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Dementia prevention, intervention, and care: 2020 report of the Lancet Commission

Gill livingston.

a Division of Psychiatry, University College London, London, UK

d Camden and Islington NHS Foundation Trust, London, UK

Jonathan Huntley

Andrew sommerlad.

f National Ageing Research Institute and Academic Unit for Psychiatry of Old Age, University of Melbourne, Royal Melbourne Hospital, Parkville, VIC, Australia

Clive Ballard

g University of Exeter, Exeter, UK

Sube Banerjee

h Faculty of Health: Medicine, Dentistry and Human Sciences, University of Plymouth, Plymouth, UK

Carol Brayne

i Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK

Alistair Burns

j Department of Old Age Psychiatry, University of Manchester, Manchester, UK

Jiska Cohen-Mansfield

k Department of Health Promotion, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

l Heczeg Institute on Aging, Tel Aviv University, Tel Aviv, Israel

m Minerva Center for Interdisciplinary Study of End of Life, Tel Aviv University, Tel Aviv, Israel

Claudia Cooper

Sergi g costafreda.

n Department of Preventive and Social Medicine, Goa Medical College, Goa, India

b Dementia Research Centre, UK Dementia Research Institute, University College London, London, UK

o Institute of Neurology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK

Laura N Gitlin

p Center for Innovative Care in Aging, Johns Hopkins University, Baltimore, MA, USA

Robert Howard

Helen c kales.

r Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California, Sacramento, CA, USA

Mika Kivimäki

c Department of Epidemiology and Public Health, University College London, London, UK

Eric B Larson

s Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA

Adesola Ogunniyi

t University College Hospital, Ibadan, Nigeria

Vasiliki Orgeta

Karen ritchie.

u Inserm, Unit 1061, Neuropsychiatry: Epidemiological and Clinical Research, La Colombière Hospital, University of Montpellier, Montpellier, France

v Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

Kenneth Rockwood

w Centre for the Health Care of Elderly People, Geriatric Medicine Dalhousie University, Halifax, NS, Canada

Elizabeth L Sampson

e Barnet, Enfield, and Haringey Mental Health Trust, London, UK

Quincy Samus

q Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MA, USA

Lon S Schneider

x Department of Psychiatry and the Behavioural Sciences and Department of Neurology, Keck School of Medicine, Leonard Davis School of Gerontology of the University of Southern California, Los Angeles, CA, USA

Geir Selbæk

y Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway

z Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

aa Geriatric Department, Oslo University Hospital, Oslo, Norway

ab Department Psychosocial and Community Health, School of Nursing, University of Washington, Seattle, WA, USA

Naaheed Mukadam

Associated data, executive summary.

The number of older people, including those living with dementia, is rising, as younger age mortality declines. However, the age-specific incidence of dementia has fallen in many countries, probably because of improvements in education, nutrition, health care, and lifestyle changes. Overall, a growing body of evidence supports the nine potentially modifiable risk factors for dementia modelled by the 2017 Lancet Commission on dementia prevention, intervention, and care: less education, hypertension, hearing impairment, smoking, obesity, depression, physical inactivity, diabetes, and low social contact. We now add three more risk factors for dementia with newer, convincing evidence. These factors are excessive alcohol consumption, traumatic brain injury, and air pollution. We have completed new reviews and meta-analyses and incorporated these into an updated 12 risk factor life-course model of dementia prevention. Together the 12 modifiable risk factors account for around 40% of worldwide dementias, which consequently could theoretically be prevented or delayed. The potential for prevention is high and might be higher in low-income and middle-income countries (LMIC) where more dementias occur.

Our new life-course model and evidence synthesis has paramount worldwide policy implications. It is never too early and never too late in the life course for dementia prevention. Early-life (younger than 45 years) risks, such as less education, affect cognitive reserve; midlife (45–65 years), and later-life (older than 65 years) risk factors influence reserve and triggering of neuropathological developments. Culture, poverty, and inequality are key drivers of the need for change. Individuals who are most deprived need these changes the most and will derive the highest benefit.

Policy should prioritise childhood education for all. Public health initiatives minimising head injury and decreasing harmful alcohol drinking could potentially reduce young-onset and later-life dementia. Midlife systolic blood pressure control should aim for 130 mm Hg or lower to delay or prevent dementia. Stopping smoking, even in later life, ameliorates this risk. Passive smoking is a less considered modifiable risk factor for dementia. Many countries have restricted this exposure. Policy makers should expedite improvements in air quality, particularly in areas with high air pollution.

We recommend keeping cognitively, physically, and socially active in midlife and later life although little evidence exists for any single specific activity protecting against dementia. Using hearing aids appears to reduce the excess risk from hearing loss. Sustained exercise in midlife, and possibly later life, protects from dementia, perhaps through decreasing obesity, diabetes, and cardiovascular risk. Depression might be a risk for dementia, but in later life dementia might cause depression. Although behaviour change is difficult and some associations might not be purely causal, individuals have a huge potential to reduce their dementia risk.

In LMIC, not everyone has access to secondary education; high rates of hypertension, obesity, and hearing loss exist, and the prevalence of diabetes and smoking are growing, thus an even greater proportion of dementia is potentially preventable.

Amyloid-β and tau biomarkers indicate risk of progression to Alzheimer's dementia but most people with normal cognition with only these biomarkers never develop the disease. Although accurate diagnosis is important for patients who have impairments and functional concerns and their families, no evidence exists to support pre-symptomatic diagnosis in everyday practice.

Our understanding of dementia aetiology is shifting, with latest description of new pathological causes. In the oldest adults (older than 90 years), in particular, mixed dementia is more common. Blood biomarkers might hold promise for future diagnostic approaches and are more scalable than CSF and brain imaging markers.

Wellbeing is the goal of much of dementia care. People with dementia have complex problems and symptoms in many domains. Interventions should be individualised and consider the person as a whole, as well as their family carers. Evidence is accumulating for the effectiveness, at least in the short term, of psychosocial interventions tailored to the patient's needs, to manage neuropsychiatric symptoms. Evidence-based interventions for carers can reduce depressive and anxiety symptoms over years and be cost-effective.

Keeping people with dementia physically healthy is important for their cognition. People with dementia have more physical health problems than others of the same age but often receive less community health care and find it particularly difficult to access and organise care. People with dementia have more hospital admissions than other older people, including for illnesses that are potentially manageable at home. They have died disproportionately in the COVID-19 epidemic. Hospitalisations are distressing and are associated with poor outcomes and high costs. Health-care professionals should consider dementia in older people without known dementia who have frequent admissions or who develop delirium. Delirium is common in people with dementia and contributes to cognitive decline. In hospital, care including appropriate sensory stimulation, ensuring fluid intake, and avoiding infections might reduce delirium incidence.

Key messages

• • New evidence supports adding three modifiable risk factors—excessive alcohol consumption, head injury, and air pollution—to our 2017 Lancet Commission on dementia prevention, intervention, and care life-course model of nine factors (less education, hypertension, hearing impairment, smoking, obesity, depression, physical inactivity, diabetes, and infrequent social contact).
• • Modifying 12 risk factors might prevent or delay up to 40% of dementias.
• • Prevention is about policy and individuals. Contributions to the risk and mitigation of dementia begin early and continue throughout life, so it is never too early or too late. These actions require both public health programmes and individually tailored interventions. In addition to population strategies, policy should address high-risk groups to increase social, cognitive, and physical activity; and vascular health.
• • Aim to maintain systolic BP of 130 mm Hg or less in midlife from around age 40 years (antihypertensive treatment for hypertension is the only known effective preventive medication for dementia).
• • Encourage use of hearing aids for hearing loss and reduce hearing loss by protection of ears from excessive noise exposure.
• • Reduce exposure to air pollution and second-hand tobacco smoke.
• • Prevent head injury.
• • Limit alcohol use, as alcohol misuse and drinking more than 21 units weekly increase the risk of dementia.
• • Avoid smoking uptake and support smoking cessation to stop smoking, as this reduces the risk of dementia even in later life.
• • Provide all children with primary and secondary education.
• • Reduce obesity and the linked condition of diabetes. Sustain midlife, and possibly later life physical activity.
• • Addressing other putative risk factors for dementia, like sleep, through lifestyle interventions, will improve general health.
• • Many risk factors cluster around inequalities, which occur particularly in Black, Asian, and minority ethnic groups and in vulnerable populations. Tackling these factors will involve not only health promotion but also societal action to improve the circumstances in which people live their lives. Examples include creating environments that have physical activity as a norm, reducing the population profile of blood pressure rising with age through better patterns of nutrition, and reducing potential excessive noise exposure.
• • Dementia is rising more in low-income and middle-income countries (LMIC) than in high-income countries, because of population ageing and higher frequency of potentially modifiable risk factors. Preventative interventions might yield the largest dementia reductions in LMIC.

For those with dementia, recommendations are:

• • Post-diagnostic care for people with dementia should address physical and mental health, social care, and support. Most people with dementia have other illnesses and might struggle to look after their health and this might result in potentially preventable hospitalisations.
• • Specific multicomponent interventions decrease neuropsychiatric symptoms in people with dementia and are the treatments of choice. Psychotropic drugs are often ineffective and might have severe adverse effects.
• • Specific interventions for family carers have long-lasting effects on depression and anxiety symptoms, increase quality of life, are cost-effective and might save money.

Acting now on dementia prevention, intervention, and care will vastly improve living and dying for individuals with dementia and their families, and thus society.

Introduction

Worldwide around 50 million people live with dementia, and this number is projected to increase to 152 million by 2050, 1 rising particularly in low-income and middle-income countries (LMIC) where around two-thirds of people with dementia live. 1 Dementia affects individuals, their families, and the economy, with global costs estimated at about US$1 trillion annually. 1

We reconvened the 2017 Lancet Commission on dementia prevention, intervention, and care 2 to identify the evidence for advances likely to have the greatest impact since our 2017 paper and build on its work. Our interdisciplinary, international group of experts presented, debated, and agreed on the best available evidence. We adopted a triangulation framework evaluating the consistency of evidence from different lines of research and used that as the basis to evaluate evidence. We have summarised best evidence using, where possible, good- quality systematic reviews, meta-analyses, or individual studies, where these add important knowledge to the field. We performed systematic literature reviews and meta-analyses where needed to generate new evidence for our analysis of potentially modifiable risk factors for dementia. Within this framework, we present a narrative synthesis of evidence including systematic reviews and meta-analyses and explain its balance, strengths, and limitations. We evaluated new evidence on dementia risk in LMIC; risks and protective factors for dementia; detection of Alzheimer's disease; multimorbidity in dementia; and interventions for people affected by dementia.

Nearly all the evidence is from studies in high-income countries (HIC), so risks might differ in other countries and interventions might require modification for different cultures and environments. This notion also underpins the critical need to understand the dementias related to life-course disadvantage—whether in HICs or LMICs.

Our understanding of dementia aetiology is shifting. A consensus group, for example, has described hippocampal sclerosis associated with TDP-43 proteinopathy, as limbic-predominant age-related TDP-43 encephalopathy (LATE) dementia, usually found in people older than 80 years, progressing more slowly than Alzheimer's disease, detectable at post-mortem, often mimicking or comorbid with Alzheimer's disease. 3 This situation reflects increasing attention as to how clinical syndromes are and are not related to particular underlying pathologies and how this might change across age. More work is needed, however, before LATE can be used as a valid clinical diagnosis.

The fastest growing demographic group in HIC is the oldest adults, those aged over 90 years. Thus a unique opportunity exists to focus on both human biology, in this previously rare population, as well as on meeting their needs and promoting their wellbeing.

Prevention of dementia

The number of people with dementia is rising. Predictions about future trends in dementia prevalence vary depending on the underlying assumptions and geographical region, but generally suggest substantial increases in overall prevalence related to an ageing population. For example, according to the Global Burden of Diseases, Injuries, and Risk Factors Study, the global age-standardised prevalence of dementia between 1990 and 2016 was relatively stable, but with an ageing and bigger population the number of people with dementia has more than doubled since 1990. 4

However, in many HIC such as the USA, the UK, and France, age-specific incidence rates are lower in more recent cohorts compared with cohorts from previous decades collected using similar methods and target populations 5 ( figure 1 ) and the age-specific incidence of dementia appears to decrease. 6 All-cause dementia incidence is lower in people born more recently, 7 probably due to educational, socio-economic, health care, and lifestyle changes. 2 , 5 However, in these countries increasing obesity and diabetes and declining physical activity might reverse this trajectory. 8 , 9 In contrast, age-specific dementia prevalence in Japan, South Korea, Hong Kong, and Taiwan looks as if it is increasing, as is Alzheimer's in LMIC, although whether diagnostic methods are always the same in comparison studies is unclear. 5 , 6 , 7

Incidence rate ratio comparing new cohorts to old cohorts from five studies of dementia incidence 5

IIDP Project in USA and Nigeria, Bordeaux study in France, and Rotterdam study in the Netherlands adjusted for age. Framingham Heart Study, USA, adjusted for age and sex. CFAS in the UK adjusted for age, sex, area, and deprivation. However, age-specific dementia prevalence is increasing in some other countries. IID=Indianapolis–Ibadan Dementia. CFAS=Cognitive Function and Ageing Study. Adapted from Wu et al, 5 by permission of Springer Nature.

Modelling of the UK change suggests a 57% increase in the number of people with dementia from 2016 to 2040, 70% of that expected if age-specific incidence rates remained steady, 10 such that by 2040 there will be 1·2 million UK people with dementia. Models also suggest that there will be future increases both in the number of individuals who are independent and those with complex care needs. 6

In our first report, the 2017 Commission described a life-course model for potentially modifiable risks for dementia. 2 Life course is important when considering risk, for example, obesity and hypertension in midlife predict future dementia, but both weight and blood pressure usually fall in later life in those with or developing dementia, 9 so lower weight and blood pressure in later life might signify illness, not an absence of risk. 11 , 12 , 13 , 14 We consider evidence on other potential risk factors and incorporate those with good quality evidence in our model.

Figure 2 summarises possible mechanisms of protection from dementia, some of which involve increasing or maintaining cognitive reserve despite pathology and neuropathological damage. There are different terms describing the observed differential susceptibility to age-related and disease-related changes and these are not used consistently. 15 , 16 A consensus paper defines reserve as a concept accounting for the difference between an individual's clinical picture and their neuropathology. It, divides the concept further into neurobiological brain reserve (eg, numbers of neurones and synapses at a given timepoint), brain maintenance (as neurobiological capital at any timepoint, based on genetics or lifestyle reducing brain changes and pathology development over time) and cognitive reserve as adaptability enabling preservation of cognition or everyday functioning in spite of brain pathology. 15 Cognitive reserve is changeable and quantifying it uses proxy measures such as education, occupational complexity, leisure activity, residual approaches (the variance of cognition not explained by demographic variables and brain measures), or identification of functional networks that might underlie such reserve. 15 , 16 , 17 , 18 , 19 , 20

Possible brain mechanisms for enhancing or maintaining cognitive reserve and risk reduction of potentially modifiable risk factors in dementia

Early-life factors, such as less education, affect the resulting cognitive reserve. Midlife and old-age risk factors influence age-related cognitive decline and triggering of neuropathological developments. Consistent with the hypothesis of cognitive reserve is that older women are more likely to develop dementia than men of the same age, probably partly because on average older women have had less education than older men. Cognitive reserve mechanisms might include preserved metabolism or increased connectivity in temporal and frontal brain areas. 17 , 18 , 19 , 20 , 21 People in otherwise good physical health can sustain a higher burden of neuropathology without cognitive impairment. 22 Culture, poverty, and inequality are important obstacles to, and drivers of, the need for change to cognitive reserve. Those who are most deprived need these changes the most and will derive the highest benefit from them.

Smoking increases air particulate matter, and has vascular and toxic effects. 23 Similarly air pollution might act via vascular mechanisms. 24 Exercise might reduce weight and diabetes risk, improve cardiovascular function, decrease glutamine, or enhance hippocampal neurogenesis. 25 Higher HDL cholesterol might protect against vascular risk and inflammation accompanying amyloid-β (Aβ) pathology in mild cognitive impairment. 26

Dementia in LMIC

Numbers of people with dementia in LMIC are rising faster than in HIC because of increases in life expectancy and greater risk factor burden. We previously calculated that nine potentially modifiable risk factors together are associated with 35% of the population attributable fraction (PAFs) of dementia worldwide: less education, high blood pressure, obesity, hearing loss, depression, diabetes, physical inactivity, smoking, and social isolation, assuming causation. 2 Most research data for this calculation came from HIC and there is a relative absence of specific evidence of the impact of risk factors on dementia risk in LMIC, particularly from Africa and Latin America. 27

Calculations considering country-specific prevalence of the nine potentially modifiable risk factors indicate PAF of 40% in China, 41% in India and 56% in Latin America with the potential for these numbers to be even higher depending on which estimates of risk factor frequency are used. 28 , 29 Therefore a higher potential for dementia prevention exists in these countries than in global estimates that use data predominantly from HIC. If not currently in place, national policies addressing access to education, causes and management of high blood pressure, causes and treatment of hearing loss, socio-economic and commercial drivers of obesity, could be implemented to reduce risk in many countries. The higher social contact observed in the three LMIC regions provides potential insights for HIC on how to influence this risk factor for dementia. 30 We could not consider other risk factors such as poor health in pregnancy of malnourished mothers, difficult births, early life malnutrition, survival with heavy infection burdens alongside malaria and HIV, all of which might add to the risks in LMIC.

Diabetes is very common and cigarette smoking is rising in China while falling in most HIC. 31 A meta-analysis found variation of the rates of dementia within China, with a higher prevalence in the north and lower prevalence in central China, estimating 9·5 million people are living with dementia, whereas a slightly later synthesis estimated a higher prevalence of around 11 million. 30 , 32 These data highlight the need for more focused work in LMIC for more accurate estimates of risk and interventions tailored to each setting.

Specific potentially modifiable risk factors for dementia

Risk factors in early life (education), midlife (hypertension, obesity, hearing loss, traumatic brain injury, and alcohol misuse) and later life (smoking, depression, physical inactivity, social isolation, diabetes, and air pollution) can contribute to increased dementia risk ( table 1 ). Good evidence exists for all these risk factors although some late-life factors, such as depression, possibly have a bidirectional impact and are also part of the dementia prodrome. 33 , 34

PAF for 12 dementia risk factors

Data are relative risk (95% CI) or %. Overall weighted PAF=39·7%. PAF=population attributable fraction.

In the next section, we briefly describe relevant newly published and illustrative research studies that add to the 2017 Commission's evidence base, including risks and, for some, mitigation. We have chosen studies that are large and representative of the populations, or smaller studies in areas where very little evidence exists. We discuss them in life-course order and within the life course in the order of magnitude of population attributable factor.

Education and midlife and late-life cognitive stimulation

Education level reached.

Higher childhood education levels and lifelong higher educational attainment reduce dementia risk. 2 , 35 , 36 , 37 New work suggests overall cognitive ability increases, with education, before reaching a plateau in late adolescence, when brain reaches greatest plasticity; with relatively few further gains with education after age 20 years. 38 This suggests cognitive stimulation is more important in early life; much of the apparent later effect might be due to people of higher cognitive function seeking out cognitively stimulating activities and education. 38 It is difficult to separate out the specific impact of education from the effect of overall cognitive ability, 38 , 39 and the specific impact of later-life cognitive activity from lifelong cognitive function and activity. 39 , 40

Cognitive maintenance

One large study in China tried to separate cognitive activity in adulthood from activities for those with more education, by considering activities judged to appeal to people of different levels of education. 40 It found people older than 65 years who read, played games, or bet more frequently had reduced risk of dementia (n=15 882, odds ratio [OR]=0·7, 95% CI 0·6–0·8). The study excluded people developing dementia less than 3 years after baseline to reduce reverse causation.

This finding is consistent with small studies of midlife activities which find them associated with better late-life cognition; so for example, in 205 people aged 30–64 years, followed up until 66–88 years, travel, social outings, playing music, art, physical activity, reading, and speaking a second language, were associated with maintaining cognition, independent of education, occupation, late-life activities, and current structural brain health. 41 Similarly, engaging in intellectual activity as adults, particularly problem solving, for 498 people born in 1936, was associated with cognitive ability acquisition, although not the speed of decline. 42

Cognitive decline

The use it or lose it hypothesis suggests that mental activity, in general, might improve cognitive function. People in more cognitively demanding jobs tend to show less cognitive deterioration before, and sometimes after retirement than those in less demanding jobs. 43 , 44 One systematic review of retirement and cognitive decline found conflicting evidence. 45 Subsequently, a 12-year study of 1658 people found older retirement age but not number of years working, was associated with lower dementia risk. 46 Those retiring because of ill health had lower verbal memory and fluency scores than those retiring for other reasons. 47 Another study found a two-fold increase in episodic memory loss attributable to retirement (n=18 575, mean age 66 years), compared to non-retirees, adjusting for health, age, sex, and wealth. 48 Similarly, in a cohort of 3433 people retiring at a mean age of 61 years, verbal memory declined 38% (95% CI 22–60) faster than before retirement. 44 In countries with younger compared to higher retirement ages, average cognitive performance drops more. 49

Cognitive interventions in normal cognition and mild cognitive impairment

A cognitive intervention or cognition-orientated treatment comprises strategies or skills to improve general or specific areas of cognition. 50 Computerised cognitive training programmes have increasingly replaced tasks that were originally paper-and-pencil format with computer-based tasks for practice and training. 51

Three systematic reviews in the general population found no evidence of generalised cognition improvement from specific cognitive interventions, including computerised cognitive training, although the domain trained might improve. 52 , 53 , 54

A meta-analysis of 17 controlled trials of at least 4 hours of computerised cognitive training, (n=351, control n=335) for mild cognitive impairment, found a moderate effect on general cognition post-training (Hedges' g=0·4, 0·2–0·5); 55 however few high quality studies and no long-term high quality evidence about prevention of dementia currently exists. A meta-analysis of 30 trials of computerised, therapy-based and multimodal interventions for mild cognitive impairment found an effect on activities of daily living (d=0·23) and metacognitive outcomes (d=0·30) compared to control. 56 A third systematic review identified five high quality studies, four group-delivered and one by computer, and concluded the evidence for the effects of cognitive training in mild cognitive impairment was insufficient to draw conclusions. 53 A comprehensive, high quality, systematic overview of meta-analyses of cognitive training in healthy older people, those with mild cognitive impairment and those with dementia, found that most were of low standard, were positive and most reached statistical significance but it was unclear whether results were of clinical value because of the poor standard of the studies and heterogeneity of results ( figure 3 ). 51

Pooled results of meta-analyses investigating objective cognitive outcomes of cognition-oriented treatment in older adults with and without cognitive impairment

K represents the number of primary trials included in the analysis. If a review reported several effect sizes within each outcome domain, a composite was created and k denotes the range of the number of primary trials that contributed to the effect estimate. AMSTAR=A MeaSurement Tool to Assess systematic Reviews (max score 16). Adapted from Gavelin et al, 51 by permission of Springer Nature.

In the only randomised controlled trial (RCT) of behavioural activation (221 people) for cognition in amnestic mild cognitive impairment, behavioural activation versus supportive therapy was associated with a decreased 2-year incidence of memory decline (relative risk [RR] 0·12, 0·02–0·74). 57

Hearing impairment

Hearing loss had the highest PAF for dementia in our first report, using a meta-analysis of studies of people with normal baseline cognition and hearing loss present at a threshold of 25 dB, which is the WHO threshold for hearing loss. In the 2017 Commission, we found an RR of 1·9 for dementia in populations followed up over 9–17 years, with the long follow-up times making reverse causation bias unlikely. 2 A subsequent meta-analysis using the same three prospective studies measuring hearing using audiometry at baseline, found an increased risk of dementia (OR 1·3, 95% CI 1·0–1·6) per 10 dB of worsening of hearing loss. 58 A cross-sectional study of 6451 individuals designed to be representative of the US population, with a mean age of 59·4 years, found a decrease in cognition with every 10 dB reduction in hearing, which continued to below the clinical threshold so that subclinical levels of hearing impairment (below 25 dB) were significantly related to lower cognition. 59

Although the aetiology still needs further clarification, a small US prospective cohort study of 194 adults without baseline cognitive impairment, (baseline mean age 54·5 years), and at least two brain MRIs, with a mean of 19 years follow-up, found that midlife hearing impairment measured by audiometry, is associated with steeper temporal lobe volume loss, including in the hippocampus and entorhinal cortex. 60

Hearing aids

A 25-year prospective study of 3777 people aged 65 years or older found increased dementia incidence in those with self-reported hearing problems except in those using hearing aids. 61 Similarly, a cross–sectional study found hearing loss was only associated with worse cognition in those not using hearing aids. 62 A US nationally representative survey of 2040 people older than 50 years, tested every two years for 18 years, found immediate and delayed recall deteriorated less after initiation of hearing aid use, adjusting for other risk factors. 63 Hearing aid use was the largest factor protecting from decline (regression coefficient β for higher episodic memory 1·53; p<0·001) adjusting for protective and harmful factors. The long follow-up times in these prospective studies suggest hearing aid use is protective, rather than the possibility that those developing dementia are less likely to use hearing aids. Hearing loss might result in cognitive decline through reduced cognitive stimulation.

Traumatic brain injury (TBI)

The International Classification of Disease (ICD) defines mild TBI as concussion and severe TBI as skull fracture, oedema, brain injury or bleed. Single, severe TBI is associated in humans, and mouse models, with widespread hyperphosphorylated tau pathology, and mice with APOE ε4 compared to APOE ε3 allele have more hippocampal hyper-phosphorylated tau after TBI. 64 , 65 TBI is usually caused by car, motorcycle, and bicycle injuries; military exposures; boxing, horse riding, and other recreational sports; firearms; and falls. 66 A nationwide Danish cohort study of nearly 3 million people aged 50 years or older, followed for a mean of 10 years, found an increased dementia (HR 1·2, 95% CI 1·2–1·3) and Alzheimer's disease risk (1·2, 1·1–1·2). 67 Dementia risk was highest in the 6 months after TBI (4·1, 3·8–4·3) and increased with number of injuries in people with TBI (one TBI 1·2, 1·2–1·3; ≥5 TBIs 2·8, 2·1–3·8). Risk was higher for TBI than fractures in other body areas (1·3, 1·3–1·3) and remained elevated after excluding those who developed dementia within 2 years after TBI, to reduce reverse causation bias. 67

Similarly, a Swedish cohort of over 3 million people aged 50 years or older, found TBI increased 1-year dementia risk (OR 3·5, 95% CI 3·2–3·8); and risk remained elevated, albeit attenuated over 30 years (1·3, 1·1–1·4). 68 ICD defined single mild TBI increased the risk of dementia less than severe TBI and multiple TBIs increased the risk further (OR 1·6, 95% CI 1·6–1·7 for single TBI; 2·1, 2·0–2·2 for more severe TBI; and 2·8, 2·5–3·2 for multiple TBI). A nested case control study of early onset clinically diagnosed Alzheimer's disease within an established cohort also found TBI was a risk factor, increasing with number and severity. 69 A stronger risk of dementia was found nearer the time of the TBI, leading to some people with early-onset Alzheimer's disease.

Military veterans have a high risk of occupational TBI, and formal record keeping allows long-term follow-up. A study of 178 779 veterans with TBI with propensity-matched veterans without TBI found dementia risk was associated with TBI severity (HR 2·4, 95% CI 2·1–2·7 for mild TBI without loss of consciousness; 2·5, 2·3–2·8 for mild TBI with loss of consciousness; and 3·8, 3·6–3·9 for moderate to severe TBI). 70 Similarly women veterans with TBI had increased risk of dementia compared to those without TBI (1·5, 1·0–2·2). 71

A cohort study of 28 815 older adults with concussion, found the risk of dementia doubled, with 1 in 6 developing dementia over a mean follow-up of 3·9 years, although those taking statins had a 13% reduced risk of dementia compared to those who were statin-free. They suggest future RCTs as statins might mitigate injury-related brain oedema, oxidative stress, amyloid protein aggregation, and neuroinflammation. 72

The term chronic traumatic encephalopathy describes sports head injury, which is not yet fully characterised and covers a broad range of neuropathologies and outcomes, with current views largely conjecture. 73 The evidence has subsequently been strengthened by a study on Scottish former soccer players reporting that they are more likely than controls to have Alzheimer's disease specified on their death certificates (HR 5·1, 95% CI 2·9–8·8) and to have been prescribed any dementia-related medications (OR 4·9, 95% CI 3·8–6·3) but not on medical records. 74 The study controlled for socio-economic class based on residential address, which in footballers might be less linked to level of education.

Hypertension

Persistent midlife hypertension is associated with increased risk of a late life dementia. In the Framingham Offspring cohort comprising 1440 people, elevated systolic blood pressure (≥140 mm Hg in midlife; mean age 55 years) was associated with an increased risk of developing dementia (HR 1·6, 95% CI 1·1–2·4) over an 18 year follow-up period. 12 In this study risk increased further if hypertension persisted into later life (mean age 69 years; HR 2·0, 95% CI 1·3–3·1). In the same cohort, people in late midlife (mean age 62 years) with ideal cardiovascular parameters (current non-smoker, body mass index [BMI] 18·5–25 kg/m 2 , regular physical activity, healthy diet, optimum blood pressure <120/<80 mm Hg, cholesterol, and normal fasting blood glucose) were compared to people with at least one of these risks. 75 Those with ideal cardiovascular parameters had a lower 10-year risk of all-cause dementia (HR 0·8, 95% CI 0·1–1·0), vascular dementia (0·5, 0·3–0·8) and clinically diagnosed Alzheimer's disease (0·8, 0·6–1·0). In a UK cohort study of 8639 civil servants, a single measure of systolic blood pressure of 130 mm Hg or higher at age 50 years but not at age 60 or 70 years was associated with increased risk of dementia (1·4, 1·1–1·7). 13 In those with persistent systolic blood pressure of 130 mm Hg or higher, from mean age 45 to 61 years, dementia risk is increased even if free of cardiovascular disease relative to those without hypertension (1·3, 1·0–1·7).

A further cohort study has provided potential insights into mechanisms, reporting that midlife hypertension, defined as from age 40 years, was associated with reduced brain volumes and increased white matter hyperintensity volume but not amyloid deposition. 76 Of note, blood pressure declines in later life and this decline is associated with and, potentially caused by, dementia development (HR 2·4, 95% CI 1·4–4·2). 12 , 13 , 77

Antihypertensive drugs, aspirin, and statins

The US and Puerto Rico Systolic Blood Pressure Intervention Trial (SPRINT) in 9361 hypertensive adults aged 50 years and older, was stopped early because of significantly fewer cardiovascular events and deaths occurring in the intensive treatment arm (aiming for systolic <120 mm Hg, n=4678) in comparison with standard treatment (systolic <140 mm Hg, n=4683). 78 Cognitive assessment continued after stopping the trial intervention in SPRINT MIND. 79 In the intensive compared with the standard treatment group, there were 7·2 dementia cases as opposed to 8·6 cases/1000 person-years (HR 0·8; 95% CI 0·7–1·0) within on average 2 years from the end of the intervention period and 5 years after baseline. Pre-specified secondary outcomes were also reduced in the intensive arm for mild cognitive impairment (14·6 vs 18·3 cases/1000 person-years; HR 0·8, 95% CI 0·7–1·0), combined mild cognitive impairment or dementia (20·2 vs 24·1 cases/1000 person-years; HR 0·9, 95% CI 0·7–1·0) 79 making this the first trial to suggest reduction of risk for mild cognitive impairment. Those who were lost to follow-up were at greater risk of dementia than those who continued but follow-up rates did not differ according to intervention group. 80

Four meta-analyses of blood pressure medications to lower high blood pressure with six studies overlap have provided combined estimates of effects. All meta-analyses suggest reduced dementia in those in the interventions arms for outcomes of any dementia as well as clinically diagnosed Alzheimer's disease. The first included randomised controlled trials (RCTs) of any drug to lower blood pressure and reported a reduction in risk of around 10% at marginal significance (RR 0·9, 95% CI 0·9–1·0). 81 Meta-regression showed risk lowered more if the achieved systolic pressure differential was larger between the intervention and control group. The second included 15 trials and observational studies of diuretics involving 52 599 people (median age 76 years) with 6·1 years median follow-up (dementia HR 0·8, 95% CI 0·8–0·9 and Alzheimer's disease 0·8, 0·7–0·9). 82 The third included used individual participant data from six observational studies; (dementia 0·9, 0·8–1·0 and Alzheimer's disease 0·8, 0·7–1·0; figure 4 ). 83 The fourth focused on people prescribed calcium channel blocker only, included 10 RCTs and observational studies comprising 75 239 hypertensive older adults (median age 72 years, median follow-up 8·2 years) found lowered dementia risk (RR 0·7, 95% CI 0·6–0·9). 84 A 2019 meta-analysis addressing which class of anti-hypertensive drug to use to lower risk of either incident dementia or cognitive decline, found over 50 000 participants in 27 studies and reported no consistent difference in effect according to which class of drug was used. 85

Associations of antihypertensive medication use with incident dementia in those with high blood pressure

Adapted from Ding et al, 83 by permission of Elsevier.

A Cochrane review reported good evidence that statins given to older people at risk of vascular disease do not prevent cognitive decline or dementia. 86 One RCT found 100 mg aspirin versus placebo in 19 114 healthy adults older than 65 years did not reduce dementia (HR 1·0, 95% CI 0·8–1·2), death, physical disability, or cardiovascular disease over a period of 4·7 years. 87

Physical inactivity, exercise, and fitness

Studies of physical activity are complex. Patterns of physical activity change with age, generation, and morbidity and are different across sex, social class, and cultures. The studies suggest a complicated relationship with the potential for both risk reduction and reverse causation.

Meta-analyses of longitudinal observational studies of 1–21 years duration showed exercise to be associated with reduced risk of dementia. 2 A further overview of systematic reviews concluded that there is convincing evidence for physical activity protecting against clinically diagnosed Alzheimer's disease. 88

Since the 2017 Commission, the HUNT study of 28 916 participants aged 30–60 years has been published, reinforcing the previous literature in this area. At least weekly midlife moderate-to-vigorous physical activity (breaking into a sweat) was associated with reduced dementia risk over a 25-year period of follow-up (HR 0·8, 95% CI 0·6–1·1) but the confidence intervals were wide. 89 In contrast the Whitehall Study reporting on the 28-year follow-up of 10 308 people, found that more than 2·5 hours of self-reported moderate-to-vigorous physical activity per week, lowered dementia risk over 10, but not 28 years. 33 Very long-term studies are unusual; however, one 44-year study recruited 191 women (mean age 50) purposively to be representative of the Swedish population and reported that 32% of the participants with low baseline peak fitness, 25% with medium, and 5% with high fitness developed dementia (high vs medium HR 0·1, 95% CI 0·03–0·5, low vs medium 1·4, 0·7–2·8). 90

An individual-level meta-analysis of 19 observational studies of relatively younger adults included 404 840 participants' data (mean baseline age 45·5 years; mean follow-up duration 14·9 years), reporting an increased incidence of all-cause dementia (HR 1·4, 95% CI 1·2–1·7) and clinically diagnosed Alzheimer's disease (1·4, 1·1–1·7) in those who were physically inactive in the 10-year period before diagnosis. 91 Notably, however, no difference in dementia risk measured 10–15 years before time of dementia incidence was found except in those with comorbid cardio-metabolic disease (RR 1·3, 95% CI 0·8–2·1).

People might stop exercising due to prodromal dementia so inactivity might be either a consequence or a cause or both in dementia and might be more of a risk in those with cardiovascular morbidity. As with other outcomes, exercise might be required to be sustained and continue nearer the time of risk. 92

Trials of exercise

Since the 2017 Commission several meta-analyses and systematic reviews have been published with three high quality meta-analyses which we include. The first included 39 RCTs with an unclear total number of participants examining moderate or vigorous exercise of any frequency lasting 45–60 min per session in cognitively normal adults aged older than 50 years. This analysis reported global cognitive improvements (standard mean difference [SMD]=0·3, 95% CI 0·2–0·4) for moderate or vigorous resistance (13 studies) or aerobic exercise (18 studies) lasting 45–60 min per session with no difference between them but no effect found for yoga. 93 A second meta-analysis of RCTs in people with mild cognitive impairment found global cognition improved in the intervention group (0·3, 0·1–0·5) with aerobic exercise having a higher effect (0·6, 0·5–0·6). 94 This study did not have dementia as an outcome measure. A third meta-analysis of RCTs of longer term exercise found five studies (four lasting 12 months and one 24 months) with 2878 participants with normal baseline cognition. 95 The incidence of dementia was 3·7% (n=949) for exercisers and 6·1% (n=1017) for controls (random effect RR 0·6, 95% CI 0·3–1·1; fixed effect as no evidence of heterogeneity 0·7, 0·4–1·0). The authors concluded that the study showed no significant effect of exercise for reducing dementia, mild cognitive impairment, or clinically significant cognitive decline but was underpowered. WHO guidelines have been published since the 2017 Commission, suggesting specific activity levels drawing on these, and one further systematic review which considered sex differences on the effect of exercise. 96 , 97 It concluded the evidence points towards physical activity having a small, beneficial effect on normal cognition, with a possible effect in mild cognitive impairment, mostly due to aerobic exercise. 97 Evidence about the effect of specific types of exercise, such as progressive muscle resistance training, on dementia risk is scarce.

In the 2017 Commission we reported on diabetes as a risk factor for dementia. Distinguishing between treated and untreated diabetes as a risk factor for dementia is challenging in observational studies. In a pooled meta-analysis from over 2·3 million individuals with type 2 diabetes across 14 cohort studies, including 102 174 with dementia, diabetes was associated with an increased risk of any dementia (RR 1·6, 95% CI 1·5–1·8 for women and 1·6, 1·4–1·8 for men). 98 The risk of dementia increased with the duration and severity of diabetes. The effect of different diabetic medications on cognition or dementia outcomes remains unclear as few studies have investigated this area. 99 However, one meta-analysis of cohort studies of diabetes reported that, cross sectionally, people with diabetes taking metformin had lower prevalence of cognitive impairment (three studies OR 0·6, 95% CI 0·4–0·8) and, longitudinally, reduced dementia incidence (six studies HR 0·8, 95% CI 0·4–0·9) compared with those taking other medications or no medication. 100 However another analysis did not find a protective effect of metformin for incident dementia (three studies, RR 1·1, 95% CI 0·5–2·4) with possible harm with insulin therapy (1·2, 1·1–1·4); but this did not account for severity of diabetes of those with type 2 diabetes on insulin. 99 A Cochrane review reported intensive compared to standard diabetes control trials with 5 year follow up (n=11 140), showing no impact on cognitive decline (1·0, 95% CI 0·9–1·1) or dementia (1·3, 0·9–1·9). 101

Overall type 2 diabetes is a clear risk factor for development of future dementia; however, whether any particular medication ameliorates this risk is unclear. Intensive diabetic control does not decrease the risk of dementia.

Combined cardiovascular risk factors

Studies of individual cardiovascular risk factors usually control for other cardiovascular risks, which cluster in individual people. This does not take into account the combinations and contexts in which risk occurs. A UK study of 7899 people aged 50 years followed up for 25 years, calculated a cardiovascular health score based on four behaviour-related (smoking, diet, physical activity, BMI) and three biological (fasting glucose, blood cholesterol, blood pressure) metrics each coded on a three-point scale (0, 1, 2). 100 A better score was associated with a lower risk of dementia (HR 0·9, 95% CI 0·9–1·0 per 1 point scale increment), for both behaviour-related (HR/1 point increment in subscales 0·9, 95% CI 0·8–0·9) and biological subscales (0·9, 0·8–1·0), maintained in people free of cardiovascular disease over the follow-up (0·9, 95% CI 0·8–1·0). These authors also reported an association of the score on the scale with hippocampal atrophy and total brain volume but not white matter hyperintensities. This finding underlines the importance of clustering of cardiovascular risk factors in midlife, as studies of individual risk factors in this sample had not shown a significant association, when controlling for other individual risks. 33

Excessive alcohol consumption

Heavy drinking is associated with brain changes, cognitive impairment, and dementia, a risk known for centuries. 102 An increasing body of evidence is emerging on alcohol's complex relationship with cognition and dementia outcomes from a variety of sources including detailed cohorts and large-scale record based studies. Alcohol is strongly associated with cultural patterns and other sociocultural and health-related factors, making it particularly challenging to understand the evidence base.

A French 5-year longitudinal study of over 31 million people admitted to hospital, found alcohol use disorders (harmful use or dependence as defined in ICD) were associated with increased dementia risk, calculated separately for men and women (women HR 3·3, 95% CI 3·3–3·4, men 3·4, 3·3–3·4). 103 The relationship of dementia with alcohol use disorders was particularly clear in the earlier onset dementias (age less than 65 years) in which 56·6% had an alcohol use disorder noted in their records (n=57 353; 5·2% all dementias).

A systematic review incorporating 45 studies of light to moderate drinking using a variety of definitions reported a reduced risk of dementia compared with not drinking (RR 0·7; 95% CI 0·6–0·91). 104 Risk was not reported separately for men and women. Drinking less than 21 units of alcohol per week (1 unit of alcohol=10 mL or 8 g pure alcohol) might be associated with a lower risk of dementia. 105 , 106 A 5-year follow-up study of 13 342 men and women volunteers from UK biobank aged 40–73 years who drank, included few heavy drinkers and did not analyse abstainers. 106 The study reported that those who drank more than 12 units per week declined slightly more in reaction time in a perceptual matching task than those who drank less (β2=−0·07, 95% CI −0·09 to −0·04). 106 The UK Whitehall study with 23 years follow-up, included 9087 participants aged 35–55 years at baseline. 107 Drinking more than 21 units per week and long-term abstinence were both associated with a 17% (95% CI 4–32 and 13–23 respectively) increase in dementia compared to drinking less than 14 units. Drinking more than 14 units was also associated with right sided hippocampal atrophy on MRI. 108

Weight control and obesity

Overweight is an emerging concern, given the changing BMI across the world's ageing population. New evidence supports the relationship between increased BMI and dementia from a review of 19 longitudinal studies including 589 649 people aged 35 to 65 years, followed up for up to 42 years. It reported obesity (BMI ≥30; RR 1·3, 95% CI 1·1–1·6) but not being overweight (BMI 25–30; 1·1, 1·0–1·2) was associated with late-life dementia. 109 In a further meta-analysis of individual level data from 1·3 million adults (aged ≥18 years), which included two studies from the meta-analysis cited above, 109 higher body mass measured before probable preclinical and prodromal dementia was associated with increased dementia risk (RR 1·3, 1·1–1·7/5-unit increase in BMI). 11

Weight loss in midlife and dementia risk

A meta-analysis of seven RCTs (468 participants) and 13 longitudinal studies (551 participants) of overweight and obese adults without dementia, mean age 50 years, found weight loss of 2 kg or more in people with BMI greater than 25 was associated with a significant improvement in attention and memory. All but one of the studies included participants aged younger than 65 years. The RCTs reported memory improvement over 8–48 weeks (SMD=0·4, 95% CI 0·2–0·6) and short-term longitudinal studies found improvement over a median of 24 weeks (SMD=0·7, 95% CI 0·5–0·8); however, data about the long-term effects or the effect of weight loss in preventing dementia are absent. 110

Smokers are at higher risk of dementia than non-smokers, 2 and at a higher risk of premature death before the age at which they might have developed dementia, introducing some bias and uncertainty in the association between smoking and risk of dementia. 111 , 112 Stopping smoking, even when older, reduces this risk. Among 50 000 men aged older than 60 years, stopping smoking for more than 4 years, compared to continuing, substantially reduced dementia risk over the subsequent 8 years (HR 0·9; 95% CI 0·7–1·0). 113 Worldwide, 35% of non-smoking adults and 40% of children are estimated to be exposed to second-hand smoke; 114 although literature on the impact of this exposure and dementia risk is scarce. One study indicated that in women aged 55–64 years, second-hand smoke exposure was associated with more memory deterioration and the risk increased with exposure duration even after controlling for other confounding factors. 115

Depression is associated with dementia incidence, with a variety of possible psychological or physiological mechanisms. It is also part of the prodrome and early stages of dementia. Reverse causation is possible whereby depressive symptoms result from dementia neuropathology that occurs years before clinical dementia onset. These explanations are not mutually exclusive. As in diabetes, few studies considering depression as a risk factor for dementia have distinguished between treated and untreated depression. In a meta-analysis of 32 studies, with 62 598 participants, with follow-up from 2 to 17 years, a depressive episode was a risk factor for dementia (pooled effect size 2·0, 95% CI 1·7–2·3). 116 Meta-regression analysis revealed a non-significant trend for the association between depression and incident dementia to be weaker when the length of follow-up was longer. The Norwegian HUNT study, suggested that symptoms of psychological distress predicted dementia 25 years later however with wide bounds of uncertainty (HR 1·3, 95% CI 1·0–1·7). 89 Two further studies differentiate between late-life and earlier life depressive symptoms. The UK Whitehall study, in a follow-up of 10 189 people, reports that in late life these symptoms increase dementia risk but not at younger ages (follow-up 11 years HR 1·7; 95% CI 1·2–2·4; follow-up 22 years 1·0, 0·7–1·4). 34 , 117 A 14-year longitudinal study of 4922 initially cognitively healthy men, aged 71–89 years, found depression was associated with 1·5 (95% CI 1·2- 2·0) times the incidence of dementia but this association was accounted for by people developing dementia within 5 years of depression. 118 The use of antidepressants did not decrease this risk.

A study of 755 people with mild cognitive impairment and with a history of depression from the Australian longitudinal Alzheimer's Disease Neuroimaging Initiative, considered the effect of selective serotonin-reuptake inhibitor (SSRI) treatment, such as citalopram, known to reduce amyloid plaque generation and plaque formation in animal models. 119 The study found that more than 4 years of such treatment was associated with delayed progression to clinically diagnosed Alzheimer's disease. People treated with antidepressants seem likely to differ from those who are not treated. Thus, the question of whether antidepressant treatment mitigates dementia risk remains open.

Social contact

Social contact, now an accepted protective factor, enhances cognitive reserve or encourages beneficial behaviours, although isolation might also occur as part of the dementia prodrome. Several studies suggest that less social contact increases the risk of dementia. Although most people in mid and later life are married, by the time they reach older age, disproportionate numbers of women are widowed as they outlive their husbands, thus reducing their social contact. In these generations, marital status is therefore an important contributor to social engagement. Additionally, most marriages are in the relatively young, and married people usually have more interpersonal contact than do single people—this gives a long-term estimate of the effect of social contact. A systematic review and meta-analysis including 812 047 people worldwide found dementia risk to be elevated in lifelong single (RR 1·4, 95% CI 1·1–1·9) and widowed people (1·2, 1·0–1·4), compared with married people and the association was consistent in different sociocultural settings. 120 Studies adjusted for sex and we do not know if a differential risk between men and women exists. Differences persisted in studies that adjusted for education and physical health so might be attributable to married people having more social contact, rather than solely because they tend to have better physical health and more education, although residual confounding is possible. A systematic review and meta-analysis of 51 longitudinal cohort studies of social isolation and cognition included 102 035 participants aged 50 or more years at baseline, with follow-up of 2–21 years. 121 High social contact (measured through either or both of social activity and social network) was associated with better late-life cognitive function (r=0·05, 95% CI: 0·04–0·065) and no differences according to sex or length of time followed up.

A new meta-analysis found that in long-term studies (≥10 years), good social engagement was modestly protective (n=8876, RR=0·9, 95% CI 0·8–1·0); but loneliness was not associated with dementia risk. 122 No long term (>10 years) studies of loneliness and dementia outcomes have been done.

A UK 28-year follow-up study of 10 308 people found that more frequent social contact at age 60 years was associated with lower dementia risk over 15 years of follow-up (HR for one standard deviation social contact frequency 0·9, 95% CI 0·8–1·0). This finding suggests more frequent social contact during late middle age is associated with a modest reduction in dementia risk, independent of socio-economic and other lifestyle factors. 123 A Japanese longitudinal cohort study of 13 984 adults aged older than 65 years with a mean of 10 years follow-up calculated a five-point social contact scale based on: marital status; exchanging support with family members; having contact with friends; participating in community groups; and engaging in paid work. It found the score to be linearly associated with reduced dementia risk; those who scored highest on the five-point scale were 46% less likely to develop incident dementia compared with those in the lowest category. 124

Despite clear cultural variation in the meaning and perception of social isolation, findings of protective effect of more social contact are largely consistent in different settings and for either sex across the studies and meta-analyses. 118 , 120 , 121

Social interventions

Little evidence of the effects of social interventions on dementia exists but a systematic review of low quality RCTs of 576 adults aged 60 or more years with normal cognition found facilitated meeting and discussion groups were associated with improved global cognition and increased brain volume at follow-up. 118

Air pollutants

Air pollution and particulate pollutants are associated with poor health outcomes, including those related to non-communicable diseases. Attention has turned to their potential effect on the brain. Animal models suggest airborne particulate pollutants accelerate neurodegenerative processes through cerebrovascular and cardiovascular disease, Aβ deposition, and amyloid precursor protein processing. 125 , 126 Although the higher levels of dementia from air pollutants are still subject to the potential for residual confounding, the effects on animal models are evidence of physiological effects over and above those driven by life-course deprivation.

High nitrogen dioxide (NO 2 ) concentration (>41·5 μg/m 3 ; adjusted HR 1·2, 95% CI 1·0–1·3), fine ambient particulate matter (PM) 2·5 from traffic exhaust (1·1, 1·0–1·2) 127 , 128 , 129 and PM 2·5 from residential wood burning (HR=1·6, 95% CI 1·0–2·4 for a 1 μg/m 3 increase) are associated with increased dementia incidence. Traffic often produces NO 2 and PM 2·5 and it is hard to separate their effects, although evidence for additive effects of different pollutants exists. 127 , 128 , 129 A systematic review of studies until 2018 including 13 longitudinal studies with 1–15 years follow-up of air pollutants exposure and incident dementia, found exposure to PM 2·5, NO 2 , and carbon monoxide were all associated with increased dementia risk. 24 The attributable burden of dementia and excess death from PM 2·5 in one large 10-year US study was particularly high in Black or African American individuals and socio-economically disadvantaged communities and related to particulate PM 2·5 concentrations above the US guidelines. 130

Mechanisms by which sleep might affect dementia remain unclear, but sleep disturbance has been linked with β-amyloid (Aβ) deposition, 131 , 132 reduced glymphatic clearance pathways activation, 133 low grade inflammation, increased Tau, hypoxia 132 , 134 and cardiovascular disease. 135 Sleep disturbance is hypothesised to increase inflammation which raises Aβ burden, leading to Alzheimer's disease and further sleep disturbance. 136

Two meta-analyses showed similar findings. The first was a synthesis of longitudinal studies with an average of 9·5 years follow-up and the second reported cross-sectional and prospective cohort studies of mixed quality with different methods of measuring sleep. Sleep disturbances were defined broadly, often self-reported and including short and long sleep duration, poor sleep quality, circadian rhythm abnormality, insomnia, and obstructive sleep apnoea. All these disturbances were associated with a higher risk of all-cause dementia (RR 1·2; 95% CI 1·1–1·3) 137 and clinically diagnosed Alzheimer's disease (1·6, 1·3–1·9) compared with no sleep disturbance, although not all cohort studies excluded those with cognitive impairment or dementia at baseline from their analyses. 138 A U-shaped association has been reported between sleep duration and risk of mild cognitive impairment or dementia with higher risks of dementia with less than 5 hours (HR=2·6; 95% CI 1·4–5·1) compared with more than 5 and less than 7 and more than 10 hours sleep (2·2, 1·4–3·5) and risks for all-cause dementia and clinically diagnosed Alzheimer's disease being similar. 135 , 139 , 140 , 141

The postulated mechanisms of reduced sleep leading to accumulation of Alzheimer's type pathology is inconsistent with the evidence that both more sleep and less sleep are associated with increased risk of dementia. New onset late-life sleep disturbance, a few years before clinical dementia, might be part of the natural history of the dementia syndrome, appearing to be a risk factor, or reflect other disorders, for example, mood disturbances or cardiovascular disease. 135 , 142 Hypnotic use might increase risks although this is unclear and a 2018 study 139 suggests that findings of a connection were related to reverse causality and confounders. 143 When benzodiazepine use was considered, in one study, sleep length was no longer significant 139 but not in all studies. 135 Those taking hypnotics were at greater risk of dementia than those who did not regardless of sleep duration. 139 Medication for sleep disturbance might be harmful and benzodiazepines are associated with falls, hospital admissions, and possibly dementia. 139 , 144

Nutrition and dietary components are challenging to research with controversies still raging around the role of many micronutrients and health outcomes in dementia. Observational studies have focused on individual components ranging from folate and B vitamins, Vitamin C, D, E, and selenium amongst others as potential protective factors. 88 There has been a move towards considering the evidence base for whole diets in the last 5 years, particularly high plant intake such as in the Mediterranean diet (high intake of vegetables, legumes, fruits, nuts, cereals, and olive oil; low intake of saturated lipids and meat) or the similar Nordic diet, rather than individual nutrients, which might reduce cognitive decline and dementia. 145 One example is a longitudinal cohort study of 960 participants, ages 58–99 years, in which those reporting the highest intake of green leafy vegetables, equivalent to 1·3 servings per day, had less cognitive decline over 4·7 years than those reporting the lowest intake (β=0·05 standardised units 95% CI 0·02–0·07). 146 The authors report this difference as being equivalent to being 11 years younger. A further prospective cohort study with three midlife dietary assessments in 8255 people, followed up for a mean of nearly 25 years, found neither healthy dietary pattern nor Mediterranean diet protected from dementia, except in those with cardiovascular disease, suggesting that diet might influence dementia risk by protecting from the excess risk of cardiovascular risk factors. 147

Dietary interventions

As well as whole diets, there has been some interest in multi-nutrient interventions. A systematic review and a Cochrane review including RCTs of supplements (A, B, C, D, and E; calcium, zinc, copper, and multivitamins trials, n-3 fatty acids, antioxidant vitamins, and herbs) found a lack of evidence for supplement use to preserve cognitive function or prevent dementia in middle-aged (45–64 years) or older people (aged 65 years and older). 148 , 149 Cochrane reviews found no evidence for beneficial effects on cognition of those with mild cognitive impairment of supplementation with B vitamins for 6 to 24 months 150 or with vitamin E in preventing progression from mild cognitive impairment to dementia. 151 A 24-month RCT of 311 people of a multi-nutrient drink containing docosahexaenoic acid, vitamins B12, B6, folic acid, and other nutrients; found no significant effect on preventing cognitive deterioration in prodromal Alzheimer's disease. 152 The authors comment that the control group's cognitive decline was much lower than expected, leading to an inadequately powered trial.

Meta-analysis of two RCTs with 471 participants with normal cognition found the Mediterranean diet improved global cognition compared to controls (SMD 0·2, 95% CI 0·0–0·4). 153 A further meta-analysis identified five RCTs (n=1888) with a weak effect on global cognition (SMD 0·2, 95% Cl 0·0–0·5) 154 but no benefit of Mediterranean diet for incident cognitive impairment or dementia.

The WHO guidelines recommend a Mediterranean diet to reduce the risk of cognitive decline or dementia, as it might help and does not harm, but conclude Vitamins B and E, polyunsaturated fatty acid, and multicomplex supplementation should not be recommended. 97

Trials of combination strategies to prevent dementia

The FINGER RCT was a 2-year multidomain intervention to prevent cognitive decline and dementia in 1260 people with cardiovascular risk factors aged 60–77 years, recruited from a Finnish national survey. Similar multidomain studies were discussed in the 2017 Commission. 2 FINGER found a small group reduction in cognitive decline in the intervention group compared with control (comprehensive neuropsychological test battery Z score 0·02, 95% Cl 0·00–0·04) regardless of baseline sociodemographic, socio-economic, cognitive, or cardiovascular status. 155 However, in a subgroup analysis, greater beneficial effects were observed on processing speed in individuals with higher baseline cortical thickness in Alzheimer's disease areas. 156

The Healthy Ageing Through Internet Counselling in the Elderly (HATICE) study recruited 2724 older people (≥65 years) in the Netherlands, Finland, and France with two or more cardiovascular risk factors. 157 , 158 It compared an interactive internet platform plus remote support by a coach, aiming to improve self-management of vascular risk factors, with a non-interactive control platform with basic health information. A small improvement in the cardiovascular risk composite primary outcome was observed in the intervention group compared with the control group at 18 months, mainly through weight loss, and the dementia risk score was slightly lower in those who received the intervention (mean difference −0·15, 95% CI −0·3 to −0·0). A larger effect was observed in the younger age group (65–70 years) and those with the lowest level of education, who had a higher baseline risk, suggesting that targeting high-risk populations might be more effective. Several multidomain preventive trials are ongoing—for example, World Wide FINGERS .

Total PAF calculation

We incorporated excessive alcohol consumption, TBI, and air pollution into our life-course model of dementia, as well as the original nine risk factors, because of the updated evidence. To calculate new RRs for excessive alcohol consumption, TBI and air pollution, we systematically reviewed the literature and did new meta-analyses for excessive alcohol consumption and TBI. For the other nine factors, we used values for RR and risk factors prevalence from our previous analysis and calculated communality using the same method as in the 2017 Commission. 2

PAF calculation

We used a representative sample of over 10 000 UK community-dwelling adults, to calculate communality (clustering of risk factors) of 11 risk factors for which data existed, 159 to allow calculation of each factor's unique risk. As we could find no datasets measuring TBI, with the other 11 risk factors of interest, we could not calculate its communality. We therefore used the mean of the other 11 communalities to calculate a weighted PAF, so we could include TBI. We used cohabitation as a proxy measure for social contact, and urbanicity for air pollution exposure. Our analysis found four principal components, explaining 55% of the total variance between the eleven risk factors, suggesting substantial overlap. The appendix (p 2) shows the PAF formula and the steps in calculating communality and we detail our new meta-analyses next, which we used to update the figure and perform our new calculations.

Incorporation of the new chosen risks in new systematic reviews

We searched, from inception to Oct 29, 2019, Embase, Allied, and Complementary Medicine, MEDLINE, and PsycINFO terms “dementia” OR “dement*” OR “AD” OR “VaD”, “Alzheimer*” AND “alcohol” OR “ethanol” OR “alcohol*” OR “drink*” OR “drunk*” to update an earlier review. 160 We used inclusion criteria: original population-based cohort studies measuring drinking during midlife, as alcohol intake tends to fall with age; 161 alcohol consumption quantified at baseline by units or number of drinks (one drink, 1·5 units) per week; and all-cause dementia ascertained at follow-up using validated clinical measures. We contacted authors for additional data. 162 Three studies met our inclusion criteria. 107 , 162 , 163 We converted HRs to RRs 164 and used raw data 162 to calculate RR, 165 for our random effects meta-analysis using Generic Inverse Variance Methods. The RR associated with drinking—more than 21 units (168 g) of alcohol weekly—compared with lighter drinking was 1·18 (95% Cl 1·06–1·31; figure 5 ). We used Health Survey England figures for heavier drinking prevalence to calculate PAF as we could not find a worldwide estimate. The weighted PAF was 0·8.

Meta-analysis of relative risk of dementia associated with drinking more than 21 units of alcohol per week in midlife compared to lighter consumption of alcohol

To estimate the RR of TBI of all severities for all cause dementia, we searched Embase, Medline, and PsycINFO from Jan 1, 2016, to Oct 21, 2019, updating an earlier search, 166 using terms (“traumatic brain injury” or “head injury” or “brain injury” or TBI) AND (neurodegeneration or “cognitive dysfunction” or dementia or “Alzheimer's disease” or “Parkinson's disease” or “frontotemporal dementia”). We converted HR figures to RR. 164 , 167 We used inclusion criteria: original population-based cohort studies, baseline TBI of all severities reported, and all-cause dementia ascertained at follow-up using validated clinical measures. We combined four new studies meeting inclusion criteria 67 , 68 , 71 , 168 with the four studies meeting criteria from the original review in a random effects meta-analysis. 166 The pooled RR was 1·84 (95% CI 1·54–2·20) for all cause dementia from all severities of TBI ( figure 6 ) although there was heterogeneity in study-specific estimates, possibly because of different populations. We used the TBI adult population prevalence of 12·1% from a meta-analysis to calculate PAF. 173 The weighted PAF was 3·4.

Meta-analysis of relative risk of all-cause dementia associated with all severity midlife traumatic brain injury

A 2019 systematic review synthesised observational studies, finding consistently increased risk of dementia from air pollution, but heterogeneous comparator groups precluded meta-analysis. 24 We updated the search, using the same search terms and searching MEDLINE, Embase, and PsycINFO from Sept 20, 2018, (the end date of the last search) to Oct 22, 2019. We included longitudinal studies with assessment of all cause air pollution exposure; use of formal assessment of cognitive function at baseline; report of incident all-cause dementia, data from adults (age ≥18 years); and a minimum follow-up of 6 months. As meta-analysis was not possible, we used data from the only study of all-cause air pollution with the outcome of all-cause dementia, with low-moderate risk of bias. This population-based, observational cohort was from Canada, where pollutant concentrations are among the lowest in the world and examined 2 066 639 people, with a mean baseline age of 67 years. 174 We calculated the RR of dementia for those in the three highest quartiles compared to the lowest was 1·09 (1·07–1·11). The attributable fraction for exposure to the highest three quartiles versus the lowest quartile of PM 2·5 and NO 2 was 6·1% (4·8–7·5). The weighted PAF was 2·3.

Table 1 displays the prevalence, communality, relative risk, unweighted and weighted PAFs adjusted for communality. Figure 7 shows the updated life-course model of potentially modifiable risk factors for dementia, including the three new risk factors.

Population attributable fraction of potentially modifiable risk factors for dementia

Strengths and limitations

This Commission is the most comprehensive analysis to date and updates the 2017 Commission with emerging risk factor evidence convincing enough to calculate PAF for potentially reversible risk factors. We reviewed the literature systematically for the chosen risk factors and provided illustrative new literature to update our synthesis and identify data to calculate communality. We find a hopeful picture with an estimate of around 40% of all cases of dementia being associated with 12 potentially modifiable risk factors.

We have made assumptions to calculate this new model. We used global figures for dementia risk although we know the risk factors prevalence varies between countries and most global research is from HIC, so LMIC are under-represented because of lack of data. We have assumed a causal relationship between risk factors and dementia, although we have been cautious and not included risk factors with less good evidence. No single database exists with all 12 risk factors together, but we found 11 of the factors in a UK database and used the mean figure for communality calculations for TBI. We calculated communality for the other 11. We do not know how far findings of communality in other geographical populations might differ, or in those with a differing distribution of age groups or sex. We found that social isolation was not explicitly measured and had to use proxies, such as cohabitation when considering prevalence, which are approximate.

Specifically, evidence for the association of alcohol misuse with dementia comes from HIC and future studies from LMIC are needed to complete the picture. Exposure to air pollution changes over a lifetime and is inextricably linked to poverty and deprivation. However, the effects on animal models suggests specific physiological effects over and above those driven by life-course deprivation. We also considered the overlap with education for this and other risk factors and the correction for education, strongly inversely linked to deprivation, will address at least some of the confounding. However, the results in one study which reported the effect of air pollution on incident dementia showed very little difference in estimates before and after adjustment for education and other risk factors, suggesting little residual confounding exists. 174 We were also unable to meta-analyse data on pollution and thus unlike the other relative risks, the figure comes from only one study, from an area of low pollution so is likely to be an underestimate.

The longitudinal evidence linking potentially modifiable risk factors to dementia generally fulfils causality criteria in observational data (strength, consistency, biological plausibility, temporality, dose–response, coherence, and quasi-experimental studies, for example, more education or using hearing aids). When measuring a risk nearer to the age of dementia onset, then it is more likely that prodromal change affects, or even causes it. Alternatively, a risk factor might act on preclinical pathology or even cause dementia near the time of exposure. Thus, excessive alcohol, and TBI are particularly important in young-onset dementia, although many early onset dementias relate to genetic risks. Risk factors might also matter more at a time of higher biological vulnerability, which the studies we have drawn on cannot establish. The length of exposure required for risk or protection effect, and their inter-relationships as they change across life is unclear—it seems probable that longer or more intense exposure has stronger effects. Additionally, as our communality figures show, risk factors overlap. We cannot establish from these data if having multiple risk factors has an additive or synergistic effect. Association does not prove causation, however, as already noted, the reductions in prevalence and incidence in several HIC suggests that at least some of the risk factors estimated here do have a causal relationship with the clinical expression of dementia.

Key points and recommendations

We judge that sufficient new evidence supports adding three additional modifiable risk factors for dementia to our 2017 Commission model (excessive alcohol, traumatic brain injury, and air pollution). We have been able to add updated evidence on the nine risk factors implicated in the 2017 Commission (education, hypertension, hearing impairment, smoking, obesity, depression, inactivity, diabetes, and social contact). Reduction of these risk factors might be protective for people with or without a genetic risk, although study findings have not been entirely consistent. 175 , 176 , 177 , 178 As we noted in the 2017 Commission, others have previously calculated an estimate of the risk associated with APOε4 at 7% taking into account some other risk factors and this estimate highlights how relatively important potentially modifiable risk factors are in dementia. 2 , 179

For some risk factors, the pattern of risk and the individual's other health, both physical and mental, might be especially important. Currently, the evidence suggests a Mediterranean or Scandinavian diet might have value in preventing cognitive decline in people with intact cognition, particularly as one component of a healthy lifestyle, although how long the exposure has to be or during which ages is unclear. We do not recommend taking additional vitamins, oils, or mixed dietary supplements as a means of preventing dementia as extensive testing in trials has not led to signals of beneficial effects.

Data from RCTs on interventions to prevent cognitive decline, all-cause dementia, or Alzheimer's disease are few. For some key life influences, only observational data, particularly related to natural experiments such as changing the statutory education age, are possible. These influences should be investigated systematically wherever possible. Others can theoretically be investigated but the long follow-up required for midlife risk and protective factors and non-random attrition in longer studies are challenging. Using intermediate endpoints, such as cognition, and dementia onset in research remains uncertain because no intermediate markers with such a close relationship to dementia outcomes exist that it would be possible to predict with certainty for any given individual, age, and sex. Overall, the evidence for treating hypertension is strongest and high blood pressure throughout midlife increases the risk of dementia even without stroke.

Although a need for more evidence is apparent, recommendations should not wait, as clear indications of ways to reduce the chances of developing dementia without causing harm will also lead to other health and wellbeing benefits.

Our recommended strategies for dementia risk reduction include both population-wide and targeted interventions ( panel ). It is important to remember that more socially disadvantaged groups, including Black, Asian, and minority ethnic groups, are particularly at risk.

Recommended strategies for dementia risk reduction

Risks are particularly high in more socially disadvantaged populations including in Black, Asian, and minority ethnic groups.

Population-wide

• • Prioritise childhood education for all, worldwide
• • Implement social public health policies that reduce hypertension risk in the entire population
• • Develop policies that encourage social, cognitive, and physical activity across the life course for all (with no evidence for any specific activities being more protective)
• • Scrutinise the risks for hearing loss throughout the life course, to reduce the risk of exposure to this risk factor
• • Reduce the risk of serious brain trauma in relevant settings, including occupational and transport
• • National and international policies to reduce population exposure to air pollution
• • Continue to strengthen national and international efforts to reduce exposure to smoking, both for children and adults, and to reduce uptake and encourage cessation

Targeted on individuals

• • Treat hypertension and aim for SBP <130 mm Hg in midlife
• • Use hearing aids for hearing loss; we need to help people wear hearing aids as many find them unacceptable, too difficult to use, or ineffective
• • Avoid or discourage drinking 21 or more units of alcohol per week
• • Prevent head trauma where an individual is at high risk
• • Stopping smoking is beneficial regardless of age
• • Reduce obesity and the linked condition of diabetes by healthy food availability and an environment to increase movement
• • Sustain midlife, and possibly late-life physical activity

Although we have more to learn about effectiveness, avoiding or delaying even a proportion of potentially modifiable dementias should be a national priority for all.

Interventions and care in dementia

Not all dementia will be preventable and we present the latest evidence on intervention and care for dementia. To date the emphasis has been on specific subtypes of dementia, most notably on Alzheimer's disease, which has been conceptualised over the years in a variety of changing diagnostic criteria—eg, DSM IV and DSM V. 180 , 181 Intense efforts have been put into biomarkers for early preclinical detection of the disease process before it becomes dementia. Biomarkers need to show reliability and validity, and for dementias they also need to be very closely and clearly related to clinical syndrome outcomes in the way that, for example, human papillomavirus is for cervical cancer, and hypertension has been for stroke.

Biomarkers and detection of Alzheimer's disease

Markers of neurodegeneration linked to clinical dementia include brain volume loss—ie, hippocampal volume loss and entorhinal cortex and medial temporal cortical thinning—seen in structural imaging. The most studied molecular markers are in Alzheimer's disease and are amyloid and tau, which PET and CSF detect clinically. The prevalence of particular pathologies at different ages is important in interpretation of such studies. So, for example, population derived studies show increases in plaques in the population from less than 3% at age 50–59 years to around 40% at age 80–89 years. 182

Amyloid imaging

Amyloid imaging detects amyloid in the brain with high sensitivity and specificity in both cognitively normal and people with Alzheimer's disease when the gold-standard comparison is either neuropathology or clinical diagnosis, distinguishing Alzheimer's disease from other neurodegenerative conditions. 183 Amyloid imaging is not a diagnostic test for dementia. A US study of randomly selected older people from the community recruited 1671 people (mean age of 71 years). 182 The prevalence of PET detected amyloid positivity increased from 2·7% (95% CI 0·5–4·9) of people without cognitive impairment aged 50–59 years to 41·3% (95% CI 33·4–49·2%) aged 80–89 years. 182 In 10-year follow-up PET positivity was associated with a higher probability of developing Alzheimer's disease compared with those who were amyloid negative (HR 2·6, 95% CI 1·4–4·9). In participants with mild cognitive impairment who were amyloid positive the probability (HR 1·9, 95% CI 0·9–3·9) was not very different to those who were amyloid negative (1·6, 0·8–3·4).

Similarly, an 8-year follow-up study of 599 volunteers (average age 70 years) in Australia found that cognitively normal PET amyloid-positive people had an elevated risk of developing Alzheimer's disease compared with amyloid negative (17·7% vs 8·1%; OR 2·4, 95% CI 1·5–4·0). 184 Over 80% of the 266 people who were PET amyloid-positive did not go onto develop a cognitive impairment within 8 years, showing positive status does not predict impairment for most people in a timeframe that might be a useful prognostic window. Follow-up at 5 years of amyloid-positive participants with normal cognition or mild cognitive impairment versus amyloid negative people found the same pattern of increased risk (2·6, 1·4–4·9). Risk also increases per 1 year of age (HR 1·05, 95% CI 0·55–2·0/year), and APOEε4 status (2·6, 1·4–5·0). 184

Most people who are amyloid positive with no other markers have not developed Alzheimer's disease dementia during their lifetime. A model of lifetime risks of people who are amyloid positive without any other biomarkers finds it to be 8·4% for a 90-year-old woman who is cognitively normal at baseline, 23·5% for a 75-year-old woman and 29·3% for a 65-year-old woman. 185 The 10-year risk is considerably less, so a 65-year-old woman with only amyloid biomarkers but who is cognitively normal and has no neurodegeneration has a 10-year Alzheimer's disease risk of 2·5% and a man 2·3%, but the risk is higher with accompanying neurodegeneration ( table 2 ). 185

Ten-year risks by age of developing Alzheimer's disease for women based on amyloidosis alone and in the presence of neurodegeneration and mild cognitive impairment

Data are relative risk (95% CI) or %. Reproduced from Brookmeyer and Abdalla 185 by permission of Elsevier.

Overall, the knowledge of PET-measured amyloid and tau status and MRI-derived cortical thickness in a general population derived sample, only adds a small improvement, which might not be clinically important for predicting memory decline over a model with clinical and genetic variables. 186

Using amyloid PET in patients with cognitive impairment of uncertain causes, results in changes to the clinical diagnosis of Alzheimer's disease 187 and sometimes to medication prescription. We do not know whether PET use improves patient care or decreases care costs. Many people have a mixed cause of dementia and a positive result does not indicate only Alzheimer's disease.

Fluid biomarkers

PET imaging is very costly (US$3000 in the USA) and although used in some clinical settings remains the topic of research to understand its usefulness in broader populations. Fluid biomarkers—ie, blood and cerebrospinal fluid tests—have become a more practical focus of interest since it has become possible to measure specific proteins linked to the proteins associated with the neuropathologies of Alzheimer's disease. 188 A composite blood biomarker for amyloid tested in a discovery dataset and then a validation cohort of participants aged 60–90 years who were already taking part in studies in Japan or Australia had areas under the receiver operating characteristic curves of 96·7% for discovery and 94·1% for validation. The blood biomarker had sensitivity and specificity above 80% against amyloid PET measurement 188 and correlated with CSF concentrations of Aβ1–42. These results are similar to other amyloid blood biomarkers 189 , 190 and harmonisation to a common reference standard is now vital. Although CSF Aβ1–42/1–40 ratio and amyloid PET are now considered interchangeable, 191 CSF tau biomarkers have only correlated weakly with brain tau as currently measured by radioligands. 192 Neurofilament light protein is measured in many cohorts; however, it is non-specific. People with Huntington's disease, multiple sclerosis, mild cognitive impairment, and Alzheimer's disease might have raised blood neurofilament light concentrations, which are a marker of neurodegeneration. 193 , 194 , 195

Key points and conclusions

To be useful in clinical practice biomarkers must be well understood in the populations to which they are going to be applied, including the effects of age and sex on results. There is now reasonable evidence that amyloid and tau measured by PET or in fluid indicate increased risk for development of cognitive impairment in older adults but at the individual level prognostication is not possible as most cognitively normal people with these markers do not develop dementia within a clinically relevant timeframe. Negative amyloid results can be useful for ruling out current Alzheimer's pathology in people with cognitive impairment when the cause is uncertain and show an individual is unlikely to develop Alzheimer's disease during the next few years. High neurofilament light concentrations indicate a neurodegenerative process but not its cause. The value of biomarkers, in terms of diagnostic value, has not been addressed in different representative populations and particularly not in those from LMIC. The potential advantages of blood biomarkers are their low cost and their wider acceptability and applicability in many settings. In many areas of medicine more reliable diagnostic tests have improved research, including epidemiological and public health research and trials, to help distinguish cause from symptom (tuberculosis from a fever) or assess risk factor and disease (hypercholesterolaemia and ischaemic heart disease). Those biomarkers developed for the underlying biology of the dementia syndrome are subject to the same assessment of value.

Principles of intervention in people with dementia

In the 2017 Commission, we discussed that when concerns are raised by patients or family, an accurate diagnosis is helpful. Such a diagnosis provides a gateway to intervention and services where available, for planning for possible futures, and support for family, as well as to research. Unfortunately, these services are not always available. National plans for dementia support timely diagnosis and offer help to individuals and their families.

We did not address screening of those not presenting with concerns but rigorous systematic reviews by the US Task Force on Prevention have found an absence of evidence of benefit and harm. 196 The first trial of population screening took place in the USA, screening 4005 primary care patients aged 65 years or older. No clear benefit or harm in terms of quality of life, mood, or increasing diagnostic rates was found. 197 Other strategies might become more valuable in time such as sensitive awareness of risk factors, when routine records suggest an individual might be deteriorating cognitively. 198

People with dementia have complex problems with symptoms in many domains. Those providing support and any interventions must consider the person as a whole, as well as their context and their close carers, whether family or friends. Individuals' medical, cognitive, psychological, environmental, cultural, and social needs must be given consideration. 2 In the context of under provision of services, this notion is and will continue to be a challenge. Dementia, as an illness which affects cognition by definition, affects the ability to organise activities and people with dementia often need help to do what they enjoy—for example, listen to music, or go to gardens and parks. Wellbeing is one of the goals of dementia care.

Interventions once a diagnosis has been made

Cholinesterase inhibitors have a useful, modest role in improving cognition and activities of daily living in patients with mild-to-moderate Alzheimer's disease and memantine can be prescribed in combination or each drug used separately for moderate and severe Alzheimer's disease. 2 , 199 , 200 However, although available in most countries these drugs are no longer remunerated in France because it is felt that they offer only a small benefit while shifting clinician's attention from other interventions. Whether non-prescribing of this drug will help patients by removing an intervention with known benefit or be detrimental to them is unknown. 201 No advances have been reported in Aβ therapeutics, with negative results from phase 3 trials of monoclonal antibodies (eg, solanezumab, crenezumab) and inhibitors of β-secretase, a protease involved in the production of Aβ peptides. 202 Aducanumab previously abandoned as futile now has further unpublished results. Three 5HT6 antagonists and the calcium channel blocker nilvadipine 203 , 204 have also been ineffective. These drugs also show substantial impact during treatments at so-called therapeutic concentrations on the leakiness of blood vessels. The long-term impact of such side-effects is unknown. Anti-tau, anti-amyloid, and anti-inflammatory drugs continue to be in focus and some argue that pre-symptomatic interventions are necessary, especially if targeting Aβ production, but no evidence of efficacy 205 and some evidence of worsening target symptoms currently exists. 206

Cognitive training in people with dementia

A meta-analysis of 12 controlled trials of 389 people with mild dementia, completing 4 or more hours of group-based computerised cognitive training (mean age 66–81 years, 63·5% female participants), found a small, statistically significant beneficial effect on overall cognition, driven by two trials of virtual reality or Video games (SMD=0·3, 95% CI 0·0–0·5), one with a low and one with a high risk of bias. 55

A Cochrane review 207 found 33 trials of cognitive training, only one of which overlapped with the study above, with around 2000 participants with mild-to-moderate dementia, most with a high or uncertain risk of bias. 207 People completing cognitive training, compared with usual treatment or non-specific activities, had small-to-moderate effects on overall cognition (SMD 0·4, 95% CI 0·2–0·6) and specific cognitive abilities such as verbal fluency and improvements lasted for a few months to 1 year. No direct evidence was observed to suggest that cognitive training was better than cognitive stimulation therapy.

Exercise and physical activity

The Dementia and Physical Activity RCT 208 found moderate-to-high intensity aerobic and strength exercise training did not slow cognitive impairment in people with mild-to-moderate dementia but improved physical fitness. The US Reducing Disability in Dementia study 209 implemented an at-home multicomponent intervention including exercise education, training to increase pleasant events, and activator-behaviour-consequence problem-solving approach over 6 weeks by case managers in 255 community dwelling people with dementia older than 60 years and their family carer and were able to follow up 140 (54·9%). The study found increased physical activity; days of taking 30 or more minutes of exercise (effect size 0·6, 95% CI 0·4–0·8 after the treatment and 0·3, 0·1–0·5 at 13 months) in a before and after intervention comparison.

Interventions for neuropsychiatric symptoms of dementia

Neuropsychiatric symptoms are common and often clustered in people with dementia. These symptoms might precede dementia and are associated with tau and amyloid neuropathology. 210 This suggests that underlying neurobiological mechanisms might underpin neuropsychiatric symptoms. However, other drivers relating to the personal history and the environment of the person with dementia are also likely to exist. Neurodegeneration could lead to increased vulnerability to stressors or triggers. Genetics, cognitive reserve, resilience, medical comorbidities, and environment including responses of carers might modify these relationships. Needs and responses will also be individual and relate to a person's own social, cultural, and historical context. First-line assessment and management of neuropsychiatric symptoms should focus on basic health: describe and diagnose symptoms; look for causes such as pain (using validated pain assessments might help), illness, discomfort, hunger, loneliness, boredom, lack of intimacy and worry that could cause the behaviours and alleviate these while considering risks of harm. 2

No new evidence of medication effectiveness for these symptoms exists; risperidone in low doses (0·5 mg daily) and some other antipsychotics are sometimes effective but often ineffective and have adverse effects. 2 Specific initiatives have led to a decrease in antipsychotic prescriptions for people with dementia, although often replaced with other psychotropics ( figure 8 ), such as benzodiazepines, antidepressants, and mood stabilisers. 211 These psychotropics lack evidence of efficacy for neuropsychiatric symptoms but show clear evidence of possible harm; for example, trazodone and benzodiazepines increase fall-related injuries. 144 Major policy changes should be assessed carefully, within and across countries for unintended consequences (and perhaps unexpected benefits) and their costs.

Proportion of patients with a diagnosis of dementia prescribed an antipsychotic drug (A) and those prescribed an anxiolytic, hypnotic, or antidepressant (B)

CPRD=Clinical Practice Research Datalink. Reproduced from Donegan et al, 211 by permission of Elsevier.

Evidence is slowly accumulating for the effectiveness, at least in the short term, of person-centred evidence-based psychosocial interventions. In Germany, a 6-month cluster RCT of nurse-delivered, supervised dementia care management used a computer-assisted nurse assessment to determine personalised intervention modules, then a multi-disciplinary team discussion and agreement with the physician for 634 people (mean age 80 years) with dementia living at home with a primary carer or alone. 212 The mean mini mental state examination (MMSE) was 23, only 38% had a formal diagnosis of dementia; the majority of participants (51%) had mild dementia but some had moderate and some severe dementia. The intervention consisted of psychosocial management of treatment and care, medication management and carer support, and education and discussion with a psychiatrist or neurologist. The intervention, compared with care as usual, was associated with better outcomes for neuropsychiatric symptoms (Neuropsychiatric Inventory [NPI] score −7·5, 95% CI −11·1 to −3·8), however this effect could be because of deterioration in care as usual (in the care as usual group NPI increased from 7·2 to 15·2; in the intervention group NPI increased from 7·6 to 8·2). This between-group reduction in neuropsychiatric symptoms was greater than that expected, extrapolating from other study results, with antipsychotic medication. Effects on quality of life were only apparent for those people living with a carer.

An eight-session home-based tailored activity programme RCT, tailored both to the person with dementia living at home and to a family member compared with eight telephone-based education sessions, recruited 160 participants with 64% follow-up, imputing values for the rest. 213 The study reported a large reduction in overall neuropsychiatric symptoms immediately after the intervention, which were better in the group receiving home-based tailored activity programme on the neuropsychiatric inventory (mean difference in score 24·3, 95% CI 3·1–45·6), and on functional dependence and pain but this was not sustained 4 months later. Non-completers had more severe neuropsychiatric symptoms.

Since the 2017 Commission two new systematic reviews of antidepressants to treat depression in dementia reported moderate quality evidence that antidepressant treatment for people with dementia does not lead to better control of symptomatology compared with placebo. 214 , 215

Agitation is distressing for people with dementia and those around them, and contributes substantially to the overall costs as the level of agitation increases. 216 The body of evidence on this key behaviour is growing, mostly focused on care-home settings. These findings are valuable as these populations are most affected; however, because many people with dementia reside at home a major gap in knowledge remains.

Care home residents with agitation often find sitting still difficult and therefore might not be included in activities. 217 , 218 Two new cluster RCTs of professionals delivering multicomponent, interdisciplinary, interventions in care homes successfully reduced agitation. The WHELD study 219 included participants with or without neuropsychiatric symptoms and provided person-centred care, aiming to improve communication with people with dementia. It implemented social, sensory experiences or other activities; educated about antipsychotic review; and addressed physical problems, finding lower Cohen Mansfield Agitation Inventory (CMAI) at 9 months (MD −4·3 points, 95% CI −7·3 to −1·2). 219 The TIME study 220 for people with moderate-to-high levels of agitation consisted of a manual-based comprehensive assessment of the resident and structured case conference for the staff and doctor, to create a tailored plan, and then implement it. This intervention led to reduced agitation at 8 weeks (NPI −1·1 points, 95% CI −0·1 to −2·1; CMAI −4·7 points, −0·6 to −8·8) and 12 weeks (NPI −1·6, −0·6 to −2·7; CMAI −5·9, −1·7 to −10·1). 220 These effect sizes are similar to those seen for medications, but without harmful side-effects. 2 , 221 A further RCT studied a six-session intervention with staff in groups, teaching staff to understand agitation as related to medical, psychological, or social unmet needs and to implement strategies to meet these needs, using the describe, investigate, create, and evaluate approach. 222 The intervention did not reduce agitation symptoms, although it was cost-effective, improving quality of life. 223 Overall, the current evidence for agitation in care homes favours multi-component interventions by clinical staff, including considering if drugs might harm, and not drug interventions. Thus a major gap remains in knowledge about people living at home who comprise the majority of those with dementia.

Psychotic symptoms in dementia

People with dementia might be wrongly thought to have delusions when they misremember, and new psychotic symptoms are often due to delirium, thus thorough assessment of symptoms is essential. 2 Management of psychosis in dementia should start with non-pharmacological interventions; however, evidence for effectiveness of these interventions for psychosis in dementia is weaker than for agitation. 224 Antipsychotics for psychosis in dementia should be prescribed in as low a dose and for the shortest duration possible. 2 However, a Cochrane review of antipsychotics withdrawal found two trials with participants with dementia who had responded to antipsychotic treatment. These reported that stopping antipsychotics was associated with symptomatic relapse 225 suggesting the need for caution in any medication withdrawal in this group. There was low-quality evidence that, in general, discontinuation might make little or no difference to overall neuropsychiatric symptoms, adverse events, quality of life or cognitive function. 226

Apathy might be conceptualised as the opposite of engagement, comprising reduced interest, initiative, and activity. Like people without dementia, those with dementia engage more in preferred activities, but require additional support to do so. 227 A study in care homes observed engagement increased during activities in those who attended the groups. 228 A Cochrane review of the few people who had been in drug RCTs of methylphenidate versus placebo for apathy in dementia found small improvements on the apathy evaluation scale (MD −5·0, 95% CI −9·6 to−0·4, n=145, three studies, low-quality evidence) but not on the NPI apathy subscale (MD −0·1, 95% CI −3·9 to 3·7, n=85, two studies). 229

There is no evidence that medication for sleep in dementia is effective 230 and considerable evidence for harm—ie, earlier death, increased hospitalisation, and falls—exists. 139 , 144 Testing of non-pharmacological interventions is ongoing. 231

Carer distress related to neuropsychiatric symptoms rather than the dementia symptoms was associated in one study with increased use and costs of health services, 232 highlighting the need for effectively identifying, educating, and supporting distressed carers. An RCT 233 reporting 6-year follow-up after the eight session STrAtegies for RelaTives intervention—manual-based coping intervention delivered by supervised psychology graduates—found continuing effectiveness for depressive symptoms in carers (adjusted MD −2·00; 95% CI −3·4 to −0·6) and risk of case-level depression, with patient-related cost being approximately 3 times lower than those who did not receive the intervention (median £5759 vs £16 964 in the final year; p=0·07). 233 Another US study 234 followed up 663 people, mean age 77 years, 55% women. Caregiver depression rather than symptoms of people with dementia predicted emergency department use for people with dementia, with a 73% (RR 1·73, 95% CI 1·3–2·3) increase. 234

Functioning

A UK RCT of 14 sessions of cognitive rehabilitation focused on individual goal attainment with therapy delivered at home by an occupational therapist or nurse to 475 participants with mild-to-moderate dementia (MMSE ≥18 for inclusion; mean 24) and a family carer. 235 Individuals had two or three goals; the most common was engaging in activities (21% of goals). The intervention group reported increased goal attainment over 3 and 9 months compared with usual treatment (effect size 0·8, 95% CI 0·6–1·0 at both 3 and 9 months). 235 The treatment did not improve participants' quality of life, mood, self-efficacy, cognition, carer stress, or health status and was not cost-effective. A systematic review 236 of RCTs without meta-analysis for overall effect size, concluded that all interventions which had improved functioning in people living with dementia in the community have been individual rather than group interventions. These were: in-home physiotherapist delivered aerobic exercise (two studies, larger one positive, 140 people with Alzheimer's disease; smaller study negative, 30 people with Alzheimer's disease), individualised cognitive rehabilitation (mild or moderate dementia; two studies; 257 cognitive reserve intervention groups and 255 controls), and in-home activities-focused occupational therapy (people with mild to moderate dementia, three studies, 201 intervention, 191 controls) reduced functional decline compared to controls but group-exercise and reminiscence therapies were ineffective. 236

People with dementia have other illnesses

Multimorbidity is a huge challenge in dementia, not only because people with dementia have increased rates of other illnesses, but also because they often find it particularly difficult to organise care. People with dementia might forget to tell their family or health professionals of symptoms, struggle to understand or follow agreed plans, and are more likely to forget to drink and eat, increasing falling and infection rates. 237 People with dementia consult primary care less often 238 and have fewer dental visits 239 than those without dementia and their family members, if involved, often feel they lack knowledge to assist. 240 Health-care professionals need education to be more comfortable, understanding, and positive in communicating with people with dementia. 241

Around 70–80% of people diagnosed with dementia in primary care have at least two other chronic illnesses. 242 , 243 People who are physically more frail are more likely to have dementia, but the relationship between pathology and symptoms in these people is comparatively weak suggesting that dementia might be from other causes. 22 Compared to the general older population, people with dementia have increased rates of cerebrovascular disease, 243 , 244 , 245 , 246 stroke, 247 Parkinson's disease, 243 , 245 diabetes, 245 , 247 skin ulcers, anxiety and depression, 243 , 245 pneumonia, incontinence, and electrolyte disturbance. 245 Multimorbidity in people with dementia is associated with faster functional decline 248 and worse quality of life for people with dementia and their family carers. 249

Dementia and COVID-19

Severe acute respiratory syndrome coronavirus 2, was first identified in patients with viral pneumonia in Hubei province, China. 250 Severity and mortality of the associated disease (COVID-19) worsen with increasing age 251 and with pre-existing illnesses such as hypertension and diabetes, 252 and thus many people with dementia are at particular risk. Death certificates from the UK indicate that dementia and Alzheimer's disease were the most common underlying conditions, specified in 11 950 deaths (25·6% of all deaths involving COVID-19) in March to May, 2020. 253 Many charities, practitioners, and academics supporting people with dementia have issued guidance based on current evidence and best practice, including advance consideration of whether people would wish to be hospitalised if they develop severe COVID-19. Concern has been expressed that the illness and consequent distancing might increase family carer stress, loneliness, neuropsychiatric symptoms and use of psychotropic medication, and lead to complications, including future dementia. Interventions delivered remotely through technology have also been implemented in some places. 254 , 255 , 256 , 257

People with dementia might struggle to adhere to measures to reduce virus transmission, as they might not understand or remember about required changes to behaviour, such as physical distancing and hygiene, leading to increased risk to themselves and their carers. 258 They might additionally be vulnerable if they depend on others for daily activities or personal care, as this necessitates close personal contact.

This situation is particularly concerning in those care homes, where many residents have dementia and where many COVID-19 deaths have occurred in many countries 259 , 260 , 261 with reports of more than half of residents being admitted to hospital. In US nursing homes, among 10 576 people with confirmed COVID-19, residents living with dementia made up 52% of COVID-19 cases; yet, accounted for 72% of all deaths (an increased risk of 1·7). 262 The number of people living together in care homes means that the infection of an individual, either staff or resident, could endanger more people than in traditional or family households. Although evidence exists that if staff are sufficiently and rigorously protected they are unlikely to develop COVID-19, many staff have become unwell and some have died. 263 , 264 Illness means that there are fewer people to care for residents at a time when they need particularly high levels of care. This situation is particularly relevant in the care of residents with dementia, if they are expected to remain in their own rooms, rather than eating and participating in activities with others. Staff or residents might also be moved between care homes and increase risk in other homes. 261 Restrictions on visitors to private homes, care homes, and hospitals might cause greater distress for people with dementia and they might not understand why people are wearing masks, recognise who is behind it, or understand speech when lips are covered. Lack of restrictions means that the visitors might also be at elevated risk. 261

The impacts of COVID-19 on people with dementia might be particularly severe in LMICs, due to smaller health budgets for testing and protective equipment, capacity of health-care systems, quality of care home provision and patterns of workforce mobility. 264

Thus, people with dementia are particularly vulnerable to COVID-19 because of their age, multimorbidity, and difficulties in maintaining physical distancing. 250 , 251 , 252

We recommend rigorous public health measures of protective equipment and hygiene, including not moving staff or residents between care homes or admitting new residents when their COVID-19 status is unknown, should mitigate impacts on people with dementia. It is also imperative that there is frequent and regular testing of staff in care homes for infection, ensuring staff have sick pay so that they do not come in when symptomatic and interim care is being set up for people discharged from hospital so that only those who are COVID-19 free come to live in care homes. Resident testing should encompass asymptomatic as well as symptomatic people, when there is exposure within the home to COVID-19. In the future, many homes might be able to start to provide oxygen therapy so that those who do not want to be admitted to hospital are still able to access oxygen therapy. In addition, it is also important to reduce isolation by providing the necessary equipment and a brief training to relatives on how to protect themselves and others from COVID-19; so that they can visit their relatives with dementia in nursing homes safely when it is allowed. Further evidence is needed to inform responses to this and future public health emergencies.

Hospital admissions

Hospitalisation in people with dementia is associated with adverse, unintended consequences, including distress, functional and cognitive decline, and high economic costs. 265 , 266 , 267 People with dementia have 1·4 to 4 times more hospital admissions than others with similar illnesses. 266 , 268 , 269 , 270

A systematic review and meta-analysis including 34 studies of 277 432 people with dementia found that in the six studies which compared the two groups, people with dementia had increased hospital admissions compared with those without dementia, after adjusting for age, sex, and physical comorbidity (RR 1·4, 95% CI 1·2–1·7; figure 9 ). 271 Hospitalisation rates in people with dementia ranged from 0·37 to 1·26 per person-year in high-quality studies. Admissions are often for conditions that might be manageable in the community (potentially preventable hospitalisations). 268 People with dementia experience longer and more frequent admissions and readmissions; health-care expenditure for people with moderate-severe dementia is around double that of people without dementia. 269 , 272 , 273 Early detection and management of physical ill-health in people with dementia, particularly of pain, falls, diabetes, incontinence, and sensory impairment, is important. 199 , 274 , 275 However, no intervention has successfully reduced number of hospital admissions of community-dwelling people with dementia, 276 although education, exercise, rehabilitation, and telemedicine have reduced admissions for older people without dementia. 277

Systematic review and meta-analysis of hospitalisation rates of people with dementia compared to those without dementia controlled for age and sex

Reproduced from Shepherd et al, 271 by permission of Springer Nature.

High-quality care for people with dementia takes longer than caring for others with the same condition. 278 Recognition of dementia in hospital inpatients is necessary for optimum care, 279 but dementia is often undetected or unrecorded. 280 In the UK however, detection rates have increased over the past 10 years. 281

Physical illness, delirium, and dementia

Dementia and delirium frequently occur together. In one hospital inpatients' survey nearly 35% of those older than 80 years experienced delirium; those with prior cognitive impairment had 15 times the risk of developing delirium than those without (OR 15·3, 95% CI 5·2–45·4). 282 People with delirium without known dementia are more likely to be diagnosed with dementia in the future than others, either because of pre-existing undiagnosed dementia or cognitive impairment, present in 20·7% (95% CI 11·9–29·5) and 37·8% (27·3–88·3) respectively of one cohort, or because delirium has neurotoxic effects and so precipitates dementia. 283 People with similar neuropathology show faster cognitive decline if they develop delirium than if they do not. 284 Additionally, older people without dementia declined cognitively more than twice as fast after an emergency hospital admission for any cause, compared with those not admitted, suggesting any severe illness is associated with cognitive decline. 285 Risk factors for delirium in dementia include sensory impairment, pain, polypharmacy, dehydration, intercurrent illnesses, such as urinary tract infections or faecal impaction, and an unfamiliar or changing environment. 286 Delirium in older people should prompt consideration of underlying dementia.

Most research on delirium prevention has been in people without dementia. It suggests targeting hydration, stopping medication predisposing to delirium, monitoring the depth of anaesthesia, and sleep promotion. However, no evidence for medication efficacy, including cholinesterase inhibitors, antipsychotic medication, or melatonin exists. 287 , 288 , 289 The Hospital Elder Life Program 290 —an intervention to prevent delirium in those admitted to hospital—reduces delirium incidence and includes people who are cognitively impaired. This multidisciplinary treatment consists of daily visits, orientation, therapeutic activities, sleep enhancement, early mobilisation, vision and hearing adaptation, fluid repletion, infection prevention and management of constipation, pain, and hypoxia, and feeding assistance. 290

A network meta-analysis of drugs for prevention and treatment of delirium did not include studies of people with dementia, thus we cannot use this to recommend drugs for people with dementia and delirium as this research might be inapplicable to them. 291

Little high-quality research exists on managing delirium in dementia. One RCT compared care at a specialist medical and mental health unit to usual care for 600 confused people older than 65 years, acutely admitted to hospital and found no difference in the primary outcome of days spent at home or in hospital, but increased family satisfaction. 292 A further RCT of cognitively stimulating activities for people with delirium in dementia did not improve the delirium. 293 No definitive evidence that any medication improves delirium in people with dementia exists: cholinesterase inhibitors, antipsychotics, and sedating benzodiazepines are ineffective and antipsychotics and benzodiazepines are associated with mortality and morbidity. 265 , 288 , 294 , 295 , 296 , 297 Given the risk of dementia in people who develop delirium, its prevention, and possibly advances in its management, might offer a means for dementia prevention. 298

Link between very old age, frailty, and dementia

The fastest growing demographic group in most advanced countries are people aged 90 years and older. One well characterised post-mortem cohort of the oldest old (n=1079; mean age 90 years) dying with dementia, found that neuropathological features of Alzheimer's disease account for about half of the cognitive decline seen as people diagnosed with Alzheimer's disease had mixed causes of dementia. 299 Although Alzheimer's disease neuropathology was the commonest cause of dementia, Alzheimer's disease changes rarely occurred on their own, so only 9% of people with dementia had pure Alzheimer's disease pathology. 300 People who have Alzheimer's disease pathology without developing dementia tend to have fewer age-related health deficits than those who develop it with even low concentrations of plaques and tangles. 301 A moderation analysis showed that the relationship between Alzheimer's disease pathology and dementia status differed according to level of frailty (adjusted for age, sex, and education) with increasing frailty weakening the relationship between Alzheimer's disease pathology and dementia ( figure 10 ). 22 As with delirium, some of this additional health risk might be modifiable. This approach suggests a new type of therapy focus on specific age-related processes that underpin many diseases of late life might reduce the incidence or severity of dementia.

Moderation analyses of the relationship between Alzheimer's disease pathology and clinical diagnosis of Alzheimer's dementia (adjusted for age, sex, and education)

As frailty increased, the odds of a neuropathological diagnosis of Alzheimer disease corresponding to a clinical diagnosis decreased. Reproduced from Wallece et al, 22 by permission of Elsevier.

End-of-life care in dementia

The numbers of people dying with dementia are increasing but the evidence for the best end-of-life care is scarce. Trends in age-standardised death rates (3·6%) for dementia increased slightly between 1990–2016, with pronounced increases in the USA and Japan and decreases in western Europe and central Latin America. 4 Dementia is more readily being included on death certificates, which accounts for some of the rise. The increase might be related to dementia manifesting at later ages, with higher physical frailty 22 leading to a faster decline.

Most people with dementia might die while still in the mild-to-moderate stages whereas only about a quarter of those dying with dementia have severe dementia. 302 , 303 The trajectory of dementia is often unpredictable 304 and palliative care initiation should reflect need not prognosis.

Decision making about end of life is complex and simple rules of thumb, co-designed with staff and carers, provided clarity in some small studies. 304 One RCT testing decision-aids about families' and doctors' goals of care for people with advanced dementia led to increased palliative care content in care plans. 305 , 306 In a 9-month UK prospective study, 85 care home residents with advanced dementia from 14 homes were likely to be living with distressing symptoms, specifically agitation (54%) or pain (61% on movement). 304

Capacity to make abstract decisions, including about the future, might be lost early in dementia. 307 Therefore, advance care planning, designed to empower people with dementia and improve quality of dying, might theoretically be something everyone should do before developing dementia. 308 However, people might not be able to predict their future wishes. This might explain why family carer proxies show only low-to-moderate agreement with stated end-of-life treatment preferences of people with dementia. 309 Advance care planning might, however, reduce carers' uncertainty in decision making and improve perceptions of quality of care. 310

Partners of people dying with dementia experience poorer mental health than those facing bereavement from other causes 311 possibly because of long and difficult caring responsibilities. This might be ameliorated through sensitive and timely information, particularly regarding the progression of dementia, 312 individually or through family and staff case-conferencing. 313 , 314

Conclusions

Knowledge about risk factors and potential prevention, detection, and diagnosis of dementia is improving although significant gaps remain. 315 In this Commission report, we have specified policy and individual changes to delay the onset of cognitive impairment and dementia and better ways to support and treat people with dementia and their families and to improve their quality of life.

Interventions, including organisation of the complex physical illness and social needs, to support people affected by dementia can have a huge effect when taken as a whole. Our ambition is for worldwide provision of resources for an adequate level of wellbeing to people with dementia and their carers with a better evidence base to guide individual care and policy making alike. With good quality care, people can live well with dementia and families can feel supported.

Acknowledgments

We are partnered by University College London (UCL), the Alzheimer's Society, UK, the Economic and Social Research Council, and Alzheimer's Research UK, and would like to thank them for financial help. These organisations funded the fares, accommodation, and food for the Commission meeting but had no role in the writing of the manuscript or the decision to submit it for publication. We would like to thank Bernadette Courtney, Jacques Gianino, and Nuj Monowari, from UCL, London, UK, for their administrative help, including managing finances, booking rooms and food, and setting up a website supported by the University College London Hospitals National Institute for Health Research Biomedical Research Centre. We would like thank Henrik Zetterberg for advice on biomarkers and dementia.

Contributors

GL, JH, AS, and NM contributed to literature searches and quality assessments for systematic reviews. JH and NM performed meta-analyses. GL, JH, AS, and NM conceived the new PAF calculation and NM led the statistical analysis. GL, JH, AS, NM, DA, CLB, SB, AB, JC-M, CC, SGC, NF, RH, HCK, EBL, VO, KRi, KRo, ELS, QS, LSS, and GS attended the conference to discuss the content. GL, JH, EBL, AS, DA, and ELS wrote first drafts of sections of the paper. GL wrote the first draft of the whole paper and revisions of drafts. CBa reviewed and contributed to revision of the final drafts. All authors contributed to sections of the reports and all revised the paper for important intellectual content.

Declaration of interests

AS reports grants from Wellcome Trust (200163/Z/15/Z), outside the submitted work. DA reports grants from Eli Lilly, during the conduct of the study. CBa reports grants and personal fees from Aca-dia and Lundbeck; and personal fees from Roche, Otsuka, Biogen, Eli Lilly, and Pfizer, outside the sub-mitted work. SB reports grants and personal fees from AbbVie, personal fees and non-financial sup-port from Eli Lilly, and personal fees from Eleusis, Daval International, Boehringer Ingelheim, Axovant Sciences, Lundbeck, and Nutricia, outside the submitted work; and he has been employed by the Department of Health for England. NF reports non-financial support from Eli Lilly, outside the submitted work. LNG and her institutions (Johns Hopkins University, Baltimore, MD, USA, Drexel University, Philadelphia, PA, USA, and Thomas Jefferson University, Philadelphia, PA, USA) are entitled to receive royalties from fees associated with online training for the tailored activity program, which is an evidence-based program referenced in the Review. RH reports grants from Department of Health, NIHR HTA Programme, outside the submitted work; and he is a Scientific Trustee of the charity Alzheimer's Research UK. MK reports grants from the UK Medical Research Council (S011676, R024227), NordForsk (the Nordic Programme on Health and Welfare, 75021) and the Academy of Finland (311492), outside the submitted work. EBL reports other (royalties) from UpToDate, outside the submitted work. KRo reports personal fees from Clinical Cardio Day-Cape Breton University, Sydney, NS, Canada, CRUIGM-Montreal, Jackson Laboratory, Bar Harbor, MA, USA (speaker fees), MouseAge, Rome, Italy (speaker fees), Lundbeck, Frontemporal Dementia Study-Group, SunLife Insurance, Japan, outside the submitted work. He is a President and Chief Science Officer of DGI Clinical, which in the last 5 years has contracts with pharma and device manufacturers (Baxter, Baxalta, Shire, Hollister, Nutricia, Roche, Otsuka) on individualised outcome measurement. In 2017, he attended an advisory board meeting with Lundbeck. He is also Associate Director of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes of Health Research, and with additional funding from the Alzheimer Society of Canada and several other charities, as well as, in its first phase (2013-2018), from Pfizer Canada and Sanofi Canada. He receives career support from the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research, and research support from the Canadian Institutes of Health Research, the QEII Health Science Centre Foundation, the Capital Health Research Fund and the Fountain Family Innovation Fund of the QEII Health Science Centre Foundation. LSS reports grants and personal fees from Eli Lilly, Merck, and Roche/Genentech; personal fees from Avraham, Boehringer Ingelheim, Neurim, Neuronix, Cognition, Eisai, Takeda, vTv, and Abbott; and grants from Biogen, Novartis, Biohaven, and Washington University DIAN-TU, outside the submitted work. The remaining authors declare no conflict of interests.

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• Published: 18 May 2023

A WHO blueprint for action to reshape dementia research

• Rodrigo Cataldi 1 ,
• Perminder S. Sachdev   ORCID: orcid.org/0000-0002-9595-3220 2 ,
• Neerja Chowdhary 1 ,
• Katrin Seeher 1 ,
• Adam Bentvelzen 2 ,
• Vasee Moorthy 3 &
• Tarun Dua   ORCID: orcid.org/0000-0002-4269-7657 1  

Nature Aging volume  3 ,  pages 469–471 ( 2023 ) Cite this article

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• Neuroscience

The World Health Organization (WHO) blueprint for dementia research provides a roadmap to addressing the challenges in the field and reshaping our approach to dementia research. This Comment focuses on how to operationalize the drivers of research highlighted in the blueprint to make research more equitable, impactful and global.

Dementia is one of the greatest health challenges of our generation. With currently 55.2 million people affected worldwide (predicted to reach 78 million by 2030 1 ), dementia is likely to have a critical impact on healthcare systems, care infrastructure and economies. This burden is particularly severe in low- and middle-income countries (LMICs), where currently over 60% of people with dementia live and the largest increase in dementia cases is expected 1 .

International commitments to address the global dementia challenge include the 2013 G8 dementia summit 2 , the adoption of the ‘Global Action Plan on the Public Health Response to Dementia 2017–2025’ (ref. 3 ) and the Okayama Declaration of the G20 Health Ministers in 2019 (ref. 4 ). Yet, dementia research often remains uncoordinated, with disparities in funding and quality 5 . Despite some countries increasing funding for dementia research, overall investment is not proportionate to the impact and cost of dementia 5 .

Accelerating research efforts is crucial for addressing any global health challenge. The COVID-19 pandemic has shown how quickly research can advance when the international community works together. However, it has also highlighted the unequal access to biomedical advances and research infrastructure globally, as well as the need to shift our approach to research toward global public-health interests.

Notwithstanding the challenges, our knowledge about dementia has substantially increased over the past four decades and many scientific advances — although mostly seen in high-income countries (HICs) — have improved diagnosis and care for people with dementia 6 . However, there is still no cure for dementia and most countries are not acting on dementia risk reduction, despite the growing evidence in its support. A holistic approach to science is needed that focuses on all aspects of research, including basic and clinical research, and its implementation into practice and policy.

To this end, the WHO has developed a blueprint for dementia research 7 , the first WHO blueprint to focus on a noninfectious disease. The blueprint aims to support the global prioritization of dementia research and provides a coordination mechanism to facilitate timely and high-quality evidence generation, fast-track innovation, foster effective research implementation and guide resource mobilization.

The blueprint summarizes the current state of dementia research across six research themes (Fig. 1 ), highlights gaps, and outlines strategic goals and actions to address them. Addressing the gaps requires an enabling research environment that can be achieved through eight essential drivers of dementia research (Fig. 1 ) that together ensure greater efficiency, equity and impact. This Comment focuses on how these drivers of research can be operationalized by different stakeholders to foster long-term systemic changes that strengthen dementia research globally.

Reproduced with permission from ref. 7 .

Operationalizing the drivers of dementia research

Including and empowering people with lived experience to achieve impact.

Globally, people with dementia are insufficiently involved in all stages of research, ranging from priority setting, planning and development of research proposals to knowledge translation and integration in policy. Data from WHO’s Global Dementia Observatory show that in almost 50% of countries, people with dementia are not at all involved in research 1 .

The blueprint therefore calls for the mandatory inclusion and active involvement of people with dementia and their carers. Concretely, the research community in partnership with civil society should develop, for example, programs that provide training in research and science so that people can contribute in a meaningful way and create processes that account for different levels of cognitive, sensory and physical impairment or disability.

Addressing inequity and diversity

Traditionally, dementia research has been driven by and conducted in HICs. For instance, genetic data are mainly representative of populations of European origin 7 . Inequities also exist in terms of funding allocation, which negatively affects mostly women and researchers from LMICs 1 . The blueprint therefore outlines equity principles to be applied by funding bodies, regulatory authorities and the scientific community (such as fairness and equal participation in resource allocation and in representation in studies) that foster real, transparent research collaborations, decrease the power imbalance and dependence on HIC institutions, as well as increase research output and the representation of LMICs in global dementia research. Funding bodies, for example, can increase the diversity of funding streams to reflect all priority areas and actively seek submissions that address inequities and promote equal partnerships between HICs and LMICs.

To promote diversity and inclusion of populations and communities who are traditionally less likely to participate in dementia research or are rarely covered by scientific studies (for example, epidemiological research), funders and research institutions can develop equity monitoring systems to ensure better representativeness and a fair distribution of resources.

Sustainable, adequate and equitable funding

Dementia research has been chronically underfunded and unevenly distributed. Of the 50 organizations and institutions that received the most grants for dementia research in 2019, 41 were in the USA, 6 in the UK and 3 in Canada 8 . This discrepancy also results in research collaborations being predominantly established with other HIC institutions 8 .

To address this imbalance, funding agencies can strategically allocate resources to LMICs towards building research capacity and infrastructure, and support the perennial development and training of a research workforce. The extent to which funders implement such recommendations can be monitored by measuring the proportion of awards being allocated to collaborations between HICs and LMICs in the design and execution of studies, as well as to researchers with geographical, gender and background underrepresentation.

The duration and continuity of funding are also crucial. Given the chronic and often slowly progressive nature of dementia, continuous funding must be provided for comprehensive longitudinal research, which is often costly. This can be facilitated by increased advocacy for resource allocation to dementia research, as well as the establishment of collaborations between funders to jointly develop funding calls. Funding agencies should strive to achieve a better balance by including underresearched aspects of dementia, such as conditions other than Alzheimer’s disease or dementia in highly vulnerable or marginalized populations.

Improving access to science, data and materials

Access to scientific knowledge, data and materials is limited in many parts of the world, with international restrictions interfering with the ability to share biosamples. Simultaneously, a lack of international standards, regulations and incentives can also hinder the ability to share materials and data. Moreover, insufficient researcher time, lack of funding, inadequate storage infrastructure and the fact that funders do not fully mandate open access to data 9 further hamper routine data sharing and global access to science.

To facilitate scientific data sharing and collaboration, governments can review existing laws and regulations concerning data sharing and protection or develop new ones, and funding agencies can promote the responsible and ethical use of data by mandating data sharing or providing incentives to do so within existing regulations.

Access to science has often been pay-walled by publishers, which creates barriers — especially in low-income settings. Recent efforts to make scientific literature more accessible through open-access publishing are encouraging, and the exemption from payment of open-access publishing fees to scientists from low-resource countries can substantially help to develop and foster scientific enterprise in LMICs.

Building capacity across different settings

Research career pathways are often insecure in dementia, owing to scarce funding and support. As a result, promising researchers (particularly women and junior researchers) may seek alternative careers, which cause a ‘brain drain’ and a reduction in research capacity 10 .

To address this, the blueprint calls for long-term strategies to invest in and build research capacity across all income settings. For instance, the academic sector should provide researchers in disciplines that are relevant to dementia research and related areas (such as aging, noncommunicable diseases and mental health) with training and resources in basic science, epidemiology, cognitive assessment, ethical research practice and dementia care, particularly in areas and settings where skills are lacking. This will strengthen capacity and broaden the pool of scientists working on dementia, and lead to a greater impact in areas such as epidemiology, disease mechanisms and risk reduction.

The career paths of female researchers also require increased attention, with academic institutions fostering their development and ensuring a fair representation in professorship and management positions. Likewise, junior researchers experience uncertainties regarding their career development. Addressing this issue requires the relevant government sectors to jointly develop strategies to provide more tenure-track positions, instead of short-term development grants and temporary positions.

Technology to provide benefits

Often considered a new determinant of health 11 , digital technologies have tremendous potential to improve the quality and reach of healthcare. Wearable devices and smartphones, for example, can collect large amounts of continuous real-time data that can be integrated and harmonized by big-data technology at different scales. However, 37% of the world’s population is still offline 12 , hampering the access to potential health benefits for many populations.

To achieve substantial global progress and eliminate the ‘digital divide’ both within and between HICs and LMICs, and across gender and socioeconomic lines, governments, funders and the private sector must invest in technological health infrastructure where it is currently lacking and ensure that innovations are applied and scaled to the benefit of societies and the elimination of health inequity. Similarly, legal frameworks regulating the access, use and protection of personal data need to be developed and regularly reviewed.

Across all income settings, infrastructure should be developed, and training provided to apply approaches grounded in data and enable the application of artificial intelligence such as machine-learning and deep-learning to dementia research. Open-source and offline software should be developed through public–private partnerships to support digitally disadvantaged groups, while an increase in capacity and investment takes place.

Knowledge translation and implementation

The time gap between evidence generation and its implementation in clinical practice is often too long 13 . Similarly, the translation and uptake of evidence into policy is also hampered owing to lack of awareness from policy-makers and communication between the various sectors 7 .

The blueprint therefore emphasizes the need for increased efforts and dedicated resources to disseminate research evidence to relevant stakeholders in a timely manner. It is also key to specifically strengthen the field of implementation science through long-term strategic investment and requirements for grant proposals to include implementation strategies, either into policy or practice, when relevant.

Often, knowledge, skills and infrastructure that are required for dementia research and implementation may not be present in all settings. To address this, the research community can share expertise by building strong international networks, shared databases and platforms that link diverse researchers, facilitate multidisciplinary collaboration and ensure that research findings can be implemented in diverse settings at local, national and international levels.

Strengthening of regulatory frameworks

Regulatory frameworks for drug development and clinical trials are sometimes seen as barriers to research progress. However, a strong, well-formulated and transparent regulatory environment is a key driver of research and an important enabler of collaboration and successful research implementation. Complexity and a lack of transparency in regulatory environments, combined with divergent international norms and standards, may create barriers that will hinder the establishment of collaborations and slow down the implementation of innovations 7 .

This warrants the creation of ethically sound guidelines by regulatory agencies that anticipate the evidence and requirements necessary for regulatory review and policy development, to fast-track life-changing scientific advances. This would be further facilitated by international harmonization of norms and standards and international agreements for the establishment of worldwide collaborations.

The next steps in implementing the drivers of research

All stakeholders have a role in operationalizing these interconnected drivers of dementia research and making research an integral part of the public health response to dementia, fully recognizing the wide societal impact of dementia with consequences that span well beyond the healthcare and social care sectors.

As outlined here, national and international research agencies and funding bodies should use this blueprint to inform their funding streams and research efforts. Civil society can support advocacy efforts that align with this blueprint, aiming to create a more equitable, efficient and collaborative research landscape. Researchers can also contribute by addressing the identified research gaps. Adopting this comprehensive approach will contribute positively to the promotion and protection of overall brain health. The WHO will use its convening power to bring together stakeholders, including regulatory bodies, funders and the scientific community, to advance the actions outlined in the blueprint.

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Acknowledgements

The WHO gratefully acknowledges the financial support of Gates Ventures for the development of the WHO blueprint for dementia research. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.

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Rodrigo Cataldi, Neerja Chowdhary, Katrin Seeher & Tarun Dua

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R.C., P.S.S., N.C., K.S. and T.D. developed the initial concept of this Comment. R.C., P.S.S., N.C., K.S., A.B., V.M. and T.D. contributed to discussing the content, and writing, reviewing and/or editing of the Comment.

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Cataldi, R., Sachdev, P.S., Chowdhary, N. et al. A WHO blueprint for action to reshape dementia research. Nat Aging 3 , 469–471 (2023). https://doi.org/10.1038/s43587-023-00381-6

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• Open access
• Published: 13 May 2020

“Current dementia care: what are the difficulties and how can we advance care globally?”

• Clarissa Giebel 1 , 2  

BMC Health Services Research volume  20 , Article number:  414 ( 2020 ) Cite this article

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Dementia is a growing global public health concern, with post-diagnostic care often very limited. Depending on where people live, both within a country and depending on high-, middle-, and low-income countries, they might also face barriers in accessing the right care at the right time. Therefore, it is important to highlight recent evidence on the facilitators and barriers to dementia care, but also evidence on how to move dementia care forward.

Current dementia care is subject to several inequalities, including living in rural regions and being from a minority ethnic background. Evidence in this collection highlights the issues that both people living with dementia and unpaid carers are facing in accessing the right care, with evidence from Australia, Canada, Uganda, to the Netherlands, and further afield. Providing improved dementia-specific training to health care professionals and supporting medication and reablement interventions have been identified as possible ways to improve dementia care for all.

Conclusions

This special issue focuses on recent evidence on inequalities in dementia care across the globe and how dementia care can be advanced in various areas.

BMC Health Services Research is pleased to launch ‘ Advancing Dementia Care’ , an article collection focused on current dementia care inequalities and what can be done to advance care. Dementia affects an estimated 50 million people worldwide [ 1 ], with numbers steadily growing. This can affect many areas in a person’s life – from struggling to do the shopping and managing medication [ 2 ] to behavioral difficulties and cognitive problems [ 3 ]. Many people with dementia have unpaid carers (family members or friends) [ 4 ] who help them with daily tasks ranging from preparing a meal to more personal tasks such as washing and dressing. However, carers are most often overlooked when it comes to supporting those people affected by dementia.

To support both people with dementia and their carers, adequate post-diagnostic support needs to be in place (which can include memory groups, support groups, respite care, day-care centers, and social activities in the community). Not everyone receives the same level of support, for multiple reasons. The World Health Organisation (WHO) defines health inequities as “differences in health status or in the distribution of health resources between different population groups, arising from the social conditions in which people are born, grow, live, work and age. Health inequities are unfair and could be reduced by the right mix of government policies.” Looking at the barriers of accessing post-diagnostic care across eight European countries, for example, Broda and colleagues [ 5 ] reported a lack of awareness and continuity of care as some of the main issues from the perspectives of policy and political decision-makers. However, some carers reject accessing specific types of post-diagnostic support, such as support groups, particularly when the person they care for is experiencing higher levels of everyday functioning and thus in less need of support [ 6 ].

One underlying reason for inequalities, that receives growing attention, is rural dementia care. Living in more rural communities can make it difficult to access both a GP or memory clinic for a diagnosis, but also to access support services afterwards [ 7 ]. If it takes an hour to access a support group, which only meets once a week, due to reduced demand, then people living in more rural regions will be unable to access the same level of post-diagnostic support than those living in more urban regions. Looking at the barriers and facilitators of implementing a strategy for dementia assessment and management in rural Canada, Morgan and colleagues [ 8 ] found that whilst it proved difficult to implement such a strategy in primary health care, the intervention proved to be successful also due to the strong partnership between researchers and clinicians. But this is just one example of how rural dementia care is being tackled.

Belonging to an ethnic minority group can also be leading to inequalities in diagnosis and care access in dementia [ 9 ]. People from black and minority ethnic (BAME) groups are often found to experience delays in receiving a diagnosis, with symptoms also being attributed to faith by some resulting in delays or general non-contact with GPs about their symptoms [ 10 ]. In line with other research on various BAME groups, Sagbaken et al. [ 11 ] reported language barriers and faith-related help-seeking attitudes as major barriers to receiving a dementia diagnosis in ethnic minority migrants in Norway. Similarly, GPs were reported to often lack cultural competency, which further created barriers to a timely diagnosis. A recent scoping review by Bieber and colleagues [ 12 ] further supports these findings, as ethnicity was found to be a barrier to service access in dementia. But ethnic minority status also leads to inequalities in accessing post-diagnostic care, including anti-dementia medication [ 13 ].

Living in rural areas or being from a BAME background are only some of the barriers to receiving the right post-diagnostic care and diagnosis in the first place. A growing body of evidence is showing that being from more disadvantaged backgrounds, for example, is linked to reduced access to anti-dementia medication [ 14 ], with more research currently being conducted on how neighborhood deprivation (measured via the Index of Multiple Deprivation, in the UK) is linked to dementia care.

Dementia care can differ not only within a country and by postcode, but also between countries. Whilst the Netherlands are renowned for their advanced dementia care both in the community and in long-term care, there are huge variations between high-income and low-and middle-income countries (LMIC). Kamoga et al. [ 15 ] explored dementia diagnosis and treatment in Uganda and found that health care workers generally had very little training on recognizing and treating dementia symptoms. Those health care workers who were able to recognize symptoms were more likely also to focus on treating other medical symptoms as opposed to those related to dementia. This suggests that there is a need for improved dementia training in the health care workforce, and possibly a better knowledge exchange across countries.

Barriers to care are not only reported in the community though, but also in long-term care institutions and thus the more advanced stages of the condition. By conducting focus groups with nursing home staff in Norway, Midtbust and colleagues [ 16 ] reported various barriers in providing palliative care in dementia. These included the lack of time spent with individual residents, as well as the frequently temporary nature of staffing. These staffing issues are not only reflective of problems in palliative care, however, but also generally in providing adequate care in long-term care institutions.

While there are many barriers and inequalities to receiving the right dementia care at the right time, there are several ways in which dementia care can be advanced though. First and foremost, getting a timely diagnosis is key, which means that barriers to getting a diagnosis, by for example people from BAME groups, need to be removed. In their paper, Watson and colleagues [ 17 ] reported how 92% of a sample of Australian health service consumers preferred a dementia diagnosis as soon as possible. This does help indeed to provide suitable care (including medication and post-diagnostic services) in time to reduce the speed of progression. Providing better support in re-enabling people with dementia is also vital and forms part of the post-diagnostic care (which also includes medication management [ 18 ]. In the Australian aged care sector, O’Connor and colleagues [ 19 ] showed that reablement interventions for people with dementia are varied, and to improve access to and usage of interventions, these should be multi-faceted with a freely available resource outlining the different intervention components. This is supported to a degree by a recent meta-analysis by Backhouse et al. [ 20 ], showing mixed evidence on the benefits of coordinated community interventions. Tackling daily functioning does not always need to involve the actual person living with dementia however, but medication management, for example, can also be addressed via community pharmacy-based interventions [ 18 ].

To advance dementia care, we need to take a global view and learn from research and clinical practice across the globe. Findings from Australia might provide important insights for improving dementia care in rural Canada or LMICs for example, and barriers identified in the Netherlands might also be relevant to the UK. This article collection thus highlights some of the recent evidence in this field and brings together inequalities research and how inequalities and general dementia care might be tackled in the future.

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CG is funded by the National Institute for Health Research Applied Research Collaboration North West Coast (ARC NWC). The views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care.

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Giebel, C. “Current dementia care: what are the difficulties and how can we advance care globally?”. BMC Health Serv Res 20 , 414 (2020). https://doi.org/10.1186/s12913-020-05307-1

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Research on Alzheimer’s Disease and Related Dementias

Alzheimer’s disease and related dementias are a series of complex brain disorders that affect millions of Americans and many more people worldwide. These disorders have an enormous impact on individuals and their families, long-term care facilities, health care providers, health care systems and infrastructure, and the communities in which we all live. As the economic, social, and personal costs of these diseases climb, the research community is working to discover solutions that will improve the lives of those with dementia, their caregivers, and their communities.

The federal government’s Alzheimer’s and related dementias research strategy focuses on engaging a cross-disciplinary team of geneticists, epidemiologists, gerontologists, behavioral scientists, disease and structural biologists, pharmacologists, clinical researchers, and others to bring the greatest and most diverse expertise to the field. This includes training new generations of researchers and clinician-scientists and engaging in innovative partnerships with private industry, nonprofit groups, and more to foster collaboration and broaden access to research resources and data.

Critically, the government’s research strategy includes the search to find treatment and prevention strategies, as well as interventions, services, and supports to improve quality of life for those already living with these diseases and their families.

Who Funds Alzheimer’s and Related Dementias Research?

The National Institutes of Health (NIH) is made up of Institutes, Centers, and Offices that conduct and fund research into all aspects of human health. The National Institute on Aging (NIA) leads NIH’s efforts in clinical, behavioral, and social research in Alzheimer’s and related dementias through efforts aimed at finding ways to treat and ultimately prevent the disorder. NIA collaborates closely with the National Institute of Neurological Disorders and Stroke (NINDS), which manages a research portfolio targeting Alzheimer’s-related dementias. While some of this research takes place in NIH laboratories, the vast majority of NIH support is provided through a competitive grants process to institutions and small businesses across the country. Other federal agencies support a range of activities focused on public health and community programs.

Advances in Alzheimer's and Related Dementias Research

As the nation’s biomedical research agency, NIH supports research ranging from basic biology to drug development and from clinical studies to evaluating public health outcomes. Within the past several decades, researchers have made great strides toward better understanding what causes Alzheimer’s and related dementias and discovering approaches that may prevent, diagnose, and treat them. Some highlights of these efforts include:

• Drug discovery and drug repurposing. Thanks to the substantial investment in Alzheimer’s and related dementias research over the past decade, NIH has increased drug discovery significantly. Of the many compounds in NIH-supported drug development programs for Alzheimer’s and related dementias, 18 new dementia drug candidates have now matured through the pipeline, from discovery in the lab all the way through preclinical development, to reach the stage of human testing. NIA currently supports more than 60 clinical trials testing drug candidates that target many different aspects of the disease. Several of these drug candidates are intended to stop or slow the disease process rather than only treat symptoms. For example, some target amyloid plaques and tau tangles in new ways. Researchers are also exploring multiple ways to repurpose drugs for the potential treatment of dementia, including FDA-approved drugs used to treat epilepsy and diabetes.
• Early detection and diagnosis. Researchers have made significant progress in developing, testing, and validating biomarkers that detect signs of the disease process. For example, in addition to PET scans that detect abnormal beta-amyloid plaques and tau tangles in the brain, NIH-supported scientists have developed the first commercial blood test for Alzheimer’s. This test and others in development can not only help support diagnosis but also be used to screen volunteers for research studies. Other discoveries are leading to the development of potential biomarkers for other dementias. These include the detection of abnormal TDP-43 protein, found in frontotemporal dementias, and a cerebrospinal fluid test to help diagnose Lewy body dementia and Parkinson’s disease. Researchers are also studying behavioral and social indicators, including problems with paying bills and a combined decline in memory and walking speed, that may be early signs of these diseases. Other early markers are also under study.
• Risks factors, genetics, and disease pathways. NIH’s research investments to identify the biological mechanisms that lead to Alzheimer’s and related dementias are fundamental for the discovery of potential drugs that target them. There are many biological pathways that scientists can target with investigational drugs. For example, several recent studies have further revealed how components of the immune system, brain inflammation, vascular disease, and possibly viruses and bacteria — including the many tiny organisms that live in the digestive system, known as the gut microbiome — contribute to the development of these diseases. Scientists are also exploring genetic variations that may contribute to or prevent disease. Recent research has revealed that the genetic risk for Alzheimer’s differs between ethnic and racial groups, highlighting the need for more diversity in genetic research studies. Scientists are also discovering genetic variants that may help protect against Alzheimer’s. Other studies are identifying the genetic underpinnings of related dementias, including new gene variants linked to the development of Lewy body dementia.
• Population studies and precision medicine. By studying large, diverse groups of people, researchers are identifying which genes, behaviors, and lifestyle choices are linked with dementia. Population studies have shown that sedentary behavior, low socioeconomic status, low level of education, and living in a poor neighborhood may increase the risk of developing dementia. These observational discoveries, along with knowledge of genetic and other factors, can be used to advance the development of methods for diagnosis, prevention, and treatment at an individualized level.
• Health disparities and dementia risk. NIH-funded researchers are examining the biological, social, and environmental factors that contribute to the higher prevalence of dementia in Hispanic Americans and Black Americans compared with other White Americans. Since dementia is also underdiagnosed in these populations, researchers are studying approaches to improve diagnoses in underserved communities. NIH is also investing in strategies to increase diversity in research study participants.
• Lifestyle interventions. Researchers are investigating interventions around exercise, healthy eating, cognitive training, preventive health care, and management of chronic conditions, such as high blood pressure, that — if made early in life — may be able to prevent or delay disease symptoms. Emerging areas of study include interventions to enhance cognitive reserve — the mind’s ability to cope with the effects of aging — and interventions to potentially compensate for premature cognitive decline and dementia linked to adverse exposures in early life, such as abuse and malnutrition. NIA currently supports more than 150 trials testing behavioral and lifestyle interventions.
• Dementia care and caregiver support. NIH has significantly expanded research on how to improve dementia care and support for care partners. Researchers are investigating new dementia care models and strategies to equip family caregivers with tools and knowledge to manage the challenges of caring for a loved one with dementia. Studies are also underway to examine ways to improve quality of life for people with dementia and their caregivers. Other studies aim to understand the costs and challenges of dementia, including lost wages and paying for long-term care. NIA currently supports more than 200 studies on dementia care and caregiving.
• Infrastructure development. NIH is continually investing in research infrastructure to advance Alzheimer’s and related dementias research. Efforts in this area include launching a consortium for Alzheimer’s clinical trials, a collaboratory to test interventions to improve care of people with dementia in real-world settings, research efforts to validate cognitive tests in a primary care setting, and centralized data-sharing platforms and other technologies.

Challenges for the Alzheimer’s Research Community

Even with the progress that we’ve made, there’s still a lot of work to do before we can find treatment and prevention strategies for the millions of people affected by Alzheimer’s and related dementias. These devastating diseases are highly complex conditions caused by an interplay of genetic, lifestyle, and environmental factors. They usually develop gradually — changes in the brain take place over years and even decades, long before the first symptoms appear. This complexity presents challenges to the discovery and development of new drugs and other prevention and treatment approaches.

Researchers believe Alzheimer’s and related dementias will likely require multiple treatments customized to individuals. We also know that as the older population continues to grow — aging remains the most important risk factor for dementia — we will see increased numbers of people living with these diseases. That’s why thousands of researchers around the country are working on this issue.

Setting the Federal Research Agenda

NIH takes a collaborative, methodical approach to reviewing progress, identifying gaps, and setting the future agenda for research into Alzheimer’s and related dementias. NIH funding in this area is guided by gaps and opportunities identified in research summits , which alternate yearly to focus on Alzheimer’s, Alzheimer’s-related dementias, or dementia care and services. Smaller, focused workshops are held more frequently on specific aspects of this research.

NIH outlines its Alzheimer’s research efforts in the NIH AD/ADRD Research Implementation Milestones , a research framework detailing specific steps and success criteria toward achieving the goals of the National Plan to Address Alzheimer’s Disease . The milestones also showcase funding initiatives, accomplishments, and highlights of progress toward accomplishing the National Plan goals.

NIH’s research progress is highlighted in the annual Alzheimer’s and related dementias professional judgment budget , which is submitted to Congress each year.

What Is a Professional Judgment Budget?

Each year NIH submits a professional judgment budget that estimates the additional funding needed to advance NIH-supported research into the treatment and prevention of Alzheimer’s and related dementias. The report also summarizes progress and promising research opportunities. Only two other areas of biomedical research — cancer and HIV/AIDS — follow a similar process designed to accelerate research discovery. This approach is often referred to as a “bypass budget” because of its direct transmission to the President and then to Congress without modification through the traditional federal budget process.

Clinical Research Into Alzheimer’s and Related Dementias

No major advance in Alzheimer’s and related dementias treatment, prevention, or care will be possible without robust clinical research. Clinical research includes studies that involve people so scientists can learn more about disease progression, how behavior and lifestyle factors may affect health, and the safety and effectiveness of an intervention. Advances made through clinical research rely on the volunteers who participate in these types of studies. NIA is working on multiple initiatives to enhance recruitment and retention of diverse populations in clinical research. View some of those resources below.

NIA-funded clinical research includes both observational studies through which researchers gather important information, and clinical trials in which researchers test interventions to treat or prevent disease, improve care and caregiver support, and enhance quality of life for people living with dementia. NIA is currently funding more than 400 active clinical trials .

NIA also funds more than 30 Alzheimer’s Disease Research Centers across the country. Scientists at these centers conduct clinical research to improve diagnosis and care for people with dementia and their families, and to find a treatment or increase prevention.

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Explore the resources on this website and linked below to find more information from federal government agencies.

View professional judgment budgets for Alzheimer’s and related dementias from NIH, including yearly updates on research progress.

Browse this database to learn more about research implementation plans and progress toward the goal of treating or preventing Alzheimer’s and related dementias.

Search this repository of resources to support the recruitment and retention of participants into clinical trials and studies on Alzheimer’s disease and related dementias.

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View a list of all active NIA-funded clinical trials, including drug trials, intervention studies, and care and caregiver interventions.

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• Jiang Bian 8 &
• Jingchuan Guo 1 , 9  

Alzheimer's Research & Therapy volume  16 , Article number:  206 ( 2024 ) Cite this article

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Previous research on the risk of dementia associated with education attainment, smoking status, and alcohol use disorder (AUD) has yielded inconsistent results, indicating potential heterogeneous treatment effects (HTEs) of these factors on dementia risk. Thus, this study aimed to identify the important variables that may contribute to HTEs of these factors in older adults.

Using 2005–2021 data from the National Alzheimer’s Coordinating Center (NACC), we included older adults (≥ 65 years) with normal cognition at the first visit. The exposure of interest included college education or above, current smoking, and AUD and the outcome was all-cause dementia. We applied doubly robust learning to estimate risk differences (RD) and 95% confidence intervals (CI) between exposed and unexposed groups in the overall cohort and subgroups identified through a decision tree model.

Of 10,062 participants included, 929 developed all-cause dementia over a median 4.4-year follow-up. College education or above was associated with a lower risk of all-cause dementia in the overall population (RD, -1.5%; 95%CI, -2.8 to -0.3), especially among the subpopulations without hypertension, regardless of the APOE4 status. Current smoking was not related to increased dementia risk overall (2.8%; -1.5 to 7.2) but was significantly associated with increased dementia risk among men with (21.1%, 3.1 to 39.1) and without (8.4%, 0.9 to 15.8) cerebrovascular disease. AUD was not related to increased dementia risk overall (2.0%; -7.7 to 11.7) but was significantly associated with increased dementia risk among men with neuropsychiatric disorders (31.5%; 7.4 to 55.7).

Conclusions

Our studies identified important factors contributing to HTEs of education, smoking, and AUD on risk of all-cause dementia, suggesting an individualized approach is needed to address dementia disparities.

Dementia, characterized by a loss of cognitive function, affects independence and daily activities [ 1 ]. It remains a major public health problem that impacts about 55 million people worldwide [ 2 ]. In the United States (US), about 6.5 million adults had a diagnosis of dementia in 2022 and this number is expected to rise to 14 million by 2060 [ 3 , 4 ]. Alzheimer’s disease (AD), the most common cause of dementia, is ranked as the sixth leading cause of death in the US [ 3 ]. Although the fundamental etiology of dementia is yet to be fully elucidated, it is posited to be caused by a combination of genetic, health behavior, and environmental determinants [ 5 , 6 ]. Currently, there is no cure for dementia, but there are interventions that can help manage the symptoms and have small disease-modifying effects [ 7 , 8 ].

Increasing evidence has shown that social and behavioral determinants of health (SBDH) have an important influence on health outcomes, accounting for 30–50% of outcomes [ 9 , 10 ]. Educational attainment, smoking status, and alcohol use disorder (AUD) have been identified as the three major factors that may contribute to the development of dementia, although their association remains inclusive [ 11 ]. Educational attainment has long been hypothesized to play a protective role against cognitive decline and dementia [ 12 , 13 ]. This protective effect is often attributed to the concept of cognitive reserve, which posits that higher levels of education may enhance neural networks and cognitive strategies, potentially delaying the clinical manifestation of dementia symptoms. Conversely, low levels of formal education or a complete lack thereof have been associated with an increased risk of dementia [ 14 ]. However, the mechanisms underlying this relationship and the extent of its impact across different populations require further elucidation. The relationship between smoking and dementia risk has been the subject of numerous studies, yielding somewhat inconsistent results [ 15 , 16 , 17 ]. These indicated a possible heterogeneity in treatment effects between smoking and risk of dementia. Alcohol consumption and its impact on dementia risk presents another area of significant interest and complexity in the population study [ 18 , 19 ], with a particular focus on the potentially detrimental effects of AUD [ 20 ].

Despite the wealth of research in these areas, significant gaps in our understanding persist, particularly regarding how these risk factors may differentially affect various subgroups within the population. Heterogeneous treatment effects (HTEs) become crucial, as it examines varying treatment effects for individuals or subgroups in a population. Doubly robust learning is a powerful data-driven approach for estimating HTEs, enabling a deeper understanding of the intricate relationships between variables and their impact on the outcome. Leveraging this advanced analytical technique, this study aimed to investigate the HTEs of educational attainment, smoking, and AUD on risk of all-cause dementia and elucidate how these factors differentially influence dementia risk across various subgroups within the older population. The findings of this study contribute valuable insights to the field of dementia prevention and risk stratification, potentially informing more targeted and effective public health interventions and clinical strategies.

Data source

This retrospective cohort study was performed using the National Alzheimer’s Coordinating Center (NACC) dataset, which was founded by the National Institute on Aging in 1999 and contains data contributed by the 39 U.S. Alzheimer’s Disease Research Centers (ADRCs). We used the NACC Uniform Data Set (UDS) collected between September 2005 and June 2021 [ 21 ]. The UDS contains data collected through a prospective, longitudinal clinical examination by trained clinicians and clinic personnel from participants and their co-participants across the ADRCs. This includes data on personal characteristics, demographics, health behaviors, current health conditions and disease history, medication use, functional abilities, depressive symptoms, and detailed neuropsychological testing. Informed consent forms were approved by the individual ADRCs’ Institutional Review Boards (IRBs), and consent was obtained from participants and study partners before research activities were carried out. Research using the NACC database was also approved by the University of Washington IRB.

Participant selection and outcome measurement

In this study, we included participants in the analytic sample if they met the following criteria: (1) had ≥ 2 clinical visits at ADRCs between September 2005 and June 2021; (2) were aged ≥ 65 years at baseline (first visit); and (3) had normal cognition at baseline. We excluded participants who took any anti-dementia drug at baseline. The participants were followed until the occurrence of a dementia diagnosis, discontinuation from the study, or completion of the study.

The cognitive diagnosis for each person was determined through clinician judgment or by a multidisciplinary consensus team using The Clinician Diagnosis form in the UDS [ 21 ]. Each person was characterized as having “normal cognition”, “impaired not mild cognitive impairment (MCI)”, “MCI”, or “dementia” at the initial visit and each subsequent follow-up visit [ 22 ]. The outcome of interest in this study was all-cause dementia. The criteria for diagnosing dementia was based on recommendations by the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease [ 23 ].

Definition of exposure of interest

The exposure of interest in this study included college education or above (yes vs. no), current smoking (yes vs. no), and AUD (yes vs. no) in the NACC dataset. Smoking status and AUD were extracted from the subject’s health history (UDS Form A5) completed by a clinician based on “ subject/informant report , medical records , and/or observation ” using the clinician’s best judgment. According to the UDS question, “smoked cigarettes in last 30 days”, the participants were divided in two groups (current smokers vs. others). Participants were also asked about their alcohol use and were classified to have AUD (yes) if they reported recent clinically significant impairment due to alcohol abuse occurring over 12 months manifested in one of the following areas: work, driving, legal, or social.

The covariates (including demographic factors, health behaviors, comorbidities, and genetic factors) that may be related to risk of dementia, were collected, and adjusted for in the study [ 24 , 25 , 26 ]. The demographic characteristics included age (≥ 80 vs. < 80 years), sex (male vs. female), race (Black vs. non-Black), ethnicity (Hispanic vs. non-Hispanic), and family history of dementia (yes vs. no). Participants were classified as obese (yes vs. no) based on their body mass index at baseline (≥ 30 kg/m 2 ). Comorbidities were classified as the presence vs. absence of self-reported history of diseases like cardiovascular disease (including heart attack/cardiac arrest, angioplasty/endarterectomy/stent, cardiac bypass procedure, pacemaker and/or defibrillator, congestive heart failure, atrial fibrillation, angina, heart valve replacement or repair, and other cardiovascular diseases), cerebrovascular disease (including stroke and transient ischemic attack), neurological diseases (including Parkinson’s disease, other Parkinson’s disease disorders, traumatic brain injury, seizures, and other neurological conditions), neuropsychiatric disorders (post-traumatic stress disorder, bipolar disorder, schizophrenia, depression, anxiety, obsessive-compulsive disorder, developmental neuropsychiatric disorders, and other psychiatric disorders), diabetes, hypercholesterolemia, and hypertension. We also controlled for the APOE4 gene, the strongest known genetic risk factor for AD and cognitive impairment [ 26 ].

Statistical analysis

We described the demographic and clinical characteristics of the study sample at baseline. Additionally, we also assessed the balance of baseline covariates between exposure and non-exposure groups using standardized mean differences (SMD) before and after the inverse probability of treatment weighting (IPTW). An SMD of ≤ 0.1 was considered to be an imbalance in the baseline covariate [ 27 ].

We estimated the conditional average treatment effect (CATE) in terms of the difference in risk of all-cause dementia between the exposed group and non-exposed group following the doubly robust learning framework [ 28 ]. In the initial models, aiming to select optimal hyperparameters of each model, we designed two predictive tasks: (1) predicting the probability of having exposure for every subject using the propensity score model and achieving balances of baseline covariate between exposure and non-exposure groups using IPTW; (2) estimating the risk of all-cause dementia for both the exposures of interest and non-exposure group using the outcome regression models. In the final model which combined the above two predictive models, we calculated the doubly robust causal estimate [ 29 ]. This model provides a correct estimate even if either the propensity score model or outcome regression is misspecified.

We randomly split the sample into a training (70%) set and a test (30%) set. The training set was used to train the machine learning models with hyperparameter optimization, and the testing set was used to evaluate the models’ prediction performance. In the initial models, we applied logistic regression for the propensity score model and least absolute shrinkage and selection operator-type regularized regression (LASSO) for the outcome regression model. In the final stage model, we estimated the CATEs in the overall cohort and predicted individualized treatment effects (ITEs, treatment effects on the personal level) using SparseLinearDRLearner within EconML package [ 28 ]. We measured the model performance using the score based on the final stage loss (a lower score is better) and assessed the out-of-sample score on the testing set. The treatment effect was quantified as the risk difference (RD) with 95% confidence interval (CI) for risk of all-cause dementia between exposed and unexposed groups in each SBDH. To identify the important covariates and discover drivers of heterogeneity, we applied a single decision tree model for the treatment effect. Based on the important covariates, the tree-based model split the participants into subgroups, in which a subgroup of samples responded to treatment differently from other subgroups.

To address the competing risk of death, we conducted a sensitivity analysis by applying a doubly robust estimation of the hazard difference for competing risk data using the R Package “HazardDiff.” [ 30 ]. All analyses were performed using SAS version 9.4 (SAS Institute Inc), and Python version 3.7 (Python Software Foundation).

Study population

The flowchart of participant selection based on the inclusion and exclusion criteria is presented in Fig.  1 . Of 43,999 participants included in the NACC from September 2005 to June 2021, we included 10,062 participants with normal cognitive function at baseline (at 1st clinical visit) in this cohort study and outlined the reasons for exclusion in Fig.  1 . The demographic and clinical characteristics of all participants at baseline are presented in Table  1 . The mean age of the sample was 74.9 years, 35.5% were men, 14.2% were Black, and 5.6% were Hispanic. Among all participants, 929 participants (9.2%) developed all-cause dementia over a median follow-up of 4.4 years (Interquartile range, 2.2 to 7.7). Participants who developed dementia were older and were more likely to have a family history of dementia and be APOE4 carriers.

Flowchart of participant selection. NACC, National Alzheimer’s Coordinating Center; FDA, Food and Drug Administration

College education or above and the risk of all-cause dementia

The baseline characteristics of the participants, stratified by college attainment (those with college education or above vs. those without), are presented in Table S1 . Among participants with college education or above, they seemed to be younger, having a higher percentage of men, and a lower percentage of Hispanic/Latino ethnicity, Black, current smoker, diabetes, hypertension, and obesity. All covariates were well-balanced after IPTW. The final parameter set of propensity score model and outcome regression model is present in Table S2 and the performance of the final model is presented in Table S3 . In the estimation of CATEs, college education or above was associated with a lower risk of all-cause dementia (RD: -1.5%; 95%CI, -2.8% to -0.3%). We estimated the heterogeneous effect of college education or above on risk of all-cause dementia (Fig.  2 ). Among all participants included, 71.0% had a decreased risk of dementia. The HTE subgroups based on a single decision tree model is presented in Fig.  3 and Figure S1 . Having hypertension and the APOE4 gene were the most important factors. We found a significant decrease in risk of all-cause dementia associated with college education or above among participants without hypertension regardless of the APOE4 gene with an RD of -5.5% (-8.3% to -2.7%) for those with APOE4 and − 2.4% (-4.3% to -0.4%) for those without APOE4 . However, we found no significant decrease in risk among those with hypertension regardless of the APOE4 gene.

Heterogeneous treatment effects of education attainment ( A ), smoking status ( B ), and alcohol use disorder ( C ) on risk of all-cause dementia. The predicted individualized treatment effect (ITE) was presented as an absolute risk difference in risk of dementia between exposure and non-exposure for each participant (Y-axis). The predicted ITE is divided into 10 groups according to the deciles (X-axis). Different colors represent participants in distinct subgroups identified by the single decision tree model

Absolute risk difference in risk of all-cause dementia associated with education attainment ( A ), smoking status ( B ), and alcohol use disorder ( C ) in the overall population and subgroups identified by the single decision tree model. CI, confidence interval

Current smoking and risk of all-cause dementia

The baseline characteristics of participants by exposure and non-exposure groups for current smoking are presented in Table S4 . Among participants with current smoking, they seemed to be younger, having a lower percentage of college education or above, and a higher percentage of Black population, AUD, neuropsychiatric disorders, and hypertension. All covariates were well-balanced after IPTW. The final parameter set of propensity score model and outcome regression model is present in Table S2 and the performance of the final model is presented in Table S3 . The final model seemed to be overfitting with a score of 5.2 for the training set and 8.8 for the testing set. In the estimation of CATEs, current smoking was not significantly associated with an increased risk of all-cause dementia (2.8%; -1.5–7.2%). The results based on the heterogeneous effect of current smoking on all-cause dementia risk showed that 39.2% of participants had a decreased risk of dementia (Fig.  2 ). Male sex, cerebrovascular disease, and neuropsychiatric disorder were the most important covariates identified in the single decision tree model (Fig.  3 and Figure S1 ). Current smoking was significantly associated with an increased risk of all-cause dementia among men with cerebrovascular disease (21.1%; 3.1–39.1%) and men without cerebrovascular disease (8.4%; 0.9–15.8%). No significant differences were observed in other HTE subgroups (Fig.  3 ).

AUD and the risk of all-cause dementia

The baseline characteristics of the participants, stratified by AUD status, are presented in Table S5 . Participants with AUD, compared to those without, seemed to be younger, having a lower percentage of Hispanic/Latino ethnicity, Black population, and hypercholesterolemia, and a higher percentage of family history of dementia, current smokers, neurological disorders, neuropsychiatric disorders, diabetes, and hypertension. The covariates were well-balanced after IPTW except for cardiovascular disease, neurological disease, and diabetes. The final parameter set of propensity score model and outcome regression model is present in Table S2 and the performance of the final model is presented in Table S3 . The final model seemed to be overfitting with a score of 16.7 for the training set and 22.1 for the testing set. In the estimation of CATEs, there was no association between AUD and risk of all-cause dementia (2.0%; -7.7–11.7%) as compared to no AUD. A heterogeneous effect of AUD on all-cause dementia risk was estimated (Fig.  2 ). Among all participants included, 47.3% of participants had a decreased risk. Having neuropsychiatric disorders and being male were the most important factors identified in the single decision tree model (Fig.  3 and Figure S1 ). AUD was significantly associated with an increased risk of all-cause dementia among male participants with neuropsychiatric disorders (RD, 31.5%; 95%CI, 7.4–55.7%). However, no significant association between AUD and all-cause dementia risk in other HTE subgroups was detected (Fig.  3 ) .

Sensitivity analysis

In the sensitivity analysis when accounting for the competing risk of death, the hazard difference for College education or above, current smoking, and AUD was − 0.3% (-0.6% to -0.1%), 0.7%(0.01–1.5%), and − 0.8% (-2.5–0.9%), respectively.

In this retrospective cohort study of 10,062 older adults from NACC, we found that college education or above was significantly associated with a decreased risk of all-cause dementia while neither current smoking nor AUD was associated with all-cause dementia risk. We identified the most important covariates that may contribute to the HTE subgroups for each exposure. Participants with college education or above had a lower risk of all-cause dementia in those without hypertension regardless of carrying the APOE4 gene, but not in those with hypertension. Current smoking was shown to be significantly associated with an increased risk of all-cause dementia among men with and without cerebrovascular disease. AUD was significantly associated with an increased risk of all-cause dementia in men with neuropsychiatric disorders.

The relationship between education attainment and risk of all-cause dementia has been widely studied [ 31 , 32 , 33 , 34 ]. Our study indicated that participants with college education or above had a lower risk for all-cause dementia, which is consistent with prior research [ 35 , 36 ]. One meta-analysis of 69 prevalence and/or incidence studies showed that lower education was significantly associated with an increased risk of dementia with an odds ratio of 2.61 and 1.88 in prevalence and incidence studies respectively [ 36 ]. Another meta-analysis of prospective studies found that higher education had a dose-response relationship with the decreased risk of dementia with a reduction in risk by 7% for per year increase in education level [ 35 ]. More importantly, several possible mechanisms underlying the protection against dementia associated with a higher education level have been proposed [ 33 , 37 , 38 ]. First, the “ reserve capacity ” hypothesis may explain the cognitive preservation among those with higher education [ 37 ]. Early education may have a direct impact on brain structure by boosting synapse quantity or vascularization, as well as establishing cognitive reserve [ 39 ]. Thus, early childhood education can slow the pace of cognitive decline in later life. Another explanation is the “ use it or lose it ” theory [ 38 ]. The population with higher education is more likely to continue searching for mental stimulation, resulting in postponing age-related cognitive decline [ 40 ]. Third, education in early life may affect late-life cognitive outcomes by changing a person’s SBDH [ 33 ]. For example, education affects a person’s occupation and health behaviors [ 33 ]. Interestingly, our findings revealed a complex interplay between education attainment, hypertension, APOE4 gene, and dementia risk. Specifically, a decreased risk of dementia associated with college education or above was more pronounced among participants without hypertension, regardless of the presence of APOE4 gene. While the exact mechanisms remain unclear, the association between hypertension and an increased risk of dementia has been well documented [ 41 , 42 ]. Our results indicated that higher education levels may influence the lifestyle, potentially reducing the risk of hypertension and, consequently, dementia risk. Moreover, APOE4 allele is a well-established genetic risk factor for Alzheimer’s disease, the most common form of demenia [ 43 ]. Notably, our study found that higher education levels may mitigate attenuate the APOE4 gene-related dementia risk, indicating that education may serve as a modifiable factor capable of offsetting genetic predisposition. The observed protective effect of higher education against dementia, even in the presence of genetic risk factors, underscores the broader societal implications of our findings. It suggests that promoting education could have far-reaching effects on public health and cognitive aging. While these results are promising, future studies should focus on elucidating the mechanisms by which educational attainment reduces dementia risk and explore the complex interactions between education, hypertension, the APOE4 gene, and risk of dementia.

Results from previous studies regarding the association between smoking and risk of dementia were mixed [ 15 , 16 , 17 , 44 ]. Many studies found that smoking was significantly associated with an increased risk of dementia [ 16 , 44 , 45 , 46 ], which is reinforced by the following plausible biological mechanisms: (1) smoking is a well-known risk factor for stroke [ 47 ], and thus may cause vascular dementia and AD [ 48 ]; and (2) smoking would adversely affect neurodegeneration through oxidative stress and inflammation [ 49 ] that were associated with increased production of amyloid-β and abnormal tau protein phosphorylation which are hypothesized to cause AD [ 50 ]. However, in our study, we found no significant association between current smoking and increased risk of all-cause dementia. Our results were consistent with one population-based longitudinal study which included 11,143 dementia-free participants aged 65 years and older [ 15 ]. The result showed that there was no significant association between smoking and the onset of all-cause dementia, AD, and vascular dementia during a mean of 3.8 years of follow-up [ 15 ]. We further explored the HTE of current smoking on dementia risk and identified key important covariates – men, history of cerebrovascular disease, and presence of neuropsychiatric disorders, that may modulate the association between smoking and dementia risk. Men typically have higher rates of cigarette smoking than women [ 51 ], which may partly explain the gender-specific effects observed in our study. Furthermore, the well-established link between smoking and increased risk of cerebrovascular disease [ 52 ], coupled with the known association between cerebrovascular disease and dementia [ 53 ], provides context for our HTE subgroup analysis findings. Specifically, we found that current smoking was significantly associated with an increased risk of dementia among men, with this effect being particularly pronounced in those with cerebrovascular disease. Neuropsychiatric disorders were associated with an increased risk of dementia, with differing impacts between men and women [ 54 ]. While further studies are warranted to fully understand the interaction between smoking, sex, cerebrovascular disease, neuropsychiatric disorders, and dementia, our research supports the potential benefits of smoking cessation programs in lowering dementia risk, even in older populations and especially among men with cerebrovascular conditions.

Prior research found detrimental effects of AUD on cognitive impairment and dementia [ 20 , 55 , 56 ]. One nationwide retrospective cohort study also found an increased risk of dementia associated with AUD among 19,769,440 adults (adjusted hazard ratio[aHR], 3.34 for women and 3.36 for men) [ 20 ]. Another cohort study involving 4,414 women veterans aged more than 55 years showed that AUD was significantly associated with an increased risk of dementia (adjusted HR, 3.12; 95%CI, 1.90–5.12) during a median follow-up of 4 years [ 55 ]. However, in our study, we found no association between AUD and all-cause dementia risk. Our single decision tree model identified neuropsychiatric disorders and men as the two key covariates that may affect the association between AUD and risk of all-cause dementia. These findings provide valuable insights into the complex interplay between AUD, neuropsychiatric disorders, and dementia risk. Neuropsychiatric disorders have been emerged as critical factors for development of all-cause dementia [ 57 ]. Simultaneously, there is a known association between AUD and risk of neuropsychiatric disorders, including depression, anxiety, and other psychiatric conditions [ 58 ]. The co-occurrence of AUD and neuropsychiatric disorders may create a synergistic effect, potentially exacerbating the risk of dementia. Our HTE subgroup analysis provided further nuance to these relationships. Specifically, we found that AUD was significantly associated with an increased risk of dementia in men who also had neuropsychiatric disorders. This finding suggests a potentially complex interaction between alcohol use, gender, mental health, and cognitive outcomes. Previous research has shown that neuropsychiatric disorders are associated with an increased risk of dementia, with differing impacts between men and women [ 54 ]. Additionally, men are more likely to smoke and have AUD, which may partly explain our study findings. Our findings suggest that individuals, particularly men, with both AUD and neuropsychiatric disorders may be at especially high risk for developing dementia. This underscores the need for integrated care approaches that address both alcohol use and mental health concerns as part of a comprehensive strategy to prevent cognitive decline. However, the HTE observed in our study also highlights the need for continued research to confirm and fully elucidate these relationships. Further investigation is warranted to better understand the complex interactions between AUD, neuropsychiatric disorders, gender, and dementia risk, and to develop more targeted prevention and intervention strategies.

This study has several advantages. First, we employed an assumption-free approach to uncover the potential HTEs of exposure of interest on the risk of all-cause dementia. This method overcomes the limitations of conventional ‘one-variable-at-a-time’ analysis, such as spurious findings and multiple hypotheses testing [ 59 ]. Second, the inclusion of detailed data on each exposure and precise dementia diagnosis enhances the overall quality of this study. However, we also acknowledge several limitations. First, as with any observational study, we cannot rule out residual confounding despite implementing advanced statistical techniques and adjusting for a list of covariates. Second, the NACC dataset is a clinic-based sample that is subjected to selection bias. The results were derived from the subjects referred by clinicians, patients, or family members, active recruitment, or volunteering, limiting the generalizability of our findings to the general population. Additionally, our sample was restricted to older adults, limiting generalizability to younger age groups. Third, smoking status, AUD, and other variables were derived from self-reported medical history, which may introduce uncertainty about the actual status and disease and potential recall bias or misclassification. It should be noted that our measure of AUD was based on a single item from the NACC questionnaire, which may not fully capture all aspects of AUD as defined by clinical diagnostic criteria such as those in the DSM-5 [ 60 ]. This limitation may have led to an underestimation of the participants with AUD in our sample and could have affected our findings regarding the association between AUD and dementia risk. Fourth, our study did not account for smoking severity and duration, which may contribute differentially to dementia risk [ 61 ]. In this study, smoking was classified into two categories (current smokers vs. all others), whereas future studies should consider more nuanced classifications. More nuanced classifications of smoking status should be considered in future studies. Similarly, educational level as a continuous variable rather than a categorical variable in future studies could offer additional insights into their association with dementia risk. Fifth, this study was limited by the small number of participants with AUD (37 out of 10,062) and current smoking (341 out of 10,062). These small sample sizes resulted in wider CIs, potentially accounting for the lack of significant association between these factors and risk of dementia in the overall cohort. Additionally, these limited sample sizes may lead to overfitting of our models for both AUD and current smoking. The findings for AUD and current smoking should be interpreted cautiously and require validation through further studies with larger sample sizes. Sixth, we included participants with normal cognition at baseline. This approach helps ensure that all participants started with relatively similar cognitive functions, reducing the likelihood that cognitive decline was influencing smoking or AUD at the study’s outset. However, subtle cognitive changes could still precede diagnosis by many years and potentially impact these behaviors. Finally, lifestyle factors, such as physical activity and diet, play a significant role in the development of dementia [ 62 , 63 , 64 ]. However, such factors are unavailable in NACC dataset, precluding a comprehensive analysis of the association between these lifestyle factors and risk of dementia in this study.

In conclusion, our study shows that participants with college education or above had a lower risk of all-cause dementia, especially among those without hypertension. While our analysis of the full cohort did not find a significant association between either current smoking or AUD and risk of all-cause dementia, we identified key variables that would contribute to their HTEs on dementia risk. These findings underscore the importance of adopting a personalized approach to addressing disparities in dementia care and prevention. Future research is warranted to confirm our findings and investigate the underlying mechanisms that drive the observed HTEs.

Data availability

The deidentified participant data could be requested through the National Alzheimer’s Coordinating Center ( https://naccdata.org/requesting-data/data-request-process ).

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Acknowledgements

The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Glenn Smith, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD).

This study is supported by the National Institute on Aging (NIA) (R01AG089445 and R01AG076234). CES is funded by NIA grant (K01AG071849).

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HT, JB, and JG conceived and designed the study. HT performed the statistical analysis. All authors provided the result interpretation. HT drafted the initial manuscript. All authors reviewed and edited the final manuscript. All authors contributed to and approved the final manuscript. HT and JG had final responsibility for the decision to submit the manuscript for publication.

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Tang, H., Shaaban, C.E., DeKosky, S.T. et al. Association of education attainment, smoking status, and alcohol use disorder with dementia risk in older adults: a longitudinal observational study. Alz Res Therapy 16 , 206 (2024). https://doi.org/10.1186/s13195-024-01569-7

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Association between alcohol consumption and incidence of dementia in current drinkers: linear and non-linear mendelian randomization analysis

Affiliations.

• 1 Department of Computational Biology and Medical Big Data, Shenzhen University of Advanced Technology, China.
• 2 Department of Computer Information Science, State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China.
• 3 School of Chinese Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China.
• 4 School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China.
• 5 School of Government, Shenzhen University, Shenzhen, Guangdong, China.
• 6 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
• 7 Division of Epidemiology, The JC School of Public Health & Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
• PMID: 39290634
• PMCID: PMC11405827
• DOI: 10.1016/j.eclinm.2024.102810

Background: Previous conventional epidemiological studies found a J-shape relationship between alcohol consumption and dementia, but this result was subject to confounding biases and reverse causation. Therefore, we aimed to investigate the potential linear or non-linear causal association between alcohol consumption and the incident risk of dementia in current drinkers.

Methods: This study used data from the UK Biobank to investigate the relationship between alcohol consumption and dementia risk. 313,958 White British current drinkers, who were free of dementia during 2006-2010, were followed up until 2021. Alcohol consumption was self-reported and calculated according to the National Health Service guideline. The primary outcome was all-cause dementia identified through hospital and mortality records. We used multivariable Cox models with restricted cubic splines for conventional analysis and both non-linear and linear Mendelian Randomization (MR) analyses to assess causal relationships, employing a genetic score based on 95 SNPs identified from a meta-genome-wide association study of 941,280 people from Europe.

Findings: 313,958 current drinkers consumed an average of 13.6 [IQR: 7.1-25.2] units/week alcohol (men averaged 20.2 [11.1-33.9] units/week and women 9.5 [5.3-16.7] units/week). During a mean follow-up of 13.2 years, 5394 (1.7%) developed dementia. Multivariable Cox model with restricted cubic spline functions identified a J-shaped relationship between alcohol consumption and dementia risk, with the lowest risk at 12.2 units/week. The non-linear MR failed to identify a significant non-linear causal relationship ( p = 0.45). Both individual-level (HR: 2.22 95%CI [1.06-4.66]) and summary-level (1.89 [1.53-2.32]) linear MR analyses indicated that higher genetically predicted alcohol consumption increased dementia risk.

Interpretation: This study identified a positive linear causal relationship between alcohol consumption and dementia among current drinkers. The J-shaped association found in conventional epidemiological analysis was not supported by non-linear MR analyses. Our findings suggested that there was no safe level of alcohol consumption for dementia.

Funding: The Shenzhen Science and Technology Program and the Strategic Priority Research Program of Chinese Academy of Sciences.

Keywords: Alcohol consumption; Dementia; Mendelian randomization.

© 2024 The Author(s).

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Conflict of interest statement

All authors declare no competing interests.

• GBDDF Collaborators Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7(2):e105–e125. - PMC - PubMed
• Livingston G., Huntley J., Sommerlad A., et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413–446. - PMC - PubMed
• Rehm J., Spuhler T. Measurement error in alcohol consumption: the Swiss Health Survey. Eur J Clin Nutr. 1993;47(Suppl 2):S25–S30. - PubMed
• Liu Y., Mitsuhashi T., Yamakawa M., et al. Alcohol consumption and incident dementia in older Japanese adults: the Okayama Study. Geriatr Gerontol Int. 2019;19(8):740–746. - PubMed
• Jeon K.H., Han K., Jeong S.M., et al. Changes in alcohol consumption and risk of dementia in a nationwide cohort in South Korea. JAMA Netw Open. 2023;6(2) - PubMed

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• Lifetime effects and cost-effectiveness of statin therapy for older people in the United Kingdom: a modelling study
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• http://orcid.org/0000-0002-0951-1304 Borislava Mihaylova 1 , 2 ,
• Runguo Wu 2 ,
• Junwen Zhou 1 ,
• Claire Williams 1 ,
• http://orcid.org/0000-0002-4154-1431 Iryna Schlackow 1 ,
• Jonathan Emberson 3 ,
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• Anthony Keech 4 ,
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• http://orcid.org/0000-0003-0239-7278 Alastair Gray 1 ,
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• 1 Health Economics Research Centre, Nuffield Department of Population Health , University of Oxford , Oxford , UK
• 2 Health Economics and Policy Research Unit, Wolfson Institute of Population Health , Queen Mary University of London , London , UK
• 3 Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health , University of Oxford , Oxford , UK
• 4 NHMRC Clinical Trials Centre , The University of Sydney , Sydney , New South Wales , Australia
• 5 Clinical Effectiveness Group, Wolfson Institute of Population Health , Queen Mary University of London , London , UK
• 6 Patient and Public Representative , Havant , UK
• Correspondence to Dr Borislava Mihaylova; boby.mihaylova{at}dph.ox.ac.uk

Background Cardiovascular disease (CVD) risk increases with age. Statins reduce cardiovascular risk but their effects are less certain at older ages. We assessed the long-term effects and cost-effectiveness of statin therapy for older people in the contemporary UK population using a recent meta-analysis of randomised evidence of statin effects in older people and a new validated CVD model.

Methods The performance of the CVD microsimulation model, developed using the Cholesterol Treatment Trialists’ Collaboration (CTTC) and UK Biobank cohort, was assessed among participants ≥70 years old at (re)surveys in UK Biobank and the Whitehall II studies. The model projected participants’ cardiovascular risks, survival, quality-adjusted life years (QALYs) and healthcare costs (2021 UK£) with and without lifetime standard (35%–45% low-density lipoprotein cholesterol reduction) or higher intensity (≥45% reduction) statin therapy. CTTC individual participant data and other meta-analyses informed statins’ effects on cardiovascular risks, incident diabetes, myopathy and rhabdomyolysis. Sensitivity of findings to smaller CVD risk reductions and to hypothetical further adverse effects with statins were assessed.

Results In categories of men and women ≥70 years old without (15,019) and with (5,103) prior CVD, lifetime use of a standard statin increased QALYs by 0.24–0.70 and a higher intensity statin by a further 0.04–0.13 QALYs per person. Statin therapies were cost-effective with an incremental cost per QALY gained below £3502/QALY for standard and below £11778/QALY for higher intensity therapy and with high probability of being cost-effective. In sensitivity analyses, statins remained cost-effective although with larger uncertainty in cost-effectiveness among older people without prior CVD.

Conclusions Based on current evidence for the effects of statin therapy and modelling analysis, statin therapy improved health outcomes cost-effectively for men and women ≥70 years old.

• Health Care Economics and Organizations
• Computer Simulation
• Cardiovascular Diseases
• Outcome Assessment, Health Care

Data availability statement

Data may be obtained from a third party and are not publicly available. The datasets used in the current study may be obtained from third parties (UK Biobank https://www.ukbiobank.ac.uk/ ; Whitehall II study www.ucl.ac.uk/epidemiology-health-care/research/epidemiology-and-public-health/research/whitehall-ii ) and are not publicly available. Researchers can apply to use the UK Biobank resource and Whitehall II study data.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/heartjnl-2024-324052

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Randomised studies showed that statins reduce the incidence of myocardial infarction and ischaemic stroke by about one quarter for every 1 mmol/L reduction in low-density lipoprotein cholesterol but direct evidence among older people without prior cardiovascular disease (CVD) is limited.

In previous studies, statin therapy has been shown to be cost-effective in older people, but it has been suggested that a small further adverse effect would offset its cardiovascular benefit.

Despite markedly increased CVD risks with advancing age, lower statin use is reported among older people.

WHAT THIS STUDY ADDS

The value of statin therapy was reassessed using a contemporary UK CVD model validated in older people together with the synthesised evidence of statins’ beneficial effects on CVD events and adverse effects on myopathy, rhabdomyolysis and incident diabetes.

The study reported that both standard and higher intensity statin therapies enhanced health outcomes, with higher intensity therapy achieving larger benefits, and were cost-effective in people ≥70 years old in the UK. These findings remained robust in scenarios with smaller CVD risk reductions and further hypothetical adverse effects with statin therapy, though with increased uncertainty among older people without CVD.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

While ongoing statin trials in older people without CVD will add valuable data, particularly in those over the age of 75 years, statin treatment of individuals should not be delayed while awaiting their findings.

Increasing statin uptake and adherence among older people will reduce CVD risks.

Introduction

Statins are widely available generically and a cornerstone in cardiovascular disease (CVD) prevention. High-quality randomised evidence has shown that statins reduce the incidence of myocardial infarction (MI) and ischaemic stroke by about one quarter for every 1 mmol/L reduction in low-density lipoprotein cholesterol (LDL-C). More intensive statin regimens achieve larger reductions in LDL-C and prevent more atherosclerotic cardiovascular events. 1 However, there is less definitive evidence for statin benefit among older patients without CVD history 2 and guidelines stop short of making specific recommendations on initiating statins for primary CVD prevention in older people. 3 4 Despite the growing proportion of older people (people ≥70 years old make up about 30% of those over the age of 40 years in the UK) and the markedly higher cardiovascular risk with increasing age, lower statin use is reported. 5 6

Evidence for treatments’ long-term effects and cost-effectiveness guides healthcare decisions in many countries and healthcare systems, including in the UK. Such evidence ensures that by implementing cost-effective treatments, healthcare systems efficiently use their resources to maximise population health. Previous evidence has indicated that statin therapy is likely to be cost-effective for older people, but the estimates were sensitive to further adverse effects of statins or lower statin effectiveness. 7–9 A recent individual participant data meta-analysis of large statin trials strengthened the evidence for efficacy and safety of statins in older people. 2 Therefore, we set out to reassess the lifetime effects and cost-effectiveness of statin therapy in people ≥70 years old in the contemporary UK population, in categories by prior CVD, sex and LDL-C level, using this evidence 2 and a new UK CVD microsimulation model. 10

Study population

The lifetime effects and cost-effectiveness of statin therapy were assessed in categories of UK adults ≥70 years old in the UK Biobank and the Whitehall II cohort studies. All UK Biobank participants ≥70 years old at recruitment into the study (2006–2010), and those who reached this age by subsequent resurveys, were included in the present study from their earliest eligible attendance. All Whitehall II participants ≥70 years old at phase 9 (2007–2009) in Whitehall II were also included. Information on the derivation of participants’ baseline characteristics is presented in the online supplemental methods . To assess the lifetime effects of statin therapy, a model is required that reliably projects individual participant’s morbidity, mortality, quality of life (QoL) and healthcare costs over their lifetimes without and with statin therapy.

Supplemental material

Cvd microsimulation model.

The CVD microsimulation model has been reported elsewhere. 10 Briefly, the model was developed using the individual participant data of large statin clinical trials, and calibrated using the UK Biobank’s participant data. The model employs a broad range of socio-demographic and clinical characteristics to project annually the first occurrence of MI, stroke, coronary revascularisation, vascular death, incident diabetes, incident cancer and non-vascular death. Participant characteristics and incident events determined health-related QoL 10 and primary care and hospital admission costs 11 in the model. The model was validated in UK Biobank and Whitehall II studies and against national data.

CVD microsimulation model validation in older people

In the present study, the model performance was further assessed among participants ≥70 years old during follow-up in the UK Biobank and Whitehall II studies using their linked electronic hospital admissions, primary care records (UK Biobank only), cancer registrations and death records to identify MIs, strokes, coronary revascularisations (UK Biobank only), incident diabetes (UK Biobank only), cancers and deaths during follow-up.

Effects and costs of statin therapy

The Cholesterol Treatment Trialists’ Collaboration (CTTC) individual participant data meta-analysis of large randomised statin trials informed the relative reductions in the risks of cardiovascular events per 1 mmol/L in LDL-C with statin therapy ( table 1 ) of 24% in MI risk, 16% in stroke, 25% in coronary revascularisation and 12% in cardiovascular death. 2 We assessed the effects of standard (eg, achieving 35%–45% LDL-C reduction: atorvastatin 20 mg/day, rosuvastatin 5–10 mg/day or simvastatin 40–80 mg/day) and higher intensity statin therapy (eg, achieving ≥45% LDL-C reduction: atorvastatin 40–80 mg/day, rosuvastatin 20–40 mg/day) ( online supplemental table 1 ). 12 The reduction in LDL-C achieved with each level of statin intensity was derived using the therapy’s proportional reduction and participant’s untreated LDL-C level (with the effects of any ongoing statin therapy removed). Meta-analyses of statin therapies informed 9% excess odds of new-onset diabetes with standard 13 and further 12% excess odds with higher intensity 14 statin therapy. An overview of cohort studies informed excess rates of myopathy (11 cases per 100 000 treated per year) and rhabdomyolysis (3.4 cases per 100 000 treated per year; 10% case fatality) with statin therapy 15 ; with myopathy and rhabdomyolysis effects on QoL informed from a modelling study. 16 Generic statin medication costs, 17 costs of consultations 18 and blood lipids tests 19 for initiation and monitoring of statin prescribing in the UK National Health Service were included ( table 1 ).

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Statin treatment effects and statin treatment costs

Cost-effectiveness of statin therapy

We employed the model to project event risks and survival and summarise life years, quality-adjusted life years (QALYs) and primary and hospital care costs over individuals’ remaining lifetimes (ie, death or 110 years of age) without and with statin therapy and to assess the cost-effectiveness of different statin therapies in categories of older individuals.

Base-case analysis

In our base-case analysis, we assessed the cost-effectiveness of lifetime statin therapy from the perspective of the UK National Health Service under a number of key assumptions based on current evidence. First, the reductions in individuals’ LDL-C levels with a particular statin therapy were assumed to correspond to the average proportional reduction achieved with the therapy. Second, we assumed that the relative effects of a particular statin therapy on event risks were independent of duration of therapy or individual person characteristics including age (ie, the overall effects reported in meta-analyses were employed). Third, disease events were assumed not to differ in severity or otherwise, irrespective of statin treatment status. Finally, statin therapy was assumed not to affect the risks of cancer or other non-vascular events, 20 nor confer any discomfort or disutility beyond the adverse events specified above.

Assessment of uncertainty

We ran 500 microsimulations per individual for each set of parameters. We summarised the parameter uncertainty, including uncertainty in effects of statin therapy on vascular and non-vascular events, all event risk equations, QoL and healthcare cost equations in the decision-analytic model using 1000 sets of parameter values, derived using a bootstrap approach, employing sampling with replacement from respective populations. 21 Values for treatment effects were sampled from lognormal distributions corresponding to the natural logarithm of relative risk reductions with statin therapy.

We report life years and QALYs gained, the additional statin and other healthcare costs (2020/2021 UK£) and the incremental costs per QALY with standard and higher-intensity statin therapies. We discounted future QALYs and costs at 3.5% per year in the summary measures for cost-effectiveness. 22 We present cost-effectiveness acceptability curves for willingness-to-pay values from £0-£40K/QALY.

Sensitivity and scenario analyses

The following parameters were varied. First, in view of the higher uncertainty in the effects of statin therapy in older people, in scenario analyses, we applied relative risk reductions in cardiovascular endpoints per 1 mmol/L LDL-C, informed from data only among: (1) people >75 years old at randomisation and (2) people >75 years old and without prior CVD at randomisation in the individual participant data meta-analysis. 2 Second, to explore sensitivity to possible double counting of statin effects in the model through its direct effect on vascular death risk and indirect effects through MI and stroke risks, we studied the impact of smaller direct relative risk reduction in cardiovascular death with statin therapy (ie, 7% instead of 12% per 1 mmol/L in LDL-C reduction). Third, to assess sensitivity to variation in major non-vascular disease risk, we ran scenario analyses with a small detrimental or beneficial statin effect on incident cancer, informed by the 95% CI limits reported in a meta-analysis of randomised statin trials. 20 Fourth, in acknowledgement of substantial rates of statin discontinuation and reinitiation, a scenario analysis assessed statin cost-effectiveness using estimated real-world compliance with statin derived from routine UK data, 23 with statin effects and costs discontinued with therapy discontinuation. Fifth, to acknowledge the uncertainty concerning any further QoL disutility from taking a daily statin pill, we included analyses with yearly disutility equal to 0.001, 0.002 or 0.005. Sixth, we present scenarios with doubled risk of non-vascular death; with lower general QoL; and both together to assess sensitivity to further reduced potential in older people to benefit from preventive treatment. We also present scenario analyses with only healthcare costs for CVD and incident diabetes included; with higher costs of statin therapy and with 1.5% discount rate for costs and outcomes.

Further details are provided in the online supplemental methods .

Patient and public involvement

Three members of the public were involved in the study management and steering groups. Study methods and results were also discussed in separate sessions with our lay members who helped us refine the study methodology and approach to presenting study findings.

The baseline characteristics of participants ≥70 years old in the UK Biobank and Whitehall II studies in categories by prior CVD are presented in table 2 and online supplemental table 2 . There were 15 019 (52% men; mean age 72.5 years) participants without CVD and 5103 (66% men; mean age 72.9 years) with history of CVD. Among participants without and with prior CVD, 29% and 58%, respectively, were prescribed a statin at baseline and the derived untreated mean LDL-C levels were 4.2 mmol/L (SD 0.78 mmol/L) and 4.3mmol/L (SD 0.98 mmol/L), respectively.

Baseline characteristics of UK Biobank and Whitehall II participants 70 years and older

In model validation, the cumulative event rates predicted by the CVD microsimulation model, using the baseline characteristics of participants ≥70 years old, corresponded mostly well to the observed rates of cardiovascular and non-vascular events in categories of participants by prior CVD, respectively, though higher MI risks, but not cardiovascular death risks, were predicted among participants with prior CVD in UK Biobank but not in Whitehall II study ( figure 1 ).

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CVD microsimulation model validation among UK Biobank and Whitehall II participants 70 years and older.In the Whitehall II study, no linked data for CRV and diabetes were available and, therefore, no model validation performed for CRV and diabetes. CRV, coronary revascularisation; CVD, cardiovascular disease; MI, myocardial infarction; NVD, nonvascular death; VD, vascular disease.

In participant categories by sex, prior CVD and LDL-C level, standard statin therapy was projected to increase individual survival (undiscounted) by 0.37 to 1.05 life years (0.24 to 0.7 QALYs), and higher intensity statin therapy by a further 0.08 to 0.21 life years (0.04 to 0.13 QALYs) ( figure 2A , online supplemental tables 3 and 4 ). Across these categories, the incremental cost per QALY gained for standard statin therapy compared with no statin ranged from £116 to £3502 and that for higher intensity compared with standard statin from £2213 to £11 778 per QALY ( figure 2B ). The analyses of parameter uncertainty indicated that at £20 000/QALY willingness to pay threshold, higher intensity statin therapy had a very high probability of being cost-effective across all categories of men and women ≥70 years old ( figure 3 ). The probability that statin therapy was cost-effective for people ≥70 years old remained high even at a cost-effectiveness threshold of £5K/QALY. However, at this lower threshold, the standard statin therapy had the highest probability of being cost-effective among women with a pretreatment LDL-C lower than 4.1 mmol/L and among men with a pretreatment LDL-C lower than 3.4 mmol/L ( figure 3 ).

Life years and QALYs gained (A) and cost-effectiveness (B) of lifetime statin therapy in categories by prior cardiovascular disease, sex and pre-treatment LDL cholesterol level. Incremental Cost-Effectiveness Ratio (ICER) is the ratio of the incremental costs divided by the incremental QALYs with costs and QALYs discounted at 3.5% per year. CVD, cardiovascular disease; LDL, low density lipoprotein; QALY, quality-adjusted life years.

Probability that lifetime statin therapy is cost-effective in categories by prior cardiovascular disease, sex and pre-treatment LDL cholesterol level. The probability that the treatment scenario provides the highest QALYs gain at the particular threshold of cost-effectiveness plotted. CVD, cardiovascular disease; LDL-C, low-density lipoprotein cholesterol; QALY, quality-adjusted life years.

These cost-effectiveness results remained robust in a wide range of sensitivity analyses ( figure 4 , online supplemental table 5 ) with higher sensitivity noted for a higher intensity statin at a five times higher price. In particular, although reduced gains in QALYs were projected, standard statin therapy remained cost-effective in people ≥70 years old if relative risk reductions after age 75 were equal to those reported in the subgroup of participants >75 years old, or indeed in the subgroup of participants >75 years old without CVD at randomisation, in the CTTC meta-analysis ( figures 4 and 5 and online supplemental figure 1 ). Higher intensity statin therapy remained cost-effective among older people with pretreatment cholesterol levels 3.4 mmol/L or higher. In these scenario analyses with lower CVD risk reductions with statin therapy, the probability of standard or higher intensity statin therapy being cost-effective remained higher than no statin therapy in all categories of older people but was substantially reduced among older women with lower LDL-C levels.

Sensitivity analyses of cost-effectiveness of statin therapy for people 70 years or older. (A) Incremental cost (£) per QALY gained (standard statin vs no statin). (B) Incremental cost (£) per QALY gained (higher intensity vs standard statin). See online supplemental methods table 7 for description of sensitivity analyses. The * on the horizontal axes represent the base-case analysis. CVD, cardiovascular disease; LDL, low-density lipoprotein; NVD, nonvascular death; QALY, quality-adjusted life year; QoL, quality of life.

Life years and QALYs gained and cost-effectiveness of lifetime statin therapy in older people: scenario analyses with CVD reductions with statin therapy in people>75 years old informed from effects of statin therapy among participants>75 years old (Scenario 1) or >75 years old without CVD (Scenario 2) from Cholesterol Treatment Trialists’ collaborative meta-analysis. Statin effects up to age 75 as in base-case analysis; statin effect thereafter as per respective scenario analysis. CVD, cardiovascular disease; ICER, Incremental Cost-Effectiveness Ratio with costs and QALYs discounted at 3.5% per year; LDL, low-density lipoprotein; QALY, quality-adjusted life year.

This assessment of the lifetime effects and cost-effectiveness of statin therapy in people ≥70 years old in the UK used contemporary patient data, a validated CVD microsimulation model and a meta-analysis of the effects of statin treatment across age categories. It concluded that lifetime statin treatment increased quality-of-life-adjusted survival in older men and women and, at UK cost of generic statins, was highly cost-effective for all, irrespective of their CVD history or LDL-C level. Higher intensity statin therapy was the strategy likely to bring the highest health benefits cost-effectively, although standard statin regimens would achieve most of these benefits. These findings remained robust in sensitivity analyses with smaller cardiovascular risk reductions with statin therapy, though smaller benefits were projected and standard statin therapy became the preferred option for older people with LDL-C levels <3.4 mmol/L.

In this analysis, we used the overall relative risk reductions in cardiovascular events per 1 mmol/L LDL-C reduction with statin therapy given the similar relative risk reductions across age categories in the individual participant meta-analysis of statin trials. 2 The meta-analysis, however, noted trends towards smaller proportional reductions in major coronary events and vascular deaths in older people. Data were particularly limited among participants >75 years old without prior CVD, where there was no direct evidence for statistically significant cardiovascular risk reductions with statin therapy. In the present report, two scenario analyses assessed the sensitivity of findings to the size of statin effects using relative risk reductions in cardiovascular events in the meta-analysis (1) among participants >75 years old, and (2) among participants >75 years old without prior CVD at randomisation. 2 In both scenarios, despite smaller net health benefits, statin therapy remained cost-effective although with larger uncertainty.

We previously reported that statin therapy, at generic prices, is highly cost-effective in UK across patients 40–70 years old irrespective of their sex, age, CVD risk and LDL-C level. 21 Here, we extend this work to older people and indicate that, although the gains in QALYs are smaller, the additional costs are also lower, and the incremental cost per QALY remains highly attractive. Moreover, with a substantially higher CVD risk (99% of ≥70 years old UK Biobank participants without prior CVD had estimated 10 year CVD risk ≥10%; and 88% had 10-year CVD risk ≥15%, data not shown), the level of risk is irrelevant in guiding statin treatment decisions in older people.

This reassessment of statins’ value in the contemporary older UK population confirms findings of earlier cost-effectiveness studies 8 9 and reaffirms that, despite substantial reductions in CVD incidence and mortality over the last decades, statins remain a cornerstone in CVD prevention in this population. Our findings differ from an earlier study of cost-effectiveness of statin therapy for the primary prevention of CVD in people ≥75 years old, which reported that, although statin treatment was highly cost-effective, even a small hypothetical increase in a geriatric-specific adverse effect (ie, reducing disability-adjusted life years by 0.003–0.004) would offset its cardiovascular benefit. 7 In our study, the known small excesses of myopathy, rhabdomyolysis and incident diabetes with statin treatment were explicitly integrated, and our findings remained robust to hypothetical further statin-associated reductions in QoL up to 0.005 QALY/ year and to lower statin efficacy, suggesting that the value of statin therapy for older people is more certain than implied. It is important to also underline that high-quality randomised evidence indicate that the vast majority of adverse effects reported on statin therapy were also reported in the absence of statin therapy, 24 25 indicating serious misattribution of adverse effects in observational and uncontrolled studies.

Our results indicate that older people are likely to cost-effectively benefit from statin treatment. Statin treatment rates in our ≥70 years old cohort (29% among people without CVD to 58% among people with prior CVD) were similar to statin treatment rates reported by the Health Survey for England. 26 Hence, from the 9.1 million adults ≥70 years old in UK, 27 a third of them with prior CVD, 26 just over 40%, or less than 4 million, are receiving statin treatment. While further evidence for statins effects in older people will be helpful, the robustness of the findings to variations in key parameters suggests that delaying statin treatment in the millions of older people while awaiting new evidence is unjustifiable.

Our study has a number of strengths. We used a contemporary UK CVD model, developed using a large and rich population biobank with demonstrable ability to predict cardiovascular and mortality risks in older people. We used the baseline characteristics of more than 20 000 people ≥70 years old to evaluate lifetime benefits and cost-effectiveness of statin therapy. A further strength of our analysis is the use of synthesised randomised evidence for the effects of statin therapy by age that allowed us to study the robustness of our findings to somewhat smaller reductions in cardiovascular risks in older people. Finally, the reported excesses in myopathy, rhabdomyolysis and incident diabetes with standard and higher intensity statin therapy were integrated allowing the net effects of treatment to be fully assessed.

The study has some limitations. First, the majority of our data is among people aged 70 to early 80s. Our findings, however, were very similar in participants 70–75 and ≥75 years old (results not shown), which suggest that they are generalisable to much older people. Second, our model and results are based on population cohorts, in which the healthy volunteer effect may limit generalisability. To address this limitation, the model used a broad range of socioeconomic, lifestyle and clinical characteristics that allow generalisations to populations with different distributions of these characteristics. Moreover, statin therapy remained cost-effective in scenario analyses with substantially higher risk of non-vascular death and lower QoL. Third, a small excess in milder muscle symptoms was recently reported with statin treatment across randomised studies with excess confined to the first year of treatment. 28 The sensitivity analyses suggest that this adverse effect is unlikely to materially alter statin’s cost-effectiveness. Fourth, two ongoing large statin trials, scheduled to complete in 2026, will add valuable further data to the direct evidence of effects of statin therapy in people aged ≥75 years without atherosclerotic CVD. 29 30 Fifth, missing baseline data were imputed using a single imputation. Moreover, while the model performance was good for most participant categories, endpoints and across the two datasets, there were some deviations. Therefore, it is possible that the uncertainty may be larger than reported by the model. However, the consistency of cost-effectiveness results across categories of participants and across a broad range of sensitivity analyses for key parameters indicate that our general findings are robust.

In conclusion, this study reports that statin therapy is highly likely to be cost-effective in older people, although there was greater uncertainty among older people without CVD in scenario analysis with substantially smaller CVD risk reductions with statin therapy. While further randomised evidence will be helpful, the robustness of these findings indicates that older people are likely to benefit cost-effectively from statin therapy and should be considered for treatment.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This work used data of participants in research studies (UK Biobank, Whitehall II) who have consented to collection and use of their data for research. Ethics committee approval was not required for this secondary research study. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

This research has been conducted using data from Cholesterol Treatment Trialists’ Collaboration https://www.cttcollaboration.org/ , UK Biobank Resource under Application Number 56757 www.ukbiobank.ac.uk , and Whitehall II study www.ucl.ac.uk/epidemiology-health-care/research/epidemiology-and-public-health/research/whitehall-ii . We thank all the participants, staff and other contributors to these resources. Project Oversight Group: Colin Baigent, Alison Gater, Borislava Mihaylova, Stephen Morris, Paul Roderick (Chair), Natalie Rowland, Peter Sever, Liam Smeeth. We also thank further members of the public with whom we discussed the project and emerging results.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

BM and RW are joint first authors.

Correction notice This article has been corrected since it was first published. Missing panel and axes titles have been added to Figure 1.

Collaborators Cholesterol Treatment Trialists’ Collaborators: CTT secretariat: J Armitage, C Baigent, E Barnes, L Blackwell, R Collins, K Davies, J Emberson, J Fulcher, H Halls, WG Herrington, L Holland, A Keech, A Kirby, B Mihaylova, R O’Connell, D Preiss, C Reith, J Simes, K Wilson. CTT Collaborating trialists: A to Z trial (phase Z): M Blazing, E Braunwald, J de Lemos, S Murphy; TR Pedersen, M Pfeffer, H White, S Wiviott; AFCAPS/TEXCAPS (AirForce/Texas Coronary Atherosclerosis Prevention Study) M Clearfield, JR Downs, A Gotto Jr, S Weis; ALERT (Assessment of Lescol in Renal Transplantation) B Fellström, H Holdaas (deceased), A Jardine, TR Pedersen; ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) D Gordon, B Davis; C Furberg, R Grimm, S Pressel, JL Probstfield, M Rahman, L Simpson; ALLIANCE (Aggressive Lipid-Lowering Initiation Abates New Cardiac Events) M Koren; ASCOT (Anglo-Scandinavian Cardiac Outcomes Trial) B Dahlöf, A Gupta, N Poulter, P Sever, H Wedel; ASPEN (Atorvastatin Study for the Prevention of Coronary Heart Disease Endpoints in Non-Insulin Dependent Diabetes Mellitus) RH Knopp (deceased); AURORA (A study to evaluate the Use of Rosuvastatin in subjects On Regular haemodialysis: an Assessment of survival and cardiovascular events) S Cobbe, B Fellström, H Holdaas (deceased), A Jardine, R Schmieder, F Zannad; CARDS (Collaborative Atorvastatin Diabetes Study) DJ Betteridge (deceased), HM Colhoun, PN Durrington, J Fuller (deceased), GA Hitman, A Neil; CARE (Cholesterol And Recurrent Events Study) E Braunwald, B Davis, CM Hawkins, L Moyé, M Pfeffer, F Sacks; CORONA (Controlled Rosuvastatin Multinational Trial in Heart Failure) J Kjekshus, H Wedel, J Wikstrand; 4D (Die Deutsche Diabetes Dialyse Studie): C Wanner, V Krane; GISSI (Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico) Heart Failure and Prevention trials: MG Franzosi, R Latini, D Lucci, A Maggioni;, R Marchioli, EB Nicolis, L Tavazzi, G Tognoni; HOPE-3: J Bosch, E Lonn, S Yusuf; HPS (Heart Protection Study): J Armitage, L Bowman, R Collins, A Keech, M Landray, S Parish, R Peto, P Sleight (deceased); IDEAL (Incremental Decrease in Endpoints through Aggressive Lipid-lowering) JJP Kastelein, TR Pedersen; JUPITER (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin) R Glynn, A Gotto Jr, JJP Kastelein, W Koenig, J MacFadyen, PM Ridker; LIPID (Long-term Intervention with Pravastatin in Ischaemic Disease) A Keech, S MacMahon, I Marschner, A Tonkin, J Shaw (deceased), J Simes, H White; LIPS (Lescol Intervention Prevention Study) PW Serruys; Post-CABG (Post-Coronary Artery Bypass Graft Study) G Knatterud (deceased); PROSPER (Prospective Study of Pravastatin in the Elderly at Risk) GJ Blauw, S Cobbe, I Ford, P Macfarlane, C Packard, N Sattar, J Shepherd (deceased), S Trompet; PROVE-IT (Pravastatin or Atorvastatin Evaluation and Infection Therapy) E Braunwald, CP Cannon, S Murphy; SEARCH (Study of Effectiveness of Additional Reductions in Cholesterol and Homocysteine): R Collins, J Armitage, L Bowman, R Bulbulia, R Haynes, S Parish, R Peto, P Sleight (deceased); SPARCL (Stroke Prevention by Aggressive Reduction in Cholesterol Levels): P Amarenco, KM Welch; (4S Scandinavian Simvastatin Survival Study) J Kjekshus, TR Pedersen, L Wilhelmsen; TNT (Treating to New Targets) P Barter, A Gotto Jr, J LaRosa, JJP Kastelein, J Shepherd (deceased); WOSCOPS (West of Scotland Coronary Prevention Study) S Cobbe, I Ford, S Kean, P Macfarlane, C Packard, M Roberston, N Sattar, J Shepherd (deceased), R Young, Other CTT Members: H Arashi, R Clarke, M Flather, S Goto, U Goldbourt, J Hopewell, GK Hovingh, G Kitas, C Newman, MS Sabatine, GG Schwartz, L Smeeth, J Tobert, J Varigos, J Yamamguchi.

Contributors BM and CB conceived the study. BM, IS, JE, CR, JR, AG, JA, CB secured funding. All authors contributed to study design. BM, RW, JZ, CW, IS performed the analyses. BM drafted the paper with support from RW. All authors provided comments on the paper. BM acts as guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding This study was funded by the UK NIHR Health Technology Assessment (HTA) Programme (17/140/02). Further support from the British Heart Foundation (PG/18/16/33570 and CH/1996001/9454), the UK Medical Research Council (MC_UU_00017/4), the National Institute for Health Research Barts Biomedical Research Centre (NIHR203330) and NHMRC, Australia is acknowledged. The study was designed and analysed independently of all funders and the views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care or any other funder. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Competing interests AK reports research support from Abbott, Amgen, ASPEN, Bayer, Mylan, Novartis, Sanofi, Viatris; speaker fees from Novartis; and is a Data Safety Monitoring Board member for Kowa. JR reports funding from North East London Integrated Care Service. JA reports receiving a grant to their research institution from Novartis for the ORION 4 trial of inclisiran. JS reports receiving grants for his institution from Amgen, Bayer, BMS, MSD, Pfizer and Roche; consulting fees from FivepHusion, and is a chair (unpaid) of STAREE DSMB. CB reports research grants from Boehringer Ingelheim and Health Data Research UK and is a chair (unpaid) of a Data Safety Monitoring Board for Merck. All other authors declare no competing interests.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer-reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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