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Systematic evaluation of the associations between environmental risk factors and

dementia: an umbrella review of systematic reviews and meta-analyses

Vanesa Bellou1, Lazaros Belbasis1, Ioanna Tzoulaki1,2,3, Lefkos T Middleton4, John

PA Ioannidis5,6,7,8, Evangelos Evangelou1,2

1Department of Hygiene and Epidemiology, University of Ioannina Medical School,

Ioannina, Greece

2Department of Biostatistics and Epidemiology, School of Public Health, Imperial

College London, London, UK

3MRC-PHE Centre for Environment and Health, School of Public Health, Imperial


4Neuroepidemiology and Ageing Research Unit, School of Public Health, Imperial


5Department of Medicine, Stanford Prevention Research Center, Stanford, CA, USA

6Department of Health Research and Policy, Stanford University School of Medicine,

Stanford, CA, USA

7Meta-Research Innovation Center at Stanford (METRICS), Stanford University,

Stanford, CA, USA

8Department of Statistics, Stanford University School of Humanities and Sciences,

Stanford, CA, USA

1

Corresponding author:

Dr. Evangelos Evangelou, PhD

Department of Hygiene and Epidemiology, University of Ioannina Medical School,

Ioannina, 45110, Greece

e-mail: [email protected]

Role of the funding source

There was no funding source for this study. All authors had full access to all the study

data. The corresponding author had final responsibility for the decision to submit for

publication.

2

Abstract

INTRODUCTION: Dementia is a heterogeneous neurodegenerative disease, whose

aetiology results from a complex interplay between environmental and genetic

factors.

METHODS: We searched PubMed to identify meta-analyses of observational studies

that examined associations between non-genetic factors and dementia. We estimated

the summary effect size using random- and fixed-effects model, the 95% CI, and the

95% prediction interval. We assessed the between-study heterogeneity (I-square),

evidence of small-study effects, and excess significance.

RESULTS: 76 unique associations were examined. By applying standardised criteria,

7 associations presented convincing evidence. These associations pertained to

benzodiazepines use, depression at any age, late-life depression and frequency of

social contacts for all types of dementia; late-life depression for Alzheimer’s disease;

and type 2 diabetes mellitus for vascular dementia and Alzheimer’s diease.

DISCUSSION: Several risk factors present substantial evidence for association with

dementia and should be assessed as potential targets for interventions, but these

associations may not necessarily be causal.

Keywords: Alzheimer’s disease, dementia, epidemiology, risk factors, umbrella

review

Abbreviations: Alzheimer’s disease (AD), confidence interval (CI), interquartile

range (IQR), standard error (SE), type 2 diabetes mellitus (T2DM), prediction interval

(PI), vascular dementia (VaD)

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Introduction

Over 46 million people live with dementia in 2016 world-wide and the number is

expected to exceed 130 million by 2050.1 This unprecedented increase of the number

of patients is mainly due to the considerable rise in life expectancy and population

ageing world-wide. The annual cost of dementia care was estimated at $818 billion,

world-wide, in 2014; and it is expected to exceed $1 trillion by 2018. No therapies are

currently available to delay or arrest the disease onset and progression and drug

development has been problematic, compared to other disease areas.2 Moreover, there

are considerable gaps in our understanding of the nosology, and aetiological

complexity of the disease.

Aimed at delaying disease onset by modulating modifiable risk factors, primary

prevention has been proposed as a potentially effective and feasible tool to address the

global challenge posed by dementia.3 It has been suggested that a third of Alzheimer’s

disease (AD) cases might be attributable to modifiable factors such as diabetes

mellitus, mid-life hypertension and obesity, physical activity, depression, smoking

and low educational attainment.4,5 An observed decline in the incidence and

prevalence of AD in western European countries and US has been ascribed to better

management of cardiovascular and metabolic risk factors.4,6–9 Unfortunately, it is

difficult to validate these speculations in randomized trials, since primary preventive

trials with clinical dementia outcomes would require large sample sizes and prolonged

follow-up. Due to the chronic and slowly progressive nature of this disease, both

pharmacological and non-pharmacological randomized clinical trials for dementia

mostly evaluate surrogate cognitive decline outcomes rather than clinical disease

outcomes.

4

We performed an umbrella review of the evidence across existing systematic reviews

and meta-analyses of observational studies to systematically map the evidence on

environmental risk factors for dementia. Our aim is to provide an overview of the

range and validity of the reported associations of diverse, potentially modifiable (non-

genetic), risk factors by evaluating whether there is evidence for biases in this

literature, and finally, pinpoint the number of previously studied associations that

have been synthesized with meta-analyses and have shown the strongest evidence for

association.

Methods

Search strategy and eligibility criteria

We conducted an umbrella review, i.e. a comprehensive and systematic collection and

evaluation of systematic reviews and meta-analyses performed on a specific research

topic.10 The methods of the umbrella review are standardized and follow the same

principles as previous umbrella reviews for other neurological disorders.11–13

We systematically searched PubMed up to January 16, 2016 to identify systematic

reviews and meta-analyses of observational studies examining associations of

potentially modifiable (environmental and other non-genetic) factors with all types of

dementia (Alzheimer’s disease, vascular dementia, dementia with Lewy bodies,

frontotemporal dementia). Relevant keywords for the search strategy were (dementia

OR Alzheimer*) AND (“systematic review” OR meta-analysis). Two independent

investigators (VB, LB) retrieved and abstracted the full text of potentially eligible

articles. We excluded meta-analyses that investigated the association between genetic

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markers and risk for dementia as these factors have been examined extensively

elsewhere14,15 and they are not modifiable. We also did not consider fluid biomarkers

as they are not directly modifiable and the literature on fluid biomarkers is being

reviewed systematically elsewhere (http://www.alzforum.org/alzbiomarker). Meta-

analyses with an outcome related to cognitive decline or impairment, progression of

dementia or severity of symptoms were excluded. We further excluded meta-analyses

including less than three component studies. When an association was covered by

more than one meta-analyses, we kept the meta-analysis with the largest number of

component studies with available data on individual studies. We did not apply any

language restrictions in our search strategy.

Data extraction

Two independent investigators (VB, LB) extracted the data, and in case of

discrepancies consensus was reached. From each eligible article, we abstracted

information on the first author, journal and year of publication, the examined risk

factors and the number of studies considered. We also extracted the study-specific

risk estimates (i.e. risk ratio, odds ratio, hazard ratio) along with their corresponding

confidence interval (CI) and the number of cases and controls in each study. If a risk

factor was examined in more than one levels of comparison, we extracted the data for

the comparison having the largest number of component studies. Also, when a meta-

analysis combined effect estimates for incidence of dementia and score in a cognitive

test, we considered the former. Furthermore, we recorded whether the eligible papers

applied any criteria to assess the quality of component studies.

Statistical analysis

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http://www.alzforum.org/alzbiomarker)

We applied standardized methods for the umbrella review and state-of-the-art

approaches to evaluate findings on putative risk factors for dementia, that have been

applied to assess the epidemiological credibility for environmental risk factors of

other neurodegenerative diseases,11–13 while similar assessments have been

successfully applied in genetic studies.16,17 Specifically, for each meta-analysis, we

estimated the summary effect size and its 95% CI using both fixed-effects and

random-effects models.18,19 We also estimated the 95% prediction interval (PI), which

accounts for the between-study heterogeneity and evaluates the uncertainty for the

effect that would be expected in a new study addressing that same association.20,21 For

the largest study of each meta-analysis, we estimated the standard error (SE) of the

effect size and we examined whether the SE was less than 0.10. In a study with SE of

less than 0.10, the difference between the effect estimate and the upper or lower 95%

CI is less than 0.20 (i.e. this uncertainty is less than what is considered a small effect

size).

Between-study heterogeneity was quantified using the I2 metric.22 I2 ranges between

0% and 100% and quantifies the variability in effect estimates that is due to

heterogeneity rather than sampling error.23 Values exceeding 50% or 75% are

considered to represent large or very large heterogeneity, respectively.

We assessed small-study effects (i.e. whether smaller studies tend to give

substantially larger estimates of effect size compared with larger studies)24 using the

Egger’s regression asymmetry test.25 A p<0.10 combined with a more conservative

effect in the largest study than in random-effects meta-analysis was judged to provide

adequate evidence for small-study effects.

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We further applied the excess statistical significance test, which evaluates whether

there is a relative excess of formally significant findings in the published literature

due to any reason (e.g. publication bias, selective reporting of outcomes or analyses).

It is a chi-square based test that assesses whether the observed (O) number of studies

with nominally significant results is larger than their expected (E) number.26 We used

the effect size of the largest study (smallest SE) in each meta-analysis to calculate the

power of each study using a non-central t distribution.27,28 Excess statistical

significance was claimed at two-sided p<0.10 with O>E as previously proposed.26

Assessment of epidemiological credibility

We identified associations that had the strongest evidence and no signals of large

heterogeneity or bias. Specifically, we considered as convincing the associations that

fulfilled all of the following criteria: statistical significance according to random-

effects model at p<10-6;29,30 based on more than 1,000 cases; without large between-

study heterogeneity (I2<50%); 95% PI excluding the null value; and no evidence of

small-study effects and excess significance. Associations with >1000 cases, p<10-6

and largest study presenting a statistically significant effect were graded as highly

suggestive. The associations supported by >1,000 cases and a significant effect at

p<10-3 were considered as suggestive. The remaining nominally significant

associations were considered as having weak evidence.

Association does not necessarily imply causation. For associations with convincing or

highly suggestive evidence, we performed a sensitivity analysis including only

prospective cohort studies in order to assess whether there is evidence also for

temporality of the association. We also qualitatively discuss the evidence on these

associations in terms of the potential for reverse causation, residual confounding,

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information bias (e.g. non-differential misclassification), and other biases not covered

by the standardized tests listed above.

The statistical analysis and the power calculations were done with STATA version

12.0.

Results

Overall, 2543 papers were searched and 43 articles were eligible. Of the 120 papers

screened in full text, 40 were excluded because a larger meta-analysis on the same

association was found, 32 because they had no quantitative synthesis and 5 for other

reasons (Figure 1). A list of the 40 excluded meta-analyses is presented in the

Supplementary Table 1.

The eligible papers were published between 2008 and 2016. The 43 articles

corresponded to 76 unique meta-analyses: 39 on Alzheimer’s disease (AD), 27 on all

types of dementia, and 10 on vascular dementia (VaD). Fifty-three unique risk factors

were considered and 14 of them were studied in more than one outcome. The median

number of studies per meta-analysis was 7 (IQR, 5-13) and the median number of

cases was 1,139 (IQR, 590-3,537). The number of cases was greater than 1,000 in 46

meta-analyses (Table 1). All eligible meta-analyses used summary-level data from

published literature and none of them had access to individual participant data.

Ten of the eligible articles used the Newcastle-Ottawa scale to qualitatively assess

259 component studies. Of the 259 studies assessed, 63 (24%) were of low quality,

124 (48%) were graded as having moderate quality, and 72 (28%) were characterized

as high quality (Supplementary Table 2).

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Associations for AD

39 of the 76 meta-analyses examined associations for AD. 26 of 39 meta-analyses

(67%) presented a nominally statictically significant effect (p<0.05), and only 6

associations (15%) reached p<10-6 (Table 1). These associations pertained to cancer,

depression at any age, late-life depression, neuroticism, physical activity, and type 2

diabetes mellitus (T2DΜ). 7 of 39 associations (aluminum, cancer, late-life

depression, neuroticism, physical activity, stroke, T2DΜ) had a 95% PI excluding the

null value (Table 1). 15 associations presented large between-study heterogeneity

estimates (I2≥50% and I2≤75%), and three associations presented very large

heterogeneity estimates (I2>75%) (Table 1). 8 associations presented evidence for

small-study effects and 12 associations had a statistically significant excess of

“positive” studies (Table 1, Supplementary Table 3).

Associations for all types of dementia

27 of the 76 meta-analyses explored associations for all types of dementia. 20 of the

27 associations (74%) had a statictically significant effect at p<0.05, and only 6 of

them were statistically significant when we applied a more stringent threshold (p<10-

6) (Table 1). These associations pertained to benzodiazepines use, depression at any

age, education, frequency of social contacts, late-life depression, and T2DΜ. In 6

associations (depression at any age, early-life depression, late-life depression, T2DΜ,

low frequency of social contacts and benzodiazepines use), the 95% PI under random-

effects model excluded the null value (Table 1). 15 associations presented large or

very large heterogeneity estimates (I2≥50%) (Table 1). Of the 27 associations for all

types of dementia, 5 had evidence for small-study effects (antihypertensive drugs, fish

intake, physical activity, social participation, and statins) and 7 had a statistically

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significant excess of “positive” studies (alcohol drinking, education, physical activity,

rural living, smoking, social participation, and tooth loss) (Table 1, Supplementary

Table 3).

Associations for VaD

Only 10 of the 76 associations examined a risk factor for VaD. Two associations

(smoking and T2DM) were supported by more than 1,000 cases. Nine associations

had a nominally statistically significant effect (p<0.05), while three of them

(education, late-life depression, and T2DM) presented a p<10-6 (Table 1). Three

associations (depression at any age, late-life depression and T2DM) had a 95% PI

excluding the null value (Table 1). One association (physical activity) had large

heterogeneity, and another association (midlife obesity) had very large heterogeneity

(Table 1). One association (smoking) had evidence for excess significance, and none

of the associations had evidence for small-study effects (Table 1, Supplementary

Table 3).

Assessment of epidemiological credibility

Βy applying the predefined methodological criteria, four risk factors for all types of

dementia (benzodiazepines use, depression at any age, late-life depression and

frequency of social contacts), two risk factors for AD (late-life depression, T2DM)

and one risk factor for VaD (T2DM) were supported by convincing evidence, by

having more than 1,000 cases, a p<10-6, not large heterogeneity (I2<50%), 95% PI

excluding the null value, no hints for small-study effects and excess significance bias.

Moreover, the association of T2DM with all types of dementia, and the association of

cancer, depression at any age and physical activity with AD were supported by highly

suggestive evidence (more than 1,000 cases, p<10-6 and a nominally significant effect

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in the largest study). Furthermore, five risk factors for AD (aluminum, herpesviridae

infection, low-frequency electromagnetic fields, education, NSAIDs) and five risk

factors for all types of dementia (early-life depression, education, midlife obesity,

physical activity, statins) presented suggestive evidence. Additional 34 associations

presented weak evidence with p<0.05. Finally, 21 associations did not present even a

nominally statistically significant result (p>0.05) (Table 2).

In a sensitivity analysis, limited to prospective cohort studies only, the summary

effect size and the p-value remained similar for all risk factors with convincing and

highly suggestive evidence (Table 3).

Discussion

We systematically collected and appraised the potentially modifiable environmental

risk factors that have previously been reported for association with dementia in

observational studies and summarized in meta-analyses. Only depression at any age,

late-life depression, benzodiazepines use, and low frequency of social contacts

showed convincing epidemiological evidence for an increased risk for all types of

dementia. For AD only late-life depression and T2DM showed convincing evidence.

T2DM was the only risk factor with convincing evidence for an association with VaD,

while the association of T2DM with all types of dementia was supported by highly

suggestive evidence. Also, the association of history of cancer, depression at any age

and physical activity with risk for AD was supported by highly suggestive evidence.

Analyses limited to prospective cohort studies gave very similar estimates for these

risk factors.

12

Half of the examined meta-analyses had large heterogeneity and one third suffered

from small-study effects or/and excess significance. Heterogeneity could be due to

biased results in some of the included studies, but it could also reflect genuine

differences across studies. Sources of heterogeneity in dementia research could be

introduced from different study designs (i.e. a mixture of prospective and

retrospective studies in the meta-analyses) and differences in exposure assessment.

Reported associations with disease should be interpreted with caution, in particular

when heterogeneity is large and small-study effects are evident.

Furthermore, the diagnostic criteria and definition of AD and, indeed, late-onset

dementia remains a controvertial issue. There is still no consensus in diagnostic

criteria used for dementia diagnosis. This could create between-study heterogeneity

and hinder the identification of robust risk factors. Indeed, the National Institute of

Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease

and Related Disorders Association 1984 diagnostic criteria for AD employed the

terms of clinically probable and possible AD, with the assumption that a definite

diagnosis requires post mortem confirmation of the typical AD-like pathology.31 The

recently revised criteria by the National Institute of Aging and Alzheimer’s

Association workgroup have differentiated all-cause dementia from AD, with (as

exclusion criterion) the presence of any clinical or other evidence of concomitant

cerebro-vascular pathology, any other known form of dementia or any concurrent

condition or medication potentially affecting cognition.32 However, whilst 70% of

dementia cases occur in people over the age of 75,33 the majority of AD patients over

the age of 75 have co-existing pathologies on brain biopsy.34

Depression at any age and late-life depression showed convincing evidence of

association with all types of dementia. It is disputed whether depression is a risk

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factor or a mere prodrome of dementia.35 To test this hypothesis, an analysis taking

into account the relative association between age at onset of depression and age at

diagnosis of dementia could more precisely define the association. Indeed, late-life

depression presented the strongest and most consistent evidence supporting an

increased risk for both all types of dementia and AD.36 On the other hand, early-life

depression yielded a significant association supported by suggestive evidence, while

only one non-significant prospective cohort study examined the association between

early-life depression and all types of dementia.35 It has also been postulated that

depression may be an early reaction to perceived cognitive decline. Our analysis also

suggests a higher risk for VaD in patients with late-life depression but the supporting

evidence was sparse, partly due to the smaller number of studies in the meta-analysis

of VaD compared to AD. According to the “vascular depression hypothesis”, vascular

disease, leading to a long-term process of subclinical cerebrovascular changes, could

be the underlying link between depression and dementia.37

Additionally, our umbrella review reveals a strong relationship of T2DΜ with VaD

and AD. VaD has been classified, based on the pattern of cerebrovascular lesions, into

multi-infarct dementia, strategic infarct dementia and subcortical vascular

encephalopathy (Binswanger’s disease).38,39 These disease patterns indicate that VaD

and cerebrovascular diseases might share common risk factors, including T2DΜ.40

The highly suggestive association of T2DΜ with all types of dementia presented large

between-study heterogeneity. This may reflect that T2DΜ confers a different

magnitude of susceptibility for different types of dementia, with more than doubling

of the risk for VaD, but a more modest increase in the risk for AD.

High level of physical activity has a protective effect for the risk of developing AD in

prospective cohort studies. No heterogeneity was observed and the 95% PI excluded

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the null value. However, the summary effect size and its significance level may be

affected by the presence of small-study effects. In support, two RCTs have suggested

that physical activity interventions can potentially reduce the cognitive decline in

elderly people.41,42

Furthermore, a statistically significant association supported by highly suggestive

evidence was observed for history of cancer and AD. Although the summary effect

estimate remained similar in the sensitivity analysis of the prospective cohort studies,

hints for small-study effects were present. Also, given that the patients with cancer

tend to have shorter life expectancy than the general population, this association could

be attributed to the competing risks between death and risk for AD.43 Finally, the

component studies used adjustment models mainly for age and sex and they did not

perform adjustment for potential confounders, like history of depression and T2DΜ.

These are significant limitations indicating that the reported association between

cancer and AD might not be genuine.

Moreover, low frequency of social contacts showed a statistically significant effect on

developing all types of dementia. Social networking is thought to be among the

modifiable factors, along with the level of educational attainment, and a range of

leisure activities, which may help maintain cognitive function in old age. This concept

of “brain reserve” refers to the ability to tolerate the age-related changes and the

disease-related pathology in the brain without developing clear clinical symptoms or

signs.44, The level of education, another factor associated with the brain reserve

theory, had suggestive evidence for an association with all types of dementia and

weak evidence for an association with AD. Both associations were highly significant,

but they presented very large between-study heterogeneity and wide 95% PI including

the null value. The observational studies might apply different diagnostic criteria and

15

different methods to measure educational attainment. These factors are potential

sources of the observed between-study heterogeneity, along with other differences in

study design (e.g., variability of adjustment models). Also, the association between

level of education and VaD was based on a small number of cases, and thus this

association was not considered to be supported by strong evidence.

The increased risk of developing dementia for those who use benzodiazepines could

be the result of limited cognitive reserve capacity, induced by the long term use of

benzodiazepines, which might reduce a person’s ability to cope with early phase brain

lesions by soliciting accessory neuronal networks.45 Of note, the cohort studies that

evaluated the association between benzodiazepines use and dementia had a

prospective design, which argues against reverse causation. However,

benzodiazepines use might have been motivated by the presence of symptoms, such

as difficulties with sleep and chronic anxiety with or without depression, which are

known psychopathological features that may precede the formal diagnosis of

dementia by years.46 Additionally, a recently published cohort study indicated the

absence of a dose-response association between use of benzodiazepines and risk for

dementia.47 This observation argues against a causative association.

Our study has several limitations. The diagnostic criteria used for case ascertainment

could influence the effect size and increase between-study heterogeneity. We could

not fully control for these factors because this information was, often, not available in

the published meta-analyses. Also, it is apparent that the mixture of both low and

high-quality studies influences the summary effect size in a meta-analysis; thus, in

meta-analyses the quantitative synthesis of the evidence should follow a critical

appraisal of the pertinent observational studies. We could not perform such an

appraisal, as this process should have been a main objective of the original meta-

16

analyses. Indeed, only a quarter of the eligible papers assessed and reported the study

quality using the Newcastle-Ottawa scale, a standardized tool for qualitative

assessment of non-randomized studies. Based on this qualitative appraisal, only a

quarter of the component studies presented high methodological quality and were of

low risk for bias. Finally, in our analysis we considered only associations that have

been evaluated in meta-analyses of observational studies and we might have missed

associations with adequate evidence that have not yet been assessed through meta-

analytic quantitative synthesis.

Taking into account these caveats, our analysis identified five risk factors with

convincing evidence and strong epidemiological credibility pertaining to

benzodiazepines use, depression at any age, low frequency of social contacts, T2DΜ

and late-life depression. The ability to modify these factors through public health

policy measures or other interventions remains to be established. Indeed, there is no

guarantee that even a convincing association in observational evidence for a

modifiable risk factor would necessarily translate into large preventive benefits for

dementia if an effort is made to modify these risk factors.48 Currently there are means

available that can achieve substantial improvements in the control of T2DM, but the

burden of T2DM is increasing in many developed and developing countries. Policy

interventions should also be feasible aiming at limitation of benzodiazepines use in

the older population. The ability to reduce the population burden of depression or

enhance social interactions is more limited. The continued search for additional risk

factors for dementia and their validation in real life are still warranted.

17

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38 O’Brien JT, Thomas A. Vascular dementia. Lancet 2015; 386: 1698–706.

39 Jellinger KA. Morphologic diagnosis of ‘vascular dementia’ - a critical update. J Neurol Sci 2008; 270: 1–12.

40 Gorelick PB, Scuteri A, Black SE, et al. Vascular Contributions to Cognitive Impairment and Dementia: A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke 2011; 42: 2672–713.

41 Ngandu T, Lehtisalo J, Solomon A, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet (London, England) 2015; 385: 2255–63.

42 Lautenschlager NT, Cox KL, Flicker L, Foster JK, Bockxmeer FM Van. Effect of Physical Activity on Cognitive Function in Older Adults at Risk for Alzheimer Disease. JAMA 2008; 300: 1027–37.

43 Zhang Q, Guo S, Zhang X, et al. Inverse relationship between cancer and Alzheimer’s disease: a systemic review meta-analysis. Neurol Sci 2015; 36: 1987–94.

44 Fratiglioni L, Wang H-X. Brain reserve hypothesis in dementia. J Alzheimers Dis 2007; 12: 11–22.

45 Billioti de Gage S, Moride Y, Ducruet T, et al. Benzodiazepine use and risk of

21

Alzheimer’s disease: case-control study. BMJ 2014; 349: g5205.

46 Barbui C, Gastaldon C, Cipriani A. Benzodiazepines and risk of dementia: true association or reverse causation? Epidemiol Psychiatr Sci 2013; 22: 307–8.

47 Gray SL, Dublin S, Yu O, et al. Benzodiazepine use and risk of incident dementia or cognitive decline: prospective population based study. BMJ 2016; 352: i90.

48 Prasad V, Jorgenson J, Ioannidis JPA, Cifu A. Observational studies often make clinical practice recommendations: an empirical evaluation of authors’ attitudes. J Clin Epidemiol 2013; 66: 361–6.e4.

49 Anstey KJ, Mack HA, Cherbuin N. Alcohol consumption as a risk factor for dementia and cognitive decline: meta-analysis of prospective studies. Am J Geriatr Psychiatry 2009; 17: 542–55.

50 Anstey KJ, Cherbuin N, Budge M, Young J. Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies. Obes Rev 2011; 12: e426–37.

51 Beckett MW, Ardern CI, Rotondi MA. A meta-analysis of prospective studies on the role of physical activity and the prevention of Alzheimer’s disease in older adults. BMC Geriatr 2015; 15: 9.

52 Cataldo JK, Prochaska JJ, Glantz SA. Cigarette smoking is a risk factor for Alzheimer’s Disease: an analysis controlling for tobacco industry affiliation. J Alzheimers Dis 2010; 19: 465–80.

53 Chang-Quan H, Hui W, Chao-Min W, et al. The association of antihypertensive medication use with risk of cognitive decline and dementia: a meta-analysis of longitudinal studies. Int J Clin Pract 2011; 65: 1295–305.

54 da Silva J, Goncalves-Pereira M, Xavier M, Mukaetova-Ladinska EB. Affective disorders and risk of developing dementia: systematic review. Br J Psychiatry 2013; 202: 177–86.

55 García AM, Sisternas A, Hoyos SP. Occupational exposure to extremely low frequency electric and magnetic fields and Alzheimer disease: A meta-analysis. Int J Epidemiol 2008; 37: 329–40.

56 Gudala K, Bansal D, Schifano F, Bhansali A. Diabetes mellitus and risk of

22

dementia: A meta-analysis of prospective observational studies. J Diabetes Investig 2013; 4: 640–50.

57 Kim Y-S, Kwak SM, Myung S-K. Caffeine intake from coffee or tea and cognitive disorders: a meta-analysis of observational studies. Neuroepidemiology 2015; 44: 51–63.

58 Li F-J, Shen L, Ji H-F. Dietary intakes of vitamin E, vitamin C, and β-carotene and risk of Alzheimer’s disease: a meta-analysis. J Alzheimers Dis 2012; 31: 253–8.

59 Maheshwari P, Eslick GD. Bacterial infection and Alzheimer’s disease: a meta-analysis. J Alzheimers Dis 2015; 43: 957–66.

60 Meng X, D’Arcy C. Education and Dementia in the Context of the Cognitive Reserve Hypothesis: A Systematic Review with Meta-Analyses and Qualitative Analyses. PLoS One 2012; 7: e38268.

61 Meng X-F, Yu J-T, Wang H-F, et al. Midlife vascular risk factors and the risk of Alzheimer’s disease: a systematic review and meta-analysis. J Alzheimers Dis 2014; 42: 1295–310.

62 O’Brien J, Jackson JW, Grodstein F, Blacker D, Weuve J. Postmenopausal Hormone Therapy Is Not Associated With Risk of All-Cause Dementia and Alzheimer’s Disease. Epidemiol Rev 2014; 36: 83–103.

63 Perry DC, Sturm VE, Peterson MJ, et al. Association of traumatic brain injury with subsequent neurological and psychiatric disease: a meta-analysis. J Neurosurg 2015; : 1–16.

64 Peters R, Booth A, Peters J. A systematic review of calcium channel blocker use and cognitive decline/dementia in the elderly. J Hypertens 2014; 32: 1945–57; discussion 1957–8.

65 Power MC, Weuve J, Gagne JJ, McQueen MB, Viswanathan A, Blacker D. The association between blood pressure and incident Alzheimer disease: a systematic review and meta-analysis. Epidemiology 2011; 22: 646–59.

66 Richardson K, Schoen M, French B, et al. Statins and cognitive function: a systematic review. Ann Intern Med 2013; 159: 688–97.

67 Seitz DP, Shah PS, Herrmann N, Beyene J, Siddiqui N. Exposure to general

23

anesthesia and risk of alzheimer’s disease: a systematic review and meta-analysis. BMC Geriatr 2011; 11: 83.

68 Steel AJ, Eslick GD. Herpes Viruses Increase the Risk of Alzheimer’s Disease: A Meta-Analysis. J Alzheimers Dis 2015; 47: 351–64.

69 Terracciano A, Sutin AR, An Y, et al. Personality and risk of Alzheimer’s disease: New data and meta-analysis. Alzheimer’s Dement 2014; 10: 179–86.

70 Virk SA, Eslick GD. Brief Report: Meta-analysis of Antacid Use and Alzheimer’s Disease: Implications for the Aluminum Hypothesis. Epidemiology 2015; 26: 769–73.

71 Wang J, Tan L, Wang H-F, et al. Anti-inflammatory drugs and risk of Alzheimer’s disease: an updated systematic review and meta-analysis. J Alzheimers Dis 2015; 44: 385–96.

72 Wang Z, Wei X, Yang J, et al. Chronic exposure to aluminum and risk of Alzheimer’s disease: A meta-analysis. Neurosci Lett 2016; 610: 200–6.

73 Zhang Y, Chen J, Qiu J, Li Y, Wang J, Jiao J. Intakes of fish and PUFAs and mild-to-severe cognitive impairment risks: a dose-response meta-analysis of 21 cohort studies. Am J Clin Nutr 2015; published online Dec 30. DOI:10.3945/ajcn.115.124081.

74 Zhou J, Yu J-T, Wang H-F, et al. Association between stroke and Alzheimer’s disease: systematic review and meta-analysis. J Alzheimers Dis 2015; 43: 479–89.

75 Blondell SJ, Hammersley-Mather R, Veerman J. Does physical activity prevent cognitive decline and dementia?: A systematic review and meta-analysis of longitudinal studies. BMC Public Health 2014; 14: 510.

76 Kuiper JS, Zuidersma M, Oude Voshaar RC, et al. Social relationships and risk of dementia: A systematic review and meta-analysis of longitudinal cohort studies. Ageing Res Rev 2015; 22: 39–57.

77 Levi Marpillat N, Macquin-Mavier I, Tropeano A-I, Bachoud-Levi A-C, Maison P. Antihypertensive classes, cognitive decline and incidence of dementia: a network meta-analysis. J Hypertens 2013; 31: 1073–82.

78 Loef M, Walach H. Midlife obesity and dementia: meta-analysis and adjusted

24

forecast of dementia prevalence in the United States and China. Obesity (Silver Spring) 2013; 21: E51–5.

79 Pedditizi E, Peters R, Beckett N. The risk of overweight/obesity in mid-life and late life for the development of dementia: a systematic review and meta-analysis of longitudinal studies. Age Ageing 2016; 45: 14–21.

80 Russ TC, Batty GD, Hearnshaw GF, Fenton C, Starr JM. Geographical variation in dementia: Systematic review with meta-analysis. Int J Epidemiol 2012; 41: 1012–32.

81 Santangeli P, Di Biase L, Bai R, et al. Atrial fibrillation and the risk of incident dementia: A meta-analysis. Hear Rhythm 2012; 9: 1761–8.e2.

82 Shen T, Lv J, Wang L, Wang W, Zhang D. Association between tooth loss and dementia among older people: a meta-analysis. Int J Geriatr Psychiatry 2015; published online Dec 7. DOI:10.1002/gps.4396.

83 Ungprasert P, Wijarnpreecha K, Thongprayoon C. Rheumatoid arthritis and the risk of dementia: A systematic review and meta-analysis. Neurol India 2016; 64: 56–61.

84 Wu S, Ding Y, Wu F, Li R, Hou J, Mao P. Omega-3 fatty acids intake and risks of dementia and Alzheimer’s disease: A meta-analysis. Neurosci Biobehav Rev 2015; 48: 1–9.

85 Zhong G, Wang Y, Zhang Y, Zhao Y. Association between Benzodiazepine Use and Dementia: A Meta-Analysis. PLoS One 2015; 10: e0127836.

86 Zhong G, Wang Y, Zhang Y, Guo JJ, Zhao Y. Smoking Is Associated with an Increased Risk of Dementia: A Meta-Analysis of Prospective Cohort Studies with Investigation of Potential Effect Modifiers. PLoS One 2015; 10: e0118333.

87 Aarsland D, Sardahaee FS, Anderssen S, Ballard C. Is physical activity a potential preventive factor for vascular dementia? A systematic review. Aging Ment Health 2010; 14: 386–95.

88 Beydoun MA, Beydoun HA, Wang Y. Obesity and central obesity as risk factors for incident dementia and its subtypes: a systematic review and meta-analysis. Obes Rev 2008; 9: 204–18.

25

89 Sharp SI, Aarsland D, Day S, Sønnesyn H, Alzheimer’s Society Vascular Dementia Systematic Review Group, Ballard C. Hypertension is a potential risk factor for vascular dementia: systematic review. Int J Geriatr Psychiatry 2011; 26: 661–9.

26

Figure 1. Flow chart of literature search

27

2543 articles reviewed by title screening

150 articles reviewed by abstract screening

120 articles reviewed by full text screening

43 eligible articles published until January 16, 2016

2393 articles were excluded

693 were treatment studies504 had outcomes other than risk for dementia457 were articles about genetic epidemiology318 were editorials or narrative reviews200 were imaging, diagnostic or prognostic studies87 were articles about biomarkers68 were incidence or prevalence studies37 were articles about health economics28 were methodological papers1 was a retracted meta-analysis


28 had outcomes other than risk for dementia1 was primary study1 was article about biomarkers


40 were not the largest meta-analyses investigating a risk factor32 were systematic reviews without a quantitative synthesis3 were pooled analyses without a systematic review of literature1 was a meta-analysis including less than 3 component studies1 was a meta-analysis with poor quality of reporting and statistical analysis

Table 1. Characteristics and quantitative synthesis of the 76 eligible meta-analyses of environmental risk factors for Alzheimer’s disease, all

types of dementia and vascular dementia

Reference Risk factorLevel of

comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Alzheimer’s disease

Anstey, 200949Alcohol

drinking

Light or moderate

drinkers vs.

Never drinkers

709/11784 6 RR 0.72 (0.61-0.86) 2.0 × 10-4 0.44-1.18 56.4 No/No Weak

Anstey, 201150 Midlife BMIUnderweight vs.

normal weight1041/7218 3 RR 1.78 (0.84-3.75) 0.133 10-4-104 71.2 No/No NS

Beckett, 201551Physical

activity

High level vs.

Low level1358/18968 9 HR 0.62 (0.52-0.72) 5.0 × 10-9 0.51-0.75 0 Yes/No

Highly

suggestive

Cataldo, 201052 SmokingEver vs. Never

smokers6927/325054 43 RR 1.05 (0.91-1.20) 0.526 0.52-2.10 64.8 No/Yes NS

Chang-Quan,

201153

Anti-

hypertensive

drugs

Exposed vs. Not

exposed906/20838 7 RR 0.90 (0.79-1.03) 0.133 0.76-1.08 0 No/No NS

da Silva, 201354Depression at

any age

Diseased vs. Non-

diseased5101/41055 25 RR 1.77 (1.48-2.13) 6.0 × 10-10 0.86-3.66 69.6 Yes/Yes

Highly

suggestive

Diniz, 201336Late-life

depression

Diseased vs. Non-

diseased3358/30111 16 RR 1.65 (1.42-1.92) 4.8 × 10-11 1.36-1.99 2.2 No/No Convincing

28


comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Garcia, 200855

Low-frequency

electromagneti

c fields

Exposed vs. Not

exposed3238/10446519 25 RR 1.74 (1.37-2.21) 5.9 × 10-6 0.77-3.91 55.2 Yes/Yes Suggestive

Gudala, 201356

Type 2

diabetes

mellitus

Diseased vs. Not

diseased3537/529160 21 RR 1.54 (1.39-1.72) 3.1 × 10-15 1.37-1.73 0 No/No Convincing

Kim, 201557 Caffeine intakeHigh intake vs.

Low intake590/6280 5 RR 0.78 (0.50-1.22) 0.275 0.17-3.53 71 No/No NS

Li, 201258Dietary intake

of vitamin C

High intake vs.

Low intake1043/13468 6 RR 0.85 (0.74-0.96) 0.011 0.71-1.01 0 No/No Weak


of vitamin E

High intake vs.

Low intake1205/14509 7 RR 0.80 (0.67-0.95) 0.011 0.52-1.24 46.7 No/No Weak


of β carotene

High intake vs.

Low intake801/9445 5 RR 0.92 (0.76-1.13) 0.435 0.59-1.45 18.4 No/No NS

Maheshwari,

201459

Chlamydia

pneumoniae

infection

Diseased vs. Not

diseased282/226 11 OR 6.00 (1.93-18.66) 2.0 × 10-3 0.19-193.81 73 No/Yes Weak

Maheshwari,

201459

Spirochetal

infection

Diseased vs. Not

diseased300/255 13 OR 10.65 (3.40-33.42) 5.0 × 10-5 0.41-279.54 51.6 No/No Weak

Meng, 201260 EducationLow level vs.

High level2769/51532 16 RR 1.82 (1.36-2.43) 5.5 × 10-5 0.55-6.05 90.1 No/No Suggestive

29


comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Meng, 201461 Midlife BMIObese vs. Normal

weight1003/16709 5 RR 1.81 (1.22-2.69) 3.0 × 10-3 0.52-6.29 63.7 No/No Weak

O’Brien, 201462 HRT Ever vs. Never 1156/13210 9 HR 0.88 (0.66-1.16) 0.361 0.41-1.87 47.7 No/No NS

Perry, 201563Mild traumatic

brain injury

Exposed vs. Not

exposed7158/21603 19 OR 1.40 (1.03-1.90) 0.034 0.39-4.98 85.2 No/No Weak

Peters, 201464

Calcium

channel

blockers

Ever vs. Never 548/8886 4 RR 0.80 (0.54-1.17) 0.242 0.17-3.75 61.1 No/No NS

Power, 201165 HypertensionDiseased vs. Not

diseased1255/25297 13 RR 0.97 (0.81-1.17) 0.745 0.57-1.64 45.7 No/Yes NS

Richardson,

201366Statins

Exposed vs. Not

exposed3785/762841 13 RR 0.72 (0.59-0.89) 1.9 × 10-3 0.39-1.35 54.7 Yes/Yes Weak

Seitz, 201167General

anesthesia

Exposed vs. Not

exposed2122/5716 15 OR 1.05 (0.93-1.19) 0.448 0.91-1.21 0 No/No NS

Steel, 201568Herpesviridae

infection

Diseased vs. Not

diseased1330/1565 33 OR 1.38 (1.14-1.65) 7.3 × 10-4 0.86-2.21 20.3 No/Yes Suggestive

Terracciano,

201469Agreeableness

High level vs.

Low level382/2960 3 HR 0.88 (0.79-0.98) 0.019 0.43-1.78 0 No/No Weak

Terracciano,

201469

Conscientiousn

ess

High level vs.

Low level382/2960 3 HR 0.77 (0.69-0.86) 1.5 × 10-6 0.38-1.54 0 Yes/Yes Weak

30


comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Terracciano,

201469Extraversion

High level vs.

Low level382/2960 3 HR 0.95 (0.82-1.11) 0.530 0.23-3.91 39.8 No/No NS

Terracciano,

201469Neuroticism

High level vs.

Low level607/4447 5 HR 1.33 (1.21-1.45) 1.9 × 10-9 1.14-1.54 0 No/Yes Weak

Terracciano,

201469Openness

High level vs.

Low level382/2960 3 HR 0.86 (0.77-0.96) 8.3 × 10-3 0.41-1.79 0 No/No Weak

Virk, 201570 Antacid drugs Ever vs. Never 939/5371 9 OR 0.96 (0.77-1.21) 0.747 0.73-1.26 0 No/No NS

Wang, 201571 Aspirin Ever vs. Never 2263/16393 11 RR 0.77 (0.63-0.95) 0.014 0.42-1.42 55.5 Yes/No Weak

Wang, 201571 Corticosteroids Ever vs. Never 351/4023 3 RR 0.62 (0.26-1.46) 0.277 10-4-103 38.3 No/Yes NS

Wang, 201571Non-aspirin

NSAIDsEver vs. Never 2108/15940 9 RR 0.65 (0.49-0.86) 2.3 × 10-3 0.29-1.45 59.1 Yes/No Weak

Wang, 201571 NSAIDs Ever vs. Never 53372/228119 16 RR 0.74 (0.64-0.86) 6.9 × 10-5 0.45-1.22 70 No/No Suggestive

Wang, 201672 AluminumExposed vs. Not

exposed1383/9184 8 OR 1.72 (1.33-2.21) 3.1 × 10-5 1.16-2.54 6.2 No/No Suggestive

Zhang, 201543 CancerDiseased vs. Not

diseased4635/40251 7 HR 0.62 (0.53-0.74) 4.6 × 10-8 0.50-0.78 0 Yes/No

Highly

suggestive

Zhang, 201573Dietary DHA

intake

High intake vs.

Low intake535/6104 3 RR 0.55 (0.24-1.23) 0.145 10-5-104 90.5 No/Yes NS

Zhang, 201573 Fish intakeHigh intake vs.

Low intake915/21026 5 RR 0.88 (0.79-0.98) 0.022 0.63-1.22 63.4 No/No Weak

31


comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Zhou, 201574 StrokeDiseased vs. Not

diseased952/13778 6 HR 1.59 (1.25-2.02) 1.7 × 10-4 1.13-2.23 0 No/No Weak

All types of dementia


drinking

Light or moderate

drinkers vs.

Never drinkers

1016/16389 7 RR 0.74 (0.61-0.91) 3.9 × 10-3 0.43-1.28 52.6 No/Yes Weak

Blondell, 201475Physical

activity

High level vs.

Low level3845/35974 21 RR 0.76 (0.66-0.86) 1.9 × 10-5 0.49-1.15 68.9 Yes/Yes Suggestive


any age

Diseased vs. Not

diseased25106/416385 33 RR 1.99 (1.84-2.16) 8.0 × 10-62 1.65-2.40 27.8 No/No Convincing

da Silva, 201354Early-life

depression

Diseased vs. Not

diseased3538/24845 9 RR 1.63 (1.27-2.11) 1.5 × 10-4 1.01-2.64 16.2 No/No Suggestive


depression

Diseased vs. Not


Gudala, 201356

Type 2

diabetes

mellitus

Diseased vs. Not

diseased15707/1125450 22 RR 1.60 (1.43-1.79) 5.4 × 10-17 1.05-2.44 72.3 No/No

Highly

suggestive

Kim, 201557 Caffeine intakeHigh intake vs.

Low intake905/7535 5 RR 0.72 (0.34-1.51) 0.385 0.05-9.50 75.7 No/No NS

Kuiper, 201576Frequency of

social contacts

Low level vs.

High level1122/14640 8 RR 1.57 (1.32-1.85) 1.9 × 10-7 1.27-1.93 0 No/No Convincing

32


comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Kuiper, 201576 LonelinessHigh level vs.

Low level280/2972 3 RR 1.58 (1.19-2.09) 1.5 × 10-3 0.25-9.78 0 No/No Weak

Kuiper, 201576

Satisfaction

with social

network

Low level vs.

High level985/5222 4 RR 1.25 (0.96-1.62) 0.103 0.46-3.36 50.3 No/No NS

Kuiper, 201576Social network

size

Low level vs.

High level1059/6691 5 RR 1.17 (0.92-1.48) 0.197 0.56-2.44 64 No/No NS

Kuiper, 201576Social

participation

Low level vs.

High level589/7125 6 RR 1.41 (1.13-1.75) 2.0 × 10-3 0.85-2.34 31.2 Yes/Yes Weak

Levi Marpillat,

201377

Anti-

hypertensive

drugs

Ever vs. Never 86422/1436995 11 HR 0.84 (0.75-0.94) 1.7 × 10-3 0.60-1.16 73.4 Yes/No Weak

Loef, 201378 Midlife BMIObese vs. Normal

weight1914/28405 5 RR 1.91 (1.40-2.62) 5.1 × 10-5 0.74-4.93 53.5 No/No Suggestive


High level8739/78504 23 RR 1.88 (1.51-2.33) 1.2 × 10-8 0.69-5.14 89.6 No/Yes Suggestive

Pedditizi, 201679 Late-life BMIObese vs. Normal

weight1053/7400 4 RR 0.83 (0.74-0.94) 3.0 × 10-3 0.64-1.09 0 No/No Weak

Pedditizi, 201679 Late-life BMIOverweight vs.

Normal weight1885/12394 5 RR 0.88 (0.76-1.02) 0.094 0.56-1.38 54.3 No/No NS

33


comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Pedditizi, 201679 Midlife BMIOverweight vs.

Normal weight1252/12792 4 RR 1.10 (0.99-1.22) 0.076 0.87-1.38 0 No/No NS

Perry, 201563Mild traumatic

brain injury

Exposed vs. Not

exposed7798/22564 23 OR 1.35 (1.01-1.78) 0.040 0.38-4.75 85 No/No Weak

Richardson,

201366Statins

Ever vs. never

users37798/4325018 12 RR 0.83 (0.76-0.91) 1.3 × 10-4 0.66-1.04 63.2 Yes/No Suggestive

Russ, 201280 Rural livingExposed vs. Not

exposed832/10504 4 RR 1.12 (0.75-1.69) 0.583 0.18-7.03 83.7 No/Yes NS

Santangeli, 201281Atrial

fibrillation

Diseased vs. Not

diseased5301/68586 9 HR 1.36 (1.12-1.65) 2.0 × 10-3 0.82-2.26 52.5 No/No Weak

Shen, 201582 Tooth loss High vs. Low 2378/17439 10 RR 1.56 (1.25-1.96) 1.1 × 10-4 0.89-2.75 44.8 No/Yes Weak

Ungprasert,

201683

Rheumatoid

arthritis

Diseased vs. Not

diseased19088/3044201 4 RR 1.58 (1.04-2.40) 0.031 0.25-10.14 93.4 No/No Weak

Wu, 201584 Fish intakeHigh intake vs.

Low intake1013/20574 5 RR 0.79 (0.62-1.01) 0.060 0.42-1.48 29.1 Yes/No NS

Zhong, 201585Benzodiazepin

es use

Ever vs. Never

users11741/29981 5 RR 1.49 (1.30-1.72) 2.7 × 10-8 1.03-2.17 35.1 No/No Convincing

Zhong, 201586 SmokingEver vs. Never

smokers14944/930070 27 RR 1.13 (1.05-1.22) 1.2 × 10-3 0.87-1.47 47.1 No/Yes Weak

Vascular dementia

34


comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Aarsland, 201087Physical

activity

High level vs.

Low level374/10108 5 RR 0.62 (0.42-0.92) 0.017 0.19-2.07 55.7 No/No Weak


drinking

Light or moderate

drinkers vs.

Never drinkers

151/8969 4 RR 0.75 (0.57-0.98) 0.037 0.38-1.45 5.2 No/No Weak

Beydoun, 200888 Midlife BMIObese vs. Normal

weight222/14017 3 RR 1.71 (0.47-6.26) 0.419 10-7-107 89.1 No/No NS

Chang-Quan,

201153

Anti-

hypertensive

drugs

Ever vs. Never 254/18558 4 RR 0.64 (0.42-0.98) 0.040 0.14-2.96 46.8 No/No Weak


any age

Diseased vs. Not

diseased227/12929 4 RR 2.92 (1.87-4.56) 2.5 × 10-6 1.10-7.78 0 No/No Weak


depression

Diseased vs. Not

diseased316/16250 5 OR 2.52 (1.77-3.59) 2.8 × 10-7 1.40-4.54 1.1 No/No Weak

Gudala, 201356

Type 2

diabetes

mellitus

Diseased vs. Not



High level379/7334 3 RR 2.75 (2.19-3.45) 2.1 × 10-18 0.63-11.96 0 No/No Weak

Sharp, 201189 HypertensionDiseased vs. Not

diseased425/7698 6 HR 1.59 (1.20-2.11) 1.4 × 10-3 0.78-3.21 36.3 No/No Weak

35


comparison

Total number

of

cases/controls

Number of

primary

studies

Effect

size

metric

Random effects

summary effect

size (95% CI)

P random 95% PI I2

Small-study

effects/Excess

statistical

significance

Level of

evidence

Zhong, 201586 SmokingEver vs. Never

smokers1406/885388 8 RR 1.26 (1.05-1.50) 0.013 0.79-2.00 43.9 No/No Weak

BMI: body mass index, CI: confidence intervanl, DHA: docosahexaenoic acid, HR: hazard ratio, HRT: hormone replacement therapy, NSAIDs: non-steroid anti-inflammatory drugs, OR: odds ratio, PI: prediction interval, RR: risk ratio

36

Table 2. Assessment of the statistically significant environmental risk factors for dementia (Alzheimer’s disease, all types of dementia, vascular dementia)

Level of evidence Criteria Alzheimer’s disease All types of dementia Vascular dementia

Convincing

>1000 cases, p<10-6, I2<50%, 95% PI excluding the null value, no small-study effects and excess significance bias

Late-life depression, type 2 diabetes mellitus

Benzodiazepines use, depression at any age, frequency of social contacts, late-life depression

Type 2 diabetes mellitus

Highly suggestive

>1000 cases, p<10-6, largest study with a statistically significant effect

Cancer, depression at any age, physical activity Type 2 diabetes mellitus None

Suggestive>1000 cases, p<10-3

Aluminum, education, herpesviridae infection, Low-frequency electromagnetic fields, NSAIDs

Early-life depression, education, physical activity, midlife BMI (Obese vs. Normal weight), statins

None

WeakThe rest associations with p<0.05

Alcohol drinking, dietary intake of vitamin C, dietary intake of vitamin E, chlamydia pneumoniae infection, spirochetal infection, midlife BMI (Obese vs. Normal weight), mild traumatic brain injury, statins, agreeableness, conscientiousness, neuroticism, openness, aspirin, non-aspirin NSAIDs, fish intake, stroke

Alcohol drinking, anti-hypertensive drugs, atrial fibrillation, loneliness, social participation, late-life BMI (Obese vs. Normal weight), mild traumatic brain injury, tooth loss, rheumatoid arthritis, smoking

Alcohol drinking, anti-hypertensive drugs, depression at any age, hypertension, late-life depression, education, physical activity, smoking

37

Table 3. Sensitivity analysis limited to prospective cohort studies for associations with convincing and highly suggestive evidence in the main analysis

References Risk factor N of studies

Number of cases/controls

Effect size

metric

Random-effects summary effect size (95% CI)

P random 95% PI I2

Small-study effects/Excess

statistical significance

Level of evidence

Alzheimer’s diseaseDiniz, 201336 Late-life depression 15 3348/29951 RR 1.64 (1.40-1.92) 1.4 × 10-9 1.27-2.12 7.5 No/No Convincing

Gudala, 201356 Type 2 diabetes mellitus 21 3537/529160 RR 1.54 (1.39-1.72) 3.1 × 10-15 1.37-1.73 0 No/No Convincing

da Silva, 201354 Depression at any age 15 1461/24937 RR 1.72 (1.39-2.13) 4.9 × 10-7 0.90-3.31 57.5 Yes/Yes Highly suggestive

Zhang, 201543 Cancer 6 1354/27127 HR 0.63 (0.53-0.75) 1.0 × 10-7 0.49-0.80 0 Yes/No Highly suggestive

Beckett. 201551 Physical activity 9 1358/18968 HR 0.62 (0.52-0.72) 5.0 × 10-9 0.51-0.75 0 Yes/No Highly suggestive

All types of dementiaZhong, 201585 Benzodiazepines use 5 11741/29981 RR 1.49 (1.30-1.72) 2.7 × 10-8 1.03-2.17 35.1 No/No Convincing

da Silva, 201354 Depression at any age 23 2781/29578 RR 1.86 (1.61-2.14) 2.6 × 10-17 1.27-2.71 26 No/No Convincing

Kuiper, 201576 Frequency of social contacts 8 1122/14640 RR 1.57 (1.32-1.85) 1.9 × 10-7 1.27-1.93 0 No/No Convincing

Diniz, 201336 Late-life depression 22 4782/45306 RR 1.83 (1.65-2.03) 3.3 × 10-29 1.63-2.05 0 No/No Convincing

Gudala, 201356 Type 2 diabetes mellitus 22 15707/1125450 RR 1.60 (1.43-1.79) 5.4 × 10-17 1.05-2.44 72.3 No/No Highly

suggestiveVascular dementia

Gudala, 201356 Type 2 diabetes mellitus 14 1396/875524 RR 2.28 (1.94-2.66) 1.1 × 10-24 1.91-2.71 0 No/No Convincing

38

HR: hazard ratio, PI: prediction interval, RR: risk ratio

39

Supplementary Table 2. Quality assessments of primary studies based on Newcastle-Ottawa scale

Reference Risk factor OutcomeHigh quality

(NOS score = 9)

Moderate quality

(NOS score = 7 or 8)

Low quality

(NOS score < 7)

Diniz, 201336

Late-life depression AD 9 6 1

Late-life depression All types of dementia 11 12 2

Late-life depression VaD 3 2 0

Kim, 201557Caffeine intake AD 0 4 1

Caffeine intake All types of dementia 1 0 4

Richardson, 201366Statins AD 3 10 0

Statins All types of dementia 2 9 1

Seitz, 201167 General anesthetics AD 4 1 10

Wang, 201571

Aspirin AD 0 6 5

Corticosteroids AD 0 1 2

Non-aspirin NSAIDs AD 0 6 3

NSAIDs AD 1 10 5

da Silva, 201354

Depression at any age All types of dementia 6 17 10

Early-life depression All types of dementia 2 7 0

Depression at any age VaD 1 3 0

Gudala, 201356Type 2 diabetes mellitus All types of dementia 16 4 0

Type 2 diabetes mellitus VaD 11 2 0

Wu, 201584 Fish intake All types of dementia 0 3 2

40

Reference Risk factor OutcomeHigh quality

(NOS score = 9)

Moderate quality

(NOS score = 7 or 8)

Low quality

(NOS score < 7)

Zhong, 201585 Benzodiazepines use All types of dementia 0 4 1

Zhong, 201586Smoking All types of dementia 1 12 14

Smoking VaD 1 5 2

AD: Alzheimer’s disease, NOS: Newcastle-Ottawa scale, NSAIDs: non-steroid anti-inflammatory drugs, VaD: vascular dementia

41

Supplementary Table 3. Heterogeneity estimates, bias assessment and largest study effect size across the 76 eligible meta-analyses of

environmental risk factors for Alzheimer’s disease, all types of dementia and vascular dementia

Reference Risk factor

Effect

size

metric

Largest study effect

size (95% CI)SE I2 (%)

Egger test p-

value

Observed

significant studies

Expected

significant studies

Excess significance

test p-value

Alzheimer’s disease

Anstey, 200949 Alcohol drinking RR 0.63 (0.55-0.72) 0.069 56.4 0.363 2 3.98 NP

Anstey, 201150Midlife BMI

(Underweight vs. Normal weight)RR 3.43 (1.90-6.20) 0.302 71.2 0.121 1 2.97 NP

Beckett, 201551 Physical activity HR 0.69 (0.50-0.96) 0.168 0 0.016 6 5.43 0.697

Cataldo, 201052 Smoking RR 0.88 (0.73-1.10) 0.105 64.8 0.702 13 4.56 NP

Chang-Quan, 201153 Anti-hypertensive drugs RR 0.88 (0.68-1.13) 0.130 0 0.161 0 0.86 NP

da Silva, 201354 Depression at any age RR 1.19 (1.07-1.32) 0.054 69.6 0.009 16 4.6 1.0 × 10-8

Diniz, 201336 Late-life depression RR 1.43 (1.05-1.96) 0.159 2.2 0.804 7 6.91 0.964

Garcia, 200855 Low-frequency electromagnetic fields RR 1.20 (1.00-1.40) 0.086 55.2 0.001 11 3.25 3.87 × 10-6

Gudala, 201356 Type 2 diabetes mellitus RR 1.60 (1.29-1.98) 0.109 0 0.629 9 15.04 NP

Kim, 201557 Caffeine intake RR 0.69 (0.50-0.96) 0.166 71 0.406 2 2.13 NP

Li, 201258 Dietary intake of vitamin C RR 0.83 (0.68-1.01) 0.101 0 0.875 1 1.45 NP

Li, 201258 Dietary intake of vitamin E RR 0.74 (0.62-0.88) 0.089 46.7 0.869 3 3.42 NP

Li, 201258 Dietary intake of β-carotene RR 0.81 (0.63-1.03) 0.125 18.4 0.901 0 1.34 NP

42


Effect

size

metric



Egger test p-

value

Observed

significant studies

Expected

significant studies

Excess significance

test p-value

Maheshwari, 201459 Chlamydia pneumoniae infection OR 1.10 (0.51-2.38) 0.393 73 0.264 4 0.59 4.62 × 10-6

Maheshwari, 201459 Spirochetal infection OR 4.47 (1.92-10.40) 0.431 51.6 0.235 6 6.4 NP

Meng, 201260 Education RR 3.83 (3.16-4.63) 0.097 90.1 0.442 11 16 NP

Meng, 201461Midlife BMI

(Obese vs. Normal weight)RR 1.68 (1.21-2.33) 0.167 63.7 0.473 2 3.97 NP

O’Brien, 201462 HRT HR 0.80 (0.58-1.09) 0.161 47.7 0.978 1 1.95 NP

Perry, 201563 Mild traumatic brain injury OR 4.03 (3.27-4.96) 0.106 85.2 0.028 8 18.88 NP

Peters, 201464 Calcium channel blockers RR 1.08 (0.81-1.45) 0.149 61.1 0.163 1 0.30 0.189

Power, 201165 Hypertension RR 1.05 (0.76-1.47) 0.168 45.7 0.784 3 0.74 0.007

Richardson, 201366 Statins RR 1.14 (0.85-1.53) 0.150 54.7 0.085 8 2.79 4.40 × 10-4

Seitz, 201167 General anesthesia OR 1.18 (0.98-1.42) 0.095 0 0.159 0 1.98 NP

Steel, 201568 Herpesviridae infection OR 1.22 (1.01-1.48) 0.097 20.3 0.830 6 2.51 0.022

Terracciano, 201469 Agreeableness HR 0.90 (0.77-1.06) 0.083 0 0.358 0 0.28 NP

Terracciano, 201469 Conscientiousness HR 0.81 (0.69-0.95) 0.080 0 0.072 3 0.68 1.39 × 10-3

Terracciano, 201469 Extraversion HR 1.09 (0.91-1.31) 0.093 39.8 0.478 0 0.24 NP

Terracciano, 201469 Neuroticism HR 1.19 (1.01-1.41) 0.085 0 0.156 5 0.82 4.60 × 10-7

Terracciano, 201469 Openness HR 0.91 (0.76-1.08) 0.090 0 0.896 1 0.25 0.120

Virk, 201570 Antacid drugs OR 0.91 (0.65-1.26) 0.169 0 0.791 0 0.64 NP

Wang, 201571 Aspirin RR 0.84 (0.63-1.11) 0.144 55.5 0.068 2 2.43 NP

Wang, 201571 Corticosteroids RR 1.03 (0.49-2.15) 0.377 38.3 0.373 1 0.16 0.029

Wang, 201571 Non-aspirin NSAIDs RR 1.19 (0.87-1.62) 0.159 59.1 0.017 4 2.20 0.160

43


Effect

size

metric



Egger test p-

value

Observed

significant studies

Expected

significant studies

Excess significance

test p-value

Wang, 201571 NSAIDs RR 0.76 (0.68-0.85) 0.057 70 0.117 10 8.80 0.545

Wang, 201672 Aluminum OR 1.70 (1.20-2.60) 0.197 6.2 0.931 3 6.18 NP

Zhang, 201543 Cancer HR 0.64 (0.50-0.81) 0.123 0 0.037 3 5.79 NP

Zhang, 201573 Fish iintake RR 0.93 (0.91-0.95) 0.011 63.4 0.174 3 0.41 2.0 × 10-5

Zhang, 201573 Dietary DHA intake RR 1.10 (0.93-1.31) 0.087 90.5 0.110 2 0.31 0.001

Zhou, 201574 Stroke HR 1.20 (0.77-1.89) 0.229 0 0.924 2 1.31 0.498

All types of dementia

Anstey, 200949 Alcohol drinking RR 0.80 (0.65-0.99) 0.107 52.6 0.634 5 1.86 0.007

Blondell, 201475 Physical activity RR 1.04 (0.98-1.10) 0.029 68.9 2.92 × 10-5 8 1.23 1.00 × 10-8

da Silva, 201354 Depression at any age RR 2.18 (2.08-2.28) 0.023 27.8 0.387 19 27.99 NP

da Silva, 201354 Early-life depression RR 1.58 (1.06-2.37) 0.205 16.2 0.484 4 6.26 NP

Diniz, 201336 Late-life depression RR 1.72 (1.38-2.13) 0.111 0 0.736 14 16.37 NP

Gudala, 201356 Type 2 diabetes mellitus RR 1.62 (1.49-1.77) 0.044 72.3 0.081 14 20.23 NP

Kim, 201557 Caffeine intake RR 1.12 (0.66-1.91) 0.271 75.7 0.600 1 0.61 0.590

Kuiper, 201576 Frequency of social contacts RR 1.67 (1.25-2.23) 0.148 0 0.961 4 6.33 NP

Kuiper, 201576 Loneliness RR 1.54 (1.08-2.19) 0.180 0 0.120 1 1.75 NP

Kuiper, 201576 Satisfaction with social network RR 1.30 (1.06-1.59) 0.103 50.3 0.932 1 2.03 NP

Kuiper, 201576 Social network size RR 0.99 (0.95-1.03) 0.021 64 0.144 1 0.25 0.128

Kuiper, 201576 Social participation RR 1.18 (1.07-1.31) 0.052 31.2 0.080 3 0.83 0.010

Levi Marpillat,

201377Anti-hypertensive drugs HR 0.94 (0.91-0.97) 0.016 73.4 0.099 4 2.61 0.326

44


Effect

size

metric



Egger test p-

value

Observed

significant studies

Expected

significant studies

Excess significance

test p-value

Loef, 201378Midlife BMI

(Obese vs. Normal weight)RR 1.74 (1.34-2.26) 0.133 53.5 0.415 3 4.88 NP

Meng, 201260 Education RR 1.29 (1.17-1.43) 0.051 89.6 0.296 16 8.63 0.001

Pedditizi, 201679Late-life BMI

(Obese vs. Normal weight)RR 0.81 (0.69-0.94) 0.079 0 0.127 1 1.48 NP

Pedditizi, 201679Late-life BMI

(Overwegith vs. Normal weight)RR 0.81 (0.72-0.91) 0.060 54.3 0.873 1 2.18 NP

Pedditizi, 201679Midlife BMI

(Overweight vs. Normal weight)RR 1.17 (0.99-1.38) 0.085 0 0.794 0 1.19 NP

Perry, 201563 Mild traumatic brain injury OR 4.03 (3.27-4.96) 0.106 85 0.012 9 22.86 NP

Richardson, 201366 Statins RR 0.88 (0.85-0.91) 0.017 63.2 0.051 8 5.65 0.174

Russ, 201280 Rural living RR 0.94 (0.79-1.13) 0.091 83.7 0.412 2 0.29 1.06 × 10-3

Santangeli, 201281 Atrial fibrillation HR 1.36 (1.13-1.63) 0.093 52.5 0.832 5 5.69 NP

Shen, 2015 Tooth loss RR 1.10 (0.89-1.36) 0.108 44.8 0.110 5 1.15 1.3 × 10-4

Ungprasert, 2016 Rheumatoid arthritis RR 2.02 (1.83-2.23) 0.050 93.4 0.810 2 2.79 NP

Wu, 201584 Fish intake RR 0.95 (0.76-1.19) 0.114 29.1 0.051 1 0.33 0.234

Zhong, 201585 Benzodiazepines use RR 1.60 (1.37-1.87) 0.079 35.1 0.516 5 4.65 0.541

Zhong, 201586 Smoking RR 1.07 (0.96-1.19) 0.055 47.1 0.641 8 3.39 7.40 × 10-3

Vascular dementia

Aarsland, 201087 Physical activity RR 0.85 (0.65-1.13) 0.141 55.7 0.115 1 0.59 0.570

Anstey, 200949 Alcohol drinking RR 0.79 (0.50-1.25) 0.234 5.2 0.616 1 0.46 0.399

45


Effect

size

metric



Egger test p-

value

Observed

significant studies

Expected

significant studies

Excess significance

test p-value

Bedoun, 200888 Obesity RR 4.95 (2.98-8.43) 0.265 89.1 0.475 1 3.00 NP

Chang-Quan, 201153 Anti-hypertensive drugs RR 0.66 (0.45-0.97) 0.196 46.8 0.363 2 1.69 0.720

da Silva, 201354 Depression at any age RR 2.41 (1.22-4.52) 0.334 0 0.665 2 3.01 NP

Diniz, 201336 Late-life depression OR 1.79 (0.99-3.22) 0.301 1.1 0.586 2 3.12 NP

Gudala, 201356 Type 2 diabetes mellitus RR 2.00 (1.50-2.66) 0.146 0 0.592 10 11.07 NP

Meng, 201260 Education RR 2.95 (2.14-4.07) 0.164 0 0.570 3 2.99 0.968

Sharp, 201189 Hypertension OR 1.53 (1.10-2.13) 0.169 36.3 0.730 3 2.62 0.754

Zhong, 201586 Smoking RR 1.02 (0.84-1.25) 0.101 43.9 0.326 2 0.42 0.012

HR: hazard ratio, OR: odds ratio, RR: relative risk

46

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