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Epidemiologic studies of modifiable factors associated with cognition and dementia: systematic review and meta-analysis



Cognitive impairment, including dementia, is a major health concern with the increasing aging population. Preventive measures to delay cognitive decline are of utmost importance. Alzheimer’s disease (AD) is the most frequent cause of dementia, increasing in prevalence from <1% below the age of 60 years to >40% above 85 years of age.


We systematically reviewed selected modifiable factors such as education, smoking, alcohol, physical activity, caffeine, antioxidants, homocysteine (Hcy), n-3 fatty acids that were studied in relation to various cognitive health outcomes, including incident AD. We searched MEDLINE for published literature (January 1990 through October 2012), including cross-sectional and cohort studies (sample sizes > 300). Analyses compared study finding consistency across factors, study designs and study-level characteristics. Selecting studies of incident AD, our meta-analysis estimated pooled risk ratios (RR), population attributable risk percent (PAR%) and assessed publication bias.


In total, 247 studies were retrieved for systematic review. Consistency analysis for each risk factor suggested positive findings ranging from ~38.9% for caffeine to ~89% for physical activity. Education also had a significantly higher propensity for “a positive finding” compared to caffeine, smoking and antioxidant-related studies. Meta-analysis of 31 studies with incident AD yielded pooled RR for low education (RR = 1.99; 95% CI: 1.30-3.04), high Hcy (RR = 1.93; 95% CI: 1.50-2.49), and current/ever smoking status (RR = 1.37; 95% CI: 1.23-1.52) while indicating protective effects of higher physical activity and n-3 fatty acids. Estimated PAR% were particularly high for physical activity (PAR% = 31.9; 95% CI: 22.7-41.2) and smoking (PAR%=31.09%; 95% CI: 17.9-44.3). Overall, no significant publication bias was found.


Higher Hcy levels, lower educational attainment, and decreased physical activity were particularly strong predictors of incident AD. Further studies are needed to support other potential modifiable protective factors, such as caffeine.

Peer Review reports


Cognitive function refers to those mental processes that are crucial for the conduct of the activities of daily living. Such mental processes include attention, short-term and long-term memory, reasoning, coordination of movement and planning of tasks [1]. The prevalence of brain disorders affecting cognition (such as stroke and dementia) increases steadily in a linear fashion with age [2]. Cognitive impairment is a major health concern affecting loss of independence in daily activities in old age. Thus, special attention should be devoted to its prevention [3].

Dementia is relatively frequent in the elderly population and was shown to affect about 6.4% of European subjects over the age of 65 years [4]. A review of 50 original articles published between 1989 and 2002 using international data showed that prevalence of dementia for the very old group (85 years and over) varied from 16.7% in China [5] to 43% in Germany [6]. This variability was also reflected within separate age groups among the very old, ranging from 9.6% to 32% for the 85–89 age category and from 41% to 58% for the 95+ age group. Incidence varied between 47 and 116.6 per 1000 and a separate meta-analytic study estimated the incidence in that age group (i.e. 85+) to be around 104 per 1000 [7, 8].

Alzheimer’s disease (AD) is the most frequent cause of dementia, increasing in prevalence from less than 1% below the age of 60 years to more than 40% above 85 years of age. The initial phase is generally marked by a progressive deterioration of episodic memory. Other impairments may be entirely absent in the beginning or consist of mild disturbances in naming and executive function. When the process advances, impairment spreads to other aspects of memory and other domains of cognition. Despite lack of curative treatment, epidemiological evidence reveals important risk factors for sporadic AD, many of which are non-modifiable (e.g. ApoE ϵ4, age and sex). This highlights the importance for further evaluation of modifiable risk and preventive factors in that these potential factors may not only delay the onset of cognitive decline, but also can be easily treated. Aside from AD, less frequently occurring forms of dementia include vascular dementia (VaD), mixed dementia, dementia with Lewy bodies (DLB) and Parkinson’s disease with dementia (PD-D), diagnostic criteria for which are described in Table 1. The relative prevalence of AD and VaD and the mixed version of both remain debatable. Ott and colleagues [9] estimated that for the very old, the prevalence of AD was 26.8% while that of VaD was 4.4%. However, VaD appears to be more frequent than AD in certain Japanese and Chinese populations [10].

Table 1 Diagnostic criteria of Alzheimer’s Disease (AD), Vascular Dementia (VaD), Mixed dementia (MD) and other dementias

The present review focuses on selected modifiable risk and protective factors of cognitive impairment, cognitive decline and dementia (including AD), given that they are commonly studied and provide reliable and comparable data. In particular, we focused on the risk and protective factors that could be grouped under three broad categories, namely socioeconomic, behavioral, and nutritional. Consequently, other known risk factors that are modifiable but did not fall under these categories were excluded (e.g. obesity, type 2 diabetes, hypertension, depression, traumatic head injury etc.). In addition, the latter risk factors are potential mediating factors in the causal pathway between our selected factors and cognitive outcomes (e.g. obesity, depression, type 2 diabetes and hypertension may be on the causal pathway between physical activity and cognition; the same for diet→depression, obesity, type 2 diabetes, hypertension→cognition) and thus are more appropriately studied together rather than with their antecedent putative causes. This is the first study to systematically review those selected modifiable risk and protective factors for cognitive health outcomes in cross-sectional and cohort studies while comparing the consistency of association between those factors and across study-level characteristics. It is also among few recent studies to compare the strength of association across those factors in relation to incident AD using a similar approach [19, 20]. Our findings could help guide future research and interventions.


Literature search

Using MEDLINE, we conducted a systematic review of the literature on cognitive function, decline and dementia focusing on specific risk factors. We considered both original research published between January 1990 and October 2012. We did not include titles prior to 1990 to ensure that diagnostic criteria for dementia and AD were comparable across studies. After an initial search using MESH keywords for risk factors (i.e. education, smoking, physical activity, caffeine/coffee/tea, alcohol, antioxidant/vitamin E, homocysteine and fatty acid) and a title containing the words (cognitive, dementia and Alzheimer), we assessed the retrieved papers for relevance by reading the titles and abstracts. Among those that were selected for review, information was retrieved including study design, contextual setting, sample size, main outcome and key findings.

Final inclusion criteria were: (1) Study sample size > 300. Although this number is arbitrary, it was based on the fact that some study outcomes were relatively rare (e.g. incident AD: <10%) and thus a smaller sample size for a cohort study for example might yield an underpowered study, depending on the distribution of the risk factor. (2) Study design is either cross-sectional or cohort study (thus case-control studies, review articles, commentaries, and basic science papers were excluded); (3) Outcomes include dementia, AD, cognitive function, cognitive decline or cognitive impairment (including MCI)). Although all types of dementia were presented in our description of selected studies, focus was on the more prevalent sub-types including AD and VaD (4) Baseline sample includes general healthy population rather than special groups at risk (e.g. coronary heart disease patients). For the “cognitive decline” and “cognitive function” outcomes, we searched risk factors in the title to expand the range of studies selected beyond those based just on MESH keywords (i.e. “caffeine,” “alcohol” and all other risk factors were also searched in titles when the outcome was “cognitive”). Both cohort and cross-sectional studies that were selected are presented in Table 2. The MEDLINE search and the studies excluded are laid out in Figure 1, showing main reasons for exclusion and final number of studies included for each risk factor. After inclusion of a study, we did not examine cross-references in order to ensure the comparability of the search strategy between risk factors.

Table 2 Summary of epidemiologic studies of risk and protective factors for cognitive outcomes included in the review
Figure 1

Flowchart of study selection for systematic review and meta-analysis. Notes: MEDLINE searches (1990–2012) included the following: (1) “Risk factor” as MESH term AND “Dementia” in title; (2) “Risk factor” as MESH term AND “Alzheimer” in title; (3) “Risk factor” as MESH term AND “Alzheimer” in title; (4) “Risk factor” as MESH term AND “cognitive” in title; (5) “Risk factor” in title and “cognitive” in title. Given that each search is not mutually exclusive of other searches, there were duplicates which were deleted from the final number of included studies. The following notations are defined follows: N1 = Studies excluded from all searches combined due to small sample size; N2 = Studies excluded from all searches combined due to design being neither cross-sectional nor cohort; N3 = Studies excluded from all searches combined due to being a review or a letter to the editor; N4 = Studies excluded from all searches combined due to lack of relevance to topic or hypothesis; N5 = Studies excluded from all searches combined for other reasons (e.g. special group of people); N6 = Final included studies; N6a = Final included cohort studies; N6b = Final included cross-sectional studies.

Out of a total search of 6,837 titles and abstracts between 1990 and 2012 (range:126 for caffeine to 1,692 for education), 247 published original epidemiologic studies (167 cohort-, 80 cross-sectional studies) were included in our review. A database was built accordingly using Endnote ver. X3 [259]. Each study was summarized in Table 2 by listing the sample characteristics (age, gender, country), study design, sample size and type of outcome. Given the diversity of types of outcomes, a quantitative meta-analysis for all studies with all outcomes was not possible. Thus, a qualitative method to assess overall consistency was conducted. This analysis was mainly based on the hypothesized direction of association and the final conclusion of the study. Thus, main findings based on the pre-set hypothesis was coded (+: supports the hypothesis; 0: no significant finding; −: against the hypothesis). In addition, within +, we coded studies as partially supporting the hypothesis for three main reasons: “some outcomes but not others”, “some exposures but not others”, “some sub-group(s) but not others”. These papers are sorted by risk factor, year of publication and first author’s last name.

Descriptive analysis

In the descriptive part of the analysis, a data point consisted of a study finding within a design/risk factor dyad (e.g. cohort/education). Using the data points, we conducted an analysis to assess consistency of positive findings across risk factors and study designs (cohort vs. cross-sectional). In particular, we estimated the % of positive findings for all participants and most outcomes; % of positive findings for some outcomes or exposures but not others; % of positive findings for sub-groups; % null findings; % of findings against hypothesized direction. In addition, study-level characteristics (e.g. year and country of publication, study design, type of cognitive outcome, sample size, age group, sex) were described in detail and compared across risk factors, using χ 2 test, independent samples t-test and one-way ANOVA.

Consistency analysis: all data points

In this part of the analysis, we modeled study finding as a binary outcome coded as 0=”null finding or finding against hypothesized direction” (referent category), 1=”positive or partially positive finding”, as a function of study-level characteristics using a logistic regression model. The study-level characteristics were entered as main effects as follows: (1) Year of publication; (2) Country of publication (1 = US, 2 = European country, 3 = Others), (3) risk factor (1 = education, 2 = smoking, 3 = alcohol, 4 = physical activity, 5 = caffeine, 6 = antioxidants, 7 = homocysteine, 8 = n-3 fatty acids); (4) sample size (when a range was provided, the average was taken), (5) Study participant age group: 1 = contains ages <65y, 0 = does not contain ages <65y; (6) Participant gender composition: 1 = Men only; 2 = Women only; 3 = Both; (7) Study design: 1 = cross-sectional; 2 = cohort; (8) Number of cognitive outcomes included in the study (e.g. 1 if only incident AD was the outcome; 2 if it is both incident AD and incident dementia); (9) General category of cognitive outcome(s): 1 = dementia/AD/impairment; 2 = cognitive function/decline; 3 = both.

Meta-analysis: data points with incident AD and selected risk or protective factors

Focusing on data points with incident AD as an outcome, we conducted further meta-analysis to assess the strength of the association between selected risk or protective factors and this outcome. This analysis was thus restricted to prospective cohort studies with available data points that had comparable measurements for each risk/protective factor, thus allowing to estimate a pooled measure of association across those data points and studies. The original reported odd ratios (ORs), relative risks (RRs) or Hazard Ratios (HRs) were combined into a pooled value with 95% confidence interval (CI). The RRs were then pooled using random effects models when included study data points were deemed heterogeneous based on the Q-test for homogeneity (p < 0.05) or fixed effect when study data points were homogenous (p > 0.05), which are also presented among results. As such, a summary or pooled RR was provided using forest plots and computed by computing the weighted average of the natural logarithm of each relative measure of interest weighting by the inverse of each RR’s respective variance [260]. Random effects models that further incorporated between-study variability were conducted using DerSimonian and Laird’s methodology.

Considering estimates of exposure prevalence from the largest study with available data on each exposure, we also computed a population attributable risk percentage (PAR%) by pooling data points from all studies together.

PAR % p , lcl , ucl ; ij = 100 × Pr exp × R R p , lcl , ucl ; ij 1 1 + ( Pr exp R R p , lcl , ucl ; ij 1 = 1 θ ij × 100
Var θ ij = Var ( 1 θ ij ) = ( 1 PA R p ; ij ) 2 × Var ( Ln ( 1 PA R p ; ij ) ) = 1 PA R p ; ij 2 × ( Ln ( 1 PA R lcl ; ij ) Ln ( 1 PA R ucl ; ij ) / 3.92 ) 2
PAR % 95 % CI ; ij = PAR % p , ij ± 1.96 × Var θ ij × 100

As shown in Equations 1.1, 1.2 and 1.3, RR (point estimates per study and data point; 95% CI) was applied to the formula and Prexp was the estimated prevalence of each exposure. The estimation of SE for PAR% was obtained using the delta method [261].

Finally, in order to examine publication bias, we used Begg’s funnel plots; each RR point estimate was plotted against their corresponding standard errors (SE) for each study on a logarithmic scale [262, 263], for all exposures combined. This type of bias was also formally tested using the Begg-adjusted rank correlation tests [264] and the Egger’s regression asymmetry test [265]. All analyses were conducted with STATA 11.0 (StataCorp, College Station, TX) [266]. Type I error was set at 0.05 for all measures of association.

Results and discussion

Socio-economic Status (SES) as indicated by education

Early life conditions are related to cognitive development and abilities in childhood and cognitive function in adulthood. Low educational attainment and other markers of low socio-economic position (SEP) were associated with poorer cognitive function in adulthood and age-related cognitive decline and impairment, as well as greater risk or prevalence of dementia and AD in the elderly. In this study, we focused our attention on education as a maker of SES, given that it is the most commonly studied protective factor.

Several possible mechanisms support the finding that less education is related to cognitive decline: First, education may exert direct effects on brain structure early in life by increasing synapse number or vascularization and creating cognitive reserve. This was named the “reserve capacity” hypothesis. Thus, this hypothesis states that early life conditions affect the pace of cognitive decline in later life [38]. Education in early life may have effects in later life if persons with more education continue searching for mental stimulation (“the use it or lose it” hypothesis), which may lead to beneficial neurochemical or structural alterations in the brain [267]. Indeed, in one study, recent mental stimulation was associated with improved cognitive functioning [268]. Alternatively, education may act through several “behavioral mediators” to improve health in general, and cognitive functioning in particular [267]. This hypothesis was confirmed by a study using the Framingham cohort which suggested that education was uniquely protective against VaD and not associated with AD [28]. This finding was explained by mediating effects of other risk factors of cognitive decline, including smoking and hypertension, which in turn can initiate cerebrovascular damage. However, Lee and colleagues [52] found evidence contrary to this hypothesis by showing a sustained strong association between education and cognitive functioning after adjustment for behavioral and health-related factors.

Based on our findings (Table 2 and Figure 2A), 18 (66.7%) of the 27 cohort studies that met our selection criteria found that lower education was linked to a worse cognitive outcome in the overall population and for all studied outcomes, 1 found this relationship with incident VaD but not AD [28], 1 found the relationship with cognitive function but not decline [64], 1 concluded that IQ was a better predictor than education [38], and 2 detected a significant association in the hypothesized direction only in women [44] and in ApoE4- individuals [53]. The remaining four cohort studies did not find an association in the hypothesized direction [26, 60, 68, 71].

Figure 2

Main findings (%) of selected studies, given hypothesis: (A) Cohort Studies (B) Cross-sectional studies.

Note: +(ALL) = positive finding, given hypothesis, for all subjects and most outcomes of interest; +(some outcomes) = positive finding, given hypothesis, for all subjects and some outcomes of interest but not others; +(some groups) = positive finding, given hypothesis, for some groups and most outcomes of interest; 0 = null finding, given hypothesis; − = finding against hypothesized direction. *P-value based on χ 2 test for independence between risk factor and finding.

The association between education and the studied cognitive outcomes was in the hypothesized direction, with higher education being protective, for the majority of the selected cross-sectional studies (21 out of 25, 84%), while 2 found an association with prevalent AD but not VaD [41, 56], 1 found that literacy was a better predictor than education [61], and 1 failed to detect a significant association [40] (See Table 2 and Figure 2B).

Behavioral factors

Several behavioral factors were selected, including smoking, alcohol drinking,and physical activity.


Smoking is a risk factor for several chronic diseases, but its long-term relationship with dementia of various sub-types is still controversial. In fact, smoking is well known to increase the risk of stroke [269] and thus subsequent vascular types of dementia (VaD). However, many studies have concluded that smoking status influenced risk of VaD independently of stroke status and thus may have an effect beyond cerebrovascular disease. In addition, studies that have shown a direct impact of smoking on AD, suggest that smoking might in fact influence neurodegeneration. A vast amount of literature points to a role of smoking in oxidative stress and inflammation, both mechanisms believed to play a key role in AD [270].

However, it is also biologically plausible that smoking might protect against cognitive decline and AD, given that nicotine, a key active component of tobacco, may enhance the release of acetylcholine, increase the density of nicotnic receptors, therefore improving attention and information processing [271]. It is now known that AD is characterized by cholinergic system deficits which may be delayed through tobacco consumption [271, 272].

Population-based evidence of an effect of smoking on cognitive outcomes was inconclusive, with most longitudinal studies reporting weak or null associations [63, 72, 74, 76, 77, 82, 84, 90]. However, a number of other cohort studies have found a positive association between smoking and risk of incident dementia and AD [78, 86, 88, 93, 95, 98, 101, 103, 104] as well as incident cognitive impairment [75, 81, 90] and age-related cognitive decline [83, 85, 89, 96, 99, 105].

For instance, the British 1946 birth cohort study pointed to the difficulty of finding an association between smoking and cognitive impairment given the differential high mortality of smokers especially among the elderly population [85]. After controlling for a range of socioeconomic and health status indicators (both physical and mental), they found that smokers who survive into later life may be at risk of clinically significant cognitive decline. However, these effects were accounted for largely by heavy smokers, i.e. those who smoked 20 cigarettes per day or more. Earlier research on middle aged adults suggested that current smoking and number of pack-years of smoking were related to reduced performance on tests of psychomotor speed and cognitive flexibility assessed approximately five years later [83]. Similar results were shown for cognitive decline in a large cohort study (Rotterdam study) conducted in multiple European countries [89] and in another more recent study conducted in the United States [92].

Among studies that examined incidence of AD in relation to smoking status, two of the largest European cohort studies reported conflicting results. While one found no relationship between smoking status and incident AD among a large sample of 34,439 older UK men (mean age: 81) [82], the recent 2011 study found that heavy smoking increased the risk of dementia and AD in a younger sample of 21,123 older Finnish adults (Mean age:60.1) that comprised both men and women [104].

In sum, 16 (55.2%) out of the 29 selected cohort studies linking smoking to the various cognitive outcomes found the relationship to be in the hypothesized direction in the entire population that was studied and for most outcomes of interest [75, 81, 83, 85, 86, 88, 89, 9296, 98, 99, 101, 104], while 2 found this relationship for some outcomes but not others [97, 103] and 2 detected it for a sub-group of the total population [78, 105], while the remaining 9 studies did not find an association [63, 72, 74, 76, 77, 82, 84, 90] or found an association in the opposite direction [102]. (See Table 2 and Figure 2A).

Only 2 (28.6%) of the 7 cross-sectional studies found an association in the hypothesized direction [36, 87], while 2 detected it for a sub-group of the total population [79, 100], and the remaining 3 did not detect a significant association [73, 80, 91], (See Table 2 and Figure 2B).


Alcohol consumption in moderation was hypothesized to be protective against cognitive decline and impairment in old age. Several mechanisms may be involved in explaining the potential protective effect of moderate alcohol consumption on various cognitive outcomes. First, this effect might be mediated through cardiovascular risk factor reduction, partly through a dampening effect of ethanol on platelet aggregation, or through a modification of the serum lipid profile. Second, another potential mechanism in which alcohol can have a direct effect on cognitive function is through acetylcholine release in the hippocampus, which in turn enhances learning and memory [273].

The Rotterdam study [83] also examined the effect of alcohol use on cognition. They found that past alcohol consumption’s effect on speed and flexibility appeared to be slightly U-shaped, with the best performance observed among those who drank 1–4 glasses of alcohol per day, although this association was stronger among women than among men. Other studies also detected sex differences [106, 107, 111]. Light to moderate alcohol consumption was also found beneficial based on findings of other cohort and cross-sectional studies with a U- or J-shaped pattern observed [108110, 113, 114, 116, 117, 119121, 124127]. However, in other studies, a linear dose–response relationship between alcohol use and improved cognition was noted, though the authors cautioned that these should not encourage increased alcohol consumption without an upper bound to this consumption [114, 115, 122, 131].

In one cross-sectional study, a linear relationship between alcohol consumption and cognitive function was found in women but a U-shaped pattern was found in men [109]. One cohort study found that overall, moderate consumption was protective against poor cognitive function, but had an opposite relationship with cognitive function among ApoE4+ individuals [113], while another found that alcohol use in general was related to better cognition without effect modification by ApoE4 status [123]. Slower memory decline with increased alcohol consumption in men was found in one study, though the opposite relationship was found in the case of psychomotor speed among women [118]. The positive association between alcohol intake and memory was also noted in at least one other cross-sectional study for both men and women combined [128]. Moreover, heavy alcohol use was linked to poorer cognitive outcomes in a few studies [87, 127, 129, 130]. Finally, only a few studies among those that were selected found no associations between alcohol consumption and cognitive outcomes [76, 77, 112].

In fact, 8 out of the 18 selected cohort studies (44%) linking alcohol consumption to the various cognitive outcomes, found the relationship to be in the hypothesized direction (but were U-shaped, J-shaped or linear) in the entire population that was studied and for most outcomes of interest [108, 114, 116, 117, 122, 124, 125, 131], while 2 found this relationship for some outcomes but not others [115, 120] and 4 detected it for a sub-group of the total population [83, 111, 113, 118]. Moreover, 1 cohort studies have indicated that alcohol use was generally linked to poor cognitive outcomes for the total population [87]. Finally, 3 did not find any significant associations between alcohol consumption and the various cognitive outcomes that were under study [76, 77, 129]. (See Table 2 and Figure 2A).

9 of the 12 cross-sectional studies (75%) found an association in the hypothesized direction for the entire study population and for most outcomes of interest [106, 109, 110, 119, 121, 123, 126128]. The remaining 3 studies either found this U-shaped or J-shaped association in a sub-group [107], and either failed to detect any association [112] or detected one that was not in line with the hypothesis, whereby alcohol use was generally found to result in poor cognitive outcomes [130]. (See Table 2 and Figure 2B).

Physical activity

Physical activity has many well-known benefits for preventing a number of chronic disorders, including coronary heart disease, stroke, diabetes mellitus and osteoporosis. However, its impact on cognitive functioning has not been studied extensively. Several mechanisms may underlie the potentially protective effects of physical activity on cognitive function, including sustained cerebral blood flow [274], improved aerobic capacity and cerebral nutrient supply [275, 276] as well as growth factors, specifically the brain-derived neurotropic factor, which is a molecule that increases neuronal survival, enhances learning, and protects against cognitive decline [277, 278].

Currently, 24 cohort and 4 cross-sectional studies have examined the hypothesized relationship. For instance, a recent cohort study of 716 dementia-free older adults from the Rush Memory and Aging Project who were followed-up for an average of 4 years found an inverse relationship between total daily physical activity and incident AD after controlling for age, sex, education, self-report physical, social, and cognitive activities, as well as current level of motor function, depressive symptoms, chronic health conditions, and ApoE4 allele status [156]. Furthermore, a recent cross-sectional study of 9344 women, 65 years and older, found a lower prevalence of cognitive impairment among those who reported being physically active versus those who reported being physically inactive at different stages of their lives [155].

These findings suggested that physical activity could represent an important and potent protective factor for cognitive decline and dementia in elderly persons. Significant findings were obtained by other recent cohort [132145, 147, 148, 150154, 156158] and cross-sectional studies [100, 146, 149]. Only one cohort study resulted in non-significant findings [143].

In sum, of the 24 selected cohort studies linking physical activity to the various cognitive outcomes, 21(87.5%) found the relationship in the hypothesized direction in the entire population that was studied and for most outcomes of interest [132, 133, 135140, 142, 144, 145, 147, 148, 150154, 156158, 279], while 1 found this relationship in ApoE4 carriers [134] and 1 in non-carriers [141]. In one cohort study, the association was against its hypothesized direction [143]. (See Table 2 and Figure 2A).

In addition, all 4 of the selected cross-sectional studies (100%) found an association in the hypothesized direction for the entire study population and for most outcomes of interest (See Table 2 and Figure 2B).

Nutritional factors

Nutritional factors being studied in relation to cognitive outcomes included caffeine consumpion, antioxidant nutrients and Hcy. In addition, special attention was devoted recently to one class of essential fatty acids, namely n-3 fatty acids.


Caffeine is known to be the most widely used psychoactive drug worldwide. Its main source is coffee particularly in Western diets. Acting as a stimulant of the central nervous system, caffeine causes heightened alertness and arousal [280]. Previous literature yielded inconsistent findings about the effects of caffeine consumption on cognitive processes. In fact, caffeine improved perceptual speed and vigilance, as well as more complex functions such as memory [281]. Caffeine is one type of compound known as methylxanthines whose effects are mainly to block adenosine receptors in the brain, resulting in cholinergic stimulation. It was hypothesized that such stimulation would lead to improved memory [282]. The earliest large cross-sectional study conducted by Jarvis and colleagues found that caffeine improved cognitive performance [159]. Later on, other cross-sectional studies focusing on tea consumption found similar results [100, 162, 167, 170]. Others, however, did not show evidence of a significant protective effect [160, 168]. In sum, 4 of 7 selected cross-sectional studies linking caffeine consumption to various cognitive outcomes found the relationship to be in the hypothesized direction in the study population and for most outcomes of interest (57.1%), one found this association in men [100] and two failed to find a significant association [160, 168]. (See Table 2 and Figure 2B).

Of 11 cohort studies, positive findings pertained to 3 (27.3%) [163, 164, 175], though this was found only for coffee intake in two studies [165, 169], while 5 recent studies detected this association only among women or for specific exposures [165, 169, 171173]. The remaining cohort studies (3 of 11, 27%) did not find an association between caffeine intake and cognitive change [161] or incident dementia [166, 174]. Given the paucity of large cohort studies, more research is needed to establish causality (See Table 2 and Figure 2A).

Antioxidants: focus on vitamin E

Several findings suggest that oxidative stress may play an important role in the pathogenesis of AD. First, the brains of AD patients have lesions that are associated with exposure to free radicals. Moreover, oxidative stress among these patients is also marked by an increased level of antioxidants in the brain that act as free radical scavengers. Finally, in vitro studies suggest that exogenous antioxidants may reduce the toxicity of β-amyloids in the brains of AD patients [283285]. Based on these findings, it may be hypothesized that dietary antioxidants may help reduce the risk of AD.

Those epidemiologic studies examined the longitudinal relationship between supplemental antioxidants and risk of AD and other dementias found conflicting results: While vitamin C supplement use was related to lower AD risk in one cohort study [178], combined supplementation of vitamin E and vitamin C was associated with reduced prevalence and incidence of AD and cognitive decline in three other cohort studies [189, 193, 198], whereas another study found this effect to be specific to Vitamin E supplements [186]. These findings of a protective effect of supplemental antioxidant use against cognitive impairment and decline was replicated in a large cohort study [185]. However, there were only borderline or little evidence of a cognitive benefit from use of antioxidant supplements, particularly vitamins C and E, according to at least five independent cohort studies [177, 180, 187, 192, 199].

There are several prospective cohort studies on the effect of dietary antioxidants on the risk of dementia. One study found that high dietary intake of vitamins C and E may reduce the risk of AD [182] with the relationship most pronounced among smokers. Morris and colleagues [183] found that dietary intake of vitamin E, but not other antioxidants, was associated with a reduced risk of incident AD, although this association was restricted to individuals without the Apolipoprotein E ϵ4 genotype. Similar findings were reported with cognitive decline as an outcome [184]. In a later study when both outcomes were considered it was concluded that certain forms of tocopherols not found in dietary supplements but found only in foods may be at play [194]. This observation was corroborated by at least one recent study [197]. Another study, however, suggested that dietary antioxidants were not able to reduce AD risk [187]. Similarly, Laurin and colleagues [188] found no association between midlife dietary intake of vitamins E and C and dementia incidence. At least five other cohort studies came to a similar conclusion [176, 181, 201, 202]. In addition to examining associations of cognition with vitamins C and E, other studies found that carotenoids, particularly β-carotene intake, may be have beneficial effects of various cognitive outcomes [176], though others were not able to detect such an association [184, 201, 202].

Irrespective of the source of antioxidants, plasma concentration may be a good biomarker for oxidative stress status. In particular, an inverse association between plasma vitamin E among others and poor cognitive outcomes was found in at least two cross-sectional studies [179, 190] and two cohort studies [196, 200]. Another cross-sectional study, however, did not find evidence of an association between plasma antioxidants, including vitamin E and prevalent AD [191]. In addition, among studies that examined the influence of plasma carotenoids [179, 195], only one detected a significant potential protective effect against cognitive impairment [195]. While these results are mixed, they suggest that at least one antioxidant has a protective effect against adverse cognitive outcomes.

In sum, of the 21 selected cohort studies linking antioxidants, with focus on vitamin E, to the various cognitive outcomes, 9 (42.9%) found the relationship to be in the hypothesized direction in the entire population that was studied and for most outcomes of interest [176, 178, 182, 184, 189, 193, 194, 197, 198], while 5 found this relationship for specific antioxidants or some outcomes but not others [180, 183, 186, 196, 200, 201] and 1 detected it for a sub-group of the total population [183]. The remaining selected cohort studies (n = 6) did not find a significant association [177, 185, 187, 188, 192, 199]. (See Table 2 and Figure 2A).

Similarly, of the 6 cross-sectional studies that were selected, 2 (33.3%) found an association in the hypothesized direction for the entire study population and for most outcomes of interest [190, 195], 1 found the association to hold only for vitamin E [179], whereas 3 found no significant association [181, 191, 202]. (See Table 2 and Figure 2B).


An elevated level of plasma concentration of the sulfur amino acid Hcy (hyperhomocysteinemia) is recognized as an independent risk factor for cardiovascular, peripheral vascular, and cerebrovascular disease [286]. Accordingly, a potential influence of hyperhomocysteinemia on cognitive functioning among older adults was postulated and several studies were able to associate high levels of Hcy with increased risk of incident AD or all-cause dementia [206, 217, 224, 228, 233, 234]. Studies have pointed to selective effect of Hcy on specific domains of cognition [214, 287, 288]. One explanation could be that Hcy might be affecting certain parts of the brain to a greater extent than others, and studies have linked Hcy to higher degree of white matter hyperintensities and with brain atrophy [289293].

Even though blood Hcy levels increase with age and diminished renal function, it is largely determined by dietary intake of B-vitamins (mainly B-6 and B-12) and folate which are needed to convert Hcy into methionine and cysteine, through the methylation reactions [294]. Thus, Hcy status in plasma can be modified by dietary interventions. Moreover, vitamin B-12 plasma level has been shown to be inversely related to that of Hcy [295] and studies looking at Hcy levels and cognitive functioning also examined the effect of B-vitamins. In particular, vitamin B-12 was found to be protective against decline in at least three recent studies [204, 213, 219]. At least five other studies [204, 213, 214, 216, 217, 219, 221] concluded that fotate was protective against cognitive impairment or decline. For Vitamin B-6, two other studies suggested a protective effect [213, 219]. An antagonistic interaction of folate and Vitamin B-12 with Hcy’s effect on cognition was noted in other studies [224, 296, 297]. Aside from its link to cardiovascular disease, Hcy was shown to have neurotoxic and excitotoxic properties in vitro[298, 299], suggesting a direct influence on cognition.

Overall, of the 19 selected cohort studies linking Hcy to the various cognitive outcomes, 12(63.2%) found the relationship in the hypothesized direction in the entire population that was studied and for most outcomes of interest [206, 215, 217, 219, 222224, 228, 231, 233235], while 2 found this relationship for some outcomes but not others or a sub-group [213, 214] and 5 were not able to detect a significant association [203, 209, 210, 229, 230]. (See Table 2 and Figure 2A).

Similarly, of the 14 cross-sectional studies that were selected, 11(78%) found an association in the hypothesized direction for the entire study population and for most outcomes of interest [204, 205, 207, 208, 216, 218, 220, 221, 225, 227, 232], 1 found an association only among older adults above age 60y [212], 1 detected it among ApoE4+ individuals [226], and 1 found no significant relationship [211]. (See Table 2 and Figure 2B).

n-3 fatty acids

Another nutritional factor hypothesized to be protective against cognitive decline is higher intake of n-3 fatty acids and/or a better balance of n-3/n-6 fatty acids. Linoleic(LA ~ 18:2n-6) and α-linolenic (LNA ~ 18:3n-3) are two types of fatty acids that are essential for all members of the animal kingdom. These fatty acids and their respective derivatives are also commonly referred to as n-6 and n-3 fatty acids. Their essentiality lies in the fact that they cannot be synthesized de novo within the human or animal organism [300].

In the past, n-3 fatty acids were classified only as essential because of their ability to alleviate deficiency symptoms that include dermatitis, growth retardation and reproductive failure. However, n-3 fatty acids have other important neurological functions, which explain their high concentrations in neural and retinal tissues [301303]. Some of the longer chain fatty acids that are synthesized from α-linolenic acid include Eicosapentanoic acid (EPA ~ 20:5 n-3), which through further elongation, desaturation and β-oxidation produces Docosahexaenoic acid (DHA ~ 22:6 n-3). On the other hand, products of linoleic acid which are also termed long-chain n-6 fatty acids include gamma-linoleic (GLA ~ 18:3 n-6), dihomogammalinolenic acid (DGLA ~ 20:3 n-6) and Arachidonic acid (AA ~ 20:4 n-6) [304]. Of all organs in the human body (excluding adipose tissue), the nervous system has the highest lipid content. The dry weight of an adult brain is 50% to 60% lipid, and 35% of the lipid content is accounted for by polyunsaturated fatty acids (PUFAs) [305].

A review of scientific articles and biochemistry textbooks [306] suggested that the fatty acid composition of neuronal cell membrane phospholipids reflects their intake in the diet. Fish oils, which contain high levels of C20 and C22 PUFA, exert the most profound influence on brain PUFA concentrations [306]. The ratio between n-3 and n-6 PUFA may influence various aspects of serotoninergic and catecholaminergic neurotransmission, and it has been shown that by increasing the density of neurotransmitter receptors for acetylcholine and dopamine, dietary n-3 PUFA can improve learning and memory processes [307].

Previous observational studies suggested that the biochemical composition of blood components in terms of fatty acids differs significantly between subjects with normal cognitive functioning and patients with some form of cognitive impairment. While the majority of these studies showed an inverse association of plasma and erythrocyte n-3 fatty acids with cognition among older adults [243245, 248, 255, 258], at least one found no association between biochemical markers of n-3 fatty acids and cognition [251].

Epidemiological studies involving self-reported dietary data of n-3 fatty acids had suggestive but slightly controversial results. One study by Morris and colleagues used cohort data on 815 subjects who were initially unaffected by AD (age range: 65-94y, mean follow-up period = 2.3y). Using standardized criteria, AD incidence was compared across n-3 fatty acid consumption groups, with those eating fish once per week compared to those who rarely or never eat fish having considerably lower incidence (RR = 0.4; 95% CI: 0.2, 0.9). Total n-3 fatty acid consumption was also associated with a reduced AD risk even after controlling for intake of other dietary fats, vitamin E and for cardiovascular conditions [239]. A similar finding was reported later on for a larger but comparable cohort when looking at fish consumption and cognitive decline over time [241].

In the Zutphen Elderly Study, cognitive functioning and decline over three years were assessed in a cohort of 476 men aged 69-89y using the Mini-Mental State Examination (MMSE). Findings indicated that high linoleic acid intake (the main n-6 fatty acid in the diet) was associated with cognitive impairment, even after controlling for age, education, cigarette smoking, alcohol consumption and energy intake (OR = 1.76, 95% CI: 1.04-3.01, comparing highest to lowest tertile). However, there was no distinctive association for n-3 fatty acids. Nevertheless, total fish consumption was suggestive of a protective effect, even though it did not reach significance [236].

Another larger cohort study–The Rotterdam Study– recruited 5,386 non-institutionalized participants, aged 55 + y at baseline, who had normal cognition and assessed their complete dietary intake with a semi-quantitative food-frequency questionnaire. After an average 2.1y of follow-up, lower risk of incident dementia and AD was found among fish consumers and therefore among those with higher intake of n-3 fatty acids (RR = 0.3; 95% CI: 0.1-0.9) [237]. However, when the study was conducted later with a longer follow-up (mean follow-up period of 6.0 years), it was concluded that high intake of total, saturated, trans fat, cholesterol and low intake of monounsatured fatty acids (MUFA), total PUFA, n-6 PUFA and n-3 PUFA were not associated with increased risk of dementia or its subtypes [238].

A cross-sectional study of 1,613 subjects aged 45–70 years that examined the association between fatty acid and fish intake with cognitive function, found that the risk of cognitive impairment was reduced with increased consumption of fatty fish and marine n-3 PUFA. per Standard Deviation (SD) increased intake, the ORs were 0.81 (95% CI: 0.66, 1.00) and 0.72 (95% CI: 0.57, 0.90), respectively [240]. Another recent study using the Athersclerosis Risk in Communities (ARIC) cohort data suggested that dietary intake of n-3 fatty acids (mainly DHA + EPA) reduced the risk of cognitive decline in verbal fluency but not other cognitive domains (i.e. delayed word recall and psychomotor speed). This protective effect was particularly strong among hypertensive subjects [246]. The potentially protective effect of dietary n-3 fatty acid was also reported in several other large epidemiological studies [242, 247, 249, 253, 254, 256258], but not in others [250252].

In sum, 7 out of the 18 (39%) selected cohort studies linking n-3 fatty acids to the various cognitive outcomes found the relationship in the hypothesized direction in the entire population that was studied and for most outcomes of interest [237, 239, 247, 248, 250, 253, 257], while 1 found this relationship for some outcomes but not others [245], 4 detected it for a sub-group of the total population [242, 243, 246, 258], and 6 found no association [236, 238, 241, 250252] (See Table 2 and Figure 2A). In addition, all of the 5 (100%) cross-sectional studies that were selected found an association in the hypothesized direction for the entire study population and for most outcomes of interest [240, 244, 249, 254, 255] (See Table 2 and Figure 2B).

Description of study-level characteristics and comparison by risk factor

Table 3 shows descriptive findings of study-level characteristics and compares their distributions across risk factors. Out of the 247 selected studies, 98 were conducted in the US (39.7%), while 104 were carried out in a European country (42.1%), and the remaining 45 studies originated from Asia, Canada and Australia among others (18.2%). The majority of the selected studies were cohort studies (n = 167). Most had only one type of cognitive outcome (72.5%), whereas 24.3% had two, and the remaining 3.2% had 3 or 4 outcomes. 152 studies had confirmed positive findings for most outcomes, exposures and for all study sub-groups (61.5%), while 18.2% (n = 45) had null findings. Partially positive findings were found in around 18.2% while 2% had a finding against the hypothesized direction. Around 40.5% of studies included participants with ages <65y, and the majority had both men and women (84.2%). Incident AD as an outcome was available in 47 studies, while 47 studies included incident dementia as a main outcome of interest. On the other hand, cognitive function as an outcome was found in 83 of included studies, while 62 of those studies had cognitive decline or change as a primary outcome of interest (data not shown). In general, there was an almost even split between studies focusing on cognitive function/decline/change (51.0%) and studies focused on AD/dementia/impairment as outcomes (46.2%). Only 2.8% of the studies examined both categories. When comparing the distribution of those study-level characteristics by risk factor, we found some significant differences for year of publication, country, age group inclusion/exclusion, study design, cognitive outcome type and study finding. In particular, studies on education and cognitive outcomes tended to be published earlier than studies of other risk factors, there were significantly more European studies of n-3 FA compared to other risk factors, while most studies with PA excluded middle aged adults unlike other risk factors. The highest proportion of cohort studies was also found for PA. The vast majority of studies on alcohol and cognitive outcomes used cognitive function/decline as their primary outcome of interest, unlike other risk factors which were more balanced in terms of cognitive outcome type. The percent positive finding was highest among PA studies (89.3%) and lowest for caffeine studies (38.9%). The significant difference in percent “positive finding” was found in cohort studies (p = 0.043) rather than cross-sectional studies (p = 0.09) (See Figure 2A-B).

Table 3 Study-level characteristics distribution, overall and comparison across risk factors

Consistency analysis: study-level characteristics and risk factor as predictors of study finding

In an attempt to examine heterogeneity in findings across risk factors and study-level characteristics, we conducted a consistency analysis using a logistic regression model (Table 4). Examining the odds ratios and their 95% CI, taking “null finding/against hypothesis finding” as the referent category for the outcome, we found that in general, a positive or partially positive finding was significantly more likely when the risk factor was “education” particularly when compared to smoking, caffeine and antioxidants/vitamin E (p < 0.05). None of the other study-level characteristics were associated with the study finding.

Table 4 Multiple logistic regression: study-level predictors of study finding*

Meta-analysis: selected risk factors for incident AD

Using random effects models, we pooled findings of 31 selected data points from 31 studies in which the outcome was incident AD and for which exposure data was adequate and comparable across studies (Figure 3A-E). Among studies used to summarize the association between low education and incident AD, the largest study (N = 3,675) found a HR = 1.81 with 95% CI: 1.30–2.53, with a prevalence of low education being ~32% [43]. In all four studies, the exposure definition was standardized as a comparison between <8y of education vs. ≥8y. In contrast, for all other exposures, definitions differed to some extent between studies but were assumed to operationalize the same concept. For instance, high vs. low physical activity was defined as a frequency of 3 times or more per week by two studies, 2 times of more per week by one, 4 activities per week by one, ≥2 vs. <2 mile walk/day by another study, and other comparable definitions by the remaining four studies combining frequency and intensity of activity. A full description of how various exposures were defined under Figure 3A-E notes.

Figure 3

Meta-analysis of selected risk and protective factors for incident AD (n = 31). (A) Education. (B) Smoking status. (C) Physical activity. (D) Homocysteine. (E) n3 fatty acids.

Sources:[28, 33, 43, 46, 78, 82, 86, 88, 93, 95, 101, 103, 104, 133, 137, 141, 142, 145, 148, 150, 156, 206, 209, 215, 217, 233, 237, 239, 242, 250, 251]: Notes: Only studies with available data points on incident AD were selected. Moreover, risk factors/protective factors needed to be measured in a comparable manner across studies to allow for estimating a pooled RR with a 95% CI. For education, only four studies out of 27 cohort studies had the required inclusion criteria. For smoking status, the common referent category was non-smoking or never smoking or « never or former » whereas exposed group consisted of either « ever smokers » [78, 93], or a pooled value for RR to obtain an approximate « ever smoker » category [86], or current smokers [82, 88, 95], or mid-life smoking or heavy smoking [101, 103, 104]. For high vs. low physical activity level, two studies used the cut-point of 3 or more times per week [133, 145], one used 2 times or more per week [142], one used the criterion of 4 activities per week vs. none [141], one used > =2 vs. <2 miles walking/day [137], and the remaining four studies used other definitions related to both frequency and intensity [148, 150, 156]. For high vs. low n-3 fatty acids, one study had fish consumption (yes vs. no) as the exposure of interest [237], another examined quintiles of total n-3 PUFA and compared the fifth to the first quintile in terms of risk for AD [239], a third study had one main exposure as « weekly consumption of fish vs. not » [242], a fourth study contrast high vs. no fish intake, 0-8y follow-up [250], and finally upper quartile vs. lowest quartile for total n-3 PUFA in erythrocyte membranes [251]. For high vs. low Hcy, two studies used a cut-point of 14.6 μmol/L [206, 215], one study used a cut-point of 15 μmol/L [217], one study used upper vs. lowest quartile [209] and one study used upper tertile vs. lowest tertile [233].

Tests of heterogeneity, including the Q-tests, determined whether to use fixed-effects or random-effects models to pool the RR. Findings indicated that, with the exception of education and smoking as main exposures, RR estimates obtained from individual studies were largely homogenous. In sum, the pooled RRs were: 1.99 (1.30, 3.04) for low vs. higher educational attainment (n = 4 data points; Q = 11.33, p = 0.010); 1.37(1.23-1.52) for smoking status (current or ever vs. never smokers (n = 9 data points, Q = 36.2, p < 0.001); 0.58(0.49, 0.70) for physical activity (n = 8 data points, Q = 3.2, p = 0.867), 0.67(0.47,0.96) for high intake of n-3 fatty acids (n = 5, Q = 7.4, p = 0.116), and RR = 1.93(1.50, 2.49) for high levels of plasma Hcy (n = 5 data points; Q = 2.64, p = 0.620).

Taking the largest study for each as a means to obtain an estimate of exposure prevalence, the following was found: low education (32%) [43], mid-life smoking (59.8%) [93], lower physical activity (62%) [150], lower n-3 fatty acids (49.4%) [242], elevated Hcy (30%) [206]. From these exposure prevalence estimates (Prevexp), we computed the PAR% and its 95% CI to assess the proportion of AD that is attributable to each exposure in a typical adult population and thus the % that can be averted if that exposure was eliminated from that population. Our findings indicated that the PAR% for low education was 24.0% with a 95% CI: 8.4-39.6; for mid-life smoking it was 31.0% with a 95% CI: 17.9-44.3; for physical activity (lower vs. higher) it was 31.9% with 95% CI: 22.7-41.2; for high vs. low Hcy, it was 21.7% with a 95% CI: 12.8-30.6; for lower vs. higher fish consumption (<weekly vs. ≥weekly), it was 21.9% with 95% CI:4.7-39.1.

Publication bias for the meta-analysis data points (n = 31) was assessed using primarily the funnel plot which plotted point estimates of RR for all exposures combined on the Loge scale against their standard errors. This plot indicated that estimates obtained from those 31 studies lay within the pseudo 95% confidence limits, an indication of non-appreciable publication bias. This finding was reinforced by a non-significant Begg-adjusted rank correlation test (Z = 0.25; P = 0.80), and by Egger’s regression asymmetry test (bias (SE): -0.43 (0.98); p = 0.66) (Additional file 1: Figure S1).


As stated earlier, this is the first study to systematically review those selected modifiable risk and protective factors for cognitive health outcomes in cross-sectional and cohort studies while comparing the consistency of association between those factors and across study-level characteristics. It is also among few recent studies to compare the strength of association across those factors in relation to incident AD using a similar approach [19, 20]. However, our study has a few limitations. First, the literature search was limited to published articles in English available in the Medline database. Second, comparing all included studies in a quantitative meta-analytic manner was not possible due to the diversity of the cognitive outcome measurements between original studies included. In fact, cognitive measures included scores from batteries of different cognitive tests, single global cognitive test scores such as the MMSE total score, as well as the use of a factor analytic approach to combine test scores into various domains of interest (e.g. memory, spatial, psychomotor, executive function, attention). Thus, meta-analysis was only possible for measures of cognitive impairment (i.e. MCI, all-cause dementia, AD, VaD) from which we selected incident AD as the most comparable outcome across studies. However, in order to measure consistency across the selected studies and compare risk factors in terms of consistency, we conducted another type of analysis in which a qualitative outcome of “study finding” was modeled against study-level characteristics. The qualification of a finding as null or positive was based on the main conclusion of each study that was included. This type of analysis does not necessarily discriminate between null findings due to low power, poor quality study vs. actual null finding. However, our logistic regression analysis indicated that overall, sample size was not a determining factor for the study finding outcome. Combining findings from meta-analysis and the consistency analyses, we compared evidence level for each risk and protective factor of cognitive health. Other limitations include the lack of comparability in measurements of risk or protective factors in studies with incident AD, which resulted in the exclusion of a few data points in our meta-analysis. However, the datapoints that were included in the meta-analysis were relatively comparable as described in the footnotes of Figure 3A-E. Finally, our study was limited by the inability to create a common quality measure for all studies given the diversity of the exposure variables and the relative importance of having a large sample size given the type of exposure (e.g., a larger sample size is needed for a questionnaire-based exposure vs. a blood level based exposure).

Our review shows that over the past several decades many risk or protective factors have been studied in relation to cognitive impairment, dementia (including AD) and cognitive decline. Overall, these studies indicate that modifiable factors including individuals’ socio-economic, behavioral characteristics and dietary intake seem to affect people’s cognitive ability and change over time, as well as the incidence of cognitive impairment, all-cause dementia and AD. It is worth noting, however, that some of the diagnostic criteria for dementia, AD and MCI have changed over time, particularly between the 1990s and the more recent years, as shown in Table 1.

In total, 247 studies were retrieved for systematic review. When conducting consistency analysis for each risk factor/design dyad, we found the % of studies with positive finding, given hypothesis, for most outcomes and study participants to range from ~38.9% for caffeine (27.3 for cohort studies (n = 11), 57.1% for cross-sectional studies(n = 7)) to ~89% for physical activity(87.5% for cohort studies(n = 24); 100% for cross-sectional studies(n = 4)). Consistency analysis confirmed that education-related studies had a significantly higher propensity for a positive or partially positive finding compared to caffeine, smoking and antioxidant-related studies. Meta-analysis of 31 studies with incident AD and selected risk/protective factors yielded pooled RR and 95% CI as follows: RR = 1.99(1.30, 3.04) for low(risk factor) vs. higher education (n = 4 studies; Q = 11.33, p = 0.010); RR = 1.37(1.23, 1.52) for smoking status (current or ever(risk factor) vs. never smokers (n = 9 studies, Q = 36.2, p < 0.001); RR = 0.58(0.49, 0.70) for higher physical activity(protective factor) vs. lower (n = 8 studies, Q = 3.2, p = 0.867), RR = 0.67(0.47,0.96) for higher intake of n-3 fatty acids(protective factor) vs. lower (n = 5, Q = 7.4, p = 0.116), and RR = 1.93(1.50, 2.49) for high levels of plasma Hcy(risk factor) vs. lower (n = 6 data points; Q = 2.64, p = 0.620). Given the observed prevalence of exposure from the largest study per risk factor included in each meta-analysis, the population attributable risk percent (PAR%) with its 95% CI was estimated as follows: for low education: 24.0% with a 95% CI: 8.4-39.6; for mid-life smoking it was 31.0% with a 95% CI: 17.9-44.3; for physical activity (lower vs. higher) it was 31.9% with 95% CI: 22.7-41.2; for high vs. low Hcy, it was 21.7% with a 95% CI: 12.8-30.6; for lower vs. higher fish consumption (<weekly vs. ≥weekly), it was 21.9% with 95% CI:4.7-39.1. There was no significant publication bias, taking all selected risk factors for incident AD together.

A large number of epidemiologic studies were initially conducted to examine the effects of socio-economic factors, mainly educational attainment, and were later used to assess the validity of alternative hypotheses regarding the presence of behavioral or health-related mediating factors. To this end, several behavioral and nutritional risk factors were studied in relation to various cognitive outcomes. For instance, it was hypothesized that low SES was associated with higher prevalence of smoking which in turn may affect cognitive performance and change over time. While few studies found weak or no association between smoking and cognitive decline, many others found a positive association whereby smoking increased the risk of decline. Alcohol was found in general to have a U-shaped association with the risk of decline, while caffeine was shown to increase perceptual speed and vigilance as well as memory and other more complex functions in at least two cohort studies and one cross-sectional study. Physical activity was shown to protect against cognitive decline as corroborated by a number of prospective cohort studies.

Among nutritional factors, dietary and supplemental antioxidants were shown in some studies to reduce the risk of cognitive decline while in others they showed no appreciable effect. Other micronutrients including B-Vitamins and folate were shown to be protective against cognitive decline, through their dampening effect on plasma Hcy which was shown to consistently increase the risk of dementia, particularly of the AD type. In addition, studies show that n-3 fatty acids with their anti-inflammatory and cardio-protective properties can help reduce the risk of cognitive decline and impairment in some studies but not in others. Among nutritional factors, caffeine seems to be the factor hypothesized to have a protective effect with the smallest number of current studies.


In conclusion, the consistency of findings between studies varied for each selected risk or protective modifiable factors (highest consistency observed for physical activity). Secondly, a moderate to strong association was observed between some selected factors and incident AD (strongest for low education and elevated Hcy). Combining both criteria (strength of association in the case of incident AD and consistency overall), the strongest evidence thus far is an increased risk with elevated plasma Hcy levels or lower educational attainment and a lowered risk with increased physical activity. Nevertheless, more studies are needed to verify the consistency, particularly regarding caffeine. A comprehensive meta-analysis requires additional research for certain risk factors of incident AD or dementia. For incident AD, selected risk factors may potentially account on average for 21.7%-31.9% of AD cases for each risk factor considered (with 95% CI: 8.4%-44.3%) (highest for mid-life smoking and physical activity), given the estimated prevalence of those factors from the largest available study. Thus, on average, one in five to one in three cases of AD can potentially be averted if those risk factors were eliminated from populations with comparable exposure prevalence.

Authors’ information

Youfa Wang and Alan B Zonderman are senior authors.



Alzheimer’s disease


Vascular dementia


Dementia with Lewy bodies


Parkinson’s disease with dementia


Mixed dementia


Fronot-parietal dementia


Other dementia


Mild cognitive impairment


Alzheimer's disease diagnostic and treatment centers


Diagnostic and statistical manual, 4th edition


International classification of disease, 10th edition


National Institute of neurological and communicative disorders and stroke -- the Alzheimer's disease and related disorders association


National institute of neurological and communicative disorders and stroke--Association Internationale pour la Recherche et l’Enseignement en Neurosciences


Apolipoprotein E


Population attributable risk percent


Odd ratio


Relative risk


Hazard ratio


Confidence interval

Prexp :

Prevalence of each exposure


Standard errors


Standard deviation




Smoking status


Alcohol consumption


Physical activity


Caffeine consumption



N-3 Fa:

n-3 fatty acids


Socio-economic status


Socio-economic position




Eicosapentanoic acid


Docosahexaenoic acid


Dihomogaminalinolenic acid


Gamma-linoleic acid


Arachidonic acid


Polyunsaturated fatty acid


monounsaturated fatty acid


Mini-mental state examination








United Kingdom


United States.


  1. 1.

    de Champlain J, Wu R, Girouard H, Karas M, ELM A, Laplante MA, Wu L: Oxidative stress in hypertension. Clin Exp Hypertens. 2004, 26: 593-601.

    CAS  PubMed  Google Scholar 

  2. 2.

    Verhaeghen P, Salthouse TA: Meta-analyses of age-cognition relations in adulthood: estimates of linear and nonlinear age effects and structural models. Psychol Bull. 1997, 122: 231-249.

    CAS  PubMed  Google Scholar 

  3. 3.

    Moritz DJ, Kasl SV, Berkman LF: Cognitive functioning and the incidence of limitations in activities of daily living in an elderly community sample. Am J Epidemiol. 1995, 141: 41-49.

    CAS  PubMed  Google Scholar 

  4. 4.

    Lobo A, Launer LJ, Fratiglioni L, Andersen K, Di Carlo A, Breteler MM, Copeland JR, Dartigues JF, Jagger C, Martinez-Lage J, Soininen H, Hofman A: Prevalence of dementia and major subtypes in Europe: a collaborative study of population-based cohorts. Neurologic Diseases in the Elderly Research Group. Neurology. 2000, 54: S4-9.

    CAS  PubMed  Google Scholar 

  5. 5.

    Li G, Shen YC, Chen CH, Zhao YW, Li SR, Lu M: An epidemiological survey of age-related dementia in an urban area of Beijing. Acta Psychiatr Scand. 1989, 79: 557-563.

    CAS  PubMed  Google Scholar 

  6. 6.

    Fichter MM, Meller I, Schroppel H, Steinkirchner R: Dementia and cognitive impairment in the oldest old in the community. Prevalence and comorbidity. Br J Psychiatry. 1995, 166: 621-629.

    CAS  PubMed  Google Scholar 

  7. 7.

    Ankri J, Poupard M: Prevalence and incidence of dementia among the very old. Review of the literature. Rev Epidemiol Sante Publique. 2003, 51: 349-360.

    CAS  PubMed  Google Scholar 

  8. 8.

    Jorm AF, Jolley D: The incidence of dementia: a meta-analysis. Neurology. 1998, 51: 728-733.

    CAS  PubMed  Google Scholar 

  9. 9.

    Ott A, Breteler MM, van Harskamp F, Claus JJ, van der Cammen TJ, Grobbee DE, Hofman A: Prevalence of Alzheimer's disease and vascular dementia: association with education. The Rotterdam study. BMJ. 1995, 310: 970-973.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Liu HC, Lin KN, Teng EL, Wang SJ, Fuh JL, Guo NW, Chou P, Hu HH, Chiang BN: Prevalence and subtypes of dementia in Taiwan: a community survey of 5297 individuals. J Am Geriatr Soc. 1995, 43: 144-149.

    CAS  PubMed  Google Scholar 

  11. 11.

    McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM: Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA work group under the auspices of Department of health and human services task force on Alzheimer's disease. Neurology. 1984, 34: 939-944.

    CAS  PubMed  Google Scholar 

  12. 12.

    McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH: The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011, 7: 263-269.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Roman GC, Tatemichi TK, Erkinjuntti T, Cummings JL, Masdeu JC, Garcia JH, Amaducci L, Orgogozo JM, Brun A, Hofman A, et al: Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN international workshop. Neurology. 1993, 43: 250-260.

    CAS  PubMed  Google Scholar 

  14. 14.

    Hachinski VC, Iliff LD, Zilhka E, Du Boulay GH, McAllister VL, Marshall J, Russell RW, Symon L: Cerebral blood flow in dementia. Arch Neurol. 1975, 32: 632-637.

    CAS  PubMed  Google Scholar 

  15. 15.

    The Lund and Manchester Groups: Clinical and neuropathological criteria for frontotemporal dementia. The Lund and Manchester groups. J Neurol Neurosurg Psychiatry. 1994, 57: 416-418.

    Google Scholar 

  16. 16.

    McKeith IG, Galasko D, Kosaka K, Perry EK, Dickson DW, Hansen LA, Salmon DP, Lowe J, Mirra SS, Byrne EJ, Lennox G, Quinn NP, Edwardson JA, Ince PG, Bergeron C, Burns A, Miller BL, Lovestone S, Collerton D, Jansen EN, Ballard C, de Vos RA, Wilcock GK, Jellinger KA, Perry RH: Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology. 1996, 47: 1113-1124.

    CAS  PubMed  Google Scholar 

  17. 17.

    Crecelius C: Diagnosis and treatment of non-Alzheimer's dementias. J Am Med Dir Assoc. 2003, 4: H25-29.

    PubMed  Google Scholar 

  18. 18.

    Zekry D, Hauw JJ, Gold G: Mixed dementia: epidemiology, diagnosis, and treatment. J Am Geriatr Soc. 2002, 50: 1431-1438.

    PubMed  Google Scholar 

  19. 19.

    Gorelick PB, Scuteri A, Black SE, Decarli C, Greenberg SM, Iadecola C, Launer LJ, Laurent S, Lopez OL, Nyenhuis D, Petersen RC, Schneider JA, Tzourio C, Arnett DK, Bennett DA, Chui HC, Higashida RT, Lindquist R, Nilsson PM, Roman GC, Sellke FW, Seshadri S, American Heart Association Stroke Council, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Cardiovascular Radiology and Intervention, and Council on Cardiovascular Surgery and Anesthesia: Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the american heart association/american stroke association. Stroke. 2011, 42: 2672-2713.

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Barnes DE, Yaffe K: The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol. 2011, 10: 819-828.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Zhang MY, Katzman R, Salmon D, Jin H, Cai GJ, Wang ZY, Qu GY, Grant I, Yu E, Levy P, et al: The prevalence of dementia and Alzheimer's disease in Shanghai, China: impact of age, gender, and education. Ann Neurol. 1990, 27: 428-437.

    CAS  PubMed  Google Scholar 

  22. 22.

    Ogunniyi A, Lekwauwa UG, Osuntokun BO: Influence of education on aspects of cognitive functions in non-demented elderly Nigerians. Neuroepidemiology. 1991, 10: 246-250.

    CAS  PubMed  Google Scholar 

  23. 23.

    Dealberto MJ, Gagnon M, Barberger-Gateau P, Dartigues JF, Alperovitch A: [Influence of educational status on a screening test for dementia, the mini-mental state examination]. Rev Epidemiol Sante Publique. 1992, 40: 93-101.

    CAS  PubMed  Google Scholar 

  24. 24.

    Koivisto K, Helkala EL, Reinikainen KJ, Hanninen T, Mykkanen L, Laakso M, Pyorala K, Riekkinen PJ: Population-based dementia screening program in Kuopio: the effect of education, age, and sex on brief neuropsychological tests. J Geriatr Psychiatry Neurol. 1992, 5: 162-171.

    CAS  PubMed  Google Scholar 

  25. 25.

    Evans DA, Beckett LA, Albert MS, Hebert LE, Scherr PA, Funkenstein HH, Taylor JO: Level of education and change in cognitive function in a community population of older persons. Ann Epidemiol. 1993, 3: 71-77.

    PubMed  Google Scholar 

  26. 26.

    Paykel ES, Brayne C, Huppert FA, Gill C, Barkley C, Gehlhaar E, Beardsall L, Girling DM, Pollitt P, O'Connor D: Incidence of dementia in a population older than 75 years in the United Kingdom. Arch Gen Psychiatry. 1994, 51: 325-332.

    CAS  PubMed  Google Scholar 

  27. 27.

    White L, Katzman R, Losonczy K, Salive M, Wallace R, Berkman L, Taylor J, Fillenbaum G, Havlik R: Association of education with incidence of cognitive impairment in three established populations for epidemiologic studies of the elderly. J Clin Epidemiol. 1994, 47: 363-374.

    CAS  PubMed  Google Scholar 

  28. 28.

    Cobb JL, Wolf PA, Au R, White R, D'Agostino RB: The effect of education on the incidence of dementia and Alzheimer's disease in the Framingham Study. Neurology. 1995, 45: 1707-1712.

    CAS  PubMed  Google Scholar 

  29. 29.

    Farmer ME, Kittner SJ, Rae DS, Bartko JJ, Regier DA: Education and change in cognitive function. The epidemiologic catchment area study. Ann Epidemiol. 1995, 5: 1-7.

    CAS  PubMed  Google Scholar 

  30. 30.

    Callahan CM, Hall KS, Hui SL, Musick BS, Unverzagt FW, Hendrie HC: Relationship of age, education, and occupation with dementia among a community-based sample of African Americans. Arch Neurol. 1996, 53: 134-140.

    CAS  PubMed  Google Scholar 

  31. 31.

    Hanninen T, Koivisto K, Reinikainen KJ, Helkala EL, Soininen H, Mykkanen L, Laakso M, Riekkinen PJ: Prevalence of ageing-associated cognitive decline in an elderly population. Age Ageing. 1996, 25: 201-205.

    CAS  PubMed  Google Scholar 

  32. 32.

    Christensen H, Korten AE, Jorm AF, Henderson AS, Jacomb PA, Rodgers B, Mackinnon AJ: Education and decline in cognitive performance: compensatory but not protective. Int J Geriatr Psychiatry. 1997, 12: 323-330.

    CAS  PubMed  Google Scholar 

  33. 33.

    Evans DA, Hebert LE, Beckett LA, Scherr PA, Albert MS, Chown MJ, Pilgrim DM, Taylor JO: Education and other measures of socioeconomic status and risk of incident Alzheimer disease in a defined population of older persons. Arch Neurol. 1997, 54: 1399-1405.

    CAS  PubMed  Google Scholar 

  34. 34.

    Freidl W, Schmidt R, Stronegger WJ, Reinhart B: The impact of sociodemographic, environmental, and behavioral factors, and cerebrovascular risk factors as potential predictors of the mattis dementia rating scale. J Gerontol A Biol Sci Med Sci. 1997, 52: M111-116.

    CAS  PubMed  Google Scholar 

  35. 35.

    Kalmijn S, Feskens EJ, Launer LJ, Kromhout D: Longitudinal study of the effect of apolipoprotein e4 allele on the association between education and cognitive decline in elderly men. BMJ. 1997, 314: 34-35.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Kilander L, Nyman H, Boberg M, Lithell H: Cognitive function, vascular risk factors and education. A cross-sectional study based on a cohort of 70-year-old men. J Intern Med. 1997, 242: 313-321.

    CAS  PubMed  Google Scholar 

  37. 37.

    Schmand B, Smit J, Lindeboom J, Smits C, Hooijer C, Jonker C, Deelman B: Low education is a genuine risk factor for accelerated memory decline and dementia. J Clin Epidemiol. 1997, 50: 1025-1033.

    CAS  PubMed  Google Scholar 

  38. 38.

    Schmand B, Smit JH, Geerlings MI, Lindeboom J: The effects of intelligence and education on the development of dementia. A test of the brain reserve hypothesis. Psychol Med. 1997, 27: 1337-1344.

    CAS  PubMed  Google Scholar 

  39. 39.

    Cerhan JR, Folsom AR, Mortimer JA, Shahar E, Knopman DS, McGovern PG, Hays MA, Crum LD, Heiss G: Correlates of cognitive function in middle-aged adults. Atherosclerosis risk in communities (ARIC) study investigators. Gerontology. 1998, 44: 95-105.

    CAS  PubMed  Google Scholar 

  40. 40.

    De Ronchi D, Fratiglioni L, Rucci P, Paternico A, Graziani S, Dalmonte E: The effect of education on dementia occurrence in an Italian population with middle to high socioeconomic status. Neurology. 1998, 50: 1231-1238.

    CAS  PubMed  Google Scholar 

  41. 41.

    Lin RT, Lai CL, Tai CT, Liu CK, Yen YY, Howng SL: Prevalence and subtypes of dementia in southern Taiwan: impact of age, sex, education, and urbanization. J Neurol Sci. 1998, 160: 67-75.

    CAS  PubMed  Google Scholar 

  42. 42.

    Lyketsos CG, Chen LS, Anthony JC: Cognitive decline in adulthood: an 11.5-year follow-up of the Baltimore epidemiologic catchment area study. Am J Psychiatry. 1999, 156: 58-65.

    CAS  PubMed  Google Scholar 

  43. 43.

    Letenneur L, Gilleron V, Commenges D, Helmer C, Orgogozo JM, Dartigues JF: Are sex and educational level independent predictors of dementia and Alzheimer's disease? Incidence data from the PAQUID project. J Neurol Neurosurg Psychiatry. 1999, 66: 177-183.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Ott A, van Rossum CT, van Harskamp F, van de Mheen H, Hofman A, Breteler MM: Education and the incidence of dementia in a large population-based study: the Rotterdam study. Neurology. 1999, 52: 663-666.

    CAS  PubMed  Google Scholar 

  45. 45.

    Aevarsson O, Skoog I: A longitudinal population study of the mini-mental state examination in the very old: relation to dementia and education. Dement Geriatr Cogn Disord. 2000, 11: 166-175.

    CAS  PubMed  Google Scholar 

  46. 46.

    Qiu C, Backman L, Winblad B, Aguero-Torres H, Fratiglioni L: The influence of education on clinically diagnosed dementia incidence and mortality data from the Kungsholmen project. Arch Neurol. 2001, 58: 2034-2039.

    CAS  PubMed  Google Scholar 

  47. 47.

    Alvarado BE, Zunzunegui MV, Del Ser T, Beland F: Cognitive decline is related to education and occupation in a Spanish elderly cohort. Aging Clin Exp Res. 2002, 14: 132-142.

    PubMed  Google Scholar 

  48. 48.

    Cagney KA, Lauderdale DS: Education, wealth, and cognitive function in later life. J Gerontol B Psychol Sci Soc Sci. 2002, 57: P163-172.

    PubMed  Google Scholar 

  49. 49.

    Herrera E, Caramelli P, Silveira AS, Nitrini R: Epidemiologic survey of dementia in a community-dwelling Brazilian population. Alzheimer Dis Assoc Disord. 2002, 16: 103-108.

    PubMed  Google Scholar 

  50. 50.

    Ravaglia G, Forti P, Maioli F, Sacchetti L, Mariani E, Nativio V, Talerico T, Vettori C, Macini PL: Education, occupation, and prevalence of dementia: findings from the Conselice study. Dement Geriatr Cogn Disord. 2002, 14: 90-100.

    PubMed  Google Scholar 

  51. 51.

    Wight RG, Aneshensel CS, Seeman TE: Educational attainment, continued learning experience, and cognitive function among older men. J Aging Health. 2002, 14: 211-236.

    PubMed  Google Scholar 

  52. 52.

    Lee S, Kawachi I, Berkman LF, Grodstein F: Education, other socioeconomic indicators, and cognitive function. Am J Epidemiol. 2003, 157: 712-720.

    PubMed  Google Scholar 

  53. 53.

    Seeman TE, Huang MH, Bretsky P, Crimmins E, Launer L, Guralnik JM: Education and APOE-e4 in longitudinal cognitive decline: MacArthur studies of successful aging. J Gerontol B Psychol Sci Soc Sci. 2005, 60: P74-83.

    PubMed  Google Scholar 

  54. 54.

    Lee S, Buring JE, Cook NR, Grodstein F: The relation of education and income to cognitive function among professional women. Neuroepidemiology. 2006, 26: 93-101.

    PubMed  Google Scholar 

  55. 55.

    Shadlen MF, Siscovick D, Fitzpatrick AL, Dulberg C, Kuller LH, Jackson S: Education, cognitive test scores, and black-white differences in dementia risk. J Am Geriatr Soc. 2006, 54: 898-905.

    PubMed  Google Scholar 

  56. 56.

    Zhang ZX, Zahner GE, Román GC, Liu XH, Wu CB, Hong Z, Hong X, Tang MN, Zhou B, Qu QM, Zhang XJ, Li H: Socio-demographic variation of dementia subtypes in china: methodology and results of a prevalence study in Beijing, Chengdu, Shanghai, and Xian. Neuroepidemiology. 2006, 27: 177-187.

    PubMed  Google Scholar 

  57. 57.

    Zhou DF, Wu CS, Qi H, Fan JH, Sun XD, Como P, Qiao YL, Zhang L, Kieburtz K: Prevalence of dementia in rural China: impact of age, gender and education. Acta Neurol Scand. 2006, 114: 273-280.

    CAS  PubMed  Google Scholar 

  58. 58.

    Galasko D, Salmon D, Gamst A, Olichney J, Thal LJ, Silbert L, Kaye J, Brooks P, Adonay R, Craig UK, Schellenberg G, Borenstein AR: Prevalence of dementia in Chamorros on Guam: relationship to age, gender, education, and APOE. Neurology. 2007, 68: 1772-1781.

    CAS  PubMed  Google Scholar 

  59. 59.

    van Hooren SA, Valentijn AM, Bosma H, Ponds RW, van Boxtel MP, Jolles J: Cognitive functioning in healthy older adults aged 64–81: a cohort study into the effects of age, sex, and education. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2007, 14: 40-54.

    CAS  PubMed  Google Scholar 

  60. 60.

    Christensen H, Batterham PJ, Mackinnon AJ, Anstey KJ, Wen W, Sachdev PS: Education, atrophy, and cognitive change in an epidemiological sample in early old age. Am J Geriatr Psychiatry. 2009, 17: 218-226.

    PubMed  Google Scholar 

  61. 61.

    Dotson VM, Kitner-Triolo MH, Evans MK, Zonderman AB: Effects of race and socioeconomic status on the relative influence of education and literacy on cognitive functioning. J Int Neuropsychol Soc. 2009, 15: 580-589.

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Gavrila D, Antunez C, Tormo MJ, Carles R, Garcia Santos JM, Parrilla G, Fortuna L, Jimenez J, Salmeron D, Navarro C: Prevalence of dementia and cognitive impairment in Southeastern Spain: the Ariadna study. Acta Neurol Scand. 2009, 120: 300-307.

    CAS  PubMed  Google Scholar 

  63. 63.

    Peters R, Beckett N, Geneva M, Tzekova M, Lu FH, Poulter R, Gainsborough N, Williams B, de Vernejoul MC, Fletcher A, Bulpitt C: Sociodemographic and lifestyle risk factors for incident dementia and cognitive decline in the HYVET. Age Ageing. 2009, 38: 521-527.

    PubMed  Google Scholar 

  64. 64.

    Wilson RS, Hebert LE, Scherr PA, Barnes LL, de Leon CF M, Evans DA: Educational attainment and cognitive decline in old age. Neurology. 2009, 72: 460-465.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Hamid TA, Krishnaswamy S, Abdullah SS, Momtaz YA: Sociodemographic risk factors and correlates of dementia in older Malaysians. Dement Geriatr Cogn Disord. 2010, 30: 533-539.

    PubMed  Google Scholar 

  66. 66.

    Mathuranath PS, Cherian PJ, Mathew R, Kumar S, George A, Alexander A, Ranjith N, Sarma PS: Dementia in Kerala, South India: prevalence and influence of age, education and gender. Int J Geriatr Psychiatry. 2010, 25: 290-297.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Scazufca M, Almeida OP, Menezes PR: The role of literacy, occupation and income in dementia prevention: the Sao Paulo ageing & health study (SPAH). Int Psychogeriatr. 2010, 22: 1209-1215.

    PubMed  Google Scholar 

  68. 68.

    Castro-Costa E, Dewey ME, Uchoa E, Firmo JO, Lima-Costa MF, Stewart R: Trajectories of cognitive decline over 10 years in a Brazilian elderly population: the Bambui cohort study of aging. Cad Saude Publica. 2011, 27 (Suppl 3): S345-350.

    PubMed  Google Scholar 

  69. 69.

    Marengoni A, Fratiglioni L, Bandinelli S, Ferrucci L: Socioeconomic status during lifetime and cognitive impairment no-dementia in late life: the population-based aging in the Chianti area (InCHIANTI) study. J Alzheimers Dis. 2011, 24: 559-568.

    PubMed  Google Scholar 

  70. 70.

    Mejia-Arango S, Gutierrez LM: Prevalence and incidence rates of dementia and cognitive impairment no dementia in the Mexican population: data from the Mexican health and aging study. J Aging Health. 2011, 23: 1050-1074.

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Zahodne LB, Glymour MM, Sparks C, Bontempo D, Dixon RA, MacDonald SW, Manly JJ: Education does not slow cognitive decline with aging: 12-year evidence from the victoria longitudinal study. J Int Neuropsychol Soc. 2011, 17: 1039-1046.

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Herbert LE, Scherr PA, Beckett LA, Albert MS, Rosner B, Taylor JO, Evans DA: Relation of smoking and low-to-moderate alcohol consumption to change in cognitive function: a longitudinal study in a defined community of older persons. Am J Epidemiol. 1993, 137: 881-891.

    CAS  PubMed  Google Scholar 

  73. 73.

    Letenneur L, Dartigues JF, Commenges D, Barberger-Gateau P, Tessier JF, Orgogozo JM: Tobacco consumption and cognitive impairment in elderly people. A population-based study. Ann Epidemiol. 1994, 4: 449-454.

    CAS  PubMed  Google Scholar 

  74. 74.

    Ford AB, Mefrouche Z, Friedland RP, Debanne SM: Smoking and cognitive impairment: a population-based study. J Am Geriatr Soc. 1996, 44: 905-909.

    CAS  PubMed  Google Scholar 

  75. 75.

    Galanis DJ, Petrovitch H, Launer LJ, Harris TB, Foley DJ, White LR: Smoking history in middle age and subsequent cognitive performance in elderly Japanese-American men. The Honolulu-Asia Aging Study. Am J Epidemiol. 1997, 145: 507-515.

    CAS  PubMed  Google Scholar 

  76. 76.

    Broe GA, Creasey H, Jorm AF, Bennett HP, Casey B, Waite LM, Grayson DA, Cullen J: Health habits and risk of cognitive impairment and dementia in old age: a prospective study on the effects of exercise, smoking and alcohol consumption. Aust N Z J Public Health. 1998, 22: 621-623.

    CAS  PubMed  Google Scholar 

  77. 77.

    Edelstein SL, Kritz-Silverstein D, Barrett-Connor E: Prospective association of smoking and alcohol use with cognitive function in an elderly cohort. J Womens Health. 1998, 7: 1271-1281.

    CAS  PubMed  Google Scholar 

  78. 78.

    Ott A, Slooter AJ, Hofman A, van Harskamp F, Witteman JC, Van Broeckhoven C, van Duijn CM, Breteler MM: Smoking and risk of dementia and Alzheimer's disease in a population-based cohort study: the Rotterdam study. Lancet. 1998, 351: 1840-1843.

    CAS  PubMed  Google Scholar 

  79. 79.

    Carmelli D, Swan GE, Reed T, Schellenberg GD, Christian JC: The effect of apolipoprotein E epsilon4 in the relationships of smoking and drinking to cognitive function. Neuroepidemiology. 1999, 18: 125-133.

    CAS  PubMed  Google Scholar 

  80. 80.

    Elwood PC, Gallacher JE, Hopkinson CA, Pickering J, Rabbitt P, Stollery B, Brayne C, Huppert FA, Bayer A: Smoking, drinking, and other life style factors and cognitive function in men in the Caerphilly cohort. J Epidemiol Community Health. 1999, 53: 9-14.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Cervilla JA, Prince M, Mann A: Smoking, drinking, and incident cognitive impairment: a cohort community based study included in the Gospel Oak project. J Neurol Neurosurg Psychiatry. 2000, 68: 622-626.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Doll R, Peto R, Boreham J, Sutherland I: Smoking and dementia in male British doctors: prospective study. BMJ. 2000, 320: 1097-1102.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Kalmijn S, van Boxtel MP, Verschuren MW, Jolles J, Launer LJ: Cigarette smoking and alcohol consumption in relation to cognitive performance in middle age. Am J Epidemiol. 2002, 156: 936-944.

    PubMed  Google Scholar 

  84. 84.

    Chen WT, Wang PN, Wang SJ, Fuh JL, Lin KN, Liu HC: Smoking and cognitive performance in the community elderly: a longitudinal study. J Geriatr Psychiatry Neurol. 2003, 16: 18-22.

    CAS  PubMed  Google Scholar 

  85. 85.

    Richards M, Jarvis MJ, Thompson N, Wadsworth ME: Cigarette smoking and cognitive decline in midlife: evidence from a prospective birth cohort study. Am J Public Health. 2003, 93: 994-998.

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Tyas SL, White LR, Petrovitch H, Webster Ross G, Foley DJ, Heimovitz HK, Launer LJ: Mid-life smoking and late-life dementia: the Honolulu-Asia aging study. Neurobiol Aging. 2003, 24: 589-596.

    PubMed  Google Scholar 

  87. 87.

    Zhou H, Deng J, Li J, Wang Y, Zhang M, He H: Study of the relationship between cigarette smoking, alcohol drinking and cognitive impairment among elderly people in China. Age Ageing. 2003, 32: 205-210.

    PubMed  Google Scholar 

  88. 88.

    Juan D, Zhou DH, Li J, Wang JY, Gao C, Chen M: A 2-year follow-up study of cigarette smoking and risk of dementia. Eur J Neurol. 2004, 11: 277-282.

    CAS  PubMed  Google Scholar 

  89. 89.

    Ott A, Andersen K, Dewey ME, Letenneur L, Brayne C, Copeland JR, Dartigues JF, Kragh-Sorensen P, Lobo A, Martinez-Lage JM, Stijnen T, Hofman A, Launer LJ, EURODEM Incidence Research Group: Effect of smoking on global cognitive function in nondemented elderly. Neurology. 2004, 62: 920-924.

    CAS  PubMed  Google Scholar 

  90. 90.

    Solfrizzi V, Panza F, Colacicco AM, D'Introno A, Capurso C, Torres F, Grigoletto F, Maggi S, Del Parigi A, Reiman EM, Caselli RJ, Scafato E, Farchi G, Capurso A, Italian Longitudinal Study on Aging Working Group: Vascular risk factors, incidence of MCI, and rates of progression to dementia. Neurology. 2004, 63: 1882-1891.

    CAS  PubMed  Google Scholar 

  91. 91.

    Aleman A, Muller M, de Haan EH, van der Schouw YT: Vascular risk factors and cognitive function in a sample of independently living men. Neurobiol Aging. 2005, 26: 485-490.

    PubMed  Google Scholar 

  92. 92.

    Reitz C, Luchsinger J, Tang MX, Mayeux R: Effect of smoking and time on cognitive function in the elderly without dementia. Neurology. 2005, 65: 870-875.

    PubMed  Google Scholar 

  93. 93.

    Whitmer RA, Sidney S, Selby J, Johnston SC, Yaffe K: Midlife cardiovascular risk factors and risk of dementia in late life. Neurology. 2005, 64: 277-281.

    CAS  PubMed  Google Scholar 

  94. 94.

    Stewart MC, Deary IJ, Fowkes FG, Price JF: Relationship between lifetime smoking, smoking status at older age and human cognitive function. Neuroepidemiology. 2006, 26: 83-92.

    PubMed  Google Scholar 

  95. 95.

    Reitz C, den Heijer T, van Duijn C, Hofman A, Breteler MM: Relation between smoking and risk of dementia and Alzheimer disease: the Rotterdam study. Neurology. 2007, 69: 998-1005.

    CAS  PubMed  Google Scholar 

  96. 96.

    Nooyens AC, van Gelder BM, Verschuren WM: Smoking and cognitive decline among middle-aged men and women: the Doetinchem cohort study. Am J Public Health. 2008, 98: 2244-2250.

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Sabia S, Marmot M, Dufouil C, Singh-Manoux A: Smoking history and cognitive function in middle age from the Whitehall II study. Arch Intern Med. 2008, 168: 1165-1173.

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Alonso A, Mosley TH, Gottesman RF, Catellier D, Sharrett AR, Coresh J: Risk of dementia hospitalisation associated with cardiovascular risk factors in midlife and older age: the Atherosclerosis risk in communities (ARIC) study. J Neurol Neurosurg Psychiatry. 2009, 80: 1194-1201.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Collins N, Sachs-Ericsson N, Preacher KJ, Sheffield KM, Markides K: Smoking increases risk for cognitive decline among community-dwelling older Mexican Americans. Am J Geriatr Psychiatry. 2009, 17: 934-942.

    PubMed  PubMed Central  Google Scholar 

  100. 100.

    Huang CQ, Dong BR, Zhang YL, Wu HM, Liu QX: Association of cognitive impairment with smoking, alcohol consumption, tea consumption, and exercise among Chinese nonagenarians/centenarians. Cogn Behav Neurol. 2009, 22: 190-196.

    PubMed  Google Scholar 

  101. 101.

    Rusanen M, Rovio S, Ngandu T, Nissinen A, Tuomilehto J, Soininen H, Kivipelto M: Midlife smoking, apolipoprotein E and risk of dementia and Alzheimer's disease: a population-based cardiovascular risk factors, aging and dementia study. Dement Geriatr Cogn Disord. 2010, 30: 277-284.

    CAS  PubMed  Google Scholar 

  102. 102.

    Wang CC, Lu TH, Liao WC, Yuan SC, Kuo PC, Chuang HL, Lee MC, Yen CH: Cigarette smoking and cognitive impairment: a 10-year cohort study in Taiwan. Arch Gerontol Geriatr. 2010, 51: 143-148.

    PubMed  Google Scholar 

  103. 103.

    Ronnemaa E, Zethelius B, Lannfelt L, Kilander L: Vascular risk factors and dementia: 40-year follow-up of a population-based cohort. Dement Geriatr Cogn Disord. 2011, 31: 460-466.

    PubMed  Google Scholar 

  104. 104.

    Rusanen M, Kivipelto M, Quesenberry CP, Zhou J, Whitmer RA: Heavy smoking in midlife and long-term risk of Alzheimer disease and vascular dementia. Arch Intern Med. 2011, 171: 333-339.

    PubMed  Google Scholar 

  105. 105.

    Sabia S, Elbaz A, Dugravot A, Head J, Shipley M, Hagger-Johnson G, Kivimaki M, Singh-Manoux A: Impact of smoking on cognitive decline in early old age: the Whitehall II cohort study. Arch Gen Psychiatry. 2012, 69: 627-635.

    PubMed  PubMed Central  Google Scholar 

  106. 106.

    Hendrie HC, Gao S, Hall KS, Hui SL, Unverzagt FW: The relationship between alcohol consumption, cognitive performance, and daily functioning in an urban sample of older black Americans. J Am Geriatr Soc. 1996, 44: 1158-1165.

    CAS  PubMed  Google Scholar 

  107. 107.

    Dufouil C, Ducimetiere P, Alperovitch A: Sex differences in the association between alcohol consumption and cognitive performance. EVA study group. Epidemiology of vascular aging. Am J Epidemiol. 1997, 146: 405-412.

    CAS  PubMed  Google Scholar 

  108. 108.

    Elias PK, Elias MF, D'Agostino RB, Silbershatz H, Wolf PA: Alcohol consumption and cognitive performance in the Framingham heart study. Am J Epidemiol. 1999, 150: 580-589.

    CAS  PubMed  Google Scholar 

  109. 109.

    Bond GE, Burr R, McCurry SM, Graves AB, Larson EB: Alcohol, aging, and cognitive performance in a cohort of Japanese Americans aged 65 and older: the Kame project. Int Psychogeriatr. 2001, 13: 207-223.

    CAS  PubMed  Google Scholar 

  110. 110.

    Zuccala G, Onder G, Pedone C, Cesari M, Landi F, Bernabei R, Cocchi A: Dose-related impact of alcohol consumption on cognitive function in advanced age: results of a multicenter survey. Alcohol Clin Exp Res. 2001, 25: 1743-1748.

    CAS  PubMed  Google Scholar 

  111. 111.

    Leroi I, Sheppard JM, Lyketsos CG: Cognitive function after 11.5 years of alcohol use: relation to alcohol use. Am J Epidemiol. 2002, 156: 747-752.

    PubMed  Google Scholar 

  112. 112.

    Krahn D, Freese J, Hauser R, Barry K, Goodman B: Alcohol use and cognition at mid-life: the importance of adjusting for baseline cognitive ability and educational attainment. Alcohol Clin Exp Res. 2003, 27: 1162-1166.

    PubMed  Google Scholar 

  113. 113.

    Anttila T, Helkala EL, Viitanen M, Kareholt I, Fratiglioni L, Winblad B, Soininen H, Tuomilehto J, Nissinen A, Kivipelto M: Alcohol drinking in middle age and subsequent risk of mild cognitive impairment and dementia in old age: a prospective population based study. BMJ. 2004, 329: 539-

    PubMed  PubMed Central  Google Scholar 

  114. 114.

    Bond GE, Burr R, McCurry SM, Rice MM, Borenstein AR, Kukull WA, Teri L, Bowen JD, McCormick WC, Larson EB: Alcohol, gender, and cognitive performance: a longitudinal study comparing older Japanese and non-Hispanic white Americans. J Aging Health. 2004, 16: 615-640.

    PubMed  Google Scholar 

  115. 115.

    Britton A, Singh-Manoux A, Marmot M: Alcohol consumption and cognitive function in the Whitehall II study. Am J Epidemiol. 2004, 160: 240-247.

    PubMed  Google Scholar 

  116. 116.

    Bond GE, Burr RL, McCurry SM, Rice MM, Borenstein AR, Larson EB: Alcohol and cognitive performance: a longitudinal study of older Japanese Americans. The Kame project. Int Psychogeriatr. 2005, 17: 653-668.

    PubMed  Google Scholar 

  117. 117.

    Ganguli M, Vander Bilt J, Saxton JA, Shen C, Dodge HH: Alcohol consumption and cognitive function in late life: a longitudinal community study. Neurology. 2005, 65: 1210-1217.

    CAS  PubMed  Google Scholar 

  118. 118.

    Richards M, Hardy R, Wadsworth ME: Alcohol consumption and midlife cognitive change in the British 1946 birth cohort study. Alcohol Alcohol. 2005, 40: 112-117.

    PubMed  Google Scholar 

  119. 119.

    Rodgers B, Windsor TD, Anstey KJ, Dear KB, FJ A, Christensen H: Non-linear relationships between cognitive function and alcohol consumption in young, middle-aged and older adults: the PATH through life project. Addiction. 2005, 100: 1280-1290.

    PubMed  Google Scholar 

  120. 120.

    Stampfer MJ, Kang JH, Chen J, Cherry R, Grodstein F: Effects of moderate alcohol consumption on cognitive function in women. N Engl J Med. 2005, 352: 245-253.

    CAS  PubMed  Google Scholar 

  121. 121.

    Reid MC, Van Ness PH, Hawkins KA, Towle V, Concato J, Guo Z: Light to moderate alcohol consumption is associated with better cognitive function among older male veterans receiving primary care. J Geriatr Psychiatry Neurol. 2006, 19: 98-105.

    CAS  PubMed  Google Scholar 

  122. 122.

    Wright CB, Elkind MS, Luo X, Paik MC, Sacco RL: Reported alcohol consumption and cognitive decline: the northern Manhattan study. Neuroepidemiology. 2006, 27: 201-207.

    PubMed  PubMed Central  Google Scholar 

  123. 123.

    Ngandu T, Helkala EL, Soininen H, Winblad B, Tuomilehto J, Nissinen A, Kivipelto M: Alcohol drinking and cognitive functions: findings from the cardiovascular risk factors aging and dementia (CAIDE) study. Dement Geriatr Cogn Disord. 2007, 23: 140-149.

    PubMed  Google Scholar 

  124. 124.

    Solfrizzi V, D'Introno A, Colacicco AM, Capurso C, Del Parigi A, Baldassarre G, Scapicchio P, Scafato E, Amodio M, Capurso A, Panza F: Alcohol consumption, mild cognitive impairment, and progression to dementia. Neurology. 2007, 68: 1790-1799.

    CAS  PubMed  Google Scholar 

  125. 125.

    Xu G, Liu X, Yin Q, Zhu W, Zhang R, Fan X: Alcohol consumption and transition of mild cognitive impairment to dementia. Psychiatry Clin Neurosci. 2009, 63: 43-49.

    CAS  PubMed  Google Scholar 

  126. 126.

    Au Yeung SL, Jiang C, Zhang W, Lam TH, Cheng KK, Leung GM, Schooling CM: Moderate alcohol use and cognitive function in the Guangzhou Biobank cohort study. Ann Epidemiol. 2010, 20: 873-882.

    PubMed  Google Scholar 

  127. 127.

    Chan KK, Chiu KC, Chu LW: Association between alcohol consumption and cognitive impairment in Southern Chinese older adults. Int J Geriatr Psychiatry. 2010, 25: 1272-1279.

    PubMed  Google Scholar 

  128. 128.

    Corley J, Jia X, Brett CE, Gow AJ, Starr JM, Kyle JA, McNeill G, Deary IJ: Alcohol intake and cognitive abilities in old age: the Lothian birth cohort 1936 study. Neuropsychology. 2011, 25: 166-175.

    PubMed  Google Scholar 

  129. 129.

    Gross AL, Rebok GW, Ford DE, Chu AY, Gallo JJ, Liang KY, Meoni LA, Shihab HM, Wang NY, Klag MJ: Alcohol consumption and domain-specific cognitive function in older adults: longitudinal data from the Johns Hopkins precursors study. J Gerontol B Psychol Sci Soc Sci. 2011, 66: 39-47.

    PubMed  Google Scholar 

  130. 130.

    Sabia S, Gueguen A, Berr C, Berkman L, Ankri J, Goldberg M, Zins M, Singh-Manoux A: High alcohol consumption in middle-aged adults is associated with poorer cognitive performance only in the low socio-economic group. Results from the GAZEL cohort study. Addiction. 2011, 106: 93-101.

    PubMed  Google Scholar 

  131. 131.

    Zanjani F, Downer BG, Kruger TM, Willis SL, Schaie KW: Alcohol effects on cognitive change in middle-aged and older adults. Aging Ment Health. 2013, 17 (1): 12-23.

    PubMed  Google Scholar 

  132. 132.

    Ho SC, Woo J, Sham A, Chan SG, Yu AL: A 3-year follow-up study of social, lifestyle and health predictors of cognitive impairment in a Chinese older cohort. Int J Epidemiol. 2001, 30: 1389-1396.

    CAS  PubMed  Google Scholar 

  133. 133.

    Laurin D, Verreault R, Lindsay J, MacPherson K, Rockwood K: Physical activity and risk of cognitive impairment and dementia in elderly persons. Arch Neurol. 2001, 58: 498-504.

    CAS  PubMed  Google Scholar 

  134. 134.

    Schuit AJ, Feskens EJ, Launer LJ, Kromhout D: Physical activity and cognitive decline, the role of the apolipoprotein e4 allele. Med Sci Sports Exerc. 2001, 33: 772-777.

    CAS  PubMed  Google Scholar 

  135. 135.

    Yaffe K, Barnes D, Nevitt M, Lui LY, Covinsky K: A prospective study of physical activity and cognitive decline in elderly women: women who walk. Arch Intern Med. 2001, 161: 1703-1708.

    CAS  PubMed  Google Scholar 

  136. 136.

    Verghese J, Lipton RB, Katz MJ, Hall CB, Derby CA, Kuslansky G, Ambrose AF, Sliwinski M, Buschke H: Leisure activities and the risk of dementia in the elderly. N Engl J Med. 2003, 348: 2508-2516.

    PubMed  Google Scholar 

  137. 137.

    Abbott RD, White LR, Ross GW, Masaki KH, Curb JD, Petrovitch H: Walking and dementia in physically capable elderly men. JAMA. 2004, 292: 1447-1453.

    CAS  PubMed  Google Scholar 

  138. 138.

    Lytle ME, Vander Bilt J, Pandav RS, Dodge HH, Ganguli M: Exercise level and cognitive decline: the MoVIES project. Alzheimer Dis Assoc Disord. 2004, 18: 57-64.

    PubMed  Google Scholar 

  139. 139.

    van Gelder BM, Tijhuis MA, Kalmijn S, Giampaoli S, Nissinen A, Kromhout D: Physical activity in relation to cognitive decline in elderly men: the FINE Study. Neurology. 2004, 63: 2316-2321.

    CAS  PubMed  Google Scholar 

  140. 140.

    Weuve J, Kang JH, Manson JE, Breteler MM, Ware JH, Grodstein F: Physical activity, including walking, and cognitive function in older women. JAMA. 2004, 292: 1454-1461.

    CAS  PubMed  Google Scholar 

  141. 141.

    Podewils LJ, Guallar E, Kuller LH, Fried LP, Lopez OL, Carlson M, Lyketsos CG: Physical activity, APOE genotype, and dementia risk: findings from the cardiovascular health cognition study. Am J Epidemiol. 2005, 161: 639-651.

    PubMed  Google Scholar 

  142. 142.

    Rovio S, Kareholt I, Helkala EL, Viitanen M, Winblad B, Tuomilehto J, Soininen H, Nissinen A, Kivipelto M: Leisure-time physical activity at midlife and the risk of dementia and Alzheimer's disease. Lancet Neurol. 2005, 4: 705-711.

    PubMed  Google Scholar 

  143. 143.

    Sturman MT, Morris MC, de Leon CF M, Bienias JL, Wilson RS, Evans DA: Physical activity, cognitive activity, and cognitive decline in a biracial community population. Arch Neurol. 2005, 62: 1750-1754.

    PubMed  Google Scholar 

  144. 144.

    Karp A, Paillard-Borg S, Wang HX, Silverstein M, Winblad B, Fratiglioni L: Mental, physical and social components in leisure activities equally contribute to decrease dementia risk. Dement Geriatr Cogn Disord. 2006, 21: 65-73.

    PubMed  Google Scholar 

  145. 145.

    Larson EB, Wang L, Bowen JD, McCormick WC, Teri L, Crane P, Kukull W: Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older. Ann Intern Med. 2006, 144: 73-81.

    PubMed  Google Scholar 

  146. 146.

    Landi F, Russo A, Barillaro C, Cesari M, Pahor M, Danese P, Bernabei R, Onder G: Physical activity and risk of cognitive impairment among older persons living in the community. Aging Clin Exp Res. 2007, 19: 410-416.

    PubMed  Google Scholar 

  147. 147.

    Psaltopoulou T, Kyrozis A, Stathopoulos P, Trichopoulos D, Vassilopoulos D, Trichopoulou A: Diet, physical activity and cognitive impairment among elders: the EPIC-Greece cohort (European Prospective Investigation into Cancer and Nutrition). Public Health Nutr. 2008, 11: 1054-1062.

    CAS  PubMed  Google Scholar 

  148. 148.

    Taaffe DR, Irie F, Masaki KH, Abbott RD, Petrovitch H, Ross GW, White LR: Physical activity, physical function, and incident dementia in elderly men: the Honolulu-Asia aging study. J Gerontol A Biol Sci Med Sci. 2008, 63: 529-535.

    PubMed  Google Scholar 

  149. 149.

    Gallucci M, Antuono P, Ongaro F, Forloni PL, Albani D, Amici GP, Regini C: Physical activity, socialization and reading in the elderly over the age of seventy: what is the relation with cognitive decline? Evidence from "The Treviso Longeva (TRELONG) study". Arch Gerontol Geriatr. 2009, 48: 284-286.

    CAS  PubMed  Google Scholar 

  150. 150.

    Scarmeas N, Luchsinger JA, Schupf N, Brickman AM, Cosentino S, Tang MX, Stern Y: Physical activity, diet, and risk of Alzheimer disease. JAMA. 2009, 302: 627-637.

    CAS  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Yaffe K, Fiocco AJ, Lindquist K, Vittinghoff E, Simonsick EM, Newman AB, Satterfield S, Rosano C, Rubin SM, Ayonayon HN, Harris TB: Predictors of maintaining cognitive function in older adults: the Health ABC study. Neurology. 2009, 72: 2029-2035.

    CAS  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Chang M, Jonsson PV, Snaedal J, Bjornsson S, Saczynski JS, Aspelund T, Eiriksdottir G, Jonsdottir MK, Lopez OL, Harris TB, Gudnason V, Launer LJ: The effect of midlife physical activity on cognitive function among older adults: AGES–Reykjavik study. J Gerontol A Biol Sci Med Sci. 2010, 65: 1369-1374.

    PubMed  Google Scholar 

  153. 153.

    Etgen T, Sander D, Huntgeburth U, Poppert H, Forstl H, Bickel H: Physical activity and incident cognitive impairment in elderly persons: the INVADE study. Arch Intern Med. 2010, 170: 186-193.

    CAS  PubMed  Google Scholar 

  154. 154.

    Gillum RF, Obisesan TO: Physical activity, cognitive function, and mortality in a US national cohort. Ann Epidemiol. 2010, 20: 251-257.

    CAS  PubMed  PubMed Central  Google Scholar 

  155. 155.

    Middleton LE, Barnes DE, Lui LY, Yaffe K: Physical activity over the life course and its association with cognitive performance and impairment in old age. J Am Geriatr Soc. 2010, 58: 1322-1326.

    PubMed  PubMed Central  Google Scholar 

  156. 156.

    Buchman AS, Boyle PA, Yu L, Shah RC, Wilson RS, Bennett DA: Total daily physical activity and the risk of AD and cognitive decline in older adults. Neurology. 2012, 78: 1323-1329.

    CAS  PubMed  PubMed Central  Google Scholar 

  157. 157.

    Lin F, Friedman E, Quinn J, Chen DG, Mapstone M: Effect of leisure activities on inflammation and cognitive function in an aging sample. Arch Gerontol Geriatr. 2012, 54: e398-404.

    PubMed  PubMed Central  Google Scholar 

  158. 158.

    Norton MC, Dew J, Smith H, Fauth E, Piercy KW, Breitner JC, Tschanz J, Wengreen H, Welsh-Bohmer K: Lifestyle behavior pattern is associated with different levels of risk for incident dementia and Alzheimer's disease: the Cache county study. J Am Geriatr Soc. 2012, 60: 405-412.

    PubMed  PubMed Central  Google Scholar 

  159. 159.

    Jarvis MJ: Does caffeine intake enhance absolute levels of cognitive performance?. Psychopharmacology (Berl). 1993, 110: 45-52.

    CAS  Google Scholar 

  160. 160.

    Johnson-Kozlow M, Kritz-Silverstein D, Barrett-Connor E, Morton D: Coffee consumption and cognitive function among older adults. Am J Epidemiol. 2002, 156: 842-850.

    PubMed  Google Scholar 

  161. 161.

    van Boxtel MP, Schmitt JA, Bosma H, Jolles J: The effects of habitual caffeine use on cognitive change: a longitudinal perspective. Pharmacol Biochem Behav. 2003, 75: 921-927.

    CAS  PubMed  Google Scholar 

  162. 162.

    Kuriyama S, Hozawa A, Ohmori K, Shimazu T, Matsui T, Ebihara S, Awata S, Nagatomi R, Arai H, Tsuji I: Green tea consumption and cognitive function: a cross-sectional study from the Tsurugaya Project 1. Am J Clin Nutr. 2006, 83: 355-361.

    CAS  PubMed  Google Scholar 

  163. 163.

    van Gelder BM, Buijsse B, Tijhuis M, Kalmijn S, Giampaoli S, Nissinen A, Kromhout D: Coffee consumption is inversely associated with cognitive decline in elderly European men: the FINE Study. Eur J Clin Nutr. 2007, 61: 226-232.

    CAS  PubMed  Google Scholar 

  164. 164.

    Ng TP, Feng L, Niti M, Kua EH, Yap KB: Tea consumption and cognitive impairment and decline in older Chinese adults. Am J Clin Nutr. 2008, 88: 224-231.

    CAS  PubMed  Google Scholar 

  165. 165.

    Eskelinen MH, Ngandu T, Tuomilehto J, Soininen H, Kivipelto M: Midlife coffee and tea drinking and the risk of late-life dementia: a population-based CAIDE study. J Alzheimers Dis. 2009, 16: 85-91.

    CAS  PubMed  Google Scholar 

  166. 166.

    Laitala VS, Kaprio J, Koskenvuo M, Raiha I, Rinne JO, Silventoinen K: Coffee drinking in middle age is not associated with cognitive performance in old age. Am J Clin Nutr. 2009, 90: 640-646.

    CAS  PubMed  Google Scholar 

  167. 167.

    Nurk E, Refsum H, Drevon CA, Tell GS, Nygaard HA, Engedal K, Smith AD: Intake of flavonoid-rich wine, tea, and chocolate by elderly men and women is associated with better cognitive test performance. J Nutr. 2009, 139: 120-127.

    CAS  PubMed  Google Scholar 

  168. 168.

    Smith AP: Caffeine, cognitive failures and health in a non-working community sample. Hum Psychopharmacol. 2009, 24: 29-34.

    PubMed  Google Scholar 

  169. 169.

    Corley J, Jia X, Kyle JA, Gow AJ, Brett CE, Starr JM, McNeill G, Deary IJ: Caffeine consumption and cognitive function at age 70: the Lothian birth cohort 1936 study. Psychosom Med. 2010, 72: 206-214.

    PubMed  Google Scholar 

  170. 170.

    Feng L, Gwee X, Kua EH, Ng TP: Cognitive function and tea consumption in community dwelling older Chinese in Singapore. J Nutr Health Aging. 2010, 14: 433-438.

    CAS  PubMed  Google Scholar 

  171. 171.

    Ritchie K, Artero S, Portet F, Brickman A, Muraskin J, Beanino E, Ancelin ML, Carriere I: Caffeine, cognitive functioning, and white matter lesions in the elderly: establishing causality from epidemiological evidence. J Alzheimers Dis. 2010, 20 (Suppl 1): S161-166.

    CAS  PubMed  Google Scholar 

  172. 172.

    Santos C, Lunet N, Azevedo A, de Mendonca A, Ritchie K, Barros H: Caffeine intake is associated with a lower risk of cognitive decline: a cohort study from Portugal. J Alzheimers Dis. 2010, 20 (Suppl 1): S175-185.

    CAS  PubMed  Google Scholar 

  173. 173.

    Arab L, Biggs ML, O'Meara ES, Longstreth WT, Crane PK, Fitzpatrick AL: Gender differences in tea, coffee, and cognitive decline in the elderly: the cardiovascular health study. J Alzheimers Dis. 2011, 27: 553-566.

    CAS  PubMed  PubMed Central  Google Scholar 

  174. 174.

    Gelber RP, Petrovitch H, Masaki KH, Ross GW, White LR: Coffee intake in midlife and risk of dementia and its neuropathologic correlates. J Alzheimers Dis. 2011, 23: 607-615.

    CAS  PubMed  PubMed Central  Google Scholar 

  175. 175.

    Feng L, Li J, Ng TP, Lee TS, Kua EH, Zeng Y: Tea drinking and cognitive function in oldest-old chinese. J Nutr Health Aging. 2012, 16: 754-758.

    CAS  PubMed  PubMed Central  Google Scholar 

  176. 176.

    Jama JW, Launer LJ, Witteman JC, den Breeijen JH, Breteler MM, Grobbee DE, Hofman A: Dietary antioxidants and cognitive function in a population-based sample of older persons. The Rotterdam Study. Am J Epidemiol. 1996, 144: 275-280.

    CAS  PubMed  Google Scholar 

  177. 177.

    Mendelsohn AB, Belle SH, Stoehr GP, Ganguli M: Use of antioxidant supplements and its association with cognitive function in a rural elderly cohort: the MoVIES project. Monongahela Valley independent elders survey. Am J Epidemiol. 1998, 148: 38-44.

    CAS  PubMed  Google Scholar 

  178. 178.

    Morris MC, Beckett LA, Scherr PA, Hebert LE, Bennett DA, Field TS, Evans DA: Vitamin E and vitamin C supplement use and risk of incident Alzheimer disease. Alzheimer Dis Assoc Disord. 1998, 12: 121-126.

    CAS  PubMed  Google Scholar 

  179. 179.

    Schmidt R, Hayn M, Reinhart B, Roob G, Schmidt H, Schumacher M, Watzinger N, Launer LJ: Plasma antioxidants and cognitive performance in middle-aged and older adults: results of the Austrian stroke prevention study. J Am Geriatr Soc. 1998, 46: 1407-1410.

    CAS  PubMed  Google Scholar 

  180. 180.

    Masaki KH, Losonczy KG, Izmirlian G, Foley DJ, Ross GW, Petrovitch H, Havlik R, White LR: Association of vitamin E and C supplement use with cognitive function and dementia in elderly men. Neurology. 2000, 54: 1265-1272.

    CAS  PubMed  Google Scholar 

  181. 181.

    Peacock JM, Folsom AR, Knopman DS, Mosley TH, Goff DC, Szklo M: Dietary antioxidant intake and cognitive performance in middle-aged adults. The Atherosclerosis Risk in Communities (ARIC) Study investigators. Public Health Nutr. 2000, 3: 337-343.

    CAS  PubMed  Google Scholar 

  182. 182.

    Engelhart MJ, Geerlings MI, Ruitenberg A, van Swieten JC, Hofman A, Witteman JC, Breteler MM: Dietary intake of antioxidants and risk of Alzheimer disease. JAMA. 2002, 287: 3223-3229.

    CAS  PubMed  Google Scholar 

  183. 183.

    Morris MC, Evans DA, Bienias JL, Tangney CC, Bennett DA, Aggarwal N, Wilson RS, Scherr PA: Dietary intake of antioxidant nutrients and the risk of incident Alzheimer disease in a biracial community study. JAMA. 2002, 287: 3230-3237.

    CAS  PubMed  Google Scholar 

  184. 184.

    Morris MC, Evans DA, Bienias JL, Tangney CC, Wilson RS: Vitamin E and cognitive decline in older persons. Arch Neurol. 2002, 59: 1125-1132.

    PubMed  Google Scholar 

  185. 185.

    Gray SL, Hanlon JT, Landerman LR, Artz M, Schmader KE, Fillenbaum GG: Is antioxidant use protective of cognitive function in the community-dwelling elderly?. Am J Geriatr Pharmacother. 2003, 1: 3-10.

    CAS  PubMed  Google Scholar 

  186. 186.

    Grodstein F, Chen J, Willett WC: High-dose antioxidant supplements and cognitive function in community-dwelling elderly women. Am J Clin Nutr. 2003, 77: 975-984.

    CAS  PubMed  Google Scholar 

  187. 187.

    Luchsinger JA, Tang MX, Shea S, Mayeux R: Antioxidant vitamin intake and risk of Alzheimer disease. Arch Neurol. 2003, 60: 203-208.

    PubMed  Google Scholar 

  188. 188.

    Laurin D, Masaki KH, Foley DJ, White LR, Launer LJ: Midlife dietary intake of antioxidants and risk of late-life incident dementia: the Honolulu-Asia aging study. Am J Epidemiol. 2004, 159: 959-967.

    PubMed  Google Scholar 

  189. 189.

    Zandi PP, Anthony JC, Khachaturian AS, Stone SV, Gustafson D, Tschanz JT, Norton MC, Welsh-Bohmer KA, Breitner JC: Reduced risk of Alzheimer disease in users of antioxidant vitamin supplements: the Cache county study. Arch Neurol. 2004, 61: 82-88.

    PubMed  Google Scholar 

  190. 190.

    Cherubini A, Martin A, Andres-Lacueva C, Di Iorio A, Lamponi M, Mecocci P, Bartali B, Corsi A, Senin U, Ferrucci L: Vitamin E levels, cognitive impairment and dementia in older persons: the InCHIANTI study. Neurobiol Aging. 2005, 26: 987-994.

    CAS  PubMed  Google Scholar 

  191. 191.

    Engelhart MJ, Ruitenberg A, Meijer J, Kiliaan A, van Swieten JC, Hofman A, Witteman JC, Breteler MM: Plasma levels of antioxidants are not associated with Alzheimer's disease or cognitive decline. Dement Geriatr Cogn Disord. 2005, 19: 134-139.

    CAS  PubMed  Google Scholar 

  192. 192.

    Fillenbaum GG, Kuchibhatla MN, Hanlon JT, Artz MB, Pieper CF, Schmader KE, Dysken MW, Gray SL: Dementia and Alzheimer's disease in community-dwelling elders taking vitamin C and/or vitamin E. Ann Pharmacother. 2005, 39: 2009-2014.

    CAS  PubMed  Google Scholar 

  193. 193.

    Maxwell CJ, Hicks MS, Hogan DB, Basran J, Ebly EM: Supplemental use of antioxidant vitamins and subsequent risk of cognitive decline and dementia. Dement Geriatr Cogn Disord. 2005, 20: 45-51.

    CAS  PubMed  Google Scholar 

  194. 194.

    Morris MC, Evans DA, Tangney CC, Bienias JL, Wilson RS, Aggarwal NT, Scherr PA: Relation of the tocopherol forms to incident Alzheimer disease and to cognitive change. Am J Clin Nutr. 2005, 81: 508-514.

    CAS  PubMed  Google Scholar 

  195. 195.

    Akbaraly NT, Faure H, Gourlet V, Favier A, Berr C: Plasma carotenoid levels and cognitive performance in an elderly population: results of the EVA study. J Gerontol A Biol Sci Med Sci. 2007, 62: 308-316.

    PubMed  Google Scholar 

  196. 196.

    Dunn JE, Weintraub S, Stoddard AM, Banks S: Serum alpha-tocopherol, concurrent and past vitamin E intake, and mild cognitive impairment. Neurology. 2007, 68: 670-676.

    CAS  PubMed  Google Scholar 

  197. 197.

    Wengreen HJ, Munger RG, Corcoran CD, Zandi P, Hayden KM, Fotuhi M, Skoog I, Norton MC, Tschanz J, Breitner JC, Welsh-Bohmer KA: Antioxidant intake and cognitive function of elderly men and women: the Cache county study. J Nutr Health Aging. 2007, 11: 230-237.

    CAS  PubMed  Google Scholar 

  198. 198.

    Fotuhi M, Zandi PP, Hayden KM, Khachaturian AS, Szekely CA, Wengreen H, Munger RG, Norton MC, Tschanz JT, Lyketsos CG, Breitner JC, Welsh-Bohmer K: Better cognitive performance in elderly taking antioxidant vitamins E and C supplements in combination with nonsteroidal anti-inflammatory drugs: the Cache county study. Alzheimers Dement. 2008, 4: 223-227.

    CAS  PubMed  PubMed Central  Google Scholar 

  199. 199.

    Gray SL, Anderson ML, Crane PK, Breitner JC, McCormick W, Bowen JD, Teri L, Larson E: Antioxidant vitamin supplement use and risk of dementia or Alzheimer's disease in older adults. J Am Geriatr Soc. 2008, 56: 291-295.

    PubMed  Google Scholar 

  200. 200.

    Ravaglia G, Forti P, Lucicesare A, Pisacane N, Rietti E, Mangialasche F, Cecchetti R, Patterson C, Mecocci P: Plasma tocopherols and risk of cognitive impairment in an elderly Italian cohort. Am J Clin Nutr. 2008, 87: 1306-1313.

    CAS  PubMed  Google Scholar 

  201. 201.

    Devore EE, Kang JH, Stampfer MJ, Grodstein F: Total antioxidant capacity of diet in relation to cognitive function and decline. Am J Clin Nutr. 2010, 92: 1157-1164.

    CAS  PubMed  PubMed Central  Google Scholar 

  202. 202.

    McNeill G, Jia X, Whalley LJ, Fox HC, Corley J, Gow AJ, Brett CE, Starr JM, Deary IJ: Antioxidant and B vitamin intake in relation to cognitive function in later life in the Lothian birth cohort 1936. Eur J Clin Nutr. 2011, 65: 619-626.

    CAS  PubMed  Google Scholar 

  203. 203.

    Kalmijn S, Launer LJ, Lindemans J, Bots ML, Hofman A, Breteler MM: Total homocysteine and cognitive decline in a community-based sample of elderly subjects: the Rotterdam study. Am J Epidemiol. 1999, 150: 283-289.

    CAS  PubMed  Google Scholar 

  204. 204.

    Duthie SJ, Whalley LJ, Collins AR, Leaper S, Berger K, Deary IJ: Homocysteine, B vitamin status, and cognitive function in the elderly. Am J Clin Nutr. 2002, 75: 908-913.

    CAS  PubMed  Google Scholar 

  205. 205.

    Prins ND, Den Heijer T, Hofman A, Koudstaal PJ, Jolles J, Clarke R, Breteler MM: Homocysteine and cognitive function in the elderly: the Rotterdam scan study. Neurology. 2002, 59: 1375-1380.

    CAS  PubMed  Google Scholar 

  206. 206.

    Seshadri S, Beiser A, Selhub J, Jacques PF, Rosenberg IH, D'Agostino RB, Wilson PW, Wolf PA: Plasma homocysteine as a risk factor for dementia and Alzheimer's disease. N Engl J Med. 2002, 346: 476-483.

    CAS  PubMed  Google Scholar 

  207. 207.

    Miller JW, Green R, Ramos MI, Allen LH, Mungas DM, Jagust WJ, Haan MN: Homocysteine and cognitive function in the Sacramento area Latino study on aging. Am J Clin Nutr. 2003, 78: 441-447.

    CAS  PubMed  Google Scholar 

  208. 208.

    Ravaglia G, Forti P, Maioli F, Muscari A, Sacchetti L, Arnone G, Nativio V, Talerico T, Mariani E: Homocysteine and cognitive function in healthy elderly community dwellers in Italy. Am J Clin Nutr. 2003, 77: 668-673.

    CAS  PubMed  Google Scholar 

  209. 209.

    Luchsinger JA, Tang MX, Shea S, Miller J, Green R, Mayeux R: Plasma homocysteine levels and risk of Alzheimer disease. Neurology. 2004, 62: 1972-1976.

    CAS  PubMed  Google Scholar 

  210. 210.

    Ariogul S, Cankurtaran M, Dagli N, Khalil M, Yavuz B: Vitamin B12, folate, homocysteine and dementia: are they really related?. Arch Gerontol Geriatr. 2005, 40: 139-146.

    CAS  PubMed  Google Scholar 

  211. 211.

    Campbell AK, Jagust WJ, Mungas DM, Miller JW, Green R, Haan MN, Allen LH: Low erythrocyte folate, but not plasma vitamin B-12 or homocysteine, is associated with dementia in elderly Latinos. J Nutr Health Aging. 2005, 9: 39-43.

    CAS  PubMed  Google Scholar 

  212. 212.

    Elias MF, Sullivan LM, D'Agostino RB, Elias PK, Jacques PF, Selhub J, Seshadri S, Au R, Beiser A, Wolf PA: Homocysteine and cognitive performance in the Framingham offspring study: age is important. Am J Epidemiol. 2005, 162: 644-653.

    PubMed  Google Scholar 

  213. 213.

    Kado DM, Karlamangla AS, Huang MH, Troen A, Rowe JW, Selhub J, Seeman TE: Homocysteine versus the vitamins folate, B6, and B12 as predictors of cognitive function and decline in older high-functioning adults: MacArthur studies of successful aging. Am J Med. 2005, 118: 161-167.

    CAS  PubMed  Google Scholar 

  214. 214.

    Mooijaart SP, Gussekloo J, Frolich M, Jolles J, Stott DJ, Westendorp RG, de Craen AJ: Homocysteine, vitamin B-12, and folic acid and the risk of cognitive decline in old age: the Leiden 85-Plus study. Am J Clin Nutr. 2005, 82: 866-871.

    CAS  PubMed  Google Scholar 

  215. 215.

    Quadri P, Fragiacomo C, Pezzati R, Zanda E, Tettamanti M, Lucca U: Homocysteine and B vitamins in mild cognitive impairment and dementia. Clin Chem Lab Med. 2005, 43: 1096-1100.

    CAS  PubMed  Google Scholar 

  216. 216.

    Ramos MI, Allen LH, Mungas DM, Jagust WJ, Haan MN, Green R, Miller JW: Low folate status is associated with impaired cognitive function and dementia in the Sacramento area Latino study on aging. Am J Clin Nutr. 2005, 82: 1346-1352.

    CAS  PubMed  Google Scholar 

  217. 217.

    Ravaglia G, Forti P, Maioli F, Martelli M, Servadei L, Brunetti N, Porcellini E, Licastro F: Homocysteine and folate as risk factors for dementia and Alzheimer disease. Am J Clin Nutr. 2005, 82: 636-643.

    CAS  PubMed  Google Scholar 

  218. 218.

    Schafer JH, Glass TA, Bolla KI, Mintz M, Jedlicka AE, Schwartz BS: Homocysteine and cognitive function in a population-based study of older adults. J Am Geriatr Soc. 2005, 53: 381-388.

    PubMed  Google Scholar 

  219. 219.

    Tucker KL, Qiao N, Scott T, Rosenberg I, Spiro A: High homocysteine and low B vitamins predict cognitive decline in aging men: the Veterans affairs normative aging study. Am J Clin Nutr. 2005, 82: 627-635.

    CAS  PubMed  Google Scholar 

  220. 220.

    Elias MF, Robbins MA, Budge MM, Elias PK, Brennan SL, Johnston C, Nagy Z, Bates CJ: Homocysteine, folate, and vitamins B6 and B12 blood levels in relation to cognitive performance: the Maine-Syracuse study. Psychosom Med. 2006, 68: 547-554.

    CAS  PubMed  Google Scholar 

  221. 221.

    Feng L, Ng TP, Chuah L, Niti M, Kua EH: Homocysteine, folate, and vitamin B-12 and cognitive performance in older Chinese adults: findings from the Singapore longitudinal ageing study. Am J Clin Nutr. 2006, 84: 1506-1512.

    CAS  PubMed  Google Scholar 

  222. 222.

    van Raamt AF, Kalmijn S, Mali WP, van Zandvoort MJ, van der Graaf Y: Homocysteine level and cognitive function in patients with arterial disease: the second manifestations of ARTerial disease study. J Am Geriatr Soc. 2006, 54: 575-579.

    PubMed  Google Scholar 

  223. 223.

    Clarke R, Birks J, Nexo E, Ueland PM, Schneede J, Scott J, Molloy A, Evans JG: Low vitamin B-12 status and risk of cognitive decline in older adults. Am J Clin Nutr. 2007, 86: 1384-1391.

    CAS  PubMed  Google Scholar 

  224. 224.

    Haan MN, Miller JW, Aiello AE, Whitmer RA, Jagust WJ, Mungas DM, Allen LH, Green R: Homocysteine, B vitamins, and the incidence of dementia and cognitive impairment: results from the Sacramento area Latino study on aging. Am J Clin Nutr. 2007, 85: 511-517.

    CAS  PubMed  PubMed Central  Google Scholar 

  225. 225.

    Kim J, Park MH, Kim E, Han C, Jo SA, Jo I: Plasma homocysteine is associated with the risk of mild cognitive impairment in an elderly Korean population. J Nutr. 2007, 137: 2093-2097.

    CAS  PubMed  Google Scholar 

  226. 226.

    Elias MF, Robbins MA, Budge MM, Elias PK, Dore GA, Brennan SL, Johnston C, Nagy Z: Homocysteine and cognitive performance: modification by the ApoE genotype. Neurosci Lett. 2008, 430: 64-69.

    CAS  PubMed  Google Scholar 

  227. 227.

    Kim JM, Kim SW, Shin IS, Yang SJ, Park WY, Kim SJ, Shin HY, Yoon JS: Folate, vitamin b(12), and homocysteine as risk factors for cognitive decline in the elderly. Psychiatry Investig. 2008, 5: 36-40.

    PubMed  PubMed Central  Google Scholar 

  228. 228.

    Kim JM, Stewart R, Kim SW, Shin IS, Yang SJ, Shin HY, Yoon JS: Changes in folate, vitamin B12 and homocysteine associated with incident dementia. J Neurol Neurosurg Psychiatry. 2008, 79: 864-868.

    PubMed  Google Scholar 

  229. 229.

    Reitz C, Tang MX, Miller J, Green R, Luchsinger JA: Plasma homocysteine and risk of mild cognitive impairment. Dement Geriatr Cogn Disord. 2009, 27: 11-17.

    CAS  PubMed  Google Scholar 

  230. 230.

    Redeen S, Ryberg A, Petersson F, Eriksson O, Nagga K, Borch K: Homocysteine levels in chronic gastritis and other conditions: relations to incident cardiovascular disease and dementia. Dig Dis Sci. 2010, 55: 351-358.

    CAS  PubMed  Google Scholar 

  231. 231.

    van den Kommer TN, Dik MG, Comijs HC, Jonker C, Deeg DJ: Homocysteine and inflammation: predictors of cognitive decline in older persons?. Neurobiol Aging. 2010, 31: 1700-1709.

    CAS  PubMed  Google Scholar 

  232. 232.

    Perneczky R, Alexopoulos P, Kurz A, Bickel H: Cognitive reserve, homocysteine, and cognition in the Bavarian school sisters study. J Am Geriatr Soc. 2011, 59: 1754-1756.

    PubMed  Google Scholar 

  233. 233.

    Zylberstein DE, Lissner L, Bjorkelund C, Mehlig K, Thelle DS, Gustafson D, Ostling S, Waern M, Guo X, Skoog I: Midlife homocysteine and late-life dementia in women. A prospective population study. Neurobiol Aging. 2011, 32: 380-386.

    CAS  PubMed  Google Scholar 

  234. 234.

    Ford AH, Flicker L, Alfonso H, Hankey GJ, Norman PE, van Bockxmeer FM, Almeida OP: Plasma homocysteine and MTHFRC677T polymorphism as risk factors for incident dementia. J Neurol Neurosurg Psychiatry. 2012, 83: 70-75.

    PubMed  Google Scholar 

  235. 235.

    Ford AH, Flicker L, Hankey GJ, Norman P, van Bockxmeer FM, Almeida OP: Homocysteine, methylenetetrahydrofolate reductase C677T polymorphism and cognitive impairment: the health in men study. Mol Psychiatry. 2012, 17: 559-566.

    CAS  PubMed  Google Scholar 

  236. 236.

    Kalmijn S, Feskens EJ, Launer LJ, Kromhout D: Polyunsaturated fatty acids, antioxidants, and cognitive function in very old men. Am J Epidemiol. 1997, 145: 33-41.

    CAS  PubMed  Google Scholar 

  237. 237.

    Kalmijn S, Launer LJ, Ott A, Witteman JC, Hofman A, Breteler MM: Dietary fat intake and the risk of incident dementia in the Rotterdam study. Ann Neurol. 1997, 42: 776-782.

    CAS  PubMed  Google Scholar 

  238. 238.

    Engelhart MJ, Geerlings MI, Ruitenberg A, Van Swieten JC, Hofman A, Witteman JC, Breteler MM: Diet and risk of dementia: does fat matter?: the Rotterdam study. Neurology. 2002, 59: 1915-1921.

    CAS  PubMed  Google Scholar 

  239. 239.

    Morris MC, Evans DA, Bienias JL, Tangney CC, Bennett DA, Wilson RS, Aggarwal N, Schneider J: Consumption of fish and n-3 fatty acids and risk of incident Alzheimer disease. Arch Neurol. 2003, 60: 940-946.

    PubMed  Google Scholar 

  240. 240.

    Kalmijn S, van Boxtel MP, Ocke M, Verschuren WM, Kromhout D, Launer LJ: Dietary intake of fatty acids and fish in relation to cognitive performance at middle age. Neurology. 2004, 62: 275-280.

    CAS  PubMed  Google Scholar 

  241. 241.

    Morris MC, Evans DA, Tangney CC, Bienias JL, Wilson RS: Fish consumption and cognitive decline with age in a large community study. Arch Neurol. 2005, 62 (12): 1849-53.

    PubMed  Google Scholar 

  242. 242.

    Barberger-Gateau P, Raffaitin C, Letenneur L, Berr C, Tzourio C, Dartigues JF, Alperovitch A: Dietary patterns and risk of dementia: the Three-City cohort study. Neurology. 2007, 69: 1921-1930.

    CAS  PubMed  Google Scholar 

  243. 243.

    Beydoun MA, Kaufman JS, Satia JA, Rosamond W, Folsom AR: Plasma n-3 fatty acids and risk of cognitive decline among older adults: the atherosclerosis risk in communities (ARIC) study. Am J Clin Nutr. 2007, 84 (4): 1103-11. In press

    Google Scholar 

  244. 244.

    Cherubini A, Andres-Lacueva C, Martin A, Lauretani F, Iorio AD, Bartali B, Corsi A, Bandinelli S, Mattson MP, Ferrucci L: Low plasma N-3 fatty acids and dementia in older persons: the InCHIANTI study. J Gerontol A Biol Sci Med Sci. 2007, 62: 1120-1126.

    PubMed  PubMed Central  Google Scholar 

  245. 245.

    Dullemeijer C, Durga J, Brouwer IA, van de Rest O, Kok FJ, Brummer RJ, van Boxtel MP, Verhoef P: n 3 fatty acid proportions in plasma and cognitive performance in older adults. Am J Clin Nutr. 2007, 86: 1479-1485.

    CAS  PubMed  Google Scholar 

  246. 246.

    Beydoun MA, Kaufman JS, Sloane PD, Heiss G, Ibrahim J: n-3 Fatty acids, hypertension and risk of cognitive decline among older adults in the atherosclerosis risk in communities (ARIC) study. Public Health Nutr. 2008, 11: 17-29.

    PubMed  Google Scholar 

  247. 247.

    Eskelinen MH, Ngandu T, Helkala EL, Tuomilehto J, Nissinen A, Soininen H, Kivipelto M: Fat intake at midlife and cognitive impairment later in life: a population-based CAIDE study. Int J Geriatr Psychiatry. 2008, 23: 741-747.

    PubMed  Google Scholar 

  248. 248.

    Samieri C, Feart C, Letenneur L, Dartigues JF, Peres K, Auriacombe S, Peuchant E, Delcourt C, Barberger-Gateau P: Low plasma eicosapentaenoic acid and depressive symptomatology are independent predictors of dementia risk. Am J Clin Nutr. 2008, 88: 714-721.

    CAS  PubMed  Google Scholar 

  249. 249.

    Albanese E, Dangour AD, Uauy R, Acosta D, Guerra M, Guerra SS, Huang Y, Jacob KS, de Rodriguez JL, Noriega LH, Salas A, Sosa AL, Sousa RM, Williams J, Ferri CP, Prince MJ: Dietary fish and meat intake and dementia in Latin America, China, and India: a 10/66 dementia research group population-based study. Am J Clin Nutr. 2009, 90: 392-400.

    CAS  PubMed  PubMed Central  Google Scholar 

  250. 250.

    Devore EE, Grodstein F, van Rooij FJ, Hofman A, Rosner B, Stampfer MJ, Witteman JC, Breteler MM: Dietary intake of fish and omega-3 fatty acids in relation to long-term dementia risk. Am J Clin Nutr. 2009, 90: 170-176.

    CAS  PubMed  PubMed Central  Google Scholar 

  251. 251.

    Kroger E, Verreault R, Carmichael PH, Lindsay J, Julien P, Dewailly E, Ayotte P, Laurin D: Omega-3 fatty acids and risk of dementia: the Canadian study of health and aging. Am J Clin Nutr. 2009, 90: 184-192.

    CAS  PubMed  Google Scholar 

  252. 252.

    van de Rest O, Spiro A, Krall-Kaye E, Geleijnse JM, de Groot LC, Tucker KL: Intakes of (n-3) fatty acids and fatty fish are not associated with cognitive performance and 6-year cognitive change in men participating in the Veterans affairs normative aging study. J Nutr. 2009, 139: 2329-2336.

    CAS  PubMed  PubMed Central  Google Scholar 

  253. 253.

    Vercambre MN, Boutron-Ruault MC, Ritchie K, Clavel-Chapelon F, Berr C: Long-term association of food and nutrient intakes with cognitive and functional decline: a 13-year follow-up study of elderly French women. Br J Nutr. 2009, 102: 419-427.

    CAS  PubMed  PubMed Central  Google Scholar 

  254. 254.

    Gonzalez S, Huerta JM, Fernandez S, Patterson AM, Lasheras C: The relationship between dietary lipids and cognitive performance in an elderly population. Int J Food Sci Nutr. 2010, 61: 217-225.

    CAS  PubMed  Google Scholar 

  255. 255.

    Muldoon MF, Ryan CM, Sheu L, Yao JK, Conklin SM, Manuck SB: Serum phospholipid docosahexaenonic acid is associated with cognitive functioning during middle adulthood. J Nutr. 2010, 140: 848-853.

    CAS  PubMed  PubMed Central  Google Scholar 

  256. 256.

    Gao Q, Niti M, Feng L, Yap KB, Ng TP: Omega-3 polyunsaturated fatty acid supplements and cognitive decline: Singapore longitudinal aging studies. J Nutr Health Aging. 2011, 15: 32-35.

    CAS  PubMed  Google Scholar 

  257. 257.

    Kesse-Guyot E, Peneau S, Ferry M, Jeandel C, Hercberg S, Galan P: Thirteen-year prospective study between fish consumption, long-chain n-3 fatty acids intakes and cognitive function. J Nutr Health Aging. 2011, 15: 115-120.

    CAS  PubMed  Google Scholar 

  258. 258.

    Samieri C, Feart C, Proust-Lima C, Peuchant E, Dartigues JF, Amieva H, Barberger-Gateau P: omega-3 fatty acids and cognitive decline: modulation by ApoEepsilon4 allele and depression. Neurobiol Aging. 2011, 32: 2317-e2313-2322

    PubMed  Google Scholar 

  259. 259.

    Thomson TM: Endnote X3. 2010, Philadelphia, PA: Thomson Reuters

    Google Scholar 

  260. 260.

    Petitti DB: Statistical methods in meat-analysis. Meta-analysis. Decision Analysis, and cost-effectiveness analysis. Edited by: Petitti DB. 2000, New York, NY: Oxford University Press, 2000

    Google Scholar 

  261. 261.

    Hildebrandt M, Bender R, Gehrmann U, Blettner M: Calculating confidence intervals for impact numbers. BMC Med Res Methodol. 2006, 6: 32-

    PubMed  PubMed Central  Google Scholar 

  262. 262.

    Egger M, Smith GD, Altman DG: Systematic Reviews in heatlh care: Meta-analysis in context. 2001, London. UK: the BMJ Publishing Group, 2

    Google Scholar 

  263. 263.

    Egger M, Smith GD, Schneider M, Minder C: Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997, 315: 629-634.

    CAS  PubMed  PubMed Central  Google Scholar 

  264. 264.

    Begg CB, Mazumdar M: Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994, 50: 1088-1101.

    CAS  PubMed  Google Scholar 

  265. 265.

    Egger M, Davey Smith G, Schneider M, Minder C, Davey Smith G, Schneider M, Minder C: Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997, 315: 629-634.

    CAS  PubMed  PubMed Central  Google Scholar 

  266. 266.

    STATA: Statistics/Data Analysis: Release 11.0. Book Statistics/Data Analysis: Release 11.0. Edited by: Editor ed. 2009, City: Stata Corporation

    Google Scholar 

  267. 267.

    Albert MS: How does education affect cognitive function?. Ann Epidemiol. 1995, 5: 76-78.

    CAS  PubMed  Google Scholar 

  268. 268.

    Wilson RS, Bennett DA, Beckett LA, Morris MC, Gilley DW, Bienias JL, Scherr PA, Evans DA: Cognitive activity in older persons from a geographically defined population. J Gerontol B Psychol Sci Soc Sci. 1999, 54: P155-160.

    CAS  PubMed  Google Scholar 

  269. 269.

    Gorelick PB, Sacco RL, Smith DB, Alberts M, Mustone-Alexander L, Rader D, Ross JL, Raps E, Ozer MN, Brass LM, Malone ME, Goldberg S, Booss J, Hanley DF, Toole JF, Greengold NL, Rhew DC: Prevention of a first stroke: a review of guidelines and a multidisciplinary consensus statement from the National Stroke Association. JAMA. 1999, 281: 1112-1120.

    CAS  PubMed  Google Scholar 

  270. 270.

    Markesbery WR, Carney JM: Oxidative alterations in Alzheimer's disease. Brain Pathol. 1999, 9: 133-146.

    CAS  PubMed  Google Scholar 

  271. 271.

    Nicotinic cholinergic receptors in Alzheimer's disease, in nicotine psychopharmacology: molecular, cellular, and behavioral aspects. Edited by: Kellar KJ, Wonnacott S. 1990, Oxford: Oxford University Press

    Google Scholar 

  272. 272.

    Nordberg A: Biological markers and the cholinergic hypothesis in Alzheimer's disease. Acta Neurol Scand Suppl. 1992, 139: 54-58.

    CAS  PubMed  Google Scholar 

  273. 273.

    Letenneur L, Larrieu S, Barberger-Gateau P: Alcohol and tobacco consumption as risk factors of dementia: a review of epidemiological studies. Biomed Pharmacother. 2004, 58: 95-99.

    CAS  PubMed  Google Scholar 

  274. 274.

    Rogers RL, Meyer JS, Mortel KF: After reaching retirement age physical activity sustains cerebral perfusion and cognition. J Am Geriatr Soc. 1990, 38: 123-128.

    CAS  PubMed  Google Scholar 

  275. 275.

    Dustman RE, Ruhling RO, Russell EM, Shearer DE, Bonekat HW, Shigeoka JW, Wood JS, Bradford DC: Aerobic exercise training and improved neuropsychological function of older individuals. Neurobiol Aging. 1984, 5: 35-42.

    CAS  PubMed  Google Scholar 

  276. 276.

    Spirduso WW: Physical fitness, aging, and psychomotor speed: a review. J Gerontol. 1980, 35: 850-865.

    CAS  PubMed  Google Scholar 

  277. 277.

    Gomez-Pinilla F, Dao L, So V: Physical exercise induces FGF-2 and its mRNA in the hippocampus. Brain Res. 1997, 764: 1-8.

    CAS  PubMed  Google Scholar 

  278. 278.

    Cotman CW, Engesser-Cesar C: Exercise enhances and protects brain function. Exerc Sport Sci Rev. 2002, 30: 75-79.

    PubMed  Google Scholar 

  279. 279.

    Pignatti F, Rozzini R, Trabucchi M: Physical activity and cognitive decline in elderly persons. Arch Intern Med. 2002, 162: 361-362.

    PubMed  Google Scholar 

  280. 280.

    James JE: Caffeine & health. 1991, London, England: Academic

    Google Scholar 

  281. 281.

    Battig K, Buzzi R: Effect of coffee on the speed of subject-paced information processing. Neuropsychobiology. 1986, 16: 126-130.

    CAS  PubMed  Google Scholar 

  282. 282.

    Riedel W, Hogervorst E, Leboux R, Verhey F, van Praag H, Jolles J: Caffeine attenuates scopolamine-induced memory impairment in humans. Psychopharmacology (Berl). 1995, 122: 158-168.

    CAS  Google Scholar 

  283. 283.

    Behl C: Amyloid beta-protein toxicity and oxidative stress in Alzheimer's disease. Cell Tissue Res. 1997, 290: 471-480.

    CAS  PubMed  Google Scholar 

  284. 284.

    Christen Y: Oxidative stress and Alzheimer disease. Am J Clin Nutr. 2000, 71: 621S-629S.

    CAS  PubMed  Google Scholar 

  285. 285.

    Grundman M: Vitamin E and Alzheimer disease: the basis for additional clinical trials. Am J Clin Nutr. 2000, 71: 630S-636S.

    CAS  PubMed  Google Scholar 

  286. 286.

    Refsum H, Ueland PM, Nygard O, Vollset SE: Homocysteine and cardiovascular disease. Annu Rev Med. 1998, 49: 31-62.

    CAS  PubMed  Google Scholar 

  287. 287.

    Garcia A, Haron Y, Pulman K, Hua L, Freedman M: Increases in homocysteine are related to worsening of stroop scores in healthy elderly persons: a prospective follow-up study. J Gerontol A Biol Sci Med Sci. 2004, 59: 1323-1327.

    PubMed  Google Scholar 

  288. 288.

    Teunissen CE, Blom AH, Van Boxtel MP, Bosma H, de Bruijn C, Jolles J, Wauters BA, Steinbusch HW, de Vente J: Homocysteine: a marker for cognitive performance? A longitudinal follow-up study. J Nutr Health Aging. 2003, 7: 153-159.

    CAS  PubMed  Google Scholar 

  289. 289.

    Scott TM, Tucker KL, Bhadelia A, Benjamin B, Patz S, Bhadelia R, Liebson E, Price LL, Griffith J, Rosenberg I, Folstein MF: Homocysteine and B vitamins relate to brain volume and white-matter changes in geriatric patients with psychiatric disorders. Am J Geriatr Psychiatry. 2004, 12: 631-638.

    PubMed  Google Scholar 

  290. 290.

    Dufouil C, Alperovitch A, Ducros V, Tzourio C: Homocysteine, white matter hyperintensities, and cognition in healthy elderly people. Ann Neurol. 2003, 53: 214-221.

    CAS  PubMed  Google Scholar 

  291. 291.

    Sachdev PS, Valenzuela M, Wang XL, Looi JC, Brodaty H: Relationship between plasma homocysteine levels and brain atrophy in healthy elderly individuals. Neurology. 2002, 58: 1539-1541.

    CAS  PubMed  Google Scholar 

  292. 292.

    Bleich S, Kornhuber J: Relationship between plasma homocysteine levels and brain atrophy in healthy elderly individuals. Neurology. 2003, 60: 1220-author reply 1220

    PubMed  Google Scholar 

  293. 293.

    den Heijer T, Vermeer SE, Clarke R, Oudkerk M, Koudstaal PJ, Hofman A, Breteler MM: Homocysteine and brain atrophy on MRI of non-demented elderly. Brain. 2003, 126: 170-175.

    CAS  PubMed  Google Scholar 

  294. 294.

    Bottiglieri T: Homocysteine and folate metabolism in depression. Prog Neuropsychopharmacol Biol Psychiatry. 2005, 29: 1103-1112.

    CAS  PubMed  Google Scholar 

  295. 295.

    Selhub J, Jacques PF, Wilson PW, Rush D, Rosenberg IH: Vitamin status and intake as primary determinants of homocysteinemia in an elderly population. JAMA. 1993, 270: 2693-2698.

    CAS  PubMed  Google Scholar 

  296. 296.

    Li L, Cao D, Desmond R, Rahman A, Lah JJ, Levey AI, Zamrini E: Cognitive performance and plasma levels of homocysteine, vitamin B12, folate and lipids in patients with Alzheimer disease. Dement Geriatr Cogn Disord. 2008, 26: 384-390.

    CAS  PubMed  PubMed Central  Google Scholar 

  297. 297.

    Vidal JS, Dufouil C, Ducros V, Tzourio C: Homocysteine, folate and cognition in a large community-based sample of elderly people–the 3C Dijon Study. Neuroepidemiology. 2008, 30: 207-214.

    PubMed  Google Scholar 

  298. 298.

    Kruman II, Culmsee C, Chan SL, Kruman Y, Guo Z, Penix L, Mattson MP: Homocysteine elicits a DNA damage response in neurons that promotes apoptosis and hypersensitivity to excitotoxicity. J Neurosci. 2000, 20: 6920-6926.

    CAS  PubMed  Google Scholar 

  299. 299.

    Parsons RB, Waring RH, Ramsden DB, Williams AC: In vitro effect of the cysteine metabolites homocysteic acid, homocysteine and cysteic acid upon human neuronal cell lines. Neurotoxicology. 1998, 19: 599-603.

    CAS  PubMed  Google Scholar 

  300. 300.

    Arab L: Biomarkers of fat and fatty acid intake. J Nutr. 2003, 133 (Suppl 3): 925S-932S.

    CAS  PubMed  Google Scholar 

  301. 301.

    Andreassi M, Forleo P, Di Lorio A, Masci S, Abate G, Amerio P: Efficacy of gamma-linolenic acid in the treatment of patients with atopic dermatitis. J Int Med Res. 1997, 25: 266-274.

    CAS  PubMed  Google Scholar 

  302. 302.

    Cerolini S, Kelso KA, Noble RC, Speake BK, Pizzi F, Cavalchini LG: Relationship between spermatozoan lipid composition and fertility during aging of chickens. Biol Reprod. 1997, 57: 976-980.

    CAS  PubMed  Google Scholar 

  303. 303.

    Zhang L: The effects of essential fatty acids preparation in the treatment of intrauterine growth retardation. Am J Perinatol. 1997, 14: 535-537.

    CAS  PubMed  Google Scholar 

  304. 304.

    Bjerve KS: Omega 3 fatty acid deficiency in man: implications for the requirement of alpha-linolenic acid and long-chain omega 3 fatty acids. World Rev Nutr Diet. 1991, 66: 133-142.

    CAS  PubMed  Google Scholar 

  305. 305.

    Wainwright PE: Dietary essential fatty acids and brain function: a developmental perspective on mechanisms. Proc Nutr Soc. 2002, 61: 61-69.

    CAS  PubMed  Google Scholar 

  306. 306.

    Haag M: Essential fatty acids and the brain. Can J Psychiatry. 2003, 48: 195-203.

    PubMed  Google Scholar 

  307. 307.

    de Wilde MC, Hogyes E, Kiliaan AJ, Farkas T, Luiten PG, Farkas E: Dietary fatty acids alter blood pressure, behavior and brain membrane composition of hypertensive rats. Brain Res. 2003, 988: 9-19.

    CAS  PubMed  Google Scholar 

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This study was supported by the Intramural Research Program of the National Institute on Aging, NIA/NIH/IRP. The authors would like to thank Mr. James Byrnes, NIA/NIH/IRP, for help with literature search and retrieval. Special thanks also go to Drs. Lori L. Beason-Held and Madhav Thambisetty, NIA/NIH/IRP, for their review and comments on the manuscript.

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Correspondence to May A Beydoun.

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The authors declare that they have no competing interests.

Authors’ contributions

MAB: conceptualization, literature search and review, plan of analysis, data management, statistical analysis (including meta-analysis), writing of the manuscript, revision of the manuscript. HAB: Literature search and review, plan of analysis, writing of parts of the manuscript, revision of the manuscript. AG: Literature search and review, plan of analysis, writing of parts of the manuscript, revision of the manuscript. AT: Literature search and reivew, writing of parts of the manuscript, revision of the manuscript. ABZ: Plan of analysis, write-up of parts of the manuscript, revision of the manuscript. YW: Plan of analysis, write-up of parts of the manuscript, revision of the manuscript. All authors read and approved the final manuscript.

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Beydoun, M.A., Beydoun, H.A., Gamaldo, A.A. et al. Epidemiologic studies of modifiable factors associated with cognition and dementia: systematic review and meta-analysis. BMC Public Health 14, 643 (2014).

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  • Cognition
  • Dementia
  • Alzheimer’s disease
  • Risk factor
  • Nutrition
  • Meta-analysis