Cross-sectional and longitudinal studies provide some evidence for an association between intake of antioxidants and B vitamins, and cognitive function in later life, but intervention studies have not provided clear evidence of beneficial effects. The possibility that those with higher cognitive ability during earlier adult life consume more nutrient-rich diets in later life could provide an alternative explanation for the associations seen in observational studies.
Survey of 1091 men and women born in 1936 living in Edinburgh, Scotland, in whom previous cognitive ability was available from intelligence quotient (IQ) measurements at age 11 years. At age 70 years, participants carried out a range of cognitive tests and completed a semiquantitative food-frequency questionnaire (FFQ).
A total of 882 participants returned completed FFQs from which intake of β-carotene, vitamin C, B12, folate and riboflavin was estimated. IQ at age 11 years was positively associated with dietary intake of vitamin C (P=0.048) and inversely associated with dietary intake of riboflavin (P<0.001) at age 70 years, and was higher in those taking folate supplements at age 70 years (P<0.005). Weak associations between intake of vitamins B12, C, riboflavin and folate and cognitive performance at age 70 years were attenuated by adjustment for confounding variables, including IQ at age 11 years. In the fully adjusted models, the proportion of total variance in cognitive function at age 70 years accounted for by intake of these nutrients was less than 1%.
These results provide no evidence for a clinically significant beneficial association between intake of these antioxidants and B vitamins, and cognitive function at age 70 years.
Decline in cognitive function occurs as a normal part of the aging process, although the rate and extent of this decline vary widely between individuals. Two recent studies have reported an association between adherence to a Mediterranean diet and risk of cognitive decline and dementia (Feart et al., 2009; Scaremas et al., 2009), whereas other studies have supported the possibility that vegetable consumption could help to maintain cognitive ability after age 70 years (Kang et al., 2005; Morris et al., 2006). One suggested mechanism for this is protection against free-radical induced-damage due to antioxidants, such as carotenoids and vitamin E (Deschamps et al., 2001), although intervention studies with supplements of these nutrients have found little evidence for beneficial effects of these nutrients (Yaffe et al., 2004; Wolters et al., 2005; Kang et al., 2006; Maylor et al., 2006; Grodstein et al., 2007; Jack et al., 2008). A number of other studies have explored possible association between B vitamins, particularly folate, and cognitive decline, as B vitamins are known to influence homocysteine levels, which in turn are associated with vascular disease (Seshadri et al., 2002; Clarke, 2008). Vitamin B12 and folate also provide methyl groups required for formation of myelin and several neurotransmitters (Selhub, 2002). However, intervention studies with supplements of B vitamins have also found no clear evidence for beneficial effects on cognition (Eussen et al., 2006; McMahon et al., 2006; McNeill et al., 2007; Aisen et al., 2008), except in two studies in populations at increased risk of cardiovascular disease (Durga et al., 2007; Kang et al., 2008). Systematic reviews of the effect of supplements of B vitamins (Balk et al., 2007; Malouf and Grimley Evans, 2008) or multivitamins, multiminerals and fatty acids (Jia et al., 2008a) also concluded that there was no consistent evidence for a beneficial effect of supplementation.
The fact that observational studies show greater evidence of beneficial associations than randomised controlled trials raises the possibility of bias in the observational studies, which could occur for many reasons, such as publication bias or unmeasured confounding in the observational studies. Alternative possibilities are that supplementation in later life is ineffective as it is provided after the onset of irreversible pathological processes or because the nutrients in the supplements are provided in a form that is not biologically available or effective. Another consideration relevant to studies of cognitive function is ‘reverse causation’, that is, modification of the independent (predictor) variable by the dependent (outcome) variable (Lawlor et al., 2006). Cognitive ability could potentially, in this way, influence nutrient intake if, for example, higher cognitive ability during adulthood leads to greater compliance with dietary advice and hence more nutrient-rich diets. This is supported by a UK-wide study of adults aged 30 years that reported that children with higher mental ability scores consumed healthy foods more frequently and unhealthy foods less frequently than those with lower childhood mental ability (Batty et al., 2007).
The follow-up studies of the Scottish Mental Survey of 1947 provide a unique opportunity to assess the relationship between diet in later life and cognitive aging independently of the influence of previous cognitive ability, as they include measurements of intelligence quotient (IQ) in childhood as well as both diet and cognitive function in later life. The aim of the present study was to investigate the possible association between antioxidant and B vitamin intake, and cognitive function in one of these cohorts using the childhood IQ measurements to adjust for any influence of previous cognitive ability on dietary intake in later life.
Subjects and methods
The Lothian Birth Cohort 1936 includes 1091 men and women born in 1936 and living in and around Edinburgh at about age 70 years. On 4 June 1947, almost all children in Scotland aged 11 years had cognitive ability (from the Moray House Test (MHT), a validated IQ-type test), measured as part of the Scottish Mental Survey of 1947 (Deary et al., 2009), an almost complete population assessment of people born in 1936. Participants in Edinburgh and its immediate surrounding area (Lothian) were traced in later life and invited to take part in a longitudinal study of cognitive aging, known as the Lothian Birth Cohort 1936. A profile of the cohort that describes the tracing, recruitment and testing of the participants has been made available elsewhere (Deary et al., 2007). The participants were invited to attend for detailed cognitive, medical and other testing between 2004 and 2007 at a mean age of about 70 years. Social class was derived using the participants' highest reported occupation (for women, their spouse's occupation was used if higher than their own) using the UK Registrar General's Classification of Occupations, 1980 (Office of Population Census and Surveys, 1980). The number of years in full-time education, past medical history, medication use and smoking habits (current, ex- or never smoker) were assessed by interviewer-administered questions during the clinic visit. Presence or absence of the APOE ɛ4 allele was assessed at the Wellcome Trust Clinical Research Facility Genetics Core at the Western General Hospital in Edinburgh. Ethical approval was gained from the Multi-Centre Research Ethics Committee for Scotland and the Lothian Research Ethics Committee, and all participants gave written informed consent to participate.
When the subjects attended for neuropsychological testing, they were given a food-frequency questionnaire (FFQ) with verbal and written instructions for completion at home. Version 7.0 was developed for this study as a modified version of the Scottish Collaborative Group food-frequency questionnaire version 6.4 used in younger adults (see http://www.foodfrequency.org.uk). This version lists 168 foods or drinks, each with an appropriate household measure, such as ‘one slice’ or ‘two tablespoons’ for estimation of portion size. Participants are asked to select one of nine possible responses (ranging from ‘rarely or never’ to ‘seven or more measures per day’) to describe the typical amount and frequency of each food consumed in the past 2–3 months. A colour photograph illustrating a range of household measures for selected foods is provided on the cover of the FFQ, along with detailed written instructions and an example for completion. Information on dietary supplements is also requested. FFQs were returned by mail and checked on return for completeness: participants with missing or ambiguous responses were contacted by letters requesting the missing information.
Full details of the cognitive tests carried out at age about 70 years have been provided elsewhere (Deary et al., 2007). Briefly, these included the MHT (verbal reasoning), the Mini-Mental State Examination (MMSE; widely used as a brief screening test for dementia), the National Adult Reading Test (NART; which estimates peak prior intelligence) and Verbal Fluency (executive function). Participants also took six subtests from Wechsler Adult Intelligence Scale-IIIUK (WAIS): matrix reasoning, block design, letter-number sequencing, backward digit span, symbol search and digit symbol coding. Using data reduction (see below), these six tests were combined to provide an assessment of general intelligence. Participants also took subtests from the Wechsler Memory Scale-IIIUK: logical memory, spatial span, verbal paired associates, letter-number sequencing and backwards digit span. Again, using data reduction techniques, these were combined to form a composite memory score. Participants also took simple and choice reaction time tests, and a visual processing speed test called inspection time. These three tests were combined with the symbol search and digit symbol-coding tests from the WAIS IIIUK to provide a processing speed score.
In line with standard operating procedures for the FFQ, questionnaires with 10 or more missing values and those with the highest and lowest energy intakes (those <2.5th or >95th centile of intake) were excluded from the analysis. Intake of nutrients was adjusted for energy using the residual method (Willett, 1998) and the energy-adjusted nutrient intakes (residual plus intake at the mean energy intake) were calculated. No participants were known to have a diagnosis of dementia, and no exclusion was made on the basis of low cognitive test scores.
MHT scores at age 11 and 70 years were corrected for exact age (in days) at the time of testing and converted to standard IQ-type scores with a mean value of 100 and s.d. of 15. A total of 46 participants did not have data for IQ at age 11 years.
Principal component analysis of the cognitive test scores from the tests described above was carried out. The first unrotated principal components in the analyses for each of the three groups listed above were termed ‘general intelligence factor’, ‘memory factor’ and ‘speed factor’, respectively. In each case, a strong first component was found and extracted, on which all of the relevant tests had high loadings.
Analysis was restricted to those antioxidants (β-carotene and vitamin C) and B vitamins (thiamine, riboflavin, vitamin B12 and folate) for which estimates of intake derived from the same FFQ and a 4-day-weighed diet diary in a separate validation study in 83 men and women with mean age 70 years had Spearman rank correlation coefficients greater than 0.30 (Jia et al., 2008b). The difference between IQ at age 11 years in those taking and those not taking supplements was assessed with unpaired t-tests. The association between IQ at age 11 years and nutrient intake at age 70 years was assessed by linear regression. Associations between nutrient intake and cognitive test scores at age 70 years were assessed using multiple linear regression, with cognitive function at age 70 years as the dependent variable and energy-adjusted nutrient intake as the independent variable in three regression models. The first model included the energy-adjusted nutrient intake plus sex and exact age at testing at about 70 years. In the second model, IQ at age 11 years was added to adjust for any influence of early-life cognitive ability on nutrient intake; in the third model, social class, years of education, smoking, statin use and presence or absence of APOE ɛ4 allele were also included. The proportion of variance in cognitive function at age 70 years, which was explained by nutrient intake, was assessed by the η2 or R2 for change in model 3. All models were fitted using both nutrient intake from food alone and total nutrient intake from food plus supplements, with all analyses carried out in SPSS v.16.0 (SPSS Inc., Chicago, IL, USA).
A total of 993 participants returned FFQs of which 26 were blank, 39 had 10 or more missing values and 46 were excluded as having very high- or very low-energy intakes, leaving 882 participants (425 males and 457 females) for the analysis. As shown in Table 1, their average number of years of full-time education was 10.8 (s.d., 1.2) years. In all, 18.5% were classed as social class 1 (professional and managerial occupations) and 38.8% as social class 2; only 19% of the participants were classed as having had manual occupations (social classes 3M, 4 or 5). In all, 12.1% were current smokers and 43.4% were ex-smokers. The average score in the MMSE was 28.9 (s.d., 1.4) out of a maximum of 30. Those included in this analysis were more likely to be from the higher socioeconomic groups and to have higher childhood IQ and higher cognitive test scores in later life than those who were not included, although the magnitude of the differences was generally small.
Intake of nutrient from foods and supplements
The intake of antioxidants and B vitamins reported by participants is shown in Table 2, along with the intake from supplements. The absolute values for nutrient intake from food was not compared with recommended intakes, as overestimation of intake of many nutrients by the FFQ relative to a 4-day-weighed diet diary was noted in the validation study (Jia et al., 2008b). In all, 49% of the participants took dietary supplements, with the proportions being very similar in men (47%) and women 49%; 34% of the participants took supplements containing retinol and 24% took supplements containing vitamin E. For all other nutrients the proportion taking supplements was under 10%.
Association between nutrient intake and cognitive function at age 70 years
Table 3 shows the results of the regression analysis for intake of nutrients from foods. Vitamin C intake was positively associated with performance on the NART and verbal fluency tests, but the associations were attenuated somewhat when IQ at age 11 years was introduced to the models and attenuated further when other confounding variables were included. For β-carotene intake, there was no significant association between intake and cognitive test results in the initial models, although for the NART, the adjustment for IQ at age 11 years in model 2 resulted in a similar regression coefficient but a decreased s.e., making this now a significant positive association. Adjusting for other covariates in model 3 reduced the regression coefficient, and the association between β-carotene and NART was no longer significant. There were negative associations between processing speed and intake of vitamin B12 that were attenuated but not abolished by adjustment for covariates. All other associations seen in the first model were no longer significant in models 2 and 3. No significant associations between intake of thiamine, vitamin B6 or folate from foods and any of the test scores were seen in any of the models.
The analysis was repeated using total nutrient intake from foods plus supplements, but in general there were fewer significant associations in these analyses (data not shown). The associations between vitamin C intake and NART and verbal fluency were no longer significant in the fully adjusted models, although the negative association between intake of vitamin B12 and processing speed remained significant (β=−0.070; P=0.029) in the fully adjusted model. There was also an association between total intake of β-carotene and NART in model 2, and between total intake of folate and NART and general intelligence in the initial model, but these were attenuated in models 2 and 3 and were not statistically significant in the fully adjusted models.
To assess the clinical significance of the associations demonstrated in Table 3, the proportion of variance in cognitive tests accounted for by nutrient intake for nutrients for which the association was significant at the 5% level in the fully adjusted models was calculated. The proportion of the total variance in the cognitive test scores explained by the nutrient intake in these models was below 0.5% for the positive associations between vitamin C intake and NART and verbal fluency, and below 1% for the negative associations between vitamin B12 intake and processing speed, and vitamin E intake and general intelligence.
Association between supplement use and cognitive test scores
Table 4 shows the IQ at age 11 years in relation to supplement use and nutrient intake at age 70 years. Participants who were taking supplements of vitamin C, thiamine, riboflavin, vitamin B6 and folate at age 70 years had higher IQ at age 11 years than other participants. There was a significant positive correlation between IQ at age 11 years and intake of vitamin C from diet or diet plus supplements at age 70 years. There was also a significant negative correlation between IQ at age 11 years and intake of riboflavin from diet, although this association was not significant when intake from supplements was included.
When these analyses were repeated with IQ at age 70 years, the same pattern was seen, although the differences between supplement takers and non-takers, and the association between IQ and intake were slightly less strong than for IQ at age 11 years. We also compared the scores on the other cognitive tests carried out at age 70 years between participants who took supplements of each of the nutrients and those who did not. There were no significant differences between supplement takers and non-takers for scores for the MMSE or speed, but scores on the NART were around 3 points higher among supplement takers for all nutrients, and for general intelligence, scores were higher for supplement takers for all the B vitamins. The memory factor scores were higher for takers of thiamine, riboflavin, vitamin B6 and folate, and verbal fluency scores were higher for those taking β-carotene or folate supplements than for the non-takers (all P<0.05).
In this study of a generally well-functioning older population there was little evidence for beneficial associations between dietary intake of β-carotene, vitamin C or any of the B vitamins and cognition in the fully adjusted models. In all the observed associations, the effect size in the final models was extremely small and probably too small to be of clinical significance.
The results provide some evidence for reverse causation in the associations between vitamin C intake and NART and verbal fluency, as there was attenuation of associations in the initial models when IQ at age 11 years was included. A substantial proportion of vitamin C intake is derived from fresh fruit and fruit juice, which have been heavily promoted for many years in healthy eating campaigns such as the ‘five a day’ campaign, so it is plausible that the uptake of this advice could have been greater in those with higher IQ in earlier adult life. A similar pattern was seen with folate intake from food plus supplements and general intelligence in the basic model, which could result from the fact that those with higher childhood IQ were more likely to take folate supplements, as shown in Table 4. Our findings are in line with those of a recent study of the influence of diet on cognition in later life that found that associations were attenuated not only by demographic and social variables but also by educational attainment (Akbaraly et al., 2009).
For nutrient intake from supplements alone, the scores for several of the tests at age 70 years, notably IQ, NART and general intelligence, were higher in those taking supplements, but the fact that the differences between supplement takers and non-takers in IQ at age 11 years were slightly greater adds further support for the possibility of reverse causation, that is, that higher lifetime trait levels of IQ predict supplement use rather than supplement use predicting IQ.
One limitation of studies of this type is that a large number of statistical tests are often carried out because of the variety of nutrients and cognitive tests studied, increasing the likelihood of type 1 errors. The associations that remained in the final models were all significant only at either the 5 or 1% level, so would have been abolished by a correction for multiple testing. There is also a possibility of type II errors due to measurement error in nutrient intake arising from the use of food-frequency questionnaires. We restricted our analyses to those nutrients with correlation coefficients above 0.30 (range 0.31–0.52; all P<0.01) in a similar population, which was similar to the range of median correlation coefficients of 0.37–0.51 reported for six nutrients in a review of published validation studies (Cade et al., 2004). Another limitation is that diet was measured at only one point in time, which may not reflect long-term intake, particularly for nutrient intake from supplements that may not be consumed for long periods of time. It is possible that the important time period for any effects of nutrient intake on cognitive ability occurs earlier in adult life or even in childhood: to investigate this possibility would require measurements of diet over the whole life course. It is also possible that nutrients from supplements act in different ways to nutrients from foods, because of differences in chemical form or interactions with other nutrient found in the foods but not supplements. Finally, we recognise that the participants with lower performance on the tests were underrepresented in this study as there were fewer participants from the manual occupational groups; also those who did not complete the FFQ to the required standard were excluded. This is a particular challenge for studies in this area, and strengthens the case for using measurements of nutrient levels in blood or other tissues wherever these are logistically feasible and are reflective of long-term as opposed to short-term dietary intake.
In this report, we have focused on the possibility of reverse causation because of the tendency, when conducting cross-sectional associations between nutrient intake and cognitive function in later life, to assume that the causal direction is from nutrient to cognition. However, we recognise that other explanations are also viable, such as there being unmeasured confounding by an additional variable, or set of variables, related to both nutrient intake and cognitive function. We also cannot rule out the possibility of effects of intake of nutrients not reported here or of associations between nutrient intake at age 70 years and cognitive function in later old age. These possibilities will be explored through further studies and longer-term follow-up of this cohort.
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The LBC1936 data were collected by a Research Into Ageing programme grant; this research continues as part of the Age UK-funded (formerly Help the Aged) Disconnected Mind project. We thank the Scottish Council for Research in Education for allowing access to the SMS1947. We thank the LBC1936 participants; for data collection, Michelle Taylor and Caroline Cameron; and LBC1936 Study Secretary, Paula Davies. This research was supported by a PhD studentship from the Institute of Applied Health Sciences, University of Aberdeen, awarded to Xueli Jia.
The authors declare no conflict of interest.
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McNeill, G., Jia, X., Whalley, L. et al. Antioxidant and B vitamin intake in relation to cognitive function in later life in the Lothian Birth Cohort 1936. Eur J Clin Nutr 65, 619–626 (2011). https://doi.org/10.1038/ejcn.2011.2
- vitamin B complex
- dietary supplements
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