METHODS

This study was part of the PAQUID research programme, a prospective cohort study of normal and pathological cerebral aging, composed of a randomly selected sample of non-institutionalised individuals aged 65 years and over living in the south west of France. The methodology of this study has already been extensively described.¹²

The survey started in 1988, following 2792 subjects who were interviewed at home by trained psychologists at one, three, five, eight, and 10 years after the baseline visit. Our sample consisted of a subgroup of 626 subjects who volunteered to give a blood sample for apoE phenotyping at the first year follow up interview.

Cognitive performance was evaluated at each visit using a comprehensive battery of neuropsychological tests. After the psychometric evaluation, the psychologists systematically completed an evaluation of the DSMIII-R criteria for dementia,¹³ and subjects who met these criteria were then seen by a senior neurologist who confirmed the diagnosis of dementia.

The results presented here are based on the MMSE, which is a global mental function screening test.⁸ For the analyses we used longitudinal MMSE score data, obtained from the one, three, five, and eight year follow up interviews; the 10 year follow up interview was only used to obtain DSMIII-R data and to establish diagnosis of dementia.

Our objective was to investigate the evolution of cognitive performance in non-demented elderly individuals. Thus in the initial analysis we excluded those subjects with a confirmed diagnosis of dementia at baseline (n = 22), and also at the first year follow up (n = 4), as the blood sample was collected at that time. In a second analysis, we then excluded any subjects in whom a diagnosis of dementia had been made between the three year and the 10 year follow up visits (n = 53). In this way, we were certain that no subjects included in the second analysis had been diagnosed as having dementia in the two year period after the last MMSE score. The sample for the first analysis comprised 600 subjects and for the second analysis, 547 subjects.

Serum samples were obtained during the first year follow up (1989–90) and frozen until determination of apoE phenotype. Homozygotes and heterozygotes for apoE ε4 were combined and designated as ε4 carriers; other phenotypes were designated as ε4 non-carriers.

Statistical analyses

To study the variables associated with cognitive performance, we used a random effects linear regression model, which takes into account the lack of independence of a subject's measurements across time.¹⁴ This intrasubject correlation was modelled by two independent subject specific random effects: a random intercept and a random slope. Parameters were estimated by the maximum likelihood method using SAS statistical software (SAS/STAT computer program, SAS Institute Inc, Cary, North Carolina, 1999).

RESULTS

At baseline, the mean (SEM) age of the 600 participants was 73.7 (0.26) years, range 65 to 94. No difference was found between mean ages for ε4 carriers (73.1 (0.53) years, range 65 to 92) and ε4 non-carriers (73.9 (0.29) years, range 65 to 94) (t = 1.37, p = 0.17). There was no difference in sex between ε4 carriers and non-carriers (men/women: carriers, 57/73; non-carriers, 203/267).

The most frequent apoE phenotypes for the group were ε3/ε3 (n = 403), ε3/ε4 (n = 114), and ε2/ε3 (n = 60). Individuals homozygous for the ε4 or ε2 alleles, or with the ε2/ε4 phenotype, were poorly represented (n = 5, 7, and 11, respectively). In our sample, approximately one in five subjects was an ε4 carrier (21.7%, 130/600), and this proportion remained stable throughout the eight year follow up.

Level of education differed among ε4 carriers and non-carriers: educational attainment (diploma from primary school (up to age 11) or higher) was much higher among ε4 non-carriers (77%, 362/470) than among ε4 carriers (58%, 76/130) (odds ratio, 2.38; 95% confidence interval, 1.55 to 3.66; p < 0.0001).

All subjects completed the MMSE at least once; 574 (95.7%), 508 (84.7%), 457 (76.2%), and 364 (60.7%) completed the MMSE at the one, three, five, and eight year follow up visits, respectively. Four MMSE measures were available in 332 participants (55.3%), and 116 (19.3%) died during follow up. There were no differences in the response patterns between ε4 carriers and ε4 non-carriers (data not shown).

Results of the linear regressions with random effects for the square root of the number of errors are presented in table 1. Three basic variables were included in all models: age, sex, and time (that is, the number of years after follow up, measuring the evolution of cognitive performance over time).

In table 1, model 1A shows that age at baseline was highly associated with the mean MMSE score (p = 0.0001)—that is, younger subjects obtained better scores at that time (fig 1A). Variables time and age by time were also significant (p = 0.01 and p = 0.006, respectively), indicating that the evolution of MMSE scores differed depending on age: the scores remained stable in the younger subjects, but in older subjects they decreased over time (fig 1A). At baseline, ε4 carriers had a significantly lower MMSE score than ε4 non-carriers (p = 0.02). A lower educational level was associated with lower performance at baseline (p < 0.0001) (model 2A), but the evolution of cognitive performance was the same over time for all levels of education (education by time, p = 0.14). This effect was the same for ε4 carriers and ε4 non-carriers (apoE ε4 by education, p = 0.26). In contrast, the effect of the presence of an ε4 allele on baseline cognitive performance disappeared when adjusted for education (fig 1B)

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Figure 1

Graphical representation of the evolution of mean mini-mental state examination scores estimated by linear mixed effects models. Scores estimated are shown for all subjects (A, C) and after exclusion of those diagnosed as demented during the 10 year follow up (B, D), before (A, B) and after (C, D) adjustment for education. ApoE, apolipoprotein E; MMSE, mini-mental state examination.

When subjects who were diagnosed as demented during the 10 year follow up were excluded from the analyses, the effect of age at baseline upon cognitive performance was still significant (p < 0.0001) (table 1, model 1B) (fig 1B). However, the evolution of cognitive performance over time, denoted as time, became non-significant, meaning that the level of performance remained stable over time. No differential evolution as a function of age at baseline was observed (age by time, p = 0.87). The apoE ε4 allele was still important for cognitive performance, as ε4 carriers had a decreased score at baseline compared with non-carriers (model 1B) (p = 0.018) (fig 1B). Education continued to have a significant effect (p < 0.0001), and the influence of the presence of an ε4 allele again disappeared when adjusted for education (model 2B) (fig 1D). In contrast to model 1B, women showed a better performance at baseline than men (p = 0.03) when cognitive performance was adjusted for education, but the evolution over time was the same for both sexes (sex by time, p = 0.16).

DISCUSSION

Four major conclusions can be drawn from this study. First, the apoE phenotype had a significant effect on cognitive performance of elderly subjects at the baseline level, in that apoE ε4 carriers had a decreased MMSE score compared with non-carriers. Second, the difference between ε4 carriers and non-carriers disappeared after adjustment for education. Third, no global cognitive decline was observed over time in elderly subjects, once those with dementia or impending dementia were excluded from the analysis; the slight decline in cognitive function observed when the analysis included the entire cohort reflected the decline in individuals who were in the early stages of dementia. Finally, the level of global cognitive performance remained stable throughout follow up and was independent of the apoE phenotype, suggesting that this phenotype did not influence cognitive performance over time.

The evoluation of MMSE scores depended on the subject's age at entry to the study and on educational level. Interestingly, the effect of apoE ε4 disappeared after taking into account the level of education. In our study, the frequency of subjects with a primary school diploma differed between ε4 carriers and ε4 non-carriers, suggesting that apoE phenotype might influence cognition through the entire life span.

It is possible that certain genetic characteristics might influence both the level of education attained and any later susceptibility to cognitive change. apoE may influence cognitive function through direct changes in brain morphology in ε4 carriers,¹⁵ and it appears that the biological influence of the apoE genotype may be assessed by differences in cognitive function in clinically healthy people. Education could also exert a direct effect on cognition. Animal studies have shown that environmental experiences can modify brain anatomy and function.¹⁶ Swaab suggested that the activation of nerve cells in people who have completed higher education protects these cells from degeneration, thereby delaying the pathological process that lead to cognitive decline.¹⁷ Along the same lines, it was hypothesised that higher education could also lead to an increased brain reserve capacity, so that other neurones can take over the tasks of ones that have died.¹⁸

To some degree at least education may be a surrogate for other factors that could influence cognition. The educational classification used in our study was the attainment of a primary school diploma. This is based on linguistic and conceptual abilities, judged by performance in tests of reading, writing, and mathematical problem solving. The number of years of education were not not taken into account, only intellectual ability—subjects who fail the examination may subsequently be involved in less demanding cognitive tasks, leading to reduced brain stimulation and thus to a more limited cognitive reserve.

Our data suggest that apoE could play a direct role in cognition since early life by influencing the level of education that could be attained. In support of our data, two recent studies have shown a significant difference in the educational background between apoE ε4 carriers and non-carriers, the former staying in school longer than the latter.^{19, 20} The level of education could have a secondary influence on the brain reserve capacity.

A possible limitation of our study is whether the subcohort was a truly representative sample, as it was composed of volunteers from the French PAQUID cohort. However, the frequency of the individual apoE phenotypes observed in our sample was comparable with other published reports.⁶ The most important form of bias linked to longitudinal studies is cohort attrition. Dropout would have altered the results if it had affected the proportion of ε4 carriers, but in our study the proportion of ε4 carriers at baseline was not significantly different from the proportion at the end of the study.

Our results appeared to provide evidence for an influence of apoE on cognition, with decreased performance in ε4 carriers compared with ε4 non-carriers before the expression of dementing illness. However, this influence disappeared when adjusted for education. The evolution of cognitive performance in non-demented elderly people seems independent of the apoE phenotype. It remains to be established, however, whether the presence of the ε4 allele in combination with an inadequate cognitive performance is conclusive evidence for preclinical Alzheimer's disease. These aspects will be of particular interest for follow up studies.