In aging societies, concern for cognitive health is on the rise. In the absence of an effective treatment for Alzheimer’s disease (AD), identification of modifiable risk factors that could delay or prevent its symptomatic stage has become a public health research priority [1
]. Indeed, the symptomatic stage of AD, including the loss of cognitive function, represents a burden not only for those who suffer this disease, but also for their caregivers [3
]. Diet has been suggested to play a role in maintaining the integrity of cognitive function, but results from experimental and epidemiological studies based on a single nutrient approach are mixed [4
]. The combination of a set of nutrients or food groups into patterns may better capture the complexity of food intake [5
]. A recent literature review reported that the evidence of a beneficial effect on cognitive outcomes was more convincing for healthy dietary patterns than for single foods or nutrients [6
Dietary and nutrient patterns can be computed in two ways [4
]. A priori patterns are defined using diet indices that measure adherence to a specific diet, such as the Mediterranean diet, or to recommended dietary guidelines for a healthy diet [8
]. Limitations of the a priori patterns are that they are hypothesis-driven and do not account for the total food intake, but only for some components of the diet. In contrast, a posteriori patterns summarize the whole dietary intake into a few representative profiles using exploratory statistical methods. In a recent systematic review on dietary patterns and cognitive health in older adults including 37 studies, only 6 studies used a posteriori dietary patterns [9
]. In Australia, a prudent healthy a posteriori dietary pattern was not associated with cognition [10
]. In contrast, in Sweden, higher adherence to a prudent a posteriori healthy dietary pattern was associated with a lower global cognitive decline, whereas a higher adherence to a Western dietary pattern was associated with a greater global cognitive decline [11
]. Among the other selected studies using a posteriori dietary patterns included in the systematic review, a Western dietary pattern was consistently associated with lower cognitive health [9
Very limited research has examined a posteriori nutrient patterns in relation to cognitive function [12
]. Reflecting the global quality of dietary intake, nutrient patterns could be a more accurate means of comparison of dietary habits in different study settings—for example, in different cohorts of older adults [13
]. We previously conducted such a comparative study in two older populations. This study allowed us to identify and describe nutrient patterns in the 3C study (France) and the Québec Longitudinal Study on Nutrition and Successful Aging (NuAge, Québec, Canada) [13
]. Similar healthy and Western nutrient patterns were observed in both cohorts, whereas a third pattern appeared to reflect traditional cultural or geographical dietary habits specific to each population. The aim of the present study was to evaluate the relationship of these nutrient patterns to global cognitive function and decline in these two cohorts of older persons, over a period of up to five years.
In 3C, 1712 participants had complete dietary intake data and 1597 of them were evaluated for cognitive function at baseline; 73 were excluded because they had been diagnosed with dementia. Among the remaining participants, 1388 had at least two MMSE scores, including one at baseline, with a mean follow-up of 4.5 years (standard deviation (SD), 1.1). The mean age of the participants was 75.7 years (SD, 4.8) and 62.8% were women. Compared to the baseline cohort, participants excluded from the analytical sample (n = 209) were significantly older (mean age = 78.4 vs. 75.7 years, p for t-test < 0.001), had a lower level of education (29.5% were in the lowest level of education vs. 9.4%, p for χ2 < 0.001), and a lower global cognitive function (mean MMSE = 26.9 vs. 27.6, p for t-test < 0.001), but showed no difference in the distribution by sex.
Among the 1596 participants with complete dietary intake data in the NuAge study, 92 were excluded because of implausible reports [26
]. In this sample, 1439 participants had at least two 3MS scores, including one at baseline, agreed to be included in secondary analyses, leaving participants with a mean follow-up of 2.9 years (SD, 0.5). The mean age of the participants was 74.3 years (SD, 4.2) and 51.8% were women. Compared to the baseline cohort, participants excluded (n
= 157) were similar in terms of age, sex, and education, but showed slightly lower values of MMSE scores (mean MMSE = 28.0 vs. 28.3, p
-test = 0.02).
Baseline characteristics of both study samples are summarized in Table 1
. Compared to 3C participants, NuAge participants were younger and less educated, with fewer women. NuAge participants showed, on average, a slightly higher baseline MMSE score (28.3 vs. 27.6), BMI (27.8 vs. 26.1 kg/m2
), and energy intake (1928 vs. 1620 kcal/day), included fewer participants with self-reported hypertension (47.4 vs. 51.0%), and more participants living with a relative or an unrelated person (68.5 vs. 45.4%), or with a self-reported history of stroke (3.2 vs. 1.7%). No difference between the two study samples was observed for self-reported diabetes.
The first nutrient patterns identified in both cohorts were qualified as “healthy” (healthy-France and healthy-Quebec, Figure 1
), and were characterized by higher intakes in carbohydrates, dietary fiber, magnesium potassium, iron, carotene, and vitamins C, E and B6. Differences between the two cohorts were notable for two specific nutrients. Compared to the healthy-Quebec, the healthy-France pattern was negatively associated with vitamin D intake (factor loadings (FLs) of −0.19 vs. 0.22), and positively associated with folate intake (FLs of 0.69 vs. 0.02). The second nutrient pattern identified in both cohorts was qualified as a Western pattern, characterized by higher intakes of proteins, lipids (saturated, monounsaturated, and n
-3 and n
-6 polyunsaturated fatty acids (PUFAs)), calcium, phosphorus, magnesium, vitamin D, and folates, but only for the Western-Quebec pattern (FLs for folates of 0.44 vs. 0.08). The third more traditional nutrient pattern in both cohorts corresponded to diets rich in vitamins A and B12. Higher intake of zinc was observed in the traditional-Quebec pattern (FLs of 0.50 vs. 0.07), whereas higher intake of folates was noted in the traditional-South West of France pattern (FLs of 0.45 vs. −0.01). Average nutrient intake across quartiles of the factor scores are presented in Table 2
The healthy-France pattern was significantly associated with higher cognitive scores at baseline (i.e., fewer number of errors in the MMSE) in the simplest and the fully-adjusted models (Table 3
). For example, in model 2, each 1-point increase of the factor score was associated with −0.053 (95% CI −0.089, −0.016) lower square root errors in the MMSE at baseline. In contrast, the Western-France pattern was significantly associated with lower cognitive scores (Table 3
). In model 2, each 1-point increase of the factor score was associated with +0.054 (95% CI 0.006, 0.102) higher square root errors in the MMSE at baseline. No association was observed between the traditional-South West of France pattern and cognitive function. Moreover, there was no association between any nutrient pattern and the slope of cognitive decline.
In NuAge, no association was observed between any of the three nutrient patterns and cognitive function at baseline or the slope of cognitive decline (all p
values >0.10, Table 2
This study examined the association of nutrient patterns with cognitive function and decline in two cohorts of older persons living in different environments. In the 3C cohort, the healthy pattern was associated with a slightly higher cognitive performance at baseline, whereas the Western pattern was associated with a slightly lower cognitive performance. We found no relation of nutrient patterns with cognitive function in the NuAge cohort, and no association with cognitive decline was observed in both cohorts. Our study proposes a methodology to harmonize and use dietary and cognitive data from two countries, using nutrient patterns. Another strength of this study is that it is based on dietary data collected by trained dietitians [14
Only one study computed nutrient patterns to estimate their associations with cognitive function in a multiethnic community-based population of 330 older participants residing in Northern Manhattan, NY [12
]. This cross-sectional study from the Washington Heights-Inwood Columbia Aging Project (WHICAP) identified an inflammatory nutrient pattern characterized by low intakes of calcium, vitamins D, E, A, B1, B2, B3, B5, B6, folate, n
-3 PUFAs, and high intake of cholesterol. This pattern was associated with lower cognitive measures of brain health. In our study, healthy nutrient patterns were characterized by lower intakes of vitamin D for the healthy-France pattern, lower intake of vitamin A for both patterns, and lower intake of n
-3 PUFAs, especially for the healthy-France pattern. Hence, a posteriori nutrient patterns, qualified as “healthy” based on the distribution of specific nutrients considered as “healthy” based on previous knowledge, may not necessarily be optimal for cognitive health. Additionally, the lower intake of folates found in the healthy pattern in NuAge could partially explain why it was not associated with better cognitive function, contrarily to what found in the 3C sample. Indeed, in the 3C study, a higher intake of folates was strongly associated with a lower risk of dementia [27
Another study based on the WHICAP cohort among 2148 older participants identified a pattern that was significantly associated with lower risk of AD after a mean follow-up of 3.9 years [28
]. This pattern was reflecting higher intakes of vitamin E and folates with higher consumption of fish, vegetables, and fruits, and lower intakes of high-fat dairy products and red meat. The healthy-France pattern in our study shares similarities with the dietary pattern identified in the WHICAP (e.g., higher intake of fruits, vegetables, and folates), although it does not include all foods and nutrients that could be protective against cognitive decline (e.g., fish and seafood). This may explain why the positive association between the healthy-France pattern and cognitive performance is small.
The lack of literature on nutrient patterns and cognitive function limits the comparison of our results with other studies. However, some of our findings could be compared to previous studies using dietary patterns. Indeed, nutrient patterns identified in our study were previously described in relation with food intake and overall diet quality [13
]. In fact, we identified an opposition between a posteriori healthy and Western nutrient patterns, similar to a posteriori dietary patterns that were previously identified in many epidemiological studies [13
]. A similar opposition between two dietary patterns was observed in a cross-sectional analysis based on a sample of middle-aged persons from the Whitehall II study, focusing on cognitive health [29
]. These two dietary patterns were also inversely associated with cognitive performance [29
]. Indeed, a “whole food” pattern, which was characterized by higher intakes of vegetables and fruits, was reported to be protective for two cognitive domains: Vocabulary and semantic fluency. In contrast, the “processed food” pattern characterized by higher intakes of high-fat and processed foods was associated with higher risk of cognitive impairment for vocabulary and phonemic fluency [29
]. Similarly, we observed in the 3C cohort a healthy a priori nutrient pattern associated with better cognitive function at baseline. The plant-based food intakes associated with this healthy a priori nutrient pattern in 3C [13
] were similar to those in the whole food dietary pattern from the Whitehall study. This was confirming that a healthy dietary pattern, positively associated with nutrients that were correlated with higher intakes of fruits and vegetables, can benefit cognition.
In our comparative study, a slightly higher baseline cognitive function was associated with a healthy diet, characterized by nutrients and other substances mostly found in plant-based food such as carotene, vitamin E, and dietary fiber, but also folates in 3C. This result is consistent with previous studies suggesting that healthy or prudent diets rich in fruits and vegetables could be protective for cognitive function and risk of dementia [4
]. This diet specific to 3C may be protective for cognitive function because of cumulative and synergic effect of nutrients, notably antioxidants, provided by these specific foods. Unexpectedly, in both cohorts, n
-3 PUFAs, generally recognized as beneficial for cognition [31
], were not associated with the healthy patterns, but rather with the Western patterns.
Heterogeneity in the results on cognition between the two cohorts may be explained by general health status and different selection criteria at baseline. In NuAge, mean MMSE at baseline was slightly higher than in 3C and the mean decline over time was greater, suggesting that cognitive status baseline in this population was higher than in the 3C population. The proportion of people with a self-reported history of stroke was higher in NuAge, but the proportion of self-reported hypertension was lower. Previous studies on the associations between cognitive performance at midlife have indicated that vascular factors, such as hypertension and diabetes mellitus, could increase the risk of dementia and cognitive decline in later life [33
]. Another explanation for the heterogeneity in the results could be related to the diet itself. The adherence to the healthy-France pattern may reflect a better overall nutritional quality for cognitive function. We previously reported that the healthy-France pattern had a stronger positive association with a dietary index estimating the adherence to dietary guidelines, than the healthy-Québec pattern [12
]. Finally, follow-up time in NuAge was shorter compared to the 3C cohort, thus reducing the possibility of cognitive decline to occur.
This work has some limitations. First, we were not able to conduct analyses on cognitive domains, unlike other previous studies (e.g., vocabulary or semantic fluency [28
]), because different tests were used for these specific domains in the two cohorts and could not be compared. The MMSE is known to show a lack of sensitivity in healthy populations, which translates to ceiling effects, a limitation to detect cognitive changes especially in healthy populations such as those investigated here [34
]. However, this test was used in previous studies based on the 3C cohort [35
], as well as other cohorts [4
], assessing cognitive changes. Moreover, this test is convenient to use in large-scale epidemiological cohort studies and is internationally validated. This is why it was used for the purpose of data harmonization between the two cohorts, as it was the only common test between them. With regard to potential confounders, apolipoprotein allele e4 (Apoe4) status is a major genetic risk factor for AD [36
]. Apoe4 status was available only in the 3C sample at the time of analysis, but was not associated with cognitive function at baseline, or with cognitive decline (data not shown). Finally, serum markers or parameters that were previously reported to be associated with cognitive function, such as HDL-cholesterol [37
], other plasma fatty acids [32
], or uric acid [38
], were not taken into consideration in this comparative study.