Abstract

Introduction

Dietary patterns have been associated with late life cognitive function. High intakes of fruit, vegetables, a Mediterranean style diet, and several micronutrients (vitamins B, C, E) have been reported to have beneficial effects [1–4]. A high caloric intake has also been associated with an increased risk of cognitive impairment [5], and caloric restriction with reduced amyloid-β deposition [6–8]. The primary determinants of total caloric intake and the largest component of any diet consist of macronutrients: carbohydrates, fat, and protein. Yet, the role of macronutrient intake relative to total caloric intake on cognitive function in older persons has received little attention. Given the associations of macronutrients with glucose metabolism, neuronal integrity, and neuronal function [9–11], relative intake of macronutrients may have an etiologic role or may be a marker for late life cognitive impairment. We investigated the associations of percent of daily energy (calories) derived from carbohydrate, fat, and protein with risk of mild cognitive impairment (MCI) in a population-based cohort of elderly persons.

METHODS

Study Participants

The details of the study design have been published previously [12]. Briefly, we identified all Olmsted County, MN, residents aged 70–89 years on October 1, 2004, using the medical records-linkage system of the Rochester Epidemiology Project [13, 14]. From among the 9,953 subjects that were enumerated, 4,398 were eligible: 2,719 agreed to participate (61.8% response) by telephone (n=669) or via a face-to-face evaluation (in-person evaluation; n=2,050). We mailed the food frequency questionnaire to eligible in-person participants between the first and second evaluations; 681 who were not included at baseline were similar to 1,233 non-demented participants who were included regarding sex, BMI, and APOE ε4 allele status, but were older, had a higher frequency of hypertension, coronary heart disease, stroke, type 2 diabetes, depressive symptoms, were less likely to be married, and had lower education [2, 15]. Of the 1,233, 161subjects had prevalent MCI at baseline, 26 had died, and 109 could not be contacted; 937 are included in this study (FIGURE 1).

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Figure 1

Study Flow Chart. Excluded data: 268 had ≥10 missing responses on frequency of food consumption; 56 had extreme caloric intake (kcal/day: <800 in men, <600 in women or >6000 in men, >5000 in women).

RESULTS

TABLE 1 describes the characteristics of the 937 subjects who were cognitively normal at baseline. Median age was 79.5 years, 51% were male, 40% had ≤ 12 years of education, and 65% were married. A total of 200 subjects developed incident MCI or dementia over a median follow-up of 3.7 years (interquartile range, 2.5–3.9; 2871 person-years).

TABLE 2 describes the demographic, clinical characteristics and dietary intakes of subjects across quartiles of % carbohydrate at baseline. Subjects in the highest % carbohydrate quartile had a higher frequency of women and incident MCI or dementia compared to the lowest quartile; they were also less likely to be married and had a lower BMI. Of note, there were no significant trends with key known risk factors for MCI or dementia: frequency of APOE ε4 allele, type 2 diabetes, stroke, depressive symptoms, moderate exercise, and years of education. Intake (as g/day or % of energy) of sugar, other carbohydrates (non-sugar, non-fiber) and fiber increased across increasing % carbohydrate quartiles, but protein, fat, and alcohol decreased. Total fruit intake increased across % carbohydrate quartiles however, vegetable intake was not different across quartiles.

TABLE 3 describes the association of % macronutrients with risk of MCI or dementia. The risk was elevated nearly 2-fold for the highest % carbohydrate quartile. In contrast, the risk was reduced at higher % fat and % protein quartiles. There was a trend toward increased risk with increasing % sugar. The significant trends persisted in the fully adjusted models that included adjusting for a history of stroke. The results did not change substantially even after exclusion of subjects with a history of stroke: HR (95% CI) for upper compared to lowest quartile were: 1.53 ([0.99–2.36]; p for trend = 0.08) for % carbohydrate; 0.66 ([0.42–1.03]; p for trend = 0.02) for % fat; 1.08 ([0.72–1.62]; p for trend = 0.15) for % protein; and 1.30 ([0.84–2.00]; p for trend = 0.14) for % sugar. In a multivariable model including carbohydrate, fat, and protein in the same model, carbohydrate remained significantly associated with MCI or dementia (upper vs. lowest quintile: 3.68 [1.61–8.38]; p for trend = 0.01); fat and protein no longer showed a significant trend (Table 4). There were no significant interactions of % carbohydrate, % fat, or % protein with age, sex, APOE ε4, or BMI.

Table 5 shows associations of intake of other carbohydrate, fiber, and fat components with MCI or dementia. The risk increased with increasing % other carbohydrate and fiber intake, and decreased with increasing % PUFA and % saturated fat, but the tests for trend were not significant.

DISCUSSION

In our population-based cohort of elderly persons, high % carbohydrate intake was associated with an increased risk of MCI. In contrast, high % fat and high % protein intake were associated with a reduced risk of MCI or dementia. These findings suggest that dietary patterns consisting of a high intake of energy derived from carbohydrates and a relatively low intake from fat and protein may have adverse implications for development of MCI. In contrast, an optimal balance in the proportions of daily calories derived from carbohydrate, fat, and protein, may maintain neuronal integrity and optimal cognitive function in the elderly.

A possible explanation for the association of carbohydrate intake with MCI is that elderly subjects with a high % carbohydrate intake may consume more foods with a high glycemic index. Indeed, subjects in our study with the highest % carbohydrate intake also had the highest intake of sugars and fruit (which are high in sugar content) but not vegetables, and the lowest intake of fat and protein. Glucose is a major source of energy for brain metabolism, and glucose administration typically enhances cognitive performance [33]. However, in elderly persons, a dietary pattern high in carbohydrate intake and in simple sugars may disrupt glucose and insulin metabolism [8, 34–38]. High insulin levels may be detrimental to cognitive function [38]. Persistence of the association of high % carbohydrate with MCI risk after simultaneous adjustment for fat and protein suggests that high intake of carbohydrate may be a key promoter of the increased risk, and relative intakes of protein and fat may also play a role.

High carbohydrate and sugar intake may adversely affect cognition through several mechanisms. Hyperglycemia and diabetes may contribute to increased formation of advanced glycation endproducts (AGE), upregulation of the soluble receptors for AGEs, and may generate oxidative stress which in turn, enhances AGE formation [39–41]. AGEs and oxidative stress have also been associated with greater cognitive decline and with AD through effects on amyloid and tau metabolism [39, 41].

The increased risk of MCI with lower intake of fats and proteins may involve non-energy related pathways [33]. Fat and protein intake may be required for the integrity of neuronal membranes and fats for the integrity of the myelin sheaths in the brain. Although we did not observe significant trends with increasing quartiles of % MUFA and % PUFA intake, the hazard ratios were reduced for higher intake. These unsaturated fatty acids, and in particular essential PUFAs, may maintain cognitive function through effects on structural, functional, and synaptic integrity of neurons [42–44], reduced amyloid-β levels [42], improved insulin sensitivity and glucose metabolism [45–47], decreased cardiovascular disease [48] and stroke [49]. High intake of fish, an important source of omega 3 PUFA, has been associated with a reduced risk of cognitive impairment in elderly persons [50] since fish is also an important source of vitamin D, the reduced risk of cognitive impairment in individuals with high fish intake may be due to the combined effects of omega 3 PUFA and vitamin D [51]. Low intake of protein may be associated with low intake of essential proteins that are required for synthesis of neurotransmitters in the brain. For example, tryptophan crosses the blood brain barrier and is a precursor for brain serotonin, an important neurotransmitter. Murine studies suggest that tryptophan transport across the blood brain barrier decreases with ageing [52]. If this is true in humans, reduced intake of proteins in the elderly may adversely impact neuronal function.

Other factors besides macronutrient intake may contribute to our findings. Subjects with the highest % carbohydrate intake had the lowest total caloric intake which is consistent with the low % fat intake, but is also consistent with low BMI in these subjects, and with previously reported decreased weight loss in the years preceding onset of dementia in elderly persons [53–55]. In addition, moderate alcohol intake has been reported to reduce risk of cognitive impairment [3] and may play a role on MCI risk in our cohort. The dietary patterns observed may be causal or alternately, may be a marker for preclinical disease and risk of cognitive impairment or dementia in elderly persons. These associations need to be examined in other longitudinal studies.

Our findings are consistent with findings from several studies. In one study, subjects with AD and vascular dementia had a high predilection for sugar and sweet foods [56]. Other investigators suggest that reducing caloric intake through carbohydrate restriction may reduce risk of cognitive impairment, AD [5, 57–62], and amyloid-β deposition and pathology [63]. In a study among non-diabetics, the highest cognitive performance was observed in subjects with the best glucose regulation [37, 64]. In the National Health and Nutrition Examination Survey, a dietary pattern with a high % fat was associated with better processing speed, learning, and memory; in contrast high % carbohydrate was associated with poor processing speed [65]. Other studies suggest that phosphatidylcholine, an essential PUFA, improved memory, learning, concentration, and the ability to memorize words in elderly subjects with memory decline [33], and that protein may enhance cognitive performance [65, 66] by improving glucose homeostasis [67]. Decreasing total calories and BMI with increasing % carbohydrate quartile may be markers for imminent cognitive impairment, and are consistent with decreasing weight prior to dementia onset in elderly persons [55].

Potential limitations of our findings include recall bias in reporting of dietary nutrients. This effect may be small in part because subjects were cognitively normal at the time the food frequency questionnaire was completed, and because our previously reported cross-sectional findings on diet and cognition [2, 15] are consistent with several other studies [1, 68–72]. Although the validity of food frequency questionnaires has been questioned, this concern may have greater bearing on studies regarding cancer risk [73]. Other experts suggest that use of the food frequency questionnaire is valid for ranking subjects according to food and nutrient intake as in the present study [74–76]. We could not estimate glycemic index (or glycemic load) since this index is impacted by foods eaten together at a meal; the food frequency questionnaire only assessed usual eating habits in the previous 12 months. There is a potential for non-participation bias, but the higher frequency of vascular risk factors in non-participants suggests that the hazard ratios may be are biased toward a null association. The potential impact of reverse causality is unclear, but it is not possible to determine whether preclinical changes of AD, cerebrovascular disease, or other neurodegenerative pathology, contributed to dietary patterns at baseline. Finally, study participants were primarily of northern European ancestry and any generalizability to other ethnicities should be performed with caution.

Several strengths of our study should be noted. The study was specifically designed to investigate risk factors for MCI. The population-based design reduced selection bias and enhanced the external generalizability of the findings to the population [14]. The comprehensive evaluation of participants for MCI or dementia by 3 independent evaluators increased the internal validity of the findings. We categorized subjects on their usual macronutrient intake using data from a previously validated food frequency questionnaire [23], and assessed nutrient intakes using an established nutrition database. The prospective study design allowed us to estimate causal associations while taking into account potential confounding factors.

ACKNOWLEDGEMENTS

Footnotes

REFERENCES