This cross-sectional study describes the nutrient biomarker patterns identified in plasma from a sample of elders at risk for dementia. This objective and multivariate approach yielded 3 distinct NBPs significant to both cognitive function and MRI measures of brain aging. To our knowledge, this is the first study to apply principal components analysis to biological markers of diet.
Dietary patterns associated with cognitive decline or Alzheimer incidence have historically derived the patterns from FFQ data. Dietary intake can be indexed as “healthy” or “unhealthy” based on existing knowledge and examined in relation to disease risk.21,24
Data-driven cluster analysis places subjects into exclusive dietary patterns a posteriori20
and reduced rank regression combines existing knowledge and the data at hand to derive dietary patterns.22
These studies using FFQ have identified an intake higher in dark and green leafy vegetables, cruciferous vegetables,22
and lower in organ meats, red meat, high-fat dairy, butter,22
and trans fat26
as favorable for cognitive health. In thinking about the plasma signature of this diet, we propose that the favorable BCDE pattern and ω-3 pattern would be sensitive to the frequent consumption of dark and green leafy and cruciferous vegetables, fruit, and fish. In addition, a NBP high in trans fat and retinol would be expected in people frequently consuming bakery and fried foods, margarine spreads,27
These consistencies are encouraging and provide impetus for further development of biological markers of diet.
The neuroimaging results suggest that the mechanisms through which the 2 favorable patterns (NBP1-BCDE and NBP5-marine ω-3) affect cognitive function are distinct. Cognitive benefit gained by a plasma profile high in antioxidants C and E, B vitamins, and vitamin D may partially operate on the neurobiology that governs rate of total brain atrophy (e.g., Alzheimer type pathology), whereas the effects of the marine ω-3s may be mediated through more vascular mechanisms.29,30
The favorable relationship between the BCDE pattern and global cognitive function was maintained after adding TCBV to the model in our study. This suggests that the effects of this combination on cognition are not entirely mediated through structural changes. Other mechanisms through which this pattern may offer cognitive benefit include the promotion of hippocampal neurogenesis,31
reduction of β-secretase activity,32
and hyperhomocysteinemia-induced neurotoxicity,35
and perhaps by maintaining blood–brain barrier integrity.36
The high trans fat pattern was consistently associated with worse cognitive performance and less TCBV. Linolelaidic acid is predominantly found in bakery foods such as cookies, doughnuts, cakes, pastries, and pies.27
These foods are often prepared with hydrogenated vegetable oils to allow for a long shelf life. Higher trans fatty acid intake increases cardiovascular risk, systemic inflammation, and endothelial dysfunction, all of which may explain an association with cognition.37,38
Unfortunately, very few studies have assessed trans fat and risk for cognitive decline.26
Trans fat may aggravate cognitive function independently and jointly through interaction with other dietary factors.e11
Trans fat may displace DHA in neuronal membranes, but apparently does not impact the neuropathologic Alzheimer hallmarks in mice.39
The consistency of the association of plasma trans fat with poorer cognitive function and more brain atrophy suggests neurologic consequences in humans, but these findings need to be confirmed.
PCA of fatty acids expressed as weight percentages of total in serum and in erythrocyte membranes have been studied.e12,e13 The patterns, including eicosapentaenoic and docosahexaenoic acid loading together, were similar to our findings using fatty acids expressed as absolute concentrations in plasma. The interactive metabolism of EPA and DHA, in addition to the similar dietary sources, may explain why these 2 fatty acids load together. PCA constructs the patterns on a basis of collinearity, and this “relatedness” may be partially attributed to interactive metabolism when applied to biological markers of diet. Our observation that the carotenoids (NBP3), total and low-density lipoprotein cholesterol (NBP4), saturated fats (NBP2), and the ω-6 fatty acids (NBP6) load together adds further support to the notion that interactive metabolism is a contributor to NBP construction.
There are limitations of this study. PCA may require investigator decisions with the data in hand. For example, using an eigenvalue of >1.0 as inclusion criteria for the number of patterns extracted to carry forward into hypothesis testing may require more field-specific criteria. Our nutrient biomarkers were selected a priori capitalizing on existing knowledge of an association with neurodegeneration, but this may not reflect the ideal set. Observational studies are susceptible to residual confounding, and our cross-sectional design is not suited for inferring any causal association since the temporal relationship is unattainable. Our sample population was restricted to a relatively healthy and well-educated cohort of white, non-Hispanic elders with minimal genetic risk for AD. These attributes may limit the generalizability of the results.
Future studies should consider validating the external consistency of these findings. The ability of NBPs to predict cognitive and brain volume changes would offer more compelling data. Gene–nutrient interactions underlying a relationship between nutrition and cognition may be important to consider since APOE
4 carriers may benefit less from nutritional interventions.6,10,40
The significance of these NBPs at different stages of cognitive status are unknown. These studies will decipher the key nutrient combinations and the population best suited for intervention studies.