Nutrient biomarker patterns, cognitive function, and MRI measures of brain aging
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"Two NBPs [nutrient biomarker patterns] associated with more favorable cognitive and MRI measures: one high in plasma vitamins B (B1, B2, B6, folate, and B12), C, D, and E, and another high in plasma marine w-3 fatty acids. A third pattern characterized by high trans fat was associated with less favorable cognitive function and less total cerebral brain volume. Depression attenuated the relationship between the marine -3 pattern and white matter hyperintensity volume."
The good, bad, and ugly? How blood nutrient concentrations may reflect cognitive
performance - Commentary
Neurology; Published online before print December 28, 2011
Christy C. Tangney, PhD, MS
Nikolaos Scarmeas, MD
From the Department of Clinical Nutrition (C.C.T.), Rush University Medical Center, Chicago, IL; and Department of Neurology (N.S.), Columbia
University, New York, NY.
A growing body of evidence is supportive of the influence of nutritional factors on cognitive health. There is evidence relating nutrition to cognitive measures to assess cognitive change over time, but such functional changes may not reflect the neuroanatomic or pathologic alterations that have occurred. Brain imaging may complement clinical assessment of dementia and hence, an understanding of how nutrients alter brain structure, specifically volumes, is key. The scarcity of such information is due in part to costs of brain imaging as well as the reluctance of participants to undergo these tests. Further, the complexity of dietary exposures or behaviors, and how best to characterize or quantify their diversity, are not only demanding of the respondent, but also demand a level of expertise in the investigator that is often overlooked. Most efforts have been directed to examining associations between single nutrients and cognitive status. More recently, nutritional epidemiologists have described dietary pattern- disease associations using a variety of approaches. These include a posteriori methods, such as factor or cluster analyses, that reduce nutrient data into investigator-named dietary patterns based on the intercorrelations between food/nutrient items or for cluster analyses on the differences1; a comparison of a priori patterns, such as the Mediterranean pattern2,3; and reduced rank regression, which is a combination of the 2 methods.4 All of these approaches are highly dependent on the performance characteristics (validity and reliability) of the dietary instruments used in the intended population. Application of one of these analytical approaches is appealing because extraction of dietary patterns can distill the synergistic and sometimes antagonistic metabolic influences of food groups or nutrients (even anti-nutrients) within foods. Additional advantages of such methods include the ability to summarize dietary behavior when examined in the context of other health behaviors, the avoidance of type I (false-positive) error inflation when many nutrients are examined, and an alternative way to account for redundancy in contribution that several nutrients have on the outcomes (here, cognitive performance, brain volumes).1-4 Moreover, if a dietary pattern is highly predictive of improved health outcomes, it may be easier to translate that pattern into practice, as has been reported for the Dietary Approaches to Stop Hypertension (DASH) diet plan.5 People eat foods, not nutrients, and they eat them in combination, not in isolation.
In the current issue of Neurology®, Bowman and coworkers6 describe how several indicator nutrients in blood (30 available in total) are cross-sectionally related to 1) cognitive performance scores of 104 adults from the Oregon Brain and Aging Study cohort(mean age of 87 years) and 2) 2 brain MRI measures in a subsample of 42 participants. These authors use factor analysis to describe patterns for nutrient indicators in blood. They extract 8 different plasma nutrient patterns-3 of which are associated with overall cognitive health (2 with better scores and 1 with poorer scores) and MRI total brain volumes (TBV) and white matter hyperintensity (WMH) volumes.
Nutrient biomarker patterns may afford complementary ways to examine the influence of food mixtures as consumed, independent of limitations
described for current food frequency questionnaires and 24-hour dietary recalls. The factor analytic approach allows several important variables to be taken into account simultaneously, such as the bioavailability of nutrients consumed in foods and the genetic variability in bioavailability. Thus, nutrient biomarker patterns may more closely reflect what is available to brain tissues. Moreover, the 2 patterns that are associated with cognitive health and brain volumes are consistent with previous reports of dietary/blood exposures and cognitive outcomes, such as the association of trans fat and cognitive decline in a biracial cohort of elders in Chicago7 or between serum vitamin B12 markers and cognitive decline.8 Bowman and coworkers show that a high trans fat pattern is related to worse cognitive scores and less TBV. Similarly, a BCDE pattern (reflecting high plasma vitamins B, C, D, and E) is related to better cognitive function and more TBV. Interestingly, the relationship between the BCDE pattern and cognition is not attenuated when TBV is entered into models, suggesting that the contribution to cognitive performance is not entirely explicable by structural changes or cerebral atrophy. The third pattern described associates marine omega 3 with better executive function and lower WMH volume, but these relationships are no longer significant when depression and hypertension are added to models.
If the relationships between cognitive scores and MRI measures with nutrient biomarker patterns are confirmed in a larger, more ethnically diverse sample of older adults, this approach should be exploited to extract nutrient biomarker patterns predictive of cognitive change. Moreover, additional biomarkers for food group and food subgroups might be explored- i.e., reservatrol for wine, hydroxytyrosol for olive oil and nuts, or proline betaine for citrus fruits.
One of the strengths of the work of Bowman et al. was the use of plasma nutrient levels rather than self-reported dietary patterns. Recall errors and biases that occur when individuals report their usual diet, in particular, in those who may be cognitively challenged, 9,10 are major threats to interpretation of dietary patterns. However, limitations are also inherent with a bioassay strategy for estimating diet. First, there are costs underlying analysis of a set of biomarkers to define the pattern. Second, the acquisition of biological specimens to measure nutrient biomarkers is not without burden to the participants, especially if fasting is necessary. Fasting conditions were required for the Oregon cohort participants. Tradeoffs between the challenges of participant burden (convenience and time), costs, and the quality of longitudinal information on dietary exposures and their influence on brain structural changes or clinically relevant function will determine the utility of the approach these authors have spearheaded.
Nutrient biomarker patterns, cognitive
function, and MRI measures of brain aging
Neurology. Published Ahead of Print on December 28, 2011
G.L. Bowman, ND,MPH et al
From the Departments of Neurology (G.L.B., L.C.S., D.H., H.H.D., J.A.K., J.F.Q.) and Public Health and Preventive Medicine (G.L.B., J.S.), and Center for Research in Occupational and Environmental Toxicology (J.S.), Oregon Health & Science University, Portland; Portland VA Medical Center (J.A.K., J.S., J.F.Q.); and the Linus Pauling Institute (M.G.T., B.F.), Oregon State University, Corvallis.
Objective: To examine the cross-sectional relationship between nutrient status and psychometric and imaging indices of brain health in dementia-free elders.
Methods: Thirty plasma biomarkers of diet were assayed in the Oregon Brain Aging Study cohort (n 104). Principal component analysis constructed nutrient biomarker patterns (NBPs) and regression models assessed the relationship of these with cognitive and MRI outcomes.
Results: Mean age was 87+/-10 years and 62% of subjects were female. Two NBPs associated with more favorable cognitive and MRI measures: one high in plasma vitamins B (B1, B2, B6, folate, and B12), C, D, and E, and another high in plasma marine w-3 fatty acids. A third pattern characterized by high trans fat was associated with less favorable cognitive function and less total cerebral brain volume. Depression attenuated the relationship between the marine -3 pattern and white matter hyperintensity volume.
"Subjects with higher marine w-3 scores had less WMH volume, but after adjustment for depression and hypertension the association was attenuated (table 4, WMH model 2). Significance of the w-3 to WMH was lost after adding depression to model 1 ( p =0.030 to 0.097). Adding hypertension had no effect. After stratifying by depression in model 1 for WMH it was apparent that -3s were significant only in those without depression (beta = - 0.845, p 0.021). The unadjusted proportions of variance explained in brain volumes by each significant NBP are provided in the figure."
Conclusion: Distinct nutrient biomarker patterns detected in plasma are interpretable and account for a significant degree of variance in both cognitive function and brain volume. Objective and multivariate approaches to the study of nutrition in brain health warrant further study. These findings should be confirmed in a separate population.
AD Alzheimer disease; CDR Clinical Dementia Rating; EDTA ethylenediaminetetraacetic acid; FFQ food frequency questionnaire; HDL high-density lipoprotein; HPLC high-performance liquid chromatography; ICC intraclass correlation coefficient; MMSE Mini-Mental State Examination; NBP nutrient biomarker pattern; OBAS Oregon Brain Aging
Study; PCA principal component analysis; TCBV total cerebral brain volume; TIV total intracranial volume; WMH white matter hyperintensity.
The epidemiology of Alzheimer disease (AD) suggests a role for nutrition.1-7 Despite studies in favor of a single or a few nutrients in the prevention of AD, the translation to formal clinical trials testing vitamin E, B vitamins, or docosahexaenoic acid have been disappointing.8-12 Given the interactive nature of nutrient action and metabolism, it is not surprising that a single or few nutrient approaches for neurodegenerative disease are tenuous.13-15 These results impart the rationale for novel methodologic approaches that appreciate the interactive features of nutrients and model their collective influence in the promotion of brain health.
Food frequency questionnaires (FFQ) have traditionally been used to construct dietary patterns.16 FFQ is relatively inexpensive and fairly comprehensive, but this method is subject to faulty recall of dietary intake and does not account for variability in nutrient absorption, both of which are issues in the elderly.17,18 We have recently reported a reliable blood test that assesses nutritional status in people at risk for dementia.19 In the current study, we examine the relationship of nutrient biomarkers with cognitive function and MRI.
To capture the effect of nutrients in combination, we construct nutrient biomarker patterns using principal component analysis (PCA). Cluster analysis,20 index scores,21 and reduced rank regression22 have each been applied to FFQ data to assemble dietary patterns, but none have applied PCA to biological markers of diet. One goal is to define dietary patterns that promote cognitive health in the same manner that dietary approaches for hypertension have been derived and applied.23
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 fish,25 and fruit21,22 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 -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 red meat,27 and offal.28 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 -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 w-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 -secretase activity,32 oxidative stress,33,34 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 -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 APOE4 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.