EXPLORING DIET’S IMPACT ON CARDIOMETABOLIC AND COGNITIVE HEALTH IN AMERICAN INDIANS
Bibliographic record
Abstract
Abstract Objective This study explores the link between cardiometabolic biomarkers and neurocognition in older American Indians, focusing on diet quality as measured by the Healthy Eating Index (HEI). Methods Participants provided biological specimens and completed health, diet, and exposome questionnaires in a mobile clinic. Cardiometabolic biomarkers (lipids, glucose) were measured with a Cholestech LDX Analyzer and log-transformed. Neurocognition was assessed using the Montreal Cognitive Assessment (MoCA). OLS regressions, adjusted for age and education, examined the association between biomarkers and neurocognition across HEI tertiles to assess effect modification. Results Across three months, we enrolled 41 participants, predominantly women (76%), with a mean age of 72 years (SD 7.8) and an average of 12.6 years of education (SD 3.2). HDL-Cholesterol levels were higher in women than in men (n=39, p=0.008). The mean HEI score was 64.3(SD 11.4), with varied diet quality among participants; lowest HEI tertile (44.2-58.1), highest HEI tertile (71.4-84). In the lowest HEI-tertile, higher MoCA scores were associated with higher LDL-C, lower triglycerides, and higher glucose. In contrast, the mid-HEI-tertile showed higher MoCA scores associated with lower TC and lower glucose, but higher LDL-C and triglycerides. For the highest HEI-tertile, higher MoCA scores were associated only with lower triglycerides. Discussion These preliminary findings suggest the relationship between cardiometabolic markers and cognitive function may depend on diet quality. Understanding the role of nutrition on brain and cardiometabolic health among older American Indians may lead to culturally tailored nutritional interventions to promote brain health in aging. Further analysis of specific nutrients, the microbiome, and the metabolome are forthcoming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".