Metabolite Profiles of Healthy Aging Index Are Associated With Cardiovascular Disease in African Americans: The Health, Aging, and Body Composition Study
Bibliographic record
Abstract
Background: Metabolic dysfunction is a hallmark of differential aging, specifically in African Americans. Investigation of systemic metabolic state, multiorgan aging, and long-term cardiovascular outcome in African Americans has not been reported. Methods: We studied 291 African American males in the Health, Aging, and Body Composition (Health ABC) study to identify circulating metabolites related to the Newman healthy aging index (HAI; a multiparametric score comprised of blood pressure, blood glucose, neurocognitive function, creatinine, and forced vital capacity). We examined the relationship of selected metabolites differential abundant at the extremes of HAI with long-term survival from cardiovascular mortality. Results: The median age was 73 years. We identified 19 metabolites differentially expressed in blood in 86 study participants at the extremes of HAI (HAI 0-3: N = 30 vs 8-10: N = 56). At a median follow-up of 10 years, 78 participants (27 per cent) died from cardiovascular causes. After adjustment for age, body mass index, presence of prevalent cardiovascular disease, creatinine, and HAI, six of these 19 metabolites were associated with long-term cardiovascular mortality. Although several metabolites had been previously reported in Caucasians (eg, isocitrate), we identified several metabolites with unreported association with cardiac disease. Metabolites associated with HAI and cardiac death in African Americans specified pathways relevant to nitric oxide, oxidative stress, mitochondrial function, urea cycle, and gut microbial metabolism. Conclusions: Metabolite profiling in African Americans identified known and novel metabolic pathways linked to HAI and cardiovascular death. Further investigation in larger patient cohorts is required to uncover race-based signatures of cardiovascular disease with aging.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".