ASSOCIATIONS BETWEEN METABOLITES AND PHYSICAL FUNCTION IN OLDER BLACK MEN
Bibliographic record
Abstract
Functional decline is a common condition among older adults but mechanisms that give rise to functional decline and disability are incompletely understood. To identify metabolic perturbations that may impact functional decline, non-targeted metabolomics was used to measure 350 metabolites in baseline plasma from 313 black men in the Health, Aging and Body Composition Study (median age 74 years, median BMI 26.7). Usual gait speed was measured over 20 meters. Cross-sectional relationships between gait speed and metabolites were explored with Pearson partial correlations adjusted for age, study site and smoking status. Risk of incident mobility disability (2 consecutive reports of inability to walk ¼ mile or climb 10 stairs) over 13 years of follow-up was additionally explored with cox regression models among 307 men who were initially free of mobility disability. Significance was determined at p≤0.01 and q≤0.30. Ten metabolites were correlated with gait speed. The most strongly correlated were hydroxyglutarate (r=-0.18), gluconurate (r=-0.18), homogentisate (r=-0.16), salicylurate (r=-0.19), and tryptophan (r=0.15). Sixteen metabolites; all uniqe from gait speed-correlated metabolites, were associated with incident mobility disability. The top metabolites were creatine (HR 5.21, 95% CI 1.85–14.7); symmetric dimethylarginine, a biomarker of kidney function (HR=3.30, 95% CI=1.46–7.48); inositol (HR 2.73, 95% CI 1.48–5.02) and quinolate; a metabolite of tryptophan degradation (HR 2.54, 95% CI 1.64–3.93). This hypothesis generating study identified 26 involved in biological mechanisms including tryptophan metabolism, prospectively associated with functional decline in older men. The novel function-related metabolites identified here may help target future investigation of perturbed metabolic pathways.
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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 | 0.001 | 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".