Metabolites Associated With Risk of Developing Mobility Disability in the Health, Aging and Body Composition Study
Bibliographic record
Abstract
Background: Metabolic pathways that give rise to functional decline and mobility disability in older adults are incompletely understood. Methods: To identify metabolic perturbations that may affect functional decline, nontargeted metabolomics was used to measure 350 metabolites in baseline plasma from 313 black men in the Health ABC Study (median age 74 years). Usual gait speed was measured over 20 m. Cross-sectional relationships between gait speed and metabolites were explored with partial correlations adjusted for age, study site, and smoking status. Risk of incident mobility disability (two consecutive reports of severe difficulty walking quarter mile or climb 10 stairs) over 13 years of follow-up was explored with Cox regression models among 307 men who were initially free of mobility disability. Significance was determined at p ≤ .01 and q (false discovery rate) ≤ 0.30. Results: Two metabolites were correlated with gait speed: salicylurate (r = -.19) and 2-hydroxyglutarate (r = -.18). Metabolites of amino acids and amino acid degradation (indoxy sulfate; hazard ratio [HR] = 1.48, 95% confidence interval [CI] = 1.09-2.03, symmetric dimethylarginine; HR = 3.58, 95% CI = 1.57-8.15, N-carbamoyl beta-alanine; HR = 1.91, 95% CI = 1.16-3.14, quinolinate; HR = 2.56, 95% CI = 1.65-3.96) and metabolites related to kidney function (aforementioned symmetric dimethylarginine and indoxy sulfate as well as creatinine; HR = 5.91, 95% CI = 2.06-16.9, inositol; HR = 2.70, 95% CI = 1.47-4.97) were among the 23 metabolites associated with incident mobility disability. Conclusions: This study highlights the potential role of amino acid derivatives and products and kidney function early in the development of mobility disability and suggests metabolic profiles could help identify individuals at risk of functional decline.
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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".