A Metabolite Composite Score Attenuated a Substantial Portion of the Higher Mortality Risk Associated With Frailty Among Community-Dwelling Older Adults
Bibliographic record
Abstract
BACKGROUND: Frailty is more prevalent among black versus white older Americans. We previously identified 37 metabolites associated with the vigor to frailty spectrum using the Scale of Aging Vigor in Epidemiology (SAVE) among older black men from the Health, Aging, and Body Composition (Health ABC) study. Here, we sought to develop a metabolite composite score based on the 37 SAVE-associated metabolites and determine whether the composite score predicts mortality and whether it attenuates the association between frailty and mortality among older black men. METHODS: Plasma metabolites were measured using liquid chromatography-mass spectrometry. Most of the 37 metabolites were organic acids/derivatives or lipids. Metabolites were ranked into tertiles: tertiles associated with more vigorous SAVE scores were scored 0, mid-tertiles were scored 1, and tertiles associated with frailer SAVE scores were scored 2. Composite scores were the sum of metabolite tertile scores. We examined mortality associations using Cox regression. Percent attenuation estimated the extent to which metabolites attenuated the association between frailty and mortality. RESULTS: One standard deviation frailer SAVE was associated with 30% higher mortality, adjusting for age and site (p = .0002); this association was attenuated by 56% after additionally adjusting for the metabolite composite score. In this model, one standard deviation higher metabolite composite score was associated with 46% higher mortality (p < .0001). Metabolite composite scores also predicted mortality (p = .045) in a validation sample of 120 older adults (40% men, 90% white). CONCLUSION: These metabolites may provide a deeper characterization of the higher mortality that is associated with frailty among older adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".