Metabolomic Pathways of Inflammation and Mitochondrial Dysfunction Are Related to Worsening Healthy Aging Index and Mortality
Bibliographic record
Abstract
BACKGROUND: Metabolic-inflammatory states are central to multiorgan mechanisms of aging, but precise functional biomarkers of physiological aging remain less clear. METHODS: In the Health, Aging, and Body Composition study, we defined metabolomic profiles of the Healthy Aging Index (HAI), a composite of cardiovascular, lung, cognitive, metabolic, and renal function (0-10, with higher scores indicating poorer health) in a split set design from 2015 older participants (mean age 73.6 years; 50% women; 35% Black). We used standard regression to identify metabolomic correlates of Year 1 and Year 10 HAI, change in HAI over time, and mortality. A metabolite score of HAI was developed using LASSO regression. RESULTS: We identified 42 metabolites consistently associated with Year 1 and Year 10 HAI, as well as change in HAI: 13 lipids, 4 amino acids, and 4 metabolites of other classes were associated with worse and worsening HAI while 20 lipids and 1 amino acid was associated with better and improving HAI. Most of these associations were no longer significant after additionally adjusting for inflammation biomarkers. A higher metabolite score of Year 1 HAI was associated with greater HAI deterioration over time (hold-out "test" set beta 0.40 [0.15-0.65]) and higher mortality (hold-out "test" set hazard ratio: 1.43 [1.23-1.67]). CONCLUSIONS: A multiorgan healthy aging phenotype was linked to lipid metabolites, suggesting potential pathways related to mitochondrial function, oxidative stress, and inflammation. Metabolomics of HAI at older age were related to worsening health and mortality, suggesting potential links between metabolism and accelerated physiological 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".