Effect of Intentional Weight Loss on Mortality Biomarkers in Older Adults With Obesity
Bibliographic record
Abstract
BACKGROUND: Observational research has identified several mortality biomarkers; however, their responsiveness to change is unknown. We tested whether the Healthy Aging Index (HAI) and other mortality biomarkers were responsive to intentional weight loss (WL), which is associated with lower mortality risk in recent meta-analyses. METHODS: Older adults (70.3 ± 3.7 years) with obesity were randomized into a 6-month WL (n = 47) or weight stability (WS: ±5% baseline weight; n = 48) program. Baseline and 6-month HAI score (0-10) was calculated from component sum (each 0-2: systolic blood pressure, forced vital capacity [FVC], creatinine, fasting blood glucose [FBG], Montreal Cognitive Assessment), and gait speed, grip strength, Digit Symbol Substitution Test, FEV1, Interleukin-6, C-Reactive Protein, and Cystatin-C were assessed at baseline and 6 months. RESULTS: Mean baseline HAI was 3.2 ± 1.6. By 6 months, WL participants lost 8.87 (95% CI: -10.40, -7.34) kg, whereas WS participants remained weight stable. WL group reduced HAI score (WL: -0.75 [95% CI: -1.11, -0.39] vs WS: -0.22 [95% CI: -0.60, 0.15]; p = .04), and components changing the most were FBG (WL: -3.89 [95% CI: -7.78, 0.00] mg/dL vs WS: 1.45 [95% CI: -2.61, 5.50] mg/dL; p = .047) and FVC (WL: 0.11 [95% CI: -0.01, 0.23] L vs WS: -0.05 [95% CI: -0.17, 0.08] L; p = .06). Among other biomarkers, only Cystatin-C significantly changed (WL: -2.53 [95% CI: -4.38, -0.68] ng/mL vs WS: 0.07 [95% CI: -1.85, 1.98] ng/mL; p = .04). Combining treatment groups, 1 kg WL was associated with a 0.07 (95% CI: 0.03, 0.12) HAI reduction (p < .01). CONCLUSION: Intentional WL via caloric restriction reduced HAI score by 0.53 points, largely attributable to metabolic and pulmonary improvements.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".