PREDICTORS OF INSULIN RESISTANCE IN COMMUNITY-DWELLING OLDER ADULTS OF THE NUAGE STUDY
Bibliographic record
Abstract
We determined insulin resistant subjects over a 3-year period by trajectory analyses of the HOMA-IR in a sample of non-diabetic, participants of the NuAge Study. Muscle mass index and % body fat were derived from DXA and bioimpedance. Physical activity was assessed. Protein intakes were calculated. Serum biomarker profile included adiponectin, leptin, CRP, TNF-α, IL-6, IL-10, lipid profile, IGF-1 and IGFBP-3. Using path analysis without biomarkers, positive associations were observed for HOMA-IR score with MMI (β=0.42) and % body fat (β=0.094). Logistic regression without biomarkers provided only 3 significant predictors of insulin resistance: MMI [OR (95% CI): 1.72 (1.26–2.3)]; %body fat [1.18 (1.12–1.25)]; male sex [0.145 (0.04–0.45)]. When the biomarker profile was included, adiponectin [0.58 (0.35–0.95)], TNF-α [1.12 (1.00–1.23)] and leptin [2.92 (1.29–6.64)] were independent predictors of insulin resistance. Our analyses showed that positive association between muscle mass and HOMA-IR is likely mediated through higher levels of TNF-α and leptin and lower adiponectin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".