Associations of the Limb Fat to Trunk Fat Ratio With Markers of Cardiometabolic Risk in Elderly Men and Women
Bibliographic record
Abstract
BACKGROUND: The ratio of limb fat to trunk fat (LF/TF) is associated with markers of cardiometabolic risk in elderly men and women. It is unknown if LF/TF is associated with cardiometabolic risk beyond that explained by LF and TF independently. METHODS: Participants included abdominally obese men (n = 58) and women (n = 78) between 60 and 80 years of age. Regional adiposity was quantified using magnetic resonance imaging. Insulin resistance, fasting glucose, high-density lipoprotein (HDL) cholesterol, plasma triglycerides, and adiponectin were determined using standard procedures. RESULTS: After control for potential confounders, TF was positively associated with fasting glucose, insulin resistance, and plasma triglycerides and negatively associated with HDL cholesterol and adiponectin (p < or = .05). These associations were strengthened after further control for LF (p < .05), with the exception of adiponectin in men (p > .05). After control for potential confounders, LF was negatively associated with adiponectin in men (p < .05) but not with any other marker of cardiometabolic risk (p > .05). After further control for TF, LF was negatively associated with plasma triglycerides and positively associated with HDL cholesterol in both genders combined (p < .05) and with adiponectin in women (p < .05) but not in men (p > .05). LF/TF was not associated with any marker of cardiometabolic risk after control for LF and TF. CONCLUSION: These results suggest that it is the absolute, rather than relative, amounts of LF and TF that have the greatest influence on cardiometabolic risk in elderly men and women.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".