Abdominal Adiposity Assessed by Dual Energy X-Ray Absorptiometry Provides a Sex-Independent Predictor of Insulin Sensitivity in Older Adults
Bibliographic record
Abstract
BACKGROUND: An increase in total adiposity and in particular an abdominal distribution of adiposity may contribute to the decline in metabolic insulin sensitivity observed in older men and women. The objective of this cross-sectional study was to determine which measure of abdominal adiposity would provide the best sex-independent predictor of metabolic insulin sensitivity in older men and women. METHODS: Insulin sensitivity and abdominal adiposity were measured in healthy, nondiabetic older (64 +/- 6 years; mean +/- standard deviation) men (n = 23) and women (n = 31). Metabolic Insulin Sensitivity Index (S(I)) was determined from a frequently sampled insulin-assisted intravenous glucose tolerance test. Body fat mass and abdominal fat mass were determined from dual energy X-ray absorptiometry (DXA) scans. Anthropometric measures included waist and hip circumferences, height, and body weight. RESULTS: Although waist circumference, waist index (waist circumference divided by height), and waist-hip ratio (WHR) were all lower in women than in men, there was no sex difference in DXA L1-L4 fat mass. In univariate analyses, S(I) was significantly inversely related with body weight, body mass index, waist circumference, waist index, percentage of total body and abdominal fat, and DXA L1-L4 fat mass but not with WHR. The DXA L1-L4 fat mass was identified as the best independent predictor of S(I), accounting for 41.2% of the variance (p <.0001) in a stepwise multiple regression model that controlled for sex. CONCLUSIONS: WHR is not associated with S(I) in either men or women. Abdominal adiposity measured by DXA L1-L4 fat mass provides a sex-independent predictor of S(I) in older 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".