Association of Visceral Adipose Tissue and Insulin Resistance with Incident Metabolic Syndrome Independent of Obesity Status: The IRAS Family Study
Bibliographic record
Abstract
OBJECTIVE: Although increasing evidence suggests that visceral adipose tissue (VAT) is a major underlying cause of metabolic syndrome (MetS), few studies have measured VAT at multiple time points in diverse populations. VAT and insulin resistance were hypothesized to differ by MetS status within BMI category in the Insulin Resistance Atherosclerosis Study (IRAS) Family Study and, further, that baseline VAT and insulin resistance and increases over time are associated with incident MetS. METHODS: Generalized estimating equations were used for differences in body fat distribution and insulin resistance by MetS status. Mixed effects logistic regression was used for the association of baseline and change in adiposity and insulin resistance with incident MetS across 5 years, adjusted for age, sex, race/ethnicity, and family correlation. RESULTS: VAT and insulin sensitivity differed significantly by MetS status and BMI category at baseline. VAT and homeostatic model assessment of insulin resistance (HOMA-IR) at baseline (VAT odds ratio [OR] = 1.16 [95% CI: 1.12-2.31]; HOMA-IR OR = 1.85 [95% CI: 1.32-2.58]) and increases over time (VAT OR = 1.55 [95% CI: 1.22-1.98]; HOMA-IR OR = 3.23 [95% CI: 2.20-4.73]) were associated with incident MetS independent of BMI category. CONCLUSIONS: Differing levels of VAT may be driving metabolic heterogeneity within BMI categories. Both overall and abdominal obesity (VAT) may play a role in the development of MetS. Increased VAT over time contributed additional risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".