Adipose Tissue Insulin Resistance Is Longitudinally Associated With Adipose Tissue Dysfunction, Circulating Lipids, and Dysglycemia: The PROMISE Cohort
Bibliographic record
Abstract
OBJECTIVE: To determine the association of adipose tissue insulin resistance with longitudinal changes in biomarkers of adipose tissue function, circulating lipids, and dysglycemia. RESEARCH DESIGN AND METHODS: = 468). Adipose tissue insulin resistance was determined using a novel validated index, Adipo-IR, calculated as the product of fasting insulin and nonesterified fatty acids measured at baseline. Fasting serum was used to measure biomarkers of adipose tissue function (adiponectin and soluble CD163 [sCD163]), circulating lipids (total cholesterol, HDL, LDL, triglyceride [TG]), and systemic inflammation (interleukin-6 [IL-6] and tumor necrosis factor-α [TNF-α]). Incident dysglycemia was defined as the onset of impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes at follow-up. Generalized estimating equation (GEE) models were used to assess the relationship of Adipo-IR with longitudinal outcomes. RESULTS: GEE analyses showed that elevated Adipo-IR was longitudinally associated with adipose tissue dysfunction (adiponectin -4.20% [95% CI -6.40 to -1.95]; sCD163 4.36% [1.73-7.06], HDL -3.87% [-5.15 to -2.57], TG 9.26% [5.01-13.69]). Adipo-IR was associated with increased risk of incident dysglycemia (odds ratio 1.59 [95% CI 1.09-2.31] per SD increase). Associations remained significant after adjustment for waist circumference and surrogate indices for insulin resistance. There were no significant longitudinal associations of Adipo-IR with IL-6, TNF-α, total cholesterol, or LDL. CONCLUSIONS: Our findings demonstrate that adipose tissue insulin resistance is prospectively associated with adipose tissue function, HDL, TG, and incident dysglycemia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".