Relationship of obesity to adipose tissue insulin resistance
Bibliographic record
Abstract
Aims This study aimed to examine the association of different anatomical forms of obesity with adipose tissue insulin resistance and to assess the diagnostic value and contribution of obesity to adipose tissue insulin resistance. Methods This cross-sectional study included a total of 499 subjects aged 50 years or over. Multivariate regression analysis was conducted to clarify the association of different forms of obesity with adipose tissue insulin resistance (calculated as fasting insulin level×fasting free fatty acids level). Receiver operating characteristic cure analyses were used to assess the diagnostic value of each anthropometric indicator for adipose tissue insulin resistance. Attributable risk per cent and population attributable risk per cent were calculated to assess the contribution of obesity to adipose tissue insulin resistance. Results After adjustment for potential confounders, we showed that anthropometric indicators were all positively associated with adipose tissue insulin resistance. In males, waist circumference (WC) was the strongest associated factor (OR, 3.43 (95% CI 2.03 to 5.82)) and indicator (area under the curve (AUC): 0.79) of adipose tissue insulin resistance among those indicators. Here, abdominal obesity (WC≥90 cm) accounted for 64.9% of adipose tissue insulin resistance in the abdominal obese males. Accordingly, body mass index (BMI) was the strongest associated factor (OR,3.08 (95% CI 2.04 to 4.66)) and indicator (AUC: 0.78) of adipose tissue insulin resistance in females. Here, general obesity of BMI≥25 kg/m 2 accounted for 66.2% of the adipose tissue insulin resistance in the general obese females. We further demonstrated that adipose tissue insulin resistance was associated or trended to be associated with the metabolic diseases of cardiovascular disease, type 2 diabetes and fatty liver in subjects with normal BMI and WC. Conclusions Maintaining WC in males and BMI in females to a normal range could be an important strategy to significantly reduce the occurrence of adipose tissue insulin resistance and the subsequent metabolic diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".