Waist Circumference as a Simple and Meaningful Means to Assess Insulin Resistance Among Outpatients with Bipolar Disorder
Bibliographic record
Abstract
Aims: To evaluate insulin resistance (IR) among outpatients with bipolar disorder (BD) in order to determine clinical correlates of IR in this patient population. Method: We performed a cross-sectional study in sixty-five DSM-IV-TR BD patients consecutively assessed from January to August 2007 at the Bipolar Disorder Program, Hospital de Clínicas de Porto Alegre, Brazil. IR was diagnosed using the homeostatic model assessment - insulin resistance (HOMA-IR). Metabolic syndrome (MS) diagnosis and metabolic variables were assessed using three definitions: National Cholesterol Educational Program - Adult Treatment Panel III (NCEP-ATP III); NCEP-ATP III modified criteria and International Diabetes Federation (IDF). Results: IR was present in 43.1% of the sample (women 40%, men 44.4%). The prevalence of MS defined by the NCEP-ATP III criteria was 32.3%, NCEP-ATP III modified was 40% and IDF was 41.5%. NCEP-ATP III modified criteria showed the best trade-off between sensitivity (78.6%) and specificity (89.2%) to detect insulin resistance. Waist circumference was the best clinical parameter associated with IR in the linear regression model (B=0.014, SE 0.002, t=6.18, p< 0.001). Areas under ROC curves were similar for waist circumference and different MS definitions (chi2=2.98, df=3, p=0.39). Conclusion: Currently MS criteria may provide reasonable sensitivity and specificity for the detection of insulin resistance in patients with bipolar disorder. Waist circumference may be a simple and inexpensive means to predict insulin resistance in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".