The Metabolically Healthy But Obese Phenotype Is Associated With Lower Plasma Levels of Persistent Organic Pollutants as Compared to the Metabolically Abnormal Obese Phenotype
Bibliographic record
Abstract
CONTEXT: Although obesity is strongly linked to insulin resistance and type 2 diabetes, a subset of obese individuals termed metabolically healthy but obese (MHO) appears relatively protected from the development of cardiometabolic complications. The origins of this metabolically healthy phenotype remain unclear. Recently, persistent organic pollutants (POPs) have emerged as potential endocrine disruptors. OBJECTIVE: The aim of this study was to test the hypothesis that the MHO phenotype presents lower circulating levels of POPs as compared to the metabolically abnormal obese (MAO) phenotype. DESIGN, SETTING, AND PATIENTS: We conducted a cross-sectional study of 76 nondiabetic obese (body mass index ≥30 kg/m(2)) postmenopausal women. MAIN OUTCOME MEASURES: Plasma concentrations of 21 POPs as well as cardiometabolic risk factors were analyzed. RESULTS: For similar age, body mass index, and fat mass index, MHO women (n = 40) showed higher insulin sensitivity levels and a more favorable cardiometabolic profile than MAO women (n = 36), as evidenced by a 2-fold increase in glucose disposal rates measured by the hyperinsulinemic-euglycemic clamp (P = .001). Among 18 detectable pollutants measured, MAO women had higher plasma concentrations of 12 POPs (fold increase, 1.4-2.9; P < .001-.036). Logistic regression analyses showed that the prevalence of the MAO phenotype was significantly associated with higher levels of total dioxin- and non-dioxin-like polychlorinated biphenyls (odds ratio, 4.7; 95% confidence interval, 1.8-12.5; P = .002), as well as trans-nonachlor (odds ratio, 6.1; 95% CI, 2.2-16.4; P < .001). CONCLUSION: Our study demonstrates that the metabolically healthy and abnormal phenotypes have distinct plasma POP profiles.
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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 | 0.000 | 0.001 |
| 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".