Are Metabolically Normal but Obese Individuals at Lower Risk for All-Cause Mortality?
Bibliographic record
Abstract
OBJECTIVE: The clinical relevance of the metabolically normal but obese phenotype for mortality risk is unclear. This study examines the risk for all-cause mortality in metabolically normal and abnormal obese (MNOB and MAOB, respectively) individuals. RESEARCH DESIGN AND METHODS: The sample included 6,011 men and women from the Third National Health and Nutrition Examination Survey (NHANES III) with public-access mortality data linkage (follow-up = 8.7 +/- 0.2 years; 292 deaths). Metabolically abnormal was defined as insulin resistance (IR) or two or more metabolic syndrome (MetSyn) criteria (excluding waist). RESULTS: A total of 30% of obese subjects had IR, and 38.4% had two or more MetSyn factors, whereas only 6.0% (or 1.6% of the whole population) were free from both IR and all MetSyn factors. By MetSyn factors or IR alone, MNOB subjects (hazard ratio [HR](MetSyn) 2.80 [1.18-6.65]; HR(IR) 2.58 [1.00-6.65]) and MAOB subjects (HR(MetSyn) 2.74 [1.46-5.15]; HR(IR) 3.09 [1.55-6.15]) had similar elevations in mortality risk compared with metabolically normal, normal weight subjects. CONCLUSIONS: Although a rare phenotype, obesity, even in the absence of overt metabolic aberrations, is associated with increased all-cause mortality 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".