Does lean body mass equal health despite body mass index?
Bibliographic record
Abstract
OBJECTIVE: To determine the association between having simultaneously high body mass index (BMI) and high relative lean body mass (LBM) and cardio-metabolic risk factors, metabolic syndrome (MetS), and diabetes in adults. MATERIALS AND METHODS: A cross-sectional analysis was performed on 4982 adults aged 19-85 years that participated in the National Health and Nutrition Examination Survey (cycles 1999-2000-2005-2006). The primary exposure variable was categorization into four groups: (a) Low-BMI/Low-LBM, (b) Low-BMI/High-LBM, (c) High-BMI/Low-LBM, and (d) High-BMI/High-LBM. LBM was assessed using dual-energy X-ray absorptiometry. The primary outcome measures were cardio-metabolic risk factors, MetS based on the ATP III definition; participants were required to have at least three of the following five criteria: high waist circumference, low HDL cholesterol, elevated triglyceride levels, high resting blood pressure, and self-reported diabetes. RESULTS: Compared to the High-BMI/High-LBM, most cardio-metabolic risk factors were significantly different among groups (P < .05) while no such differences were observed for the High-BMI/Low-LBM (P > .05). Exception of waist circumference (OR [95%]: 21.8 [8.84-53.82]), there was no increased odds of having cardio-metabolic risk factors in the High-BMI/Low-LBM compared with the High-BMI/High-LBM (P > .05). The odds of having MetS and diabetes for the High-BMI/Low-LBM compared with the High-BMI/High-LBM were OR (95% CI): 1.68 (0.84-3.36) and 0.59 (0.26-1.34), respectively. CONCLUSIONS: appears to be clinically relevant, regardless of LBM levels.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".