Adiposity and muscle mass phenotyping is not superior to BMI in detecting cardiometabolic risk in a cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: Classifying adiposity based on dual-energy x-ray absorptiometry (DXA) muscle and fat mass phenotypes has been proposed. Whether these phenotypes are more accurate in predicting cardiometabolic risk than BMI weight status is unknown. METHODS: Data were from the National Health and Nutrition Examination Survey (NHANES; 1999-2006 cycles, n = 5,475). Weight status was defined by BMI. Phenotypes of adiposity and muscle were based on high (≥50th percentile) and low (<50th percentile) permutations of sex- and age-specific fat and muscle mass population curves. The area under the curves of receiver operating characteristic curves (ROC-AUCs), which predicted the presence of abnormal lipids, glucose, and blood pressure, were compared. All analyses were stratified by sex and incorporated the complex survey design and weighting of NHANES. RESULTS: The ROC-AUCs from weight status models used to correctly identify cardiometabolic risk ranged from 0.57 to 0.68, indicating generally weak predictive power. However, the ROC-AUCs from DXA phenotypes were lower (ranging from 0.53-0.68), indicating weaker predictive power than weight status, and were statistically inferior for nearly all of the comparisons. CONCLUSIONS: Despite DXA's high cost and detailed output regarding body composition, its phenotype classification was inferior to weight status in predicting cardiometabolic risk. Further studies investigating the utility of the phenotypes are needed.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".