The prediction of Metabolic Syndrome alterations is improved by combining waist circumference and handgrip strength measurements compared to either alone
Bibliographic record
Abstract
BACKGROUND: Adiposity is a major component of the metabolic syndrome (MetS), low muscle strength has also been identified as a risk factor for MetS and for cardiovascular disease. We describe the prevalence of MetS and evaluate the relationship between muscle strength, anthropometric measures of adiposity, and associations with the cluster of the components of MetS, in a middle-income country. METHODS: MetS was defined by the International Diabetes Federation criteria. To assess the association between anthropometric variables (waist circumference (WC), waist-to-hip ratio (W/H), body mass index (BMI)), strength (handgrip/kg bodyweight (HGS/BW)) and the cluster of MetS, we created a MetS score. For each alteration (high triglycerides, low HDLc, dysglycemia, or high blood pressure) one point was conferred. To evaluate the association an index of fat:muscle and MetS score, participants were divided into 9 groups based on combinations of sex-specific tertiles of WC and HGS/BW. RESULTS: The overall prevalence of MetS in the 5,026 participants (64% women; mean age 51.2 years) was 42%. Lower HGS/BW, and higher WC, BMI, and W/H were associated with a higher MetS score. Amongst the 9 HGS/BW:WC groups, participants in the lowest tertile of HGS/BW and the highest tertile of WC had a higher MetS score (OR = 4.69 in women and OR = 8.25 in men;p < 0.01) compared to those in the highest tertile of HGS/BW and in the lowest tertile of WC. CONCLUSION: WC was the principal risk factor for a high MetS score and an inverse association between HGS/BW and MetS score was found. Combining these anthropometric measures improved the prediction of metabolic alterations over either alone.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".