Association between ratio indexes of body composition phenotypes and metabolic risk in Italian adults
Bibliographic record
Abstract
The ratio between fat mass (FM) and fat-free mass (FFM) has been used to discriminate individual differences in body composition and improve prediction of metabolic risk. Here, we evaluated whether the use of a visceral adipose tissue-to-fat-free mass index (VAT:FFMI) ratio was a better predictor of metabolic risk than a fat mass index to fat-free mass index (FMI:FFMI) ratio. This is a cross-sectional study including 3441 adult participants (age range 18-81; men/women: 977/2464). FM and FFM were measured by bioelectrical impedance analysis and VAT by ultrasonography. A continuous metabolic risk Z score and harmonised international criteria were used to define cumulative metabolic risk and metabolic syndrome (MetS), respectively. Multivariate logistic and linear regression models were used to test associations between body composition indexes and metabolic risk. In unadjusted models, VAT:FFMI was a better predictor of MetS (OR 8.03, 95%CI 6.69-9.65) compared to FMI:FFMI (OR 2.91, 95%CI 2.45-3.46). However, the strength of association of VAT:FFMI and FMI:FFMI became comparable when models were adjusted for age, gender, clinical and sociodemographic factors (OR 4.06, 95%CI 3.31-4.97; OR 4.25, 95%CI 3.42-5.27, respectively). A similar pattern was observed for the association of the two indexes with the metabolic risk Z score (VAT:FFMI: unadjusted b = 0.69 ± 0.03, adjusted b = 0.36 ± 0.03; FMI:FFMI: unadjusted b = 0.28 ± 0.028, adjusted b = 0.38 ± 0.02). Our results suggest that there is no real advantage in using either VAT:FFMI or FMI:FFMI ratios as a predictor of metabolic risk in adults. However, these results warrant confirmation in longitudinal studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".