Association between ratio indexes of body composition phenotypes and metabolic risk in Italian adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it