The Association Between Lean-to-Fat Mass Ratio and Cardiometabolic Abnormalities: An Analytical Cross-Sectional Study
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVDs) are a global health threat, significantly impacting Latin America. Cardiometabolic abnormalities (CAs), encompassing lipid profile, fasting plasma glucose, and blood pressure, contribute to CVD prevalence. Despite high CA incidence, research in Latin America has primarily focused on traditional adiposity indices, overlooking the intricate relationship between fat and lean body components. The study aimed to analyze the association between the lean-to-fat mass ratio (LFMR) and CAs in the adult Peruvian population. Methods: This was an analytical cross-sectional study using secondary data from the PERU MIGRANT study (2007, 989 participants). The main outcome variable was CA defined as having ≥ 2 out of six metabolic components (high triglycerides, impaired fasting glucose, high blood pressure, low high-density lipoprotein (HDL)-cholesterol, insulin resistance, and high C-reactive protein). The main exposure variable LFMR was divided into tertiles. A generalized linear model was used with log link and robust variance Poisson family to calculate crude (cPR) and adjusted prevalence ratios (aPRs) and 95% confidence intervals (95% CIs). Results: A total of 959 adults aged 30 years or older were included in the analysis (53% females). The prevalence of CA was 50.9%. Females aged 30 - 44 years old showed statistically significant inverse associations for the middle (aPR: 0.57, 95% CI: 0.42 - 0.78) and highest (aPR: 0.22, 95% CI: 0.14 - 0.35) LFMR categories. Similar trends were seen for females aged 45 - 59 years and ≥ 60 years, and males aged 30 - 44 years, while for males aged 45 - 59 years, only the middle LFMR category was associated. No statistically significant association between LFMR and CA was found among old males. Conclusions: LFMR was negatively associated with CA, among the Peruvian adult population. These findings underscore the relevance of LFMR in understanding cardiometabolic health disparities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".