Prevalence of sarcopenic obesity and association with metabolic syndrome in an adult Iranian cohort: The Fasa PERSIAN cohort study
Bibliographic record
Abstract
Sarcopenic obesity (SO) is characterised by a concomitant high fat mass (FM) and low fat free mass (FFM) leading to an increased cardio-metabolic risk. This analysis aims to estimate the SO prevalence in Iranian adults and evaluate the association of SO with metabolic syndrome (MetS) risk. This cross-sectional analysis included 4296 subjects (age 35-70 years, 55.2% females). Body composition parameters, measured by bioelectrical impedance included: FM, FFM, appendicular lean mass (ALM) and skeletal mass index. SO was classified according to five criteria: (1) FM%-SMI; (2) FM%-ALM/% weight (wt%); (3) FM%-ALM/body mass index (BMI); (4) Residuals of ALM and FM and (5) FM/FFM Ratio. Multivariate logistic regression was applied to explore the association between SO models with MetS risk stratified by gender. Receiving operating characteristic (ROC) curves were used to identify the best FM/FFM ratio cut-off value for detecting MetS cases in males and females. The prevalence of SO varied between 4% and 26% depending upon the classification method. The prevalence of MetS was 12.8% and 31.6% in males and females, respectively. SO models based on ALM/wt% and FM/FFM ratio showed the strongest association with MetS risk in males (OR: 11.5, 95%CI: 7.5-17.7, p < 0.001 and OR: 10.1, 95%CI: 6.9-14.7, p < 0.001, respectively) and females (OR: 4.1, 95%CI: 3.0-5.6, p < 0.001 and OR: 4.6, 95%CI: 3.5-5.9, p < 0.001, respectively). SO is a prevalent condition in an adult Iranian population and the ALM/wt% and the FM/FFM ratio models of SO appeared to be associated with higher MetS risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".