Association between obesity phenotypes in adolescents and adult metabolic syndrome: Tehran Lipid and Glucose Study
Bibliographic record
Abstract
Obesity phenotypes can be regarded as an indicator of CVD risk factors. The aim of the present study was to determine the prevalence of adolescents with different obesity phenotypes and the role of obesity phenotypes in prediction of the metabolic syndrome (MetS) in adults. For this population-based cohort study, 2159 adolescents aged 11-18 years were included. Subjects were divided into four obesity phenotype groups: metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW) and metabolically unhealthy obese (MUO). Cox proportional hazard modelling was used to estimate the incidence of the MetS in adults after a median follow-up of 11·3 years. The incidence rate of the MetS in early adulthood was 111·6 (95 % CI 98·7, 126·3) per 10 000 person-years, with higher values in boys (210·1 (95 % CI 183·0, 241·3)), compared with girls (39·7 (95 % CI 30·2, 52·1)). In the age- and adult BMI-adjusted model, the hazard ratio of the MetS in adulthood for boys was 3·33 (95 % CI 2·08, 5·32) among MUO phenotype followed less than 6 years, 1·71 (95 % CI 1·01, 2·90) among MHO, and 2·52 (95 % CI 1·72, 3·68) among MUNW. All associations were attenuated in girls except for MUO phenotype followed less than 6 years (5·72 (95 % CI 2·14, 15·3)). In conclusion, MUNW and MHO phenotypes in boys, but not in girls, and MUO phenotype in both sexes with less than 6 years of follow-up increased the risk of adult MetS compared with MHNW. It seems that lack of obesity at least in boys does not protect them from MetS development in adulthood.
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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 | 0.000 |
| 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 | 0.001 |
| 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".