The Most Important Predictors of Metabolic Syndrome Persistence after 10-year Follow-Up: YHHP Study
Bibliographic record
Abstract
Background: Metabolic syndrome (MetS) is one of the world's largest health epidemics, and its management is a major challenge worldwide. The aim of this 10-year follow-up study was to assess the most important predictors of MetS persistence among an Iranian adult population. Methods: In this cohort study, 887 out of 2000 participants with MetS aged 20–74 years in the central part of Iran were followed-up for about 10 years from 2005–2006 to 2015–2016. MetS was defined based on the criteria of NCEP-ATP III adopted for the Iranian population. Cox proportional hazards regression was conducted to evaluate the predictors of MetS persistence in crude- and multivariate-adjusted models. Results: Our analyses showed that 648 out of 887 participants (73%) completed the follow-up and 565 (87.2%) of them had persistence of MetS after 10-year follow-up. There was a significant association between age, weight, body mass index, triglyceride, and waist circumference in participants who had MetS compared to those without MetS after 10-year follow-up ( P < 0.05). There was a direct association between increases in the mean changes of systolic/diastolic blood pressure, waist circumference, and low HDL-C and risk of MetS persistence after adjusting the model for sex and age in the total population ( P trend < 0.05). The trends were the same for women except in diastolic blood pressure. After adjustment for potential confounders, the risk of MetS persistence in men was significantly higher than women (HR = 1.98, 95% CI: 1.38–2.85, Ptrend = 0.001). Conclusions: Most of the risk factors of MetS were positively associated with persistence of MetS. Therefore, modification of lifestyle is recommended to reduce MetS.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".