The Incidence and Determinants of Metabolic Syndrome Amongst a Group of Migrants to Qatar: A Prospective Longitudinal Observational Cohort Study 24-Months Post-Migration
Bibliographic record
Abstract
While there is some evidence that migration to Western countries increases metabolic syndrome (MetS) risk, there is a lack of data pertaining to migration to the Middle East. This study aimed to investigate the relationship between migration and MetS incidence following 24-months of residency in Qatar and identify possible MetS determinants. Migrants to Qatar employed at Hamad Medical Corporation (the national health service) aged 18–65 years were invited to participate. Baseline and follow-up screening for MetS included HbA1c, triglycerides, HDL-cholesterol, blood pressure, and waist circumference. MetS-free migrants were rescreened 24-months post-migration, and the World Health Organization STEPwise questionnaire was administered, assessing changes in lifestyle from baseline. Of 1095 migrants contacted, 472 consented to participate, 205 of whom had normal metabolic parameters at baseline; 160 completed follow-up screening. Most participants were males (74.6%, n = 153) and Asian (81.0%, n = 166/205), and two thirds (66.3%, n = 136/205) were nurses. The incidence of new-onset MetS was 17.0% (n = 27/160, 95%CI; 11.0–23.0%), with 81.0% (n = 129/160, 95%CI; 73.8–86.0%) having at least one MetS element 24-months post-residency in Qatar. Male gender was a risk factor for MetS (adjusted odds ratio (AOR) = 3, p = 0.116), as was consuming medication that could induce MetS (AOR = 6.3, p < 0.001). There is merit in further research targeting these groups.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".