Qatar's Silent Epidemic: A Comprehensive Meta-analysis on the Prevalence of Metabolic Syndrome
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome comprises various conditions like abdominal obesity, insulin resistance, elevated triglyceride levels, reduced HDL, and high blood pressure, which pose significant health challenges globally. It's imperative to determine its prevalence in specific populations to formulate effective preventive measures. OBJECTIVE: This systematic review and meta-analysis aimed to determine the prevalence of metabolic syndrome in the Qatari population. METHODS: Using the PRISMA guidelines, a systematic search was executed on PubMed until July 2023 with keywords "Metabolic syndrome" and "Qatar." Eligibility criteria included human subjects, studies assessing metabolic syndrome components, and research conducted in Qatar or on Qatari subjects. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS). Pooled prevalence rates were calculated using the inverse variance weighting metaanalysis. RESULTS: Out of 237 studies, 14 met our inclusion criteria, with a combined sample size of 14,772 from the Qatari population. The overall pooled prevalence of metabolic syndrome was 26%. The ATP III and IDF criteria exhibited significant differences in prevalence rates, with the IDF criteria showing a higher prevalence. Patients in the age of 40 or older demonstrated a higher prevalence compared to the younger group. Studies post-2018 reported a decreasing trend in metabolic syndrome prevalence. CONCLUSION: The prevalence of metabolic syndrome in the Qatari population is comparable to rates in the Middle East. The study underscores the need for tailored interventions and strategies, especially targeting the older age group. Continuous research and monitoring are essential to track and understand the disease's progression in Qatar.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.045 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".