The prevalence of hypercholesterolemia and associated risk factors in Al-Kharj population, Saudi Arabia: a cross-sectional survey
Bibliographic record
Abstract
Abstract Background Hypercholesterolemia (HC) is an important precursor to many cardiovascular, cerebrovascular, and peripheral vascular diseases. A report conducted by the American Heart Association showed the prevalence of HC to be 11.9%, with around 28.5 million adults age ≥ 20 years having high cholesterol levels. This study aimed to evaluate the prevalence of HC and its associated risk factors among the general population of Al-Kharj, Saudi Arabia. Method A cross-sectional study was conducted on the general population of Al-Kharj, Saudi Arabia in 2016. The representative sample consisted of 1019 individuals, who all participated on a voluntary basis. The statistical analysis was performed using SPSS version 25. Results The results of this study showed the prevalence of HC in the sample to be 12.5%. There was a significant moderate positive association between increasing age and the prevalence of HC (r = 0.240, P < 0.0001). Males had a significantly higher prevalence of HC (56.7%) compared to their female counterparts (43.3%) ( X 2 = 23.093, P ≤ 0.0001). BMI was positively and significantly associated with high cholesterol status. Participants in the overweight category had a significantly higher risk of HC (OR = 1.727; 95% CI = 1.58–1.914; P = 0.046). The non-obese (< 25 kg/m 2 ) participants had an inverse significant association with the risk of hypercholesterolemia. (OR = 0.411; 95% CI = 0.216–0.783; P = 0.007). Conclusion In this population-based study, the predominant risk factors of HC in Al-Kharj region were being of a Saudi nationality, male, having obesity, being unemployed, and being a civilian worker. There is a clear need for future screening studies of HC, as most previous studies have reported contradictory prevalence data (because they were conducted in different regions of KSA). Furthermore, well-designed prospective cohort studies are needed in the future to assess how the association between lifestyle behavioural factors such as dietary intake patterns and levels of physical activity may affect the relative risk of HC status.
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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 | 0.000 |
| 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".