Dyslipidemia Prevalence and Risk Factors in Al Ain: A Retrospective Cross-Sectional Analysis
Bibliographic record
Abstract
Background: Dyslipidemia, defined by abnormal lipid profiles, is a key modifiable risk factor for cardiovascular diseases (CVDs), contributing significantly to morbidity and mortality globally. Although dyslipidemia prevalence is high in the United Arab Emirates (UAE), data specific to Al Ain City remain limited. The aim of the study was to assess the prevalence of dyslipidemia and its associated risk factors among adults in Al Ain City, UAE, and to inform targeted public health strategies. Methods: A retrospective, cross-sectional study was conducted at Burjeel Royal Hospital, Al Ain, UAE, utilizing data collected during September - October 2023. Data from 398 adults were analyzed. Participants included outpatient clinic attendees aged ≥ 18 years, with exclusions for pregnant or lactating women and incomplete medical records. Results: The primary outcome was dyslipidemia prevalence, defined using lipid profile abnormalities. Secondary outcomes included associations with modifiable risk factors such as obesity, hypertension, diabetes, and lifestyle factors. Dyslipidemia prevalence was 66.1%, with males demonstrating higher low-density lipoprotein (LDL) cholesterol levels and females exhibiting higher high-density lipoprotein (HDL) cholesterol levels. Significant risk factors included obesity, hypertension, diabetes, poor dietary habits, and physical inactivity. Older age groups exhibited higher dyslipidemia rates, with notable gender-specific differences in lipid profiles. Conclusions: The findings reveal a high burden of dyslipidemia in Al Ain, driven by modifiable risk factors. Public health interventions, including lifestyle modifications and routine lipid screening, are urgently needed to mitigate cardiovascular risks. This study establishes a baseline for future regional surveillance and intervention strategies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".