Prevalence of plasma lipid abnormalities and associated risk factors among Iranian adults based on the findings from STEPs survey 2021
Bibliographic record
Abstract
The study aimed to estimate the prevalence of lipid abnormalities in Iranian adults by demographic characterization, geographical distribution, and associated risk factors using national and sub-national representative samples of the STEPs 2021 survey in Iran. In this population-based household survey, a total of 18,119 individuals aged over 25 years provided blood samples for biochemical analysis. Dyslipidemia was defined by the presence of at least one of the lipid abnormalities of hypertriglyceridemia (≥ 150 mg/dL), hypercholesterolemia (≥ 200 mg/dL), high LDL-C (≥ 130 mg/dL), and low HDL-C (< 50 mg/dL in women, < 40 mg/dL in men), or self-reported use of lipid-lowering medications. Mixed dyslipidemia was characterized as the coexistence of high LDL-C with at least one of the hypertriglyceridemia and low HDL-C. The prevalence of each lipid abnormality was determined by each population strata, and the determinants of abnormal lipid levels were identified using a multiple logistic regression model. The prevalence was 39.7% for hypertriglyceridemia, 21.2% for hypercholesterolemia, 16.4% for high LDL-C, 68.4% for low HDL-C, and 81.0% for dyslipidemia. Hypercholesterolemia and low HDL-C were more prevalent in women, and hypertriglyceridemia was more prevalent in men. The prevalence of dyslipidemia was higher in women (OR = 1.8), obese (OR = 2.8) and overweight (OR = 2.3) persons, those residents in urban areas (OR = 1.1), those with inappropriate physical activity (OR = 1.2), patients with diabetes (OR = 2.7) and hypertension (OR = 1.9), and participants with a history (OR = 1.6) or familial history of CVDs (OR = 1.2). Mixed dyslipidemia prevalence was 13.6% in women and 11.4% in men (P < 0.05). The prevalence of lipid abnormalities was highly heterogeneous among provinces, and East Azarbaijan with 85.3% (81.5-89.1) and Golestan with 68.5% (64.8-72.2) had the highest and lowest prevalence of dyslipidemia, respectively. Although the prevalence of high cholesterol and LDL-C had a descending trend in the 2016-2021 period, the prevalence of dyslipidemia remained unchanged. There are modifiable risk factors associated with dyslipidemia that can be targeted by the primary healthcare system. To modify these risk factors and promote metabolic health in the country, action plans should come to action through a multi-sectoral and collaborative approach.
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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 | 0.000 | 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) | 0.000 | 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".