| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 14
| Issue : 1 | Page : 69 |
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Identifying factors related to serum lipids using multilevel quantile model: Analysis of nationwide STEPs survey 2016
Parisa Mohseni1, Davood Khalili2, Shirin Djalalinia3, Farshad Farzadfar4, Yadollah Mehrabi1
1 Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran
2 Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran
3 Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
4 Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
Correspondence Address:
Yadollah Mehrabi
Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran
Islamic Republic of Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijpvm.ijpvm_464_21
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Background: Lipid disorder is a modifiable risk factor for diseases related to plaque formation in arteries such as heart attack, stroke, and peripheral vascular diseases. Identifying related factors and diagnosis and treatment in time reduces the incidence of non-communicable diseases (NCDs). The aim of this study was to determine factors associated with lipids based on a national survey data. Methods: Data of 16757 individuals aged 25–64 years obtained from the Iranian STEPwise approach to NCD risk factor surveillance (STEPs) performed in 2016, through multistage random sampling, were analyzed. Because of clustered, hierarchical, and skewed form of the data, factors related to total holesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol) (LDL-C), high-density lipoprotein-cholesterol) (HDL-C), TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C were determined applying multilevel quantile mixed model. Parameters of the model were estimated on the basis of random effect of the province as well as urban or rural area for 10th, 25th, 50th, 75th, and 90th quantiles. Statistical analyses were performed by R software version 4.0.2. Results: Significant relationship was found between age, body mass index (BMI), waist circumference (WC), diabetes, hypertension, smoking, physical activity, education level, and marital status with TC, LDL-C, HDL-C, LDL-C, and LDL-C/HDL-C. With increasing BMI and WC, subjects had higher levels of serum lipids, especially in higher quantiles of lipid levels. Lipid levels were significantly increased among smokers and those with diabetes or hypertension. The random effects were also significant showing that there is a correlation between the level of lipids in provincial habitants as well as urban and rural areas. Conclusions: This study showed that the effect of each factor varies depending on the centiles of the lipids. Significant relationship was found between sociodemographic, behaviors, and anthropometric indices with lipid parameters.
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