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Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016

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 a...

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Autores principales: Mohseni, Parisa, Khalili, Davood, Djalalinia, Shirin, Farzadfar, Farshad, Mehrabi, Yadollah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10284246/
https://www.ncbi.nlm.nih.gov/pubmed/37351060
http://dx.doi.org/10.4103/ijpvm.ijpvm_464_21
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author Mohseni, Parisa
Khalili, Davood
Djalalinia, Shirin
Farzadfar, Farshad
Mehrabi, Yadollah
author_facet Mohseni, Parisa
Khalili, Davood
Djalalinia, Shirin
Farzadfar, Farshad
Mehrabi, Yadollah
author_sort Mohseni, Parisa
collection PubMed
description 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 10(th), 25(th), 50(th), 75(th), and 90(th) 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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spelling pubmed-102842462023-06-22 Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016 Mohseni, Parisa Khalili, Davood Djalalinia, Shirin Farzadfar, Farshad Mehrabi, Yadollah Int J Prev Med Original Article 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 10(th), 25(th), 50(th), 75(th), and 90(th) 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. Wolters Kluwer - Medknow 2023-05-27 /pmc/articles/PMC10284246/ /pubmed/37351060 http://dx.doi.org/10.4103/ijpvm.ijpvm_464_21 Text en Copyright: © 2023 International Journal of Preventive Medicine https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Original Article
Mohseni, Parisa
Khalili, Davood
Djalalinia, Shirin
Farzadfar, Farshad
Mehrabi, Yadollah
Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016
title Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016
title_full Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016
title_fullStr Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016
title_full_unstemmed Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016
title_short Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016
title_sort identifying factors related to serum lipids using multilevel quantile model: analysis of nationwide steps survey 2016
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10284246/
https://www.ncbi.nlm.nih.gov/pubmed/37351060
http://dx.doi.org/10.4103/ijpvm.ijpvm_464_21
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