Cargando…
Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey
People living in the same area are more likely to experience similar socioeconomic characteristics, which leads to cluster effect and influences the generalizability of data regarding metabolic syndrome (MetS). However, previous studies did not consider or adjust for the cluster effect of living cir...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598811/ https://www.ncbi.nlm.nih.gov/pubmed/33126337 http://dx.doi.org/10.1097/MD.0000000000022883 |
_version_ | 1783602719546671104 |
---|---|
author | Xu, Tao Zhu, Guangjin Han, Shaomei |
author_facet | Xu, Tao Zhu, Guangjin Han, Shaomei |
author_sort | Xu, Tao |
collection | PubMed |
description | People living in the same area are more likely to experience similar socioeconomic characteristics, which leads to cluster effect and influences the generalizability of data regarding metabolic syndrome (MetS). However, previous studies did not consider or adjust for the cluster effect of living circumstances. The aim of this study was to determine the prevalence of MetS and associated lifestyle factors in Chinese adults 18 to 80 years of age, using multi-level generalized estimation equation (GEE). The participants came from a large-scale cross-sectional population survey. A total of 28,062 participants underwent all the blood tests. Participants meeting at least 3 of the 5 diagnostic criteria were defined as having MetS. Multi-level GEE was used to evaluate the relationship between MetS and lifestyle covariates to control the cluster effect of living circumstances. Odds ratios (ORs) and their 95% confidence intervals (CIs) were used to assess the strength of each relationship. A total of 65.70% of the participants had at least 1 clinical feature of MetS, and 2926 were diagnosed with MetS (prevalence 14.03%). 32.74%, 18.93%, 10.25%, 3.25%, and 0.53% of the participants had 1, 2, 3, 4, and 5 components, respectively. The prevalence of MetS in men (12.31%) was lower than in women (15.57%). After controlling for the cluster effect of living circumstances, many demographic and lifestyle characteristics were associated with MetS. Overweight (OR = 1.670, 95%CI: 1.600–1.743), obesity (OR = 2.287, 95% CI: 2.136–2.449), current alcohol consumption (OR = 1.053, 95% CI: 1.020–1.086), physical labor (OR=1.070, 95% CI: 1.040–1.101), a high-salt diet (OR=1.040, 95% CI: 1.009–1.071), hyperuricemia (OR=1.264, 95% CI: 1.215–1.316), short sleep duration (OR=1.032, 95% CI: 1.009–1.055), and a family history of cardiovascular disease (OR=1.065, 95% CI: 1.019–1.113), or cerebrovascular disease (OR=1.055, 95% CI: 1.007–1.104) increased the risk of MetS. The risk of MetS increased 6.9% (OR = 1.069, 95% CI: 1.053–1.085) with each 5% increase in body fat percentage. MetS has become a serious public health challenge in China. Many lifestyle factors have been found to be closely associated with MetS, including obesity, a high-salt diet, alcohol consumption, and short sleep duration. Therefore, changes in lifestyle are very important for adults to reduce the prevalence of MetS. |
format | Online Article Text |
id | pubmed-7598811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-75988112020-11-02 Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey Xu, Tao Zhu, Guangjin Han, Shaomei Medicine (Baltimore) 6600 People living in the same area are more likely to experience similar socioeconomic characteristics, which leads to cluster effect and influences the generalizability of data regarding metabolic syndrome (MetS). However, previous studies did not consider or adjust for the cluster effect of living circumstances. The aim of this study was to determine the prevalence of MetS and associated lifestyle factors in Chinese adults 18 to 80 years of age, using multi-level generalized estimation equation (GEE). The participants came from a large-scale cross-sectional population survey. A total of 28,062 participants underwent all the blood tests. Participants meeting at least 3 of the 5 diagnostic criteria were defined as having MetS. Multi-level GEE was used to evaluate the relationship between MetS and lifestyle covariates to control the cluster effect of living circumstances. Odds ratios (ORs) and their 95% confidence intervals (CIs) were used to assess the strength of each relationship. A total of 65.70% of the participants had at least 1 clinical feature of MetS, and 2926 were diagnosed with MetS (prevalence 14.03%). 32.74%, 18.93%, 10.25%, 3.25%, and 0.53% of the participants had 1, 2, 3, 4, and 5 components, respectively. The prevalence of MetS in men (12.31%) was lower than in women (15.57%). After controlling for the cluster effect of living circumstances, many demographic and lifestyle characteristics were associated with MetS. Overweight (OR = 1.670, 95%CI: 1.600–1.743), obesity (OR = 2.287, 95% CI: 2.136–2.449), current alcohol consumption (OR = 1.053, 95% CI: 1.020–1.086), physical labor (OR=1.070, 95% CI: 1.040–1.101), a high-salt diet (OR=1.040, 95% CI: 1.009–1.071), hyperuricemia (OR=1.264, 95% CI: 1.215–1.316), short sleep duration (OR=1.032, 95% CI: 1.009–1.055), and a family history of cardiovascular disease (OR=1.065, 95% CI: 1.019–1.113), or cerebrovascular disease (OR=1.055, 95% CI: 1.007–1.104) increased the risk of MetS. The risk of MetS increased 6.9% (OR = 1.069, 95% CI: 1.053–1.085) with each 5% increase in body fat percentage. MetS has become a serious public health challenge in China. Many lifestyle factors have been found to be closely associated with MetS, including obesity, a high-salt diet, alcohol consumption, and short sleep duration. Therefore, changes in lifestyle are very important for adults to reduce the prevalence of MetS. Lippincott Williams & Wilkins 2020-10-30 /pmc/articles/PMC7598811/ /pubmed/33126337 http://dx.doi.org/10.1097/MD.0000000000022883 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 |
spellingShingle | 6600 Xu, Tao Zhu, Guangjin Han, Shaomei Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey |
title | Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey |
title_full | Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey |
title_fullStr | Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey |
title_full_unstemmed | Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey |
title_short | Prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in Chinese adults from a cross-sectional survey |
title_sort | prevalence of and lifestyle factors associated with metabolic syndrome determined using multi-level models in chinese adults from a cross-sectional survey |
topic | 6600 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598811/ https://www.ncbi.nlm.nih.gov/pubmed/33126337 http://dx.doi.org/10.1097/MD.0000000000022883 |
work_keys_str_mv | AT xutao prevalenceofandlifestylefactorsassociatedwithmetabolicsyndromedeterminedusingmultilevelmodelsinchineseadultsfromacrosssectionalsurvey AT zhuguangjin prevalenceofandlifestylefactorsassociatedwithmetabolicsyndromedeterminedusingmultilevelmodelsinchineseadultsfromacrosssectionalsurvey AT hanshaomei prevalenceofandlifestylefactorsassociatedwithmetabolicsyndromedeterminedusingmultilevelmodelsinchineseadultsfromacrosssectionalsurvey |