Literature DB >> 29885819

Patterns of clustering of the metabolic syndrome components and its association with coronary heart disease in the Multi-Ethnic Study of Atherosclerosis (MESA): A latent class analysis.

Seyed Mohammad Riahi1, Soraya Moamer2, Mahshid Namdari3, Yaser Mokhayeri4, Mohammad Amin Pourhoseingholi5, Seyed Saeed Hashemi-Nazari6.   

Abstract

BACKGROUND: The Metabolic syndrome (MetS), refers to one of the most challenging public health issues across the world. The aim of this study was to explore the clusters of participants on the basis of MetS components and determine its effect on coronary heart disease (CHD).
METHODS: This study used the information from Multi-Ethnic Study of Atherosclerosis (MESA). MESA was performed at 6 US sites and was a population-based cohort study of 6776 adults (3576 females; 3200 males), aged 45 to 84 years. The participants were free of clinical cardiovascular disease at baseline. Latent class analysis (LCA) was conducted to achieve the study's objectives. The outcome variable was CHD during the study period (2000-2012).
RESULTS: The prevalence of all Mets components (except triglyceride (TG) and fasting blood glucose (FBS)) is more common in females than in males. Three latent classes were recognized: (1) Non-MetS, (2) low risk, and (3) MetS. Notably, MetS latent class included 29.88% and 35.38% in females and males, respectively. After adjustment for covariates (e.g. demographic, biomarker etc.), MetS latent class showed a positive association with CHD events in both genders.
CONCLUSIONS: Results showed that clustering pattern of the MetS components, as well as the association between latent classes and risk of incident CHD events, are different in females and males. Notable percentages of individuals are in the MetS class, which emphasizes the necessity of implementing preventive interventions for this sub-group of the population.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Coronary heart disease; Latent class analysis; MESA; Metabolic syndrome; NECP-ATPIII criteria; USA

Mesh:

Year:  2018        PMID: 29885819     DOI: 10.1016/j.ijcard.2018.05.080

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  6 in total

1.  Correlation Analysis Between Required Surgical Indexes and Complications in Patients With Coronary Heart Disease.

Authors:  Meiyi Tao; Xiaoling Yao; Shengli Sun; Yuelan Qin; Dandan Li; Juan Wu; Yican Xiong; Zhiyu Teng; Yunfei Zeng; Zuoheng Luo
Journal:  Front Surg       Date:  2022-07-06

Review 2.  Recent advances in managing/understanding the metabolic syndrome.

Authors:  Carlos A Aguilar-Salinas; Tannia Viveros-Ruiz
Journal:  F1000Res       Date:  2019-04-03

3.  Application of Latent Class Analysis to Identify Metabolic Syndrome Components Patterns in adults: Tehran Lipid and Glucose study.

Authors:  Noushin Sadat Ahanchi; Farzad Hadaegh; Abbas Alipour; Arash Ghanbarian; Fereidoun Azizi; Davood Khalili
Journal:  Sci Rep       Date:  2019-02-07       Impact factor: 4.379

4.  Subtypes of Premorbid Metabolic Syndrome and Associated Clinical Outcomes in Older Adults.

Authors:  Chu-Sheng Lin; Wei-Ju Lee; Shih-Yi Lin; Hui-Ping Lin; Ran-Chou Chen; Chi-Hung Lin; Liang-Kung Chen
Journal:  Front Med (Lausanne)       Date:  2022-02-11

5.  The neighbourhood environment and profiles of the metabolic syndrome.

Authors:  Anthony Barnett; Ester Cerin; Erika Martino; Luke D Knibbs; Jonathan E Shaw; David W Dunstan; Dianna J Magliano; David Donaire-Gonzalez
Journal:  Environ Health       Date:  2022-09-03       Impact factor: 7.123

6.  Non-alcoholic fatty liver disease (NAFLD), metabolic syndrome and cardiovascular events in atrial fibrillation. A prospective multicenter cohort study.

Authors:  Daniele Pastori; Angela Sciacqua; Rossella Marcucci; Maria Del Ben; Francesco Baratta; Francesco Violi; Pasquale Pignatelli
Journal:  Intern Emerg Med       Date:  2021-03-13       Impact factor: 3.397

  6 in total

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