Prevalence and pattern of metabolic syndrome among residents in the chronic diseases comprehensive prevention and control demonstration area of Shandong Province
Objective This study aimed to examine the current prevalence of Metabolic Syndrome(MetS)within the comprehensive prevention and control demonstration areas for chronic diseases in Shandong Province and to investigate the disease patterns of MetS among patients,thereby providing a foundation for assessing the disease burden associated with MetS and developing evidence-based prevention and control strategies.Methods From July to September 2022,a multi-stage stratified cluster sampling method was conducted on 3,216 community-dwelling residents aged 35 and above from eight counties(districts,cities)within Shandong Province as the study subjects.Data were collected through questionnaires,physical examinations,and biochemical tests to gather demographic information and health-related indicators.The proportion of the population meeting the definition of MetS was evaluated,and the disease patterns of MetS among the community residents were explored using latent class analysis.Results A total of 3 208 participants(51.15％males,48.85％females,mean age 54.8±12.03 years)were included in the final analysis;42.43％met the criteria for MetS.The prevalence of MetS increased with age(P＜0.01).The highest component rates of MetS were elevated blood pressure(60.66％),central obesity(57.95％),and low HDL-C(54.71％),and the most common disease pattern identified was a combination of central obesity,high blood pressure,low HDL-C hyperlipidemia,high TG hyperlipidemia.Conclusions Compared with the prevalence rate of MetS reported in previous studies,the prevalence rate of MetS in comprehensive prevention and control demonstration areas of Shandong Province was higher.Multiple measures should be taken to further strengthen the prevention and control of metabolic syndrome in the demonstration area.
Chronic diseasesCommunity residentsMetabolic syndromeEpidemic statusLatent class analysis
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NSTL
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