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印度南部城乡18至60岁成年人代谢综合征的患病率及其决定因素:一项基于社区的横断面研究

Prevalence and Determinants of Metabolic Syndrome Among Adults (18-60 Years) in Urban and Rural South India: A Community-Based Cross-Sectional Study.

作者信息

Prasad Bidhu Revathy, Muraleedharan Aswani, Daniel Roy A, Surya B N

机构信息

Major Trauma, St Mary's Hospital, Imperial College Healthcare National Health Service (NHS) Trust, London, GBR.

Trauma and Orthopaedics, Pilgrim Hospital, United Lincolnshire Hospitals National Health Service (NHS) Trust, Boston, GBR.

出版信息

Cureus. 2025 May 19;17(5):e84384. doi: 10.7759/cureus.84384. eCollection 2025 May.

DOI:10.7759/cureus.84384
PMID:40539174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12178443/
Abstract

Background Metabolic syndrome (MetS) constitutes a multifaceted clinical condition characterised by a constellation of metabolic risk factors including central obesity, atherogenic dyslipidemia, elevated blood pressure, and impaired glucose metabolism, which synergistically elevate the risk of cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). Rapid shifts in lifestyle have notably elevated the incidence of MetS in India, particularly in urban regions. Yet, sparse comparative data is available regarding the urban-rural differences in MetS prevalence within Tamil Nadu. Thus, this research sought to assess the prevalence of MetS and to identify associated risk factors among adults residing in urban and rural locales of Tamil Nadu. Methods Employing a multistage random sampling approach, this community-centric cross-sectional study encompassed 1,000 adults, evenly divided between urban (n=500) and rural (n=500) settings. Data were gathered through structured face-to-face interviews, anthropometric assessments, and biochemical analyses. The National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) guidelines were utilised to define MetS. Analytical methods included descriptive statistics, chi-square tests, independent samples t-tests, and multivariate logistic regression modelling to pinpoint the critical determinants of MetS. Results The cumulative prevalence of MetS was 31.6% (95% confidence interval (CI): 28.7%-34.6%; 316 participants), with a higher prevalence in urban areas at 34.8% (95% CI: 31.9%-37.8%; 174 participants) compared to 28.4% in rural areas (95% CI: 25.6%-31.3%; 142 participants). The study identified significant predictors of MetS, such as age 40 years or above (adjusted odds ratio (AOR): 2.2, p<0.01), female sex (AOR: 2.8, p<0.01), tobacco smoking (AOR: 2.7, p<0.01), sedentary lifestyle (AOR: 2.7, p=0.02), minimal fruit intake (AOR: 4.4, p=0.01), habitual consumption of fried foods (AOR: 5.3, p=0.01), existing diabetes mellitus (AOR: 3.2, p=0.02), and obesity (AOR: 2.3, p=0.03). Conclusion Urban populations exhibited a higher MetS prevalence, likely attributed to modifiable dietary patterns and lifestyle choices. Regular screening initiatives, lifestyle adjustments, and targeted nutritional interventions emerge as vital preventive and therapeutic measures. Public health strategies should emphasise early detection and encourage behavioural modifications, specifically targeting at-risk populations.

摘要

背景 代谢综合征(MetS)是一种多方面的临床病症,其特征是一系列代谢危险因素,包括中心性肥胖、致动脉粥样硬化血脂异常、血压升高和糖代谢受损,这些因素协同增加心血管疾病(CVD)和2型糖尿病(T2DM)的风险。生活方式的迅速转变显著提高了印度代谢综合征的发病率,尤其是在城市地区。然而,关于泰米尔纳德邦城乡代谢综合征患病率差异的比较数据稀少。因此,本研究旨在评估泰米尔纳德邦城乡地区成年人代谢综合征的患病率,并确定相关危险因素。方法 本以社区为中心的横断面研究采用多阶段随机抽样方法,纳入1000名成年人,城乡各500人。通过结构化面对面访谈、人体测量评估和生化分析收集数据。采用美国国家胆固醇教育计划成人治疗组第三次报告(NCEP ATP III)指南来定义代谢综合征。分析方法包括描述性统计、卡方检验、独立样本t检验和多变量逻辑回归建模,以确定代谢综合征的关键决定因素。结果 代谢综合征的累积患病率为31.6%(95%置信区间(CI):28.7%-34.6%;316名参与者),城市地区患病率较高,为34.8%(95%CI:31.9%-37.8%;174名参与者),而农村地区为28.4%(95%CI:25.6%-31.3%;142名参与者)。该研究确定了代谢综合征的重要预测因素,如40岁及以上年龄(调整优势比(AOR):2.2,p<0.01)、女性(AOR:2.8,p<0.01)、吸烟(AOR:2.7,p<0.01)、久坐不动的生活方式(AOR:2.7,p=0.02)、水果摄入量极少(AOR:4.4,p=0.01)、经常食用油炸食品(AOR:5.3,p=0.01)、现患糖尿病(AOR:3.2,p=0.02)和肥胖(AOR:2.3,p=0.03)。结论 城市人群代谢综合征患病率较高,可能归因于可改变的饮食模式和生活方式选择。定期筛查举措、生活方式调整和有针对性的营养干预是重要的预防和治疗措施。公共卫生策略应强调早期发现,并鼓励行为改变,特别是针对高危人群。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/12178443/9a6ef10cbca7/cureus-0017-00000084384-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/12178443/b69b746b55dd/cureus-0017-00000084384-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/12178443/9a6ef10cbca7/cureus-0017-00000084384-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/12178443/b69b746b55dd/cureus-0017-00000084384-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c027/12178443/9a6ef10cbca7/cureus-0017-00000084384-i02.jpg

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