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Epidemiological predictors of metabolic syndrome in urban West Bengal, India
INTRODUCTION: Metabolic syndrome is one of the emerging health problems of the world. Its prevalence is high in urban areas. Though pathogenesis is complex, but the interaction of obesity, sedentary lifestyle, dietary, and genetic factors are known as contributing factors. Community-based studies we...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776605/ https://www.ncbi.nlm.nih.gov/pubmed/26985412 http://dx.doi.org/10.4103/2249-4863.174279 |
Sumario: | INTRODUCTION: Metabolic syndrome is one of the emerging health problems of the world. Its prevalence is high in urban areas. Though pathogenesis is complex, but the interaction of obesity, sedentary lifestyle, dietary, and genetic factors are known as contributing factors. Community-based studies were very few to find out the prevalence or predictors of the syndrome. OBJECTIVES: To ascertain the prevalence and epidemiological predictors of metabolic syndrome. MATERIALS AND METHODS: A total of 690 study subjects were chosen by 30 clusters random sampling method from 43 wards of Durgapur city. Data were analyzed in SPSS version 20 software and binary logistic regression was done to find out statistical significance of the predictors. RESULTS: Among 32.75% of the study population was diagnosed as metabolic syndrome according to National Cholesterol Education Program Adult Treatment Panel III definition with a modification for Asia Pacific cut-off of waist circumference. Odds were more among females (2.43), upper social class (14.89), sedentary lifestyle (17.00), and positive family history. CONCLUSION: The overall prevalence of metabolic syndrome was high in urban areas of Durgapur. Increased age, female gender, higher social status, sedentary lifestyle, positive family history, and higher education were the statistically significant predictors of metabolic syndrome. |
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