Prevalence and correlates of metabolic syndrome among women living in urban slums, Mysore, India.
Karl KruppArathi Prahallada RaoBenjamin PopeKavitha RaviAnisa KhanVijaya SrinivasPurnima MadhivananArun SrinivasPublished in: PLOS global public health (2023)
Metabolic Syndrome (MetSyn) is a predictor of cardiovascular disease (CVD). About a third of urban Indians suffer from MetSyn. This study examined the prevalence of MetSyn among women living in urban slums. A cross-sectional survey was carried out between October 2017 and May 2018 among a non-probability sample of slum-dwelling women, 40-64 years of age, in six government-designated slums in Mysore, India. Data were collected on demographics, diet, behavioral risks, anthropometry, blood pressure, serum glucose, hemoglobin A1c, and serum lipids. The study used a definition of MetSyn from the International Diabetes Federation Task Force on Epidemiology and Prevention with an HbA1c measure for average blood glucose. About two-fifths of the 607 participants had MetSyn (41.5%; 95% CI: 37.7-45.5). Of those, 40.9% met three criteria, 38.1% four, and 25.0%, all five criteria. Elevated BP was the most prevalent MetSyn factor (79.6%), followed by increased waist circumference (54.5%), low HDL (50.1%), elevated Hb A1c (37.1%), and elevated triglycerides (36.1%). Odds for MetSyn were 1.52 times greater for those who were 50-59 years of age compared with those 40-49 years of age (adjusted odds ratio[AOR]:1.52; 95% CI:0.96-2.40). Women with mobility issues had 1.29 times higher odds of MetSyn than those without it (AOR: 0.76, 95% CI: 0.96, 1.75). Housewives had 1.29 times greater odds of MetSyn (AOR: 1.29, 95% CI: 1.00, 1.67). There is a high prevalence of MetSyn among urban slum-dwelling women in Mysore. There is a need for interventions aimed at reducing CVD risk factors in this population.
Keyphrases
- risk factors
- blood glucose
- polycystic ovary syndrome
- metabolic syndrome
- cardiovascular disease
- blood pressure
- pregnancy outcomes
- body mass index
- type diabetes
- insulin resistance
- glycemic control
- cervical cancer screening
- breast cancer risk
- weight loss
- heart rate
- risk assessment
- machine learning
- tyrosine kinase
- hypertensive patients
- big data
- high density
- red blood cell
- artificial intelligence