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High prevalence of cardiometabolic risk factors in young employees of Information Technology industry.
BACKGROUND: We assessed the burden of cardiometabolic risk factors in Information Technology (IT) employees as they are exposed to adverse lifestyle.
MATERIALS AND METHODS: In this cross-sectional study, health records were obtained from two IT industries in Pune. Prevalence of cardiometabolic risk factors [hyperglycemia, high blood pressure (BP), hypertriglyceridemia, high low-density lipoprotein (LDL)-cholesterol, low high-density lipoprotein (HDL)-cholesterol, and overweight/obesity] was determined using standard cutoffs. We also examined clustering of risk factors (≥two risk factors).
RESULTS: Data were available on 1,350 of 5,800 employees (mean age: 33 ± 6 years, 78% men). Prevalence of diabetes and hypertension was 2.5% and 13.5%, respectively. Prevalence of prediabetes, borderline high BP, hypertriglyceridemia, high LDL-cholesterol, low HDL-cholesterol, and overweight/obesity was 6.5%, 20.3%, 21%, 22.1%, 70.1%, and 51.4%, respectively. Risk factor clustering was observed in 63.5% that increased with age (P < 0.001).
CONCLUSION: Given the high burden of risk factors at relatively young age, spreading awareness and promoting healthy lifestyle through workplace interventions are warranted.
MATERIALS AND METHODS: In this cross-sectional study, health records were obtained from two IT industries in Pune. Prevalence of cardiometabolic risk factors [hyperglycemia, high blood pressure (BP), hypertriglyceridemia, high low-density lipoprotein (LDL)-cholesterol, low high-density lipoprotein (HDL)-cholesterol, and overweight/obesity] was determined using standard cutoffs. We also examined clustering of risk factors (≥two risk factors).
RESULTS: Data were available on 1,350 of 5,800 employees (mean age: 33 ± 6 years, 78% men). Prevalence of diabetes and hypertension was 2.5% and 13.5%, respectively. Prevalence of prediabetes, borderline high BP, hypertriglyceridemia, high LDL-cholesterol, low HDL-cholesterol, and overweight/obesity was 6.5%, 20.3%, 21%, 22.1%, 70.1%, and 51.4%, respectively. Risk factor clustering was observed in 63.5% that increased with age (P < 0.001).
CONCLUSION: Given the high burden of risk factors at relatively young age, spreading awareness and promoting healthy lifestyle through workplace interventions are warranted.
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