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Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
Published on: September 27, 2024
Incidence and prediction nomogram for metabolic syndrome in a middle-aged Vietnamese population: a 5-year follow-up
Tran Quang Thuyen1,2, Dinh Hong Duong1, Bui Thi Thuy Nga3
1Department of Epidemiology, Vietnam Military Medical University, 104 Phung Hung, Ha Dong, Hanoi, Vietnam.
Purpose:
We aimed to determine the incidence and prediction nomogram for new-onset metabolic syndrome (MetS) in a middle-aged Vietnamese population.
Methods:
A population-based prospective study was conducted in 1150 participants aged 40-64 years without MetS at baseline and followed-up for 5 years. Data on lifestyle factors, socioeconomic status, family diabetes history, and anthropometric measures were collected. MetS incidence was estimated in general population and subgroup of age, gender, and MetS components. A Cox proportional hazards regression was used to estimate hazard ratios (HRs) with 95% confidence intervals (CI) for MetS. A prediction nomogram was developed and checked for discrimination and calibration.
Results:
During median follow-up of 5.14 years, the accumulate MetS incidence rate was 23.4% (95% CI: 22.2-24.7). The annual incidence rate (95% CI) was 52.9 (46.7-60.1) per 1000 person-years in general population and higher in women [56.6 (48.7-65.9)] than men [46.5 (36.9-59.3)]. The HRs (95% CI) for developing MetS were gender [females vs males: 2.04 (1.26-3.29)], advanced age [1.02 (1.01-1.04) per one year], waist circumference [1.08 (1.06-1.10) per one cm] and other obesity-related traits, and systolic blood pressure [1.02 (1.01-1.03) per one mmHg]. The prediction nomogram for MetS had a good discrimination (C-statistics = 0.742) and fit calibration (mean absolute error = 0.009) with a positive net benefit in the predicted probability thresholds between 0.13 and 0.70.
Conclusions:
The study is the first to indicate an alarmingly high incidence of MetS in a middle-aged population in Vietnam. The nomogram with simply applicable variables would be useful to qualify individual risk of developing MetS.
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