Sultana Sabera, Nomura Shuhei, Sheng Chris Fook, Hashizume Masahiro
Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan.
AJPM Focus. 2024 Aug 24;3(6):100273. doi: 10.1016/j.focus.2024.100273. eCollection 2024 Dec.
Metabolic comorbidities are involved in the development and progression of noncommunicable diseases. There is convincing evidence that lifestyles are important contributors to metabolic comorbidities. This study measured the metabolic comorbidity score of South Asian adults and identified its relationship with lifestyles.
The authors studied 5 South Asian countries, including Afghanistan, Bangladesh, Bhutan, Nepal, and Sri Lanka, using the World Health Organization's STEPwise approach to noncommunicable disease risk factor surveillance data between 2014 and 2019. This was a nationally representative and cross-sectional survey on participants aged 15-69 years. The sample size was 27,616. The outcome was metabolic comorbidity score, calculated on the basis of total cholesterol, fasting plasma glucose, blood pressure, and abdominal obesity. Total metabolic comorbidity score of each participant varied between 0 and 8. It was then divided into 3 ranges: the lowest range (total metabolic comorbidity score <3), medium range (total metabolic comorbidity score ≥3 and ≤5), and the highest range (total metabolic comorbidity score ≥6). On the basis of the outcome of nonparametric receiver operating characteristics analysis, the medium and the highest ranges together were considered as higher metabolic comorbidity score. The lowest range was considered as lower metabolic comorbidity score. The higher metabolic comorbidity score was coded as 1, and the lower metabolic comorbidity score was coded as 0. Thus, the outcome variable, metabolic comorbidity score, became a binary variable. Exposures included physical inactivity (<150 minutes of medium-to-vigorous physical activity/week), high daily sedentary time (≥9 hours/day), use of tobacco (present or past smoking or daily use of smokeless tobacco products), and consumption of alcohol (at least once per month in the last 1 year). Binomial logistic regression model produced the OR with corresponding 95% CIs.
The prevalence of higher metabolic comorbidity score was 34% among South Asian adults, 25% among the male respondents, and 41% among the female respondents. Participants who were physically inactive (OR=1.26; 95% CI= 1.17, 1.36), had high sedentary time (OR=1.24; 95% CI=1.11, 1.33), and consumed alcohol (OR=1.40; 95% CI=1.23, 1.53) showed higher metabolic comorbidity score than participants who were physically active, had low sedentary time, and did not consume alcohol respectively. However, the authors found an inverse association (OR=0.75; 95% CI=0.71, 0.81) between the use of tobacco and metabolic comorbidity score.
One third of South Asian adults had higher metabolic comorbidity score. Physical inactivity, daily sedentary hours, and minimal alcohol consumption were associated with higher metabolic comorbidity score.
代谢合并症与非传染性疾病的发生和发展有关。有确凿证据表明,生活方式是代谢合并症的重要促成因素。本研究测量了南亚成年人的代谢合并症评分,并确定了其与生活方式的关系。
作者使用世界卫生组织的逐步非传染性疾病危险因素监测方法,研究了包括阿富汗、孟加拉国、不丹、尼泊尔和斯里兰卡在内的5个南亚国家2014年至2019年期间的数据。这是一项针对15至69岁参与者的具有全国代表性的横断面调查。样本量为27616。结果变量为代谢合并症评分,根据总胆固醇、空腹血糖、血压和腹型肥胖计算得出。每位参与者的总代谢合并症评分在0至8之间。然后将其分为3个范围:最低范围(总代谢合并症评分<3)、中等范围(总代谢合并症评分≥3且≤5)和最高范围(总代谢合并症评分≥6)。根据非参数受试者工作特征分析结果,将中等范围和最高范围合并视为较高的代谢合并症评分,最低范围视为较低的代谢合并症评分。较高的代谢合并症评分编码为1,较低的代谢合并症评分编码为0。因此,结果变量代谢合并症评分成为一个二元变量。暴露因素包括身体活动不足(每周中等至剧烈身体活动<150分钟)、每日久坐时间长(≥9小时/天)、吸烟(目前或过去吸烟或每日使用无烟烟草制品)和饮酒(过去1年中每月至少一次)。二项式逻辑回归模型得出比值比及相应的95%置信区间。
南亚成年人中较高代谢合并症评分的患病率为34%,男性受访者中为25%,女性受访者中为41%。与身体活动活跃、久坐时间短且不饮酒的参与者相比,身体活动不足(比值比=1.26;95%置信区间=1.17, 1.36)、久坐时间长(比值比=1.24;95%置信区间=1.11, 1.33)和饮酒(比值比=1.40;95%置信区间=1.23, 1.53)的参与者代谢合并症评分更高。然而,作者发现吸烟与代谢合并症评分之间存在负相关(比值比=0.75;95%置信区间=0.71, 0.81)。
三分之一的南亚成年人有较高的代谢合并症评分。身体活动不足、每日久坐时间和少量饮酒与较高的代谢合并症评分相关。