Teufel Felix, Seiglie Jacqueline A, Geldsetzer Pascal, Theilmann Michaela, Marcus Maja E, Ebert Cara, Arboleda William Andres Lopez, Agoudavi Kokou, Andall-Brereton Glennis, Aryal Krishna K, Bicaba Brice Wilfried, Brian Garry, Bovet Pascal, Dorobantu Maria, Gurung Mongal Singh, Guwatudde David, Houehanou Corine, Houinato Dismand, Jorgensen Jutta M Adelin, Kagaruki Gibson B, Karki Khem B, Labadarios Demetre, Martins Joao S, Mayige Mary T, McClure Roy Wong, Mwangi Joseph Kibachio, Mwalim Omar, Norov Bolormaa, Crooks Sarah, Farzadfar Farshad, Moghaddam Sahar Saeedi, Silver Bahendeka K, Sturua Lela, Wesseh Chea Stanford, Stokes Andrew C, Essien Utibe R, De Neve Jan-Walter, Atun Rifat, Davies Justine I, Vollmer Sebastian, Bärnighausen Till W, Ali Mohammed K, Meigs James B, Wexler Deborah J, Manne-Goehler Jennifer
Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.
Diabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Lancet. 2021 Jul 17;398(10296):238-248. doi: 10.1016/S0140-6736(21)00844-8.
The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings.
In this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5-22·9 kg/m], upper-normal [23·0-24·9 kg/m], overweight [25·0-29·9 kg/m], or obese [≥30·0 kg/m]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region.
Our pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6-27·8), of obesity was 21·0% (19·6-22·5), and of diabetes was 9·3% (8·4-10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5-22·9 kg/m. Diabetes risk also increased steeply in individuals aged 35-44 years and in men aged 25-34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m among men in east, south, and southeast Asia to 28·3 kg/m among women in the Middle East and north Africa and in Latin America and the Caribbean.
The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines.
Harvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program.
低收入和中等收入国家(LMICs)超重、肥胖及糖尿病的患病率正在迅速上升,但在这些环境中,关于体重指数(BMI)与糖尿病之间关联的实证数据却很少。
在这项横断面研究中,我们汇总了来自57个低收入和中等收入国家具有全国代表性调查的个体层面数据。我们确定了所有在某一年进行了世界卫生组织逐步监测方法(STEPS)调查且该国属于符合条件的世界银行收入组类别的国家。对于没有进行STEPS调查、没有有效联系信息或拒绝我们数据请求的低收入和中等收入国家,我们对调查数据集进行了系统搜索。符合条件的调查在2008年及以后进行;有个体层面数据;在低收入、中低收入或中高收入国家进行;具有全国代表性;回复率达到50%或更高;包含糖尿病生物标志物(血糖测量值或糖化血红蛋白[HbA]);并且包含身高和体重数据。糖尿病在生物学上定义为空腹血糖浓度7.0 mmol/L(126.0 mg/dL)或更高;随机血糖浓度11.1 mmol/L(200.0 mg/dL)或更高;或HbA 6.5%(48.0 mmol/mol)或更高,或者通过自我报告使用糖尿病药物来确定。我们纳入了年龄在25岁及以上且具有糖尿病状态、BMI(定义为正常[18.5 - 22.9 kg/m²]、超正常[23.0 - 24.9 kg/m²]、超重[25.0 - 29.9 kg/m²]或肥胖[≥30.0 kg/m²])、性别和年龄完整数据的个体。国家被分为六个地理区域:拉丁美洲和加勒比地区、欧洲和中亚、东亚、南亚和东南亚、撒哈拉以南非洲、中东和北非以及大洋洲。我们通过多变量泊松回归和受试者工作特征曲线分析,按性别和地理区域分层,估计BMI与糖尿病风险之间的关联。
我们从57个低收入和中等收入国家的58项具有全国代表性调查中汇总的数据集包括685616名个体。超重的总体患病率为27.2%(95%置信区间26.6 - 27.8),肥胖为21.0%(19.6 - 22.5),糖尿病为9.3%(8.4 - 10.2)。在汇总分析中,BMI为23 kg/m²及以上时观察到糖尿病风险更高,与BMI为18.5 - 22.9 kg/m²相比,男性患糖尿病的风险高43%,女性高41%。在撒哈拉以南非洲,35 - 44岁的个体以及25 - 34岁的男性中,糖尿病风险也急剧增加。在分层分析中,这种关联存在相当大的区域差异。糖尿病筛查的最佳BMI阈值范围从东亚、南亚和东南亚男性的23.8 kg/m²到中东和北非以及拉丁美洲和加勒比地区女性的28.3 kg/m²。
低收入和中等收入国家中BMI与糖尿病风险之间的关联存在很大的区域差异。与目前用于评估糖尿病风险的BMI临界值相比,在较低的BMI阈值和较年轻的年龄时糖尿病风险更高。这些发现为制定针对具体情况的糖尿病筛查指南提供了重要见解。
哈佛T.H.陈公共卫生学院麦克伦南基金:院长挑战资助计划。