Nakanga Wisdom P, Crampin Amelia C, Mkandawire Joseph, Banda Louis, Andrews Rob C, Hattersley Andrew T, Nyirenda Moffat J, Rodgers Lauren R
National Institute for Health Research (NIHR), Exeter Clinical Research Facility, University of Exeter, Exeter, United Kingdom.
Malawi Epidemiology and Intervention Research Unit (MEIRU), Karonga and Lilongwe, Malawi.
PLOS Glob Public Health. 2023 Sep 27;3(9):e0001263. doi: 10.1371/journal.pgph.0001263. eCollection 2023.
Sub-Saharan Africa is projected to have the highest increase in the number of people with diabetes worldwide. However, the drivers of diabetes in this region have not been clearly elucidated. The aim of this study was to evaluate the incidence of diabetes and the predictors of progression in a population-based cohort with impaired fasting glucose (IFG) in Malawi. We used data from an extensive rural and urban non-communicable disease survey. One hundred seventy-five, of 389 individuals with impaired fasting glucose (IFG) at baseline, age 48 ±15 years and body mass index 27.5 ±5.9 kg/m2 were followed up for a median of 4.2 years (714 person-years). Incidence rates were calculated, and predictors of progression to diabetes were analysed using multivariable logistic regression models, with overall performance determined using receiver operator characteristics (ROC) curves. The median follow-up was 4.2 (IQR 3.4-4.7) years. Forty-five out of 175 (26%) progressed to diabetes. Incidence rates of diabetes were 62.9 per 1000 person-years 95% CI, 47.0-84.3. The predictors of progression were higher; age (odds ratio [OR] 1.48, P = 0.046), BMI (OR 1.98, P = 0.001), waist circumference (OR 2.50,P<0.001), waist-hip ratio (OR 1.40, P = 0.03), systolic blood pressure (OR 1.56, P = 0.01), fasting plasma glucose (OR 1.53, P = 0.01), cholesterol (OR 1.44, P = 0.05) and low-density lipoprotein cholesterol (OR 1.80, P = 0.002). A simple model combining fasting plasma glucose and waist circumference was predictive of progression to diabetes (ROC area under the curve = 0.79). The incidence of diabetes in people with IFG is high in Malawi and predictors of progression are like those seen in other populations. Our data also suggests that a simple chart with probabilities of progression to diabetes based on waist circumference and fasting plasma glucose could be used to identify those at risk of progression in clinical settings in sub-Saharan Africa.
预计撒哈拉以南非洲地区糖尿病患者人数的增长幅度将位居全球之首。然而，该地区糖尿病的驱动因素尚未得到明确阐释。本研究旨在评估马拉维一个基于人群的空腹血糖受损（IFG）队列中糖尿病的发病率以及病情进展的预测因素。我们使用了一项广泛的城乡非传染性疾病调查的数据。在基线时，389名空腹血糖受损（IFG）个体中有175人，年龄为48±15岁，体重指数为27.5±5.9kg/m²，随访时间中位数为4.2年（714人年）。计算发病率，并使用多变量逻辑回归模型分析糖尿病进展的预测因素，通过受试者工作特征（ROC）曲线确定总体表现。中位随访时间为4.2（四分位间距3.4 - 4.7）年。175人中有45人（26%）进展为糖尿病。糖尿病发病率为每1000人年62.9例（95%置信区间，47.0 - 84.3）。病情进展的预测因素包括年龄较大（比值比[OR]1.48，P = 0.046）、体重指数（OR 1.98，P = 0.001）、腰围（OR 2.50，P<0.001）、腰臀比（OR 1.40，P = 0.03）、收缩压（OR 1.56，P = 0.01）、空腹血糖（OR 1.53，P = 0.01）、胆固醇（OR 1.44，P = 0.05）和低密度脂蛋白胆固醇（OR 1.80，P = 0.002）。一个结合空腹血糖和腰围的简单模型可预测糖尿病进展（曲线下面积=0.79）。马拉维空腹血糖受损人群中糖尿病发病率较高，病情进展的预测因素与其他人群相似。我们的数据还表明，一个基于腰围和空腹血糖的糖尿病进展概率简单图表可用于在撒哈拉以南非洲的临床环境中识别有进展风险的人群。
