Hirabai Cowasji Jehangir Medical Research Institute, Jehangir Hospital, Pune, Maharashtra 411001, India.
Interdisciplinary School of Health Sciences, Savitribai Phule University, Pune, India; Senior Pediatric Endocrinologist, Jehangir Hospital, Pune and Bombay Hospital, India.
Prim Care Diabetes. 2022 Jun;16(3):466-470. doi: 10.1016/j.pcd.2022.03.006. Epub 2022 Mar 16.
Mid upper arm circumference (MUAC) measurement is an easy and low-cost method to determine nutritional status. MUAC cut-offs for screening for overnutrition in Indian children from 5 to 17 years age are recently published. We conducted this study to validate MUAC cut-offs against BMI to screen overnutrition in children with T1D in comparison with age-gender-matched healthy controls and to compare the predictive value of BMI and MUAC to assess adiposity.
This cross sectional, observational study included 249 children and adolescents (5-17 years) with T1D attending a pediatric endocrine clinic along with same number of age and gender matched healthy controls. Demographic, anthropometric and body composition data were obtained using standardized protocols and questionnaires.
The co-relation between MUAC with BMI was significant for cases and controls and percent body fat with MUAC and BMI in T1D(r = 0.854,0.917 and 0.546,0.616). The AUC of MUAC to identify obesity based on BMI cut-offs for cases and controls and of BMI and MUAC for adiposity were similar(0.745,0.918 and 0.867,0.814). Sensitivity, specificity and PPV were significantly higher in controls than in cases.
MUAC is an accurate method to identify obesity and adiposity in T1D thereby reducing the risk of development of double diabetes.
上臂中部周长(MUAC)测量是一种简单且低成本的方法,可用于确定营养状况。最近发表了印度 5 至 17 岁儿童用于筛查营养过剩的 MUAC 截断值。我们进行这项研究是为了验证 MUAC 截断值与 BMI 的一致性,以筛查 1 型糖尿病儿童的营养过剩,与年龄性别匹配的健康对照组进行比较,并比较 BMI 和 MUAC 的预测价值,以评估肥胖。
这项横断面观察性研究纳入了 249 名 5 至 17 岁的 1 型糖尿病患儿及其年龄和性别相匹配的健康对照组。使用标准化方案和问卷获得了人口统计学、人体测量学和身体成分数据。
病例组和对照组的 MUAC 与 BMI 之间存在显著相关性,T1D 患儿的体脂百分比与 MUAC 和 BMI 之间也存在显著相关性(r=0.854、0.917 和 0.546、0.616)。基于 BMI 截断值的 MUAC 识别肥胖的曲线下面积(AUC),以及 BMI 和 MUAC 对肥胖的 AUC,在病例组和对照组中相似(0.745、0.918 和 0.867、0.814)。在对照组中,敏感性、特异性和阳性预测值均显著高于病例组。
MUAC 是一种识别 1 型糖尿病肥胖和肥胖的准确方法,从而降低了双重糖尿病发展的风险。