Nguyen Phuong, Ramakrishnan Usha, Katz Benjamin, Gonzalez-Casanova Ines, Lowe Alyssa E, Nguyen Hieu, Pham Hoa, Truong Truong, Nguyen Son, Martorell Reynaldo
Food Nutr Bull. 2014 Sep;35(3):301-11. doi: 10.1177/156482651403500303.
Mid-upper-arm circumference (MUAC) and calf circumference (CC) are correlated with body mass index (BMI) in adults and may be useful for screening women with underweight (BMI < 18.5 kg/m2). However, there is no consensus on appropriate MUAC and CC cutoff points in diverse populations, especially in women of reproductive age.
To assess the accuracy of different MUAC and CC cutoff points to screen for underweight and to identify the most appropriate cutoff points in a sample of women of reproductive age from rural northern Vietnam.
Anthropometric measurements (weight, height, MUAC, CC, and triceps and subscapular skinfold thicknesses) were obtained for 4,981 women of reproductive age who participated in a micronutrient intervention trial (PRECONCEPT) in Thái Nguyên Province, Vietnam. Receiver operating characteristic (ROC) analysis was used to evaluate different cutoff values of MUAC and CC and identify the most appropriate cutoff values to predict underweight.
The overall prevalence of underweight was 32%. The MUAC value of 23.5 cm and the CC value of 31 cm were identified as the best cutoffs based on low misclassification (16% for MUAC and 21% for CC) and good balance of sensitivity (89% and 85%, respectively) and specificity (71% and 67%, respectively. The ROC curves were similar across different ethnic groups, with the area under the curve (AUC) values reaching 0.89 to 0.93 for MUAC and 0.83 to 0.89 for CC.
MUAC and CC perform adequately in screening for underweight in women. The utility of these measurements in predicting functional outcomes should be examined.
上臂中部周长(MUAC)和小腿周长(CC)与成年人的体重指数(BMI)相关,可能有助于筛查体重过轻(BMI<18.5kg/m²)的女性。然而,对于不同人群,尤其是育龄妇女,合适的MUAC和CC切点尚无共识。
评估不同MUAC和CC切点筛查体重过轻的准确性,并确定越南北方农村育龄妇女样本中最合适的切点。
对参与越南太原省一项微量营养素干预试验(孕前)的4981名育龄妇女进行人体测量(体重、身高、MUAC、CC、肱三头肌和肩胛下皮褶厚度)。采用受试者工作特征(ROC)分析来评估MUAC和CC的不同切点,并确定预测体重过轻的最合适切点。
体重过轻的总体患病率为32%。基于低误分类率(MUAC为16%,CC为21%)以及良好的敏感性(分别为89%和85%)和特异性平衡(分别为71%和67%),确定23.5cm的MUAC值和31cm的CC值为最佳切点。不同种族群体的ROC曲线相似,MUAC的曲线下面积(AUC)值达到0.89至0.93,CC的AUC值达到0.83至0.89。
MUAC和CC在筛查女性体重过轻方面表现良好。应研究这些测量方法在预测功能结局方面的效用。