Effectiveness of Anthropometric Measurements for Identifying Diabetes and Prediabetes among Civil Servants in a Regional City of Northern Ethiopia: A Cross-Sectional Study

Abstract

Back ground. Diabetes mellitus is an emerging noncommunicable disease in Ethiopia. Overlooking an appropriate tool for identifying diabetes and prediabetes would have significant impact for future diabetes and prediabetes projections and its management. Therefore, the study aims to examine the effectiveness of anthropometric measurements for identifying prediabetes and diabetes in Mekelle city, Tigray, Northern Ethiopia. Methods. The study involved a cross-sectional survey carried out from October 2015 to February 2016 among 1504 subjects aged from 18 to 75 years of age. Receiver operating characteristic (ROC) was used to select the most effective anthropometric cut-off point among waist circumference, waist-to-hip ratio, waist-to-height ratio, and BMI for identifying prediabetic and diabetes. Statistical significance was declared at p value of ≤0.05. Results. Waist circumference was found better for identifying diabetes (AUC = 0.69) and prediabetes (AUC = 0.63) in women, respectively. Waist-to-hip ratio was better identifying diabetes (AUC = 0.67) while waist circumference-to-height ratio was better identifying prediabetes (AUC = 0.63) in men compared to body mass index. The optimal cut-off point with maximum sensitivity and specificity of waist circumference for identifying diabetes and prediabetes was 83.5 cm and 82.9 cm in women, respectively. The optimal ut-off point with maximum sensitivity and specificity of waist-to-hip ratio for identifying diabetes and prediabetes was 0.97 and 0.82 in men, respectively. Conclusion. Waist circumference and waist-to-hip ratio exhibited better discriminate performance than BMI for identifying prediabetes and diabetes in women and men, respectively.

Optimal cut-offs of four anthropometric measures and their predictive ability of diabetes in a nationally representative Kenyan study

“…Further, WC and WHtR had a better discriminatory ability for T2D than BMI. This agrees with other findings from Guinea, Kenya, Cameroon, and Ethiopia which have shown that WC is a better predictor of T2D (7,(9)(10)(11)(12). Globally, these findings are consistent with a meta-analysis that found that WC and WHtR had better discrimination for T2D than BMI (6).…”

“…The optimal BMI cut-off was 27.1 kg.m -2 and 24.8 kg.m -2 in women and men, respectively. These findings were consistent with a Ghanaian study which found an overweight BMI of 26.2 kg.m -2 as the optimal cut-off in women ( 8) and an Ethiopian study that reported a normal weight BMI of 23.0 kg.m -2 as the optimal cut-off in men (12). Also, this is in agreement with increasing evidence that Africans develop T2D at a lower BMI (23) than the current obesity cut-off of 30kg.m -2 (24).…”

“…We found the optimal WC cut-off as 87cm for women and 91cm for men. These cut-offs differ from those from other SSA settings: in Ghana, the optimal cut-off was 91.7cm for women and 83.4cm for men (8) while in Ethiopia the optimal cut-off was 82.9cm for women and 88.6cm for men (12). Despite these contrasts, our findings show a similar trend to the other two studies, that is, the cut-offs for women are above the WHO cut-off for metabolic complications (≥80cm) while the cut-offs in men are below the WHO thresholds (≥94cm) (24).…”

“…However, it remains unclear which measure has a better predictive ability. For instance in Ghana and Ethiopia, WHR was a better predictor of diabetes in three studies (8,11,12) while WC was a better predictor of diabetes in five studies from Ethiopia, Cameroon, Kenya, and Guinea (7,(9)(10)(11)(12).…”

“…In Ghana, the optimal cut-offs for BMI, WC and WHR were 26.2 kg.m -2 , 91.7cm and 0.88 for women, and 26.7 kg.m -2 , 83.4cm and 0.90 for men (8). Recently, an Ethiopian study reported that the optimal cut-offs for BMI, WC, WHR and WHtR were 20.5 kg.m -2 , 82.9cm, 0.86 and 0.51 in women, and 23.0 kg.m -2 , 88.6cm, 0.97 and 0.52 in men (12). This study, therefore, aimed to determine the optimal anthropometric cut-offs of four measures (BMI, WC, WHR, WHtR) for predicting T2D in Kenya and compare their predictive ability.…”