Clinical Usefulness of Anthropometric Indices to Predict the Presence of Prediabetes. Data from the ILERVAS Cohort

Abstract

Prediabetes is closely related to excess body weight and adipose distribution. For this reason, we aimed to assess and compare the diagnostic usefulness of ten anthropometric adiposity indices to predict prediabetes. Cross-sectional study with 8188 overweight subjects free of type 2 diabetes from the ILERVAS project (clinicaltrials.gov: NCT03228459). Prediabetes was diagnosed by levels of glycated hemoglobin (HbA1c). Total body adiposity indices [BMI, Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) and Deurenberg’s formula] and abdominal adiposity (waist and neck circumferences, conicity index, waist to height ratio, Bonora’s equation, A body shape index, and body roundness index) were calculated. The area under the receiver-operating characteristic (ROC) curve, the best cutoff and the prevalence of prediabetes around this value were calculated for every anthropometric index. All anthropometric indices other than the A body adiposity were higher in men and women with prediabetes compared with controls (p < 0.001 for all). In addition, a slightly positive correlation was found between indices and HbA1c in both sexes (r ≤ 0.182 and p ≤ 0.026 for all). None of the measures achieved acceptable levels of discrimination in ROC analysis (area under the ROC ≤ 0.63 for all). Assessing BMI, the prevalence of prediabetes among men increased from 20.4% to 36.2% around the cutoff of 28.2 kg/m2, with similar data among women (from 29.3 to 44.8% with a cutoff of 28.6 kg/m2). No lonely obesity index appears to be the perfect biomarker to use in clinical practice to detect individuals with prediabetes.

Diagnostic Accuracy of Anthropometric Markers of Obesity for Prediabetes: A Systematic Review and Meta-Analysis

“…From these, 2,506 studies were excluded, leaving 11 manuscripts to be evaluated in full text. Finally, 7 manuscripts [15][16][17][24][25][26][27] were selected (Figure 1).…”

“…Among all the manuscripts chosen for review, we had four cross-sectional studies [15,[24][25][26], and three were classified as cohort studies [16,17,27]. Each article included in this comprehensive review was disseminated to the public from the year 2018 through to 2021.…”

“…The summary of the cross-sectional studies are found in Table 1, as well as the cohort studies in Table 2. The selected studies were conducted in nine countries: Spain (n=1) [15], China (n=5) [16,17,24,25,27] and Mexico (n=1) [26]. The sample reached a total of 24,367 in cross-sectional studies, ranging from 130 to 15,078.…”

“…These measures, derived from simple and accessible indicators, are crucial for identifying patients at risk; this allows for the implementation of strategies to preventively intervene to significantly halt the onset of diabetes. Despite these findings, there is still no clarity regarding the precise cut-off points for these measures and their accurate diagnosis, which includes different values from each study, such as specificity and sensitivity [15][16][17]. This results in some ambiguity in their application, which potentially limits their effectiveness as detection tools.…”

Measurement of visceral fat for early prediction of prediabetes—Cross-sectional study from Southern India

“… 13 Moreover, the prediction of metabolic risk using anthropometric measures is challenging in non-obese individuals, and any single anthropometric measure is insufficient to predict prediabetes. 14 …”