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Dr. Villagran-Orellana, Marcelo
Nombre de publicación
Dr. Villagran-Orellana, Marcelo
Nombre completo
Villagran Orellana, Marcelo Alejandro
Facultad
Email
marcelo.villagran@ucsc.cl
ORCID
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- PublicationIs waist-to-height ratio a better predictor of hypertension and type 2 diabetes than body mass index and waist circumference in the Chilean population?(Elsevier, 2020)
; ; ;Petermann-Rocha, Fanny ;Ulloa, Natalia ;MartÃnez-Sanguinetti, MarÃa ;Leiva, Ana ;Martorell, Miquel ;Ho, Frederick ;Celis-Morales, CarlosPizarro, AlonsoObjective: The aim of this study was to identify which anthropometric measurement (body mass index [BMI], waist circumference [WC], or waist-to-height ratio [WHtR]) is a better predictor of type 2 diabetes and hypertension in the Chilean population. Methods: The study included 13 044 participants (59.7% women) from the Chilean National Health Surveys conducted in 2003, 2009-2010, and 2016-2017. BMI, WC, and WHtR were the anthropometric measurements evaluated. Hypertension was defined as systolic blood pressure ≥140 mm Hg and diastolic blood pressure -90 mm Hg or on medication for hypertension. Diabetes was defined as fasting glucose -7 mmol/L or on medication for diabetes. The receiver operating characteristics (ROC) curve and the area under curve (AUC) were computed to derive the specificity and sensitivity using a bootstrapping approach. Results: Compared with BMI and WC, WHtR was the anthropometric measurement with the highest AUC curve in both sexes for hypertension (AUC for women: 0.70; 95% confidence interval [CI], 0.67-0.73; AUC for men: 0.71; 95% CI, 0.69-0.74) and diabetes (AUC for women: 0.71; 95% CI, 0.66-0.77; AUC for men: 0.71; 95% CI, 0.67-0.76). The sex-specific cutoff points of WHtR to predict hypertension were 0.59 and 0.55 for women and men, respectively. Those used to predict diabetes were 0.60 and 0.58 for women and men, respectively. Conclusion: WHtR was a better predictor of hypertension and diabetes than BMI and WC in Chile. The definition of cutoff points specific for the Chilean population could be implemented in future screening programs aiming to identify high-risk individuals.