Obesity Science & Practice (Feb 2023)

Waist‐to‐height ratio for the prediction of gallstone disease among different obesity indicators

  • Tien‐Shin Chou,
  • Chih‐Lang Lin,
  • Li‐Wei Chen,
  • Ching‐Chih Hu,
  • Jia‐Jang Chang,
  • Cho‐Li Yen,
  • Shuo‐Wei Chen,
  • Ching‐Jung Liu,
  • Cheng‐Hung Chien

DOI
https://doi.org/10.1002/osp4.650
Journal volume & issue
Vol. 9, no. 1
pp. 30 – 41

Abstract

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Abstract Background Factors of metabolic syndrome such as obesity are well‐known risk factors for gallstone disease (GSD). There are different indicators of obesity, including weight, body mass index, waist circumference, and waist‐to‐height ratio. The predictive ability of different obesity indicators for GSD remains unclear. Objective To explore the most efficient predictor of GSD among the different anthropometric indicators of obesity. Methods This population‐based cross‐sectional study included 2263 participants who completed a questionnaire detailing their demographics, medical history, and lifestyle between 2014 and 2017 in Taiwan. Blood samples were collected and physical examinations, including anthropometric measurements, were performed. Gallstone disease was ascertained using ultrasonography. Multivariate analyses were performed to identify independent risk factors for GSD. Results The overall prevalence of GSD was 8.8%. According to the multivariate analysis, individuals with a waist‐to‐height ratio ≥0.5 (odds ratio|odds ratios (OR) = 1.65, 95% confidence interval (CI) = 1.10−2.48, p = 0.017) had an increased risk of GSD. Diabetes was the main risk factor for GSD in men (OR = 2.06, 95% CI = 1.17−3.65, p = 0.013). Among women, waist‐to‐height ratio >0.5 (OR = 1.76, 95% CI = 1.03−3.02, p = 0.040) and current hormone drug use (OR = 2.73, 95% CI = 1.09−6.84, p = 0.033) were significant risk factors for gallstones. Conclusion GSD was independently associated with central obesity and exogenous hormone intake in women. Among the anthropometric indicators used to assess central obesity, waist‐to‐height ratio was the most accurate predictor of GSD.

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