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Association of waist-to-height ratio with hypertension and its subtypes in southern China.

Peng LuLingjuan ZhuLihua HuHuihui BaoXiao HuangWei ZhouTao WangXi LiuJuxiang LiPing LiYanqing WuQinghua WuZengwu WangRunlin GaoMinghui LiXiaoshu Cheng
Published in: Journal of human hypertension (2021)
Data regarding the association of the waist-to-height ratio (WHtR) with hypertension (HTN) are conflicting. Moreover, little information is available on the association between WHtR and HTN subtypes. Therefore, we aimed to investigate the associations between WHtR and the prevalence of HTN and its subtypes in a Chinese population. In the cross-sectional analysis, 13,947 adults from the China Hypertension Survey study were analysed. We examined the relationship between WHtR and the prevalence of HTN and its subtypes (isolated systolic hypertension (ISH), isolated diastolic hypertension (IDH) and systodiastolic hypertension (SDH)) using multivariate logistic regression analysis. A generalized additive model (GAM) and smooth curve fitting (penalized spline method) were also used. Overall, the mean WHtR was 0.50. The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for HTN, ISH, IDH and SDH for each standard deviation (SD) increase in WHtR were 1.53 (1.45-1.61), 1.36 (1.28-1.44), 1.41 (1.20-1.65) and 1.47 (1.36-1.59), respectively. The fully adjusted smooth curve fitting revealed a linear association between WHtR and HTN, ISH, IDH, and SDH. Moreover, the positive associations between WHtR and HTN and its subtypes were more strong among younger adults (<60 compared with ≥60 years, P values for interaction <0.001). These findings suggested that WHtR was positively associated with HTN and its subtypes, especially among younger adults (<60 years) in southern China.
Keyphrases
  • blood pressure
  • body mass index
  • cross sectional
  • heart failure
  • risk factors
  • low grade
  • healthcare
  • machine learning
  • data analysis
  • arterial hypertension
  • electronic health record
  • health information
  • social media
  • big data