Lee Bum Ju, Kim Jong Yeol
Medical Research Division, Korea Institute of Oriental Medicine, Yuseong-gu, Deajeon, Republic of Korea.
PLoS One. 2014 Jan 23;9(1):e84897. doi: 10.1371/journal.pone.0084897. eCollection 2014.
It is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives of the present study were to determine the best predictors of hypertension and hypotension among various anthropometric indices and to assess the use of combined indices as a method of improving the predictive power in adult Korean women and men.
For 12789 subjects 21-85 years of age, we assessed 41 anthropometric indices using statistical analyses and data mining techniques to determine their ability to discriminate between hypertension and normotension as well as between hypotension and normotension. We evaluated the predictive power of combined indices using two machine learning algorithms and two variable subset selection techniques.
The best indicator for predicting hypertension was rib circumference in both women (p = <0.0001; OR = 1.813; AUC = 0.669) and men (p = <0.0001; OR = 1.601; AUC = 0.627); for hypotension, the strongest predictor was chest circumference in women (p = <0.0001; OR = 0.541; AUC = 0.657) and neck circumference in men (p = <0.0001; OR = 0.522; AUC = 0.672). In experiments using combined indices, the areas under the receiver operating characteristic curves (AUC) for the prediction of hypertension risk in women and men were 0.721 and 0.652, respectively, according to the logistic regression with wrapper-based variable selection; for hypotension, the corresponding values were 0.675 in women and 0.737 in men, according to the naïve Bayes with wrapper-based variable selection.
The best indicators of the risk of hypertension and the risk of hypotension may differ. The use of combined indices seems to slightly improve the predictive power for both hypertension and hypotension.
人们普遍认为体脂分布与高血压有关，但关于高血压风险最强的人体测量指标仍存在争议。此外，尚无关于低血压与人体测量指数之间关联的研究报道。本研究的目的是确定各种人体测量指数中高血压和低血压的最佳预测指标，并评估联合指数作为提高韩国成年男女预测能力的一种方法的应用。
对于12789名年龄在21至85岁之间的受试者，我们使用统计分析和数据挖掘技术评估了41项人体测量指数，以确定它们区分高血压与正常血压以及低血压与正常血压的能力。我们使用两种机器学习算法和两种变量子集选择技术评估联合指数的预测能力。
预测高血压的最佳指标在女性中是肋围（p < 0.0001；OR = 1.813；AUC = 0.669），在男性中是肋围（p < 0.0001；OR = 1.601；AUC = 0.627）；对于低血压，最强的预测指标在女性中是胸围（p < 0.0001；OR = 0.541；AUC = 0.657），在男性中是颈围（p < 0.0001；OR = 0.522；AUC = 0.672）。在使用联合指数的实验中，根据基于包装器的变量选择的逻辑回归，预测女性和男性高血压风险的受试者工作特征曲线下面积（AUC）分别为0.721和0.652；对于低血压，根据基于包装器的变量选择的朴素贝叶斯算法，女性和男性的相应值分别为0.675和0.737。
高血压风险和低血压风险的最佳指标可能不同。联合指数的使用似乎能略微提高对高血压和低血压的预测能力。
