Ang Lin, Lee Bum Ju, Kim Honggie, Yim Mi Hong
Clinical Medicine Division, Korea Institute of Oriental Medicine (KIOM), 1672, Yuseong-daero, Yuseong-gu, Daejeon 34054, Korea.
Korean Convergence Medicine, University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea.
Diagnostics (Basel). 2021 Mar 17;11(3):540. doi: 10.3390/diagnostics11030540.
This study aims to investigate the association between hypertension and facial complexion and determine whether facial complexion is a predictor for hypertension. Using the Commission internationale de l'éclairage Lab* (CIELAB) color space, the facial complexion variables of 1099 subjects were extracted in three regions (forehead, cheek, and nose) and the total face. Logistic regression was performed to analyze the association between hypertension and individual color variables. Four variable selection methods were also used to assess the association between hypertension and combined complexion variables and to compare the predictive powers of the models. The a* (green-red) complexion variables were identified as strong predictors in all facial regions in the crude analysis for both genders. However, this association in men disappeared, and L* (lightness) variables in women became the strongest predictors after adjusting for age and body mass index. Among the four prediction models based on combined complexion variables, the Bayesian approach obtained the best predictive in men. In women, models using three different methods but not the stepwise Akaike information criterion (AIC) obtained similar AUC values between 0.82 and 0.83. The use of combined facial complexion variables slightly improved the predictive power of hypertension in all four of the models compared with the use of individual variables.
本研究旨在探讨高血压与面色之间的关联，并确定面色是否为高血压的预测指标。采用国际照明委员会Lab*（CIELAB）颜色空间，在三个区域（额头、脸颊和鼻子）以及整个面部提取了1099名受试者的面色变量。进行逻辑回归分析高血压与个体颜色变量之间的关联。还使用了四种变量选择方法来评估高血压与综合面色变量之间的关联，并比较模型的预测能力。在未经调整的分析中，a*（绿-红）面色变量在所有面部区域均被确定为男女两性的强预测指标。然而，在调整年龄和体重指数后，男性中的这种关联消失，女性中的L*（亮度）变量成为最强的预测指标。在基于综合面色变量的四个预测模型中，贝叶斯方法在男性中获得了最佳预测效果。在女性中，使用三种不同方法但不使用逐步Akaike信息准则（AIC）的模型获得了相似的AUC值，介于0.82和0.83之间。与使用个体变量相比，使用综合面色变量在所有四个模型中均略微提高了高血压的预测能力。
