Symptoms Based on Deficiency Syndrome in Traditional Chinese Medicine Might Be Predictor of Frailty in Elderly Community Dwellers

• Main Authors: Zhen Qi, Bei-Ling Wu, Chuan Chen, Zhi-Hua Yu, Ding-Zhu Shen, Jiu-Lin Chen, Hong-Bin Zhao, Lin Sun
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Evidence-Based Complementary and Alternative Medicine
• Online Access: http://dx.doi.org/10.1155/2021/9918811

Background. The most widely used frailty phenotype and frailty indexes are either time-consuming or complicated, thus restricting their generalization in clinical practice; and therefore, an easier and faster screening tool is needed to be developed. Objective. To select sensitive symptoms in traditional Chinese medicine (TCM) and study whether they can improve the risk prediction of frailty. Methods. This is a cross-sectional study enrolling 2249 Chinese elderly community dwellers. Data were collected via face-to-face inquiries, anthropometric measurements, laboratory tests, and community health files. Frailty was the main outcome measure, and it was evaluated by Fried’s frailty phenotype (FP). The ordinal logistic regression model was used to identify the factors associated with frailty. The risk assessment plot was used to compare the discriminative ability for frailty among models with and without TCM symptoms. Results. The identified sensitive influential factors for frailty included age, education level, dietary habits, chronic obstructive pulmonary disease, diabetes, cerebral infarction, osteoporosis, cold limbs, lethargy and laziness in speaking and moving, weakness of lower limbs, slow movement, dry mouth and throat, and glazed expression. The risk prediction for “frailty cumulative components ≥1” was not significantly increased, while for “frailty cumulative components ≥2”, a new model developed with the above selected TCM symptoms had a higher AUC than the baseline model without it (0.79 VS 0.81, P=0.002). And the NRI and IDI for the new model were 41.4% (P=0.016) and 0.024% (P=0.041), respectively. Conclusion. This research might provide an easier and faster way for early identification and risk prediction of frailty in elderly community dwellers.