Correlation analysis between the obesity classification and human metabolic indexes based on Huangdi Neijing
LU Chen-xia1,2,3, ZHU Hui4, HUI Chen-yang1,2,3, LI Xiao-dong1,2,3△, TONG Xiao-lin5△, et al.
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1. Institute of Liver Diseases, Hubei Key Laboratory of the theory and application research of liver and kidney in traditional Chinese medicine, Hubei Provincial Hospital of Traditional Chinese Medicine(Wuhan Hubei,430061),China
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Revised
Published
2023-06-24
2023-08-28
Issue Date
2024-02-23
Abstract
Objective: Combining Huangdi Neijing with clinical practice,anthropometric indexes were used to identify the obesity subtypes. Methods: A total of 5 926 subjects from Hubei Hospital of Traditional Chinese Medicine from October 2016 to April 2023 were included. Age, gender, anthropometric indicators, liver function, kidney function, blood lipids, uric acid and fasting blood glucose of the subjects were collected. Body fat percentage, visceral fat area and waist-hip ratio(WHR) were used as the classification variables of obesity subtypes by K-means clustering. R software was used to compare among groups, and multi-factor disordered multiple classification logistic regression was used to analyze the impact on the risk of obesity subtypes. Results:K-means cluster analysis can be used to divide overweight/obese objects into four categories. At the same time, there were 377 individuals with normal BMI but abnormal anthropometric indicators (Xiao-gao Ren), mainly female and older.The rate of abnormal liver function (55.20%), the rate of abnormal uric acid (58.85%) and fasting blood glucose (39.41%) in Gao Ren is higher than that in other subtypes; the rate of abnormal lipids in Zhi Ren (63.15%) is higher than that in other subtypes;the rate of abnormal renal function in Rou Ren(56.00%) is higher than that in other subtypes (P<0.001). Multivariate logistic regression analysis showed that high UA level [OR=2.408, 95% CI(1.775,3.267), P<0.001], high FPG level [OR=1.488, 95% CI(1.075,2.061)], high FPG level [OR=1.488, 95% CI(1.075,2.061), P<0.001], high UA level [OR=2.408, 95% CI(1.775,3.267),P<0.001], high FPG level [OR=1.488, 95% CI(1.075), P<0.05] is a risk factor for the development of Zhi-gao Ren. High ALT level [OR=2.332, 95% CI(1.363,3.989), P<0.001], high GGT level [OR=1.234, 95% CI(0.794,1.918),P<0.05], high TG level [OR=2.837, 95% CI(1.983,4.057), P<0.001], high UA level [OR=2.955, 95% CI(2.045,4.268),P<0.001] and high FPG level [OR=1.597, 95% CI(1.065,2.395),P<0.05] were risk factors for Gao Ren. High Cr level[OR=2.627, 95% CI(1.144, 6.033), P<0.001] and high UA level [OR=1.562, 95% CI(1.265,1.93),P<0.001] were risk factors for the occurrence of Rou Ren. High ALT [OR=1.578, 95% CI(1.053,14.673), P<0.001], TG [OR=2.143, 95% CI(1.686,2.724), P<0.001], high UA [OR=1.776, 95% CI(1.380,2.285), P<0.001] was a risk factor for developing Zhi Ren. Femal [OR=1.290, 95% CI(0.556,2.995),P<0.001] and older age[OR=1.021, 95% CI(1.001,1.042),P<0.001] were the risk factors for the development of Xiao-Gao Ren. Conclusion: The metabolic abnormal characteristics and risk factors of Gao Ren,Rou Ren,Zhi Ren,Zhi-gao Ren,Xiao-gao Ren were identified by preliminary quantitative determination.
LU Chen-xia, ZHU Hui, HUI Chen-yang, LI Xiao-dong, TONG Xiao-lin, et al..
Correlation analysis between the obesity classification and human metabolic indexes based on Huangdi Neijing[J]. Chinese Journal of Integrated Traditional and Western Medicine on Liver Diseases, 2023, 33(8): 682-687 https://doi.org/10.3969/j.issn.1005-0264.2023.008.002
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