Objective:To develop a clinical predictive model for dampness constitution based on laboratory testing indicators.Methods:A retrospective cohort study was conducted on 1 355 healthy individuals who underwent physical examinations at the Health Examination Center of Guangdong Provincial Hospital of Traditional Chinese Medicine from October 1 st, 2022 to October 31 st, 2023. Basic information and blood routine, blood glucose, liver function, lipid metabolism, and kidney function test results of 1 355 apparently healthy individuals were collected. According to the diagnostic criteria for dampness constitution in traditional Chinese medicine, they were divided into a dampness constitution group (683 cases, including 394 with phlegm-dampness constitution and 289 with damp-heat constitution) and a non-dampness constitution group (672 cases). Among them, there were 547 males and 136 females in the dampness constitution group, with an age of 38.0 (32.0, 45.0) years; and there were 355 males and 317 females in the non-dampness constitution group, with an age of 33.0 (27.0, 41.0) years. A total of 1 355 apparently healthy individuals were randomly divided into a training set ( n=948) and a validation set ( n=407) using computer-generated random numbers in a 7∶3 ratio. Logistic regression analysis was employed to identify risk factors associated with dampness constitution. Utilizing these identified risk factors, a predictive model was constructed and subsequently visualized. The model′s predictive accuracy, consistency, and clinical utility were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Results:Among 1 355 subjects, there were statistically significant differences ( P<0.05) in gender, age, body mass index (BMI), blood glucose, some indicators of renal function, some indicators of blood routine, liver function, and four indicators of lipid metabolism between the dampness constitution group and the non-dampness constitution group. Gender ( OR=0.434,95 %CI 0.253-0.738), Cr ( OR=0.981,95 %CI 0.967-0.996), BMI ( OR=1.366,95 %CI 1.290-1.450), and LDL-C ( OR=1.388,95 %CI 1.014-1.897) were independent risk factors for dampness constitution ( P<0.05). A nomogram was subsequently developed based on these identified risk factors. The areas under the ROC curves (AUC) of the training set and validation set were 0.810 (95 %CI 0.783-0.837) and 0.804 (95 %CI 0.762-0.846), respectively. Conclusion:Gender,BMI,Cr and LDL-C were risk factors for the development of dampness constitution, and the clinical predictive model has clinical application value in predicting the risk of dampness constitution.

Objective To explore the risk factors associated with the occurrence of dampness syndrome based on peripheral blood multiple laboratory indicators,construct predictive model and validate it.Methods A retrospective analysis was conducted on 180 patients who visited the Preventive Treatment Center of Guangdong Provincial Hospital of Chinese Medicine from May 2022 to December 2023.They were divided into two groups according to the diagnostic criteria:the damp syndrome of TCM group(n=118)and the balanced yin-yang constitution group(n=62),with the latter serving as the"non-syndrome"control group for dampness syndrome.Serum biochemical indicators were detected by electrochemiluminescence(ECL),immune cell subsets were analyzed through flow cytometer,and routine blood parameters were assessed using an automatic hematology analyzed.Logistic regression analysis was employed to screen risk factors and develop a predictive model.The Bootstrap method was used for data resampling to draw the receiver operating characteristic(ROC)curve,calibration curve,and clinical decision curve analysis(DCA)to evaluate the predictive value,consistency,and clinical efficacy of the model.Results Compared with the balanced yin-yang constitution group,the damp syndrome of TCM group showed increased levels of insulin(INS),non-high-density lipoprotein cholesterol(non HDL-C),red blood cells(RBC)and the proportion of CD4+T cells,the proportion of triglyceride(TG)>1.70 mmol/L,total cholesterol(TC)>5.20 mmol/L,low-density lipoprotein cholesterol(LDL-C)>3.37 mmol/L,and high-density lipoprotein cholesterol(HDL-C)≤1.15 mmol/L were also significantly higher,with statistical significance(U/t/χ2=-2.900～4 626,all P＜0.05).Logistic regression analysis showed that INS,TC>5.20 mmol/L,HDL-C≤1.15 mmol/L,and the proportion of CD4+T cells were independent risk factors for the occurrence of damp syndrome of TCM(all P＜0.05).Based on the screened risk factors,a forecasting model was established and a nomogram was plotted.The model had an area under the ROC curve area under curve(AUC)of 0.747(95%CI=0.672～0.822),a Brier score of 0.184 for the calibration curve,and demonstrated clinical net benefit at threshold probabilities ranging from 0.30 to 1.00.Conclusion The forecasting model constructed based on INS,TC>5.20 mmol/L,HDL-C≤1.15 mmol/L,and CD4+T cells ratio has a high predictive value for damp syndrome of TCM.

Objective To explore the risk factors associated with the occurrence of dampness syndrome based on peripheral blood multiple laboratory indicators,construct predictive model and validate it.Methods A retrospective analysis was conducted on 180 patients who visited the Preventive Treatment Center of Guangdong Provincial Hospital of Chinese Medicine from May 2022 to December 2023.They were divided into two groups according to the diagnostic criteria:the damp syndrome of TCM group(n=118)and the balanced yin-yang constitution group(n=62),with the latter serving as the"non-syndrome"control group for dampness syndrome.Serum biochemical indicators were detected by electrochemiluminescence(ECL),immune cell subsets were analyzed through flow cytometer,and routine blood parameters were assessed using an automatic hematology analyzed.Logistic regression analysis was employed to screen risk factors and develop a predictive model.The Bootstrap method was used for data resampling to draw the receiver operating characteristic(ROC)curve,calibration curve,and clinical decision curve analysis(DCA)to evaluate the predictive value,consistency,and clinical efficacy of the model.Results Compared with the balanced yin-yang constitution group,the damp syndrome of TCM group showed increased levels of insulin(INS),non-high-density lipoprotein cholesterol(non HDL-C),red blood cells(RBC)and the proportion of CD4+T cells,the proportion of triglyceride(TG)>1.70 mmol/L,total cholesterol(TC)>5.20 mmol/L,low-density lipoprotein cholesterol(LDL-C)>3.37 mmol/L,and high-density lipoprotein cholesterol(HDL-C)≤1.15 mmol/L were also significantly higher,with statistical significance(U/t/χ2=-2.900～4 626,all P＜0.05).Logistic regression analysis showed that INS,TC>5.20 mmol/L,HDL-C≤1.15 mmol/L,and the proportion of CD4+T cells were independent risk factors for the occurrence of damp syndrome of TCM(all P＜0.05).Based on the screened risk factors,a forecasting model was established and a nomogram was plotted.The model had an area under the ROC curve area under curve(AUC)of 0.747(95%CI=0.672～0.822),a Brier score of 0.184 for the calibration curve,and demonstrated clinical net benefit at threshold probabilities ranging from 0.30 to 1.00.Conclusion The forecasting model constructed based on INS,TC>5.20 mmol/L,HDL-C≤1.15 mmol/L,and CD4+T cells ratio has a high predictive value for damp syndrome of TCM.

Objective:To develop a clinical predictive model for dampness constitution based on laboratory testing indicators.Methods:A retrospective cohort study was conducted on 1 355 healthy individuals who underwent physical examinations at the Health Examination Center of Guangdong Provincial Hospital of Traditional Chinese Medicine from October 1 st, 2022 to October 31 st, 2023. Basic information and blood routine, blood glucose, liver function, lipid metabolism, and kidney function test results of 1 355 apparently healthy individuals were collected. According to the diagnostic criteria for dampness constitution in traditional Chinese medicine, they were divided into a dampness constitution group (683 cases, including 394 with phlegm-dampness constitution and 289 with damp-heat constitution) and a non-dampness constitution group (672 cases). Among them, there were 547 males and 136 females in the dampness constitution group, with an age of 38.0 (32.0, 45.0) years; and there were 355 males and 317 females in the non-dampness constitution group, with an age of 33.0 (27.0, 41.0) years. A total of 1 355 apparently healthy individuals were randomly divided into a training set ( n=948) and a validation set ( n=407) using computer-generated random numbers in a 7∶3 ratio. Logistic regression analysis was employed to identify risk factors associated with dampness constitution. Utilizing these identified risk factors, a predictive model was constructed and subsequently visualized. The model′s predictive accuracy, consistency, and clinical utility were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Results:Among 1 355 subjects, there were statistically significant differences ( P<0.05) in gender, age, body mass index (BMI), blood glucose, some indicators of renal function, some indicators of blood routine, liver function, and four indicators of lipid metabolism between the dampness constitution group and the non-dampness constitution group. Gender ( OR=0.434,95 %CI 0.253-0.738), Cr ( OR=0.981,95 %CI 0.967-0.996), BMI ( OR=1.366,95 %CI 1.290-1.450), and LDL-C ( OR=1.388,95 %CI 1.014-1.897) were independent risk factors for dampness constitution ( P<0.05). A nomogram was subsequently developed based on these identified risk factors. The areas under the ROC curves (AUC) of the training set and validation set were 0.810 (95 %CI 0.783-0.837) and 0.804 (95 %CI 0.762-0.846), respectively. Conclusion:Gender,BMI,Cr and LDL-C were risk factors for the development of dampness constitution, and the clinical predictive model has clinical application value in predicting the risk of dampness constitution.

Age is the main risk factor for cardiovascular disease. This paper mainly discusses the mechanism of cardiovascular aging, status of detection, opportunities and challenges of research, in order to provide theoretical basis and guidance for the prevention and detection of cardiovascular aging.