[1]刘光明,孙波,杨佳裕,等.膝骨关节炎-肌少症共病风险预测模型的构建与验证[J].中医正骨,2026,38(02):33-41.
　LIU Guangming,SUN Bo,YANG Jiayu,et al.Construction and validation of a risk prediction model for the comorbidity of knee osteoarthritis and sarcopenia[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2026,38(02):33-41.
点击复制

膝骨关节炎-肌少症共病风险预测模型的构建与验证()

《中医正骨》[ISSN:1001-6015/CN:41-1162/R]

卷:
第38卷
期数:
2026年02期
页码:
33-41
栏目:
临床研究
出版日期:
2026-02-20

文章信息/Info

Title:
Construction and validation of a risk prediction model for the comorbidity of knee osteoarthritis and sarcopenia
作者:
刘光明1; 孙波1; 杨佳裕1; 容承宜1; 王颖2; 胡毓敏2; 庞坚3; 詹红生3
1.上海市黄浦区香山中医医院,上海 200020; 2.上海市黄浦区豫园街道社区卫生服务中心,上海 200010; 3.上海中医药大学附属曙光医院,上海 201203
Author(s):
LIU Guangming1; SUN Bo1; YANG Jiayu1; RONG Chengyi1; WANG Ying2; HU Yumin2; PANG Jian3; ZHAN Hongsheng3
1.Shanghai Xiangshan Hospital of Traditional Chinese Medicine of Huangpu District, Shanghai 200020, China; 2.Yuyuan Community Health Service Center of Huangpu District, Shanghai 200010, China; 3.Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
关键词:
骨关节炎; 膝;  肌肉衰减征;  共患疾病;  老年人;  Logistic模型;  因素分析; 统计学;  列线图表;  风险;  预测
Keywords:
osteoarthritis; knee;  sarcopenia;  multimorbidity;  aged;  logistic models;  factor analysis; statistical;  Nomograms;  risk;  forecasting
摘要:
目的:构建并验证膝骨关节炎(knee osteoarthritis,KOA)-肌少症共病风险预测模型。方法:以2024年1—12月在上海市黄浦区豫园街道社区卫生服务中心招募的老年人为研究对象。将纳入的受试者按照7:3的比例随机分为训练集和验证集。分别采用美国风湿病学会提出的KOA诊断标准和亚洲肌少症工作组制定的肌少症诊断标准诊断KOA和肌少症。收集受试者性别、年龄、体质量指数(body mass index,BMI)、吸烟史、饮酒史、合并糖尿病、合并冠心病、合并高血压、合并骨质疏松、身体活动水平、膳食情况、Kellgren-Lawrence分级、膝关节疼痛视觉模拟量表(visual analogue scale,VAS)评分、握力、步速、四肢骨骼肌指数(appendicular skeletal muscle index,ASMI)、小腿最大周径等信息。将训练集患者按照是否存在KOA-肌少症共病分为共病组和非共病组。比较2组受试者的一般资料与相关指标。采用R语言Gtsummary包对2组受试者的相关变量进行单因素和多因素 Logistic回归分析。基于单因素和多因素分析结果及临床经验,在剔除可能导致信息泄露和共线性的变量后,确定纳入模型构建的变量; 构建模型并基于贝叶斯信息准则(Bayesian information criterion,BIC)对所有构建的模型进行比较,筛选BIC值最小的5个模型,并计算各模型的McFadden's R2值、区分度、校准度等性能评价指标。综合模型的简洁性、解释性、区分度、校准度等性能筛选最优模型,并基于最优模型构建KOA-肌少症共病列线图风险预测模型。分别基于训练集和验证集受试者数据,采用受试者操作特征(receiver operating characteristic,ROC)曲线评价KOA-肌少症共病风险预测模型的区分度,采用校准曲线与Hosmer-Lemeshow检验评价KOA-肌少症共病风险预测模型的校准度。结果:共纳入受试者640例,训练集448例、验证集192例; 训练集中共病组56例、非共病组392例。2组受试者性别、年龄、BMI、合并糖尿病、身体活动水平、膝关节疼痛VAS评分、握力、步速、ASMI、小腿最大周径的组间差异均有统计学意义(χ2=5.853,P=0.016; Z=-4.210,P=0.000; Z=-5.630,P=0.000; χ2=21.863,P=0.000; χ2=7.382,P=0.007; Z=-2.700,P=0.007; Z=-3.892,P=0.000; Z=-2.977,P=0.003; Z=-4.624,P=0.000; Z=-4.955,P=0.000)。单因素Logistic回归分析结果显示,性别(女)、年龄、BMI、合并糖尿病、低身体活动水平、膝关节疼痛VAS评分、握力、步速、ASMI及小腿最大周径与KOA-肌少症共病的关联有统计学意义[OR=2.23,95%CI(1.22,4.28),P=0.012; OR=1.13,95%CI(1.06,1.20),P=0.000; OR=0.72,95%CI(0.64,0.80),P=0.000; OR=3.90,95%CI(2.18,6.98),P=0.000; OR=2.63,95%CI(1.45,5.00),P=0.002; OR=1.91,95%CI(1.59,2.32),P=0.000; OR=0.92,95%CI(0.88,0.96),P=0.000; OR=0.03,95%CI(0.00,0.17),P=0.000; OR=0.22,95%CI(0.13,0.33),P=0.000; OR=0.49,95%CI(0.40,0.57),P=0.000]。多因素Logistic回归分析结果显示,性别(女)、步速快、ASMI高、小腿最大周径大是KOA-肌少症共病的独立保护因素[OR=0.08,95%CI(0.01,0.68),P=0.025; OR=0.02,95%CI(0.00,0.38),P=0.011; OR=0.10,95%CI(0.02,0.39),P=0.002; OR=0.50,95%CI(0.37,0.65),P=0.000],合并糖尿病、低身体活动水平、膝关节疼痛VAS评分高是KOA-肌少症共病的独立危险因素[OR=6.28,95%CI(2.10,20.40),P=0.001; OR=3.13,95%CI(1.06,10.00),P=0.045; OR=2.10,95%CI(1.57,2.91),P=0.000]。纳入年龄、BMI、性别、小腿最大周径、合并糖尿病、身体活动水平、膝关节疼痛VAS评分7个变量进行KOA-肌少症共病风险预测模型构建,确定基于BMI、合并糖尿病、身体活动水平、膝关节疼痛VAS评分、小腿最大周径5个变量构建的KOA-肌少症共病风险预测模型为最优模型(BIC值为156.73,McFadden's R2值为0.644,敏感度为0.875,特异度为0.911,准确率为0.906,F1得分为0.700,马修斯相关系数为0.666,Brier评分为0.071)。基于训练集数据,KOA-肌少症共病风险预测模型的ROC曲线下面积为0.971[P=0.000,95%CI(0.953,0.990)],校准截距为0.000,校准斜率为1.000,E/O值为1.000,MAE值为0.077(χ2=1.584,P=0.991); 基于验证集数据,KOA-肌少症共病风险预测模型的ROC曲线下面积为0.938[P=0.000,95%CI(0.900,0.975)],校准截距为-0.588,校准斜率为0.625,E/O值为1.211,MAE值为0.119(χ2=17.866,P=0.022)。结论:基于BMI、合并糖尿病、身体活动水平、膝关节疼痛VAS评分、小腿最大周径5个变量构建的KOA-肌少症共病风险预测模型具有良好的区分度和准确度,能够用于临床KOA-肌少症共病的风险预测。
Abstract:
Objective:To construct and validate a risk prediction model for the comorbidity of knee osteoarthritis(KOA)and sarcopenia.Methods:Elderly individuals recruited from the Yuyuan Community Health Service Center in Huangpu District of Shanghai from January to December 2024 were selected as the study subjects.The included subjects were randomly divided into a training set and a validation set at a ratio of 7:3.The KOA and sarcopenia were diagnosed based on the KOA diagnostic criteria proposed by the American College of Rheumatology and the sarcopenia diagnostic criteria established by the Asian Working Group for Sarcopenia, respectively.Information including gender, age, body mass index(BMI), smoking history, alcohol consumption history, comorbid diabetes, comorbid coronary heart disease, comorbid hypertension, comorbid osteoporosis, physical activity level, dietary status, Kellgren-Lawrence grade, knee pain visual analogue scale(VAS)score, grip strength, gait speed, appendicular skeletal muscle index(ASMI), and maximum circumference of leg was collected.Patients in the training set were divided into a comorbidity group and a non-comorbidity group based on the presence or absence of KOA-sarcopenia comorbidity.The general data and relevant indicators of the two groups were compared.The Gtsummary package in R language was used to perform univariate and multivariate logistic regression analyses on the relevant variables of the two groups.Based on the results of univariate and multivariate analyses and clinical experience, after excluding variables that might cause information leakage and multicollinearity, the variables included in the model construction were determined.The models were constructed and compared based on the Baye-sian information criterion(BIC), and the five models with the smallest BIC values were selected.Performance evaluation indicators such as McFadden's R2, discrimination, and calibration were calculated for each model.The optimal model was selected based on the model's simplicity, interpretability, discrimination, calibration, and other performances, and a nomogram risk prediction model for KOA-sarcopenia comorbidity was constructed based on the optimal model.Based on the data of the training set and validation set subjects, the receiver operating characteristic(ROC)curve was used to evaluate the discrimination of the KOA-sarcopenia comorbidity risk prediction model, and the calibration curve and Hosmer-Lemeshow test were used to evaluate the calibration of the KOA-sarcopenia comorbidity risk prediction model.Results:A total of 640 subjects were included, with 448 in the training set and 192 in the validation set.In the training set, there were 56 cases in the comorbidity group and 392 cases in the non-comorbidity group.The differences between the two groups in gender, age, BMI, comorbid diabetes, physical activity level, knee pain VAS score, grip strength, gait speed, ASMI, and maximum circumference of leg were statistically significant(χ2=5.853, P=0.016; Z=-4.210, P=0.000; Z=-5.630, P=0.000; χ2=21.863, P=0.000; χ2=7.382, P=0.007; Z=-2.700, P=0.007; Z=-3.892, P=0.000; Z=-2.977, P=0.003; Z=-4.624, P=0.000; Z=-4.955, P=0.000).Univariate logistic regression analysis showed that gender(female), age, BMI, comorbid diabetes, low physical activity level, knee pain VAS score, grip strength, gait speed, ASMI, and maximum circumference of leg were significantly associated with KOA-sarcopenia comorbidity(OR=2.23, 95%CI(1.22, 4.28), P=0.012; OR=1.13, 95%CI(1.06, 1.20), P=0.000; OR=0.72, 95%CI(0.64, 0.80), P=0.000; OR=3.90, 95%CI(2.18, 6.98), P=0.000; OR=2.63, 95%CI(1.45, 5.00), P=0.002; OR=1.91, 95%CI(1.59, 2.32), P=0.000; OR=0.92, 95%CI(0.88, 0.96), P=0.000; OR=0.03, 95%CI(0.00, 0.17), P=0.000; OR=0.22, 95%CI(0.13, 0.33), P=0.000; OR=0.49, 95%CI(0.40, 0.57), P=0.000).Multivariate logistic regression analysis showed that gender(female), fast gait speed, high ASMI, and large maximum circumference of leg were independent protective factors for KOA-sarcopenia comorbidity(OR=0.08, 95%CI(0.01, 0.68), P=0.025; OR=0.02, 95%CI(0.00, 0.38), P=0.011; OR=0.10, 95%CI(0.02, 0.39), P=0.002; OR=0.50, 95%CI(0.37, 0.65), P=0.000), while comorbid diabetes, low physical activity level, and high knee pain VAS score were independent risk factors(OR=6.28, 95%CI(2.10, 20.40), P=0.001; OR=3.13, 95%CI(1.06, 10.00), P=0.045; OR=2.10, 95%CI(1.57, 2.91), P=0.000).Seven variables including age, BMI, gender, maximum circumference of leg, comorbid diabetes, physical activity level, and knee pain VAS score were included to construct the KOA-sarcopenia comorbidity risk prediction model.The KOA-sarcopenia comorbidity risk prediction model constructed based on 5 variables of BMI, comorbid diabetes, physical activity level, knee pain VAS score, and maximum circumference of leg was determined as the optimal model(BIC value was 156.73, McFadden's R2 value was 0.644, sensitivity was 0.875, specificity was 0.911, accuracy was 0.906, F1 score was 0.700, Matthews correlation coefficient was 0.666, Brier score was 0.071).Based on the training set data, the area under the ROC curve of the KOA-sarcopenia comorbidity risk prediction model was 0.971(P=0.000, 95%CI(0.953, 0.990)), the calibration intercept was 0.000, the calibration slope was 1.000, the E/O value was 1.000, and the MAE value was 0.077(χ2=1.584, P=0.991).Based on the validation set data, the area under the ROC curve of the KOA-sarcopenia comorbidity risk prediction model was 0.938(P=0.000, 95%CI(0.900, 0.975)), the calibration intercept was -0.588, the calibration slope was 0.625, the E/O value was 1.211, and the MAE value was 0.119(χ2=17.866, P=0.022).Conclusion:The KOA-sarcopenia comorbidity risk prediction model constructed based on 5 variables of BMI, comorbid diabetes, physical activity level, knee pain VAS score, and maximum circumference of leg demonstrates good discrimination and accuracy, and can be used for clinical risk prediction of KOA-sarcopenia comorbidity.

参考文献/References:

[1] 中华医学会骨科学分会关节外科学组,中国医师协会骨科医师分会骨关节炎学组,国家老年疾病临床医学研究中心(湘雅医院),等.中国骨关节炎诊疗指南(2021年版)[J].中华骨科杂志,2021,41(18):1291-1314.
[2] CHEN L K,WOO J,ASSANTACHAI P,et al.Asian Working Group for Sarcopenia:2019 consensus update on sarcopenia diagnosis and treatment[J].J Am Med Dir Assoc,2020,21(3):300-307.
[3] CRUZ-JENTOFT A J,SAYER A A.Sarcopenia[J].Lancet,2019,393(10191):2636-2646.
[4] IIJIMA H,AOYAMA T.Increased recurrent falls experience in older adults with coexisting of sarcopenia and knee osteoarthritis:a cross-sectional study[J].BMC Geriatr,2021,21(1):698.
[5] 刘娟,丁国宪.骨关节病和肌少症[J].中国实用内科杂志,2022,42(8):631-633.
[6] WANG H,WANG N,WANG Y,et al.Association between sarcopenia and osteoarthritis:a protocol for meta-analy-sis[J].PLoS One,2022,17(8):e0272284.
[7] 中国中医药研究促进会骨伤科分会.膝骨关节炎中医诊疗指南(2020年版)[J].中医正骨,2020,32(10):1-14.
[8] CRAIG C L,MARSHALL A L,SJÖSTRÖM M,et al.International physical activity questionnaire:12-country reliability and validity[J].Med Sci Sports Exerc,2003,35(8):1381-1395.
[9] BULL F C,AL-ANSARI S S,BIDDLE S,et al.World Health Organization 2020 guidelines on physical activity and sedentary behaviour[J].Br J Sports Med,2020,54(24):1451-1462.
[10] 中国营养学会.中国居民膳食指南(2022)[M].北京:人民卫生出版社,2022:47.
[11] PEREIRA D,RAMOS E,BRANCO J.Osteoarthritis[J].Acta Med Port,2015,28(1):99-106.
[12] ASLAN S G,GENÇ H.Sarcopenia in knee osteoarthritis:the association with clinical and sonographic findings,physical function,and nutrition[J].Ir J Med Sci,2023,192(3):1231-1239.
[13] PEGREFFI F,BALESTRA A,DE LUCIA O,et al.Prevalence of sarcopenia in knee osteoarthritis:a systematic review and meta-analysis[J].J Clin Med,2023,12(4):1532.
[14] PARK D,PARK C J,JUNG M G,et al.High prevalence of sarcopenia in Asian female patients awaiting primary total knee arthroplasty:application of updated diagnostic tools from the Asian working group for sarcopenia[J].J Orthop Surg(Hong Kong),2022,30(2):10225536221113034.
[15] 陈敏,余泽芸,宋丹,等.老年膝骨关节炎合并肌少症的影响因素分析及风险预测模型构建[J].中医正骨,2025,37(3):23-28.
[16] ZHENG H,CHEN C.Body mass index and risk of knee osteoarthritis:systematic review and meta-analysis of prospective studies[J].BMJ Open,2015,5(12):e007568.
[17] CURTIS M,SWAN L,FOX R,et al.Associations between body mass index and probable sarcopenia in community-dwelling older adults[J].Nutrients,2023,15(6):1505.
[18] FEELEY B T,GALLO R A,SHERMAN S,et al.Management of osteoarthritis of the knee in the active patient[J].J Am Acad Orthop Surg,2010,18(7):406-416.
[19] NEGM A M,LEE J,HAMIDIAN R,et al.Management of sarcopenia:a network meta-analysis of randomized controlled trials[J].J Am Med Dir Assoc,2022,23(5):707-714.
[20] RAPOSO F,RAMOS M,LUCIA CRUZ A.Effects of exercise on knee osteoarthritis:a systematic review[J].Musculoskelet Care,2021,19(4):399-435.
[21] 林舒静,何凌骁,方亚.中国老年人慢性病共病模式与肌少症发生风险的关联性研究[J].中国慢性病预防与控制,2024,32(5):327-331.
[22] CHUNG S M,HYUN M H,LEE E,et al.Novel effects of sarcopenic osteoarthritis on metabolic syndrome,insulin resistance,osteoporosis,and bone fracture:the national survey[J].Osteoporos Int,2016,27(8):2447-2457.
[23] 张强强.肌少因素对肝肾亏虚型KOA患者的影响及与膝关节X线影像相关性分析的研究[D].天津:天津中医药大学,2022.
[24] ALGHADIR A H,ANWER S,IQBAL A,et al.Test-retest reliability,validity,and minimum detectable change of visual analog,numerical rating,and verbal rating scales for measurement of osteoarthritic knee pain[J].J Pain Res,2018,11:851-856.
[25] KIM S,KIM M,LEE Y,et al.Calf circumference as a simple screening marker for diagnosing sarcopenia in older Korean adults:the Korean frailty and aging cohort study(KFACS)[J].J Korean Med Sci,2018,33(20):e151.
[26] CHEN C Y,TSENG W C,YANG Y H,et al.Calf circumference as an optimal choice of four screening tools for sarcopenia among ethnic Chinese older adults in assisted living[J].Clin Interv Aging,2020,15:2415-2422.
[27] 杨丽君.苏州市老年人肌少症的相关因素研究[D].南京:南京医科大学,2018.
[28] 钟清枝.广义线性模型在不同种类膝骨关节炎预测中的应用[J].数理医药学杂志,2016,29(3):317-318.
[29] SANGHI D,SRIVASTAVA R N,SINGH A,et al.The association of anthropometric measures and osteoarthritis knee in non-obese subjects:a cross sectional study[J].Clinics(Sao Paulo),2011,66(2):275-279.
[30] MOONS K G,ROYSTON P,VERGOUWE Y,et al.Prognosis and prognostic research:what,why,and how?[J].BMJ,2009,338:b375.
[31] 牛喆昀,沈佳莹,张子涵,等.预测模型在临床研究中的应用[J].上海预防医学,2023,35(1):56-65.

相似文献/References:

[1]樊庆阳,任凯晶.定制3D打印切模辅助全膝关节置换术治疗 膝骨关节炎合并股骨干骨折畸形愈合[J].中医正骨,2015,27(11):37.
[2]刘晓雅,孙永强,刘国杰.主动快速康复锻炼对全膝关节置换术后关节活动度的影响[J].中医正骨,2015,27(09):73.
[3]郑春松,叶蕻芝,李西海,等.透骨消痛胶囊中补肾柔肝药和活血祛风药治疗 骨关节炎作用方式的计算机模拟比较[J].中医正骨,2015,27(07):6.
　ZHENG Chunsong,YE Hongzhi,LI Xihai,et al.Comparison of the mode of action of Bushen Rougan(补肾柔肝)drugs versus Huoxue Qufeng(活血祛风)drugs contained in Tougu Xiaotong Jiaonang(透骨消痛胶囊)for the treatment of osteoarthritis:A computer simulation study[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2015,27(02):6.
[4]宋兵华,孙俊英,倪增良,等.全膝关节置换术前CT测量股骨后髁角的临床意义[J].中医正骨,2015,27(07):38.
[5]郑春松,叶蕻芝,李西海,等.独活寄生汤含药血清对白细胞介素1β诱导的 退变关节软骨细胞中基质金属蛋白酶 和环氧化酶2表达的影响[J].中医正骨,2015,27(12):1.
　ZHENG Chunsong,YE Hongzhi,LI Xihai,et al.Impact of Duhuo Jisheng Tang(独活寄生汤)medicated serum on expression of matrix metalloproteinase and cyclooxygenase 2 in degenerative articular chondrocytes induced by interleukin-1 beta[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2015,27(02):1.
[6]王金良,孙京涛,李玲,等.骨水泥联合螺钉修复全膝关节置换术中 胫骨平台内侧骨缺损[J].中医正骨,2015,27(12):55.
[7]冯荣,王平,李炳奇,等.铍针刺络拔罐结合中药口服治疗膝骨关节炎合并 原发性血小板增多症1例[J].中医正骨,2015,27(12):73.
[8]蔡云仙.围手术期耳穴按压联合平衡针疗法 在全膝关节置换术后镇痛中的应用[J].中医正骨,2015,27(06):41.
[9]张荣,王健.人工全膝关节置换术的围手术期心理护理[J].中医正骨,2015,27(05):77.
[10]喻长纯,杨明路,王战朝.不同手术方式治疗胫骨平台骨折畸形愈合的体会[J].中医正骨,2015,27(03):37.
[11]孟维娜,明立功,王新德,等.关节镜下清理联合腓骨近1/3段截骨治疗膝骨关节炎[J].中医正骨,2015,27(11):40.
[12]明立功,孟维娜,王新德,等.腓骨近端截骨治疗内侧间室膝骨关节炎的近期疗效观察[J].中医正骨,2015,27(10):25.
[13]张杰,王人彦,张玉柱.膝骨关节炎的治疗进展[J].中医正骨,2015,27(10):68.
[14]梁朝,蔡静怡,闫立,等.针刀疗法改善膝骨关节炎早期疼痛症状的疗效评价[J].中医正骨,2015,27(09):9.
　LIANG Zhao,CAI Jingyi,YAN Li,et al.Evaluation of the curative effect of needle-knife therapy for relieving knee pain in patients with early knee osteoarthritis[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2015,27(02):9.
[15]王建武,党建军,李强,等.四联疗法治疗膝骨关节炎[J].中医正骨,2015,27(08):44.
[16]刘红娟,郭会利,郭树农.云克联合中药治疗膝骨关节炎的护理[J].中医正骨,2015,27(08):75.
[17]陈卫衡.探索建立系统的膝骨关节炎中医临床科研范式 和理论体系[J].中医正骨,2015,27(07):1.
[18]帅波,沈霖,杨艳萍,等.加味青娥丸治疗膝骨关节炎的作用机制研究[J].中医正骨,2015,27(07):15.
　SHUAI Bo,SHEN Lin,YANG Yanping,et al.Study on the mechanism of action of Jiawei Qing'e Wan(加味青娥丸)for the treatment of knee osteoarthritis[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2015,27(02):15.
[19]梅其杰,袁长深,段戡,等.壮药骨痹方烫熨联合运动疗法治疗膝骨关节炎的临床研究[J].中医正骨,2015,27(07):27.
　MEI Qijie,YUAN Changshen,DUAN Kan,et al.Clinical study of the curative effect of hot compressing and rubbing with packet of Gubi Fang(骨痹方)combined with exercise therapy in the treatment of knee osteoarthritis[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2015,27(02):27.
[20]王丹辉,张燕,刘丽娟,等.重组人Ⅱ型肿瘤坏死因子受体-抗体融合蛋白 关节腔注射联合中药薰洗治疗膝骨关节炎的临床研究[J].中医正骨,2015,27(07):31.
　WANG Danhui,ZHANG Yan,LIU Lijuan,et al.Clinical study on intra-articular injection of TypeⅡrecombinant human tumor necrosis factor receptor-Fc fusion protein combined with Chinese herbal steaming and washing therapy for treatment of knee osteoarthritis[J].The Journal of Traditional Chinese Orthopedics and Traumatology,2015,27(02):31.

备注/Memo

备注/Memo:
基金项目:国家自然科学基金项目(82074466,82374481); 全国名老中医药专家传承工作室建设项目(国中医药人教函〔2022〕75号); 上海市中医药传承创新工作室建设项目(2025CXGZS-03); 上海市黄浦区卫生健康系统专业人才梯队建设项目(2023BJ05); 上海市黄浦区科研项目(HLM202420); 上海市黄浦区名医名师工作室项目(2023MY05); 上海市黄浦区卫生健康系统重点学科项目(2025ZDXK05)
通信作者:詹红生 E-mail:shgsyjs@139.com
(收稿日期:2025-04-20 本文编辑:吕宁)
更新日期/Last Update: 2026-02-20