摘要
目的探究卵巢储备功能减退(DOR)的中医证候特点及分型规律,并分析与DOR发病相关的独立影响因素。方法选取2022年9月至2024年9月北京市丰台中西医结合医院收治的100例患者作为研究对象,根据卵巢储备功能是否正常分为观察组(50例)和对照组(50例)。采用因子分析和聚类分析DOR的中医证候特点、分型规律,通过单因素分析及多因素logistic回归分析DOR的独立影响因素。结果50例DOR患者归类为5种证型,其分布频率按肾虚肝郁证、肝肾阴虚证、脾肾阳虚证、肾虚血瘀证、心肾不交证的排序递减。单因素分析结果提示,观察组和对照组的年龄、文化程度、吸烟史、心理因素、妇科手术史、流产次数、避孕方式比较,差异有统计学意义(P<0.05)。多因素logistic回归分析结果提示,年龄(β=0.138,OR=1.148,95%CI:1.032~1.278)、硕士及以上文化程度(β=-1.184,OR=0.306,95%CI:0.122~0.768)、有吸烟史(β=2.057,OR=7.823,95%CI:1.773~34.522)、焦虑/抑郁(β=1.695,OR=5.449,95%CI:1.274~23.305)、卵巢相关手术史(β=0.814,OR=2.257,95%CI:1.130~4.509)、流产次数≥3次(β=0.948,OR=2.580,95%CI:1.230~5.411)、口服避孕药避孕(β=1.379,OR=3.972,95%CI:1.550~10.177)是DOR的独立危险因素(P<0.05)。结论DOR患者常见的中医证型依次为肾虚肝郁证、肝肾阴虚证、脾肾阳虚证、肾虚血瘀证、心肾不交证,可为中医辨证施治提供参考。年龄、硕士及以上文化程度、有吸烟史、焦虑/抑郁、卵巢相关手术史、流产次数≥3次、口服避孕药避孕可为临床识别高风险人群和制定干预策略提供理论依据。
Objective To explore the traditional Chinese medicine syndrome characteristics and classification patterns of diminished ovarian reserve(DOR),and to analyze the independent influencing factors related to the onset of DOR.Methods A total of 100 patients admitted to Beijing Fengtai Hospital of Integrated Traditional Chinese and Western Medicine from September 2022 to September 2024 were selected as the research subjects and they were divided into the observation group(50 cases)and the control group(50 cases)according to whether their ovarian reserve function was normal.Factor analysis and cluster analysis were used to analyze the traditional Chinese medicine syndrome characteristics and classification rules of DOR.Univariate analysis and multivariate logistic regression analysis were conducted to analyze the independent influencing factors of DOR.Results Fifty patients with DOR were classified into five syndrome types,and their distribution frequency decreased in the order of kidney deficiency and liver depression syndrome,liver and kidney yin deficiency syndrome,spleen and kidney yang deficiency syndrome,kidney deficiency and blood stasis syndrome,and heart-kidney disharmony syndrome.The results of univariate analysis indicated that there were statistically significant differences in age,educational level,smoking history,psychological factors,gynecological surgery history,number of miscarriages,and contraceptive methods between the observation group and the control group(P<0.05).The results of multivariate logistic regression analysis indicated that age(β=0.138,OR=1.148,95%CI:1.032-1.278),master's degree or above(β=-1.184,OR=0.306,95%CI:0.122-0.768),history of smoking(β=2.057,OR=7.823,95%CI:1.773-34.522),anxiety/depression(β=1.695,OR=5.449,95%CI:1.274-23.305),ovaria-related surgical history(β=0.814,OR=2.257,95%CI:1.130-4.509),number of miscarriages≥3 times(β=0.948,OR=2.580,95%CI:1.230-5.411)and oral contraceptive use(β=1.379,OR=3.972,95%CI:1.550-10.177)were independent risk factors for DOR(P<0.05).Conclusion The common traditional Chinese medicine syndrome types of DOR patients in sequence are kidney deficiency and liver depression syndrome,liver and kidney yin deficiency syndrome,spleen and kidney yang deficiency syndrome,kidney deficiency and blood stasis syndrome,and heart-kidney disharmony syndrome,which can provide a reference for traditional Chinese medicine syndrome differentiation and treatment.Age,master's degree or above,history of smoking,anxiety/depression,history of ovaries related surgeries,≥3 miscarriages,and oral contraceptives for contraception can provide theoretical basis for clinical identification of high-risk populations and formulation of intervention strategies.
作者
吕尤
马丽灵
雷旭露
马瑶
牛嘉琦
李柯
孟令宇
LYU You;MA Liling;LEI Xulu;MA Yao;NIU Jiaqi;LI Ke;MENG Lingyu(Department of Gynecology of Traditional Chinese Medicine,Huaishuling Campus,Beijing Fengtai Hospital of Integrated Traditional Chinese and Western Medicine,Beijing 100072,China;College of Traditional Chinese Medicine,Hebei North University,Hebei Province,Zhangjiakou 075000,China)
出处
《中国当代医药》
2026年第3期17-22,共6页
China Modern Medicine
基金
北京市丰台中西医结合医院院所协同创新科研专项基金资助项目(YS2022-03)。
关键词
卵巢储备功能减退
中医证候
因子分析
聚类分析
影响因素
Diminished ovarian reserve
Traditional Chinese medicine syndromes
Factor analysis
Cluster analysis
Influencing factor
