卵巢衰老预警模型的建立和意义

发布时间：2018-03-18 14:05

本文选题：卵巢储备　切入点：卵巢年龄　出处：《山东大学》2017年硕士论文　论文类型：学位论文

【摘要】：目的:目前有很多指标用来评估卵巢储备功能,内分泌学指标如AMH、FSH等,超声学指标如AFC、OV等,克罗米芬刺激试验等及年龄,目前临床上最常用的指标是年龄、FSH、AMH、AFC,然而没有一项指标可以获得满意的特异度及灵敏度,许多学者提出将不同的指标共同检测可能会提高准确性。许多研究显示不同指标预测卵巢反应性、妊娠率等的能力不同,且不同的检测指标的预测能力存在不一致性。国际生殖领域关于不孕女性卵巢储备功能模型或评分、分期系统的构建近年来为热点问题,然而健康女性卵巢储备功能模型或分期系统构建的研究报道国内外均较少,根据健康女性卵巢储备功能构建卵巢功能模型对于准确的评估卵巢储备功能有重要意义,种族对卵巢功能存在一定的影响,根据我国女性特点制定符合中国女性的卵巢储备功能模型意义重大。由于代谢因素对卵巢功能存在一定影响,本研究拟根据评估卵巢储备功能的内分泌学指标,同时考虑代谢因素,综合分析,建立卵巢储备功能预警模型,以期发现卵巢储备功能减退,及时进行干预,改善生殖潜能,围绝经期女性及时行MHT治疗,对于预防心脑血疾病、认知障碍等慢性疾病的发生都具有重要价值和意义。方法:研究对象为2014年3月～2015年7月189例入选本研究的健康女性,同时将自愿参加本研究的山东省立医院妇科门诊PO]、PCOS患者纳入,分别为19例。月经第2～4天对研究人群采血,收集 FSH、FSH/LH、E2、AMH、DHEA、IGF-1、25(OH)D、FINS、FPG、LDL、HDL、TG、HbAlc,记录身高、体重,同时计算BM]、IRI、AMH*FSH,通过统计学方法,构建"卵巢年龄"。假设健康女性"卵巢年龄"与实际年龄相同,将"卵巢年龄"作为因变量,余指标作为自变量,采用单因素分析对自变量进行初步选择,利用逐步回归分析法建立多元回归模型,获得"卵巢年龄"。由健康女性构建的"卵巢年龄"等式,将POI及PCOS患者相关指标代入等式,获得相应的"卵巢年龄",分别将POI、PCOS患者的"卵巢年龄"与相对应的实际年龄进行独立样本y检验,比较POI组、PCOS组"卵巢年龄"与实际年龄有无统计学差异。结果:将检验水准设为0.2,通过单因素分析入选的变量有AMH(p0.0001)、FSH(P0.0001)、FSH/LH(p=0.0313)、DHEA-S(p0.0001)、FPG(p=0.0759)、BMI(p=0.0012)、LDL(p=0.0115)、TG(p=0.108)、HbAlC(0.0001)、IGF-I(p0.0001)、25(OH)D(p=0.0483)、AMH*FSH(p0.0001)。将上述变量利用多元逐步回归建立多元回归模型,最终纳入模型的变量有 AMH、FSH、DHEA-S、FPG、TG、IGF-I、25(OH)D,模型为"卵巢年龄"=39.71-1.96*AMH+0.13*FSH-0.52*DHEA-S+1.72*FPG+0.64*TG-0.01*IGF-I-0.19*VitD-T,模型的拟合度R2=0.614,AIC为628.3。将该模型应用于POI、PCOS患者中,分别将POI、PCOS组的实际年龄与"卵巢年龄"进行独立样本t检验,结果发现POI患者"卵巢年龄"大于实际年龄,PCOS患者"卵巢年龄"小于实际年龄,均有显著统计学差异。结论:本研究构建的"卵巢年龄"为临床医师提供了准确评估卵巢储备的"新指标",简便、易操作,准确性高,该模型将评估卵巢储备功能的内分泌学指标联合检测,解决了临床上指标检测不一致的问题,同时考虑到代谢因素,全面评估卵巢储备功能,为本文的创新点之一,为评估卵巢储备功能提供了新的方向。然而其预测效能需要进一步的临床验证。
[Abstract]:Objective: at present, there are many indicators to assess the ovarian reserve function, endocrinology index such as AMH, FSH, ultrasound indexes such as AFC, OV, clomiphene stimulation test and age, currently the most commonly used clinical indexes were age, FSH, AMH, AFC, but not a specificity index can get satisfied and the sensitivity of many scholars put forward different indicators of common detection may improve the accuracy. Many studies show that prediction of ovarian response ability of different indicators of pregnancy rate of different indexes and different prediction ability are not consistent. The international field of reproductive on infertile women with ovarian reserve model or score, staging system construction in recent years, as a hot issue, but healthy female ovarian reserve model or staging system construction research reports at home and abroad are less, according to the ovarian reserve function of healthy women in construction of eggs The nest function model has important significance for the accurate assessment of ovarian reserve function, race has certain influence on ovarian function, according to the characteristics of Chinese women to develop in line with the significance of ovarian reserve model Chinese women. Because major metabolic factors influence on ovarian function, this study intends to endocrinology evaluation index of ovarian reserve, at the same time considering the metabolic factors, comprehensive analysis, establish the ovarian reserve function of early warning model, in order to find the ovarian reserve function, timely intervention, improve the reproductive potential of perimenopausal women and performed MHT therapy for the prevention of cardio cerebrovascular diseases, has the important value and significance of cognitive disorders and other chronic diseases. Methods: the research object for March 2014 to July 2015, 189 cases of healthy women enrolled in this study, at the same time, volunteered to participate in this study from Shangdong Province-owned Hospital gynecology clinic PO] PCOS patients, 19 cases were included, second ~ 4 days of menstruation. The study population were collected from FSH, FSH/LH, E2, AMH, DHEA, IGF-1,25 (OH) D, FINS, FPG, LDL, HDL, TG, HbAlc, record the height, weight, and calculate the BM], IRI, AMH*FSH, through the statistics the construction method, "ovarian age." that healthy women "ovarian age" with the same age, the "ovarian age" as the dependent variable, other indexes as independent variables, using single factor analysis of the independent variable initial selection, using stepwise regression analysis method to establish regression model, obtain the "ovarian age". By the construction of healthy women "ovarian age" equation, will POI index and PCOS were substituted into the equation, obtain the corresponding "ovarian age", respectively, POI, PCOS in patients with ovarian age "and the corresponding actual age independent samples y test, POI group, PCOS group of ovarian age" and there is no actual age statistical difference ISO. Results: the test level is set to 0.2, through the single factor analysis of selected variables AMH (P0.0001), FSH (P0.0001), FSH/LH (p=0.0313), DHEA-S (P0.0001), FPG (p=0.0759), BMI (p=0.0012), LDL (p=0.0115), TG (p=0.108), HbAlC (0.0001). IGF-I (P0.0001), 25 (OH) D (p=0.0483), AMH*FSH (P0.0001). The above variables to establish regression model using multiple stepwise regression, included in the final model variables were AMH, FSH, DHEA-S, FPG, TG, IGF-I, D, 25 (OH) model for the "ovarian age" = 39.71-1.96*AMH+0.13*FSH-0.52*DHEA-S+1.72*FPG+0.64*TG-0.01*IGF-I-0.19*VitD-T. The fit of the R2=0.614 model, AIC 628.3. the model is applied to POI, PCOS patients, respectively, POI, PCOS and the actual age group "ovarian age" of independent sample t test, the results showed that POI patients with ovarian age older than PCOS, patients with ovarian age were significantly less than the actual age. Significant difference. Conclusion: the establishment of the "ovarian age" for clinicians to provide an accurate assessment of ovarian reserve "new index" is simple, easy operation, high accuracy, the model will assess the combined detection indexes endocrinology of ovarian reserve, to solve the clinical detection index is not the same problem, considering the factors of metabolism, a comprehensive assessment of ovarian reserve function, is one of the innovations of this paper provide a new direction for the assessment of ovarian reserve function. However, the prediction efficiency need further clinical validation.

【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R711.75

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