QUANTITIVE MICRBIOLOGIC MODEL FOR PRETERM DELIVERY

早产是美国婴儿发病率和死亡率的主要原因，预防早产是围产期保健的首要目标。最近的数据表明，包括细菌性阴道病在内的母体生殖道感染与PTD之间有很强的相关性，有令人信服的证据表明微生物是PTD的原因。然而，阴道微生物浓度与PTD之间的关系，对PTD的重要危险因素的关键微生物种群的识别，以及细菌促进PTD的病理生理机制仍有待确定。此外，对PTD有意义的微生物种群之间的相互作用尚不清楚。早产可能是由细菌磷脂酶A2引发的，它会释放花生四烯酸，这是前列腺素的主要物质，被认为是启动分娩的基础。有人假设，脂肪酶和磷脂酶的结合活性也解释了与膜损伤相关的各种影响。然而，阴道中这些结合酶活性与妊娠结局之间的关系尚未确定。拟议项目的目标是前瞻性地收集关于微生物种群和酶活性的定量数据，并了解两个PTD高危人群和一个PTD高危人群和一个低风险人群细菌性阴道病的存在或不存在。采用广义估计方程回归分析重复测量数据，建立预测PTD的定量模型。将解决以下具体目标：(1)定量确定关键微生物种群为PTD的危险因素，(2)确定导致PTD的微生物种群之间的相互作用，(3)确定阴道酶(脂肪酶、磷脂酶)活性与PTD之间是否存在关联，以及(4)记录细菌性阴道病与PTD之间的任何关系，以及(4)记录无细菌性阴道病与PTD之间的任何关系。识别PTD的预测途径(S)可以识别PTD的风险个体，可以识别PTD的风险个体，并最终实施预防PTD的新策略。

Preterm delivery (PTD) is the leading cause of infant morbidity and mortality in the United States, and prevention of PTD is a primary goal in perinatal health care. Recent data indicates a strong association a strong association between maternal genital tract infection, including bacterial vaginosis, and PTD, with compelling evidence that micro- organisms are a cause of PTD. However, the relationship between concentrations of vaginal microorganisms and PTD, the identification of key microbial populations that are significant risk factors for PTD, and the pathophysiologic mechanisms by which bacteria contribute to PTD remain to be determined. In addition, the interactions between microbial populations of significance to PTD are unknown. Preterm labor may be initiated by bacterial phospholipases A2 and which release arachidonic acid-the primary substance for prostaglandins which are considered fundamental to the initiation of labor. It has been hypothesized that the combined activities of lipase and phospholipases also account for a variety of effects associated with membrane damage. However, the relationship between these combined enzyme activities in the vagina and pregnancy outcome has not been determined. The objective of the proposed project is to prospectively collect quantitative data on microbiologic populations and enzyme activities and to receptor the presence or absence of bacterial vaginosis in two cohorts at high risk for PTD and one to record the presence or absence of bacterial vaginosis in two cohorts at high risk for PTD and one cohort at low risk. Generalized estimating equation regression will be used to analyze repeat measurement data and to formulate quantitative models to predict PTD. The following Specific Aims will be addressed: (1) quantitatively identify key microbial population as risk factors for PTD, (2) identify interactions between microbial populations that contribute to PTD, (3) determine whether there is an association between vaginal enzyme (lipase, phospholipase) activities and PTD, and (4) document any relationship between the presence or absence of bacterial vaginosis and PTD, and (4) document any relationship between the presence of absence of bacterial vaginosis and PTD. Recognition of predictive pathway(s) for PTD may permit the identification of individuals at risk for PTD may permit the identification of individuals at risk for PTD and, ultimately, the implementation of noel strategies for its prevention.