Targeted metabolic profiling to predict major morbidity in very preterm newborns

在全球范围内，每年约有1500万婴儿早产，110万死亡是由于早产（PTB），定义为在月经后37周之前分娩婴儿。由于死亡率和发病率取决于胎龄，极早产儿（<32周妊娠）发生并发症的风险最高，可导致死亡或严重的终身残疾。其中最重要和最常见的主要新生儿疾病是脑室内出血（IVH）、支气管肺发育不良（BPD）、坏死性小肠结肠炎（NEC）、败血症、动脉导管未闭（PDA）和早产儿视网膜病变（ROP）。虽然新生儿疾病严重程度的措施已成功地预测极早产儿的死亡风险，我们的能力，以确定新生儿可能发展显着的发病率仍然有限。新生儿疾病严重程度指数具有多种重要的临床和研究应用，包括风险分层、家庭咨询、医院间性能比较的外部基准以及确定具有特定风险特征的婴儿的个体治疗。评分系统是必要的，不仅预测死亡率，而且发病率在非常早产儿。我们的研究小组已经表明，在常规新生儿筛查时的代谢状态是早产儿新生儿发病率和死亡率的一个新的预测因子。需要进一步的工作来优化这些预测模型在非常早产的新生儿和量化的能力代谢物作为强大的，强大的和潜在的纵向生物标志物的新生儿疾病的严重程度。我们假设代谢生物标志物可用于准确预测极早产儿的复合结局风险，包括新生儿发病率和住院死亡率。我们的研究目标是：目的1：开发和外部验证代谢模型预测新生儿发病率在极早产儿;和目的2：评估动态代谢模型预测新生儿发病率在多个时间点内的第一周的生活。拟议的工作将检查新生儿发病率和死亡率的代谢预测因子在一个回顾性样本中的大约8,500个非常早产儿从加州和1,500个非常早产儿从爱荷华州。此外，我们将评估代谢物预测新生儿发病率和死亡率的能力在四个关键时间点内的第一周的前瞻性样本中的500个非常早产新生儿接受护理的NICU在加州大学旧金山分校贝尼奥夫儿童医院（UCSF-BCH）和爱荷华州大学Stead家庭儿童医院（UI-SFCH）。了解特定代谢物和新生儿发病率之间的关系将导致改善诊断的长期目标，更有效的治疗药物，以及对极早产儿的临床管理的精确方法。

Globally, approximately 15 million babies are born preterm each year and 1.1 million deaths are due to preterm birth (PTB), defined as delivery of an infant before 37 post-menstrual weeks. Because mortality and morbidity rates are dependent upon gestational age, the very preterm neonate (<32 weeks gestation) is at the highest risk of developing complications that can result in death or significant life-long disability. Among the most significant and common of the major neonatal morbidities are intraventricular hemorrhage (IVH), bronchopulmonary dysplasia (BPD), necrotizing enterocolitis (NEC), sepsis, patent ductus arteriosus (PDA) and retinopathy of prematurity (ROP). While measures of neonatal illness severity have been successful in predicting the risk for mortality in very preterm neonates, our ability to identify newborns likely to develop significant morbidity remains limited. Neonatal illness severity indices have a variety of important clinical and research applications including risk stratification, family counseling, external benchmarking for inter-hospital performance comparisons, and determining individual treatments for infants with a specific risk profile. Scoring systems are needed that not only predict mortality but also morbidity in the very preterm neonate. Our team has shown that metabolic status at the time of routine newborn screening is a novel predictor of neonatal morbidity and mortality in preterm newborns. Further work is needed to optimize these prediction models in very preterm neonates and quantify the ability of metabolites to act as strong, robust and potentially longitudinal biomarkers of neonatal illness severity. We hypothesize that metabolic biomarkers can be used to accurately predict the risk of a composite outcome in very preterm neonates that includes neonatal morbidity and in-hospital mortality. The objectives of our study are: Aim 1: Develop and externally validate metabolic models for predicting neonatal morbidity in very preterm newborns; and Aim 2: Evaluate dynamic metabolic models for predicting neonatal morbidity at multiple time points within the first week of life. The proposed work will examine metabolic predictors of neonatal morbidity and mortality in a retrospective sample of approximately 8,500 very preterm births from California and 1,500 very preterm births from Iowa. Furthermore, we will evaluate the ability of metabolites to predict neonatal morbidity and mortality at four critical time points within the first week of life in a prospective sample of 500 very preterm newborns receiving care in the NICU at UCSF Benioff Children's Hospital (UCSF-BCH) and the University of Iowa Stead Family Children's Hospital (UI-SFCH). Understanding the relationship between specific metabolites and neonatal morbidity will lead to the long-term goal of improved diagnostics, more effective therapeutic agents, and a precision approach to clinical management of the very preterm neonate.