Targeted metabolic profiling to predict major morbidity in very preterm newborns

全球每年约有1500万名婴儿早产，110万人死于早产(PTB)，即婴儿在月经后37周前分娩。由于死亡率和发病率取决于胎龄，早产儿(妊娠32周)发生并发症的风险最高，可能导致死亡或严重终身残疾。最重要和最常见的新生儿疾病是脑室出血(IVH)、支气管肺发育不良(BPD)、坏死性小肠结肠炎(NEC)、脓毒症、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测量已经成功地预测了极早产儿的死亡风险，但我们识别可能发生重大发病率的新生儿的能力仍然有限。新生儿疾病严重程度指数具有各种重要的临床和研究应用，包括风险分层、家庭咨询、用于医院间绩效比较的外部基准，以及确定具有特定风险特征的婴儿的个别治疗。评分系统不仅需要预测早产儿的死亡率，还需要预测发病率。我们的团队已经证明，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新预测因子。还需要进一步的工作来优化这些早产儿的预测模型，并量化代谢物作为新生儿疾病严重程度的强大、强大和潜在的纵向生物标志物的能力。我们假设代谢生物标记物可以用来准确预测早产儿综合结局的风险，包括新生儿发病率和住院死亡率。我们研究的目标是：目标1：建立和外部验证预测早产儿新生儿发病率的代谢模型；以及目标2：评估动态代谢模型预测出生第一周内多个时间点的新生儿发病率。这项拟议的工作将在加利福尼亚州约8500名极早产儿和爱荷华州1500名极早产儿的回顾样本中检查新生儿发病率和死亡率的代谢预测因素。此外，我们将评估代谢产物在出生第一周内的四个关键时间点预测新生儿发病率和死亡率的能力，这些样本包括500名在加州大学伯尼奥夫分校贝尼奥夫儿童医院(UCSF-BCH)和爱荷华大学斯特德家庭儿童医院(UI-SFCH)NICU接受护理的极早产儿。了解特定代谢物与新生儿发病率之间的关系将导致改善诊断、更有效的治疗药物以及临床治疗早产儿的精确方法的长期目标。

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.