Machine Learning and Multiomics for Predictive Models and Biomarker Discovery in Preterm Infants.

用于早产儿预测模型和生物标志物发现的机器学习和多组学。

• 批准号: 10729640
• 负责人: Mohan Pammi
• 金额: $ 64.08万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729640
• 关键词: Adolescent; Age; Artificial Intelligence; Bioinformatics; Biological Markers; Blood specimen; Bronchopulmonary Dysplasia; Child; Clinical; Clinical Data; Collaborations; Collection; Data Set; Databases; Digestive System Disorders; Disease; Early Intervention; Enrollment; Feces; Functional disorder; Future; Genetic Transcription; Goals; Health; Histone Deacetylation; Immunity; Infant; Inflammation; Intervention; Knowledge; Life; Machine Learning; Mediating; Medicine; Metabolic; Metabolic Pathway; Metabolism; Mission; Modeling; Monitor; Morbidity - disease rate; Multiomic Data; National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; Necrotizing Enterocolitis; Neonatal; Neonatology; Outcome; Pathogenesis; Pathway interactions; Patient-Focused Outcomes; Patients; Pediatric Hospitals; Premature Infant; Prospective Studies; Prospective cohort; Public Health; Research; Research Design; Retinopathy of Prematurity; Retrospective cohort; Sampling; Science; Sepsis; Survivors; Techniques; Testing; Texas; Traditional Medicine; United States National Institutes of Health; Universities; Urine; Validation; Vermont; Very Low Birth Weight Infant; Volatile Fatty Acids; biomarker discovery; clinical predictive model; cohort; college; disability; dysbiosis; high risk; host microbiome; improved; innovation; intraventricular hemorrhage; knowledge base; knowledgebase; late onset sepsis; medical schools; metabolome; microbial; microbial disease; microbiome; mortality; multiple omics; novel; precision medicine; predictive marker; predictive modeling; premature; prognostication; prospective; survival prediction; tool

PROJECT SUMMARY Preterm infants born at < 32 weeks and <1500 g (very low birth weight, VLBW) suffer from increased mortality (10-15%) and less than 70% survive without major morbidity. Microbial dysbiosis has been associated with major preterm morbidities but the microbial metabolites or the mechanisms by which they impact pathophysiology, survival and morbidity is not known. The purpose of this proposal is to develop holistic prediction models integrating clinical data and multi-omic signatures, aid biomarker discovery and advance the paradigm in Neonatal Medicine from traditional to targeted precision medicine. The overarching hypothesis is that integrating metabolic and multi-omic signatures with clinical data will reliably predict survival and major morbidity in preterm, VLBW infants. The long-term goal of this research is to establish causal association between identified microbial metabolites and disease in preterm infants, contribute to the knowledgebase of microbial metabolites and improve preterm outcomes. We will test our hypothesis using the following Specific Aims; Aim 1) Leverage machine learning techniques to develop clinical prediction models for mortality and specific morbidities in preterm, VLBW infants: We will test the hypothesis, that a model integrating clinical variables in the first 2 wks. of age, will accurately predict mortality, and morbidities of late-onset sepsis, NEC, BPD, severe ROP and severe IVH. We will employ a retrospective cohort from the Vermont Oxford Database (VON) from Texas Children’s Hospital, (n= 3385 VLBW infants). We will validate the clinical predictive models derived from aim 1A with the prospective clinical data from the first 2 weeks, from Aim 2 (n=300), Aim 2) Delineate microbial metabolites and multi-omic signatures that differentiate preterm VLBW infants with mortality and morbidity, refine predictive models and enhance biomarker discovery: We will test the hypothesis that integrating multi-omics signatures with clinical data using machine learning techniques will refine our predictive models (mortality and specific morbidities of late-onset sepsis, NEC, BPD, ROP and IVH/PVL) for better accuracy and enhance biomarker discovery. We will accomplish this in a prospective study design of enrolled preterm (< 32weeks), VLBW infants (n= 300) and collect stool, urine and blood samples, longitudinally twice a week for 2 weeks of age. We anticipate identifying known and novel metabolites and delineating metabolic pathways hitherto unidentified that influence preterm pathophysiology and outcomes. Holistic prediction models using information from the first 2 weeks of life will enable us to introduce interventions early to improve health trajectories and patient outcomes, thereby facilitating the paradigm of proactive precision medicine in Neonatology. The impact of our results extend beyond the field of neonatology, to other patients and diseases where microbial dysbiosis and altered metabolome are key factors in the pathogenesis.

项目摘要 早产婴儿在<32周且<1500 g（非常低的出生体重，VLBW）死亡率增加 （10-15％）和少于70％的生存，没有重大发病率。微生物营养不良与 主要的早产，但微生物代谢产物或它们影响的机制 病理生理学，生存和发病率尚不清楚。该提议的目的是发展整体 预测模型整合临床数据和多摩尼克特征，帮助生物标志物发现并推进 从传统到有针对性的精确医学的新生儿医学范式。总体假设是 将代谢和多摩尼克特征与临床数据相结合的，将可靠地预测生存和主要 早产的发病率，VLBW婴儿。这项研究的长期目标是建立因果关系 在早产儿中发现的微生物代谢产物和疾病之间，有助于知识基础 微生物代谢物并改善早产结果。我们将使用以下特定的特定来检验我们的假设 目标；目标1）利用机器学习技术开发死亡率和 早产，VLBW婴儿的特定病态：我们将检验一个假设，即一个整合临床的模型 前2周中的变量。年龄将准确预测败血症的死亡率和病态，NEC， BPD，严重的ROP和严重的IVH。我们将使用佛蒙特州牛津数据库的回顾性队列 （von）来自德克萨斯州儿童医院（n = 3385 VLBW婴儿）。我们将验证临床预测模型 源自AIM 1A，并带有前2周的前瞻性临床数据，AIM 2（n = 300），AIM 2） 描述微生物代谢物和多摩尼克特征，使早产儿与婴儿 死亡率和发病率，完善预测模型并增强生物标志物的发现：我们将检验假设 使用机器学习技术将多摩学特征与临床数据相结合的将会完善我们的 预测模型（败血症的死亡率和特定病态，NEC，BPD，ROP和IVH/PVL） 更好的准确性并增强生物标志物发现。我们将在一项前瞻性研究设计中实现这一目标 注册早产（<32周），VLBW婴儿（n = 300）并收集凳子，尿液和血液样本，纵向 每周两次2周大。我们预计识别已知和新颖的代谢物并描绘 迄今未识别的代谢途径会影响早产性病理生理和结果。整体 使用生命前2周的信息的预测模型将使我们能够尽早引入干预措施 为了改善健康轨迹和患者的结果，从而支持主动精度的范式 新生儿学的医学。我们的结果的影响范围超出了新生儿学领域，对其他患者的影响 以及微生物营养不良和代谢组改变的疾病是发病机理中的关键因素。