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.

项目总结 32周和1500克(极低出生体重，VLBW)出生的早产儿死亡率增加 (10%-15%)，不到70%的人存活下来，没有大的发病率。微生物的微生态失调与 主要的早产儿疾病，但微生物代谢物或其影响机制 病理生理学、存活率和发病率尚不清楚。这项建议的目的是发展全面的 结合临床数据和多组特征的预测模型，有助于生物标记物的发现和推进 新生儿医学从传统到靶向精确医学的范式。最重要的假设是 将代谢和多组学特征与临床数据相结合将可靠地预测生存率和主要 早产儿、极低出生体重儿的发病率。这项研究的长期目标是建立因果关系 已识别的微生物代谢物与早产儿疾病之间的关系，有助于 微生物代谢物和改善早产结局。我们将使用以下具体内容来测试我们的假设 目标1)利用机器学习技术开发死亡率和 早产儿、极低出生体重儿的特殊发病率：我们将检验这一假设，即结合临床的模型 前两周的变量。年龄，将准确预测死亡率和迟发性败血症，NEC， BPD、重度ROP和重度IVH。我们将使用佛蒙特州牛津数据库中的回溯性队列 (Von)来自德克萨斯州儿童医院，(n=3385名极低出生体重儿)。我们将验证临床预测模型 来自目标1的前瞻性临床数据来自目标2(n=300)，目标2) 描述微生物代谢物和多组学特征以区分早产儿和 死亡率和发病率、改进预测模型和加强生物标记物发现：我们将检验这一假设 使用机器学习技术将多组学签名与临床数据相结合将完善我们的 预测模型(迟发性脓毒症的死亡率和特定发病率、NEC、BPD、ROP和IVH/PVL) 更好的准确性和加强生物标志物的发现。我们将在一项前瞻性研究设计中实现这一点 登记早产(32周)，极低出生体重儿(n=300)，并纵向收集粪便、尿液和血样 每周两次，持续两周。我们期待着识别已知和新的代谢物并描绘出 影响早产病理生理和结局的代谢途径迄今尚未确定。整体性 使用出生前两周的信息的预测模型将使我们能够及早引入干预措施 改善健康轨迹和患者结果，从而促进前瞻性精确度的范例 新生儿科医学。我们的研究结果不仅影响到新生儿领域，还影响到其他患者。 以及微生物代谢失调和代谢改变是发病机制的关键因素的疾病。