Nutritional and clinical predictors of intestinal maturation and feeding tolerance in the preterm infant

早产儿肠道成熟和喂养耐受性的营养和临床预测因素

• 批准号: 10717469
• 负责人: Robert Stephen Chapkin
• 金额: $ 72.25万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-12 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10717469
• 关键词: Abdomen; Affect; Antibiotics; Benign; Biological Markers; Caring; Cells; Characteristics; Clinical; Data; Development; Developmental Biology; Diagnosis; Diet; Disease; Early Diagnosis; Enteral; Enteral Feeding; Enteral Nutrition; Exhibits; Functional disorder; Gene Expression; Genes; Gestational Age; Growth; Health; Hospitalization; Immune; Infant; Infant Health; Inflammatory; Intervention; Intestinal Diseases; Intestinal Mucosa; Intestines; Ischemia; Life; Measures; Metabolic; Methodology; Mucous Membrane; Necrotizing Enterocolitis; Neonatal; Neonatal Intensive Care Units; Neurocognitive; Normal Range; Nutritional; Nutritional Support; Outcome; Pathologic; Pathology; Physiological; Physiology; Premature Infant; Process; Prospective cohort; Recommendation; Research; Risk; Risk Factors; Route; Symptoms; Testing; Time; analytical method; artificial neural network; biomarker identification; clinical care; clinical predictors; clinically relevant; dietary; disorder risk; early detection biomarkers; evidence base; extreme prematurity; fecal microbiota; feeding; gastrointestinal; gastrointestinal sign; gastrointestinal symptom; gut microbiome; high risk; host microbiome; improved; insight; intestinal injury; intestinal maturation; ischemic injury; machine learning algorithm; medical complication; metabolome; metagenome; microbial; microbiome; microbiome signature; neonatal period; novel; novel marker; nutrition; precision medicine; precision nutrition; prediction algorithm; prevent; response; tool; transcriptome

Project Summary/Abstract: In 2020, 10% of U.S. infants were born preterm and ~2%, or 60,000 infants, were born very preterm (VPI; <32 weeks PMA). VPI infants are at high risk for of substantial medical complications, including necrotizing enterocolitis (NEC). In VPI, advancing and maintaining nutritional support reduces disease risk and improves neurodevelopmental outcomes; however, up to 25% of preterm infants demonstrate feeding intolerance, which may be benign or may progress to NEC. However, precise measures and clinical tools that reliably differentiate benign, intestinal immaturity from life-threatening symptoms are lacking. Therefore, the overall objective of this application is to establish intestinal host and microbial biomarkers of intestinal function from an existing, longitudinal, prospective cohort of 400 analyzable VPI and to relate those biomarkers to the spectrum of intestinal function, from consistent enteral nutrition tolerance to intermittent intolerance to ischemic injury. For this purpose, we will utilize our novel non-invasive (exfoliated mucosal cell) methodology to simultaneously assess host- microbiome interactions in the VPI gut. Our central hypothesis is that the transgenomic cross-talk between intestinal mucosal cells and the fecal metagenome and metabolome will provide mechanistic insight into the spectrum of clinical presentations ranging from normal gut developmental biology to abnormal pathophysiology. Three specific aims will test our central hypothesis. Aim 1 will annotate the host exfoliated mucosal cell transcriptome and fecal bacterial metagenome and metabolome profiles to identify biomarkers for preterm infants who have consistent tolerance to enteral feeding or who are diagnosed with feeding intolerance. Aim 2 will annotate the host exfoliated mucosal cell transcriptome and fecal bacterial metagenome and metabolome profiles to identify biomarkers for preterm infants who are diagnosed with feeding intolerance compared to those who develop ischemia. Aim 3 will utilize machine learning algorithms to construct putative diet-health outcome driven Artificial Neural Networks (ANNs). Completion of these aims will provide the necessary data to develop predictive algorithms to enable identification of at-risk VPI who would benefit from precision medicine/nutrition guided interventions targeting specific risk factors.

项目概要/摘要： 2020年，10%的美国婴儿早产，约2%或60，000名婴儿极早产（VPI; <32 PMA周）。VPI婴儿是在高风险的实质性医疗并发症，包括坏死 小肠结肠炎（NEC）。在VPI中，推进和维持营养支持可降低疾病风险， 然而，高达25%的早产儿表现出喂养不耐受， 可能是良性的，也可能发展为NEC。然而，精确的测量和临床工具，可靠地区分 良性的、危及生命的肠道不成熟症状缺乏。因此，这一总体目标 本发明的应用是从现有的， 400例可分析VPI的纵向前瞻性队列，并将这些生物标志物与肠道疾病谱相关 功能，从一致的肠内营养耐受间歇性不耐受缺血性损伤。为此目的， 我们将利用我们的新的非侵入性（脱落的粘膜细胞）方法来同时评估宿主- VPI肠道中的微生物组相互作用。我们的中心假设是， 肠粘膜细胞和粪便宏基因组和代谢组将提供机制的见解， 从正常肠道发育生物学到异常病理生理学的临床表现谱。 三个具体目标将检验我们的中心假设。目的1将注释宿主脱落的粘膜细胞 转录组和粪便细菌宏基因组和代谢组谱，以鉴定早产儿的生物标志物 对肠道喂养有持续耐受性或被诊断为喂养不耐受的患者。目标2将 注释宿主脱落粘膜细胞转录组和粪便细菌宏基因组和代谢组 与那些被诊断为喂养不耐受的早产儿相比， 会出现局部缺血目标3将利用机器学习算法来构建推定的饮食健康结果 驱动的人工神经网络（ANN）。这些目标的完成将提供必要的数据， 预测算法，以识别将从精准医疗/营养中受益的风险VPI 针对具体风险因素的指导干预措施。