AI-Driven Quantitative Metabolomics for Early and Precise HIE Diagnosis: Challenges and Solutions.

Adequate cerebral oxygenation is vital for neonatal survival and the establishment of a healthy brain function. A disruption in this critical balance, particularly during the perinatal period, can result in hypoxic-ischemic encephalopathy (HIE)a severe and potentially fatal form of neonatal brain injury caused by diminished oxygen and blood flow to the brain. HIE is a major contributor to neonatal morbidity and mortality worldwide, with an even greater burden in low-resource settings, where delays in diagnosis and limited access to timely intervention exacerbate long-term neurodevelopmental outcomes. Affected neonates frequently suffer from a spectrum of sequelae, including cerebral palsy, epilepsy, intellectual disability, and other persistent neurological impairments. Currently, therapeutic hypothermia (TH) is the standard-of-care neuroprotective intervention for moderate to severe HIE. However, its efficacy is highly time-dependent and constrained by the critical need for early and accurate diagnosis often within the first 6 h of life. Traditional diagnostic modalities, including clinical assessment, serum protein biomarkers, electroencephalography (EEG), and neuroimaging, frequently lack the sensitivity and specificity required for early risk stratification, thereby limiting their clinical utility during this narrow therapeutic window. In this context, metabolomics-based approaches have emerged as powerful tools to detect subtle early biochemical changes associated with neuronal injury and energy failure. Biomarkers such as lactate, glutamate, succinate, S-100 protein, and CK-BB have shown potential in reflecting metabolic disturbances characteristic of the HIE. Recent advances in nuclear magnetic resonance (NMR) spectroscopy, when integrated with artificial intelligence (AI)-based pattern recognition algorithms, have further enabled the identification of complex metabolic signatures that are both disease-specific and prognostically informative. Despite their promise, the clinical translation of metabolomics-derived biomarkers remains limited by significant preanalytical variability, particularly in sample handling, processing, and storage conditions. Factors such as anticoagulant type, temperature fluctuations, and delays in sample processing can profoundly alter metabolite stability, leading to inconsistent results and reduced reproducibility across cohorts. To address these limitations, this review highlights common pitfalls in blood-based metabolomics workflows and presents a novel, rigorously standardized, multipanel metabolomics strategy for HIE evaluation. By coupling high-resolution NMR spectroscopy with machine learning techniques, we propose the development of a composite "Metabolic Index of Brain Health" that quantitatively captures the extent and severity of hypoxic-ischemic injury. This approach not only enhances diagnostic precision but also enables early risk stratification, paving the way for timely therapeutic interventions.