Case Study
Metabolomics Provides Insight into the Pathology of Sudden Infant Death Syndrome and Identifies Biomarker Candidates
In this report, Aldridge and colleagues used global metabolomics to generate a comprehensive analysis of biochemical changes that occur in sudden infant death syndrome.
This study supports the potential use of metabolomic screening for a sudden infant death syndrome(SIDS) risk assessment and emphasizes the importance of validating these findings in follow-up studies so the metabolomic basis of SIDS pathophysiology can be better understood.
This study supports the potential use of metabolomic screening for a sudden infant death syndrome(SIDS) risk assessment and emphasizes the importance of validating these findings in follow-up studies so the metabolomic basis of SIDS pathophysiology can be better understood.
The Challenge: Understanding How and Why Sudden Infant Death Syndrome Occurs
Sudden infant death syndrome (SIDS) is the demise of a healthy infant during sleep that cannot be explained by a postmortem autopsy, scene of death, or medical history. This devastating condition is a leading cause of infant mortality in the United States and remains poorly understood. The Triple Risk Model1 suggests SIDS occurs at the intersection of a vulnerable infant, a critical development period, and external stressors. However, this model lacks specific biomarkers to assist with SIDS diagnosis and risk. Metabolomics is a promising approach to advancing our understanding of SIDS owing to its ability to reveal deep phenotypic information about pathologic processes and environmental factors that may contribute to this condition.
Metabolon’s Insight: Using Comprehensive Metabolomics Profiling to Understand Biochemical Disturbances in SIDS and Identify Candidate Biomarkers of Higher Risk
To address the gap in knowledge of SIDS pathology and risk factors on a cellular level, one group leveraged the sensitivity and comprehensiveness of metabolomics to retrospectively compare metabolomic profiles in SIDS cases and non-SIDS controls in a large cohort2.
Biobanked serum samples were analyzed using Metabolon’s Global Discovery Panel. Compounds were identified by comparing them to a library of standard biochemical entries, quantified based on area under the curve, then normalized to quality control samples. Welch’s t-tests were used to identify differential metabolites between the SIDS and non-SIDS groups. Potential metabolite predictors of SIDS were identified using logistic regression.
The Solution: Metabolomics Reveals Key Phenotypic Insight into SIDS Pathology and Identifies Candidate Biomarkers of Higher Risk
Thirty-five metabolites were significantly associated with SIDS after adjusting for confounding variables. The top five metabolite predictors—ornithing, 5-hydroxylysine, 1-stearoyl-2-linoleoyl-GPC (18:0/18:2), ribitol, and arabitol/xylitol—are involved in key biological pathways including nitrogen metabolism, lipid metabolism, oxidative stress, and neuron communication. Other pathways were also altered in SIDS cases: 1) tyrosine metabolism, which could potentially impact stress response and arousal mechanisms, 2) sphingomyelin pathways, which suggests disruptions in neuronal function and surfactant composition, 3) the urea cycle, which may be the result of metabolic stress and disrupted ammonia detoxification, and 4) the pentose phosphate pathway, which suggests the involvement of oxidative stress.
Other metabolite biomarkers that strongly associated with SIDS were also tied to environmental risk factors of the condition. For example, N1-Methyl-2-pyridone-5-carboxamide is a uremic toxin that increases in response to smoking exposure, and maternal smoking is a known risk factor for SIDS. Additionally, levels of the stress hormone cortisol were significantly different between infants who died of SIDS who were bed sharing compared to those who were not. It has been theorized that some SIDS deaths are due to uncontrolled inflammatory reactions and cortisol levels may be an indicator for stress and/or a lack of inflammatory control in SIDS.
The Outcome: Using Metabolomics to Better Understand SIDS Pathology and Biomarkers of Increased Risk
In this report, Aldridge and colleagues used global metabolomics to generate a comprehensive analysis of biochemical changes that occur in SIDS. Altogether, their findings identified biomarker candidates of higher risk and support the potential use of metabolomic screening for SIDS risk assessment. Their work also emphasizes the importance of validating these findings in follow-up studies so the metabolomic basis of SIDS pathophysiology can be better understood.
References
1. J.J. Filiano, H.C. Kinney. A perspective on neuropathologic findings in victims of the sudden infant death syndrome: the triple-risk model. Biol Neonate 1994;65(3-4):194-7.
2. Aldridge, Chad et.al. Metabolomics profiles of infants classified as sudden infant death syndrome: a case-control analysis. EBioMedicine 2025 Jan:111:105484.
