Metabolomics biomarkers in prediction of Sudden Infant Death Syndrome: the role of short chain fatty acids

Maria Aslam, Omer Riaz, Jawaria Aslam, Dost Muhammad Khan, Mustafa Hameed, Muhammad Suleman, Rizwan Shahid, Turke Althobaiti, Naeem Ramzan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Sudden Infant Death Syndrome (SIDS) presents a significant challenge, necessitating ongoing research and preventive measures. The intricate landscape of lipid metabolism plays a crucial role in SIDS, with disruptions in key lipid components like Short Chain Fatty Acids (SCFA), alongside other lipids such as triglycerides (TG) and phospholipids (PL), being significant. In this context, SCFA are essential products of the fermentation process by gut microbiota, hold particular interest. SCFA are integral to energy regulation and metabolism, influencing overall well-being. Their unique characteristics, such as chain length and saturation level, provide insights into their potential effects. Alterations in SCFA metabolism can disrupt energy balance, adding to the complexity of SIDS. Leveraging machine learning (ML) presents a promising avenue for unraveling the intricate profiles of SCFA and decoding patterns indicative of heightened SIDS risk. Ensuring interpretability in healthcare is essential for building trust and developing effective prevention strategies. This research delves into understanding SIDS, with a specific focus on SCFA and their role in metabolic health. The application of machine learning, particularly the Artifical Neural Network (ANN) and Stacking model, demonstrated exceptional accuracy of 94% and 96.15% with a recall of 100% and 92.31%, respectively. The models also demonstrated strong classification capabilities, as indicated by a high True Positive Rate (TPR) in the AUC, a low Root Mean Square Error (RMSE) of 0.20, Mean Absolute Error (MAE) of 0.04 and Standard deviation (SD) of 0.10, emphasizing the robustness and precision of the approach. These results underscore the potential of machine learning in the early assessment of SIDS risk, highlighting the critical role of SCFA and advancing the prospects for preventative healthcare.
Original languageEnglish
Pages (from-to)14820-14836
Number of pages17
JournalIEEE Access
Volume13
Early online date23 Dec 2024
DOIs
Publication statusE-pub ahead of print - 23 Dec 2024

Keywords

  • Sudden Infant Death syndrome
  • biomarkers
  • machine learning
  • fatty acids
  • short chain fatty acids
  • healthcare and vulnerable infants

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