Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening

Eissa, Tarek; Leonardo, Cristina; Kepesidis, Kosmas V.; Fleischmann, Frank; Linkohr, Birgit; Meyer, Daniel; Zoka, Viola; Huber, Marinus; Voronina, Liudmila; Richter, Lothar; Peters, Annette; Žigman, Mihaela (2024): Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening. Cell Reports Medicine, 5 (7): 101625. ISSN 26663791

Abstract

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

• Dokumententyp: Artikel (LMU)
• Organisationseinheit (Fakultäten): 17 Physik
• DFG-Fachsystematik der Wissenschaftsbereiche: Naturwissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892