S. Decherchi

Metabolomics models trained on one cohort often lose accuracy on another because platform, sample handling and quantification differences shift the data. Using 12,000 plasma NMR samples from the Estonian Biobank to classify heart failure (ICD-10 I50.9), a study evaluated 295 samples re-measured on a different NMR platform and calibrated alignment with up to 108 biospecimens duplicated across both. Harmonization generally beat unaligned data and stabilized as matched pairs grew, yet simple within-domain normalization stayed a strong baseline, reaching balanced accuracy 0.72 and MCC 0.46 with a support vector machine at all 108 pairs.

Metabolomics models trained on one cohort often lose accuracy on another because platform, sample handling and quantification differences shift the data. Using 12,000 plasma NMR samples from the Estonian Biobank to classify heart failure (ICD-10 I50.9), a study evaluated 295 samples re-measured on a different NMR platform and calibrated alignment with up to 108 biospecimens duplicated across both. Harmonization generally beat unaligned data and stabilized as matched pairs grew, yet simple within-domain normalization stayed a strong baseline, reaching balanced accuracy 0.72 and MCC 0.46 with a support vector machine at all 108 pairs.