---
title: "Matched samples improve domain harmonization for cross-cohort NMR metabolomics"
id: "pubmed-42888323"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42888323"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42888323/"
doi: "10.1007/s11306-026-02543-0"
published_at: "2026-10-17T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Matched samples improve domain harmonization for cross-cohort NMR metabolomics
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42888323
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42888323/)
- **DOI:** [10.1007/s11306-026-02543-0](https://doi.org/10.1007%2Fs11306-026-02543-0)
- **Published At:** 2026-10-17T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Investigators developed a machine-learning framework to classify ICD-10 I50.9 (unspecified heart failure) using plasma **NMR** **metabolomics** data from the Estonian Biobank. - The source training set contained 12,000 samples measured at Nightingale Health; the destination cohort comprised 295 samples measured on a different NMR platform, with up to 108 duplicated biospecimens available as **matched samples** for calibration. - Harmonization methods compared included **within-domain normalization**, **Optimal Transport**, Bridge mapping, and **Empirical Bayes** correction; the source did not report detailed algorithmic hyperparameters for each method in the abstract. - Data indicated that domain harmonization improved cross-cohort prediction versus unaligned data, and that predictive performance rose as the number of matched samples used for calibration increased, with variability in gains across methods. - The study team observed that simple **within-domain normalization** often delivered strong performance and in many cases equaled or outperformed more complex alignment strategies. - External validation used independently generated human gestational diabetes and mouse diabetes metabolomics datasets from Metabolomics Workbench; harmonization approaches improved classification on these fully unmatched datasets relative to raw data. - Authors noted that the effectiveness of advanced alignment methods depended on the availability and representativeness of matched reference samples; the abstract does not provide per-method accuracy metrics or statistical significance values. - The report emphasizes practical guidance: matched biospecimens substantially enhance harmonization and model transferability, while robust normalization is a reliable baseline when matched samples are limited. - The abstract documents ethical approvals and declarations: participants provided informed consent and the Estonian Biobank analysis was conducted under ethical approval 1.1-12/15; the authors reported no competing interests.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia. aigar.ottas@ut.ee. * 2 Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy. aigar.ottas@ut.ee. * 3 Fondazione Istituto Italiano di Tecnologia, Genoa, Italy. aigar.ottas@ut.ee. * 4 Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy. * 5 Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy. andrea.armirotti@iit.it. * 6 Fondazione Istituto Italiano di Tecnologia, Genoa, Italy. sergio.decherchi@iit.it. # Contributed equally. * PMID: **42888323** * DOI: [ 10.1007/s11306-026-02543-0 ](https://doi.org/10.1007/s11306-026-02543-0) Item in Clipboard # Impact of matched samples on domain harmonization in cross-cohort NMR metabolomics A Ottas et al. Metabolomics. 2026. Show details Display options Display options Format Abstract PubMed PMID Metabolomics Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Metabolomics%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Metabolomics%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) . 2026 Oct 17;22(6):172. doi: 10.1007/s11306-026-02543-0. ### Authors [A Ottas](https://pubmed.ncbi.nlm.nih.gov/?term=Ottas+A&cauthor_id=42888323)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42888323/#short-view-affiliation-1 "Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia. aigar.ottas@ut.ee.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42888323/#short-view-affiliation-2 "Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy. aigar.ottas@ut.ee.")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42888323/#short-view-affiliation-3 "Fondazione Istituto Italiano di Tecnologia, Genoa, Italy. aigar.ottas@ut.ee."), [L Goldoni](https://pubmed.ncbi.nlm.nih.gov/?term=Goldoni+L&cauthor_id=42888323)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42888323/#short-view-affiliation-4 "Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy."), [A Armirotti](https://pubmed.ncbi.nlm.nih.gov/?term=Armirotti+A&cauthor_id=42888323)[#](https://pubmed.ncbi.nlm.nih.gov/42888323/#short-view-equal-contrib-explanation "Contributed equally")[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42888323/#short-view-affiliation-5 "Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy. andrea.armirotti@iit.it."), [S Decherchi](https://pubmed.ncbi.nlm.nih.gov/?term=Decherchi+S&cauthor_id=42888323)[#](https://pubmed.ncbi.nlm.nih.gov/42888323/#short-view-equal-contrib-explanation "Contributed equally")[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42888323/#short-view-affiliation-6 "Fondazione Istituto Italiano di Tecnologia, Genoa, Italy. sergio.decherchi@iit.it.") ### Affiliations * 1 Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia. aigar.ottas@ut.ee. * 2 Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy. aigar.ottas@ut.ee. * 3 Fondazione Istituto Italiano di Tecnologia, Genoa, Italy. aigar.ottas@ut.ee. * 4 Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy. * 5 Fondazione Istituto Italiano di Tecnologia, Analytical Chemistry Facility, Genoa, Italy. andrea.armirotti@iit.it. * 6 Fondazione Istituto Italiano di Tecnologia, Genoa, Italy. sergio.decherchi@iit.it. # Contributed equally. * PMID: **42888323** * DOI: [ 10.1007/s11306-026-02543-0 ](https://doi.org/10.1007/s11306-026-02543-0) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID Linked Discoveries ## Abstract **Introduction:** Machine learning models trained on metabolomics data frequently show reduced performance when applied to independent cohorts because of domain shifts introduced by differences in analytical platforms, sample handling, storage conditions, and metabolite quantification procedures. Robust approaches for harmonizing metabolomics datasets across cohorts are therefore needed to improve model transferability and reproducibility. **Objectives:** To evaluate the impact of matched samples on domain harmonization and cross-cohort prediction performance in NMR metabolomics data, and to compare multiple harmonization strategies for improving model generalization across independently acquired datasets. **Methods:** A machine-learning framework was developed to classify ICD-10 code I50.9 (unspecified heart failure) using plasma NMR metabolomics data from the Estonian Biobank. Models were trained on a source dataset comprising 12,000 samples measured at Nightingale Health and evaluated on an independently measured destination dataset of 295 samples acquired on a different NMR platform. Harmonization approaches included within-domain normalization, Optimal Transport, Bridge mapping, and Empirical Bayes correction. The effect of matched samples was assessed using up to 108 duplicated biospecimens measured in both datasets. Additional validation was performed using independently generated human gestational diabetes and mouse diabetes metabolomics datasets from Metabolomics Workbench. **Results:** Domain harmonization generally improved cross-cohort prediction compared with unaligned data, particularly when matched samples were available. Predictive performance increased as the number of matched samples used for calibration increased, although gains varied across methods. Nevertheless, simple within-domain normalization consistently provided strong performance and often matched or exceeded more complex alignment approaches. External validation using fully unmatched human and mouse metabolomics datasets demonstrated that harmonization strategies improved classification performance relative to raw data, supporting the broader applicability of the framework. **Conclusion:** Matched samples substantially enhance domain harmonization and improve the transferability of metabolomics prediction models across analytical platforms and cohorts. However, robust normalization remains a strong baseline, and the benefits of more sophisticated alignment methods depend on the availability and representativeness of matched reference samples. These findings provide practical guidance for developing reproducible cross-cohort metabolomics machine-learning models while preserving biologically relevant signals. **Keywords:** Cardiovascular disease; Cross-cohort; Data transformation; Domain alignment; Independent dataset; Machine learning; Matched samples; Metabolomics; NMR. © 2026. The Author(s). [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declarations. Competing interests: The authors declare they have no known competing financial or personal relationships that could have influenced the work reported in this paper. Ethical approval: All participating subjects have signed an informed consent form and the privacy rights of human subjects have been observed. The activities of the EstBB are regulated by the Human Genes Research Act, which was adopted in 2000 specifically for the operations of the EstBB. Individual level data analysis in the EstBB was carried out under ethical approval 1.1-12/15 from the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs), using data according to release application 3-10/GI-9133 from the Estonian Biobank. ## References 1. 1. Aguilar, J. A., Nilsson, M., Bodenhausen, G., & Morris, G. A. (2012). Spin echo nmr spectra without j modulation. Chemical Communications, 48(6), 811–813. - [PubMed](https://pubmed.ncbi.nlm.nih.gov/22143456/) 2. 1. Behdenna, A., Colange, M., Haziza, J., Gema, A., Appé, G., Azencott, C.-A., & Nordor, A. (2023). pycombat, a python tool for batch effects correction in high-throughput molecular data using empirical bayes methods. BMC Bioinformatics, 24(1), 459. - [DOI](https://doi.org/10.1186/s12859-023-05578-5) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/38057718/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/10701943/) 3. 1. Bharti, S. K., & Roy, R. (2012). Quantitative 1h nmr spectroscopy. TrAC, Trends in Analytical Chemistry, 35, 5–26. 4. 1. Bland, J. M., & Altman, D. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The lancet, 327(8476), 307–310. 5. 1. Bosso, G., Mercurio, V., Diab, N., Pagano, A., Porta, G., Allegorico, E., Serra, C., Guiotto, G., Numis, F. G., Tocchetti, C. G., & Schiraldi, F. (2020). Time-weighted lactate as a predictor of adverse outcome in acute heart failure. ESC Heart Failure, 8(1), 539–545. - [PubMed](https://pubmed.ncbi.nlm.nih.gov/33231918/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/7835560/) Show all 67 references ## MeSH terms * Animals Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Animals%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Animals) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Cohort Studies Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Cohort+Studies%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Cohort+Studies) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Diabetes, Gestational / metabolism Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Diabetes%2C+Gestational%2Fmetabolism%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Diabetes%2C+Gestational) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Female Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Female%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Female) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Humans Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Humans%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Humans) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Machine Learning Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Machine+Learning%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Machine+Learning) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Magnetic Resonance Spectroscopy / methods Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Magnetic+Resonance+Spectroscopy%2Fmethods%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Magnetic+Resonance+Spectroscopy) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Metabolomics* / methods Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Metabolomics%2Fmethods%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Metabolomics) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Mice Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Mice%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Mice) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) * Reproducibility of Results Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Reproducibility+of+Results%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Reproducibility+of+Results) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42888323/) ## Grants and funding * [ PUTJD914/Eesti Teadusagentuur ](https://pubmed.ncbi.nlm.nih.gov/?term=PUTJD914%2FEesti+Teadusagentuur%5BGrants+and+Funding%5D&sort=date&sort_order=desc "All articles for grant PUTJD914/Eesti Te
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