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Nguyen, B.H.P. ; Garger, D. ; Lu, D. ; Maalmi, H.* ; Prokisch, H. ; Thorand, B. ; Adamski, J. ; Kastenmüller, G. ; Waldenberger, M. ; Gieger, C. ; Peters, A. ; Suhre, K.* ; Bönhof, G.J.* ; Rathmann, W.* ; Roden, M.* ; Grallert, H. ; Ziegler, D.* ; Herder, C.* ; Menden, M.P.

Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy.

Commun. Med. 4:265 (2024)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
BACKGROUND: Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactorial disease and not fully understood yet. METHODS: Here, we developed the Interpretable Multimodal Machine Learning (IMML) framework for predicting DSPN prevalence and incidence based on sparse multimodal data. Exploiting IMMLs interpretability further empowered biomarker identification. We leveraged the population-based KORA F4/FF4 cohort including 1091 participants and their deep multimodal characterisation, i.e. clinical data, genomics, methylomics, transcriptomics, proteomics, inflammatory proteins and metabolomics. RESULTS: Clinical data alone is sufficient to stratify individuals with and without DSPN (AUROC = 0.752), whilst predicting DSPN incidence 6.5 ± 0.2 years later strongly benefits from clinical data complemented with two or more molecular modalities (improved ΔAUROC > 0.1, achieved AUROC of 0.714). Important and interpretable features of incident DSPN prediction include up-regulation of proinflammatory cytokines, down-regulation of SUMOylation pathway and essential fatty acids, thus yielding novel insights in the disease pathophysiology. CONCLUSIONS: These may become biomarkers for incident DSPN, guide prevention strategies and serve as proof of concept for the utility of IMML in studying complex diseases.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Population; Neuropathy; Kora; Inflammation; Integration; Severity; Pathway; Health; Set
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 2730-664X
e-ISSN 2730-664X
Zeitschrift Communications Medicine
Quellenangaben Band: 4, Heft: 1, Seiten: , Artikelnummer: 265 Supplement: ,
Verlag Springer
Verlagsort Campus, 4 Crinan St, London, N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Neurogenomics (ING)
Institute of Epidemiology (EPI)
Institute of Experimental Genetics (IEG)
Institute of Bioinformatics and Systems Biology (IBIS)
POF Topic(s) 30205 - Bioengineering and Digital Health
90000 - German Center for Diabetes Research
30202 - Environmental Health
30201 - Metabolic Health
30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
Genetics and Epidemiology
PSP-Element(e) G-554700-001
G-501900-382
G-503292-001
G-504000-002
G-500600-001
G-503891-001
G-504091-001
G-504091-004
G-504000-010
G-504091-002
G-503700-001
Förderungen German Federal Ministry of Health
Ministry of Culture and Science of the State of North Rhine-Westphalia
Munich Centre of Health Sciences (MC-Health), Ludwig-Maximilians-Universitaet
German Federal Ministry of Education and Research, State of Bavaria
Helmholtz Zentrum Muenchen-German Research Centre for Environmental Health
DOI 10.1038/s43856-024-00637-1
WOS ID 001379560500001
Scopus ID 85212091971
PubMed ID 39681608
Erfassungsdatum 2024-12-18
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