| Title: |
Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy |
| Authors: |
Nguyen, PBH; Garger, D; Lu, D; Maalmi, H; Prokisch, H; Thorand, B; Adamski, J; Kastenmüller, G; Waldenberger, M; Gieger, C; Peters, A; Suhre, K; Bönhof, GJ; Rathmann, W; Roden, M; Grallert, H; Ziegler, D; Herder, C; Menden, MP |
| Publisher Information: |
Nature Portfolio |
| Publication Year: |
2024 |
| Collection: |
The University of Melbourne: Digital Repository |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| ISSN: |
2730-664X |
| Relation: |
https://hdl.handle.net/11343/359495 |
| Availability: |
https://hdl.handle.net/11343/359495 |
| Rights: |
https://creativecommons.org/licenses/by/4.0 ; CC BY |
| Accession Number: |
edsbas.B675F30C |
| Database: |
BASE |