Machine learning approach on plasma proteomics identifies signatures associated with obesity in the <scp>KORA FF4</scp> cohort
The protein signatures identified in our study showed promising predictive potential for obesity. These findings warrant further investigation to evaluate their clinical applicability, offering insights into obesity prevention and treatment strategies.
Authors
- Jochen Seißler (ORCID: https://orcid.org/0000-0002-7404-2364)
- Esienanwan Esien Efiong (ORCID: https://orcid.org/0000-0003-2863-5772)
- Thomas Skurk (ORCID: https://orcid.org/0000-0002-6875-1658)
- Qiuling Dong (ORCID: https://orcid.org/0000-0002-3369-4120)
- Harald Grallert (ORCID: https://orcid.org/0000-0002-6876-9655)
- Christian Gieger (ORCID: https://orcid.org/0000-0001-6986-9554)
- Jonathan Adam
- Sapna Sharma (ORCID: https://orcid.org/0000-0003-1039-8879)
- Jiefei Niu (ORCID: https://orcid.org/0000-0003-4104-1426)
- Annette Peters (ORCID: https://orcid.org/0000-0003-0984-2258)
Institutions
- University of Antwerp (BE)
- Federal University Lafia (NG)
- Helmholtz Zentrum München (DE)
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich (DE)
- Deutsches Diabetes-Zentrum e.V. (DE)
- German Center for Diabetes Research (DE)
- Heinrich Heine University Düsseldorf (DE)
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Diabetes Obesity and Metabolism
- Published
- 2025-02-27
- DOI
- https://doi.org/10.1111/dom.16264
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- China Scholarship Council