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. 2025 Feb:163:156047.
doi: 10.1016/j.metabol.2024.156047. Epub 2024 Oct 12.

GDF-15 improves the predictive capacity of steatotic liver disease non-invasive tests for incident morbidity and mortality risk for cardio-renal-metabolic diseases and malignancies

Michail Kokkorakis  1 , Pytrik Folkertsma  2 , José Castela Forte  3 , Bruce H R Wolffenbuttel  2 , Sipko van Dam  2 , Christos S Mantzoros  4
Affiliations
Free article

GDF-15 improves the predictive capacity of steatotic liver disease non-invasive tests for incident morbidity and mortality risk for cardio-renal-metabolic diseases and malignancies

Michail Kokkorakis et al. Metabolism. 2025 Feb.
Free article

Abstract

Background & aims: Noninvasive tools (NITs) are currently used to stratify the risk of having or developing hepatic steatosis or fibrosis. Their performance and a proteomic-enabled improvement in forecasting long-term cardio-renal-metabolic morbidity, malignancies, as well as cause-specific and all-cause mortality, are lacking. Therefore, the performance of established NITs needs to be investigated in identifying cardio-renal-metabolic morbidity, malignancies, cause-specific and overall mortality and improve their performance with novel, proteomic-enabled NITs, including growth differentiation factor 15 (GDF-15), allowing multipurpose utilization.

Methods: 502,359 UK Biobank participants free of the study outcomes at baseline with a 14-year median follow-up were grouped into three categories: a) general population, b) potentially metabolic dysfunction-associated steatotic liver disease (MASLD) population, c) individuals with type 2 diabetes mellitus. The investigated NITs include Aspartate aminotransferase to Platelet Ratio Index (APRI), Fibrosis 4 Index (FIB-4), Fatty Liver Index (FLI), Hepatic Steatosis Index (HSI), Lipid Accumulation Product (LAP), and metabolic dysfunction-associated fibrosis (MAF-5) score.

Results: Adding GDF-15 to the existing NITs led to significantly increased prognostic performance compared to the traditional NITs in almost all instances, reaching substantially high C-indices, ranging between 0.601 and 0.808, with an overall >0.2 improvement in C-index. Overall, with the GDF-15 enhanced NITs, up to more than seven times fewer individuals need to be screened to identify more incident cases of adverse outcomes compared to the traditional NITs. The cumulative incidence of all outcomes, based on the continuous value percentiles of NITs, is increasing exponentially in the upper quintile of the GDF-15 enhanced NITs.

Conclusions: The herein-developed GDF-15 enhanced indices demonstrate higher screening effectiveness and significantly improved prognostic abilities, which are reduced to practice through an easy-to-use web-based calculator tool (https://clinicalpredictor.shinyapps.io/multimorbidity-mortality-risk/).

Keywords: AI; MASLD; ML; NITs; Omics.

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Conflict of interest statement

Declaration of competing interest All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; MK declares no competing interests. PF, JCF, and SvD are employees of Ancora Health B.V. and own shares of Ancora Health B.V., and BHRW is a scientific advisory board member of Ancora Health B.V. without being compensated for this position. C.S.M. reports grants through his institution from Merck, Massachusetts Life Sciences Center, and Boehringer Ingelheim, has been a shareholder of and has received grants through his institution and personal consulting fees from Coherus Inc. and AltrixBio; he reports personal consulting fees and support with research reagents from Ansh Inc., collaborative research support from LabCorp Inc., reports personal consulting fees from Genfit, Lumos, Amgen, Corcept, Aligos, Intercept, 89 Bio, Madrigal, and Regeneron, reports travel support and fees from TMIOA, Elsevier, and the Cardio Metabolic Health Conference. None is related to the work presented herein.

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