Growth differentiation factor-15 and the incidence, bidirectional progression, and risk prediction of atherosclerotic cardiovascular disease and metabolic dysfunction-associated steatotic liver disease in individuals with cardiovascular-kidney-metabolic syndrome stages 0–3
Cardiovascular Diabetology, 2026
Chen X., Tang H., Xu Z., Chen X., Tian C., Luo N., Huang J., Lin H., Zhang X., Yang Q., Liang K., Chen P., Qiu X., Jiang L., Lin W., Chen W., Zhang Y., Tan X., Lai J., Chen Y.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Metabolic Diseases CVD Nephrology | Pathophysiology | Plasma |  Olink Explore 3072/384 |
Abstract
Background
Growth differentiation factor 15 (GDF-15) is a circulating biomarker reflecting oxidative stress, inflammation, and cellular aging. However, its role in disease risk assessment amongst individuals with cardiovascular-kidney-metabolic (CKM) syndrome stages 0–3 remains unclear.
Methods
This study included 29,697 UK Biobank participants with CKM stages 0–3 defined in accordance with the American Heart Association criteria. Associations of GDF-15 with metabolic, inflammatory and liver fibrosis markers and CKM stage were examined using linear or multinomial logistic regression models. Fine-Gray competing risk regression models were used to evaluate associations with incident atherosclerotic cardiovascular disease (ASCVD), metabolic dysfunction-associated steatotic liver disease (MASLD) and their comorbidity (coexistence of both conditions). Bidirectional disease transitions were assessed using a multi-state Markov model. The relative importance of GDF-15 was evaluated using SHapley Additive exPlanations (SHAP) and likelihood ratio (LR) statistics. Improvements in risk prediction models were assessed using time-dependent area under the receiver operating characteristic curve, Brier score, integrated discrimination improvement and continuous net reclassification improvement.
Results
Amongst 29,697 participants (mean age of 56.16 years; 57.32% female), 2,786 developed ASCVD and 456 developed MASLD during follow-up. Higher GDF-15 levels were associated with poorer CKM health and showed the strongest associations with renal function markers, followed by insulin resistance indices. Each 1-unit increase in GDF-15 (normalised protein expression, log2 scale) was associated with increased risks of ASCVD (HR = 1.25, 95%CI 1.15–1.36, P = 1.35 × 10–7), MASLD (HR = 1.62, 95%CI 1.41–1.86, P = 2.06 × 10–11) and their comorbidity (HR = 1.62, 95%CI 1.32–2.18, P = 4.36 × 10–7) after multivariable adjustment for age, sex, smoking status, body mass index, diabetes mellitus, glycated haemoglobin, systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, C-reactive protein, creatinine, estimated glomerular filtration rate, urinary albumin-to-creatinine ratio, cystatin C, Townsend deprivation index, physical activity, and CKM stage. These associations remained consistent across subgroup analyses. Multi-state analyses indicated that GDF-15 predicted bidirectional progression between ASCVD and MASLD, with 10-year cumulative incidences of ASCVD and MASLD reaching 15.75% and 2.03%, respectively, among individuals in the top 10% of GDF-15 levels, and further increasing to 20.05% and 2.32% in those in the top 5%. SHAP and LR analyses showed that GDF-15 had high relative importance in predicting ASCVD and MASLD. Incorporating GDF-15 into established risk scores (PREVENT, SCORE2, FLI, FIB-4 and ARPI) showed modest improvements in risk discrimination, reclassification, and prediction error, particularly for ASCVD. In several settings, GDF-15 outperformed established biomarkers, including insulin resistance, systemic inflammation, apolipoprotein A/B, lipoprotein(a), cardiac troponin I, and N-terminal prohormone of brain natriuretic peptide.
Conclusions
GDF-15 may serve as a promising biomarker for cardiovascular-kidney-liver-metabolic syndrome risk stratification and management.