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Haller, P.M.* ; Goßling, A.* ; Magnussen, C.* ; Brenner, H.* ; Schöttker, B.* ; Iacoviello, L.* ; Costanzo, S.* ; Kee, F.* ; Koenig, W.* ; Linneberg, A.* ; Sujana, C. ; Thorand, B. ; Salomaa, V.* ; Niiranen, T.J.* ; Söderberg, S.* ; Völzke, H.* ; Dörr, M.* ; Sans, S.* ; Padró, T.* ; Felix, S.B.* ; Nauck, M.* ; Petersmann, A.* ; Palmieri, L.* ; Donfrancesco, C.* ; De Ponti, R.* ; Veronesi, G.* ; Ferrario, M.M.* ; Kuulasmaa, K.* ; Zeller, T.* ; Ojeda, F.M.* ; Blankenberg, S.* ; Westermann, D.*

Biomarker-based prediction of fatal and non-fatal cardiovascular outcomes in individuals with diabetes mellitus.

Eur. J. Prev. Cardiol. 30, 1218-1226 (2023)

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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.

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AIMS: The role of biomarkers in predicting cardiovascular outcomes in high-risk individuals is not well established. We aimed to investigate benefits of adding biomarkers to cardiovascular risk assessment in individuals with and without diabetes. METHODS AND RESULTS: We used individual-level data of 95 292 individuals of the European population harmonized in the Biomarker for Cardiovascular Risk Assessment across Europe consortium and investigated the prognostic ability of high-sensitivity cardiac troponin I (hs-cTnI), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and high-sensitivity C-reactive protein (hs-CRP). Cox-regression models were used to determine adjusted hazard ratios of diabetes and log-transformed biomarkers for fatal and non-fatal cardiovascular events. Models were compared using the likelihood ratio test. Stratification by specific biomarker cut-offs was performed for crude time-to-event analysis using Kaplan-Meier plots. Overall, 6090 (6.4%) individuals had diabetes at baseline, median follow-up was 9.9 years. Adjusting for classical risk factors and biomarkers, diabetes [HR 2.11 (95% CI 1.92, 2.32)], and all biomarkers (HR per interquartile range hs-cTnI 1.08 [95% CI 1.04, 1.12]; NT-proBNP 1.44 [95% CI 1.37, 1.53]; hs-CRP 1.27 [95% CI 1.21, 1.33]) were independently associated with cardiovascular events. Specific cut-offs for each biomarker identified a high-risk group of individuals with diabetes losing a median of 15.5 years of life compared to diabetics without elevated biomarkers. Addition of biomarkers to the Cox-model significantly improved the prediction of outcomes (likelihood ratio test for nested models P < 0.001), accompanied by an increase in the c-index (increase to 0.81). CONCLUSION: Biomarkers improve cardiovascular risk prediction in individuals with and without diabetes and facilitate the identification of individuals with diabetes at highest risk for cardiovascular events.

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