OBJECTIVE: Although patients with type 2 diabetes (T2D) with nephropathy are at high risk for renal and cardiovascular complications, relevant biomarkers have been poorly identified. Because renal impairment may increase biomarker levels, this potentially confounds associations between biomarker levels and risk. To investigate the predictive value of a biomarker in such a setting, we examined baseline levels of growth differentiation factor-15 (GDF-15), N-terminal prohormone of B-type natriuretic peptide (NTproBNP), and high-sensitivity troponin T (hs-TnT) in relation to renal and cardiovascular risk in T2D patients with nephropathy. RESEARCH DESIGN AND METHODS: Eight hundred sixty-one T2D patients from the sulodexide macroalbuminuria (Sun-MACRO) trial were included in our post hoc analysis. Prospective associations of baseline serum GDF-15, NTproBNP, and hs-TnT with renal and cardiovascular events were determined by Cox multiple regression and C-statistic analysis. Renal base models included albumin-to-creatinine ratio (ACR), serum creatinine, hemoglobin, age, and sex. Cardiovascular base models included diastolic blood pressure, ACR, cholesterol, age, and sex. RESULTS: The mean (±SD) estimated glomerular filtration rate was 33 ± 9 mL/min/1.73 m2, and the median serum concentration for GDF-15 was 3,228 pg/mL (interquartile range 2,345-4,310 pg/mL), for NTproBNP was 380 ng/L (155-989 ng/L), and for hs-TnT was 30 ng/L (20-47 ng/L). In multiple regression analysis, GDF-15 (hazard ratio [HR] 1.83, P = 0.04), NTproBNP (HR 2.34, P = 0.004), and hs-TnT (HR 2.09, P = 0.014) were associated with renal events, whereas NTproBNP (HR 3.45, P < 0.001) was associated with cardiovascular events. The C-statistic was improved by adding NTproBNP and hs-TNT to the renal model (0.793 vs. 0.741, P = 0.04). For cardiovascular events, the C-statistic was improved by adding NTproBNP alone (0.722 vs. 0.658, P = 0.018). CONCLUSIONS: Biomarkers GDF-15, NTproBNP, and hs-TnT associate independently with renal risk, whereas NTproBNP independently predicts cardiovascular risk.
RCT Entities:
OBJECTIVE: Although patients with type 2 diabetes (T2D) with nephropathy are at high risk for renal and cardiovascular complications, relevant biomarkers have been poorly identified. Because renal impairment may increase biomarker levels, this potentially confounds associations between biomarker levels and risk. To investigate the predictive value of a biomarker in such a setting, we examined baseline levels of growth differentiation factor-15 (GDF-15), N-terminal prohormone of B-type natriuretic peptide (NTproBNP), and high-sensitivity troponin T (hs-TnT) in relation to renal and cardiovascular risk in T2D patients with nephropathy. RESEARCH DESIGN AND METHODS: Eight hundred sixty-one T2D patients from the sulodexide macroalbuminuria (Sun-MACRO) trial were included in our post hoc analysis. Prospective associations of baseline serum GDF-15, NTproBNP, and hs-TnT with renal and cardiovascular events were determined by Cox multiple regression and C-statistic analysis. Renal base models included albumin-to-creatinine ratio (ACR), serum creatinine, hemoglobin, age, and sex. Cardiovascular base models included diastolic blood pressure, ACR, cholesterol, age, and sex. RESULTS: The mean (±SD) estimated glomerular filtration rate was 33 ± 9 mL/min/1.73 m2, and the median serum concentration for GDF-15 was 3,228 pg/mL (interquartile range 2,345-4,310 pg/mL), for NTproBNP was 380 ng/L (155-989 ng/L), and for hs-TnT was 30 ng/L (20-47 ng/L). In multiple regression analysis, GDF-15 (hazard ratio [HR] 1.83, P = 0.04), NTproBNP (HR 2.34, P = 0.004), and hs-TnT (HR 2.09, P = 0.014) were associated with renal events, whereas NTproBNP (HR 3.45, P < 0.001) was associated with cardiovascular events. The C-statistic was improved by adding NTproBNP and hs-TNT to the renal model (0.793 vs. 0.741, P = 0.04). For cardiovascular events, the C-statistic was improved by adding NTproBNP alone (0.722 vs. 0.658, P = 0.018). CONCLUSIONS: Biomarkers GDF-15, NTproBNP, and hs-TnT associate independently with renal risk, whereas NTproBNP independently predicts cardiovascular risk.
Authors: Axel C Carlsson; Christoph Nowak; Lars Lind; Carl Johan Östgren; Fredrik H Nyström; Johan Sundström; Juan Jesus Carrero; Ulf Riserus; Erik Ingelsson; Tove Fall; Johan Ärnlöv Journal: Ups J Med Sci Date: 2019-12-05 Impact factor: 2.384
Authors: Marie Frimodt-Møller; Bernt Johan von Scholten; Henrik Reinhard; Peter Karl Jacobsen; Tine Willum Hansen; Frederik Ivar Persson; Hans-Henrik Parving; Peter Rossing Journal: PLoS One Date: 2018-04-26 Impact factor: 3.240
Authors: Marcus V B Malachias; Pardeep S Jhund; Brian L Claggett; Magnus O Wijkman; Rhonda Bentley-Lewis; Nishi Chaturvedi; Akshay S Desai; Steven M Haffner; Hans-Henrik Parving; Margaret F Prescott; Scott D Solomon; Dick De Zeeuw; John J V McMurray; Marc A Pfeffer Journal: J Am Heart Assoc Date: 2020-09-23 Impact factor: 5.501