Kirsten E Peters1, Wendy A Davis2, Jun Ito3, Scott D Bringans3, Richard J Lipscombe3, Timothy M E Davis4. 1. Medical School, University of Western Australia, Western Australia, Australia; Proteomics International, Perth, Western Australia, Australia. 2. Medical School, University of Western Australia, Western Australia, Australia. 3. Proteomics International, Perth, Western Australia, Australia. 4. Medical School, University of Western Australia, Western Australia, Australia. Electronic address: tim.davis@uwa.edu.au.
Abstract
AIMS: To validate the prognostic utility of a novel plasma biomarker panel, PromarkerD, for predicting renal decline in an independent cohort of people with type 2 diabetes. METHODS: Models for predicting rapid estimated glomerular filtration rate (eGFR) decline defined as i) incident diabetic kidney disease (DKD), ii) eGFR decline ≥30% over four years, and iii) annual eGFR decline ≥5 mL/min/1.73 m2 were applied to 447 participants from the longitudinal observational Fremantle Diabetes Study Phase II. Model performance was assessed using discrimination and calibration. RESULTS: During 4.2 ± 0.3 years of follow-up, 5-10% of participants experienced a rapid decline in eGFR. A consensus model comprising apolipoprotein A-IV (apoA4), CD5 antigen-like (CD5L), insulin-like growth factor-binding protein 3 (IGFBP3), age, serum HDL-cholesterol and eGFR showed the best performance for predicting incident DKD (AUC = 0.88 (95% CI 0.84-0.93)); calibration Chi-squared = 5.6, P = 0.78). At the optimal score cut-off, this model provided 86% sensitivity, 78% specificity, 30% positive predictive value and 98% negative predictive value for four-year risk of developing DKD. CONCLUSIONS: The combination of readily available clinical and laboratory features and the PromarkerD biomarkers (apoA4, CD5L, IGFBP3) proved an accurate prognostic test for future renal decline in an independent validation cohort of people with type 2 diabetes.
AIMS: To validate the prognostic utility of a novel plasma biomarker panel, PromarkerD, for predicting renal decline in an independent cohort of people with type 2 diabetes. METHODS: Models for predicting rapid estimated glomerular filtration rate (eGFR) decline defined as i) incident diabetic kidney disease (DKD), ii) eGFR decline ≥30% over four years, and iii) annual eGFR decline ≥5 mL/min/1.73 m2 were applied to 447 participants from the longitudinal observational Fremantle Diabetes Study Phase II. Model performance was assessed using discrimination and calibration. RESULTS: During 4.2 ± 0.3 years of follow-up, 5-10% of participants experienced a rapid decline in eGFR. A consensus model comprising apolipoprotein A-IV (apoA4), CD5 antigen-like (CD5L), insulin-like growth factor-binding protein 3 (IGFBP3), age, serum HDL-cholesterol and eGFR showed the best performance for predicting incident DKD (AUC = 0.88 (95% CI 0.84-0.93)); calibration Chi-squared = 5.6, P = 0.78). At the optimal score cut-off, this model provided 86% sensitivity, 78% specificity, 30% positive predictive value and 98% negative predictive value for four-year risk of developing DKD. CONCLUSIONS: The combination of readily available clinical and laboratory features and the PromarkerD biomarkers (apoA4, CD5L, IGFBP3) proved an accurate prognostic test for future renal decline in an independent validation cohort of people with type 2 diabetes.
Authors: Scott Bringans; Jason Ito; Tammy Casey; Sarah Thomas; Kirsten Peters; Ben Crossett; Orla Coleman; Holger A Ebhardt; Stephen R Pennington; Richard Lipscombe Journal: Clin Proteomics Date: 2020-10-20 Impact factor: 3.988
Authors: Lauren Fusfeld; Jessica T Murphy; YooJin Yoon; Li Ying Kam; Kirsten E Peters; Pearl Lin Tan; Michael Shanik; Alexander Turchin Journal: PLoS One Date: 2022-08-01 Impact factor: 3.752
Authors: Lili Chan; Girish N Nadkarni; Fergus Fleming; James R McCullough; Patricia Connolly; Gohar Mosoyan; Fadi El Salem; Michael W Kattan; Joseph A Vassalotti; Barbara Murphy; Michael J Donovan; Steven G Coca; Scott M Damrauer Journal: Diabetologia Date: 2021-04-02 Impact factor: 10.122