TY - JOUR
T1 - Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes
AU - Mayer, Gert
AU - Heerspink, Hiddo J L
AU - Aschauer, Constantin
AU - Heinzel, Andreas
AU - Heinze, Georg
AU - Kainz, Alexander
AU - Sunzenauer, Judith
AU - Perco, Paul
AU - de Zeeuw, Dick
AU - Rossing, Peter
AU - Pena, Michelle
AU - Oberbauer, Rainer
AU - SYSKID Consortium
N1 - © 2017 by the American Diabetes Association.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - OBJECTIVE Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (EGFR) in a large group of patients with type 2 diabetes and CKD at various stages. RESEARCH DESIGN AND METHODS We used publicly available "omics" data to develop a molecular process model of CKD in diabetes and identified a representative parsimonious set of nine molecular biomarkers: chitinase 3-like protein 1, growth hormone 1, hepatocyte growth factor, matrix metalloproteinase (MMP) 2, MMP7, MMP8, MMP13, tyrosine kinase, and tumor necrosis factor receptor-1. These biomarkers were measured in baseline serumsamples from1,765 patients recruited into two large clinical trials. EGFR decline was predicted based on molecular markers, clinical risk factors (including baseline EGFR and albuminuria), and both combined, and these predictions were evaluated using mixed linear regression models for longitudinal data. RESULTS The variability of annual EGFR loss explained by the biomarkers, indicated by the adjusted R2 value, was 15% and 34% for patientswith EGFR 60 and <60 mL/min/1.73m2, respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted R2 to 35% and 64%, respectively. Calibration analysis of marker models showed significant (all P < 0.0001) but largely irrelevant deviations from optimal calibration (calibration-in-The-large: 21.125 and 0.95; calibration slopes: 1.07 and 1.13 in the two groups, respectively). CONCLUSIONS A small set of serum protein biomarkers identified using a systems biology approach, combined with clinical variables, enhances the prediction of renal function loss over awide range of baseline EGFR values in patientswith type 2 diabetes and CKD .
AB - OBJECTIVE Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (EGFR) in a large group of patients with type 2 diabetes and CKD at various stages. RESEARCH DESIGN AND METHODS We used publicly available "omics" data to develop a molecular process model of CKD in diabetes and identified a representative parsimonious set of nine molecular biomarkers: chitinase 3-like protein 1, growth hormone 1, hepatocyte growth factor, matrix metalloproteinase (MMP) 2, MMP7, MMP8, MMP13, tyrosine kinase, and tumor necrosis factor receptor-1. These biomarkers were measured in baseline serumsamples from1,765 patients recruited into two large clinical trials. EGFR decline was predicted based on molecular markers, clinical risk factors (including baseline EGFR and albuminuria), and both combined, and these predictions were evaluated using mixed linear regression models for longitudinal data. RESULTS The variability of annual EGFR loss explained by the biomarkers, indicated by the adjusted R2 value, was 15% and 34% for patientswith EGFR 60 and <60 mL/min/1.73m2, respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted R2 to 35% and 64%, respectively. Calibration analysis of marker models showed significant (all P < 0.0001) but largely irrelevant deviations from optimal calibration (calibration-in-The-large: 21.125 and 0.95; calibration slopes: 1.07 and 1.13 in the two groups, respectively). CONCLUSIONS A small set of serum protein biomarkers identified using a systems biology approach, combined with clinical variables, enhances the prediction of renal function loss over awide range of baseline EGFR values in patientswith type 2 diabetes and CKD .
KW - Aged
KW - Albuminuria/blood
KW - Biomarkers/blood
KW - Blood Glucose/metabolism
KW - Chitinase-3-Like Protein 1/blood
KW - Creatinine/blood
KW - Diabetes Mellitus, Type 2/blood
KW - Disease Progression
KW - Female
KW - Follow-Up Studies
KW - Glomerular Filtration Rate
KW - Growth Hormone/blood
KW - Hepatocyte Growth Factor/genetics
KW - Humans
KW - Linear Models
KW - Longitudinal Studies
KW - Male
KW - Matrix Metalloproteinases/blood
KW - Middle Aged
KW - Protein-Tyrosine Kinases/blood
KW - Receptors, Tumor Necrosis Factor, Type I/blood
KW - Renal Insufficiency, Chronic/blood
KW - Risk Factors
KW - Systems Biology
U2 - 10.2337/dc16-2202
DO - 10.2337/dc16-2202
M3 - Journal article
C2 - 28077457
SN - 0149-5992
VL - 40
SP - 391
EP - 397
JO - Diabetes Care
JF - Diabetes Care
IS - 3
ER -
