Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes

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 -