Results of untargeted analysis using the SOMAscan proteomics platform indicates novel associations of circulating proteins with risk of progression to kidney failure in diabetes

Abstract

This study applies a large proteomics panel to search for new circulating biomarkers associated with progression to kidney failure in individuals with diabetic kidney disease. Four independent cohorts encompassing 754 individuals with type 1 and type 2 diabetes and early and late diabetic kidney disease were followed to ascertain progression to kidney failure. During ten years of follow-up, 227 of 754 individuals progressed to kidney failure. Using the SOMAscan proteomics platform, we measured baseline concentration of 1129 circulating proteins. In our previous publications, we analyzed 334 of these proteins that were members of specific candidate pathways involved in diabetic kidney disease and found 35 proteins strongly associated with risk of progression to kidney failure. Here, we examined the remaining 795 proteins using an untargeted approach. Of these remaining proteins, 11 were significantly associated with progression to kidney failure. Biological processes previously reported for these proteins were related to neuron development (DLL1, MATN2, NRX1B, KLK8, RTN4R and ROR1) and were implicated in the development of kidney fibrosis (LAYN, DLL1, MAPK11, MATN2, endostatin, and ROR1) in cellular and animal studies. Specific mechanisms that underlie involvement of these proteins in progression of diabetic kidney disease must be further investigated to assess their value as targets for kidney-protective therapies. Using multivariable LASSO regression analysis, five proteins (LAYN, ESAM, DLL1, MAPK11 and endostatin) were found independently associated with risk of progression to kidney failure. Thus, our study identified proteins that may be considered as new candidate prognostic biomarkers to predict risk of progression to kidney failure in diabetic kidney disease. Furthermore, three of these proteins (DLL1, ESAM, and MAPK11) were selected as candidate biomarkers when all SOMAscan results were evaluated.

Keywords: circulating biomarker; diabetes; diabetic kidney disease; end-stage kidney disease; proteomics analysis.

Figure 1.. Two stage study design for SOMAscan measurements

* Parenthesis shows number of proteins which we measured only in ½ Discovery cohort

Figure 2.. Results of logistic regression analysis for circulating proteins associated with risk of 10-year ESKD.

A. Volcano plot of effect sizes (Odds Ratio per 1-quartile increase in concentration of circulating proteins) and strengths of associations (p value- y axis) with risk of 10-year ESKD. Proteins that are considered in 2^nd stage are presented in the figure. A total of 32 potential candidate proteins were significantly associated with risk of 10-year ESKD. Red indicates 11 proteins that were confirmed in all study cohorts. B. Odds Ratios and 95% CIs for risk of 10-year ESKD are presented per 1-quartile increase in protein level for each of the 11 candidate proteins in each cohort (univariate logistic analysis) and in combined cohort (multivariable logistic analysis adjusted by sex, duration of diabetes, HbA1c, systolic blood pressure, eGFR, and ACR). See also Supplementary Table 1 and Table 2

Figure 3.. Spearman rank correlation analysis for 11 candidate proteins in type 1 and type 2 diabetic subjects with CKD stage 3 in Joslin cohorts

***P<0.001

Figure 4.. Paths of regression coefficient for proteins and clinical factors selected as predictors of risk of 10-year ESKD shrinking towards zero using penalized LASSO logistic regression

A total of 11 candidate proteins and clinical factors are included in the least absolute shrinkage and selection operator (LASSO) regression model and the coefficient of 8 selected variables are shown. Optimal lambda, a penalty factor for penalized maximum likelihood estimation, was calculated by 10-fold cross-validation at its minimum level. Each curve corresponds to a protein selected as a result of shrinkage for selection, and draws shrinkage during estimation of regression coefficient. LASSO penalizes the sum of the absolute values of regression coefficients, and a predictor with a coefficient of zero was excluded from the model and was not presented in the figure. C-statistics of logistic regression for clinical model (HbA1c, log₂ACR, and eGFR/10): 0.847. C-statistics of logistic regression for selected variables by LASSO regression analysis (HbA1c, log₂ACR, eGFR/10, LAYN. ESAM. DLL1, MAPK11, endostatin): 0.869. Clinical model vs New model: Difference in C-statistics, 0.022 (P=0.006); NRI, 0.55 (P<0.0001).