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Abstract: TH-PO247

Proteomic Analyses Identify Novel Predictors of Diabetic Kidney Disease in Youth-Onset Type 2 Diabetes

Session Information

Category: Diabetic Kidney Disease

  • 602 Diabetic Kidney Disease: Clinical


  • Pyle, Laura, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
  • Vigers, Tim, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
  • El ghormli, Laure K., The George Washington University Milken Institute of Public Health, Washington, District of Columbia, United States
  • de Boer, Ian H., University of Washington, Seattle, Washington, United States
  • Nelson, Robert G., National Institutes of Health, Bethesda, Maryland, United States
  • Layton, Anita T., University of Waterloo, Waterloo, Ontario, Canada
  • Sharma, Kumar, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States
  • Waikar, Sushrut S., Boston University, Boston, Massachusetts, United States
  • L Heerspink, Hiddo Jan, University Medical Center Gronigen, Gronigen, Netherlands
  • White, Neil H., Washington University in St Louis, St Louis, Missouri, United States
  • Tommerdahl, Kalie L., University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
  • Shah, Amy Sanghavi, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
  • Gubitosi-Klug, Rose, Case Western Reserve University, Cleveland, Ohio, United States
  • Bjornstad, Petter, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States

Diabetic kidney disease (DKD) develops by young adulthood in up to 50% of people with youth-onset type 2 diabetes (Y-T2D), increasing risk of dialysis and premature death. Understanding mechanisms responsible for early DKD is key to management and prevention; accordingly, we sought to identify multiprotein signatures of DKD in Y-T2D.


We measured 7604 Aptamers in 374 baseline plasma samples from the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) study, using the SomaScan 7K Proteomic (SomaLogic) platform. Urine albumin-to-creatinine ratio (UACR) was assessed annually for up to 15 years. Incident micro- and macroalbuminuria were defined as UACR ≥30 and ≥300 mg/g on ≥2 of 3 measures. We evaluated prediction of micro- and macroalbuminuria in separate Cox regression models adjusted for HbA1c, triglycerides, blood pressure, and estimated insulin sensitivity. Gene set enrichment analysis (GSEA) identified pathways of interest. The false discovery rate was controlled at 5% and we report q-values.


Participants were 14±2 years of age, 37% male; 43% developed either micro- or macroalbuminuria. Seven proteins predicted time to microalbuminuria, while 8 proteins predicted time to macroalbuminuria, with 2 proteins in common: nerve epidermal growth factor-like 1 (NELL1) (micro: HR 1.56 per 1 SD [95% CI 1.33, 1.83], q=0.0003; macro: 1.95 [1.44-2.63], q=0.017) and FAM189A2 (micro: 1.58 [1.33, 1.87], q=0.0008; macro: 1.79 [1.36, 2.35], q=0.038). GSEA identified gene sets, including one related to semaphorin interactions, associated with microalbuminuria and macroalbuminuria (Figure).


Novel proteins, including those interacting with semaphorins, which play an important role in inflammation and cellular repair, predict incident albuminuria in Y-T2D.


  • NIDDK Support