Publications
Transformative Research in Diabetic Nephropathy (TRIDENT) is redefining how diabetic kidney disease is studied, diagnosed, and treated. These publications reflect a decade-long, multi-center effort that integrates deep clinical phenotyping with kidney biopsies, advanced multi-omic profiling, and artificial intelligence–driven pathology. This is not incremental science. It is infrastructure-building, field-shifting work.
From Biopsy to Prognosis: AI-Powered Kidney Pathology
Unbiased self-supervised learning of kidney histology reveals phenotypic and prognostic insights (2025)Pandit K, Coudray N, Quiros AC, et al. Scientific Reports.
TRIDENT moves kidney pathology into the AI era. Using self-supervised deep learning on biopsy images, this study demonstrates that machine learning can uncover reproducible and prognostically meaningful kidney injury patterns that are not reliably scored by humans. This work establishes a foundation for objective, scalable, and clinically actionable digital pathology in diabetic kidney disease.
Building the Right Cohorts to Answer the Right Questions
CureGN-Diabetes Study: Rationale, Design, and Methods (2023)Mottl AK, Bomback AS, Mariani LH, et al. Glomerular Diseases.
Together, CureGN and TRIDENT establish rigorously phenotyped comparator cohorts that integrate retrospective and prospective clinical data with patient-reported outcomes. This design enables cross-disease and cross-etiology comparisons that were previously not possible at scale.
Glomerular Injury as a Driver of Kidney Function Decline
The Role of Glomerular Epithelial Injury in Kidney Function Decline in Patients with Diabetic Kidney Disease in the TRIDENT Cohort (2021)Palmer MB, Abedini A, Jackson C, et al. Kidney International Reports.
This TRIDENT analysis demonstrates that specific patterns of glomerular epithelial injury strongly associate with kidney function decline in patients with biopsy-confirmed diabetic kidney disease. The findings underscore a critical point: diabetic kidney disease is biologically heterogeneous, and histology still matters when analyzed quantitatively.
Urine as a Window into the Kidney
Urinary Single-Cell Profiling Captures the Cellular Diversity of the Kidney (2021)Abedini A, Zhu YO, Chatterjee S, et al. Journal of the American Society of Nephrology.
TRIDENT demonstrates that single-cell RNA sequencing of urine can non-invasively capture kidney cell diversity and injury states. This work opens the door to repeatable and scalable molecular monitoring of kidney disease without relying exclusively on tissue biopsy.
Proving That Precision Biopsy Is Safe and Feasible
The Feasibility and Safety of Obtaining Research Kidney Biopsy Cores in Patients with Diabetes (2020)Hogan JJ, Owen JG, Blady SJ, et al. Clinical Journal of the American Society of Nephrology.
Before discovery comes trust. TRIDENT established, across multiple centers, that research kidney biopsies in patients with diabetes are safe and feasible when performed within a rigorous protocol. This work enabled every subsequent tissue-based TRIDENT discovery.
The Blueprint
Rationale and Design of the Transformative Research in Diabetic Nephropathy (TRIDENT) Study (2020)Townsend RR, Guarnieri P, Argyropoulos C, et al. Kidney International.
This foundational paper laid out the TRIDENT vision: integrate kidney biopsies, deep clinical data, molecular profiling, and longitudinal follow-up to understand diabetic kidney disease at a mechanistic level. Everything that followed traces back to this design.
Why this matters
TRIDENT is not a single paper or a single technique. It is a platform. Collectively, these studies enable AI-driven pathology, molecularly defined disease subtypes, biomarker discovery, and biologically informed clinical trials in diabetic kidney disease. The field is now catching up to what TRIDENT was designed to do from the start.
More is coming. The next phase integrates spatial transcriptomics, long-read sequencing, and circulating biomarkers to translate kidney biology directly into patient care.
© The Trustees of the University of Pennsylvania | Site best viewed in a supported browser. | Report Accessibility Issues and Get Help | Privacy Policy | Site Design: PMACS Web Team.