RecruitingNANCT07512804

Post-Transplant Diabetes Outcomes Prediction Through Machine Learning and Deep Phenotyping

Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Trial Details

Condition	New Onset Diabetes After Transplantation
Intervention	Whole genome sequencing (WGS)
Phase	NA
Status	Recruiting
Participants	120
Est. Completion	28 Feb 2027
Last Updated	6 Apr 2026

Primary Outcome

Composite Diabetic Micro- and Macroangiopathy Assessed by Imaging and Graft Biopsy

Eligibility Criteria

Inclusion Criteria

Inclusion Criteria: * age between 18 and 70 years old, * kidney graft recipients with a diagnosis of de novo post-transplant diabetes between 1 and 10 years from the time of enrollment, * ability to sign a valid informed consent form. Exclusion Criteria: * diagnosis of neoplasia, * the presence of an active infection, * previous biopsy-proven diagnosis of a recurrent renal disease, * NYHA class III-IV heart failure, * hepatic failure.

Trial Sites

Countries

Italy

View Registry Entry

Related Analysis

Italian Study Launches Deep Phenotyping Approach to Predict Post-Transplant Diabetes Complications — Genomic Insights for Transplant Teams

Italian researchers launch genomic study to predict diabetic complications in kidney transplant recipients. The study uses whole genome sequencing and deep phenotyping to identify high-risk patients with new-onset diabetes after transplantation.

Read full article →

This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service