Chronic Kidney Diseases
Conditions
Brief summary
Chronic kidney disease (CKD) affects about 10% of the world population, with high morbidity and mortality. Genetic kidney diseases are increasingly recognized across all age groups and represent over 20% of all the causes of CKD. Accurate diagnosis allows necessary and unnecessary diagnostic procedures to be defined, avoids unnecessary treatments, improves prognosis prediction, identifies other family members for genetic counseling, and defines risks for living donor kidney transplantation. The research group coordinated by the Principal Investigator has recently developed an algorithm for the genetic diagnosis in pediatric and adult patients with CKD. The application of this personalized diagnostic algorithm on a local study led to a global diagnostic yield of 70%, suggesting that this strategy has the potential to substantially improve the diagnostic approach to patients with rare kidney disorders. The aim of this study is to validate and implement these results by extending its application in a multicentric study involving nephrology units that are referral centers for rare kidney diseases at national level.
Interventions
DIAGNOSTIC_TESTImplementation of the diagnostic algorithm
Patients will be selected based on specific clinical criteria and referred to the tertiary center for genetic testing. All selected patients will undergo genetic testing by whole-exome sequencing (WES), followed by in silico analysis for an extended panel of genes associated with kidney diseases. The results of genetic testing will be evaluated by a multidisciplinary team of experts to establish conclusive diagnosis.
Sponsors
Meyer Children's Hospital IRCCS
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
0 Years to 70 Years
Healthy volunteers
No
Inclusion criteria
* proteinuria and/or hematuria in the absence of immune deposits on renal biopsy or immune-mediated glomerulopathy resistant to treatment (e.g., steroids, immunosuppressive drugs); * family history of kidney diseases and/or consanguinity; * extrarenal involvement; * ultrasound evidence of at least two cysts in each kidney or hyperechogenic kidneys or nephrocalcinosis; * persistent metabolic abnormalities (metabolic acidosis or alkalosis without kidney function impairment; calcium phosphate metabolism abnormalities) after exclusion of secondary causes; * availability of clinical information. * signed informed consent form
Exclusion criteria
* Refusal by the patient, parents, or legal guardian to provide informed consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Implementation of a diagnostic algorithm for personalized diagnosis of rare kidney diseases | From enrollment of the first patient until the end of the study (up to 24 months) | The previously established diagnostic algorithm for rare kidney diseases will be extended to out-of-region centers with a multicenter study design. This outcome will be assessed as diagnostic rate of the algorithm, i.e., number of conclusive genetic diagnosis/number of patients enrolled. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Analysis of the functional role of variant of unknown clinical significance (VUS) | Form enrollment until the last follow up visit (up to 12 months) | Analysis of the functional role of VUS identified by WES by in vitro functional studies and 3D-organ-on-a-chip models obtained by patients-specific urine-derived renal progenitor cells (u-RPC) cultures |
| Identification of immunological and/or structural factors in genetic and nongenetic forms. | Form enrollment until the last follow up visit (up to 12 months) | Identification of immunological and/or structural factors in genetic and nongenetic forms. In particular, the investigators will assess: \- the presence of anti-nephrin antibodies on serum samples and/or kidney biopsy of patients (performed for diagnostic purposes) |
| Cost-effectiveness of the diagnostic algorithm. | From enrollment of the last patient until the end of the study (up to 24 months) | A modeled cost-effectiveness analysis (including direct and indirect medical costs) will be performed to compare the proposed diagnostic algorithm with the current standard-of-care. |
Countries
Italy
Contacts
Primary ContactPaola Romagnani, Prof, MD, PhD
Outcome results
None listed