The Utility of an Inherited Kidney Disease Clinic Employing a Broad Range of Genomic Testing Platforms: Experience of the Irish Kidney Gene Project
Bibliographic record
Abstract
Background: Inherited kidney diseases (IKD) are increasingly identified in adult patients. Here we demonstrate the diagnostic and clinical impact of evaluating patients with potential IKD in a dedicated IKD clinic (IKDC) utilising various genomic testing technologies (whole-exome sequencing, comprehensive gene-panel, and dedicated MUC-1 sequencing) and immunostaining. Methods: We undertook a prospective cohort study of adult patients referred to an academic medical centre with suspected monogenic cause as part of the Irish Kidney Gene Project (IKGP), between 2014 and 2020. Patients with chronic kidney disease (CKD) who had either a positive family history, extrarenal features, or had CKD of “unknown cause” (uCKD) were recruited from various centres across Ireland. We attempted to identify disease-causing variants and to assess the impact of the IKDC from diagnostic and clinical perspectives. Results: During this period, genetic testing was performed for 677 adults (n= 501 families). The median age was 53 years (range, 18-93 years) and 73.9% participants had reported a family history of renal disease. We achieved a molecular diagnostic rate of 56.7 % (384/677). Among the identified disease-causing variants, PKD was the largest cohort (n= 183, 47.8% for PKD1 and PKD2), while mutations in three other causative genes were most prevalent among the remaining identified 42 genes encompassing several Mendelian disorders; MUC-1 (n=31, 8.1%); COL4A5 (n=30, 7.8%); UMOD (n= 12, 3.3%). In the remaining 167 disease-causing variants, excluding PKD, the clinical diagnosis was confirmed in 60.5% and 18% of cases were reclassified. A molecular diagnosis was established in 27 (36.5%) patients with uCKD, implying the end of their diagnostic odyssey. Clinically, a diagnostic kidney biopsy was unnecessary in 13 (7.7%) patients based on the genomic testing, 80 (47.3%) had their treatment plan altered and further 76 (45%) patients had appropriate cascade testing. Conclusions: The IKDC is a valuable resource and the implementation of a broad range of diagnostic platforms has a direct clinical and therapeutic impact on treatment of patients with CKD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".