Investigation of the Monogenic Causation of Kidney Disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) affects 11-13% of the global population; the third fastest growing cause of death worldwide. On average, 2% of CKD patients will develop end-stage kidney disease (ESKD), resulting in the need for dialysis or transplantation. Kidney transplantation is the most common transplant surgery in Canada, with living donation offering the best outcomes for recipients. It is now predicted that 10-20% of adults with CKD have a genetic cause of disease, though implementation into the diagnostic pathway has not been routinely available. My thesis aims to determine the monogenic causes of kidney disease by determining the diagnostic yield and clinical utility of genetic testing in patients with CKD seen in a Kidney Genetics Clinic, older adults (³50 years), transplant recipients, living kidney donors, and by systematically reviewing reported CKD populations across literature. Testing strategies included: 1) multigene-panel approach, testing a subset of genes for a specific phenotype for patients with a presumed known etiology of CKD, and 2) comprehensive testing including exome sequencing (ES) for patients with unknown etiology or transplant recipients and donors. Our systematic review including 60 studies determined the overall diagnostic yield of genetic testing in adults with CKD to be 40%. This was reflected in the clinical setting (34%), and in older adults (38%). Participants seen in the clinic also had high clinical utility, with direct treatment changes for a third of genetically solved participants. ES in patients unsolved with multigene panels determined the novel association of ABCC6 pathogenic variants with CKD due to vascular calcification. A high proportion of genetic CKD (26%) was observed in transplant recipients. Living kidney donors prospectively recruited had a low positivity rate of 4%, whereas the rate in donors with adverse outcomes after donation was 20%. Overall, these findings support the integration of genetic testing in the diagnostic assessment for CKD patients, including those ³50 years of age, and in transplant recipients. The low yield in donors supports the current donor workup process with the integration of genetic testing through targeted-gene panels in living donors biologically related to the recipient who has a known genetic disease.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".