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Record W4416235331 · doi:10.1101/2025.11.07.25339114

Genetic Evaluation in Chronic Kidney Disease: A Canadian Single-Centre Experience from a Multicultural Urban Population

2025· preprint· W4416235331 on OpenAlexafffundabout
Zachary T. Sentell, Felicia Russo, M. Henein, Lina Mougharbel, Zachary W. Nurcombe, Ahsan Alam, Dana Baran, Lorraine Bell, Daniel Blum, Marcelo Cantarovich, Andrey V. Cybulsky, Sonali de Chickera, Mallory L. Downie, Bethany J. Foster, Gershon Frisch, Paul Goodyer, Indra R. Gupta, Laura Horowitz, Serge Lemay, Mark L. Lipman, Sharon J. Nessim, Tiina Podymow, Ratna Samanta, Shaifali Sandal, Rita S. Suri, Tomoko Takano, Emilie Trinh, Murray Vasilevsky, Ruth Sapir‐Pichhadze, Thomas M. Kitzler

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Language
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsMontreal General HospitalJewish General HospitalMontreal Children's HospitalMcGill Genome CentreMcGill UniversityMcGill University Health Centre
FundersHospital for Sick ChildrenChugai PharmaceuticalMcGill University Health CentreSanofi
KeywordsGenetic testingKidney diseaseGenetic counselingDiseasePopulationMedical geneticsEtiologyHealth careNephropathy

Abstract

fetched live from OpenAlex

Background: Chronic kidney disease (CKD) affects over 10% of the global population. A genetic diagnosis can be identified in about 30% of pediatric and 10-30% of adults, informing treatment, prognosis, and family-based risk assessment. However, access to renal genetics services remains limited across many healthcare systems. Objectives: To characterize the clinical and genetic landscape of CKD in patients referred for genetic evaluation within a Canadian single-centre nephrology-genetics program, and to evaluate the diagnostic yield and clinical utility of an integrated renal genetics clinic. Methods: We conducted a retrospective study of 206 probands referred for suspected hereditary kidney disease to the McGill University Health Centre Renal Genetics Clinic between 2019 and 2024. Genetic testing was performed in accredited laboratories, predominantly through comprehensive multi-gene panels or phenotype-directed exome sequencing. All reported variants were classified according to the ACMG/AMP criteria, and variants of uncertain significance were reevaluated post hoc using standardized quantitative and evidence-tier frameworks to determine whether they trended toward "likely pathogenic" ("hot") or "likely benign" ("cold"), without implying formal reclassification. Results: A molecular diagnosis was established in 34.5% of probands (71/206), implicating pathogenic or likely pathogenic variants across 35 genes representing diverse monogenic kidney disease etiologies. The highest diagnostic yields were observed in cystic nephropathy (51.9%), tubulopathy (38.5%), and glomerulopathy (35.6%). Genetic results affected clinical management in 23.9% of diagnosed cases, leading to changes in treatment for 16.9%, modification of transplant management in 5.6%, informed living donor risk assessment in 14.1%, and facilitated cascade testing in 66.2% of families. CKD of unknown etiology comprised 28% of the cohort, with a genetic diagnosis reached in 25.9% of these cases. Variants of uncertain significance (VUS) were reported in 39.3% of probands, with higher overall variant burden and lower diagnostic yields among individuals of non-European ancestry. Post hoc internal reassessment stratified 67.4% of VUS as mid or lower confidence ("cold") and 31.4% as higher confidence ("hot") or likely pathogenic. Conclusions: In a diverse urban population, integration of a dedicated renal genetics service within nephrology care achieved high diagnostic yield, substantially influenced management, and facilitated family risk assessment. Structured referral pathways and multidisciplinary variant interpretation optimize the clinical utility of genetic testing in CKD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0060.002
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.286
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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