Genetic Testing in Adults over 50 Years with Chronic Kidney Disease: Diagnostic Yield and Clinical Implications in a Specialized Kidney Genetics Clinic
Bibliographic record
Abstract
Background: Genetic causes of chronic diseases, once considered rare in adult-onset disease, now account for between 10 and 20% of cases of chronic kidney disease (CKD). Confirming a genetic diagnosis can influence disease management; however, the utility of genetic testing in older adults remains poorly understood, partly due to age-based restrictions on testing access. To better evaluate the diagnostic yield and clinical utility of genetic testing in this population, we analyzed data from adults aged ≥50 years with CKD who were assessed in a specialized kidney genetics clinic. Methods: We studied a cohort of 125 adults with CKD aged ≥50 years at the time of genetic testing. Genetic testing included gene panels targeting disease-related genes based on clinical phenotype, and/or exome sequencing for additional monogenic causes if the initial panel testing was inconclusive. Results: Pathogenic variants in disease-related genes were identified in 38% of patients. The highest diagnostic yield (48%) was in patients aged 50–54 years. The most common diagnosis post-testing was glomerulopathies (32%). Clinical utility, shown through the case series, included modifications to treatment and clinical management, as well as a reduction in the diagnostic odyssey. Conclusions: Our findings from a dedicated Kidney Genetics Clinic show that genetic testing in adults ≥50 years with CKD has significant diagnostic and clinical utility. These results support guideline recommendations that there should be no upper age limit for genetic testing. Future research in unselected CKD populations is needed to establish the broader applicability and feasibility of genetic testing in older adults.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".