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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".