Autosomal Dominant Tubulointerstitial Kidney Disease—Uromodulin Misclassified as Focal Segmental Glomerulosclerosis or Hereditary Glomerular Disease
Bibliographic record
Abstract
Introduction Focal segmental glomerulosclerosis (FSGS) is a histopathologically defined kidney lesion. FSGS can be observed with various underlying causes, including highly penetrant monogenic renal disease. We recently identified pathogenic variants of UMOD , a gene encoding the tubular protein uromodulin, in 8 families with suspected glomerular disease. Methods To validate pathogenic variants of UMOD , we reviewed the clinical and pathology reports of members of 8 families identified to have variants of UMOD . Clinical, laboratory, and pathologic data were collected, and genetic confirmation for UMOD was performed by Sanger sequencing. Results Biopsy-proven cases of FSGS were verified in 21% (7 of 34) of patients with UMOD variants. The UMOD variants seen in 7 families were mutations previously reported in autosomal dominant tubulointerstitial kidney disease-uromodulin (ADTKD-UMOD). For one family with 3 generations affected, we identified p.R79G in a noncanonical transcript variant of UMOD co-segregating with disease. Consistent with ADTKD, most patients in our study presented with autosomal dominant inheritance, subnephrotic range proteinuria, minimal hematuria, and renal impairment. Kidney biopsies showed histologic features of glomerular injury consistent with secondary FSGS, including focal sclerosis and partial podocyte foot process effacement. Conclusion Our study demonstrates that with the use of standard clinical testing and kidney biopsy, clinicians were unable to make the diagnosis of ADTKD-UMOD; patients were often labeled with a clinical diagnosis of FSGS. We show that genetic testing can establish the diagnosis of ADTKD-UMOD with secondary FSGS. Genetic testing in individuals with FSGS histology should not be limited to genes that directly impair podocyte function.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".