An International Cohort Study of Mutations in RENIN Causing Autosomal Dominant Tubulointerstitial Kidney Disease
Bibliographic record
Abstract
Background: There have been few clinical reports of Autosomal Dominant Tubulointerstitial Kidney Disease due to REN Mutations (ADTKD-REN), limiting clinical characterization. Methods: We formed an international collaboration that identified and characterized 111 individuals from 30 families with heterozygous REN mutations. Results: Sixty-nine (62%) individuals had a REN mutation in the signal peptide region (signal group), 27 (24%) in the prosegment (prosegment group), and 15 (14%) in the mature renin peptide (mature group). Laboratory investigations revealed that REN signal peptide mutations prevented recognition and translocation of preprorenin into the endoplasmic reticulum (ER), prosegment mutations led to abnormal deposition of prorenin and renin in the ER Golgi intermediate compartment (ERGIC), and mutations in mature renin led to deposition of prorenin and renin in the ER. Signal and prosegment patients were most severely affected, often presenting at <10 years (see Table 1) with anemia, hyperkalemia, and acute and chronic kidney disease. While eGFR was approximately 50 ml/min in children < 10 years, eGFR remained stable until age 20, with mean age of end-stage kidney disease (ESKD) >50 in this cohort. The mean hemoglobin level in children <10 y not receiving erythropoietin was 9.6±1.04 g/dL (7.4-13.8 g/dl), which improved with erythropoietin administration. The serum potassium values decreased and bicarbonate values increased in 9 patients taking fludrocortisone (4.77±0.55 mEq/L vs. 4.37±0.54 mEq/L, p< 0.01 and 23.7±3.5 mEq/L vs. 25.9±2.3 mEq/L, p=0.003). Patients with mutations in mature renin presented >20y with gout and chronic kidney disease. Conclusions: There are 3 subtypes of heterozygous REN mutations that are pathophysiologically and clinically distinct. Funding: Private Foundation SupportPatient Characteristics
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".