Association between Genotype and Phenotype in Uromodulin-Associated Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Uromodulin-associated kidney disease (UAKD) is an autosomal dominant disease caused by uromodulin (UMOD) gene mutations. This study explored genotype-phenotype correlations by examining the relationship between the type of UMOD mutation and the age at onset of ESRD. DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS: Extensive bibliographic research was used to ascertain patient-level data of all patients with UAKD published up to October 2011. Data included sex; ages at onset of hyperuricemia, gout, and ESRD; and UMOD genotype. Kaplan-Meier analysis and Cox proportional hazards models fitted with shared gamma frailty terms to adjust for within-family correlations were used to model time to event. RESULTS: Thirty-one peer-reviewed publications reporting on 202 patients from 74 families with 59 different UMOD mutations were included. Median ages at onset of hyperuricemia, gout, and ESRD were 24, 40, and 56 years, respectively. Men developed gout and ESRD significantly earlier than did women (age at ESRD was 50 years for men and 60 for women; P=0.04, shared frailty model). Median ages at ESRD development were lowest with Cys77Tyr (37.5 years) and highest with Gln316Pro (65.5 years) UMOD mutations. Onset of ESRD was significantly earlier with UMOD mutations located within the epidermal growth factor domains 2 and 3 (range, 45-52 years; P<0.01 and 0.04, respectively) compared with the cysteine-rich domains (range, 60-65 years; by shared frailty model). CONCLUSIONS: The UMOD genotype is related to the clinical phenotype of UAKD. This finding may assist in counseling of patients.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".