How Does ADPKD Severity Differ Between Family Members?
Bibliographic record
Abstract
Thousands of pathogenic variants in more than 100 genes can cause kidney cysts with substantial variability in phenotype and risk of subsequent kidney failure. Despite an established genotype-phenotype correlation in cystic kidney diseases, incomplete penetrance and variable disease expressivity are present as is the case in all monogenic diseases. In family members with autosomal dominant polycystic kidney disease (ADPKD), the same causal variant is responsible in all affected family members; however, there can still be striking discordance in phenotype severity. This narrative review explores contributors to within-family discordance in ADPKD severity. Cases of biallelic and digenic inheritance, where 2 rare pathogenic variants in cystogenic genes are coexistent in one family, account for a small proportion of within-family discordance. Genetic background, including cis and trans factors and the polygenic propensity for comorbid disease, also plays a role but has not yet been exhaustively quantified. Environmental exposures, including diet; smoking; alcohol, salt, and protein intake, and comorbid diseases, including obesity, diabetes, hypertension, kidney stones, dyslipidemia, and additional coexistent kidney diseases all contribute to ADPKD phenotypic variability among family members. Given that many of the factors contributing to phenotype variability are preventable, modifiable, or treatable, health care providers and patients need to be aware of these factors and address them in the treatment of ADPKD.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".