Deciphering Variability of PKD1 and PKD2 in an Italian Cohort of 643 Patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD)
Bibliographic record
Abstract
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common hereditary kidney disease. We analysed PKD1 and PKD2, in a large cohort of 440 unrelated Italian patients with ADPKD and 203 relatives by direct sequencing and MLPA. Molecular and detailed phenotypic data have been collected and submitted to the PKD1/PKD2 LOVD database. This is the first large retrospective study in Italian patients, describing 701 variants, 249 (35.5%) already associated with ADPKD and 452 (64.5%) novel. According to the criteria adopted, the overall detection rate was 80% (352/440). Novel variants with uncertain significance were found in 14% of patients. Among patients with pathogenic variants, in 301 (85.5%) the disease is associated with PKD1, 196 (55.7%) truncating, 81 (23%) non truncating, 24 (6.8%) IF indels, and in 51 (14.5%) with PKD2. Our results outline the high allelic heterogeneity of variants, complicated by the presence of variants of uncertain significance as well as of multiple variants in the same subject. Classification of novel variants may be particularly cumbersome having an important impact on the genetic counselling. Our study confirms the importance to improve the assessment of variant pathogenicity for ADPKD; to this point databasing of both clinical and molecular data is crucial.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".