Patients with Protein-Truncating PKD1 Mutations and Mild ADPKD
Bibliographic record
Abstract
Background and objectives Progression of autosomal dominant polycystic kidney disease (ADPKD) is highly variable. On average, protein-truncating PKD1 mutations are associated with the most severe kidney disease among all mutation classes. Here, we report that patients with protein-truncating PKD1 mutations may also have mild kidney disease, a finding not previously well recognized. Design, setting, participants, & measurements From the extended Toronto Genetic Epidemiologic Study of Polycystic Kidney Disease, 487 patients had PKD1 and PKD2 sequencing and typical ADPKD imaging patterns by magnetic resonance imaging or computed tomography. Mayo Clinic Imaging Classification on the basis of age- and height-adjusted total kidney volume was used to assess their cystic disease severity; classes 1A or 1B were used as a proxy to define mild disease. Multivariable linear regression was performed to test the effects of age, sex, and mutation classes on log-transformed height-adjusted total kidney volume and eGFR. Results Among 174 study patients with typical imaging patterns and protein-truncating PKD1 mutations, 32 (18%) were found to have mild disease on the basis of imaging results ( i.e ., Mayo Clinic Imaging class 1A–1B), with their mutations spanning the entire gene. By multivariable analyses of age, sex, and mutation class, they displayed mild disease similar to patients with PKD2 mutations and Mayo Clinic Imaging class 1A–1B. Most of these mildly affected patients with protein-truncating PKD1 mutations reported a positive family history of ADPKD in preceding generations and displayed significant intrafamilial disease variability. Conclusions Despite having the most severe mutation class, 18% of patients with protein-truncating PKD1 mutations had mild disease on the basis of clinical and imaging assessment. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_02_18_CJN11100720_final.mp3
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".