Progressive Loss of Renal Function Is an Age-Dependent Heritable Trait in Type 1 Autosomal Dominant Polycystic Kidney Disease
Bibliographic record
Abstract
Significant intrafamilial phenotypic variability is well documented in autosomal dominant polycystic kidney disease (ADPKD) and suggests a modifier effect. In this study, variance components analysis was performed to estimate the contribution of genetic factors for within-family renal disease variability in 406 patients from 66 type 1 ADPKD families. Overall, 39% of the study patients had ESRD at their last follow-up, and their renal survival did not differ by gender (P = 0.35, log-rank test). Because their frequency plot of creatinine clearance (Ccr) assumed a bimodal distribution with a marked kurtosis that was not improved by transformations, the study cohort was decomposed into two separate groups (non-ESRD [n = 247] and ESRD [n = 159]) in which the Ccr plots were normally distributed. The heritability (h(2)) of Ccr and age at ESRD (age(ESRD)) and the genetic correlations between these measures and their covariates were estimated. In patients without ESRD, a significant heritability was found for Ccr (h(2) = 0.42; P = 0.0015) after adjusting for age (P = 0.0001), systolic BP (P = 0.0006), and treatment with angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (P = 0.00001). Birth year, gender, BMI, diastolic and mean BP, and pack-years of cigarette smoking did not significantly influence the heritability of this trait. In patients with ESRD, age(ESRD) provides a better measure than Ccr, which was very narrowly distributed. A significant heritability was found for age(ESRD) (h(2) = 0.78; P = 0.00009) in these latter patients. None of the above covariates influenced the heritability of this trait. It is concluded that a significant modifier gene effect influences the progression of renal disease in type 1 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".