Vascular calcification in chronic kidney disease associated with pathogenic variants in ABCC6
Bibliographic record
Abstract
Vascular calcification is prevalent in chronic kidney disease (CKD). Genetic causes of CKD account for 10-20% of adult-onset disease. Vascular calcification is thought to be one of the most important risk factors for increased cardiovascular morbidity and mortality in CKD patients and is detectable in 80% of patients with end stage kidney disease (ESKD). Despite the high prevalence of vascular calcification in CKD, no single gene cause has been described. We hypothesized that variants in vascular calcification genes may contribute to disease pathogenesis in CKD, particularly in families who exhibit a predominant vascular calcification phenotype. We developed a list of eight genes that are hypothesized to play a role in vascular calcification due to their involvement in the ectopic calcification pathway: ABCC6, ALPL, ANK1, ENPP1, NT5E, SLC29A1, SLC20A2, and S100A12. With this, we assessed exome data from 77 CKD patients, who remained unsolved following evaluation for all known monogenic causes of CKD. We also analyzed an independent cohort (Ontario Neurodegenerative Disease Research Initiative (ONDRI), n = 520) who were screened for variants in ABCC6 and compared this to a control cohort of healthy adults (n = 52). We identified two CKD families with heterozygous pathogenic variants (R1141X and A667fs) in ABCC6. We identified 10 participants from the ONDRI cohort with heterozygous pathogenic or likely pathogenic variant in ABCC6. Replication in a healthy control cohort did not reveal any variants. Our study provides preliminary data supporting the hypothesis that ABCC6 may play a role in vascular calcification in CKD. By screening CKD patients for genetic causes early in the diagnostic pathway, patients with genetic causes associated with vascular calcification can potentially be preventatively treated with new therapeutics with aims to decrease mortality.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".