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Record W4412484755 · doi:10.1681/asn.0000000803

Genetic Variation and Ultrafiltration with Peritoneal Dialysis

2025· article· en· W4412484755 on OpenAlexaff
Ian B. Stanaway, Inês P. D. Costa, Simon Davies, Jeffrey Perl, Mark Lambie, Johann Morelle, Gail P. Jarvik, Arsh K. Jain, Jonathan Himmelfarb, Olof Heimbürger, David W. Johnson, James L. Pirkle, Bruce Robinson, Peter Stenvinkel, Angela Yee‐Moon Wang, Olivier Devuyst, Rajnish Mehrotra

Bibliographic record

VenueJournal of the American Society of Nephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsWestern UniversityUniversity of TorontoSt. Michael's Hospital
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsHeritabilityGenome-wide association studyPeritoneal dialysisGenetic associationSingle-nucleotide polymorphismBiologyFalse discovery rateGenetic variationUltrafiltration (renal)GeneticsMedicineInternal medicineGeneGenotype

Abstract

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Key Points There is a large person-to-person variability in ultrafiltration with peritoneal dialysis at the time of starting treatment. In this international cohort study, heritability of peritoneal ultrafiltration with peritoneal dialysis was estimated at 50%. In genome-wide association study, two single-nucleotide variants reached genome-wide significance—rs72631501 in CRK intron with European ancestry and rs1416265, intergenic, with South Asian ancestry. Background There is a large person-to-person variability in ultrafiltration volume with peritoneal dialysis (PD), most of which cannot be accounted for by demographic and clinical differences. In this article, we tested the hypothesis that common genetic variants are associated with peritoneal ultrafiltration and explored one mechanistic pathway identified by genetic studies. Methods We generated estimates of heritability and undertook genome-wide and gene-wise association studies, adjusted for peritoneal solute transfer rate, to test associations of genetic variation with ultrafiltration on peritoneal equilibration test conducted at PD initiation in 2723 participants in the international Biological Determinants of PD (Bio-PD) study. We used a mouse model of PD to study the mechanistic basis for the association of PTGES gene with peritoneal ultrafiltration. Results The peritoneal equilibration test was conducted at a median of 61 (interquartile range, 38–118) days from PD start with a median 4-hour ultrafiltration volume of 250 (interquartile range, 25–465) ml. The heritability of peritoneal ultrafiltration was estimated to be 50% ( P = 0.001). In single-nucleotide variant–wise multiancestry genome-wide association study using TRACTOR software, one single-nucleotide variant reached genome-wide significance in participants with European local ancestry (rs72631501, CRK intron, P = 2.6×10 −8 ) and one in participants with South Asian local ancestry (rs1416265, intergenic, P = 4.2×10 −8 ). Gene-wise analyses showed significant association of 21 genes at false discovery rates (FDRs) <0.10 in the European strata, notably PTGES (FDR=0.053), SLC24A3 (FDR=0.0003), and CRK (FDR=0.04). SLC24A3 remained significant (FDR=0.03) in meta-analysis of the four ancestry strata. Using single-cell RNA sequencing, PTGES localized in peritoneal adipocytes. In a mouse PD model, pharmacologic modulation of prostaglandin E synthase altered dialysate PGE2 levels with changes in adipocyte volume, peritoneal small solute transfer rate, and ultrafiltration volume. Conclusions Common genetic variants accounted for a substantial proportion of the variability in peritoneal ultrafiltration with potential associations with 21 genes, including CRK , PTGES , and SLC24A3 . Functional studies substantiated a potential role for prostaglandin E synthase/prostaglandin E2 in regulating peritoneal ultrafiltration. Clinical Trial registry name and registration number: ClinicalTrials.gov, NCT02694068.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.006
GPT teacher head0.245
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2025
Admission routes1
Has abstractyes

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Same venueJournal of the American Society of NephrologySame topicDialysis and Renal Disease ManagementFrench-language works237,207