MétaCan
Menu
← Back to cohort
Record W4399488514 · doi:10.1681/asn.0000000000000419

Kidney Volume and Risk of Incident Kidney Outcomes

2024· article· en· W4399488514 on OpenAlexafffund
Jianhan Wu, Yifan Wang, Caitlyn Vlasschaert, Ricky Lali, James Feiner, Pukhraj S. Gaheer, Serena Yang, Nicolas Perrot, Michael Chong, Guillaume Paré, Matthew B. Lanktree

Bibliographic record

VenueJournal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsSt. Joseph’s Healthcare HamiltonQueen's UniversityMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health Research
KeywordsKidneyMedicineVolume (thermodynamics)UrologyIntensive care medicineInternal medicinePhysics

Abstract

fetched live from OpenAlex

Key Points Low kidney volume was a risk factor of incident CKD. A nonlinear relationship existed whereby individuals in the bottom tenth percentile of kidney volume exhibited exaggerated risk of CKD and albuminuria. Kidney volume could improve the classification of kidney disease risk. Background Low total kidney volume (TKV) is a risk factor of CKD. However, evaluations of nonlinear relationships, incident events, causal inference, and prognostic utility beyond traditional biomarkers are lacking. Methods TKV, height-adjusted TKV, and body surface area–adjusted TKV of 34,595 White British ancestry participants were derived from the UK Biobank. Association with incident CKD, AKI, and cardiovascular events were assessed with Cox proportional hazard models. Prognostic thresholds for CKD risk stratification were identified using a modified Mazumdar method with bootstrap resampling. Two-sample Mendelian randomization was performed to assess the bidirectional association of genetically predicted TKV with kidney and cardiovascular traits. Results Adjusted for eGFR and albuminuria, a lower TKV of 10 ml was associated with a 6% higher risk of incident CKD (hazard ratio, 1.06; 95% confidence interval [CI], 1.03 to 1.08; P = 5.8×10 −6 ) in contrast to no association with incident AKI (hazard ratio, 1.00; 95% CI, 0.98 to 1.02; P = 0.66). Comparison of nested models demonstrated improved accuracy over the Chronic Kidney Disease Prognosis Consortium Incident CKD Risk Score with the addition of body surface area–adjusted TKV or prognostic thresholds at 119 (tenth percentile) and 145 ml/m 2 (50th percentile). In Mendelian randomization, a lower genetically predicted TKV by 10 ml was associated with 10% higher CKD risk (odds ratio, 1.10; 95% CI, 1.06 to 1.14; P = 1.3×10 −7 ). Reciprocally, an elevated risk of genetically predicted CKD by two-fold was associated with a lower TKV by 7.88 ml (95% CI, −9.81 to −5.95; P = 1.2×10 −15 ). There were no significant observational or Mendelian randomization associations of TKV with cardiovascular complications. Conclusions Kidney volume was associated with incident CKD independent of traditional risk factors, including baseline eGFR and albuminuria. Mendelian randomization demonstrated a bidirectional relationship between kidney volume and CKD.

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)0.005
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.275
Teacher spread0.266 · 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".

Quick stats

Citations11
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Society of Nephrology→Same topicChronic Kidney Disease and Diabetes→French-language works237,207→