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Relationship between androgen deprivation therapy and kidney function in patients with prostate cancer: An analysis of the RADICAL-PC study.

2025· article· en· W4407699982 on OpenAlexaff
Geethan Baskaran, Jehonathan H. Pinthus, Celestia S. Higano, Cristina Cano Garcia, Rajibul Mian, Germano Dallegrave Cavalli, Guila Delouya, Cesar O.L. Dusilek, Bobby Shayegan, Rafaela Kirchner Piccoli, Tamim Niazi, Margot K. Davis, Avirup Guha, Joseph B. Selvanayagam, Patrick Luke, Darryl P. Leong

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcGill UniversityCentre Hospitalier de l’Université de MontréalImpactUniversity of British ColumbiaPopulation Health Research InstituteLondon Health Sciences CentreMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineProstate cancerAndrogen deprivation therapyCancerOncologyInternal medicineProstateUrologyKidney cancerAndrogenKidneyGynecologyHormone

Abstract

fetched live from OpenAlex

115 Background: Androgen deprivation therapy (ADT) is an effective treatment for patients with advanced prostate cancer (PC). However, its impact on renal function remains underexplored. We evaluated the impact of ADT on kidney function in a cohort of patients with PC. Methods: We analyzed the RADICAL-PC study (Role of Androgen-Deprivation Therapy in Cardiovascular Disease—A Longitudinal Prostate Cancer study, NCT04127631), a prospective study of patients with PC in 10 countries. ADT use was assessed at baseline and follow-up visits. We quantified kidney function using glomerular filtration rate (GFR), calculated using the CKD-EPI equation and creatinine measurements obtained at annual visits. We used a linear mixed-effects regression model to analyze the association between ADT and change in GFR (ΔGFR), adjusting for age, diabetes, hypertension, renal disease, heart failure, chemotherapy, medications, alcohol use, and smoking. Using a Cox regression model, we assessed the association between ADT and acute kidney injury (AKI) as reported by sites. Results: Our analysis included 4119 participants with a median age of 68 (IQR 63-74) years. In 1791 (43%) participants using ADT at baseline, the ΔGFR over 2 years was -0.64 [95%CI -1.05 to -0.22] ml/min/1.73m 2 /year. In 2328 (57%) ADT-naïve participants at baseline, the ΔGFR was -1.00 [95%CI -1.34 to -0.64] ml/min/1.73m 2 /year. There was no significant difference in ΔGFR between the two groups (p=0.20). Age (β=-0.58, p<0.001), hypertension (β=-1.98, p<0.01), and anticoagulation use (β=-3.92, p<0.001) were associated with GFR decline. When analyzed as a time-varying covariate, ADT use was associated with less GFR decline than no ADT use (-0.21 vs. -1.07 ml/min/1.73m 2 /year, respectively, p<0.01). Duration of ADT use was not associated with ΔGFR (+0.04 [95%CI -0.02 to 0.10] ml/min/1.73m 2 /month, p=0.18). AKI developed in 91 (2.2%) participants over a median follow-up of 25 (IQR 12-36) months. Baseline ADT use was associated with an increased risk of AKI in univariate analysis (HR 2.58 [95%CI 1.67 to 3.98], p<0.001), but was not significant after adjustment for covariates (HR 1.37 [95%CI 0.83 to 2.26], p=0.22). Conclusions: This analysis suggests that ADT use is not associated with GFR decline or AKI incidence in patients with PC, challenging previous literature concerning ADT-related renal dysfunction. Further studies with longer follow-up periods are needed to confirm these findings.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.130
GPT teacher head0.475
Teacher spread0.346 · 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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Citations0
Published2025
Admission routes1
Has abstractyes

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