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Androgen deprivation therapy for prostate cancer, measures of adiposity and strength, and adverse cardiovascular outcomes: observations from 3000 men in 5 countries in the RADICAL PC study

2023· article· en· W4388600581 on OpenAlexafffund
Darryl P. Leong, Felipe Homem Valle, Guila Delouya, Vincent Fradet, Philippe D. Violette, Tamim Niazi, Celestia S. Higano, Bobby Shayegan, Parminder Nain, Ludhmila Abrahão Hajjar, Zaza Iakobishvili, Guilherme José Pimentel Lopes de Oliveira, Álvaro Avezum, Joseph B. Selvanayagam, Jehonathan H. Pinthus

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcMaster UniversityUniversity of British ColumbiaPopulation Health Research InstituteUniversité LavalUniversité de MontréalJewish General Hospital
FundersProstate Cancer Canada
KeywordsMedicineAndrogen deprivation therapyProstate cancerAdverse effectInternal medicineMyocardial infarctionStroke (engine)Weight changeHazard ratioWeight lossPhysical therapyCardiologyObesityCancerConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background Androgen deprivation therapy (ADT) is prescribed to nearly half of patients with prostate cancer (PC). ADT is associated with weight gain, reduced muscle strength and increased cardiovascular (CV) risk. However, no PC study has examined the relationship between these physical characteristics, their change with ADT and cardiovascular outcomes. Purpose In men with PC: To compare 1-year changes in physical measures in those receiving versus not receiving ADT To evaluate the relationship between physical measures and adverse CV outcomes Methods We prospectively studied 3080 men with PC from 37 sites in 5 countries (mean age 68 years), 41% on ADT. Inclusion criteria were one of: PC diagnosed in the past 12 months; started ADT within the past 6 months; or plan to start ADT in the next month. Weight, waist and hip circumference, handgrip strength and get-up-and-go time were recorded at baseline and 1 year. Change in measurements were adjusted for age, education, race, tobacco and alcohol use, baseline cardiovascular disease and renal function. Participants were followed for a median 3 years from enrolment to document adverse CV outcomes associated with adiposity: myocardial infarction, angina, stroke, cerebrovascular disease, peripheral arterial disease, arterial revascularization, heart failure, venous thromboembolism and atrial fibrillation. The relationship between baseline physical measures and outcomes was evaluated by Cox proportional hazards models adjusted for age, diabetes, hypertension, total cholesterol, tobacco use and physical activity levels. Results Changes in physical measures are displayed in Table. ADT recipients gained 5x more weight than non-recipients. Weight gain was distributed evenly between waist and hips, resulting in no difference in waist:hip ratio. Muscle strength decreased in both groups but with 43% larger decrease among ADT recipients. The ratio of strength to body weight fell twice as much in ADT recipients as non-recipients. Gait speed slowed by 13% after 12 months’ ADT. We observed 144 adverse CV events. There was a continuous relationship between handgrip strength and CV events. Each quartile reduction in strength was associated with adjusted hazard ratio (95% CI) 1.24 (1.01-1.52). There was no detectable association between other physical measures and CV events. Conclusion ADT use is associated with increased adiposity and reduced muscle strength. Low muscle strength, reflected by low handgrip strength is a better predictor of adverse CV outcomes than increased adiposity. These findings may explain help the association between ADT use and adverse CV outcomes.Table

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)0.002
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 score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
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 head0.120
GPT teacher head0.350
Teacher spread0.231 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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