Evidence map›Paper›PMID 41883289›Full record

ArticleDiabetes, obesity & metabolism2026

Polygenic Risk Score Predicts Prostate Cancer Risk Independent of Type 2 Diabetes.

Guk Jin Lee, Sang-Hyuk Jung, Jonghyun Lee, Ki Won Moon, Penn Medicine Biobank, Jae-Seung Yun, Dokyoon Kim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Guk Jin LeeDivision of Medical Oncology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-8658-7225
Sang-Hyuk JungDepartment of Medical Informatics, Kangwon National University College of Medicine, Chuncheon, Republic of Korea.ORCID https://orcid.org/0000-0003-4116-3327
Jonghyun LeeDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ki Won MoonDepartment of Medical Informatics, Kangwon National University College of Medicine, Chuncheon, Republic of Korea.
Penn Medicine BiobankInstitute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jae-Seung YunDivision of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-5949-1826
Dokyoon KimDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-4592-9564

Funding

Methods for Enhancing Polygenic Risk Prediction Models for Complex DiseaseR01HL169458 · UNIVERSITY OF PENNSYLVANIA · 2025 to 2025
$770k
Ministry of Education 2026-RISE-10-002Ministry of Health and Welfare, Republic of Korea RS-2025-24535069NHLBI NIH HHS R01 HL169458NIGMS NIH HHS R01 GM138597
6 · The paper itself

Abstract

aimsType 2 diabetes mellitus (T2DM) has been inversely associated with prostate cancer (PrCa) risk. However, it remains unclear whether a polygenic risk score (PRS) for PrCa can effectively stratify risk among men with T2DM. The primary objective of this study was to assess whether a PrCa PRS predicts PrCa risk independently of T2DM status. The secondary objective was to evaluate potential mediating factors, including insulin-like growth factor-1 (IGF-1) and sex hormones. MATERIALS AND

methodsWe analysed data from over 140 000 men in the UK Biobank and Penn Medicine Biobank. A PrCa PRS was constructed using summary statistics from a large-scale genome-wide association study. Cox proportional hazards models were used to evaluate the association between PRS and incident PrCa, adjusting for relevant covariates and testing for interaction by T2DM status. Additionally, sex hormones and IGF-1 levels were analysed to explore potential mediators.

resultsT2DM was associated with a reduced incidence of PrCa. The PrCa PRS was significantly associated with PrCa risk regardless of T2DM status (p < 0.001), and men in the highest PRS category exhibited the greatest risk, especially among those without T2DM. IGF-1 levels were positively associated with PrCa risk among both diabetic and non-diabetic men, while sex hormone levels showed no significant association in men with T2DM. Adjusting for testosterone and IGF-1 did not attenuate the association between PRS and PrCa.

conclusionsPrCa PRS effectively stratifies risk among men with and without T2DM, highlighting the independent contribution of genetic susceptibility. Lower IGF-1 levels in T2DM patients may partly mediate the reduced PrCa risk, suggesting a possible biological mechanism underlying these observations.

Indexed as

Diabetes Mellitus, Type 2Prostatic NeoplasmsAgedGenetic Predisposition to DiseaseGenetic Risk ScoreGenome-Wide Association StudyGonadal Steroid HormonesHumansIncidenceInsulin-Like Growth Factor IMaleMiddle AgedRisk FactorsUnited KingdomGonadal Steroid HormonesInsulin-Like Growth Factor Idiabetes mellitusinsulin‐like growth factor‐1polygenic risk scoreprostatic neoplasms

Identifiers

PMID41883289
PMCPMC13146153

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.