Evidence map›Paper›PMID 39076779›Full record

ArticlePeerJ2024

Association between the metabolic score for insulin resistance and prostate cancer: a cross-sectional study in Xinjiang.

Jinru Wang, Aireti Apizi, Ning Tao, Hengqing An

Abstract read
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jinru Wang *College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
Aireti Apizi *Department of Urology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
Ning TaoCollege of Public Health, Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
Hengqing AnDepartment of Urology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.

Funding

Excellence Youth Science Foundation of Xinjiang Uygur Autonomous RegionKey Projects of Xinjiang Uyghur Autonomous RegionNational Natural Science Foundation of ChinaXinjiang Uygur Autonomous Region “Tianshan Talents” youth science and technology top talent project
6 · The paper itself

Abstract

Background: Insulin resistance is associated with the development and progression of various cancers. However, the epidemiological evidence for the association between insulin resistance and prostate cancer is still limited. Objectives: To investigate the associations between insulin resistance and prostate cancer prevalence. Methods: A total of 451 patients who were pathologically diagnosed with prostate cancer in the First Affiliated Hospital of Xinjiang Medical University were selected as the case population; 1,863 participants who conducted physical examinations during the same period were selected as the control population. The metabolic score for insulin resistance (METS-IR) was calculated as a substitute indicator for evaluating insulin resistance. The Chi-square test and Mann-Whitney U test were performed to compare the basic information of the case population and control population. Univariate and multivariate logistic regression analyses to define factors that may influence prostate cancer prevalence. The generalized additive model (GAM) was applied to fit the relationship between METS-IR and prostate cancer. Interaction tests based on generalized additive model (GAM) and contour plots were also carried out to analyze the interaction effect of each factor with METS-IR on prostate cancer. Results: METS-IR as both a continuous and categorical variable suggested that METS-IR was negatively associated with prostate cancer prevalence. Smoothed curves fitted by generalized additive model (GAM) displayed a nonlinear correlation between METS-IR and prostate cancer prevalence ( Conclusions: METS-IR is nonlinearly and negatively associated with the prevalence of prostate cancer. The interaction between METS-IR and low-density lipoprotein cholesterol (LDL-c) has an impact on the prevalence of prostate cancer. The study suggests that the causal relationship between insulin resistance and prostate cancer still needs more research to confirm.

Indexed as

Insulin ResistanceProstatic NeoplasmsAgedCase-Control StudiesChinaCross-Sectional StudiesHumansMaleMetabolic SyndromeMiddle AgedPrevalenceRisk FactorsGeneralized additive model (GAM)Insulin resistanceInteraction testMetabolic score for insulin resistance (METS-IR)Prostate cancer

Identifiers

PMID39076779
PMCPMC11285359

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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 OpenQuestion graph.