Evidence map›Paper›PMID 40605151›Full record

ArticleDiabetes, obesity & metabolism2025

Dynamic phenotypes of preclinical and clinical obesity in relation to new-onset cancer risk: A longitudinal analysis from the UK biobank.

Manrong Xu, Menghan Li, Yawen Zhang, Lianxi Li, Yun Shen, Gang Hu

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In one paragraph

Article in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

6 authors.

Manrong XuDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China.
Menghan LiDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China.
Yawen ZhangDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China.
Lianxi LiDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China.ORCID 0000-0001-6073-4901
Yun ShenChronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA.ORCID 0000-0002-9850-122X
Gang HuChronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA.ORCID 0000-0002-6172-8017

Funding

Tracking & Evaluation CoreU54GM104940 · NIGMS · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI Peter Todd Katzmarzyk · 2012 to 2026
$69.1M
Lake Health & Louisiana State University LSU-OLOL-2024-06National Natural Science Foundation of China 81770813National Natural Science Foundation of China 82070866NIGMS NIH HHS U54 GM104940NIGMS NIH HHS U54GM104940
6 · The paper itself

Abstract

aimThe definition of clinical obesity was newly announced. Our study aims to investigate the relationship between different states of obesity and dysfunctions due to obesity with cancer incidence and mortality.

methodsThe prospective cohort study from the UK Biobank included 220 016 participants. Anthropometric parameters, in combination with obesity-induced dysfunctions, were used to diagnose clinical obesity. Six clusters were categorized according to individual's baseline and follow-up dysfunction status. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for cancer incidence risk were estimated using the landmark analysis.

resultsAfter a mean follow-up period of 11.0 years, a total of 24 066 cancer incidence was observed. Using Cluster 1 (participants without obesity and dysfunction at baseline and during follow-up) as the reference group, Cluster 5 (preclinical obesity with follow-up dysfunctions; HR = 3.17, 95% CI: 3.05-3.29) exhibited the highest multivariable-adjusted cancer incidence risk, while Cluster 4 (preclinical obesity without follow-up dysfunctions; HR = 0.88, 95% CI: 0.85-0.92) showed the lowest. Additionally, the fully adjusted HRs for cancer mortality showed the highest in Cluster 6 (clinical obesity; HR = 1.82, 95% CI: 1.65-2.00), compared with Cluster 1. Site-specific analyses showed consistently higher cancer risks in Cluster 5 and 6 across various types of cancer, notably the incidence of pancreatic cancer and the mortality of prostate or bladder cancer.

conclusionObesity-induced dysfunction was significantly associated with cancer risk. For future clinical practice, the early identification and intervention of clinical obesity and obesity-induced dysfunctions are of critical importance for reducing cancer risks.

Indexed as

NeoplasmsObesityAdultAgedBiological Specimen BanksBody Mass IndexFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedPhenotypeProspective StudiesRisk FactorsUK Biobankcancer incidencecancer mortalityclinical obesityobesity‐induced dysfunctionsUK biobank

Identifiers

PMID40605151
PMCPMC12326904

What Socratic holds

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LicenceCC BY-NC
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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.