Evidence map›Paper›PMID 40825749›Full record

ArticleObesity (Silver Spring, Md.)2025

Visceral Adiposity Thresholds for Cardiovascular Risk Stratification: A Simplified Biomarker-Driven Model.

Tangmeng Guo, Ping He, Weilin Lu, Lili Huang, Chengyun Liu, Bei Cheng, Yuanyuan Zhang, Qi Zhang, Yanxu Chen, Minghao Liu and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Obesity (Silver Spring, Md.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

16 authors.

Tangmeng GuoDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0000-0002-0463-8349
Ping HeDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Weilin LuDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Lili HuangDepartment of Clinical Laboratory Medicine, People's Hospital of Dongxihu District Wuhan City and Union Dongxihu Hospital, Huazhong University of Science and Technology, Wuhan, China.
Chengyun LiuDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Bei ChengDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yuanyuan ZhangEchocardiography and Vascular Ultrasound Centre, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Qi ZhangDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Yanxu ChenDepartment of Organ Transplantation, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Minghao LiuDepartment of Computer Science, University of Oxford, Oxford, UK.
Peien ZhouNuffield Department of Population Health, Oxford Population Health, University of Oxford, Oxford, UK.
Junxi LiuNuffield Department of Population Health, Oxford Population Health, University of Oxford, Oxford, UK.
Xinchun GuNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Zhengyang SunOxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Qiang ZhangOxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Sihao XiaoNuffield Department of Population Health, Oxford Population Health, University of Oxford, Oxford, UK.

Funding

National Natural Science Foundation of China 81701376National Natural Science Foundation of China 82171574
6 · The paper itself

Abstract

objectiveVAT volume is a critical determinant of cardiometabolic risk, yet population-specific thresholds and accessible predictive tools remain undefined.

methodsUsing US National Health and Nutrition Examination Survey (NHANES) data, we analyzed dual-energy x-ray absorptiometry (DXA)-derived VAT volume. Threshold effects and sex/ethnicity interactions were evaluated via multivariable regression, while LASSO regularization and ROC analyses identified a simplified predictive model.

resultsA VAT volume threshold of 327.0 cm

conclusionsThis study establishes the first sex-specific and ethnicity-specific VAT thresholds for CVD risk stratification and provides a clinically actionable tool for visceral adiposity screening.

Indexed as

AdiposityCardiovascular DiseasesIntra-Abdominal FatObesity, AbdominalAbsorptiometry, PhotonAdultAgedBiomarkersFemaleHeart Disease Risk FactorsHumansMaleMiddle AgedNutrition SurveysRisk AssessmentRisk FactorsBiomarkers

Identifiers

PMID40825749
PMCPMC12477109

What OpenQuestion holds

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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 OpenQuestion graph.