Evidence map›Paper›PMID 40928997›Full record

ArticleObesity facts2026

Visceral Adipose Tissue Cut Points for Increased Metabolic Risk in Chinese Adults Using Low-Dose Computed Tomography: A Nationwide Multicenter Study.

Yandong Liu, Yanhui Lu, Yongli Li, Dong Yan, Yan Wu, Chunwei Wu, Limei Ran, Yong Lu, Kaiping Zhao, Glen M Blake and 3 more

Abstract readMulticenter Study
In one paragraph

Article in Obesity facts, 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. Review
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

13 authors.

Yandong LiuDepartment of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Yanhui LuDepartment of Endocrinology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Centre for Geriatric Disease, Beijing, China.
Yongli LiDepartment of Health Management, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.
Dong YanDepartment of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Yan WuDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Chunwei WuDepartment of Health Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Limei RanDepartment of Health Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Yong LuDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Kaiping ZhaoDepartment of Medical Records and Statistics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
Glen M BlakeSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Mingzhu ZouDepartment of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Xiaoguang ChengDepartment of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China, xiao65@263.net.
Min ChenDepartment of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China, cjr.chenmin@vip.163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionVisceral adipose tissue (VAT) is a significant driver for metabolic disease risk. Low-dose computed tomography (LDCT) imaging obtained for other clinical indications is useful for the opportunistic screening of osteoporosis and demonstrates additional potential for the screening of metabolic risk through the measurement of VAT. In this study, we explored LDCT-derived VAT and calculated VAT thresholds indicative of elevated metabolic risk in a population cohort of Chinese men and women.

methodsA total of 21,772 adults (64.2% men, 35.8% women) received a LDCT chest scan for routine lung cancer screening between 2018 and 2019, and abdominal VAT area (L2 region) was derived from these scans using QCT software. The presence of metabolic syndrome (MetS) was ascertained from clinical records and based on the Chinese national guidelines. All data for this current study were obtained from the China Biobank Project, a prospective, nationwide, multicenter population study.

resultsMetS was prevalent in 29.5% of men and 10.5% of women. Using receiver operating characteristic (ROC) curves, we determined the optimum VAT area cut points for identifying MetS: 210 cm2 in men (odds ratio [OR] 6.15, 95% confidence interval [CI] 5.65, 6.69) and 136 cm2 in women (OR 9.25, 95% CI: 7.58, 11.4). A population attributable risk for MetS by VAT area above sex-specific cut points was 70.9% in men and 74.1% in women. The area under the ROC curves (AUCs) of VAT area for distinguishing MetS are 0.780 (95% CI: 0.772, 0.788) in men and 0.854 (95% CI: 0.842, 0.866) in women, both exceeding the AUCs for body mass index (BMI). ROC curves showed that VAT area demonstrates superiority over BMI and WC in identifying hypertension and hyperglycemia in men, as well as hypertension, hyperglycemia, and hypertriglyceridemia in women.

conclusionLDCT-based opportunistic measured VAT area is a superior predictor of metabolic risk, better than BMI and WC, and should be monitored and interventions made to control the metabolic risk. This study proposes VAT risk thresholds for the Chinese population: 210 cm2 for men and 136 cm2 for women.

Indexed as

Intra-Abdominal FatMetabolic SyndromeTomography, X-Ray ComputedAdultAgedChinaFemaleHumansMaleMiddle AgedProspective StudiesRisk FactorsROC CurveAdiposityMetabolic syndromeQuantitative computed tomographyScreeningVisceral fat

Identifiers

PMID40928997
PMCPMC13046328

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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.