Evidence map›Paper›PMID 39144009›Full record

ArticleQuantitative imaging in medicine and surgery2024

Assessment of body compositions in the prediction of metabolic syndrome in adults.

Bowen Hou, Yitong Li, Chanyuan Liu, Yao Zhang, Donglin Wen, Xiaoming Li

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2024. 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
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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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Bowen HouDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yitong LiDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Chanyuan LiuDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yao ZhangDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Donglin WenDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaoming LiDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The musculoskeletal system participates in the pathology of metabolic disorders. Several studies have focused on body composition changes; however, the adipose tissue between muscle bundles with different metabolic statuses has rarely been studied. This study sought to explore the association between body compositions and metabolic disorders in Asians, and identify whether these body compositions can be used to detect metabolic disorders with different waist circumferences (WCs) by computed tomography (CT). Methods: A total of 116 subjects were included in the study and categorized into the following four groups according to WC and metabolic syndrome (MS): (I) the healthy control group; (II) the normal WC with metabolic disorder group; (III) the normal WC with MS group; and (IV) the larger WC with MS group. The International Diabetes Federation (IDF) criteria based on WC, laboratory tests, body mass index (BMI), and medical history was used to diagnose MS. Body composition parameters, such as muscle attenuation, the cross-sectional area of subcutaneous adipose tissue (SAT), muscle, extramyocellular lipid (EMCL), visceral adipose tissue (VAT), and the ratios between different compositions [e.g., the SMR (SAT/muscle), EMR (EMCL/muscle), and VMR (VAT/muscle)] were calculated for the thigh and abdomen. The areas under the curve (AUCs) of the receiver operating characteristic (ROC) curves adjusted for multiple comparisons were used to discriminate among metabolic disorders. Results: The groups with metabolic disorders had more SAT (P=0.001) and EMCL (P=0.040) in the thigh, and more VAT (P=0.001) and a higher SMR (P<0.001) in the abdomen. EMCL and muscle attenuation in the thigh (AUCs =0.790 and 0.791), and the VMR and SMR in the abdomen were better able to diagnose metabolic disorders (AUCs =0.752 and 0.746) than other body composition parameters. While SAT and EMCL in the thigh (AUCs =0.768 and 0.760), and VAT and the VMR in the abdomen (AUCs =0.788 and 0.775) were better able to diagnose MS than other parameters. Conclusions: Body composition parameters for the thigh and abdomen could assist in detecting patients with an increased risk of MS.

Indexed as

computed tomography (CT)extramyocellular lipid (EMCL)metabolic syndrome (MS)muscleSubcutaneous adipose tissue (SAT)

Identifiers

PMID39144009
PMCPMC11320537

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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.