Evidence map›Paper›PMID 39252042›Full record

ArticleNutrition journal2024

Sex-specific associations between total and regional Fat-to-muscle Mass ratio and cardiometabolic risk: findings from the China National Health Survey.

Zhiming Lu, Yaoda Hu, Xingming Chen, Qiong Ou, Yawen Liu, Tan Xu, Ji Tu, Ang Li, Binbin Lin, Qihang Liu and 8 more

Abstract read
In one paragraph

Article in Nutrition journal, 2024. 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

18 authors.

Zhiming LuDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Yaoda HuDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Xingming ChenDepartment of Otolaryngology-Head and Neck Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
Qiong OuSleep Center, Department of Respiratory and Critical Care Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Yawen LiuDepartment of Epidemiology and Biostatistics, School of Public Health of Jilin University, Changchun, China.
Tan XuDepartment of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
Ji TuDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Ang LiDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Binbin LinDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Qihang LiuDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Tianshu XiDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Weihao WangDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Haibo HuangDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Da XuDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Zhili ChenDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Zichao WangDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
Huijing HeDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China. huijing_he@ibms.pumc.edu.cn.
Guangliang ShanDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China. guangliang_shan@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe fat-to-muscle mass ratio (FMR), integrating the antagonistic effects of fat and muscle mass, has been suggested as a valuable indicator to assess cardiometabolic health independent of overall adiposity. However, the specific associations of total and regional FMR with cardiometabolic risk are poorly understood. We aimed to examine sex-specific associations of total and regional FMR with single and clustered cardiometabolic risk factors (CRFs).

methods13,505 participants aged 20 years and above were included in the cross-sectional study. Fat mass and muscle mass were assessed using a bioelectrical impedance analysis device. FMR was estimated as fat mass divided by muscle mass in corresponding body parts (whole body, arm, leg, and trunk). Clustered CRFs was defined as the presence of two or more risk factors, including hypertension, elevated blood glucose, dyslipidemia, insulin resistance (IR), and hyperuricemia. IR was assessed by the triglyceride glucose (TyG) index. Multivariable logistic regression models were applied to explore the associations of FMR in the whole body and body parts with single and clustered CRFs.

resultsThe odds ratios (ORs) increased significantly for all single and clustered CRFs with the per quartile increase of total and regional FMR in both sexes (P for trend < 0.001), following adjustment for confounders. Among the regional parts, FMRs of the legs presented the strongest associations for clustered CRFs in both men and women, with adjusted OR of 8.54 (95% confidence interval (CI): 7.12-10.24) and 4.92 (95% CI: 4.24-5.71), respectively. Significant interactions (P for interaction < 0.05) were identified between age and FMRs across different body parts, as well as between BMI status and FMRs in different regions for clustered CRFs. Restricted cubic splines revealed significant non-linear relationships between FMRs of different body parts and clustered CRFs in both sexes (P for nonlinear < 0.05).

conclusionsFMRs in the whole body and different regions were significantly associated with single and clustered CRFs in the general Chinese population. The association between FMR and clustered CRFs was more pronounced in youngers than in the elderly.

Indexed as

Cardiometabolic Risk FactorsHealth SurveysAdipose TissueAdiposityAdultAgedBody CompositionCardiovascular DiseasesChinaCross-Sectional StudiesFemaleHumansInsulin ResistanceMaleMiddle AgedMuscle, SkeletalCardiometabolic riskFat massFat-to-muscle mass ratioMuscle massObesity

Identifiers

PMID39252042
PMCPMC11385979

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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.