Evidence map›Paper›PMID 39833326›Full record

ArticleScientific reports2025

Relationship Between Body Mass Index and Low Skeletal Muscle Mass in Adults Based on NHANES 2011-2018.

Rong-Zhen Xie, Xu-Song Li, Fang-Di Zha, Guo-Qing Li, Wei-Qiang Zhao, Yu-Feng Liang, Jie-Feng Huang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Observational
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Association of Body Metrics and Ocular Diseases.Journal of clinical medicine · 2025
    Article
  17. 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

7 authors.

Rong-Zhen Xie *Department of Orthopaedics & Traumatology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, China.
Xu-Song Li *Department of Orthopaedics and Traumatology, Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, 528401, Guangdong, China.
Fang-Di ZhaWenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
Guo-Qing LiZhejiang Chinese Medical University, Hangzhou, 310006, Zhejiang, China.
Wei-Qiang ZhaoDepartment of Orthopaedics & Traumatology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, China.
Yu-Feng LiangDepartment of Orthopaedics & Traumatology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, China.
Jie-Feng HuangDepartment of Orthopaedics & Traumatology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Shangcheng District, Hangzhou, 310006, China. 40983285@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sarcopenia, a syndrome characterized by declining muscle mass and function, is associated with various adverse health outcomes. While body mass index (BMI) is a fundamental health indicator, its relationship with sarcopenia is complex and remains inadequately explored. Low BMI has been linked to muscle loss, but the role of other demographic and clinical factors in this relationship is unclear. This cross-sectional study analyzed data from the NHANES 2011-2018 cohort, including 7,455 adults aged ≥ 20 years. Low muscle mass was diagnosed based on appendicular skeletal muscle mass using DXA criteria. BMI was categorized into quartiles for analysis. Weighted multivariable logistic regression assessed associations between BMI and low muscle mass, adjusting for confounders such as age, gender, ethnicity, income-to-poverty ratio, and chronic diseases (e.g., diabetes and hypertension). Variance inflation factors (VIF) confirmed the absence of multicollinearity. Lower BMI was significantly associated with higher odds of low muscle mass (adjusted OR: 0.508, 95% CI: 0.483-0.533, p < 0.001), while higher BMI exhibited a protective effect. Age (OR: 1.035, 95% CI: 1.025-1.045, p < 0.001) and female gender (OR: 1.570, 95% CI: 1.267-1.949, p < 0.001) were independent risk factors. Racial disparities were noted, with non-Hispanic Black individuals at lower risk compared to non-Hispanic Whites (OR: 0.242, 95% CI: 0.152-0.384, p < 0.001). Other significant factors included diabetes and alcohol consumption, while education and smoking status were not significantly associated. BMI is inversely associated with low muscle mass prevalence, with lower BMI posing a higher odds. The findings underscore the clinical importance of monitoring BMI and addressing multifactorial risk profiles for low muscle mass management and prevention.

Indexed as

Body Mass IndexMuscle, SkeletalSarcopeniaAdultAge FactorsBlack or African AmericanCross-Sectional StudiesFemaleHealth Status DisparitiesHumansLogistic ModelsMaleMiddle AgedNutrition SurveysRisk FactorsSex FactorsAgingBody statusDiabetesMuscle lossObesity paradox

Identifiers

PMID39833326
PMCPMC11747499

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.