Evidence map›Paper›PMID 35898717›Full record

ArticleFrontiers in nutrition2022

Association Between Serum Iron Status and Muscle Mass in Adults: Results From NHANES 2015-2018.

Zhi Chen, Jing Chen, Chenyang Song, Jun Sun, Wenge Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
4.2field-weighted citation impact, top 4% of its field
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

29 citing papers in PubMed, 38 citations in OpenAlex.

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  20. Drug-related sarcopenia as a secondary sarcopenia.Geriatrics & gerontology international · 2024
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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

5 authors at 4 institutions in 1 country.

Zhi ChenDepartment of Orthopedic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
Jing ChenDepartment of Ophthalmology, Fujian Provincial Hospital, Fuzhou, China.
Chenyang SongDepartment of Orthopedic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
Jun SunDepartment of Emergency, Zhaotong Traditional Chinese Medicine Hospital, Zhaotong, China.
Wenge LiuDepartment of Orthopedic Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
Fujian Medical University · CNFujian Provincial Hospital · CNJining Traditional Chinese Medicine Hospital · CNUnion Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Iron deficiency or overload may contribute to complications associated with diseases, but the link between iron status and skeletal muscle disorder is poorly understood. This study aimed to investigate the relationship between serum iron status, reflected by serum ferritin concentration, and muscle mass in U.S. adults. Methods: We utilized data from National Health and Nutrition Examination Survey (NHANES) 2015-2018 for analysis. Data on serum ferritin, appendicular skeletal muscle mass (ASM), body mass index (BMI) and confounding factors were extracted and analyzed. Multivariate linear regression analyses and smooth curve fittings were employed to investigate the association between serum ferritin and muscle mass. Subgroup analysis based on iron status, age, gender and race were performed. Results: A total of 2,078 participants were included, and divided into iron deficiency ( Conclusions: Our study revealed an inverse relationship between serum iron status and muscle mass in adults. This finding improves our understanding of the impact of serum iron status on muscle mass, and sheds new light on the prevention and treatment of muscle loss.

Indexed as

ferritinironNHANESsarcopeniaskeletal muscle

Identifiers

PMID35898717
PMCPMC9309789
OpenAlexW4285009488

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.