Evidence map›Paper›PMID 38454497›Full record

ArticleSkeletal muscle2024

Metabolic signatures and potential biomarkers of sarcopenia in suburb-dwelling older Chinese: based on untargeted GC-MS and LC-MS.

Peipei Han, Chunhua Yuan, Xiaoyu Chen, Yuanqing Hu, Xiaodan Hu, Zhangtao Xu, Qi Guo

Open access · goldAbstract read
In one paragraph

Article in Skeletal muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.3field-weighted citation impact, top 8% 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

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Trial
  2. Observational
  3. Review
  4. Article
  5. Review
  6. Article
  7. LC/MS-Based Untargeted Lipidomics Reveals Lipid Signatures of Sarcopenia.International journal of molecular sciences · 2024
    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 at 2 institutions in 1 country.

Peipei HanDepartment of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China.
Chunhua Yuan *Comprehensive Surgical Rehabilitation Ward, Shanghai Health Rehabilitation Hospital, Shanghai, China.
Xiaoyu Chen *College of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China.
Yuanqing HuCollege of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China.
Xiaodan HuCollege of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China.
Zhangtao XuCollege of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China.
Qi GuoDepartment of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China. guoqijp@gmail.com.
Shanghai University of Medicine and Health Sciences · CNShanghai Sunshine Rehabilitation Center · CN

Funding

National Natural Science Foundation of China 82172552National Natural Science Foundation of China 82202814Shanghai Municipal Health Commission 20214Y0329
6 · The paper itself

Abstract

backgroundUntargeted metabolomics can be used to expand our understanding of the pathogenesis of sarcopenia. However, the metabolic signatures of sarcopenia patients have not been thoroughly investigated. Herein, we explored metabolites associated with sarcopenia by untargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) and identified possible diagnostic markers.

methodsForty-eight elderly subjects with sarcopenia were age and sex matched with 48 elderly subjects without sarcopenia. We first used untargeted GC/LC-MS to analyze the plasma of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of sarcopenia.

resultsAccording to variable importance in the project (VIP > 1) and the p-value of t-test (p < 0.05), a total of 55 metabolites by GC-MS and 85 metabolites by LC-MS were identified between sarcopenia subjects and normal controls, and these were mostly lipids and lipid-like molecules. Among the top 20 metabolites, seven phosphatidylcholines, seven lysophosphatidylcholines (LysoPCs), phosphatidylinositol, sphingomyelin, palmitamide, L-2-amino-3-oxobutanoic acid, and palmitic acid were downregulated in the sarcopenia group; only ethylamine was upregulated. Among that, three metabolites of LysoPC(17:0), L-2-amino-3-oxobutanoic acid, and palmitic acid showed very good prediction capacity with AUCs of 0.887 (95% CI = 0.817-0.957), 0.836 (95% CI = 0.751-0.921), and 0.805 (95% CI = 0.717-0.893), respectively.

conclusionsThese findings show that metabonomic analysis has great potential to be applied to sarcopenia. The identified metabolites could be potential biomarkers and could be used to study sarcopenia pathomechanisms.

Indexed as

Liquid Chromatography-Mass SpectrometrySarcopeniaAgedBiomarkersChinaChromatography, LiquidGas Chromatography-Mass SpectrometryHumansPalmitic AcidTandem Mass SpectrometryBiomarkersPalmitic AcidBiomarkersGC–MSLC–MSMetabolomicsSarcopenia

Identifiers

PMID38454497
PMCPMC10921582
OpenAlexW4392551603

What Socratic holds

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LicenceCC BY
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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 Socratic graph.