Evidence map›Paper›PMID 36107781›Full record

Observational studyAnnals of clinical and translational neurology2022

Metabolome and transcriptome analysis on muscle of sporadic inclusion body myositis.

Ayuka Murakami, Seiya Noda, Tomoyuki Kazuta, Satoko Hirano, Seigo Kimura, Hirotaka Nakanishi, Koji Matsuo, Koyo Tsujikawa, Madoka Iida, Haruki Koike and 7 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Annals of clinical and translational neurology, 2022. 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
1.6field-weighted citation impact, top 17% 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, 11 citations in OpenAlex.

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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

17 authors at 5 institutions in 1 country.

Ayuka MurakamiDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Seiya NodaDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Tomoyuki KazutaDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Satoko HiranoDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Seigo KimuraDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Hirotaka NakanishiDepartment of Neurology, Yokkaichi Municipal Hospital, Yokkaichi, Japan.
Koji MatsuoDepartment of Neurology, Kariya Toyota General Hospital, Kariya, Japan.
Koyo TsujikawaDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Madoka IidaDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Haruki KoikeDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Kazuma SakamotoDepartment of Biochemistry, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Yuichiro HaraDepartment of Genetics, Research Institute of Environmental Medicine (RLeM), Nagoya University, Nagoya, Japan.
Satoshi KuruDepartment of Neurology, National Hospital Organization Suzuka Hospital, Suzuka, Japan.
Kenji KadomatsuDepartment of Biochemistry, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Teppei ShimamuraDivision of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Tomoo OgiDepartment of Genetics, Research Institute of Environmental Medicine (RLeM), Nagoya University, Nagoya, Japan.
Masahisa KatsunoDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.ORCID 0000-0001-9453-9311
Nagoya University · JPNational Hospital Organization · JPNagoya Industrial Science Research Institute · JPKariya Toyota General Hospital · JPYokkaichi Municipal Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSporadic inclusion body myositis (sIBM) is the most common acquired myopathy in patients older than 50 years of age. sIBM is hardly responds to any immunosuppressing theraphies, and its pathophysiology remains elusive. This study aims to explore pathogenic pathways underlying sIBM and identify novel therapeutic targets using metabolomic and transcriptomic analyses.

methodsIn this retrospective observational study, we analyzed biopsied muscle samples from 14 sIBM patients and six non-diseased subjects to identify metabolic profiles. Frozen muscle samples were used to measure metabolites with cation and anion modes of capillary electrophoresis time of flight mass spectrometry. We validated the metabolic pathway altered in muscles of sIBM patients through RNA sequencing and histopathological studies.

resultsA total of 198 metabolites were identified. Metabolomic and transcriptomic analyses identified specific metabolite changes in sIBM muscle samples. The pathways of histamine biosynthesis and certain glycosaminoglycan biosynthesis were upregulated in sIBM patients, whereas those of carnitine metabolism and creatine metabolism were downregulated. Histopathological examination showed infiltration of mast cells and deposition of chondroitin sulfate in skeletal muscle samples, supporting the results of metabolomic and transcriptomic analyses.

interpretationWe identified alterations of several metabolic pathways in muscle samples of sIBM patients. These results suggest that mast cells, chondroitin sulfate biosynthesis, carnitine, and creatine play roles in sIBM pathophysiology.

Indexed as

Myositis, Inclusion BodyCarnitineChondroitin SulfatesCreatineGene Expression ProfilingHistamineHumansMetabolomeMuscle, SkeletalCarnitineChondroitin SulfatesCreatineHistamine

Identifiers

PMID36107781
PMCPMC9539386
OpenAlexW4295900971

What OpenQuestion holds

Textmetadata
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.