Evidence map›Paper›PMID 39863658›Full record

ArticleScientific reports2025

Branched-chain amino acids and specific phosphatidylinositols are plasma metabolite pairs associated with menstrual pain severity.

Atsushi Sato, Kanako Yuyama, Yuko Ichiba, Yasushi Kakizawa, Yuki Sugiura

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

5 authors.

Atsushi SatoResearch & Development Headquarters, Advanced Analytical Science Research Laboratories, Lion Corporation, Tokyo, Japan. a-sato@lion.co.jp.ORCID 0009-0005-8775-1687
Kanako YuyamaResearch & Development Headquarters, Advanced Analytical Science Research Laboratories, Lion Corporation, Tokyo, Japan.
Yuko IchibaResearch & Development Headquarters, Strategy Management Department, Lion Corporation, Tokyo, Japan.
Yasushi KakizawaResearch & Development Headquarters, Advanced Analytical Science Research Laboratories, Lion Corporation, Tokyo, Japan.ORCID 0009-0001-7830-1669
Yuki SugiuraCenter for Cancer Immunotherapy and Immunobiology, Kyoto University Graduate School of Medicine, Kyoto, Japan. yuki.sgi@gmail.com.ORCID 0000-0002-6983-8958

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Menstrual pain affects women's quality of life and productivity, yet objective molecular markers for its severity have not been established owing to the variability in blood levels and chemical properties of potential markers such as plasma steroid hormones, lipid mediators, and hydrophilic metabolites. To address this, we conducted a metabolomics study using five analytical methods to identify biomarkers that differentiate menstrual pain severity. This study included 20 women, divided into mild (N = 12) and severe (N = 8) pain groups based on their numerical pain rating scale. We developed pretreatment procedures that allowed all analyses from only 100 µL of finger-prick blood collected across the menstrual cycle. Among the 692 quantified metabolites, branched-chain amino acids and specific phosphatidylinositol (PI), especially PI(36:2), were identified as potential biomarkers. Furthermore, the ratio of PI(36:2) to each BCAA or total BCAA effectively discriminated between the severity levels of menstrual pain. These ratios correlated positively with NPRS, indicating high accuracy in pain assessment. This study highlights the potential of small molecular markers to objectively assess menstrual pain severity, aiding evidence-based support and intervention.

Indexed as

Amino Acids, Branched-ChainDysmenorrheaPhosphatidylinositolsAdultBiomarkersFemaleHumansMetabolomicsSeverity of Illness IndexYoung AdultAmino Acids, Branched-ChainBiomarkersPhosphatidylinositols

Identifiers

PMID39863658
PMCPMC11762980

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LicenceCC BY-NC-ND
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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.