Evidence map›Paper›PMID 39351127›Full record

ReviewReproductive medicine and biology

Recent progress in metabolomics for analyzing common infertility conditions that affect ovarian function.

Hiroshi Kobayashi, Shogo Imanaka

Abstract readReview
In one paragraph

Review in Reproductive medicine and biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

12 citing papers in PubMed.

  1. Article
  2. Review
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  4. Article
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  6. Article
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  8. Article
  9. Review
  10. Mitochondrial DNA Damage and Its Repair Mechanisms in Aging Oocytes.International journal of molecular sciences · 2024
    Review
  11. Review
  12. 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

2 authors.

Hiroshi KobayashiDepartment of Gynecology and Reproductive Medicine Ms.Clinic MayOne Kashihara Japan.ORCID https://orcid.org/0000-0002-8124-6269
Shogo ImanakaDepartment of Gynecology and Reproductive Medicine Ms.Clinic MayOne Kashihara Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Numerous efforts have been undertaken to identify biomarkers associated with embryo and oocyte quality to improve the success rate of in vitro fertilization. Metabolomics has gained traction for its ability to detect dynamic biological changes in real time and provide comprehensive metabolite profiles. This review synthesizes the most recent findings on metabolomic analysis of follicular fluid (FF) in clinical conditions leading to infertility, with a focus on the dynamics of energy metabolism and oocyte quality, and discusses future research directions. Methods: A literature search was conducted without time constraints. Main findings: The metabolites present in FF originate from five primary pathways: glycolysis, oxidative phosphorylation, lipid metabolism and β-oxidation, nucleic acid synthesis, and ketogenesis. Metabolomic profiling can broadly categorize infertile women into two groups: those with infertility due to aging and endometriosis, and those with infertility associated with polycystic ovarian syndrome and obesity. In the former group, glycolysis and lipid metabolism are upregulated to compensate for mitochondrial dysfunction, whereas the latter group exhibits the opposite trend. Assessing the levels of glucose, pyruvate, lactate, and plasmalogens in FF may be valuable for evaluating oocyte quality. Conclusion: Metabolomic analysis, particularly focusing on energy metabolism in FF, holds promise for predicting female reproductive outcomes.

Indexed as

glycolysisinfertilitylipid metabolismmetabolomicsoxidative phosphorylation

Identifiers

PMID39351127
PMCPMC11442066

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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.