Evidence map›Paper›PMID 33154644›Full record

ArticleNeuropsychiatric disease and treatment2020

Association of Serum Kynurenine Levels and Neural Networks in Patients with First-Episode, Drug-Naïve Major Depression: A Source-Based Morphometry Study.

Naomichi Okamoto, Keita Watanabe, LeHoa Ngyuyen, Atsuko Ikenouchi, Taro Kishi, Nakao Iwata, Shingo Kakeda, Yukunori Korogi, Reiji Yoshimura

Abstract read
In one paragraph

Article in Neuropsychiatric disease and treatment, 2020. 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

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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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Naomichi OkamotoDepartment of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan.ORCID 0000-0003-2791-8113
Keita WatanabeOpen Innovation Institute, Kyoto University, Kyoto, Japan.
LeHoa NgyuyenDepartment of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan.
Atsuko IkenouchiDepartment of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan.ORCID 0000-0001-8328-4608
Taro KishiDepartment of Psychiatry, Fujita Medical University, Toyoake, Japan.ORCID 0000-0002-9237-2236
Nakao IwataDepartment of Psychiatry, Fujita Medical University, Toyoake, Japan.ORCID 0000-0003-3189-6076
Shingo KakedaDepartment of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan.
Yukunori KorogiDepartment of Radiology, University of Occupational and Environmental Health, Kitakyushu, Japan.
Reiji YoshimuraDepartment of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe kynurenine (KYN) pathway can directly or indirectly influence cerebral volume and neural integrity in patients with major depression (MD). The aim of the present study was to investigate neural network systems and the KYN pathway in patients with first-episode, drug-naïve MD. PATIENTS AND

methodsTwenty right-handed drug-naïve patients, with MD diagnosed using the Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition, Text Revision, Research Version, were included in this study. Magnetic resonance imaging scans and scores on the Hamilton Rating Scale for Depression were assessed, and serum sampling was performed prior to the start of pharmacological treatment. Image processing and data analysis were performed according to our recently published procedure. Serum metabolomes were measured in the cation and anion modes of CE-FTMS-based metabolome analysis.

resultsWe found that serum KYN levels were positively correlated with the Z-scores of the salience network but not with those of the central executive network or default mode network. No associations were observed between serum glutamate levels and the Z-score of any of the three networks.

conclusionOur results indicate that serum KYN levels might affect the activity of the salience network in first-episode, drug-naïve patients with MD.

Indexed as

kynureninemajor depressionneural networksource-based morphometry

Identifiers

PMID33154644
PMCPMC7605945

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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.