Evidence map›Paper›PMID 31455063›Full record

ArticlePsychiatry investigation2019

Epigenetics and Depression: An Update.

Eugene Lin, Shih-Jen Tsai

Open access · diamondAbstract read
In one paragraph

Article in Psychiatry investigation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
3.3field-weighted citation impact, top 7% 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.

  1. Pooled it
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  9. MiR-182-5p: A Novel Biomarker in the Treatment of Depression in CSDS-Induced Mice.The international journal of neuropsychopharmacology · 2024
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  12. Adverse Childhood Experiences and Mental Health among Students Seeking Psychological Counseling Services.International journal of environmental research and public health · 2023
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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

2 authors at 2 institutions in 2 countries.

Eugene LinDepartment of Biostatistics, University of Washington, Seattle, WA , USA.
Shih-Jen TsaiDepartment of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.
National Yang Ming Chiao Tung University · TWUniversity of Washington · US

Funding

Ministry of Science and Technology, Taiwan 107-2634-F-075-002Taipei Veterans General Hospital V105D17-002-MY2-2
6 · The paper itself

Abstract

objectiveDepression is associated with various environmental risk factors such as stress, childhood maltreatment experiences, and stressful life events. Current approaches to assess the pathophysiology of depression, such as epigenetics and gene-environment (GxE) interactions, have been widely leveraged to determine plausible markers, genes, and variants for the risk of developing depression.

methodsWe focus on the most recent developments for genomic research in epigenetics and GxE interactions.

resultsIn this review, we first survey a variety of association studies regarding depression with consideration of GxE interactions. We then illustrate evidence of epigenetic mechanisms such as DNA methylation, microRNAs, and histone modifications to influence depression in terms of animal models and human studies. Finally, we highlight their limitations and future directions.

conclusionIn light of emerging technologies in artificial intelligence and machine learning, future research in epigenetics and GxE interactions promises to achieve novel innovations that may lead to disease prevention and future potential therapeutic treatments for depression.

Indexed as

BiomarkersDepressionEpigeneticsGene-environment interactionsStress

Identifiers

PMID31455063
PMCPMC6761788
OpenAlexW2970586921

What OpenQuestion holds

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LicenceCC BY-NC
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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.