Evidence map›Paper›PMID 30374314›Full record

ArticleFrontiers in psychiatry2018

Lipidomics in Major Depressive Disorder.

Andreas Walther, Carlo Vittorio Cannistraci, Kai Simons, Claudio Durán, Mathias J Gerl, Susanne Wehrli, Clemens Kirschbaum

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 2 pooled it
–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

46 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  9. [Role of dysregulation brain lipid metabolism in depression].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025
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  19. LC-HRMS Lipidomic Fingerprints in Serbian Cohort of Schizophrenia Patients.International journal of molecular sciences · 2024
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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

7 authors.

Andreas WaltherBiological Psychology, TU Dresden, Dresden Germany.
Carlo Vittorio CannistraciBiomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, TU Dresden, Dresden, Germany.
Kai SimonsLipotype GmbH, Dresden, Germany.
Claudio DuránBiomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, TU Dresden, Dresden, Germany.
Mathias J GerlLipotype GmbH, Dresden, Germany.
Susanne WehrliBiological Psychology, TU Dresden, Dresden Germany.
Clemens KirschbaumBiological Psychology, TU Dresden, Dresden Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Omic sciences coupled with novel computational approaches such as machine intelligence offer completely new approaches to major depressive disorder (MDD) research. The complexity of MDD's pathophysiology is being integrated into studies examining MDD's biology within the omic fields. Lipidomics, as a late-comer among other omic fields, is increasingly being recognized in psychiatric research because it has allowed the investigation of global lipid perturbations in patients suffering from MDD and indicated a crucial role of specific patterns of lipid alterations in the development and progression of MDD. Combinatorial lipid-markers with high classification power are being developed in order to assist MDD diagnosis, while rodent models of depression reveal lipidome changes and thereby unveil novel treatment targets for depression. In this systematic review, we provide an overview of current breakthroughs and future trends in the field of lipidomics in MDD research and thereby paving the way for precision medicine in MDD.

Indexed as

computational psychiatrycortisoldepressioninflammationlipidomicsmachine learningmajor depressive disorderprostaglandin

Identifiers

PMID30374314
PMCPMC6196281

What OpenQuestion holds

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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.