Evidence map›Paper›PMID 38826420›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Causes and consequences of major depressive disorder: An encompassing Mendelian randomization study.

Joëlle A Pasman, Jacob Bergstedt, Arvid Harder, Tong Gong, Ying Xiong, Sara Hägg, Fang Fang, Jorien L Treur, Karmel W Choi, Patrick F Sullivan and 1 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Joëlle A PasmanDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.ORCID 0000-0001-7558-0482
Jacob BergstedtDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.ORCID 0000-0002-9279-6936
Arvid HarderDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.
Tong GongDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.
Ying XiongDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.
Sara HäggDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.
Fang FangInstitute of Environmental Medicine, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.ORCID 0000-0002-3310-6456
Jorien L TreurAmsterdam UMC location University of Amsterdam, Department of Psychiatry, Genetic Epidemiology, Meibergdreef 9, Amsterdam, The Netherlands.
Karmel W ChoiDepartment of Psychiatry, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-3914-2431
Patrick F SullivanDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.
Yi LuDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels Väg 12, Stockholm, Sweden.ORCID 0000-0001-9933-3654

Funding

A Trans-Nordic Study of Extreme Major DepressionR01MH123724 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI PATRICK F SULLIVAN, Lu Yi · 2020 to 2026
$4.7M
1/7 PGC: Advancing Discovery and ImpactR01MH124871 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BULIK, CYNTHIA M, SULLIVAN, PATRICK F · 2021 to 2025
$3.5M
2/2 Genetics at an extreme: an efficient genomic study of individuals with clinically severe major depression receiving ECTR01MH121545 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SULLIVAN, PATRICK F · 2019 to 2023
$2.9M
Integrating Health Records, Genomic, and Social Data to Stratify Adolescent Depression RiskK08MH127413 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI CHOI, KARMEL · 2021 to 2025
$987k
NIMH NIH HHS K08 MH127413NIMH NIH HHS R01 MH121545NIMH NIH HHS R01 MH123724NIMH NIH HHS R01 MH124871
6 · The paper itself

Abstract

Background: Major depressive disorder (MDD) is a prevalent and debilitating disorder that has been associated with a range of risk factors and outcomes. Causal pathways between MDD and other traits can be studied using genetic variants as instrumental variables. Methods: A literature review was conducted to identify 201 MDD-associated traits. For 115 traits, there were well-powered genome-wide association study (GWAS) results available that could be used to assess the genetic correlation with MDD. Of these, there were 89 meeting criteria for investigating causal associations in both directions using two-sample Mendelian randomization (TSMR). Of the traits that were not captured by GWAS, 43 could be included as outcomes of MDD using one-sample MR (OSMR). A range of methods and sensitivity tests was applied to gauge robustness of results, together with statistical power analyses to aid interpretation. Outcomes: Moderate to strong genetic overlap was found between MDD and most traits. Support for causal effects of MDD liability were found for circadian, cognitive, diet, medical disease, endocrine, functional, inflammatory, metabolic, mortality, physical activity, reproduction, risk behavior, social, socioeconomic, and suicide outcomes. Most associations were bidirectional, although there was less evidence for diet, disease, and endocrine traits causing MDD risk. Results were robust across sensitivity analyses. Interpretation: This study provides a systematic overview of traits putatively causally related to MDD, confirming previous findings as well as identifying new associations. Our results highlight the importance of MDD as a risk factor cross-cutting across medical, functional, and psychosocial domains and emphasize the need for concerted efforts at reducing this highly prevalent disorder.

Identifiers

PMID38826420
PMCPMC11142270

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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.