Loneliness and depression: bidirectional mendelian randomization analyses using data from three large genome-wide association studies
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Author
Sbarra, David A.Ramadan, Ferris A.
Choi, Karmel W.
Treur, Jorien L.
Levey, Daniel F.
Wootton, Robyn E.
Stein, Murray B.
Gelernter, Joel
Klimentidis, Yann C.
Affiliation
Department of Psychology, University of ArizonaMel & Enid Zuckerman College of Public Health, University of Arizona
BIO5 Institute, University of Arizona
Issue Date
2023-09-21
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Springer Science and Business Media LLCCitation
Sbarra, D. A., Ramadan, F. A., Choi, K. W., Treur, J. L., Levey, D. F., Wootton, R. E., ... & Klimentidis, Y. C. (2023). Loneliness and depression: bidirectional mendelian randomization analyses using data from three large genome-wide association studies. Molecular psychiatry, 1-8.Journal
Molecular PsychiatryRights
© 2023, The Author(s), under exclusive licence to Springer Nature Limited.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Major depression (MD) is a serious psychiatric illness afflicting nearly 5% of the world’s population. A large correlational literature suggests that loneliness is a prospective risk factor for MD; correlational assocations of this nature may be confounded for a variety of reasons. This report uses Mendelian Randomization (MR) to examine potentially causal associations between loneliness and MD. We report on analyses using summary statistics from three large genome wide association studies (GWAS). MR analyses were conducted using three independent sources of GWAS summary statistics. In the first set of analyses, we used available summary statistics from an extant GWAS of loneliness to predict MD risk. We used two sources of outcome data: the Psychiatric Genomics Consortium (PGC) meta-analysis of MD (PGC-MD; N = 142,646) and the Million Veteran Program (MVP-MD; N = 250,215). Finally, we reversed analyses using data from the MVP and PGC samples to identify risk variants for MD and used loneliness outcome data from UK Biobank. We find robust evidence for a bidirectional causal relationship between loneliness and MD, including between loneliness, depression cases status, and a continuous measure of depressive symptoms. The estimates remained significant across several sensitivity analyses, including models that account for horizontal pleiotropy. This paper provides the first genetically-informed evidence that reducing loneliness may play a causal role in decreasing risk for depressive illness, and these findings support efforts to reduce loneliness in order to prevent or ameliorate MD. Discussion focuses on the public health significance of these findings, especially in light of the SARS-CoV-2 pandemic.Note
6 month embargo; first published: 21 September 2023ISSN
1359-4184EISSN
1476-5578PubMed ID
37735503Version
Final accepted manuscriptSponsors
U.S. Department of Health & Human Services | NIH | National Institute on Agingae974a485f413a2113503eed53cd6c53
10.1038/s41380-023-02259-w
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