{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:36:58Z","timestamp":1767339418090,"version":"3.40.4"},"reference-count":42,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"51","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>Recent studies have explored the impact of personality traits, including mood swings, on physical health. However, it remains unclear whether there is a direct cause-and-effect link between mood swings and cardiovascular diseases (CVDs). A STROBE-compliant cross-sectional observational study was conducted and analyzed using a two-sample Mendelian randomization (MR) approach to examine the potential causal relationship between mood swings and a range of CVDs, such as arrhythmia, artery aneurysm, coronary heart disease (CHD), heart failure, hypertension, stroke, ischemic stroke, and peripheral artery disease. We sourced genome-wide association studies (GWAS) summary data for mood swings from the UK Biobank, and for CVDs from the GWAS Catalog and FinnGen databases. We excluded single-nucleotide polymorphisms (SNPs) linked to potential confounders such as obesity, smoking, sex, diabetes, as well as SNPs suspected of horizontal pleiotropy, as identified by MR-PRESSO and the MR-pleiotropy method, prior to the final analysis. Sensitivity analyses were conducted using the MR-Egger, inverse variance weighted, and leave-one-out methods. After screening, 57 SNPs were identified as instrumental variables for mood swings, and 9 SNPs related to confounding factors were excluded. An increase in mood swing frequency is correlated with a significant increase in the likelihood of various conditions. Notably, arrhythmia in the FinnGen dataset showed an odds ratio (OR: 2.28, 95% confidence interval [CI]: 1.44\u20133.61, <jats:italic toggle=\"yes\">P<\/jats:italic>\u2005&lt;\u2005.001), and atrial fibrillation had an OR (OR: 2.25, 95% CI: 1.23\u20134.11, <jats:italic toggle=\"yes\">P<\/jats:italic>\u2005=\u2005.01). CHD risk was elevated in both the IEU OpenGWAS project (OR: 2.05, 95% CI: 1.30\u20133.21, <jats:italic toggle=\"yes\">P<\/jats:italic>\u2005&lt;\u2005.001) and GWAS Catalog (OR: 4.45, 95% CI: 1.75\u201311.33, <jats:italic toggle=\"yes\">P<\/jats:italic>\u2005&lt;\u2005.001). Increased risks were also noted for heart failure (GWAS Catalog: OR: 1.75, 95% CI: 1.09\u20132.83, <jats:italic toggle=\"yes\">P<\/jats:italic>\u2005=\u2005.02) and hypertension (FinnGen: OR, 2.17; 95% CI: 1.47\u20133.19, <jats:italic toggle=\"yes\">P<\/jats:italic>\u2005&lt;\u2005.001). However, no significant associations were found for conditions such as arterial aneurysms or ischemic stroke. In combined analyses, mood swings were associated with a higher risk of CHD (OR: 2.21, 95% CI: 1.64\u20132.97, <jats:italic toggle=\"yes\">P<\/jats:italic>\u2005&lt;\u2005.01), heart failure (OR: 1.74, 95% CI: 1.21\u20132.50, <jats:italic toggle=\"yes\">P<\/jats:italic>\u2005&lt;\u2005.01), and other CVDs. This study revealed a causal link between mood swings and various CVDs, highlighting intriguing findings. This suggests that implementing proper psychological interventions to stabilize mood may be beneficial for preventing negative cardiovascular events.<\/jats:p>","DOI":"10.1097\/md.0000000000041003","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T20:02:28Z","timestamp":1734724948000},"page":"e41003","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["The adverse effect of mood swings on the risk of cardiovascular diseases: Evidence from Mendelian randomization analysis"],"prefix":"10.1097","volume":"103","author":[{"given":"Yuanhui","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Center, The First Affiliated Hospital, Key Laboratory of High Incidence Disease Research in Xinjiang, Ministry of Education, Xinjiang Medical University, Urumqi, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1807-8773","authenticated-orcid":false,"given":"Yuqi","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Center, The First Affiliated Hospital, Key Laboratory of High Incidence Disease Research in Xinjiang, Ministry of Education, Xinjiang Medical University, Urumqi, China."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"276","reference":[{"key":"R1-20250503","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990-2019: Update From the GBD 2019 Study.","volume":"76","author":"Roth","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"R2-20250503","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/nrcardio.2016.179","article-title":"Peripheral artery disease: epidemiology and global 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