Editorial: Mood Instability: What It Is, Why It Matters, and What to Do About It

Psychological distress and uterine fibroids: a bidirectional two-sample mendelian randomization study

BACKGROUND

Observational data indicates a connection between emotional discomfort, such as anxiety and depression, and uterine fibroids (UFs). However, additional investigation is required to establish the causal relationship between them. Hence, we assessed the reciprocal causality between four psychological disorders and UFs utilizing two-sample Mendelian randomization (MR).

METHODS

To evaluate the causal relationship between four types of psychological distress (depressive symptoms, severe depression, anxiety or panic attacks, mood swings) and UFs, bidirectional two-sample MR was employed, utilizing single nucleotide polymorphisms (SNPs) associated with these conditions. Both univariate MR (UVMR) and multivariate MR (MVMR) primarily applied inverse variance weighted (IVW) as the method for estimating potential causal effects. Complementary approaches such as MR Egger, weighted median, simple mode, and weighted mode were utilized to validate the findings. To assess the robustness of our MR results, we conducted sensitivity analyses using Cochran's Q-test and the MR Egger intercept test.

RESULTS

The results of our UVMR analysis suggest that genetic predispositions to depressive symptoms (Odds Ratio [OR] = 1.563, 95% Confidence Interval [CI] = 1.209-2.021, P = 0.001) and major depressive disorder (MDD) (OR = 1.176, 95% CI = 1.044-1.324, P = 0.007) are associated with an increased risk of UFs. Moreover, the IVW model showed a nominally significant positive correlation between mood swings (OR: 1.578; 95% CI: 1.062-2.345; P = 0.024) and UFs risk. However, our analysis did not establish a causal relationship between UFs and the four types of psychological distress. Even after adjusting for confounders like body mass index (BMI), smoking, alcohol consumption, and number of live births in the MVMR, the causal link between MDD and UFs remained significant (OR = 1.217, 95% CI = 1.039-1.425, P = 0.015).

CONCLUSIONS

Our study presents evidence supporting the causal relationship between genetic susceptibility to MDD and the incidence of UFs. These findings highlight the significance of addressing psychological health issues, particularly depression, in both the prevention and treatment of UFs.

Genetically predicted mood swings increased risk of cardiovascular disease: Evidence from a Mendelian randomization analysis

BACKGROUND

Mood swings is linked to a higher risk of cardiovascular diseases (CVDs). However, the causal relationships between them remain unknown.

METHODS

We conducted this Mendelian randomization (MR) analysis to evaluate the causal associations between mood swings (n = 373,733) and 5 CVDs, including CAD, MI, HF, AF, and stroke using summary data of large-scale genome-wide association studies (GWAS). FinnGen datasets validated the results. Various MR approaches, sensitivity analyses, multivariable MR (MVMR), and two-step MR mediation analyses were applied.

RESULTS

The MR analysis revealed significant causal effects of mood swings on CAD (OR = 1.45, 95 % CI 1.24-1.71; P = 5.52e-6), MI (OR = 1.60, 95 % CI 1.32-1.95; P = 1.77e-6), HF (OR = 1.42, 95 % CI 1.18-1.71; P = 2.32e-4), and stroke (OR = 1.48, 95 % CI 1.19-1.83; P = 3.46e-4), excluding AF (P = 0.16). In the reverse MR analysis, no causal relationships were observed. The results were reproducible using FinnGen data. In the MVMR analysis, the causal effects of mood swings on CAD, MI, HF and stroke still remain significant after adjusting potential confounding factors including BMI, smoking and T2DM, but not for LDL and hypertension. Further mediation analysis indicated hypertension may mediate the causal pathways from mood swings to CAD (18.11 %, 95 % CI: 8.83 %-27.39 %), MI (16.40 %, 95 % CI: 7.93 %-24.87 %), HF (13.06 %, 95 % CI: 6.25 %-19.86 %), and stroke (18.04 %, 95 % CI: 8.73 %-27.34 %).

CONCLUSION

Mood swings has a significant causal impact on the development of CAD, MI, HF, and stroke, partly mediated by hypertension.

A cognitive-computational account of mood swings in adolescence

Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

Mood instability and low back pain: a mendelian randomization study

Objective

Low back pain is a prevalent and debilitating condition worldwide, with significant implications for individuals' quality of life and productivity. The aim of this study was to assess the relationship between mood instability and the risk of developing chronic low back pain, using a rigorously designed mendelian randomization methodology.

Method

The study incorporated both univariate and multivariate mendelian randomization to analysis the causal relationship between mood instability and the risk of developing chronic low back pain. The data on mood instability from the Integrative Epidemiology Unit (IEU) opened Genome-Wide Association Studies (GWAS) project (IEU-opened GWAS project). Data on low back pain were collected from two sources: One source is the IEU open GWAS project (discovery data). Another source is a GWAS meta-analysis (replication data). Inverse variance weighted method, weighted median method, MR-Egger regression, and mendelian randomization pleiotropy residual sum and outlier method were used for mendelian randomization analysis.

Result

The univariable mendelian randomization analysis shows a statistically significant correlation between mood instability and the risk of low back pain. Several methods were performed, including inverse variance weighting (discovery data: odds ratio = 3.544, 95% confidence interval = 1.785-7.039, p = 0.000; replication data: odds ratio = 3.167, 95% confidence interval = 2.476-4.052, p = 0.000), MR-Egger (discovery data: odds ratio = 7.178, 95% confidence interval = 0.057-909.525, p = 0.429; replication data: odds ratio = 2.262, 95% confidence interval = 0.580-8.825, p = 0.246), weighted median (discovery data: odds ratio = 2.730, 95% confidence interval = 1.112-6.702, p = 0.028; replication data: odds ratio = 3.243, 95% confidence interval = 2.378-4.422, p = 0.000), MR-PRESSO (discovery data: odds ratio = 3.544, 95% confidence interval = 1.785-7.039, p = 0.001; replication data: odds ratio = 3.167, 95% confidence interval = 2.476-4.052, p = 0.000) methods. The results were consistent across these methods. The results obtained from discovery data are consistent with those obtained from discovery data. In the multivariable mendelian randomization, after adjusting for various covariates such as body mass index, current tobacco smoking, alcohol intake frequency, Total body bone mineral density, and vigorous physical activity, there is a consistent correlation between mood instability and chronic low back pain.

Conclusion

This study provides robust evidence supporting a causal relationship between mood instability and the development of low back pain. Our findings suggest that addressing mood instability may play a crucial role in prevention and management strategies for individuals experiencing low back pain.

Causal relationships between mood instability and autoimmune diseases: A mendelian randomization analysis

OBJECTIVES

The interrelationship between mental health and autoimmunity gains more and more attention in recent years. However, the causality between personality traits and autoimmune diseases remained largely unknown.

METHODS

We first conducted two-sample mendelian randomization (MR) analysis on the relationships between mood instability, which is a common personality trait in the general population, and 10 autoimmune diseases. The results were further validated in secondary analyses of sensitivity where different MR methods, ethnicities, genders, and ascertainment methods were compared.

RESULTS

In the primary analyses, three autoimmune diseases showed genetical predisposition to mood instability: asthma (OR [95%CI] = 3.45 [2.48, 4.78], P = 1.33E- 13), hypothyroidism (OR [95%CI] = 1.02 [1.00, 1.03], P = 1.71E-02), and systemic lupus erythematosus (OR [95%CI] = 5.25 [1.21, 22.76], P = 2.67E-02). The results were consistent in subsequent secondary analyses. Three diseases remained significantly correlated with mood instability by different MR methods with asthma remaining significant in Finnish and mixed populations, and in females from the UK biobank, while hypothyroidism remained significant in both genders from the UK biobank.

CONCLUSION

Mood instability is a modifiable risk factor for three autoimmune diseases including asthma, hypothyroidism and systemic lupus erythematosus.