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Abstract

Bipolar disorder (BD) is a prevalent psychiatric condition characterized by extreme mood fluctuations between manic and depressive episodes, significantly affecting social and occupational functioning. The etiology of BD is multifactorial, involving genetic, environmental, and lifestyle factors. While previous research has focused on the genetic and environmental contributors to BD, the role of physical activity as a modifiable lifestyle factor remains underexplored. This study investigates the causal relationship between different types of physical activity, particularly heavy do-it-yourself (DIY) activities, and BD using Mendelian randomization (MR). The study employs MR to examine the causal link between physical activity and BD. Genetic variants associated with various forms of physical activity were selected from large-scale genome-wide association studies. The study uses several MR techniques, including inverse variance weighting (IVW), MR-Egger, and weighted median methods, to analyze the relationship between physical activity (e.g., heavy DIY, light DIY, vigorous exercise, and walking) and BD. Instrumental variables were chosen based on their strong association with physical activity and their independence from other potential confounders. The MR analysis revealed a significant causal relationship between heavy DIY activities and reduced BD risk (OR = 0.333; 95% CI [0.111, 0.997]; p = .049). In contrast, no significant causal associations were found for the other types of physical activity examined. The IVW method indicated significant heterogeneity, prompting the use of a random-effects model, which confirmed that the results were not biased by heterogeneity or pleiotropy. Sensitivity analyses, including MR-Egger and MR-PRESSO, showed no significant pleiotropy, reinforcing the reliability of the findings. Leave-One-Out analysis and funnel plots further supported the robustness of the causal estimate. This study provides compelling evidence for the protective role of heavy DIY activities in reducing the risk of BD, suggesting that high-intensity physical activities may have a beneficial impact on mood regulation and the prevention of BD. The findings highlight the importance of considering gender differences in physical activity interventions for BD prevention and management. Future research should explore the neurobiological mechanisms underlying this association and further investigate the effectiveness of different types of physical activities in BD prevention and treatment strategies.

Keywords

bipolar disorder heavy DIY activities gender differences stress urinary incontinence

Introduction

Bipolar disorder (BD) is a chronic psychiatric condition characterized by extreme mood fluctuations, including manic (or hypomanic) and depressive episodes (Bauer, 2022; Scott & McClung, 2023). Globally, the prevalence of BD is approximately 1% to 2%, rendering it one of the most common psychiatric disorders. Patients typically experience alternating periods of mania and depression, with these mood swings exerting a significant negative impact on social functioning, work productivity, and daily living activities (Lane & Smith, 2023; Malhi et al., 2023; Nierenberg et al., 2023). The etiology of BD is multifactorial, involving genetic predisposition, environmental influences, and neurobiological mechanisms. Genetic studies have demonstrated a substantial heritable component to BD; however, the contributions of environmental factors, lifestyle choices, and physical activity should not be overlooked. In recent years, an increasing number of studies have highlighted that physical activity not only improves physical health but may also exert beneficial effects on mental health, particularly in the prevention and management of BD (Gabriel et al., 2023; Retamal et al., 2023; Robinson et al., 2023).

Sex differences play a critical role in the prevalence, clinical presentation, and progression of bipolar disorder. Epidemiological studies have revealed significant disparities between males and females in terms of clinical manifestations, incidence, responsiveness, disease course, and treatment outcomes (Colic et al., 2022; Hu et al., 2023; Liu et al., 2022). Males are more likely to exhibit pronounced manic symptoms, whereas females tend to experience depressive episodes more frequently. These gender differences may be attributable to a combination of physiological factors, social roles, and mechanisms of emotional regulation. Notably, males generally engage in higher levels of physical activity and are more inclined toward high-intensity activities, such as heavy do-it-yourself (DIY) tasks, which may be associated with the elevated incidence of BD observed in this population (Y. Kim et al., 2019; J. Kim et al., 2023).

In recent years, Mendelian randomization (MR) has gained widespread application as a method for causal inference (Ding et al., 2024; Li, Huang, et al., 2024; Li, Peng, et al., 2024). Unlike traditional observational studies, MR leverages the natural randomization of genetic variants to help eliminate confounding factors, thereby providing a more robust evidence base for elucidating causal relationships between lifestyle factors, such as physical activity, and diseases (Levin & Burgess, 2024; Sekula et al., 2016). Specifically, MR allows for the examination of genetic variants associated with physical activity to infer whether physical activity directly influences the risk of developing BD, rather than merely exhibiting a correlation.

This study will focus on the relationship between various types of physical activity (e.g., heavy DIY, light DIY, and intense exercise) and BD. Different forms of activity may exert distinct effects on mental health, and understanding these differences is crucial for further exploring the potential of physical activity in the management of BD. Using MR methods, we aim to investigate the potential causal link between physical activity and BD by analyzing genetic data. With large-scale genome-wide association study (GWAS) data from the UK Biobank, we seek to define the impact of physical activity on BD through scientifically validated genetic instrumental variables, providing a basis for evidence-based prevention and treatment strategies.

Study Design

This study employs MR to explore the causal relationship between physical activity (including heavy DIY activities, light DIY activities, vigorous exercise, and recreational walking) and BD. MR utilizes genetic variants as instrumental variables, which helps mitigate confounding biases and reverse causality issues inherent in traditional observational studies, thus providing more reliable causal inferences.

Data Sources

We selected single-nucleotide polymorphisms (SNPs) from published GWAS that are significantly associated with various types of physical activity (heavy DIY, light DIY, vigorous exercise, recreational walking). Summary statistics for these physical activities were extracted from the publicly available online database (IEU OpenGWAS project, https://gwas.mrcieu.ac.uk/). We used five distinct physical activity intensities to investigate their causal relationship with BD: heavy DIY (197,006 cases, 263,370 controls), light DIY (236,244 cases, 224,132 controls), vigorous exercise (47,468 cases, 412,908 controls), walking (329,755 cases, 130,621 controls), and other activities (222,470 cases, 237,906 controls). In this study, heavy DIY refers to activities classified as ≥4 METs on the UK Biobank Accelerometer Questionnaire, including lifting and carrying heavy loads, gardening work, and house repairs. These activities typically involve a significant amount of physical labor and meet the criteria for high-intensity physical activity.

The statistical data for BD also come from GWAS databases, encompassing large-scale data from both patients and control groups. The data include information on the incidence, epidemiological characteristics, and other relevant aspects of BD, sourced from multiple independent GWAS studies to ensure both breadth and reliability of the data.

To minimize the impact of potential confounders on our study results, we explicitly identified factors that might be associated with both physical activity and BD. These included genetic predisposition to psychiatric conditions, medication use history, and other socioeconomic variables. By carefully selecting SNPs unrelated to these confounders, we ensured that the chosen SNPs influence BD only through the physical activity pathway.

Selection of Instrumental Variables

In this study, we carefully selected SNPs that are significantly associated with physical activity to serve as instrumental variables, ensuring the validity and reliability of the causal inferences. The selection of instrumental variables followed strict criteria, including the following: (a) Relevance: The selected instrumental variables must be strongly associated with physical activity, with a statistical significance level of p <5 × 10⁻⁸. This criterion ensures that the instrumental variables are sufficiently correlated with the exposure (i.e., physical activity), providing the necessary explanatory power to infer causal relationships. (b) Independence: To avoid confounding due to genetic correlations, the chosen instrumental variables must be genetically independent. This means that the linkage disequilibrium (LD) between the instrumental variables should be below r ² < .01, ensuring that each instrumental variable provides independent genetic information and avoids bias due to genetic population structure or correlations. (c) Exclusion of pleiotropy: We prioritized excluding SNPs that might affect the outcome through multiple pathways. We consulted the latest BD GWAS results and excluded all SNPs with genome-wide significant associations with bipolar disorder (p <5 × 10⁻⁸) that could influence BD through pathways other than physical activity. Ideally, the instrumental variables should influence BD solely through the physical activity pathway (Birney, 2022; Yeung et al., 2025). Consequently, SNPs with pleiotropic effects—that is, those influencing BD through other biological mechanisms—were excluded, as they would undermine the reliability of causal inferences. According to the assumptions of MR, the SNPs used as instrumental variables must be strongly related to the exposure variable (physical activity).To assess the effectiveness of the instrumental variables, we used the F-statistic (F = R ² (N − k − 1)/k (1 − R ²)), where N is the sample size, k is the number of instrumental variables, and R ² represents the variance in the exposure explained by the instrumental variables. We consider that when the F-statistic is significantly greater than 10, the bias from weak instrumental variables can be excluded, thus ensuring that the selected instruments have sufficient power for causal inference.

Through the process described above, we minimized the interference of confounders and multiple effects on causal inferences and ensured that the effects of the selected instrumental variables were derived solely from the effects of physical activity on BD, thereby increasing the validity and confidence of the Mendelian randomization analyses.

MR Estimation Methods

To assess the causal relationship between physical activity and BD, this study employs a range of MR methods to ensure the robustness and reliability of the results. Specifically, the methods employed included inverse variance weighting (IVW), MR-Egger regression, weighted median (WM), simple mode, and weighted mode, with the IVW method being the primary analytical technique. Each method has its unique advantages in estimating causal effects and can complement each other, providing a comprehensive evaluation of the causal relationship.

IVW Method

The IVW method estimates the causal effect between physical activity and BD by calculating the weighted average of the effects of each SNP. The weighting is based on the inverse of each SNP’s standard error, meaning that SNPs with more precise effect estimates are given more weight. The advantage of this method lies in its ability to make full use of the information from each instrumental variable, thus providing the most precise causal effect estimates. This method is particularly powerful when the selected instrumental variables are valid and there is no pleiotropy.

WM Method

The WM method estimates the causal effect by using the median of the effect sizes from all instrumental variables. Each instrumental variable is weighted according to the precision of its effect estimate. An important advantage of the WM method is its robustness to invalid or pleiotropic instrumental variables. Even if some instruments are weak or affect the outcome through other pathways, the median-based approach still provides reliable causal effect estimates. This robustness makes the WM method particularly useful when the validity of some instrumental variables is questionable. Simple mode, Weighted mode are auxiliary sensitivity methods. Through the combined application of these methods, we ensure the comprehensiveness and robustness of the causal relationship between physical activity and BD. These analytical approaches complement each other and provide multifaceted evidence to reveal the potential causal link between physical activity and BD.

Sensitivity Analysis

To ensure the robustness and reliability of the MR analysis results, this study conducted a series of sensitivity analyses. Cochran’s Q test was used to assess the heterogeneity among the instrumental variables. If significant heterogeneity is present, it may indicate diversity in the effects of the instrumental variables or the presence of uncorrected pleiotropy. A significant Q statistic suggests heterogeneity among the instrumental variables, which may require further examination and correction for pleiotropy (Hoaglin, 2016). The MR-Egger intercept is used to evaluate and adjust for pleiotropy among the instrumental variables. MR-Egger regression detects pleiotropic effects by examining the intercept term; if the intercept significantly deviates from zero, it suggests the presence of pleiotropy. A significant deviation from zero in the intercept term indicates the existence of pleiotropic effects, necessitating consideration of this impact. The MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) method was employed to detect and correct for outliers and pleiotropy in the instrumental variables. This method identifies and removes SNPs with significant pleiotropy, providing a more robust causal effect estimate (Bowden et al., 2016). Leave-One-Out Analysis (LOO) was conducted by sequentially excluding each instrumental variable to assess its influence on the overall causal effect estimate. This method helps to determine whether any single instrumental variable significantly impacts the overall results (Grabowski et al., 2012). Specifically, each time an instrumental variable was excluded, the causal effect estimate was recalculated, and the changes in the results were observed. Funnel plots were used to visually inspect the distribution of effect sizes among the instrumental variables and detect potential biases. By plotting the effect sizes of the instrumental variables against their standard errors, the plot allows for the identification of any bias or heterogeneity. Through the application of these sensitivity analysis methods, this study ensured the robustness and reliability of the MR results, providing a more comprehensive assessment of the causal effects.

Results

Table S1 provides detailed information on the SNPs associated with the five types of physical activity. These SNPs meet the criteria of relevance (p <5 × 10⁻⁸) and independence (LD r ² < .001). The F-statistics for each physical activity SNP are greater than 10, indicating that these instrumental variables are not subject to weak instrument bias.

Causal Impact of Physical Activity on Bipolar Disorder

In the primary MR analysis, five types of physical activity were evaluated for their causal association with BD (Table 1). The IVW analysis indicated that heavy DIY activity was associated with a reduced risk of BD (OR = 0.333; 95% CI [0.111, 0.997]; p = .049) (Figure 1). No causal associations were found for the other four types of physical activity. Cochran’s Q test using the IVW method revealed significant heterogeneity (see Table S1), prompting the use of a random-effects IVW approach for further analysis. Both MR-Egger and MR-PRESSO methods indicated no horizontal pleiotropy (see Table S2), ensuring that the results were not affected by heterogeneity or genetic pleiotropy. Based on the LOO analysis (Figure 2), no SNPs were found to significantly influence the causal effect between heavy DIY and BD. The funnel plot showed overall symmetry (Figure 3), further supporting the robustness of the findings.

Table 1.

MR Results for Association of Physical Activity and BD.

Exposure	Outcome	Method	No. of SNP	p Value	OR	95% CI
Heavy DIY	BD	Weighted median	15	.345	0.571	[0.178, 1.829]
		IVW	15	.049	0.333	[0.111, 0.997]
		Simple mode	15	.994	1.01	[0.082, 12.409]
		Weighted mode	15	.987	0.982	[0.116, 8.313]
Light DIY	BD	Weighted median	9	.034	0.166	[0.032, 0.877]
		IVW	9	.665	0.57	[0.045, 7.234]
		Simple mode	9	.027	0.053	[0.006, 0.444]
		Weighted mode	9	.035	0.11	[0.020, 0.607]
High-intensity exercise	BD	Weighted median	4	.522	3.037	[0.908, 1.064]
		IVW	4	.385	38.852	[0.924, 1.051]
		Simple mode	4	.691	0.087	[0.862, 1.089]
		Weighted mode	4	.425	0.087	[0.872, 1.100]
Leisurely walking	BD	Weighted median	17	.536	0.668	[0.187, 2.393]
		IVW	17	.298	0.493	[0.130, 1.868]
		Simple mode	17	.943	0.907	[0.064, 12.939]
		Weighted mode	17	.951	1.088	[0.075, 15.694]
Other exercises	BD	Weighted median	13	.304	1.927	[0.552, 6.727]
		IVW	13	.066	3.212	[0.927, 11.133]
		Simple mode	13	.142	7.792	[0.601, 101.051]
		Weighted mode	13	.169	7.792	[0.499, 121.564]

Note. DIY = do-it-yourself; IVW = inverse variance weighting; BD = bipolar disorder; MR = Mendelian randomization.

Figure 1.

Scatter plot of the association between heavy DIY activities and bipolar disorder.

Figure 2.

Leave-one-out analysis of the association between heavy DIY activities and bipolar disorder.

Figure 3.

Funnel plots of the association between heavy DIY activities and bipolar disorder.

Discussion

This study, utilizing MR methods, reveals a significant negative association between heavy DIY activities and the risk of developing BD (OR = 0.333; 95% CI [0.111, 0.997]; p = .049). This finding provides compelling evidence for the potential role of physical activity in the prevention of BD, particularly highlighting heavy DIY activities as a form of high-intensity physical activity that may play a crucial role in mitigating BD risk and emotional fluctuations. This result aligns with previous research, suggesting that high-intensity physical activity may improve neurobiological mechanisms, influence emotional regulation, and reduce the incidence of BD. Heavy DIY activities, which involve significant physical exertion, typically require intense muscular work, physical labor, and stress-regulating bodily adjustments. These activities can have beneficial effects on neurotransmitter regulation, reduction of stress hormones, and enhancement of brain plasticity, which may, in turn, help reduce the fluctuations of manic and depressive symptoms.

In the original manuscript, the simple mode method yielded ORs in the opposite direction to IVW. We suggest that this bias may be related to the strong dependence of the simple mode on instrumental variables and the high data heterogeneity. We have verified the consistency of the effect directions of IVW, WM, and MR-Egger methods by MR-PRESSO outlier rejection and LOO analyses, supporting IVW as the main conclusion method. Although bias in simple mode may be caused by unresolved confounders or data heterogeneity, the IVW method still showed robust causal effects.

Although the protective effect of heavy DIY activities on BD has garnered some support, gender differences remain a critical moderating factor in how physical activity influences BD. In this study, while we did not directly analyze the results by gender subgroups, existing literature indicates that there are substantial differences between males and females in the clinical presentation, disease progression, and psychological and physiological responses to BD. Men typically exhibit more pronounced manic symptoms, while women are more likely to experience depressive episodes. This phenomenon may be closely linked to gender differences in emotional regulation mechanisms and the manner in which physical activity is engaged with, highlighting the need for further research into how gender may influence the effects of physical activity on BD (Arnold, 2003; Bayes et al., 2019; Buoli et al., 2019; Diflorio & Jones, 2010).

High-intensity physical activity may reduce the risk of mood swings and BD by modulating the hypothalamic-pituitary-adrenal axis and decreasing the secretion of stress hormones such as cortisol. Recent studies have shown that exercise also promotes neuroplasticity and mood regulation by increasing the expression of brain-derived neurotrophic factor (BDNF; Chan et al., 2024; Zarza-Rebollo et al., 2024). Men generally exhibit greater variability in their participation and choice of physical activity types, particularly in heavy DIY activities, where their frequency and intensity of engagement are typically higher than those of women (Espinoza et al., 2023; Garg, 2023). As a form of high-intensity physical activity, heavy DIY activities can significantly influence the brain’s neurotransmitter systems, including dopamine and serotonin, which are closely related to emotional regulation. Several studies have shown that exercise, particularly high-intensity exercise, can improve mood, alleviate anxiety, and reduce depressive symptoms through modulation of these neurotransmitters (Alizadeh Pahlavani, 2024; Conio et al., 2020; Kandola & Stubbs, 2020; Philippot et al., 2022). Men’s more frequent engagement in heavy DIY activities may lead to greater physiological and neurobiological stimulation, helping alleviate manic symptoms and reducing the risk of BD. In contrast, women are generally more likely to participate in low-to-moderate intensity physical activities, such as walking or yoga. While these activities contribute to physical and mental well-being, their effect in reducing emotional fluctuations and meeting the high-intensity demands required to manage mental health disorders may not be as pronounced as that of men’s high-intensity activities (Feraco et al., 2024).

Gender differences in the effects of physical activity on BD may be influenced by a variety of factors, including social role identification, psychological regulation mechanisms, and hormonal differences. Men are often more inclined to engage in strength training and intense physical activities, behaviors that may correlate with stronger social roles (such as being the primary family provider) and higher psychological endurance and emotional regulation capabilities (Feldman, 2023). Furthermore, gender differences may also be shaped by the sociocultural context of physical activity. Men are culturally encouraged to engage in high-intensity, challenging physical labor, such as heavy DIY activities, while women are typically encouraged to participate in lower-intensity, health-focused activities. This cultural bias may make men’s physical activities more intense and challenging, potentially affecting their psychological health through different mechanisms. Thus, when designing physical activity interventions for different gender groups, it is essential to consider these gender roles and cultural backgrounds (Bowen et al., 2011; Sanchez-Johnsen et al., 2019; Valdez et al., 2019).

This study also suggests that the intensity and diversity of physical activity could be a key factor in BD prevention strategies. Existing literature generally acknowledges the mental health benefits of low-intensity activities such as recreational walking or yoga; however, their effectiveness in reducing the risk of BD may be limited. In contrast, heavy DIY activities, as a more challenging and intense form of activity, may be more effective in regulating neurotransmitters, reducing emotional fluctuations, and managing BD in the long term (Goodarzi et al., 2024; McCartan et al., 2024; Sa Filho et al., 2020). Future research should further explore the role of different intensities and types of physical activity in BD management, with a particular focus on gender-specific intervention strategies.

This study has several strengths. First, the use of MR methods effectively reduced the confounding effects often seen in observational studies, thereby strengthening the inference of causal relationships. Second, the study not only examined the impact of general physical activity on BD but also focused on specific activity types, such as heavy DIY, providing more detailed insights. However, this study also has limitations. First, if the genetic data used predominantly comes from a specific ethnic group or region, the findings may not be generalizable to other populations. Second, while an association between physical activity and BD was established, the precise biological mechanisms remain to be further explored. Future studies should increase the sample size and optimize the selection of instrumental variables to validate and extend the findings of this study.

In conclusion, this study provides robust evidence for the preventive role of physical activity, particularly heavy DIY activities, in BD, while emphasizing the critical role of gender differences in the impact of physical activity on BD. Gender-specific intervention strategies should take into account the differing levels of participation, activity intensity, and emotional regulation responses between men and women. Future research should further explore the effects of different types of physical activity across gender groups, with the goal of offering more precise guidance for personalized prevention and treatment strategies for BD. In addition, as research in the field of physical activity progresses, integrating neurobiological mechanisms will enhance our understanding of how physical activity regulates brain neurotransmitters and physiological responses, promoting mental health and alleviating BD symptoms.

Conclusion

In summary, this study, through MR, revealed a significant causal relationship between heavy DIY activities and BD. This finding offers new perspectives and scientific evidence for the prevention of BD. Future research should continue to explore the preventive effects of different types of physical activity and further validate the results of this study.

Supplemental Material

sj-docx-1-jmh-10.1177_15579883251359452 – Supplemental material for The Impact of Physical Activities on Men’s Mental Health: A Focus on Bipolar Disorder Prevention

Supplemental material, sj-docx-1-jmh-10.1177_15579883251359452 for The Impact of Physical Activities on Men’s Mental Health: A Focus on Bipolar Disorder Prevention by jie Liu, Sheng Zheng, Peiqi Yu and Xiaomeng Shi in American Journal of Men's Health

Footnotes

ORCID iD

Xiaomeng Shi

Ethical Considerations

We used the publicly available GWAS catalog to conduct a two-sample MR study. No additional ethical approval was required.

Author Contributions

Weijie Liu and Sheng Zheng completed the conceptualization of this study and wrote the initial draft of the manuscript. Sheng Zheng and Peiqi Yu were responsible for polishing the article, improving the language expression, and overall readability to make the manuscript more in line with academic writing norms. Xiaomeng Shi was responsible for revising the manuscript and securing the funding for this research.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data used in this study are available from the corresponding author upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

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