Major depressive disorder (MDD) is a significant global health concern. Its etiology is complex, involving behavioral and biological factors, necessitating comprehensive strategies for prevention. While previous research often focused on individual lifestyle factors, this study adopts a holistic approach, examining the combined impact of seven key lifestyle factors: alcohol consumption, smoking, physical activity, diet, sleep patterns, sedentary behavior, and social connection. These factors are known to influence brain function and immune systems. For instance, exercise releases myokines impacting hippocampal function and mood, while overeating and sedentary lifestyles suppress adaptive stress responses. Circadian disruption through sleep disturbances affects melatonin levels and increases depression risk. Smoking and alcohol abuse damage brain circuits, and reduced social connection negatively impacts metabolic and brain health. By integrating these factors into a composite score, this study aims to provide a more effective strategy for depression risk reduction. Furthermore, the study incorporates polygenic risk scores (PRS) to investigate the interplay between genetic predisposition and lifestyle in determining depression risk. The hypothesis is that a healthy lifestyle reduces depression risk across individuals with diverse genetic profiles, and multiple shared neurobiological mechanisms, modulated by genetic variants, underpin the lifestyle-depression association. The UK Biobank dataset, rich in behavioral, brain imaging, biochemical, and genetic data, provides the ideal platform to test these hypotheses.

Existing literature demonstrates links between individual lifestyle factors and depression risk. Studies show that lack of social connection, high sedentary behavior, inadequate physical activity, smoking, unhealthy diet, insufficient or excessive sleep, and problematic alcohol consumption are associated with increased depression risk. These associations are not always linear; for example, the relationship between alcohol consumption and depression often exhibits a U-shaped curve, with moderate consumption sometimes showing a protective effect. Previous research also highlights the complex interaction between genetic predisposition and lifestyle factors in influencing depression risk, indicating a need for studies exploring the combined effects of these factors on depression risk and the underlying neurobiological mechanisms.

This study utilized data from the UK Biobank, a large prospective cohort study. The researchers employed a multifaceted approach involving several analyses: 1. **Survival Analyses:** A multivariate Cox proportional hazard model was used to analyze the association between the composite lifestyle score and the incidence of depression in 287,282 participants. The model adjusted for age, sex, socioeconomic status, BMI, and education level. Each lifestyle factor was also analyzed individually. 2. **Combined Genetic and Lifestyle Risk:** The study examined the interaction between polygenic risk scores (PRS) for depression and lifestyle categories in 197,344 participants. The PRS was categorized into low, intermediate, and high risk. The analysis explored whether the protective effects of healthy lifestyle were consistent across these genetic risk groups. 3. **Mendelian Randomization:** To establish causal relationships, a two-sample Mendelian randomization (MR) analysis was performed using genetic variants as instrumental variables. This investigated the causal effect of lifestyle on depression risk and vice versa. 4. **Correlation Analyses:** Pearson correlation analyses assessed the relationships between the lifestyle score and brain structure (32,839 participants) and peripheral markers (blood biochemistry, blood cell counts, and NMR metabolic biomarkers), adjusting for various covariates. 5. **Structural Equation Modeling (SEM):** A structural equation model was used (18,244 participants) to investigate the relationships between lifestyle, PRS, brain structure, immunometabolic function, and depression, elucidating the potential mediating pathways. Confirmatory factor analysis was performed to validate the latent variables (depression, brain structure, and immunometabolic function) before incorporating them into the SEM. The SEM tested various hypothetical paths, considering the possible bidirectional and complex interactions among variables. **Data Sources:** The UK Biobank provided data on demographics, lifestyle factors (through questionnaires), depression diagnoses (from hospital and primary care records), brain structural imaging (MRI), blood biochemistry and cell count, and NMR-based metabolic markers. Polygenic risk scores for depression were calculated using publicly available GWAS summary statistics, ensuring that the UK Biobank data used in the PRS calculation were excluded from the GWAS summary statistics to avoid sample overlap. **Statistical Software:** The analyses were primarily conducted using R, employing packages such as *survival*, *TwoSampleMR*, *PRSice*, and *lavaan*. **Sensitivity Analyses:** Several sensitivity analyses were performed, including analyses stratified by depression subtypes (single episode, recurrent, and treatment-resistant depression) to assess the robustness of the findings and exploration of different smoking statuses and lifestyle categorization approaches. Additional SEMs were created to investigate alternative causal paths.

The study yielded several key findings: 1. **Protective Effect of Healthy Lifestyle:** Each healthy lifestyle factor (moderate alcohol consumption, healthy diet, regular physical activity, never smoking, healthy sleep, low-to-moderate sedentary behavior, and frequent social connection) independently reduced the risk of depression. The combined lifestyle score showed a strong, monotonic inverse relationship with depression risk; higher scores were strongly associated with a lower risk. These findings suggest that adhering to a healthy lifestyle is significantly protective against depression. 2. **Lifestyle's Protective Effect Across Genetic Risk:** The protective effect of a healthy lifestyle persisted across different levels of polygenic risk for depression. Individuals with a favorable lifestyle had a significantly lower risk of depression, regardless of their genetic predisposition. 3. **Causal Relationship Confirmed:** Mendelian randomization analysis provided robust causal evidence for the protective effect of a healthy lifestyle on depression risk, indicating that lifestyle changes might play a causal role in preventing depression. The analysis also suggested a possible bidirectional causal relationship, where depression might also influence lifestyle choices. 4. **Lifestyle's Impact on Brain Structure and Peripheral Markers:** Higher lifestyle scores were associated with larger brain volumes in various regions, including the pallidum and precentral cortex. Positive correlations were also observed between lifestyle scores and specific blood and metabolic markers. This indicates a broad impact of lifestyle on both brain health and overall physiological functioning. 5. **Neurobiological Mechanisms Revealed:** The structural equation model demonstrated a significant pathway where lifestyle positively influences brain structure and immunometabolic function, which in turn reduce depression risk. Genetic predisposition also significantly affected the risk of depression but interacted with lifestyle in a way that did not alter lifestyle's protective role. This complex interplay highlights the importance of both genetic susceptibility and modifiable lifestyle factors in determining depression risk. Sensitivity analyses generally confirmed the findings in subtypes of depression, although the most protective lifestyle factor varied somewhat across subtypes.

The findings of this large-scale study strongly support the notion that a comprehensive, healthy lifestyle plays a crucial protective role in the prevention of depression, regardless of genetic predisposition. The causal relationship established by Mendelian randomization strengthens the implication for public health interventions targeting lifestyle modification as a potent strategy for depression prevention. The observed associations between lifestyle, brain structure, and peripheral biomarkers emphasize the multifaceted impact of lifestyle on both neurological and physiological well-being. The identified neurobiological pathways, as revealed by the structural equation modeling, provide valuable mechanistic insight into how lifestyle factors might exert their protective effects. These pathways underscore the importance of integrated interventions addressing not just one aspect, but multiple dimensions of lifestyle. The relatively greater impact of lifestyle compared to other factors in the SEM suggests that lifestyle modification may be a particularly effective intervention target. This study adds to the growing body of evidence highlighting the critical role of lifestyle in mental health, complementing other research and paving the way for more targeted and effective preventive measures.

This study provides compelling evidence for a causal association between a healthy lifestyle and reduced depression risk, highlighting the importance of lifestyle interventions in depression prevention and potentially treatment. The findings across diverse genetic risk groups underscore the broad applicability of promoting healthy habits as a preventative strategy. Future research should focus on further refining the understanding of the neurobiological mechanisms and exploring the most effective interventions for promoting a healthy lifestyle to prevent and manage depression.

The study's limitations include reliance on self-reported lifestyle data, which might be prone to biases, the potential for selection bias within the UK Biobank cohort (participants tend to be healthier than the general population), and the limitation of not considering the time lag between onset of depressive episode and diagnosis. Additionally, the study's interpretation of the SEM results might be affected by the potential for confounding factors, given the large number of variables analyzed. Furthermore, the categorization methods for lifestyle factors and PRS might influence the results; however, sensitivity analyses indicate that the core findings are robust. Future research would benefit from objective lifestyle measures, independent validation in diverse populations, and potentially longitudinal studies examining changes in brain structure and peripheral markers over time.