Obsessive-compulsive disorder (OCD) is a mental disorder characterized by recurrent, intrusive thoughts and repetitive behaviors or rituals performed to alleviate anxiety (Hiranandani et al., 2023). It is one of the leading causes of disability worldwide, affecting up to 2 % of the global population (Sharma et al., 2021). The incidence of post-traumatic stress disorder in adults with OCD varies substantially from 2 % to 75 % (Adams et al., 2018). Despite its relatively low prevalence compared to other psychiatric disorders, OCD is strongly associated with severe functional impairment and poses a substantial financial burden on families, estimated to be over 21 times higher than average (Thompson et al., 2020).

The etiology of OCD is multifactorial, with both genetic and environmental risk factors playing crucial roles in its development (Bozorgmehr et al., 2017). In recent years, numerous studies have highlighted the association between OCD and changes in environmental factors. For instance, some studies indicated that a negative impact of the COVID-19 pandemic was present on OCD symptoms in children and adolescents (Luginaah et al., 2023). Understanding gene-environment interactions and mechanisms underlying the disease through clinical research is crucial for advancing precision medicine and developing effective therapeutic interventions to improve the lives of patients with these devastating disorders (Wilson et al., 2023). However, systematic investigation of the causal relationship between these factors and OCD is lacking.

Mendelian randomization (MR) is an analytical approach that leverages genetic variants as instrumental variables (IVs) to assess the causal effects between environmental factors (known as “exposure”) and studied disease (known as “outcome”) using reliable statistical analysis (Burgess et al., 2023). Since genetic variants are highly associated with exposure and are not influenced by confounding factors, MR analysis can infer if exposure has a causal effect on the outcome. MR analysis can be divided into one-sample and two-sample approaches. Two-sample MR uses summary-level data from two independent samples: one for the association between genetic variants and the exposure, and another for the association between the same genetic variants and the outcome. This method can achieve higher statistical power by combining large-scale GWAS datasets. With the advancement of this method, MR analysis has frequently been applied to infer the causal association between environmental factors and disease, such as educational attainment and psychiatric diagnosis (Demange et al., 2024), Epilepsy and psychiatric comorbidities (Chu et al., 2024), brain functional networks, and risk of psychiatric disorders (Mu et al., 2024).

Traditional observational studies are often susceptible to confounding biases and reverse causation. In contrast, MR employs genetic variants that are randomly allocated during gamete formation as instrumental variables, thereby mimicking the design of a randomized controlled trial. This approach allows for more robust inference of causal relationships between exposures and outcomes. In certain scenarios, directly investigating exposure factors may raise ethical concerns. MR circumvents these issues by leveraging genetic proxies to indirectly assess causal effects. A recent MR study uncovered a causal relationship between specific immune phenotypes and asthma risk, providing novel insights into asthma pathogenesis (Xu et al., 2025). Similarly, studies have revealed strong associations between specific immune signatures and the development of lung cancer by MR analysis (Xu et al., 2024). Additionally, in the context of blood metabolites, researchers have identified correlations between abdominal aortic aneurysm (AAA) and some important metabolites by MR analysis, providing valuable insights for AAA screening and prevention strategies (Guo et al., 2024). Building on the strength of this methodology, we employed two-sample MR methods to investigate if different environmental factors exert causal effects on OCD.

In this study, we aimed to elucidate the role of environmental factors by employing single nucleotide polymorphisms (SNPs) as instrumental variables to systematically analyze the causal relationships between 44 different environmental factors and the risk of OCD. Through the results of two-sample MR analyses, we conclude that significant risk factors and protective factors for OCD. The important SNPs in each EF were also identified and analyzed. Our findings could provide novel clues for understanding the mechanism of OCD progression.