Postpartum depression (PPD), also termed postnatal depression, is a type of depressive episode related to childbirth with a variety of symptoms of mood changes, negative attitudes to life, and mental and psychiatric changes, with few severe cases showing suicidality (1). The diagnostic criteria of PPD comprise five or more symptoms, such as depressed mood, loss of previous interest, weight or appetite alteration, somnipathy, fatigue, attention deficit, and feeling worthless or guilty (2). PPD occurs in approximately 13% of women, which may cause severe consequences for mothers and their children (3). Moreover, the reported overall incidence of paternal PPD between 3 months and 12 months after birth was about 10.4% (4), which suggested that both new parents may suffer from this common psychiatric disorder. Apart from causing mental disorders or even suicides of parents, the risks of PPD on children have been well-investigated, and it suggested that children with parents experiencing PPD are more likely to suffer from depressive disorders (5). Regarding the risk factors associated with PPD, a variety of research found that multiple factors, including a history of mental illness, hormonal changes, and social factors, were related to PPD (1, 6). The causal relationship between potential risk factors and PPD is still unelucidated, and a comprehensive exploration of the causal effects of factors on PPD would be helpful for the intervention of PPD.

Mendelian randomization (MR) is a useful way to infer a causal relationship, using genetic variants as instrumental variants (IVs) to estimate the causal assumption between exposure and outcome (7). Due to the random allocation of genetic variants, MR is less susceptible to other confounding factors and reverse causation that can impede causal inference in conventional observational studies (8). Multivariable MR (MVMR) takes pleiotropy among multiple exposures into account, which is an extension of univariable MR and is important for pleiotropic pathways (9). Thus, accumulating research has taken advantage of MR to gain insights into the causes of multiple diseases (10). Recently, MR analysis initially demonstrated a potential causal relationship between opioid use and the risk of PPD. In turn, PPD was also associated with a higher risk of opioid and non-opioid analgesic use (11). Another recent study investigated the causal association between PPD and cerebrovascular diseases and cognitive impairment, and the result recommended that cognitive impairment was a significant outcome induced by PPD (12). In addition, modifiable risk factors, such as body mass index (BMI) and glucose, are usually considered exposures that may be associated with diseases in MR analysis (13). However, the causal association of other potential risk factors, particularly modifiable risk factors with PPD, still remains unclear.

In the present study, we focused on the association of various potential risk factors and modifiable factors with PPD. Based on the summarized data of the genome-wide association study (GWAS), we investigated the genetic correlations between 40 potential risk factors and PPD by linkage disequilibrium score regression (LDSC) analysis and then performed a two-sample MR analysis.

In the present MR study, we found that higher BMI, MD, SCZ, and earlier SI increased the risk of PPD. On the contrary, the risk factors of increased age at first birth, older age at first sexual intercourse, increased income, and higher education level led to a decreased probability of PPD. In sensitivity analysis, contradictory results of SI were reported, which should be interpreted with caution. The other 32 risk factors did not show a statistically causal correlation with PPD. Adjustment for all eight significant traits in the MVMR model further found that MD and older age at first birth were still associated with an increased risk of PPD. Thus, our study shed fresh light on multiple causal factors associated with PPD.

Younger maternal age was reported to be associated with an increased risk of PPD in women (44). There was an MR analysis illustrating that the older age of first birth was a protective factor for mental disorders (45). Consistently, we found a negative causal relationship between age at first birth and PPD; thus, we suggested that relatively late-to-be parents may be less likely to develop PPD. Early sexual intercourse was associated with mental health outcomes (46). A novel MR study established a risky causal relationship between early sexual intercourse and MD (47). In our study, we initially brought forward that the experience of early sexual intercourse exerted a deleterious effect on PPD. As one of the social determinants, higher education level is identified as a protective factor for PPD (48). Our study also suggested that longer years of schooling were negatively associated with the risks of PPD, which was readily understandable as the higher the level of education, the higher the capability to withstand health and wealth risks. Whether income level or urbanization is associated with depression remains controversial (49). Here, we found that a lower average total household income before tax was associated with an increased risk of PPD. This conclusion needs further investigation using genetic biobanks of middle- and low-income countries. Based on these results, clinicians and parents should take advantage of these viable socioeconomic factors to prevent PPD, such as avoiding earlier first birth and immature sexual intercourse and acquiring a higher education level and a higher income.

Regarding the negative causalities of PPD, we found that modifiable risk factors, both higher BMI and earlier SI, significantly increased the likelihood of PPD. Recently, MR analysis has already found a promoting role of BMI in the risk of MD. In addition, it suggested that depression increases the genetic susceptibility to high BMI (50), which means a positive feedback between BMI and depression and needs the clinician’s close attention and intervention. Combined with our findings, further clinical trials should investigate the effect of preventing PPD by getting rid of excess weight. Non-smoking has been considered a protective causal factor in the incidence of MD (51). This was consistent with our MR results, which indicated a direct effect of earlier SI on an increased risk for PPD. As the risk factors above are relatively easy to modify, we postulate that reducing BMI and postponing SI may decrease the risk of PPD. Although insomnia was associated with an increased risk of perinatal depression (52), we did not conclude a causal relationship between insomnia and PPD. An observational study reported that 11% of women and 8% of men had PPD after the first birth, and it was partly attributed to postpartum sleep deprivation (53). In contrast, our finding suggested no causal effect of insomnia on PPD. Although insomnia is prevalently comorbid with depression, and treating insomnia is beneficial to the mood of patients (54, 55), further research is warranted to identify the role of insomnia in the pathogenesis of PPD.

Finally, we reported that a history of MD increased the risk of PPD, which was in line with previous findings (56, 57). In addition, there was evidence of a positive causal effect between SCZ and PPD in our study. An observational study suggested that PPD and postpartum psychosis were related to a higher risk of SCZ (58). Our MR analysis further strengthened the robustness of the positive causal connection between mental disorders, such as SCZ and MD, and the risk of PPD. More than half of women with PPD have a comorbidity of bipolar disorder, which usually being misdiagnosed or neglected (59). In our study, we did not conclude a causal relationship between these two diseases. We speculated that the negative result may be related to limited cases, distinct diagnostic criteria, and sequencing depth for bipolar disorder. Anyway, future studies should try to illustrate the association between bipolar disorder and PPD. Subsequently, we conducted an MVMR analysis to control potential multicollinearity. We included all eight significant traits from the IVW analysis, of which the MR Egger analysis showed that SI had an opposite direction compared to IVW and weighted median analysis, which should be interpreted with caution. In addition, BMI did not reach a significance following BH correcting. Importantly, compared to univariable MR, the effect of MD and older age at first birth on the increased risks of PPD were still observed. Thus, our study brought forward the most important causal factors of PPD, which may help future studies to better prevent and treat PPD.

Our study has limitations. First, the data sources are mainly from participants with European ancestry; thus, our result inevitably may not adapt to different ethnic groups, and the non-European participants in the GWAS of attention-deficit/hyperactivity disorder might lead to biases in results due to variations in allele frequencies and genetic associations among different populations and ancestry-specific effects. Second, the causal relationship of our findings does not conduct external verification as we only obtained one GWAS data of PPD. Therefore, we utilize more approaches to promote the robustness of our results, such as using the MR egger and weighted median analysis. In addition, one of the limitations of the methodology of MR is the potential confounding of the genetic variants and the outcome (Violation of IV condition two: exchangeability). Other than that, another bias of MR analysis that is uneasily corrected is the selection bias (10). In all, we should be prudent with our findings of MR because of the limitations above.

In summary, our MR analysis provided suggestive evidence of the protective effect of older age at first birth and older age at first sexual intercourse, higher education, and higher income on the risk of PPD. We also suggested that higher BMI and a history of MD and SCZ were hazardous causal factors for PPD. In addition, MD and older age at first birth were independently related to the increased risks of PPD. These novel shreds of evidence may help to guide the prevention and intervention strategies for PPD.

Publicly available datasets were analyzed in this study. See Table 1 for detailed information. Further inquiries can be directed to the corresponding author.

Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.

MZ: Conceptualization, Data curation, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. ZW: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. WL: Data curation, Methodology, Validation, Visualization, Writing – review & editing. SC: Formal analysis, Investigation, Validation, Writing – review & editing. YunY: Data curation, Formal analysis, Investigation, Project administration, Validation, Writing – review & editing. YuaY: Funding acquisition, Supervision, Validation, Writing – review & editing. QM: Data curation, Supervision, Validation, Writing – review & editing. YL: Conceptualization, Funding acquisition, Investigation, Supervision, Validation, Writing – review & editing.