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Specificity of environmental risk factors for schizophrenia, bipolar disorders, and depressive disorders – umbrella review

Published online by Cambridge University Press:  03 December 2025

Jouko Miettunen Open the ORCID record for Jouko Miettunen [Opens in a new window] ,
Heidi Ruotsalainen Open the ORCID record for Heidi Ruotsalainen [Opens in a new window] ,
Nea Vainio ,
Hala AlSaadi Open the ORCID record for Hala AlSaadi [Opens in a new window] ,
Erika Jääskeläinen Open the ORCID record for Erika Jääskeläinen [Opens in a new window]  and
Nina Rautio Open the ORCID record for Nina Rautio [Opens in a new window]
Jouko Miettunen*
Affiliation:
Research Unit of Population Health, University of Oulu , Oulu, Finland Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
Heidi Ruotsalainen
Affiliation:
Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland Department of Social Services, Rehabilitation and Culture, Oulu University of Applied sciences , Oulu, Finland
Nea Vainio
Affiliation:
Research Unit of Population Health, University of Oulu , Oulu, Finland Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
Hala AlSaadi
Affiliation:
Research Unit of Population Health, University of Oulu , Oulu, Finland
Erika Jääskeläinen
Affiliation:
Research Unit of Population Health, University of Oulu , Oulu, Finland Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland Department of Psychiatry, Oulu University Hospital and Wellbeing Services County of North Ostrobothnia, Oulu, Finland
Nina Rautio
Affiliation:
Research Unit of Population Health, University of Oulu , Oulu, Finland Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
*
Corresponding author: Jouko Miettunen; Email: jouko.miettunen@oulu.fi
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Abstract

Schizophrenia (SZ), bipolar disorder (BD), and depressive disorder (DEP) are disabling diseases influenced by genetic and environmental factors. Several risk factors have been identified for these disorders in various systematic reviews, meta-analyses, and umbrella reviews. Identifying risk factors for these disorders is essential to be able to target disorder-specific or transdiagnostic interventions. We aimed to systematically review existing meta-analyses on selected risk factors for SZ, BD, and DEP. We systematically searched for meta-analyses of risk factors relating to pregnancy and birth, childhood and adolescence, lifestyle, somatic conditions, infectious agents, and environmental exposures published since 2000. The transdiagnostic comparison included 70 meta-analyses, encompassing results for 55 risk factors that were studied across at least two of the three disorders. In our extensive transdiagnostic umbrella, 74% of reported effect sizes for the risk factors from meta-analyses were statistically significant. Childhood maltreatment was a robust transdiagnostic risk factor for all three disorders. We also found differences in risk factors, for example, pregnancy and birth complications associated strongly with SZ risk, and several somatic conditions were associated with DEP. It should be noted that many meta-analyses were low quality and based on a small number of original studies. More high-quality longitudinal research is needed on many risk factors to be able to evaluate their validity in single outcomes and their potential specificity or non-specificity.

Keywords

bipolar disordersdepressive disordersrisk factorsschizophreniaumbrella review

Information

Type
Invited Review
Information
Psychological Medicine , Volume 55 , 2025 , e368
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© The Author(s), 2025. Published by Cambridge University Press

Introduction

Bipolar disorder (BD), depressive disorder (DEP), and schizophrenia (SZ) are disabling diseases influenced by genetic and environmental factors. BDs are chronic disabling conditions characterized by both manic and depressive episodes, with an estimated lifetime prevalence of 2.3% (Clemente et al., Reference Clemente, Diniz, Nicolato, Kapczinski, Soares, Firmo and Castro-Costa2015). DEPs are currently the leading cause of disability worldwide, affecting more than 300 million people worldwide, with a prevalence of 12% in the European region (World Health Organization, 2017). Although SZ is a low-prevalence disorder (0.4% lifetime prevalence) (Saha, Chant, Welham, & McGrath, Reference Saha, Chant, Welham and McGrath2005), the burden of disease is substantial, and based on a meta-analysis, only 13.5% of SZ patients recover (Jääskeläinen et al., Reference Jääskeläinen, Juola, Hirvonen, McGrath, Saha, Isohanni, Veijola and Miettunen2013). All these disorders typically begin in the mid-20s, though onset varies widely; especially in DEPs, there are new onset cases also after age 60 years among females (Dagani et al., Reference Dagani, Baldessarini, Signorini, Nielssen, de Girolamo, Large, de Girolamo, McGorry and Sartorius2019; Miettunen, Immonen, McGrath, Isohanni, & Jääskeläinen, Reference Miettunen, Immonen, McGrath, Isohanni, Jääskeläinen, de Girolamo, McGorry and Sartorius2019; Yalin & Young, Reference Yalin, Young, de Girolamo, McGorry and Sartorius2019).

Numerous risk factors have been identified for these disorders. The candidate risk factors for SZ include environmental, biological, and genetic factors, for example, complications of pregnancy, developmental delays, early adversity and trauma, substance use, low intelligence quotient (IQ), and family history (Belbasis et al., Reference Belbasis, Köhler, Stefanis, Stubbs, van Os, Vieta, Seeman, Arango, Carvalho and Evangelou2018). Female gender, being unmarried, family history and genetics, substance use, somatic morbidity, and personality traits are examples of factors that have been linked with the onset of DEPs (Köhler et al., Reference Köhler, Evangelou, Stubbs, Solmi, Veronese, Belbasis, Bortolato, Melo, Coelho, Fernandes, Olfson, Ioannidis and Carvalho2018). There are fewer consistent findings in BDs, but for example, family history and genetics, delays in motor development, good school performance, and irritable bowel syndrome have been found to be risk factors (Rowland & Marwaha, Reference Rowland and Marwaha2018). The proportion of risk that is derived from environmental sources is estimated to be 15–40% in SZ and BDs (Robinson & Bergen, Reference Robinson and Bergen2021) and 63% in DEPs (Sullivan, Neale, & Kendler, Reference Sullivan, Neale and Kendler2000).

Some risk factors, especially genetic ones (Prata, Costa-Neves, Cosme, & Vassos, Reference Prata, Costa-Neves, Cosme and Vassos2019), appear to be pluripotent, increasing risk for both SZ and affective disorders. However, the specificity of environmental risk factors has not been studied systematically. Robinson and Bergen (Reference Robinson and Bergen2021) have compared obstetric complications, infections, season of birth, migration, urbanicity, adverse childhood experiences, and cannabis use in their review for SZ and BDs. Their conclusion was that although some risk factors have been identified for SZ, a few have been identified for BDs. A common risk factor in their review was childhood adversity. There is no extensive systematic review that includes risk factors for DEPs. Psychotic disorders have often been studied so that psychotic symptoms and non-affective and affective psychoses have been studied as one outcome, for example, in studies on cannabis use (Groening et al., Reference Groening, Denton, Parvaiz, Brunet, Von Daniken, Shi and Bhattacharyya2024); thus, specificity can be unclear in some risk factors.

Umbrella reviews allow comparison across results from different previous systematic reviews and meta-analyses. Few umbrella reviews have examined environmental risk factors across all three disorders. The earlier transdiagnostic umbrella review focusing on person-level antecedents identified psychotic symptoms, depressive symptoms, anxiety, disruptive behaviors, affective lability, and sleep problems as transdiagnostic antecedents associated with the onset of SZ, BD, and DEPs (Uher et al., Reference Uher, Pavlova, Najafi, Adepalli, Ross, Howes Vallis, Freeman, Parker, Propper and Palaniyappan2024). Synthesizing earlier meta-analyses and their quality is needed to identify shared, disorder-specific, and not yet meta-analyzed risk factors across the three disorders. Understanding the risk factors for these disorders is essential in designing disorder-specific or transdiagnostic interventions. Our aim was to systematically evaluate the specificity of selected risk factors using existing meta-analyses on SZ, BD, and DEPs.

Methods

The protocol of this umbrella review of meta-analysis is preregistered on PROSPERO (CRD420251037819).

Search strategies

We did a systematic search in PubMed and Scopus databases for studies since January 1st, 2000. We limited the search to studies from 2000 onward based on the earlier umbrella reviews, as meta-analyses before this were rare, and those meta-analyses have been updated since then. General search was ‘risk and (SZ or BD or DEP or psychotic or affective) and (“systematic review” or meta-analysis)’. The last literature search was done on May 2nd, 2025. First, we performed a general search on risk factors and selected risk factors of the included categories and disorders. Second, in cases where we identified meta-analyses on only one or two of the disorders, we searched for meta-analyses on that topic using specific search terms for the missing outcomes. Search was not limited to language. We (JM) also did an additional search in Elsevier Science Direct and Web of Science database to check for possible missed studies and a manual search on references of earlier umbrella reviews. The search strategy is presented in Supplementary Table 1.

Eligibility criteria

Inclusion criteria

We included only meta-analyses focusing on observational studies of adult populations. If a meta-analysis included only one or two original studies that utilized normative controls or samples from randomized controlled trials, those were included. The inclusion and exclusion of the articles were evaluated by one researcher (JM) and checked by another (HA). In the first phase, we included all identified meta-analyses of selected risk factors in SZ, BD, and DEPs. To the transdiagnostic comparison, we included meta-analyses on risk factors, which were available for at least two of the disorders.

Risk/protective factor definition

We included the following risk factor categories:

  1. 1. Pregnancy and birth-related factors,

  2. 2. Childhood and adolescence-related factors,

  3. 3. Lifestyle factors,

  4. 4. Somatic conditions,

  5. 5. Infectious agents, and

  6. 6. Environmental exposures.

We did not include studies on person-level antecedents (Uher et al., Reference Uher, Pavlova, Najafi, Adepalli, Ross, Howes Vallis, Freeman, Parker, Propper and Palaniyappan2024), personality (Christensen & Kessing, Reference Christensen and Kessing2006; Ohi et al., Reference Ohi, Shimada, Nitta, Kihara, Okubo, Uehara and Kawasaki2016), temperament and character traits (Komasi et al., Reference Komasi, Rezaei, Hemmati, Rahmani, Amianto and Miettunen2022), biomarkers (Carvalho et al., Reference Carvalho, Solmi, Sanches, Machado, Stubbs, Ajnakina, Sherman, Sun, Liu, Brunoni, Pigato, Fernandes, Bortolato, Husain, Dragioti, Firth, Cosco, Maes, Berk and Herrmann2020), genetic risk (e.g.; Gatt, Burton, Williams, & Schofield, Reference Gatt, Burton, Williams and Schofield2015; Mistry, Harrison, Smith, Escott-Price, & Zammit, Reference Mistry, Harrison, Smith, Escott-Price and Zammit2018; Prata et al., Reference Prata, Costa-Neves, Cosme and Vassos2019), migration (Henssler et al., Reference Henssler, Brandt, Müller, Liu, Montag, Sterzer and Heinz2020; Swinnen & Selten, Reference Swinnen and Selten2007), and parental psychiatric illnesses (Rasic, Hajek, Alda, & Uher, Reference Rasic, Hajek, Alda and Uher2014; Uher et al., Reference Uher, Pavlova, Radua, Provenzani, Najafi, Fortea, Ortuño, Nazarova, Perroud, Palaniyappan, Domschke, Cortese, Arnold, Austin, Vanyukov, Weissman, Young, Hillegers, Danese and Fusar-Poli2023) as those have been reviewed earlier.

Outcome criteria

The included outcomes were diagnosis of SZ or any non-affective psychosis, BD, or DEPs. Also, meta-analyses including original studies using depressive symptoms (using various cutoffs to define depression or, if not available, standardized mean differences [SMD]) as outcomes were included as those studies were commonly included in meta-analyses of DEPs.

Exclusion criteria

We excluded studies focusing only on children, adolescents, young adults, or older people. We also excluded meta-analyses focusing on selected populations, for example, risk factors among cancer patients or meta-analyses focusing on specific geographical areas. Also, meta-analyses of studies with randomized controlled trials were excluded. In addition, we excluded studies focusing only on particular types of diagnoses within selected outcomes, for example, postpartum depression.

Data extraction and study selection

One author (JM) extracted all relevant data, which was then cross-checked by another researcher (HA). From the articles we extracted references, included study designs, studied risk factors and outcomes, number of studies, used effect size metrics and their estimates with 95% confidence intervals (CI), and between-study heterogeneity (I2 metric) (Higgins, Thompson, Deeks, & Altman, Reference Higgins, Thompson, Deeks and Altman2003). Most of the meta-analyses summarized results using relative risk estimates (odds ratio [OR], risk ratio [RR], hazard ratio [HR], or incidence rate ratio [IRR]) and their 95% CI; if the studies used correlation coefficients (r) or SMDs, those were transformed into equivalent ORs (eORs) using available formulas (Lenhard & Lenhard, Reference Lenhard and Lenhard2022). All risk estimates are presented; however, when comparing risk estimates between disorders, we included the study with the largest number of original studies. Meta-analyses with depression diagnoses were preferred over those using depressive symptoms. When selecting pooled results from meta-analyses, we initially selected results from the total sample. However, if this was not suitable (e.g., due to the inclusion of children), we then selected results from subsamples (e.g., adults).

The methodological quality of the meta-analyses included in the transdiagnostic comparison was evaluated by two reviewers alternately (HR and JM) using the AMSTAR 2. When interpreting AMSTAR 2 ratings, two (items 3 and 10) of the 16 original items were considered irrelevant as we excluded randomized controlled trials. Seven domains assessed by AMSTAR 2 are considered critical domains. Missing one of these critical domains contributes to an overall rating of low methodological quality, and missing more than one of these critical domains contributes to an overall rating of critically low methodological quality (Shea et al., Reference Shea, Reeves, Wells, Thuku, Hamel, Moran, Moher, Tugwell, Welch, Kristjansson and Henry2017). For the studies in transdiagnostic comparison, we also recorded the total number of cases or participants and whether the eligible articles applied any criteria to assess the quality of component studies. We also included a short summary of the quality results presented.

We summarized results of transdiagnostic comparisons using common statistically significant risk factors and common statistically significant risk factors in combination with an eOR >2.

Results

Original search identified 7135 articles, and 141 articles were eligible. Manual search and the second phase, using specific search terms, identified 102 additional articles, bringing the total to 243 articles. The transdiagnostic comparison regarding specificity included 70 articles (see Figure 1).

Figure 1. Flow chart of the selection of studies.

Of the risk factors, 55 were studied in meta-analyses for at least two of the three disorders. Only twelve risk factors were studied in meta-analyses from all three diagnoses: winter birth, maternal smoking, low birth weight, premature birth, any childhood maltreatment, current smoking, traumatic brain injury, psoriasis, Borna disease virus, cytomegalovirus, human herpesvirus-1, and Toxoplasma gondii. The number of original studies varied, with 34 (38%) meta-analyses including 2 to 4 studies, 43 (36%) including 5 to 9 studies, and 43 (36%) including 10 or more studies. Of the included meta-analyses, 90 (74%) of 120 reported statistically significant pooled effect estimates. Heterogeneity (I2) was reported in 103 (86%) meta-analyses; of these, 54 (52%) studies reported heterogeneity to be over 50%.

When we compare the three diagnoses pairwise, of the risk factors which were studied both for SZ and BD, about half (57%) were associated statistically significantly with both outcomes. The percentage was similar when comparing SZ and DEP (55%), whereas in comparison between BD and DEP, 81% of the risk factors were statistically significant in transdiagnostic comparison. It is worth noting that some statistically nonsignificant effect sizes were relatively large. We also checked this comparison using statistical significance in combination with an eOR larger than two. Here, we found support for transdiagnostic risk regarding Borna disease virus for all three outcomes. Any maltreatment, epilepsy, and celiac disease are associated with SZ and DEP; polycystic ovary syndrome, irritable bowel syndrome, and gastroesophageal reflux are associated with BD and DEP; and minor physical anomalies are associated with SZ and BD. In all these pairwise comparisons, the proportion of associations which were statistically significant with eOR > 2 was about 30%.

Meta-analyses included in the transdiagnostic comparison are described by risk factor categories shortly in Table 1 and in more detail in Supplementary Table 2. All the results (ORs with 95% CIs) from meta-analyses are also presented as forest plots in Supplementary Figures 1 to 12. Meta-analyses analyzing only one diagnosis and studies with a smaller number of original articles than those included in the meta-analysis on the same topic are presented in Supplementary Table 3. Tables include 579 effect sizes in total, 135 for SZ, 76 for BD, and 368 for DEPs. The excluded articles, which were read in full, are listed with exclusion reasons in Supplementary Table 4.

Table 1. Summary of meta-analyses of risk factors included in transdiagnostic comparison between schizophrenia, bipolar, and depressive disorders

Abbreviations: CI = confidence interval, CO = carbon monoxide, eOR = equivalent OR, HR = hazard ratio, IRR = incidence rate ratio, k = number of studies, NO2 = nitrogen dioxide, OR = odds ratio, PM10 = particulate matter with diameter ≤ 10 μm, PM2.5 = particulate matter with diameter ≤ 2.5 μm, RR = relative risk, SO2 = sulfur dioxide.

Quality of original studies and meta-analyses

The quality of original studies was evaluated in 49 of 70 (70%) meta-analyses. The most commonly used method was the Newcastle-Ottawa Quality Assessment Scale (NOQAS), either in its original form of scoring or as modified, which was employed in 31 meta-analyses. Twenty-three meta-analyses using the original NOQAS reported the proportion of studies with high quality (scoring at least 7 of 9 points), with a median being 67%. The quality methods and results are summarized in Supplementary Table 5.

Based on an AMSTAR 2 evaluation of methodological quality, two of the meta-analyses (Hu et al., Reference Hu, Cui, Chen and Zhang2025; Zhang et al., Reference Zhang, Sidorchuk, Sevilla-Cermeño, Vilaplana-Pérez, Chang, Larsson, Mataix-Cols and Fernández de la Cruz2019) were of high quality, whereas the remaining ones were of low or critically low quality. The critical items that contributed most to low ratings were items 7 (the list of excluded studies was missing) and 2 (no prior protocol). AMSTAR 2 instrument and ratings are presented in Supplementary Table 6.

Results by risk factor categories

Pregnancy and birth-related factors

Pregnancy and birth-related risk factors were mainly studied in SZ and BD. Winter birth was associated with a statistically significant risk in all three disorders, with the highest effect size observed for SZ (OR = 1.05, 95% CI 1.03–1.07). Maternal smoking was a statistically significant risk factor for all three disorders, with risk estimates (OR/RR) from 1.3 to 1.5. Maternal stress was a risk factor for DEPs with a small effect (OR = 1.12, 1.04–1.22) and for BD with a very large effect (OR = 12.00, 3.30–43.59); however, the meta-analysis included only two studies. Emergency cesarean section was a risk factor for SZ (OR 3.24, 1.40–7.50), but not for BD. Advanced paternal age was studied using different cutoffs; it associated with increased risk, with somewhat larger and more robust effect sizes in SZ than in BD.

Childhood and adolescence-related factors

The meta-analyses on childhood and adolescence-related risk factors included were on academic achievement, parental death, maltreatment, and mainly from BD and DEPs. There were several meta-analyses on maltreatment, and in most of them, there was a clear association with risk estimates mostly larger than 2, indicating the non-specificity of abuse and neglect as risk factors. Good school success was a protective factor both for SZ and DEPs.

Lifestyle factors

Only two lifestyle factors, both relating to substance use, were studied in relation to at least two disorders. The associations in current smoking were quite similar between the three disorders, with ORs from 1.30 to 1.80, with the highest risk for SZ. Cannabis use was a more substantial risk factor for BD (OR 2.63, 1.95–3.53) than for DEPs (OR 1.29, 1.13–1.46).

Somatic conditions

Obesity was more strongly associated with BD (OR 1.77, 1.40–2.23) than with DEPs (OR 1.18, 1.01–1.37). Traumatic brain injury and psoriasis were risk factors for all outcomes, with ORs between 1.4 and 2.2. Thirteen other somatic conditions were studied in two different diagnoses. Bullous pemphigoid was strongly associated with SZ (HR 2.86, 1.55–5.28), whereas association with DEPs was weaker (HR 1.49, 1.13–1.96). Similarly, epilepsy was strongly associated with SZ (OR 5.22, 2.99–9.11) and also associated with DEPs with an OR/RR of 2.05 (1.77–2.37). Celiac disease was more strongly associated with DEPs (OR 4.91, 2.17–11.15) than SZ (OR 2.03, 1.45–2.86). Rheumatoid arthritis, irritable bowel disease, and polycystic ovary syndrome were associated with both BD and DEPs, with ORs between 1.5 and 2.8. Minor physical anomalies were associated with both SZ and BD.

Infectious agents

Toxoplasma gondii was associated with risk for SZ (OR 1.81, 1.52–2.17) and BD (OR 1.52, 1.06–2.18), but not for DEPs (OR 1.15, 0.95–1.39). Human herpesvirus-1 was associated with DEPs (OR 1.98, 1.20–3.29), but not with SZ or BD. Cytomegalovirus was also studied in all disorders, but with statistically nonsignificant results. Borna disease virus is associated with all three outcomes, with ORs between 2.0 and 3.3. Other infectious agents were studied mainly in SZ and DEPs.

Environmental exposures

Environmental risk factor meta-analyses were conducted only in SZ and DEPs. Urbanicity was associated with SZ (IRR 2.25, 2.00–2.52), but not with DEPs (pooled estimated OR of developing and developed countries was 1.1). Studies on air pollution showed quite similar statistically significant associations with SZ and DEPs, but also some differences in the magnitudes of the effect sizes were found.

Discussion

Summary of findings

Our umbrella review identified several statistically significant risk factors. In total, 74% of meta-analyses included in the review reported statistically significant associations. The meta-analyses were often of low quality. Childhood maltreatment was a robust transdiagnostic risk factor for SZ, BD, and DEPs. Also, maternal smoking and traumatic brain injury were transdiagnostic risk factors. We also found differences in risk factors, for example, several somatic conditions were strong risk factors for DEPs, and pregnancy and birth complications were strongly associated with SZ risk. Moreover, the infectious agents varied notably in their associated disorders.

Comparison to other studies by risk factor categories

Pregnancy and birth-related factors

Pregnancy and birth-related factors have been commonly studied in SZ, and several risk factors have been identified. Some risk factors were also identified for BD and DEPs. The large Swedish register study comparing pregnancy and birth-related factors between SZ and BD, similarly to our review, found an association in both disorders, but often stronger effects in SZ than in BD (Robinson et al., Reference Robinson, Ploner, Leone, Lichtenstein, Kendler and Bergen2023). Advanced paternal age was associated with SZ and also with BD, although not as strongly. There was no meta-analysis for DEPs, and the systematic transdiagnostic review by de Kluiver, Buizer-Voskamp, Dolan, and Boomsma (Reference de Kluiver, Buizer-Voskamp, Dolan and Boomsma2017) included only one original study on DEPs, which also found a risk for DEP (OR 1.65, 1.33–2.05). Regarding maternal age, both younger and older ages have been linked with BD in a meta-analysis (Fico et al., Reference Fico, Oliva, De Prisco, Giménez-Palomo, Sagué-Vilavella, Gomes-da-Costa, Garriga, Solé, Valentí, Fanelli, Serretti, Fornaro, Carvalho, Vieta and Murru2022).

Childhood and adolescence-related factors

Childhood maltreatment was a consistent risk factor for all three disorders. Regarding possible pathways to the onset of psychiatric illness, it is possible that these early experiences may make a person more vulnerable to exposures later in life (Starr, Hammen, Conway, Raposa, & Brennan, Reference Starr, Hammen, Conway, Raposa and Brennan2014). Regarding childhood sexual abuse, a large umbrella review by Hailes, Yu, Danese, & Fazel (Reference Hailes, Yu, Danese and Fazel2019) concludes that higher-quality meta-analyses for specific outcomes and more empirical studies on the developmental pathways from childhood sexual abuse to later outcomes are needed. Good academic achievement was a protective factor for SZ and DEPs in meta-analyses. There was no meta-analysis on BDs, but in a Swedish national cohort study, those with poorest grades were at increased risk (HR 1.86, 1.06–3.28), but interestingly, excellent school performance was even a larger risk (HR 3.79, 2.11–6.82) of adult BD (MacCabe et al., Reference MacCabe, Lambe, Cnattingius, Sham, David, Reichenberg, Murray and Hultman2010).

Lifestyle factors

Cannabis is a well-studied risk factor for mental illnesses, although it has been linked with psychotic outcomes in many meta-analyses (Groening et al., Reference Groening, Denton, Parvaiz, Brunet, Von Daniken, Shi and Bhattacharyya2024), we were not able to find a meta-analysis focusing specifically on SZ or non-affective psychoses. However, several meta-analyses address related outcomes (Large, Sharma, Compton, Slade, & Nielssen, Reference Large, Sharma, Compton, Slade and Nielssen2011; Marconi, Di Forti, Lewis, Murray, & Vassos, Reference Marconi, Di Forti, Lewis, Murray and Vassos2016); thus, it can be assumed that there is an association also with SZ. There are only a few original studies linking cannabis use to SZ, probably due to the need for large samples and long follow-ups. In the well-known Swedish conscript study, OR for SZ was 3.7 among frequent cannabis users in a 35-year follow-up (Manrique-Garcia et al., Reference Manrique-Garcia, Zammit, Dalman, Hemmingsson, Andreasson and Allebeck2012). Association between cannabis use and DEPs was less clear in the included meta-analysis (Lev-Ran et al., Reference Lev-Ran, Roerecke, Le Foll, George, McKenzie and Rehm2014). Physical activity and diet were studied mostly in DEPs, whereas the research on psychotic outcomes is scarce (Aucoin, LaChance, Cooley, & Kidd, Reference Aucoin, LaChance, Cooley and Kidd2020; Brokmeier et al., Reference Brokmeier, Firth, Vancampfort, Smith, Deenik, Rosenbaum, Stubbs and Schuch2020; Johnstad, Reference Johnstad2024).

Somatic conditions

Obesity was linked with BD and DEPs, with a higher risk for BD (OR 1.77), but there was no meta-analysis on SZ. Interestingly, in a Finnish cohort study, SZ was associated with childhood and adolescent underweight (RR 2.44, 1.03–5.80) and, although nonsignificantly, with an earlier overweight (RR 2.25, 0.98–5.20) (Sormunen et al., Reference Sormunen, Saarinen, Salokangas, Hutri-Kähönen, Viikari, Raitakari and Hietala2019). Several single medical illnesses were studied as risk factors, and the published meta-analyses often had statistically significant findings. The risk factors were typically studied in one or two outcomes; only traumatic brain injury was studied across all outcomes. Of the outcomes not studied across all outcomes, for example, epilepsy was strongly associated with SZ (OR 5.22) and DEPs (OR 2.05), and it is also identified as a risk factor for BDs (Li, Ledoux-Hutchinson, & Toffa, Reference Li, Ledoux-Hutchinson and Toffa2022). Regarding obesity and medical illnesses, the direction of association is often unclear, and the medical illnesses can also be consequences of mental illness or treatment. Rheumatoid arthritis was found to be associated with BD and DEPs in meta-analyses, but regarding SZ, there is a potential inverse relationship, which could be explained by genetic correlations between the two illnesses (Zamanpoor, Ghaedi, & Omrani, Reference Zamanpoor, Ghaedi and Omrani2020). Alcohol use disorder was a predictor for DEP (OR 1.57) and especially for BDs (OR 4.09). Some studies have investigated the association in both directions, concluding that alcohol use is causally linked to DEP, but not vice versa (Boden & Fergusson, Reference Boden and Fergusson2011).

Infectious agents

We found several significant associations in infectious agents, with the strongest association between Borna disease virus and DEPs. Toxoplasma gondii is the most studied infection, associated with a risk for SZ and BD, but not with DEPs. Regarding the timing of exposure, childhood infections were meta-analyzed only in SZ with an OR of 1.80 (1.04–3.11) (Khandaker, Zimbron, Dalman, Lewis, & Jones, Reference Khandaker, Zimbron, Dalman, Lewis and Jones2012). Maternal infection during gestation is associated with an increased risk of SZ in the offspring (RR 1.65; 1.23–2.22) (Saatci, van Nieuwenhuizen, & Handunnetthi, Reference Saatci, van Nieuwenhuizen and Handunnetthi2021), whereas findings regarding maternal influenza infection in pregnancy and offspring psychiatric illnesses have been inconsistent (Fung et al., Reference Fung, Fakhraei, Condran, Regan, Dimanlig-Cruz, Ricci, Foo, Sarna, Török and Fell2022). Regarding original comparative studies on childhood infections, a Swedish register study found increased risk with later BD (IRR 1.21; 1.17–1.26), but not with SZ (Robinson et al., Reference Robinson, Ploner, Leone, Lichtenstein, Kendler and Bergen2024).

Environmental exposures

Urbanicity was associated with SZ, but not with DEPs. There was no meta-analysis regarding BD. The meta-analysis by Rodriguez et al. (Reference Rodriguez, Alameda, Trotta, Spinazzola, Marino, Matheson, Laurens, Murray and Vassos2021) also included other affective psychoses, with the only study focusing on BD finding a linear trend with increasing population density and an increased risk for BD (Kaymaz et al., Reference Kaymaz, Krabbendam, de Graaf, Nolen, Ten Have and van Os2006). Notably, urbanicity is associated with SZ in developed countries (OR 1.30), but not in developing countries (OR 0.89) (Xu, Miao, Turner, & DeRubuis, Reference Xu, Miao, Turner and DeRubeis2023). There were no meta-analyses regarding urbanicity at birth, but it has been linked with SZ (IRR = 1.84, 1.77–1.91) and BD (IRR = 1.29; 1.21–1.37) in an extensive Danish population-based register study (Vassos, Agerbo, Mors, & Pedersen, Reference Vassos, Agerbo, Mors and Pedersen2016). Green space has been identified as a protective factor for DEPs (Liu et al., Reference Liu, Chen, Cui, Ma, Gao, Li, Meng, Lin, Abudou, Guo and Liu2023), but studies on SZ are limited. However, an extensive Danish register study suggests green space to be a protective factor also for SZ (Engemann et al., Reference Engemann, Pedersen, Arge, Tsirogiannis, Mortensen and Svenning2018). Short-term air pollution was associated with relatively similar effect sizes in SZ (Song et al., Reference Song, Liu, Wei, Li, Liu, Yuan, Yan, Sun, Mei, Liang, Li, Jin, Wu, Pan, Yi, Song, He, Tang, Liu and Su2023) and DEPs (Borroni et al., Reference Borroni, Pesatori, Bollati, Buoli and Carugno2022). Borroni et al. (Reference Borroni, Pesatori, Bollati, Buoli and Carugno2022) summarized results also regarding long-term air pollution and DEPs, and there, the risk estimates were typically higher than in the corresponding short-term exposure studies. There have been only a few studies on BD, but, for example, a study using large US and Danish datasets found poor air quality to be associated with BD in both countries (Khan et al., Reference Khan, Plana-Ripoll, Antonsen, Brandt, Geels, Landecker, Sullivan, Pedersen and Rzhetsky2019). Despite the relatively small effect sizes in air pollution, it should be noted that they may have significant population-level effects as, for example, in the study by Borroni, Pesatori, Bollati, Buoli, & Carugno (Reference Borroni, Pesatori, Bollati, Buoli and Carugno2022), authors calculated that the risk of long-term PM2.5 exposure corresponds to 0.64 to 1.3 million attributable cases in Europe.

General discussion

Due to similarities in heritability, neurobiology, and symptomatology (Dines et al., Reference Dines, Kes, Ailán, Cetkovich-Bakmas, Born and Grunze2024), one would expect some overlap also in environmental risk factors. As expected, meta-analyses of BD and DEP identified several common statistically significant risk factors for these disorders, but there were also many transdiagnostic risk factors in other diagnostic comparisons. The meta-analyses on DEP had typically more original studies, which partly explains that there were more statistically significant findings. When we added magnitude of eORs to comparison, the amount of transdiagnostic risk factors was quite similar across the three outcomes.

Our review focused on environmental risk factors on which specificity is not well known, and this review adds to earlier findings on transdiagnostic overlaps in, for example, genetic (Prata et al., Reference Prata, Costa-Neves, Cosme and Vassos2019), psychiatric (Uher et al., Reference Uher, Pavlova, Najafi, Adepalli, Ross, Howes Vallis, Freeman, Parker, Propper and Palaniyappan2024), and psychological (Komasi et al. Reference Komasi, Rezaei, Hemmati, Rahmani, Amianto and Miettunen2022; Uher et al., Reference Uher, Pavlova, Najafi, Adepalli, Ross, Howes Vallis, Freeman, Parker, Propper and Palaniyappan2024) risk factors. There are various potential neurochemical mechanisms which could explain the association between different environmental risk factors and brain function; these have been discussed, for example, by Stilo & Murray (Reference Stilo and Murray2019). There are also several articles discussing potential mechanisms regarding specific environmental risk factors. Regarding, for example, lifestyle factors and DEPs, there are some potential biological mechanisms, for example, monoamine imbalance, inflammation, altered stress response, oxidative stress, and dysfunction of brain-derived neurotrophic factor (Kunugi, Reference Kunugi2023). Regarding advanced paternal age, genetic changes have been suggested to explain the risk for SZ (Torrey, et al., Reference Torrey, Buka, Cannon, Goldstein, Seidman, Liu, Hadley, Rosso, Bearden and Yolken2009).

Most of the effect sizes found were relatively small. Although the effect sizes can be small, they may in some cases have large public health effect (e.g., in air pollution as mentioned earlier). The effect sizes also represent group level associations; thus, direct clinical significance is limited. Many of the risk factors found are modifiable. Dragioti et al. (Reference Dragioti, Radua, Solmi, Arango, Oliver, Cortese, Jones, Il Shin, Correll and Fusar-Poli2022) have estimated population attributable fractions (PAFs) in their meta-umbrella systematic review for different risk factors and outcomes. Regarding the topics of the current umbrella review, large global PAFs not confounded by indication were, for example, 37.8% for childhood adversities and SZ spectrum disorders, 13.4% for childhood sexual abuse and DEPs, and 9.7% for cannabis use and SZ spectrum disorders. Taking into account these calculations, the transdiagnostic results of this current umbrella review support childhood maltreatment as a potential intervention target.

Strengths and limitations

The general quality of the meta-analyses was low; this may not directly indicate low-quality research, but also unclear reporting. Especially, older meta-analyses were of poorer quality as reporting guidelines were not so commonly in use earlier. It is also worth noting that AMSTAR 2 primarily focuses on randomized controlled trials, rather than observational studies. The quality of original studies and thus some of the meta-analyses is limited regarding causality, for example, there are not many prospective longitudinal high-quality studies on early risk factors in all three disorders. The associations are not necessarily causal, for example, in lifestyle or somatic conditions, and bidirectional relationships are possible, especially regarding risk factors, which have been investigated primarily in cross-sectional designs. Only very few risk factors were studied in all three disorders, and only a few resulted in robust evidence for association. In some cases, it was not possible to define based on meta-analyses if risk factors were disorder specific or transdiagnostic ones due to lack of power, as effect sizes were uncertain due to low number of studies. It can be also noted that many meta-analyses are likely drawn from clinical populations, and some observed associations may reflect general vulnerability rather than true disorder-specific risk.

The number of original studies in some meta-analyses was low. For instance, in about half (46%) of meta-analyses on infectious agents, there were fewer than five original studies. Additionally, heterogeneity was substantial in many meta-analyses, with more than half exceeding 50%. Our finding that 74% of included meta-analyses yielded statistically significant results is an indication of publication bias in the research field and affects the conclusions of this umbrella review. Due to the potential publication bias and the fact that many meta-analyses included cross-sectional and unadjusted risk estimates, many risk estimates presented here are likely to be larger than the actual risk. The umbrella review was limited to meta-analyses; thus, we have missed evidence from several original studies which have not been meta-analyzed.

Our study has several strengths. The umbrella review was extensive; thus, we used a two-phase systematic search to identify eligible meta-analyses. We offer all data collected in our umbrella review as online Supplementary Material. The online material can be utilized in the future to synthesize earlier research further. Limitations of this umbrella meta-analysis include the inclusion of only two literature databases; on the other hand, we searched for studies from earlier reviews and made additional searches in two more databases. The selection of risk factors was partly artificial, as the categorization and relevance of these risk factors are not straightforward. For those interested, we have also given references to earlier umbrella reviews and meta-analyses of excluded risk factors (see Supplementary Material).

Remaining gaps in the literature

We found several remaining gaps in the literature; in some topics, there was a lack of primary studies and/or meta-analyses. Examples of the gaps include lack of meta-analyses of birth- and pregnancy-related factors in DEP, childhood and adolescent factors in SZ, and environmental factors in BP. It can be noted also that lifestyle factors in transdiagnostic comparison included only substance-use-related risk factors. There have been various reviews also of other risk factors which have not been meta-analyzed, so more original research studies, preferably with a longitudinal design, and meta-analyses are needed.

It would be important to study several predictors in all these outcomes simultaneously in extensive prospective studies. So far, there have been some register studies in Sweden and Denmark. Laursen, Munk-Olsen, Nordentoft, & Bo Mortensen (Reference Laursen, Munk-Olsen, Nordentoft and Bo Mortensen2007) studied several risk factors in a Danish register study and found, for example, that loss of a parent was associated with all these disorders and that high paternal age and urbanization were associated only with the risk of SZ. Additionally, comparative studies beyond register studies, such as birth cohorts, are necessary to encompass more potential risk factors that are not captured in registers.

Most of the earlier meta-analyses have not considered sex or gender differences, but there is evidence that different risk factors may impact individuals across sexes and genders (Brosch & Dhamala, Reference Brosch and Dhamala2024; Pence et al., Reference Pence, Pries, Ferrara, Rutten, van Os and Guloksuz2022). In future original studies and meta-analyses, it is important to consider differences in effects also by sex or gender.

Conclusions

We found several single risk factors with relatively small risk estimates. Childhood maltreatment was a robust transdiagnostic risk factor for all three disorders. We also found differences in risk factors, for example, pregnancy and birth complications associated strongly with SZ risk, and several somatic conditions were associated with DEP. It should be noted that our findings were based on relatively low-quality meta-analyses and a small number of original studies. More high-quality longitudinal research is needed on many risk factors to be able to evaluate their validity in single outcomes and their potential specificity or non-specificity.

Supplementary material

The supplementary material for this article can be found at http://doi.org/10.1017/S0033291725102584.

Funding statement

This work was supported by the Jalmari and Rauha Ahokas Foundation (JM).

Competing interests

The authors declare none.

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Figure 0

Figure 1. Flow chart of the selection of studies.

Figure 1

Table 1. Summary of meta-analyses of risk factors included in transdiagnostic comparison between schizophrenia, bipolar, and depressive disorders

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