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. 2025 Dec 3;55:e368. doi: 10.1017/S0033291725102584

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

Jouko Miettunen 1,2,✉, Heidi Ruotsalainen 2,3, Nea Vainio 1,2, Hala AlSaadi 1, Erika Jääskeläinen 1,2,4, Nina Rautio 1,2
PMCID: PMC13058633  PMID: 41332356

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 disorders, depressive disorders, risk factors, schizophrenia, umbrella review

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., 2015). 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, 2005), the burden of disease is substantial, and based on a meta-analysis, only 13.5% of SZ patients recover (Jääskeläinen et al., 2013). 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., 2019; Miettunen, Immonen, McGrath, Isohanni, & Jääskeläinen, 2019; Yalin & Young, 2019).

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., 2018). 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., 2018). 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, 2018). The proportion of risk that is derived from environmental sources is estimated to be 15–40% in SZ and BDs (Robinson & Bergen, 2021) and 63% in DEPs (Sullivan, Neale, & Kendler, 2000).

Some risk factors, especially genetic ones (Prata, Costa-Neves, Cosme, & Vassos, 2019), 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 (2021) 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., 2024); 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., 2024). 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. Pregnancy and birth-related factors,

  2. Childhood and adolescence-related factors,

  3. Lifestyle factors,

  4. Somatic conditions,

  5. Infectious agents, and

  6. Environmental exposures.

We did not include studies on person-level antecedents (Uher et al., 2024), personality (Christensen & Kessing, 2006; Ohi et al., 2016), temperament and character traits (Komasi et al., 2022), biomarkers (Carvalho et al., 2020), genetic risk (e.g.; Gatt, Burton, Williams, & Schofield, 2015; Mistry, Harrison, Smith, Escott-Price, & Zammit, 2018; Prata et al., 2019), migration (Henssler et al., 2020; Swinnen & Selten, 2007), and parental psychiatric illnesses (Rasic, Hajek, Alda, & Uher, 2014; Uher et al., 2023) 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, 2003). 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, 2022). 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., 2017). 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.

Figure 1.

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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

Category Schizophrenia Bipolar disorders Depressive disorders
Reference (k) Effect size (95% CI) Reference (k) Effect size (95% CI) Reference (k) Effect size (95% CI)
Pregnancy- and birth-related factors (12 factors)
Winter birth Coury et al., 2023 (k = 43) OR 1.05 (1.03–1.07) Hsu et al., 2021 (k = 5) RR 1.02 (1.00–1.04) Hsu et al., 2021 (k = 9) RR 1.01 (1.00–1.02)
Maternal stress - - Shintani et al., 2023 (k = 2) OR 12.0 (3.30–43.6) Su, D’Arcy, & Meng, 2021 (k = 4) OR 1.12 (1.04–1.22)
Maternal smoking Hunter, Murray, Asher, & Leonardi-Bee, 2020 (k = 7) RR 1.29 (1.10–1.51) Duko, Ayano, Pereira, Betts, & Alati, 2020 (k = 4) RR 1.44 (1.15–1.80) Su et al., 2021 (k = 13) OR 1.45 (1.28–1.63)
Maternal drinking - - Shintani et al., 2023 (k = 2) OR 1.59 (0.27–9.34) Su et al., 2021 (k = 2) OR 1.09 (0.96–1.24)
Birth weight < 2500 g Cannon, Jones, & Murray, 2002 (k = 5) OR 1.67 (1.22–2.29) Shintani et al., 2023 (k = 5) OR 1.28 (1.04–1.56) Su et al., 2021 (k = 10) OR 1.44 (1.17–1.76)
Gestational age < 37 weeks Cannon et al., 2002 (k = 5) OR 1.22 (0.90–1.65) Shintani et al., 2023 (k = 2) OR 1.27 (0.28–5.65) Su et al., 2021 (k = 7) OR 1.38 (1.00–1.90)
Small for gestational age Cannon et al., 2002 (k = 5) OR 1.21 (0.91–1.61) - - de Mola, de França, de Avila Quevedo, & Horta, 2014 (k = 5) OR 1.14 (0.64–2.03)
Bleeding in pregnancy Cannon et al., 2002 (k = 6) OR 1.69 (1.14–2.52) Shintani et al., 2023 (k = 5) OR 1.05 (0.47–2.37) - -
Cesarean section Zhang et al., 2019 (k = 7) OR 0.97 (0.78–1.21) Shintani et al., 2023 (k = 4) OR 1.05 (0.87–1.27) - -
Cesarean section emergency Cannon et al., 2002 (k = 3) OR 3.24 (1.40–7.50) Shintani et al., 2023 (k = 4) OR 0.95 (0.84–1.08) - -
Asphyxia Cannon et al., 2002 (k = 3) OR 1.74 (1.15–2.62) Shintani et al., 2023 (k = 5) OR 1.46 (1.02–2.11) - -
Advanced paternal age:
≥35y (ref: others) Torrey et al., 2009 (k = 10) OR 1.28 (1.11–1.48) Shintani et al., 2023 (k = 3) OR 1.13 (1.00–1.58) - -
30–34y (ref: 25–29y) Miller et al., 2011 (k = 12) RR 1.03 (0.97–1.08) Fico et al., 2022 (k = 4) OR 1.02 (0.95–1.09) - -
35–39y (ref: 25–29y) Miller et al., 2011 (k = 12) RR 1.12 (1.06–1.19) Fico et al., 2022 (k = 4) OR 1.03 (0.91–1.16) - -
40–44y (ref: 25–29y) Miller et al., 2011 (k = 12) RR 1.21 (1.11–1.32) Fico et al., 2022 (k = 3) OR 1.16 (0.97–1.38) - -
>45y (ref: 25–29y) Miller et al., 2011 (k = 12) est. RR 1.49 (1.28–1.73) Fico et al., 2022 (k = 4) OR 1.29 (1.15–1.46) - -
Childhood- and adolescence-related factors (8 factors)
Academic achievement Dickson et al., 2020 (k = 11) eOR 0.59 (0.46–0.78) - - Huang, 2015 (k = 43) eOR 0.58 (0.51–0.62)
Parental death - - Palmier-Claus, Berry, Bucci, Mansell, & Varese, 2016 (k = 5) OR 1.16 (0.75–1.78) Simbi, Zhang, & Wang, 2020 (k = 4) OR 1.76 (1.13–2.73)
Any maltreatment Matheson, Shepherd, Pinchbeck, Laurens, & Carr, 2013 (k = 7) OR 3.60 (2.08–6.23) - - Watson, Sharpley, Bitsika, Evans, & Vessey, 2025 (k = 101) OR 2.49 (2.25–2.76)
Neglect (emotional) - - Palmier–Claus et al., 2016 (k = 7) OR 2.62 (2.03–3.38) Nelson, Klumparendt, Doebler, & Ehring, 2017 (k = 9) OR 3.54 (2.48–5.04)
Neglect (physical) - - Palmier–Claus et al., 2016 (k = 7) OR 2.26 (1.74–2.93) Nelson et al., 2017 (k = 7) OR 2.45 (1.63–3.68)
Sexual abuse Chen et al., 2010 (k = 3) OR 1.36 (0.81–2.30) Palmier–Claus et al., 2016 (k = 12) OR 2.58 (2.08–3.20) Amado, Arce, & Herraiz, 2015 (k = 87) eOR 2.45 (2.36–2.55)
Emotional abuse - - Palmier–Claus et al., 2016 (k = 9) OR 2.26 (1.74–2.93) Li et al., 2023 (k = 21) eOR 1.87 (1.49–2.36)
Physical abuse - - Palmier–Claus et al., 2016 (k = 12) OR 2.86 (2.22–3.69) Gardner et al., 2019 (k = 41) OR 1.78 (1.57–2.01)
Lifestyle factors (2 factors)
Cannabis use - - Maggu et al., 2023 (k = 4) OR 2.63 (1.95–3.53) Churchill, Chubb, Popova, Spears, & Pigott, 2025 (k = 22) OR 1.29 (1.13–1.46)
Current smoking Hu et al., 2025 (k = 6) RR 1.84 (1.07–3.19) Hu et al., 2025 (k = 3) RR 1.54 (1.22–1.95) Hu et al., 2025 (k = 18) RR 1.30 (1.18–1.43)
Somatic conditions (16 factors)
Obesity - - Zhao, Okusaga, Quevedo, Soares, & Teixeira, 2016 (k = 9) OR 1.77 (1.40–2.23) de Wit et al., 2010 (k = 28) OR 1.18 (1.01–1.37)
Celiac disease Wijarnpreecha, Jaruvongvanich, Cheungpasitporn, & Ungprasert, 2018 (k = 4) OR 2.03 (1.45–2.86) - - Clappison, Hadjivassiliou, & Zis, 2020 (k = 11) OR 4.91 (2.17–11.15)
Bullous pemphigoid Huang, Wu, Liu, & Huang, 2022 (k = 4) HR 2.86 (1.55–5.28) - - Huang et al., 2022 (k = 6) HR 1.49 (1.13–1.96)
Polycystic ovary syndrome - - Shahraki, Rastkar, Ramezanpour, & Ghajarzadeh, 2024 (k = 9) OR 2.06 (1.61–2.63) Brutocao et al., 2018 (k = 24) OR 2.79 (2.23–3.50)
Epilepsy Thapa et al., 2024 (k = 5) OR 5.22 (2.99–9.11) - - Chu, 2022 (k = 23) OR/RR 2.05 (1.77–2.37)
Asthma - - Wu et al., 2016 (k = 4) OR 2.12 (1.57–1.87) Lu et al., 2018 (k = 39) RR 1.59 (1.46–1.74)
Irritable bowel syndrome - - Tseng et al., 2016 (k = 6) OR 2.48 (2.35–2.61) Zamani, Alizadeh-Tabari, & Zamani, 2019 (k = 10) OR 2.72 (2.45–3.02)
Minor physical anomalies Xu, Chan, & Compton, 2011 (k = 14) eOR 5.58 (3.13–9.96) Varga et al., 2021 (k = 4) eOR 3.07 (1.44–6.45) - -
Autoimmune diseases Cao et al., 2023 (k = 8) RR 1.48 (1.15–1.89) Chen, Jiang, & Zhang, 2021 (k = 16) OR 1.54 (1.28–1.86) - -
Psoriasis Ungprasert, Wijarnpreecha, & Cheungpasitporn, 2019 (k = 5) OR 1.41 (1.19–1.66) Chen et al., 2021 (k = 4) OR 1.58 (1.21–2.05) Dowlatshahi, Wakkee, Arends, & Nijsten, 2014 (k = 5) 1.47 (1.40–1.76)
Multiple sclerosis Cao et al., 2023 (k = 10) RR 1.36 (1.12–1.66) Chen et al., 2021 (k = 2) OR 6.93 (0.26–185.7) - -
Gastroesophageal reflux - - Nurita, Faturohman, Santoso, Magdalena, & Ilyas, 2025 (k = 3) OR 2.29 (1.64–3.21) Zamani, Alizadeh-Tabari, Chan, & Talley, 2023 (k = 5) OR 2.56 (1.11–5.87)
Rheumatoid arthritis - - Charoenngam, Ponvilawan, & Ungprase, 2019 (k = 6) OR 2.06 (1.34–3.17) Dickens, McGowan, Clark-Carter, & Creed, 2002 (k = 8) eOR 1.87 (1.61–2.18)
Alcohol use disorder - - Hunt, Malhi, Cleary, Lai, & Sitharthan, 2016 (k = 21) OR 4.09 (3.37–4.96) Li et al., 2020 (k = 42) RR 1.57 (1.41–1.76)
Hearing loss Linszen, Brouwer, Heringa, & Sommer, 2016 (K = 3) OR 3.15 (1.25–7.95) - - Wei, Li, & Gui, 2024 (K = 24) OR 1.35 (1.27–1.44)
Traumatic brain injury Molloy, Conroy, Cotter, & Cannon, 2011 (k = 9) OR 1.65 (1.17–2.32) Perry et al., 2016 (k = 3) OR 1.85 (1.17–2.94) Dehbozorgi et al., 2024 (k = 29) OR 2.10 (1.71–2.59)
Infectious agents (10 factors)
Toxoplasma gondii Sutterland et al., 2015 (k = 42) OR 1.81 (1.52–2.17) Sutterland et al., 2015 (k = 11) OR 1.52 (1.06–2.18) Nayeri Chegeni et al., 2019 (k = 29) OR 1.15 (0.95–1.39)
Human Herpesvirus–1 Gutiérrez-Fernández et al., 2015 (k = 14) OR 1.17 (0.88–1.54) Snijders et al., 2019 (k = 7) OR 0.92 (0.73–1.14) Wang et al., 2014 (k = 4) OR 1.98 (1.20–3.29)
Human Herpesvirus–2 Arias et al., 2012 (k = 6) OR 1.34 (1.09–1.70) Snijders et al., 2019 (k = 5) OR 1.23 (0.86–1.75) - -
Borna disease virus Azami et al., 2018a (k = 30) OR 2.72 (1.76–4.21) Azami et al., 2018b (k = 7) OR 2.00 (1.29–3.08) Wang et al., 2014 (k = 15) OR 3.25 (1.61–6.55)
Cytomegalovirus Arias et al., 2012 (k = 15) OR 0.86 (0.54–1.38) Snijders et al., 2019 (k = 9) OR 1.19 (0.86–1.64) Wang et al., 2014 (k = 3) OR 1.94 (0.91–4.14)
Epstein–Barr virus Arias et al., 2012 (k = 5) OR 1.67 (0.57–4.94) - - Wang et al., 2014 (k = 4) OR 1.98 (1.20–3.29)
Varicella zoster virus Arias et al., 2012 (k = 4) OR 1.17 (0.16–8.58) - - Wang et al., 2014 (k = 3) OR 2.10 (1.02–4.32)
Chlamydiaceae trachomatis Arias et al., 2012 (k = 2) OR 4.39 (0.03–571.2) - - Wang et al., 2014 (k = 2) OR 11.59 (1.40–95.8)
Chlamydiaceae pneumoniae Gutiérrez-Fernández et al., 2015 (k = 3) OR 5.96 (3.42–10.39) - - Wang et al., 2014 (k = 2) OR 3.32 (0.53–20.58)
Chlamydiaceae psittaci Arias et al., 2012 (k = 2) OR 29.05 (8.91–94.70) - - Wang et al., 2014 (k = 2) OR 5.85 (0.63–54.54)
Environmental exposures (7 factors)
Urbanicity Castillejos, Martín-Pérez, & Moreno-Küstner, 2018 (k = 5) IRR 2.25 (2.00–2.52) - - Xu et al., 2023 (k = 96) est. OR 1.10 (0.94–1.29)
Ethnic density, 10% increase Baker, Jackson, Jongsma, & Saville, 2021 (k = 5) OR 1.15 (1.04–1.25) - - Bécares, Dewey, & Das-Munshi, 2018 (k = 6) OR 0.83 (0.47–1.48)
PM10 (short-term exposure), per increase in 10 μg/m3 Song et al., 2023 (k = 10) RR 1.005 (1.003–1.007) - - Borroni et al., 2022 (k = 16) RR 1.009 (1.006–1.012)
PM2.5 (short-term exposure), per increase in 10 μg/m3 Song et al., 2023 (k = 10) RR 1.005 (1.002–1.008) - - Borroni et al., 2022 (k = 19) RR 1.009 (1.007–1.011)
SO2 (short-term exposure), per increase in 10 μg/m3 Song et al., 2023 (k = 7) RR 1.029 (1.015–1.043) - - Borroni et al., 2022 (k = 18) RR 1.024 (1.010–1.037)
NO2 (short-term exposure), per increase in 10 μg/m3 Song et al., 2023 (k = 9) RR 1.028 (1.013–1.042) - - Borroni et al., 2022 (k = 19) RR 1.022 (1.012–1.033)
CO (short-term exposure), per increase in 1 mg/m3 Song et al., 2023 (k = 3) RR 1.030 (0.990–1.072) - - Borroni et al., 2022 (k = 12) RR 1.062 (1.020–1.105)
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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., 2025; Zhang et al., 2019) 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., 2023). 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 (2017) 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., 2022).

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, 2014). Regarding childhood sexual abuse, a large umbrella review by Hailes, Yu, Danese, & Fazel (2019) 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., 2010).

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., 2024), 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, 2011; Marconi, Di Forti, Lewis, Murray, & Vassos, 2016); 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., 2012). Association between cannabis use and DEPs was less clear in the included meta-analysis (Lev-Ran et al., 2014). Physical activity and diet were studied mostly in DEPs, whereas the research on psychotic outcomes is scarce (Aucoin, LaChance, Cooley, & Kidd, 2020; Brokmeier et al., 2020; Johnstad, 2024).

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., 2019). 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, 2022). 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, 2020). 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, 2011).

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, 2012). 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, 2021), whereas findings regarding maternal influenza infection in pregnancy and offspring psychiatric illnesses have been inconsistent (Fung et al., 2022). 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., 2024).

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. (2021) 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., 2006). Notably, urbanicity is associated with SZ in developed countries (OR 1.30), but not in developing countries (OR 0.89) (Xu, Miao, Turner, & DeRubuis, 2023). 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, 2016). Green space has been identified as a protective factor for DEPs (Liu et al., 2023), 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., 2018). Short-term air pollution was associated with relatively similar effect sizes in SZ (Song et al., 2023) and DEPs (Borroni et al., 2022). Borroni et al. (2022) 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., 2019). 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 (2022), 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., 2024), 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., 2019), psychiatric (Uher et al., 2024), and psychological (Komasi et al. 2022; Uher et al., 2024) 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 (2019). 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, 2023). Regarding advanced paternal age, genetic changes have been suggested to explain the risk for SZ (Torrey, et al., 2009).

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. (2022) 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 (2007) 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, 2024; Pence et al., 2022). 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.

Supporting information

Miettunen et al. supplementary material

Miettunen et al. supplementary material

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.

References

  1. Amado, B. G., Arce, R., & Herraiz, A. (2015). Psychological injury in victims of child sexual abuse: A meta-analytic review. Psychosocial Intervention, 24(1), 49–62. 10.1016/j.psi.2015.03.002. [DOI] [Google Scholar]
  2. Arias, I., Sorlozano, A., Villegas, E., de Dios Luna, J., McKenney, K., Cervilla, J., Gutierrez, B., & Gutierrez, J. (2012). Infectious agents associated with schizophrenia: A meta-analysis. Schizophrenia Research, 136(1–3), 128–136. 10.1016/j.schres.2011.10.026. [DOI] [PubMed] [Google Scholar]
  3. Aucoin, M., LaChance, L., Cooley, K., & Kidd, S. (2020). Diet and psychosis: A scoping review. Neuropsychobiology, 79(1), 20–42. 10.1159/000493399. [DOI] [PubMed] [Google Scholar]
  4. Azami, M., Jalilian, F. A., Khorshidi, A., Mohammadi, Y., & Tardeh, Z. (2018a). The association between Borna disease virus and schizophrenia: A systematic review and meta-analysis. Asian Journal of Psychiatry, 34, 67–73. 10.1016/j.ajp.2017.11.026. [DOI] [PubMed] [Google Scholar]
  5. Azami, M., Jalilian, F. A., Mojarad, M. R. A., Mohammadi, Y., & Tardeh, Z. (2018b). The association between Borna disease virus and mood disorders: A systematic review and meta-analysis. Archives of Neuroscience, 5(2), e57779. 10.5812/archneurosci.57779. [DOI] [PubMed] [Google Scholar]
  6. Baker, S. J., Jackson, M., Jongsma, H., & Saville, C. W. N. (2021). The ethnic density effect in psychosis: A systematic review and multilevel meta-analysis. The British Journal of Psychiatry, 219(6), 632–643. 10.1192/bjp.2021.96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bécares, L., Dewey, M. E., & Das-Munshi, J. (2018). Ethnic density effects for adult mental health: Systematic review and meta-analysis of international studies. Psychological Medicine, 48(12), 2054–2072. 10.1017/S0033291717003580. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Belbasis, L., Köhler, C. A., Stefanis, N., Stubbs, B., van Os, J., Vieta, E., Seeman, M. V., Arango, C., Carvalho, A. F., & Evangelou, E. (2018). Risk factors and peripheral biomarkers for schizophrenia spectrum disorders: An umbrella review of meta-analyses. Acta Psychiatrica Scandinavica, 137(2), 88–97. 10.1111/acps.12847. [DOI] [PubMed] [Google Scholar]
  9. Boden, J. M., & Fergusson, D. M. (2011). Alcohol and depression. Addiction, 106(5), 906–914. 10.1111/j.1360-0443.2010.03351.x. [DOI] [PubMed] [Google Scholar]
  10. Borroni, E., Pesatori, A. C., Bollati, V., Buoli, M., & Carugno, M. (2022). Air pollution exposure and depression: A comprehensive updated systematic review and meta-analysis. Environmental Pollution, 292(Pt A), 118245. 10.1016/j.envpol.2021.118245. [DOI] [PubMed] [Google Scholar]
  11. Brokmeier, L. L., Firth, J., Vancampfort, D., Smith, L., Deenik, J., Rosenbaum, S., Stubbs, B., & Schuch, F. B. (2020). Does physical activity reduce the risk of psychosis? A systematic review and meta-analysis of prospective studies. Psychiatry Research, 284, 112675. 10.1016/j.psychres.2019.112675. [DOI] [PubMed] [Google Scholar]
  12. Brosch, K., & Dhamala, E. (2024). Influences of sex and gender on the associations between risk and protective factors, brain, and behavior. Biology of Sex Differences, 15(1), 97. 10.1186/s13293-024-00674-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brutocao, C., Zaiem, F., Alsawas, M., Morrow, A. S., Murad, M. H., & Javed, A. (2018). Psychiatric disorders in women with polycystic ovary syndrome: A systematic review and meta-analysis. Endocrine, 62(2), 318–325. 10.1007/s12020-018-1692-3. [DOI] [PubMed] [Google Scholar]
  14. Cannon, M., Jones, P. B., & Murray, R. M. (2002). Obstetric complications and schizophrenia: Historical and meta-analytic review. The American Journal of Psychiatry, 159(7), 1080–1092. 10.1176/appi.ajp.159.7.1080. [DOI] [PubMed] [Google Scholar]
  15. Cao, Y., Ji, S., Chen, Y., Zhang, X., Ding, G., & Tang, F. (2023). Association between autoimmune diseases of the nervous system and schizophrenia: A systematic review and meta-analysis of cohort studies. Comprehensive Psychiatry, 122, 152370. 10.1016/j.comppsych.2023.152370. [DOI] [PubMed] [Google Scholar]
  16. Carvalho, A. F., Solmi, M., Sanches, M., Machado, M. O., Stubbs, B., Ajnakina, O., Sherman, C., Sun, Y. R., Liu, C. S., Brunoni, A. R., Pigato, G., Fernandes, B. S., Bortolato, B., Husain, M. I., Dragioti, E., Firth, J., Cosco, T. D., Maes, M., Berk, M., … Herrmann, N. (2020). Evidence-based umbrella review of 162 peripheral biomarkers for major mental disorders. Translational Psychiatry, 10(1), 152. 10.1038/s41398-020-0835-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Castillejos, M. C., Martín-Pérez, C., & Moreno-Küstner, B. (2018). A systematic review and meta-analysis of the incidence of psychotic disorders: The distribution of rates and the influence of gender, urbanicity, immigration and socio-economic level. Psychological Medicine, 48(13), 2101–2115. 10.1017/S0033291718000235. [DOI] [PubMed] [Google Scholar]
  18. Charoenngam, N., Ponvilawan, B., & Ungprasert, P. (2019). Patients with rheumatoid arthritis have a higher risk of bipolar disorder: A systematic review and meta-analysis. Psychiatry Research, 282, 112484. 10.1016/j.psychres.2019.112484. [DOI] [PubMed] [Google Scholar]
  19. Chen, L. P., Murad, M. H., Paras, M. L., Colbenson, K. M., Sattler, A. L., Goranson, E. N., Elamin, M. B., Seime, R. J., Shinozaki, G., Prokop, L. J., & Zirakzadeh, A. (2010). Sexual abuse and lifetime diagnosis of psychiatric disorders: Systematic review and meta-analysis. Mayo Clinic Proceedings, 85(7), 618–629. 10.4065/mcp.2009.0583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Chen, M., Jiang, Q., & Zhang, L. (2021). The prevalence of bipolar disorder in autoimmune disease: A systematic review and meta-analysis. Annals of Palliative Medicine, 10(1), 350–361. 10.21037/apm-20-2293. [DOI] [PubMed] [Google Scholar]
  21. Christensen, M. V., & Kessing, L. V. (2006). Do personality traits predict first onset in depressive and bipolar disorder? Nordic Journal of Psychiatry, 60(2), 79–88. 10.1080/08039480600600300. [DOI] [PubMed] [Google Scholar]
  22. Chu, C. (2022). Association between epilepsy and risk of depression: A meta-analysis. Psychiatry Research, 312, 114531. 10.1016/j.psychres.2022.114531. [DOI] [PubMed] [Google Scholar]
  23. Churchill, V., Chubb, C. S., Popova, L., Spears, C. A., & Pigott, T. (2025). The association between cannabis and depression: An updated systematic review and meta-analysis. Psychological Medicine, 55, e44. 10.1017/S0033291724003143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Clappison, E., Hadjivassiliou, M., & Zis, P. (2020). Psychiatric manifestations of coeliac disease, a systematic review and meta-analysis. Nutrients, 12(1), 142. 10.3390/nu12010142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Clemente, A. S., Diniz, B. S., Nicolato, R., Kapczinski, F. P., Soares, J. C., Firmo, J. O., & Castro-Costa, É. (2015). Bipolar disorder prevalence: A systematic review and meta-analysis of the literature. Revista Brasileira de Psiquiatria, 37(2), 155–161. 10.1590/1516-4446-2012-1693. [DOI] [PubMed] [Google Scholar]
  26. Coury, S. M., Lombroso, A., Avila-Quintero, V. J., Taylor, J. H., Flores, J. M., Szejko, N., & Bloch, M. H. (2023). Systematic review and meta-analysis: Season of birth and schizophrenia risk. Schizophrenia Research, 252, 244–252. 10.1016/j.schres.2022.12.016. [DOI] [PubMed] [Google Scholar]
  27. Dagani, J., Baldessarini, R. J., Signorini, G., Nielssen, O., de Girolamo, G., Large, M. (2019). The age of onset of bipolar disorders. In de Girolamo G., McGorry P., & Sartorius N. (Eds.), Age of onset of mental disorders (pp. 75–110). Springer. 10.1007/978-3-319-72619-9_5 [DOI] [Google Scholar]
  28. de Kluiver, H., Buizer-Voskamp, J. E., Dolan, C. V., & Boomsma, D. I. (2017). Paternal age and psychiatric disorders: A review. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics, 174(3), 202–213. 10.1002/ajmg.b.32508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. de Mola, C., de França, G. V., de Avila Quevedo, L., & Horta, B. L. (2014). Low birth weight, preterm birth and small for gestational age association with adult depression: Systematic review and meta-analysis. The British Journal of Psychiatry, 205(5), 340–347. 10.1192/bjp.bp.113.139014. [DOI] [PubMed] [Google Scholar]
  30. de Wit, L., Luppino, F., van Straten, A., Penninx, B., Zitman, F., & Cuijpers, P. (2010). Depression and obesity: A meta-analysis of community-based studies. Psychiatry Research, 178(2), 230–235. 10.1016/j.psychres.2009.04.015. [DOI] [PubMed] [Google Scholar]
  31. Dehbozorgi, M., Maghsoudi, M. R., Rajai, S., Mohammadi, I., Nejad, A. R., Rafiei, M. A., Soltani, S., Shafiee, A., & Bakhtiyari, M. (2024). Depression after traumatic brain injury: A systematic review and meta-analysis. The American Journal of Emergency Medicine, 86, 21–29. 10.1016/j.ajem.2024.08.039. [DOI] [PubMed] [Google Scholar]
  32. Dickens, C., McGowan, L., Clark-Carter, D., & Creed, F. (2002). Depression in rheumatoid arthritis: A systematic review of the literature with meta-analysis. Psychosomatic Medicine, 64(1), 52–60. 10.1097/00006842-200201000-00008. [DOI] [PubMed] [Google Scholar]
  33. Dickson, H., Hedges, E. P., Ma, S. Y., Cullen, A. E., MacCabe, J. H., Kempton, M. J., Downs, J., & Laurens, K. R. (2020). Academic achievement and schizophrenia: A systematic meta-analysis. Psychological Medicine, 50(12), 1949–1965. 10.1017/S0033291720002354. [DOI] [PubMed] [Google Scholar]
  34. Dines, M., Kes, M., Ailán, D., Cetkovich-Bakmas, M., Born, C., & Grunze, H. (2024). Bipolar disorders and schizophrenia: Discrete disorders? Frontiers in Psychiatry, 15, 1352250. 10.3389/fpsyt.2024.1352250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Dowlatshahi, E. A., Wakkee, M., Arends, L. R., & Nijsten, T. (2014). The prevalence and odds of depressive symptoms and clinical depression in psoriasis patients: A systematic review and meta-analysis. The Journal of Investigative Dermatology, 134(6), 1542–1551. 10.1038/jid.2013.508. [DOI] [PubMed] [Google Scholar]
  36. Dragioti, E., Radua, J., Solmi, M., Arango, C., Oliver, D., Cortese, S., Jones, P. B., Il Shin, J., Correll, C. U., & Fusar-Poli, P. (2022). Global population attributable fraction of potentially modifiable risk factors for mental disorders: A meta-umbrella systematic review. Molecular Psychiatry, 27(8), 3510–3519. 10.1038/s41380-022-01586-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Duko, B., Ayano, G., Pereira, G., Betts, K., & Alati, R. (2020). Prenatal tobacco use and the risk of mood disorders in offspring: A systematic review and meta-analysis. Social Psychiatry and Psychiatric Epidemiology, 55(12), 1549–1562. 10.1007/s00127-020-01949-y. [DOI] [PubMed] [Google Scholar]
  38. Engemann, K., Pedersen, C. B., Arge, L., Tsirogiannis, C., Mortensen, P. B., & Svenning, J. C. (2018). Childhood exposure to green space – A novel risk-decreasing mechanism for schizophrenia? Schizophrenia Research, 199, 142–148. 10.1016/j.schres.2018.03.026. [DOI] [PubMed] [Google Scholar]
  39. Fico, G., Oliva, V., De Prisco, M., Giménez-Palomo, A., Sagué-Vilavella, M., Gomes-da-Costa, S., Garriga, M., Solé, E., Valentí, M., Fanelli, G., Serretti, A., Fornaro, M., Carvalho, A. F., Vieta, E., & Murru, A. (2022). The U-shaped relationship between parental age and the risk of bipolar disorder in the offspring: A systematic review and meta-analysis. European Neuropsychopharmacology, 60, 55–75. 10.1016/j.euroneuro.2022.05.004. [DOI] [PubMed] [Google Scholar]
  40. Fung, S. G., Fakhraei, R., Condran, G., Regan, A. K., Dimanlig-Cruz, S., Ricci, C., Foo, D., Sarna, M., Török, E., & Fell, D. B. (2022). Neuropsychiatric outcomes in offspring after fetal exposure to maternal influenza infection during pregnancy: A systematic review. Reproductive Toxicology, 113, 155–169. 10.1016/j.reprotox.2022.09.002. [DOI] [PubMed] [Google Scholar]
  41. Gardner, M. J., Thomas, H. J., & Erskine, H. E. (2019). The association between five forms of child maltreatment and depressive and anxiety disorders: A systematic review and meta-analysis. Child Abuse & Neglect, 96, 104082. 10.1016/j.chiabu.2019.104082. [DOI] [PubMed] [Google Scholar]
  42. Gatt, J. M., Burton, K. L., Williams, L. M., & Schofield, P. R. (2015). Specific and common genes implicated across major mental disorders: A review of meta-analysis studies. Journal of Psychiatric Research, 60, 1–13. 10.1016/j.jpsychires.2014.09.014. [DOI] [PubMed] [Google Scholar]
  43. Groening, J. M., Denton, E., Parvaiz, R., Brunet, D. L., Von Daniken, A., Shi, Y., & Bhattacharyya, S. (2024). A systematic evidence map of the association between cannabis use and psychosis-related outcomes across the psychosis continuum: An umbrella review of systematic reviews and meta-analyses. Psychiatry Research, 331, 115626. 10.1016/j.psychres.2023.115626. [DOI] [PubMed] [Google Scholar]
  44. Gutiérrez-Fernández, J., Luna Del Castillo, J.deD., Mañanes-González, S., Carrillo-Ávila, J. A., Gutiérrez, B., Cervilla, J. A., & Sorlózano-Puerto, A. (2015). Different presence of Chlamydia pneumoniae, herpes simplex virus type 1, human herpes virus 6, and Toxoplasma gondii in schizophrenia: Meta-analysis and analytical study. Neuropsychiatric Disease and Treatment, 11, 843–852. 10.2147/NDT.S79285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Hailes, H. P., Yu, R., Danese, A., & Fazel, S. (2019). Long-term outcomes of childhood sexual abuse: An umbrella review. The Lancet Psychiatry, 6(10), 830–839. 10.1016/S2215-0366(19)30286-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Henssler, J., Brandt, L., Müller, M., Liu, S., Montag, C., Sterzer, P., & Heinz, A. (2020). Migration and schizophrenia: Meta-analysis and explanatory framework. European Archives of Psychiatry and Clinical Neuroscience, 270(3), 325–335. 10.1007/s00406-019-01028-7. [DOI] [PubMed] [Google Scholar]
  47. Higgins, J. P., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ, 327(7414), 557–560. 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Hsu, C. W., Tseng, P. T., Tu, Y. K., Lin, P. Y., Hung, C. F., Liang, C. S., Hsieh, Y. Y., Yang, Y. H., Wang, L. J., & Kao, H. Y. (2021). Month of birth and mental disorders: A population-based study and validation using global meta-analysis. Acta Psychiatrica Scandinavica, 144(2), 153–167. 10.1111/acps.13313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Hu, Z., Cui, E., Chen, B., & Zhang, M. (2025). Association between cigarette smoking and the risk of major psychiatric disorders: A systematic review and meta-analysis in depression, schizophrenia, and bipolar disorder. Frontiers in Medicine (Lausanne), 12, 1529191. 10.3389/fmed.2025.1529191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Huang, C. (2015). Academic achievement and subsequent depression: A meta-analysis of longitudinal studies. Journal of Child and Family Studies, 24, 434–442. 10.1007/s10826-013-9855-6. [DOI] [Google Scholar]
  51. Huang, I. H., Wu, P. C., Liu, C. W., & Huang, Y. C. (2022). Association between bullous pemphigoid and psychiatric disorders: A systematic review and meta-analysis. Journal of the German Society of Dermatology, 20(10), 1305–1312. 10.1111/ddg.14852. [DOI] [PubMed] [Google Scholar]
  52. Hunt, G. E., Malhi, G. S., Cleary, M., Lai, H. M., & Sitharthan, T. (2016). Comorbidity of bipolar and substance use disorders in national surveys of general populations, 1990-2015: Systematic review and meta-analysis. Journal of Affective Disorders, 206, 321–330. 10.1016/j.jad.2016.06.051. [DOI] [PubMed] [Google Scholar]
  53. Hunter, A., Murray, R., Asher, L., & Leonardi-Bee, J. (2020). The effects of tobacco smoking, and prenatal tobacco smoke exposure, on risk of schizophrenia: A systematic review and meta-analysis. Nicotine & Tobacco Research, 22(1), 3–10. 10.1093/ntr/nty160. [DOI] [PubMed] [Google Scholar]
  54. Jääskeläinen, E., Juola, P., Hirvonen, N., McGrath, J. J., Saha, S., Isohanni, M., Veijola, J., & Miettunen, J. (2013). A systematic review and meta-analysis of recovery in schizophrenia. Schizophrenia Bulletin, 39(6), 1296–1306. 10.1093/schbul/sbs130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Johnstad, P. G. (2024). Unhealthy behaviors associated with mental health disorders: A systematic comparative review of diet quality, sedentary behavior, and cannabis and tobacco use. Frontiers in Public Health, 11, 1268339. 10.3389/fpubh.2023.1268339 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Kaymaz, N., Krabbendam, L., de Graaf, R., Nolen, W., Ten Have, M., & van Os, J. (2006). Evidence that the urban environment specifically impacts on the psychotic but not the affective dimension of bipolar disorder. Social Psychiatry and Psychiatric Epidemiology, 41(9), 679–685. 10.1007/s00127-006-0086-7. [DOI] [PubMed] [Google Scholar]
  57. Khan, A., Plana-Ripoll, O., Antonsen, S., Brandt, J., Geels, C., Landecker, H., Sullivan, P. F., Pedersen, C. B., & Rzhetsky, A. (2019). Environmental pollution is associated with increased risk of psychiatric disorders in the US and Denmark. PLoS Biology, 17(8), e3000353. 10.1371/journal.pbio.3000353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Khandaker, G. M., Zimbron, J., Dalman, C., Lewis, G., & Jones, P. B. (2012). Childhood infection and adult schizophrenia: A meta-analysis of population-based studies. Schizophrenia Research, 139(1–3), 161–168. 10.1016/j.schres.2012.05.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Köhler, C. A., Evangelou, E., Stubbs, B., Solmi, M., Veronese, N., Belbasis, L., Bortolato, B., Melo, M. C. A., Coelho, C. A., Fernandes, B. S., Olfson, M., Ioannidis, J. P. A., & Carvalho, A. F. (2018). Mapping risk factors for depression across the lifespan: An umbrella review of evidence from meta-analyses and Mendelian randomization studies. Journal of Psychiatric Research, 103, 189–207. 10.1016/j.jpsychires.2018.05.020. [DOI] [PubMed] [Google Scholar]
  60. Komasi, S., Rezaei, F., Hemmati, A., Rahmani, K., Amianto, F., & Miettunen, J. (2022). Comprehensive meta-analysis of associations between temperament and character traits in Cloninger’s psychobiological theory and mental disorders. The Journal of International Medical Research, 50(1), 3000605211070766. 10.1177/03000605211070766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Kunugi, H. (2023). Depression and lifestyle: Focusing on nutrition, exercise, and their possible relevance to molecular mechanisms. Psychiatry and Clinical Neurosciences, 77(8), 420–433. 10.1111/pcn.13551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Large, M., Sharma, S., Compton, M. T., Slade, T., & Nielssen, O. (2011). Cannabis use and earlier onset of psychosis: A systematic meta-analysis. Archives of General Psychiatry, 68(6), 555–561. 10.1001/archgenpsychiatry.2011.5. [DOI] [PubMed] [Google Scholar]
  63. Laursen, T. M., Munk-Olsen, T., Nordentoft, M., & Bo Mortensen, P. (2007). A comparison of selected risk factors for unipolar depressive disorder, bipolar affective disorder, schizoaffective disorder, and schizophrenia from a Danish population-based cohort. The Journal of Clinical Psychiatry, 68(11), 1673–1681. 10.4088/jcp.v68n1106. [DOI] [PubMed] [Google Scholar]
  64. Lenhard, W. & Lenhard, A. (2022). Computation of effect sizes. Psychometrica. 10.13140/RG.2.2.17823.92329. [DOI] [Google Scholar]
  65. Lev-Ran, S., Roerecke, M., Le Foll, B., George, T. P., McKenzie, K., & Rehm, J. (2014). The association between cannabis use and depression: A systematic review and meta-analysis of longitudinal studies. Psychological Medicine, 44(4), 797–810. 10.1017/S0033291713001438. [DOI] [PubMed] [Google Scholar]
  66. Li, J., Ledoux-Hutchinson, L., & Toffa, D. H. (2022). Prevalence of bipolar symptoms or disorder in epilepsy: A systematic review and meta-analysis. Neurology, 98(19), e1913–e1922. 10.1212/WNL.0000000000200186. [DOI] [PubMed] [Google Scholar]
  67. Li, J., Wang, H., Li, M., Shen, Q., Li, X., Zhang, Y., Peng, J., Rong, X., & Peng, Y. (2020). Effect of alcohol use disorders and alcohol intake on the risk of subsequent depressive symptoms: A systematic review and meta-analysis of cohort studies. Addiction (Abingdon, England), 115(7), 1224–1243. 10.1111/add.14935. [DOI] [PubMed] [Google Scholar]
  68. Li, M., Gao, T., Su, Y., Zhang, Y., Yang, G., D’Arcy, C., & Meng, X. (2023). The timing effect of childhood maltreatment in depression: A systematic review and meta-analysis. Trauma, Violence & Abuse, 24(4), 2560–2580. 10.1177/15248380221102558. [DOI] [PubMed] [Google Scholar]
  69. Linszen, M. M., Brouwer, R. M., Heringa, S. M., & Sommer, I. E. (2016). Increased risk of psychosis in patients with hearing impairment: Review and meta-analyses. Neuroscience and Biobehavioral Reviews, 62, 1–20. 10.1016/j.neubiorev.2015.12.012. [DOI] [PubMed] [Google Scholar]
  70. Liu, Z., Chen, X., Cui, H., Ma, Y., Gao, N., Li, X., Meng, X., Lin, H., Abudou, H., Guo, L., & Liu, Q. (2023). Green space exposure on depression and anxiety outcomes: A meta-analysis. Environmental Research, 231(Pt 3), 116303. 10.1016/j.envres.2023.116303 [DOI] [PubMed] [Google Scholar]
  71. Lu, Z., Chen, L., Xu, S., Bao, Q., Ma, Y., Guo, L., Zhang, S., Huang, X., Cao, C., & Ruan, L. (2018). Allergic disorders and risk of depression: A systematic review and meta-analysis of 51 large-scale studies. Annals of Allergy, Asthma & Immunology, 120(3), 310–e2-317. 10.1016/j.anai.2017.12.011. [DOI] [PubMed] [Google Scholar]
  72. MacCabe, J. H., Lambe, M. P., Cnattingius, S., Sham, P. C., David, A. S., Reichenberg, A., Murray, R. M., & Hultman, C. M. (2010). Excellent school performance at age 16 and risk of adult bipolar disorder: National cohort study. The British Journal of Psychiatry, 196(2), 109–115. 10.1192/bjp.bp.108.060368. [DOI] [PubMed] [Google Scholar]
  73. Maggu, G., Choudhary, S., Jaishy, R., Chaudhury, S., Saldanha, D., & Borasi, M. (2023). Cannabis use and its relationship with bipolar disorder: A systematic review and meta-analysis. Industrial Psychiatry Journal, 32(2), 202–214. 10.4103/ipj.ipj_43_23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Manrique-Garcia, E., Zammit, S., Dalman, C., Hemmingsson, T., Andreasson, S., & Allebeck, P. (2012). Cannabis, schizophrenia and other non-affective psychoses: 35 years of follow-up of a population-based cohort. Psychological Medicine, 42(6), 1321–1328. 10.1017/S0033291711002078. [DOI] [PubMed] [Google Scholar]
  75. Marconi, A., Di Forti, M., Lewis, C. M., Murray, R. M., & Vassos, E. (2016). Meta-analysis of the association between the level of cannabis use and risk of psychosis. Schizophrenia Bulletin, 42(5), 1262–1269. 10.1093/schbul/sbw003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Matheson, S. L., Shepherd, A. M., Pinchbeck, R. M., Laurens, K. R., & Carr, V. J. (2013). Childhood adversity in schizophrenia: A systematic meta-analysis. Psychological Medicine, 43(2), 225–238. 10.1017/S0033291712000785. [DOI] [PubMed] [Google Scholar]
  77. Miettunen, J., Immonen, J., McGrath, J. J., Isohanni, M., Jääskeläinen, E. (2019). The age of onset of schizophrenia spectrum disorders. In de Girolamo G., McGorry P., & Sartorius N. (Eds.), Age of onset of mental disorders (pp. 55–730). Springer. 10.1007/978-3-319-72619-9_4 [DOI] [Google Scholar]
  78. Miller, B., Messias, E., Miettunen, J., Alaräisänen, A., Järvelin, M. R., Koponen, H., Räsänen, P., Isohanni, M., & Kirkpatrick, B. (2011). Meta-analysis of paternal age and schizophrenia risk in male versus female offspring. Schizophrenia Bulletin, 37(5), 1039–1047. 10.1093/schbul/sbq011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Mistry, S., Harrison, J. R., Smith, D. J., Escott-Price, V., & Zammit, S. (2018). The use of polygenic risk scores to identify phenotypes associated with genetic risk of bipolar disorder and depression: A systematic review. Journal of Affective Disorders, 234, 148–155. 10.1016/j.jad.2018.02.005. [DOI] [PubMed] [Google Scholar]
  80. Molloy, C., Conroy, R. M., Cotter, D. R., & Cannon, M. (2011). Is traumatic brain injury a risk factor for schizophrenia? A meta-analysis of case-controlled population-based studies. Schizophrenia Bulletin, 37(6), 1104–1110. 10.1093/schbul/sbr091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Nayeri Chegeni, T., Sharif, M., Sarvi, S., Moosazadeh, M., Montazeri, M., Aghayan, S. A., Balalami, N. J., Gholami, S., Hosseininejad, Z., Saberi, R., Anvari, D., Gohardehi, S., & Daryani, A. (2019). Is there any association between toxoplasma gondii infection and depression? A systematic review and meta-analysis. PLoS One, 14(6), e0218524. 10.1371/journal.pone.0218524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Nelson, J., Klumparendt, A., Doebler, P., & Ehring, T. (2017). Childhood maltreatment and characteristics of adult depression: Meta-analysis. The British Journal of Psychiatry, 210(2), 96–104. 10.1192/bjp.bp.115.180752. [DOI] [PubMed] [Google Scholar]
  83. Nurita, R., Faturohman, A., Santoso, F. M., Magdalena, B., & Ilyas, M. F. (2025). Bidirectional association between gastroesophageal reflux disease and bipolar disorder: A systematic review and meta-analysis of longitudinal studies. Middle East Journal of Digestive Diseases, 17(1), 68–75. 10.34172/mejdd.2025.411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Ohi, K., Shimada, T., Nitta, Y., Kihara, H., Okubo, H., Uehara, T., & Kawasaki, Y. (2016). The five-factor model personality traits in schizophrenia: A meta-analysis. Psychiatry Research, 240, 34–41. 10.1016/j.psychres.2016.04.004. [DOI] [PubMed] [Google Scholar]
  85. Palmier-Claus, J. E., Berry, K., Bucci, S., Mansell, W., & Varese, F. (2016). Relationship between childhood adversity and bipolar affective disorder: Systematic review and meta-analysis. The British Journal of Psychiatry, 209(6), 454–459. 10.1192/bjp.bp.115.179655. [DOI] [PubMed] [Google Scholar]
  86. Pence, A. Y., Pries, L. K., Ferrara, M., Rutten, B. P. F., van Os, J., & Guloksuz, S. (2022). Gender differences in the association between environment and psychosis. Schizophrenia Research, 243, 120–137. 10.1016/j.schres.2022.02.039. [DOI] [PubMed] [Google Scholar]
  87. Perry, D. C., Sturm, V. E., Peterson, M. J., Pieper, C. F., Bullock, T., Boeve, B. F., Miller, B. L., Guskiewicz, K. M., Berger, M. S., Kramer, J. H., & Welsh-Bohmer, K. A. (2016). Association of traumatic brain injury with subsequent neurological and psychiatric disease: A meta-analysis. Journal of Neurosurgery, 124(2), 511–526. 10.3171/2015.2.JNS14503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Prata, D. P., Costa-Neves, B., Cosme, G., & Vassos, E. (2019). Unravelling the genetic basis of schizophrenia and bipolar disorder with GWAS: A systematic review. Journal of Psychiatric Research, 114, 178–207. 10.1016/j.jpsychires.2019.04.007. [DOI] [PubMed] [Google Scholar]
  89. Rasic, D., Hajek, T., Alda, M., & Uher, R. (2014). Risk of mental illness in offspring of parents with schizophrenia, bipolar disorder, and major depressive disorder: A meta-analysis of family high-risk studies. Schizophrenia Bulletin, 40(1), 28–38. 10.1093/schbul/sbt114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Robinson, N., & Bergen, S. E. (2021). Environmental risk factors for schizophrenia and bipolar disorder and their relationship to genetic risk: Current knowledge and future directions. Frontiers in Genetics, 12, 686666. 10.3389/fgene.2021.686666. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Robinson, N., Ploner, A., Leone, M., Lichtenstein, P., Kendler, K. S., & Bergen, S. E. (2023). Impact of early-life factors on risk for schizophrenia and bipolar disorder. Schizophrenia Bulletin, 49(3), 768–777. 10.1093/schbul/sbac205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Robinson, N., Ploner, A., Leone, M., Lichtenstein, P., Kendler, K. S., & Bergen, S. E. (2024). Environmental risk factors for schizophrenia and bipolar disorder from childhood to diagnosis: A Swedish nested case-control study. Psychological Medicine, 54(9), 2162–2171. 10.1017/S0033291724000266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Rodriguez, V., Alameda, L., Trotta, G., Spinazzola, E., Marino, P., Matheson, S. L., Laurens, K. R., Murray, R. M., & Vassos, E. (2021). Environmental risk factors in bipolar disorder and psychotic depression: A systematic review and meta-analysis of prospective studies. Schizophrenia Bulletin, 47(4), 959–974. 10.1093/schbul/sbaa197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Rowland, T. A., & Marwaha, S. (2018). Epidemiology and risk factors for bipolar disorder. Therapeutic Advances in Psychopharmacology, 8(9), 251–269. 10.1177/2045125318769235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Saatci, D., van Nieuwenhuizen, A., & Handunnetthi, L. (2021). Maternal infection in gestation increases the risk of non-affective psychosis in offspring: A meta-analysis. Journal of Psychiatric Research, 139, 125–131. 10.1016/j.jpsychires.2021.05.039. [DOI] [PubMed] [Google Scholar]
  96. Saha, S., Chant, D., Welham, J., & McGrath, J. (2005). A systematic review of the prevalence of schizophrenia. PLoS Medicine, 2(5), e141. 10.1371/journal.pmed.0020141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Shahraki, Z., Rastkar, M., Ramezanpour, M. R., & Ghajarzadeh, M. (2024). The prevalence and odds of bipolar disorder in women with polycystic ovary syndrome (PCO) disease: A systematic review and meta-analysis. Archives of Women’s Mental Health, 27(3), 329–336. 10.1007/s00737-024-01420-w. [DOI] [PubMed] [Google Scholar]
  98. Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., Moher, D., Tugwell, P., Welch, V., Kristjansson, E., & Henry, D. A. (2017). AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 358, j4008. 10.1136/bmj.j4008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Shintani, A. O., Rabelo-da-Ponte, F. D., Marchionatti, L. E., Watts, D., de Souza, F., Machado, C. D. S., Pulice, R. F., Signori, G. M., Luzini, R. R., Kauer-Sant’Anna, M., & Passos, I. C. (2023). Prenatal and perinatal risk factors for bipolar disorder: A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 144, 104960. 10.1016/j.neubiorev.2022.104960. [DOI] [PubMed] [Google Scholar]
  100. Simbi, C. M. C., Zhang, Y., & Wang, Z. (2020). Early parental loss in childhood and depression in adults: A systematic review and meta-analysis of case-controlled studies. Journal of Affective Disorders, 260, 272–280. 10.1016/j.jad.2019.07.087. [DOI] [PubMed] [Google Scholar]
  101. Snijders, G. J. L. J., van Mierlo, H. C., Boks, M. P., Begemann, M. J. H., Sutterland, A. L., Litjens, M., Ophoff, R. A., Kahn, R. S., & de Witte, L. D. (2019). The association between antibodies to neurotropic pathogens and bipolar disorder: A study in the Dutch bipolar (DB) cohort and meta-analysis. Translational Psychiatry, 9(1), 311. 10.1038/s41398-019-0636-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Song, R., Liu, L., Wei, N., Li, X., Liu, J., Yuan, J., Yan, S., Sun, X., Mei, L., Liang, Y., Li, Y., Jin, X., Wu, Y., Pan, R., Yi, W., Song, J., He, Y., Tang, C., Liu, X., … Su, H. (2023). Short-term exposure to air pollution is an emerging but neglected risk factor for schizophrenia: A systematic review and meta-analysis. The Science of the Total Environment, 854, 158823. 10.1016/j.scitotenv.2022.158823. [DOI] [PubMed] [Google Scholar]
  103. Sormunen, E., Saarinen, M. M., Salokangas, R. K. R., Hutri-Kähönen, N., Viikari, J. S. A., Raitakari, O. T., & Hietala, J. (2019). Body mass index trajectories in childhood and adolescence – risk for non-affective psychosis. Schizophrenia Research, 206, 313–317. 10.1016/j.schres.2018.10.025. [DOI] [PubMed] [Google Scholar]
  104. Starr, L. R., Hammen, C., Conway, C. C., Raposa, E., & Brennan, P. A. (2014). Sensitizing effect of early adversity on depressive reactions to later proximal stress: Moderation by polymorphisms in serotonin transporter and corticotropin releasing hormone receptor genes in a 20-year longitudinal study. Development and Psychopathology, 26(4 Pt 2), 1241–1254. 10.1017/S0954579414000996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Stilo, S. A., & Murray, R. M. (2019). Non-genetic factors in schizophrenia. Current Psychiatry Reports, 21(10), 100. 10.1007/s11920-019-1091-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Su, Y., D’Arcy, C., & Meng, X. (2021). Research review: Developmental origins of depression – A systematic review and meta-analysis. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 62(9), 1050–1066. 10.1111/jcpp.13358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. The American Journal of Psychiatry, 157(10), 1552–1562. 10.1176/appi.ajp.157.10.1552. [DOI] [PubMed] [Google Scholar]
  108. Sutterland, A. L., Fond, G., Kuin, A., Koeter, M. W., Lutter, R., van Gool, T., Yolken, R., Szoke, A., Leboyer, M., & de Haan, L. (2015). Beyond the association. Toxoplasma gondii in schizophrenia, bipolar disorder, and addiction: Systematic review and meta-analysis. Acta Psychiatrica Scandinavica, 132(3), 161–179. 10.1111/acps.12423. [DOI] [PubMed] [Google Scholar]
  109. Swinnen, S. G., & Selten, J. P. (2007). Mood disorders and migration: Meta-analysis. The British Journal of Psychiatry, 190, 6–10. 10.1192/bjp.bp.105.020800. [DOI] [PubMed] [Google Scholar]
  110. Thapa, S., Panah, M. Y., Vaheb, S., Dahal, K., Maharjan, P. M., Shah, S., & Mirmosayyeb, O. (2024). Psychosis and schizophrenia among patients with epilepsy: A systematic review and meta-analysis. Epilepsy Research, 207, 107452. 10.1016/j.eplepsyres.2024.107452. [DOI] [PubMed] [Google Scholar]
  111. Torrey, E. F., Buka, S., Cannon, T. D., Goldstein, J. M., Seidman, L. J., Liu, T., Hadley, T., Rosso, I. M., Bearden, C., & Yolken, R. H. (2009). Paternal age as a risk factor for schizophrenia: How important is it? Schizophrenia Research, 114(1–3), 1–5. 10.1016/j.schres.2009.06.017. [DOI] [PubMed] [Google Scholar]
  112. Tseng, P. T., Zeng, B. S., Chen, Y. W., Wu, M. K., Wu, C. K., & Lin, P. Y. (2016). A meta-analysis and systematic review of the comorbidity between irritable bowel syndrome and bipolar disorder. Medicine, 95(33), e4617. 10.1097/MD.0000000000004617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Uher, R., Pavlova, B., Najafi, S., Adepalli, N., Ross, B., Howes Vallis, E., Freeman, K., Parker, R., Propper, L., & Palaniyappan, L. (2024). Antecedents of major depressive, bipolar, and psychotic disorders: A systematic review and meta-analysis of prospective studies. Neuroscience and Biobehavioral Reviews, 160, 105625. [DOI] [PubMed] [Google Scholar]
  114. Uher, R., Pavlova, B., Radua, J., Provenzani, U., Najafi, S., Fortea, L., Ortuño, M., Nazarova, A., Perroud, N., Palaniyappan, L., Domschke, K., Cortese, S., Arnold, P. D., Austin, J. C., Vanyukov, M. M., Weissman, M. M., Young, A. H., Hillegers, M. H. J., Danese, A., … Fusar-Poli, P. (2023). Transdiagnostic risk of mental disorders in offspring of affected parents: A meta-analysis of family high-risk and registry studies. World Psychiatry, 22(3), 433–448. 10.1002/wps.21147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  115. Ungprasert, P., Wijarnpreecha, K., & Cheungpasitporn, W. (2019). Patients with psoriasis have a higher risk of schizophrenia: A systematic review and meta-analysis of observational studies. Journal of Postgraduate Medicine, 65(3), 141–145. 10.4103/jpgm.JPGM_253_18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  116. Varga, E., Hajnal, A., Soós, A., Hegyi, P., Kovács, D., Farkas, N., Szebényi, J., Mikó, A., Tényi, T., & Herold, R. (2021). Minor physical anomalies in bipolar disorder – A meta-analysis. Frontiers in Psychiatry, 12, 598734. 10.3389/fpsyt.2021.598734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  117. Vassos, E., Agerbo, E., Mors, O., & Pedersen, C. B. (2016). Urban-rural differences in incidence rates of psychiatric disorders in Denmark. The British Journal of Psychiatry, 208(5), 435–440. 10.1192/bjp.bp.114.161091. [DOI] [PubMed] [Google Scholar]
  118. Wang, X., Zhang, L., Lei, Y., Liu, X., Zhou, X., Liu, Y., Wang, M., Yang, L., Zhang, L., Fan, S., & Xie, P. (2014). Meta-analysis of infectious agents and depression. Scientific Reports, 4, 4530. 10.1038/srep04530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  119. Watson, C. B., Sharpley, C. F., Bitsika, V., Evans, I., & Vessey, K. (2025). A systematic review and meta-analysis of the association between childhood maltreatment and adult depression. Acta Psychiatrica Scandinavica, 151(5), 572–599. 10.1111/acps.13794. [DOI] [PMC free article] [PubMed] [Google Scholar]
  120. Wei, J., Li, Y., & Gui, X. (2024). Association of hearing loss and risk of depression: A systematic review and meta-analysis. Frontiers in Neurology, 15, 1446262. 10.3389/fneur.2024.1446262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  121. Wijarnpreecha, K., Jaruvongvanich, V., Cheungpasitporn, W., & Ungprasert, P. (2018). Association between celiac disease and schizophrenia: A meta-analysis. European Journal of Gastroenterology & Hepatology, 30(4), 442–446. 10.1097/MEG.0000000000001048. [DOI] [PubMed] [Google Scholar]
  122. World Health Organization. (2017). Depression and other common mental disorders: Global health estimates. World Health Organization. [Google Scholar]
  123. Wu, M. K., Wang, H. Y., Chen, Y. W., Lin, P. Y., Wu, C. K., & Tseng, P. T. (2016). Significantly higher prevalence rate of asthma and bipolar disorder co-morbidity: A meta-analysis and review under PRISMA guidelines. Medicine, 95(13), e3217. 10.1097/MD.0000000000003217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  124. Xu, C., Miao, L., Turner, D., & DeRubeis, R. (2023). Urbanicity and depression: A global meta-analysis. Journal of Affective Disorders, 340, 299–311. 10.1016/j.jad.2023.08.030. [DOI] [PubMed] [Google Scholar]
  125. Xu, T., Chan, R. C., & Compton, M. T. (2011). Minor physical anomalies in patients with schizophrenia, unaffected first-degree relatives, and healthy controls: A meta-analysis. PLoS One, 6(9), e24129. 10.1371/journal.pone.0024129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  126. Yalin, N., Young, A. H. (2019). The age of onset of unipolar depression. In de Girolamo G., McGorry P., & Sartorius N. (Eds.), Age of onset of mental disorders (pp. 111–124). Springer. 10.1007/978-3-319-72619-9_6 [DOI] [Google Scholar]
  127. Zamani, M., Alizadeh-Tabari, S., Chan, W. W., & Talley, N. J. (2023). Association between anxiety/depression and gastroesophageal reflux: A systematic review and meta-analysis. The American Journal of Gastroenterology, 118(12), 2133–2143. 10.14309/ajg.0000000000002411. [DOI] [PubMed] [Google Scholar]
  128. Zamani, M., Alizadeh-Tabari, S., & Zamani, V. (2019). Systematic review with meta-analysis: The prevalence of anxiety and depression in patients with irritable bowel syndrome. Alimentary Pharmacology & Therapeutics, 50(2), 132–143. 10.1111/apt.15325. [DOI] [PubMed] [Google Scholar]
  129. Zamanpoor, M., Ghaedi, H., & Omrani, M. D. (2020). The genetic basis for the inverse relationship between rheumatoid arthritis and schizophrenia. Molecular Genetics & Genomic Medicine, 8(11), e1483. 10.1002/mgg3.1483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  130. Zhang T, Sidorchuk A, Sevilla-Cermeño L, Vilaplana-Pérez A, Chang Z, Larsson H, Mataix-Cols D, Fernández de la Cruz L. Association of cesarean delivery with risk of neurodevelopmental and psychiatric disorders in the offspring: A systematic review and meta-analysis. JAMA Network Open 2019. Aug 2;2(8):e1910236. 10.1001/jamanetworkopen.2019.10236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  131. Zhao, Z., Okusaga, O. O., Quevedo, J., Soares, J. C., & Teixeira, A. L. (2016). The potential association between obesity and bipolar disorder: A meta-analysis. Journal of Affective Disorders, 202, 120–123. 10.1016/j.jad.2016.05.059. [DOI] [PubMed] [Google Scholar]

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