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Genetic overlap between schizophrenia and constipation: insights from a genome-wide association study in a European population

Annals of General Psychiatry volume 24, Article number: 11 (2025) Cite this article

Abstract

Background

Patients with schizophrenia (SCZ) experience constipation at significantly higher rates compared with the general population. This relationship suggests a potential genetic overlap between these two conditions.

Methods

We analyzed genome-wide association study (GWAS) data for both SCZ and constipation using a five-part approach. The first and second parts assessed the overall and local genetic correlations using methods such as linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (HESS). The third part investigated the causal association between the two traits using Mendelian randomization (MR). The fourth part employed conditional/conjunctional false discovery rate (cond/conjFDR) to analyze the genetic overlap with different traits based on the statistical theory. Finally, an LDSC-specifically expressed gene (LDSC-SEG) analysis was conducted to explore the tissue-level associations.

Results

Our analyses revealed both overall and specific genetic correlations between SCZ and constipation at the genomic level. The MR analysis suggests a positive causal relationship between SCZ and constipation. The ConjFDR analysis confirms the genetic overlap between the two conditions and identifies two genetic risk loci (rs7583622 and rs842766) and seven mapped genes (GPR75-ASB3, ASB3, CHAC2, ERLEC1, GPR75, PSME4, and ACYP2). Further investigation into the functions of these genes could provide valuable insights. Interestingly, disease-related tissue analysis revealed associations between SCZ and constipation in eight brain regions (substantia nigra, anterior cingulate cortex, hypothalamus, cortex, hippocampus, cortex, amygdala, and spinal cord).

Conclusion

This study provides the first genetic evidence for the comorbidity of SCZ and constipation, enhancing our understanding of the pathophysiology of both conditions.

Introduction

Schizophrenia (SCZ) is a chronic mental disorder characterized by multifactorial neurodevelopment in clinical practice [1], affecting approximately 1 in 100 people worldwide [2]. Research indicates that individuals with SCZ experience varying degrees of impairment in thinking, emotion, behavior, and perception, leading to deviations in their psychological aspects and interactions with the external environment [3]. Although the specific etiology of SCZ remains unclear, genetics, environmental factors, and dopamine receptors are believed to play crucial roles in its development [4]. Furthermore, SCZ is often accompanied by comorbidities, such as psychiatric disorders (depression and anxiety) [5], cardiovascular diseases [6], skin diseases [7], and gastrointestinal disorders [8]. Constipation, one of the most common gastrointestinal disorders, is closely related to SCZ, with a meta-analysis reporting the comorbidity rate of constipation in patients with SCZ to be 21% [9]. Recent epidemiological studies have demonstrated a positive association between SCZ and constipation [10,11,12], highlighting the importance of investigating their relationship further. Additionally, constipation may lead to paralytic ileus or even death [13]. However, previous investigations into this relationship have had certain limitations in their design, making it challenging to exclude confounding factors. To address this gap, no work has been conducted on the genetic correlation between SCZ and constipation at the GWAS level. Therefore, conducting such an investigation would overcome the aforementioned limitations and provide valuable biological evidence and insights into the relationship between SCZ and constipation.

Genetic factors play pivotal roles in the pathogenesis of both SCZ and constipation. First-degree relatives of individuals with SCZ exhibit a staggering 10-fold increased risk of developing SCZ compared with normal controls [14]. A genetic epidemiological study conducted on SCZ within a Finnish twin cohort estimated its heritability at an astounding 83% [15]. Similarly, in a comprehensive questionnaire survey involving 338 pairs of twins, the prevalence of constipation was identified to be approximately 8.7%. Notably, the incidence of constipation among monozygotic twins was nearly 4-fold than that observed among dizygotic twins [16]. Moreover, the likelihood of constipation in children increases if one of the parents, siblings, or twins has a history of constipation [16]. Given the polygenic nature of these two diseases, conducting an in-depth analysis is imperative to ascertain whether SCZ and constipation share genetic etiologies. Furthermore, delving into their genetic overlap at the single gene locus level and identifying shared genetic risk loci are pivotal steps in effectively diagnosing and treating these diseases.

In this study, we adopted a longitudinal progressive approach to investigate the genetic basis underlying the relationship between SCZ and constipation. We selected genome-wide association study (GWAS) datasets for both diseases as our research objects and conducted the following five analyses: ① linkage disequilibrium score regression (LDSC) analysis [17] was performed on both SCZ and constipation to obtain their overall correlation; ② heritability estimation from summary statistics (HESS) method [18] was applied to SCZ and constipation to acquire their local genetic correlations; ③ Mendelian randomization (MR) was utilized to examine the causal relationship between the two conditions [19]; ④ conditional/conjunctional false discovery rate (cond/conjFDR) method was used to identify shared genetic risk variants between the traits and analyze the polygenic overlap of genetic correlations; and ⑤ LDSC-specifically expressed gene (LDSC-SEG) analysis was conducted to determine the tissues markedly associated with both diseases [20].

Methods and materials

GWAS data

In the IEU GWAS database (https://gwas.mrcieu.ac.uk/), we selected the summary statistics data for SCZ GWAS [21] (ID: ieu-b-5102) by considering factors such as sample size, number of SNPs, study population (European), and publication year. The dataset includes 52,017 patients and 75,889 controls.To minimise sample overlap, the GWAS data for constipation were sourced from the FinnGen database (https://r10.finngen.fi/) [22], which comprises 41,124 cases and 371,057 controls. Both GWAS cohorts were derived from European populations.

Genetic correlation analysis

LDSC (version 1.0.1) is a robust tool for assessing the overall correlation between these two traits and elucidating the extent of shared genetic effects [20]. The genetic correlation (Rg), a pivotal metric in this analysis, ranges from “−1” to “+1,” with “−” and “+” denoting negative and positive correlations, respectively. Following the instructions provided in the LDSC manual, the first step involves converting the GWAS data of both traits into LDSC format, followed by the calculation of Rg. We used parameters such as munge_sumstats.py, -rg, -ref-ld-chr, and -w-ld-chr, which can be obtained from the website (https://alkesgroup.broadinstitute.org/LDSCORE/). The European ancestry data from the 1,000 Genomes Project served as the linkage disequilibrium (LD) reference panel for this analysis [23].

Local genetic correlation analysis

HESS allows for the calculation of local heritability for each trait and analysis of genetic correlation within independent LD blocks [18].When analyzing the local genetic correlation within each segment, it is necessary to pre-divide the genetic components into 1703 segments, which should be uncorrelated with pre-specified LD. Utilizing the 1 kG reference panel for LD blocks, the analysis encompasses three steps: preparation of LD blocks and eigenvalues, assessment of the local SNP heritability for each trait, and determination of the local genetic covariance and standard error [24]. In order to ensure the reliability of the conclusions, the results of HESS were subjected to a Bonferroni correction (P < 2.9E–05) [18].

MR analysis

Following the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) statement, we conducted an MR analysis for both diseases. Initially, we performed a genome-wide significance and LD check with the conditions set as “P < 5 × 10–8,” “r2 = 0.001,” and “kbp = 10,000” [25]. Subsequently, we calculated the F-statistic (F = β2/SE2, where β refers to the effect value of the allele and SE refers to the standard error) for all single nucleotide polymorphisms (SNPs), excluding samples with values less than 10 [26, 27]. Using the “TwoSampleMR” package in R software, we employed four MR methods, inverse variance weighted (IVW) [28], MR Egger [29], weighted median (WM) [30], and maximum likelihood (ML) [31], to investigate the causal relationship between SCZ and constipation. Sensitivity analyses, including the “leave-one-out” sensitivity test [32], “pleiotropy” test [33], and “heterogeneity” test [34], were conducted to ensure result reliability. The entire MR analysis was bidirectional.

Conditional quantile–quantile plots

We generated conditional quantile-quantile (Q–Q) plots to illustrate multiepitope genes. Increasing proportions of SNPs associated with the primary phenotype (SCZ) relative to the strength of association with the secondary phenotype (constipation) suggest enrichment between the two [35]. P-values for all Q–Q plots were categorized into the following three levels: “P < 0.10”, “P < 0.01”, and “P < 1.0E-03”. The precimed/mixer software package in Python 3.11 (https://github.com/precimed/mixer) facilitated Q–Q plot generation.

CondFDR/ConjFDR analysis

The condFDR and conjFDR in the empirical Bayesian statistical framework are recently popular statistical methods uncovering shared genetic risk variants for various traits that may not surpass the significance threshold [36]. FDR serves as a reference value for pleiotropic assessment. Based on Bayesian statistics, the condFDR method identifies genetic loci associated with the primary phenotype (SCZ) by referencing genetic loci of the secondary phenotype (constipation) [37]. This bidirectional process re-ranks test statistics by leveraging variant–phenotype associations and recalculates associations between variants and the primary phenotype. The entire process requires the primary and secondary phenotypes to be swapped, thereby obtaining the reverse condFDR values. After repeatedly calculating the condFDR of the two traits, the maximum value of the condFDR is considered the conjFDR. The conjFDR analysis is then conducted to determine the genetic loci commonly associated with both phenotypes. Notably, the FDR significance threshold for conjFDR is 0.05, with detailed analysis available on the website (https://github.com/precimed/pleiofdr).

Functional annotation

To delineate LD-independent genomic regions proximal to the identified signals, we utilized functional mapping and annotation (FUMA) (https://fuma.ctglab.nl/) [38]. We screened for independent significant SNPs using conditions “conjFDR < 0.05” and “r2 < 0.6” and identified the lead SNPs with the condition “r2 < 0.1.” Locus mapping and functional annotation for the newly identified loci, shared loci, and specific loci were conducted. The SNP2Gene module of FUMA mapped SNP information to corresponding genes, with functional enrichment analyses conducted using GeneMANIA (http://www.genemania.org) [39].

Tissue enrichment analysis

LDSC-SEG analysis identified tissues significantly associated with traits [20, 40]. Calculating the t-statistic value for each gene’s expression across 53 human tissues, LDSC-SEG ranks all genes for the trait from high to low, considering the top 10% of genes as markedly associated with the trait. Notably, 100 kb windows are set at the front and back of the transcription regions in each gene set. Finally, GWAS summary statistics evaluates the role of key genomic annotations in trait heritability, with gene expression data for 53 tissue types provided by Finucane et al. serving as genomic annotation files [40]. The detailed LDSC-SEG analysis workflow is available on the website (https://github.com/bulik/ldsc/wiki/Cell-type-specific-analyses).

Results

Genetic correlation

In the LDSC analysis results, the SNP heritability of SCZ was found to be 37%, whereas for constipation, it was 1.4%. The calculated genetic correlation (Rg) between the two conditions was 0.24 (P = 6.9e–11), indicating a significant positive association between SCZ and constipation.

Within the local genetic correlation map, we identified a local genetic overlap between SCZ and constipation on chromosome 5 (Fig. 1).

Fig. 1
figure 1

HESS analysis of SCZ and constipation. The top and middle sections of each subgraph represent local genetic correlations and covariances, respectively, and the colored bars represent loci with significant local genetic correlations and covariances. The bottom portion represents the local snp heritability of an individual trait, and the colored bars represent loci with significant local snp heritability. SCZ, schizophrenia

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Mendelian randomization

When SCZ was considered as the exposure and constipation as the outcome, the results indicated a positive causal relationship (Fig. 2). Conversely, in the reverse scenario, no causal association was observed (Fig. 2). Bidirectional pleiotropy was not significant (positive P = 0.71 > 0.05, negative P = 0.65 > 0.05). The leave-one-out analysis plot (Fig. 3A-B) did not identify potential SNPs influencing the causal relationship, affirming the result’s reliability. The instrumental variables data pertinent to this study can be found in Supplementary Tables S1 and S2.

Fig. 2
figure 2

The MR analysis results of SCZ and constipation.SCZ, schizophrenia

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Fig. 3
figure 3

(A) Forest plot for the leave-one-out analysis of SCZ on constipation. (B) Forest plot for the leave-one-out analysis of constipation on SCZ.SCZ, schizophrenia

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ConjFDR analysis identified shared genomic loci between SCZ and constipation

Observing the Q–Q plots (Fig. 4A-B), we note a consistent leftward shift in the curve of SCZ as the association P-value of constipation gradually decreases from 0.1 to 0.001. This indicates a strong correlation between the two traits, suggesting shared genetic overlap and consistent genetic risk loci. Under the condition of “conjFDR < 0.05,” we can obtain two high-confidence shared loci (rs7583622 and rs842766) (Table 1) between SCZ and constipation, which have an inverse regulatory effect (z < 0).

Fig. 4
figure 4

Conditional quantile-quantile plot. The dashed line indicates the expected line under the null hypothesis, and the deflection to the left indicates the degree of pleiotropic enrichment. SCZ, schizophrenia

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Table 1 The 2 LD-independent loci jointly associated with SCZ and constipation identified by conjFDR analyses. The table presents reference allele (A1), alternative allele (A2), allele frequency of reference allele, and gene and its functional category. CADD score was used for predicting the deleteriousness of variants.The conjunctional FDR (conjFDR) columns report the maximum condFDR value, from each pair of condFDR analyses. Z-scores and P-values from the original summary statistics on SCZ and constipation were also shown.SCZ, schizophrenia
Full size table

Functional annotations

Among the 111 candidate SNPs shared by SCZ and constipation, the majority exhibit functional attributes primarily within intronic (93), intergenic [7], UTR3 [4], exonic [3], and downstream [2] regions (Fig. 5, Supplementary Table S3). Mapped genes for both diseases include GPR75-ASB3, ASB3, CHAC2, ERLEC1, GPR75, PSME4, and ACYP2. Subsequently, GeneMania was used to construct a refined gene–gene interaction network based on these mapped genes and their neighboring genes (Fig. 6). The network analysis identified 20 frequently mutated genes closely associated with the target genes, revealing multiple key biological pathways between the two conditions. These pathways include endoplasmic reticulum to cytosol transport, protein exit from the endoplasmic reticulum, ERAD pathway, proteasome-mediated ubiquitin-dependent protein catabolic process, regulation of retrograde protein transport from the ER to the cytosol, and response to endoplasmic reticulum stress. These in-depth network analysis results offer valuable perspectives for a more comprehensive understanding of these mapped genes in the progression of comorbidity.

Fig. 5
figure 5

ConjFDR Manhattan plot. The shared risk loci between SCZ and constipation were marked. The statistically significant causality is defined to be conjFDR < 0.05. SCZ, schizophrenia

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Fig. 6
figure 6

Mapping genes and their co-expression genes were analyzed via GeneMANIA

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Trait-related tissues

Finally, referring to tissue expression data from GTEx, LDSC-SEG analyses were conducted for both diseases to examine the tissue origins closely associated with them. With a coefficient of P < 0.05, SCZ demonstrated associations with 13 regions of the human brain (Fig. 7A), whereas constipation was associated with eight brain regions (Fig. 7B). Furthermore, these eight brain regions (substantia nigra, anterior cingulate cortex, hypothalamus, cortex, hippocampus, cortex, amygdala, and spinal cord) were found to be shared by both conditions, suggesting that the brain may have shared tissue origins for both conditions. Specific analysis results can be found in Supplementary Tables S4 and S5.

Fig. 7
figure 7

Tissues enrichment results of SCZ (A) and constipation (B) using gene expression data of 53 tissues from GTEx. SCZ, schizophrenia

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Discussion

The findings from the five investigations in this study suggest a profound association between SCZ and constipation. At the genome-wide level, they reveal significant genetic and local genetic correlations, particularly on chromosome 5. The MR results indicate a positive causal relationship between SCZ and constipation. At the SNP level, conjFDR analysis facilitated the identification of two genetic risk loci (rs7583622 and rs842766) and seven mapped genes (GPR75-ASB3, ASB3, CHAC2, ERLEC1, GPR75, PSME4, and ACYP2) shared between SCZ and constipation. Finally, the LDSC-SEG analysis highlighted eight brain regions (substantia nigra, anterior cingulate cortex, hypothalamus, cortex, hippocampus, cortex, amygdala, and spinal cord) as shared tissue origins for both SCZ and constipation, providing histological evidence for their comorbidity. These research findings deepen our understanding of the genetic structure of SCZ and constipation, confirming the existence of genetic overlap between the two conditions.

Several studies have underscored a strong relationship between SCZ and constipation. A study conducted in Taiwan found that the prevalence of constipation in patients with SCZ reached 60%, with an odds ratio (OR) (95% confidence interval [CI]) of 1.02 (1.00–1.03), P < 0.05 for the average score of the Positive and Negative Syndrome Scale (PANSS) [10]. Xiaoquan et al. found that 37% of 503 hospitalized patients with SCZ met the diagnostic criteria for constipation, with 80% of them failing to disclose their condition to their doctors [41]. Another retrospective study revealed that 36% of patients with SCZ had received at least one constipation medication treatment [42]. Importantly, in some previous studies, constipation has frequently been overlooked in patients with SCZ [41], possibly due to a lack of awareness of somatic comorbidities, leading to disregard for bowel habits and self-care deficits [43]. Furthermore, the underrecognition of constipation may be attributed to the lower pain threshold commonly associated with patients with SCZ [44]. The genetic-level insights provided by this study offer new perspectives on the shared pathogenic mechanisms between the two conditions, underlining the importance for psychiatrists to actively inquire about patients’ bowel habits and conduct physical examinations to prevent adverse consequences.

The genetic signatures obtained in this study warrant attention. In a recent methylation study, GPR75-ASB3 was identified as a pathogenic marker for SCZ [45]. ASB not only influences intestinal status by mediating its receptor TNF-R2 but also targets inflammasomes to activate IL-1β and IL-18 [46, 47], which are involved in neuroinflammation and neurodegeneration. Therefore, it has an impact on constipation and SCZ. Mutations in CHAC2 are implicated in the brain pathogenesis of SCZ [48].In a Mendelian randomization study, ERLEC1 has been identified as a circulating protein closely associated with SCZ [49], and its aberrant expression is linked to numerous gastrointestinal complications, including constipation [50]. GPR75, as a component of G protein-coupled receptors, plays a role in the pathogenesis of central nervous system diseases such as SCZ and represents one of the most promising targets for drug development in neuropsychopharmacology [51].Through machine learning, PSME4 has been identified as a significant risk locus for SCZ [52]. ACYP2 has been recognized as an abnormally expressed gene in a transcriptome analysis of SCZ [53]. Although research on the association between GPR75-ASB3 [54], CHAC2 [55], GPR75 [56], PSME4 [57], and ACYP2 [58]with constipation has not yet been conducted, they are involved in the pathogenesis of colorectal cancer, indicating a close relationship with intestinal disorders and warranting further investigation in the future.The functional enrichment results of these mapped genes suggest an association with endoplasmic reticulum transport and degradation, which is a significant finding. Endoplasmic reticulum transport and degradation represent common pathogenic pathways in neurodegenerative and psychiatric disorders [59], and may also offer a novel avenue for future SCZ treatment [60]. A study using a constipation mouse model confirmed that endoplasmic reticulum transport and degradation can impact the bowel movement process [61].

Brain tissue serves as a prominent source for understanding SCZ. The brain governs various higher functions of the body, such as cognition, decision-making, and expression of different emotions and language [62]. Various neural networks are distributed throughout the regions of the brain, serving as rich projection sites for neurons associated with crucial neurotransmitters like dopamine, serotonin, and norepinephrine and can also serve as targets for SCZ treatment [63]. Several previous studies have demonstrated morphological changes in the brains of patients with SCZ. The most consistent finding is the reduced volumes of the prefrontal cortex, temporal cortex, and limbic regions of gray matter in patients with SCZ [64,65,66]. Recent large-scale meta-analyses have provided more detailed insights [67]. SCZ affects the volumes of subcortical structures such as the hippocampus, amygdala, thalamus, nucleus accumbens, pallidum, and lateral ventricles [68,69,70]. Cortical structures are also impacted in patients with SCZ. Van Erp et al. [71] and Matsumoto et al. [72] demonstrated a widespread impact of SCZ on the prefrontal and temporal cortical regions, with a greater magnitude of impact on the prefrontal and temporal cortical regions. Longitudinal brain imaging studies have highlighted that brain volumes in patients with SCZ are smaller compared with those in normal individuals, and this reduction becomes more pronounced with increasing disease duration [73]. Capturing morphological changes in the brain during the disease process is crucial for objectively inferring the progression of SCZ. Constipation is also closely linked to brain tissues. Neuroimaging studies have demonstrated that primary and secondary somatic pain sensations associated with constipation are primarily localized in the thalamus, insular cortex, hypothalamus, and amygdala [74]. In a study using voxel-based morphometry, a significant reduction in GM volume of the anterior cingulate cortex was found in constipation (P < 0.05) [75]. When investigating abnormal structural connectivity changes in constipated patients using diffusion tensor imaging and probabilistic tractography, it was discovered that patients exhibited lower fractional anisotropy of fibers connecting the thalamus with the amygdala and hippocampus, leading to difficulty in defecation [76].

The gut–brain axis is a prominent area of interest in biomedical research, with its core concept highlighting the direct link between gut dysfunction and inflammation and brain development and mental disorders [77]. This finding aligns with our study results, which unveil a potential genetic overlap between SCZ and constipation, along with their enrichment in brain tissues. Abnormal gut–brain connections not only impair gut motility but also impact brain function, particularly emotional-related functional disorders in patients with constipation. This implies that personalized treatment could integrate constipation management with targeted antipsychotic medications, rather than solely focusing on alleviating constipation symptoms [78]. The findings of this study further substantiate the existence of the gut–brain axis.

In this study, we used five methods (LDSC, HESS, MR, conjFDR, and LDSC-SEG analysis) to comprehensively examine the genetic overlap between SCZ and constipation across genomic, SNP, and tissue levels. However, certain limitations are unavoidable. First, we cannot completely rule out the absence of LD. There might still be potential sample overlap in the research process. Second, the GWAS data used in this study were sourced from a European ancestry population, which could potentially limit the generalizability of our findings to other populations. Finally, as this study is a computational simulation based on publicly available GWAS data, independent cohort validation at the population level has not been undertaken.

Conclusion

In conclusion, our study confirms a robust positive genetic correlation and overlap between SCZ and constipation, identifying two SNPs with shared risk. Moreover, our analysis unveils a shared genetic overlap in brain tissues between these two conditions, potentially bolstering evidence for the gut–brain axis and their comorbidities. These findings open up new avenues for future research endeavors.

Data availability

All the GWAS data and statistical software used in this study were publicly available (which can be accessed through the following URLs), and all the generated results in this study were provided in the main text and supplemental data.

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Acknowledgements

The authors thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

Funding

This work was supported by the National Administration of Traditional Chinese Medicine's Key Discipline for High-Level Traditional Chinese Medicine (ZYYZDXK-2023064), the National Natural Science Foundation of China (Grant Nos. 82260938, 82374454, 82004374), the 2024 Jiangxi University of Traditional Chinese Medicine Graduate Innovation Project (XJ-B202404), and the Clinical Medicine Leadership Project (YD202223) under the "Dragon Medical Science and Technology Innovation Cultivation Program" of Longhua Hospital affiliated with Shanghai University of Traditional Chinese Medicine.

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  1. Qinghua Luo and Mingwei An contributed equally to this work.

Authors and Affiliations

  1. Department of Anorectal Surgery, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Qinghua Luo, Jiawen Wang & Chen Wang

  2. Clinical Medical College, Jiangxi University of Chinese Medicine, Nanchang, China

    Qinghua Luo & Yuanting Mao

  3. Department of Anorectal Surgery, Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, China

    Mingwei An, Yunxiang Wu, Yuanting Mao & Leichang Zhang

  4. Formula-Pattern Research Center, Jiangxi University of Chinese Medicine, Jiangxi, China

    Leichang Zhang

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Contributions

Qinghua Luo: Conceptualization, methodology, formal analysis, data curation, writing-original draft preparation; Mingwei An and Yunxiang Wu: writing-original draft preparation, visualization, Funding acquisition; Jiawen Wang and Yuanting Mao: Data curation, writing-original draft preparation, visualization; Leichang Zhang: Supervision, writing-review and editing, Funding acquisition; Chen Wang: writing-review and editing, Data curation, visualization. All authors contributed to the article and approved the final version of the manuscript.

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Correspondence to Leichang Zhang or Chen Wang.

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Not applicable. The data used for analysis were obtained from published studies and public databases. The GWAS database is a database of publicly available datasets, where each study has been approved by local institutional review boards and ethics committees.

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Luo, Q., An, M., Wu, Y. et al. Genetic overlap between schizophrenia and constipation: insights from a genome-wide association study in a European population. Ann Gen Psychiatry 24, 11 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12991-025-00551-3

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Annals of General Psychiatry

ISSN: 1744-859X