Identification of Common Pathogenetic Processes between Schizophrenia and Diabetes Mellitus by Systems Biology Analysis

One path, two solutions: Network-based analysis identifies targetable pathways for the treatment of comorbid type II diabetes and neuropsychiatric disorders

Comorbid diseases complicate patient outcomes and escalate healthcare costs, necessitating the need for a deeper mechanistic understanding. Neuropsychiatric disorders (NPDs) such as Neurotic Disorder, Major Depression, Bipolar Disorder, Anxiety Disorder, and Schizophrenia significantly exacerbate Type 2 Diabetes Mellitus (DM2), often leading to suboptimal treatment outcomes. The neurobiological mechanisms underlying this comorbidity remain poorly understood. To address this gap, we developed a novel pathway-based network computational framework to identify critical shared disease mechanisms between DM2 and these five prevalent comorbid NPDs. Our approach involves reconstructing an integrated DM2 ∩ NPDs KEGG pathway-pathway network and employs two complementary analytical methods, including the "minimum path to comorbidity" method to identify the shortest path fostering comorbid development. This analysis uncovered shared pathways like the PI3K-Akt signaling pathway and highlighted key nodes such as calcium signaling, MAPK, estrogen signaling, and apoptosis pathways. Dysregulation of these pathways likely contributes to the development of DM2-NPDs comorbidity. These findings have significant clinical implications, as they identify promising therapeutic targets that could lead to more effective treatments addressing both DM2 and NPDs simultaneously. Our model not only elucidates the intricate molecular interactions driving this comorbidity but also identifies promising therapeutic targets, paving the way for innovative treatment strategies. Additionally, the framework developed in this study can be adapted to study other complex comorbid conditions, advancing personalized medicine for comorbidities and improving patient care.

Impact of Opuntia ficus-indica Juice and Empagliflozin on Glycemic Control in Rats

Diabetes mellitus (DM) is a major global health concern characterized by high blood glucose levels. This study investigates the effects of Opuntia ficus-indica (cactus) juice and empagliflozin, both alone and in combination, on glycated hemoglobin (HbA1c) levels in healthy and streptozotocin-induced diabetic rats. Eighty Wistar albino male rats were divided into eight groups, with four groups being diabetic. Treatment options included cactus juice, empagliflozin, or both. HbA1c levels were measured at baseline and 100 days later using ELISA. In diabetic and non-diabetic rats treated with cactus juice or empagliflozin, HbA1c levels were significantly reduced, but diabetic rats had significantly lower HbA1c values than non-diabetic rats. The combined treatment provided no additional benefits over individual therapies. These findings indicate that cactus juice and empagliflozin effectively lower HbA1c levels, making their use a promising complementary approach to diabetes management. However, the combined treatment of Opuntia ficus-indica juice and empagliflozin did not yield additional reductions in HbA1c levels compared to individual treatments, with no significant synergistic effects observed throughout the study period. More research is needed to better understand the clinical applications and mechanisms in humans.

Sex differences in plasma lipid profiles, but not in glucose metabolism in patients with first-episode antipsychotics-naïve schizophrenia

BACKGROUND

First-episode antipsychotics-naïve schizophrenia (FEAN-SCZ) is associated with abnormalities in glucose and lipid metabolism. While sex differences in the incidence and severity of SCZ and metabolic abnormalities have been documented, the specific metabolic abnormalities between the sexes remain unclear. The study aimed to investigate sex-specific differences in plasma glycolipid profiles in FEAN-SCZ patients.

METHODS

A total of 172 FEAN-SCZ patients (male/female: 83/89) and 31 healthy controls (male/female: 14/17) were recruited. Psychopathology assessment was conducted using the Positive and Negative Syndrome Scale (PANSS). Glycolipid profiles, including oral glucose tolerance test (OGTT), fasting glucose, insulin, total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were examined in all participants.

RESULTS

FEAN patients displayed significantly higher fasting and 2-hour glucose levels compared to healthy controls (both p < 0.001). Impaired glucose tolerance (IGT) prevalence in male patients was 24.1 % (n = 20) and 25.9 % (n = 23) in females, contrasting with 0 % (n = 0) in the control group. FEAN patients exhibited elevated blood insulin and TC levels (both p < 0.05) and increased insulin resistance measured by HOMA-IR (p < 0.01). Among male patients, those with IGT had significantly higher TC, TG and LDL levels than non-IGT patients (all p < 0.05), while no significant differences were observed in female patients between IGT and non-IGT groups. Body mass index (BMI), TG and HDL levels were identified as significant predictors of IGT in male FEAN patients.

CONCLUSIONS

IGT is present in a subset of FEAN-SCZ patients. Male patients with IGT exhibit distinct alterations in plasma lipid profiles compared to non-IGT patients.

Relationship between eating attitudes, depression, and insight in schizophrenic patients with and without type 2 diabetes mellitus: a comparative study in Guangdong, China

Background

Schizophrenia, a severe mental disorder, is often complicated by Type 2 Diabetes Mellitus (T2DM), which can further impact patients' psychological health. This study investigated the differences in eating attitudes, depression, and insight between schizophrenic patients with and without comorbid T2DM and explored the correlations among these factors to provide empirical support for clinical interventions.

Methods

This case-control study was conducted in Guangdong Province, China. From December 2022 to May 2023, a total of 300 hospitalized patients with schizophrenia (92 with comorbid T2DM and 208 without T2DM) were recruited. Data were collected using the Personal Information Form, Eating Attitudes Test (EAT-26), Hamilton Depression Scale (HAMD), and Insight and Treatment Attitudes Questionnaire (ITAQ). Statistical analyses, including t-tests, ANOVA, and multiple linear regression, were performed to examine differences and predictive factors of eating attitudes among patients. This study was approved by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University (approval number: 2020028), and written informed consent was obtained from all participants.

Results

Patients with schizophrenia and comorbid T2DM exhibited significantly higher risks of eating disorders (EAT-26: 12.54 ± 9.77 vs. 9.07 ± 7.90, P=0.003), more severe depression (HAMD: 14.71 ± 7.36 vs. 11.80 ± 6.04, P=0.001), and poorer insight (ITAQ: 10.46 ± 6.01 vs. 12.16 ± 6.09, P=0.025) compared to those without T2DM. Regression analysis revealed that gender, weekly exercise frequency, depression, and insight were significant predictors of eating attitudes among patients with T2DM. For patients without T2DM, weekly exercise frequency, smoking status, and insight were significant predictors.

Conclusion

Schizophrenic patients with comorbid T2DM are facing increasing risks related to eating attitudes, depression, and insight which highlight the need for targeted interventions. Regular psychological assessment and tailored support strategies might improve their mental health and quality of life. Future research should focus on longitudinal studies to clarify causal relationships and develop more effective interventions.

A network of transcriptomic signatures identifies novel comorbidity mechanisms between schizophrenia and somatic disorders

The clinical burden of mental illness, in particular schizophrenia and bipolar disorder, are driven by frequent chronic courses and increased mortality, as well as the risk for comorbid conditions such as cardiovascular disease and type 2 diabetes. Evidence suggests an overlap of molecular pathways between psychotic disorders and somatic comorbidities. In this study, we developed a computational framework to perform comorbidity modeling via an improved integrative unsupervised machine learning approach based on multi-rank non-negative matrix factorization (mrNMF). Using this procedure, we extracted molecular signatures potentially explaining shared comorbidity mechanisms. For this, 27 case-control microarray transcriptomic datasets across multiple tissues were collected, covering three main categories of conditions including psychotic disorders, cardiovascular diseases and type II diabetes. We addressed the limitation of normal NMF for parameter selection by introducing multi-rank ensembled NMF to identify signatures under various hierarchical levels simultaneously. Analysis of comorbidity signature pairs was performed to identify several potential mechanisms involving activation of inflammatory response auxiliarily interconnecting angiogenesis, oxidative response and GABAergic neuro-action. Overall, we proposed a general cross-cohorts computing workflow for investigating the comorbid pattern across multiple symptoms, applied it to the real-data comorbidity study on schizophrenia, and further discussed the potential for future application of the approach.

Glucose dysregulation in antipsychotic-naive first-episode psychosis: in silico exploration of gene expression signatures

Antipsychotic (AP)-naive first-episode psychosis (FEP) patients display early dysglycemia, including insulin resistance and prediabetes. Metabolic dysregulation may therefore be intrinsic to psychosis spectrum disorders (PSDs), independent of the metabolic effects of APs. However, the potential biological pathways that overlap between PSDs and dysglycemic states remain to be identified. Using meta-analytic approaches of transcriptomic datasets, we investigated whether AP-naive FEP patients share overlapping gene expression signatures with non-psychiatrically ill early dysglycemia individuals. We meta-analyzed peripheral transcriptomic datasets of AP-naive FEP patients and non-psychiatrically ill early dysglycemia subjects to identify common gene expression signatures. Common signatures underwent pathway enrichment analysis and were then used to identify potential new pharmacological compounds via Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS). Our search results yielded 5 AP-naive FEP studies and 4 early dysglycemia studies which met inclusion criteria. We discovered that AP-naive FEP and non-psychiatrically ill subjects exhibiting early dysglycemia shared 221 common signatures, which were enriched for pathways related to endoplasmic reticulum stress and abnormal brain energetics. Nine FDA-approved drugs were identified as potential drug treatments, of which the antidiabetic metformin, the first-line treatment for type 2 diabetes, has evidence to attenuate metabolic dysfunction in PSDs. Taken together, our findings support shared gene expression changes and biological pathways associating PSDs with dysglycemic disorders. These data suggest that the pathobiology of PSDs overlaps and potentially contributes to dysglycemia. Finally, we find that metformin may be a potential treatment for early metabolic dysfunction intrinsic to PSDs.

Integrated analysis of potential biomarkers associated with diabetic periodontitis development based on bioinformatics: An observational study

Based on the importance of chronic inflammation in the pathogenesis of periodontitis and diabetes, the bidirectional relationship between these 2 diseases has been widely confirmed. However, the molecular mechanisms of bidirectional relationship still need to be studied further. In this study, gene expression profile data for diabetes and periodontitis were obtained from Gene Expression Omnibus (GEO) database. Integrative analytical platform were constructed, including common differentially expressed genes (cDEGs), Gene Ontology-Kyoto Encyclopedia of Genes and Genomes (GO-KEGG), and protein-protein interaction. Hub genes and essential modules were detected via Cytoscape. Key hub genes and signaling pathway that mediate chronic inflammation were validated by qPCR and Western blot. Eleven cDEGs were identified. Function analysis showed that cDEGs plays an important role in inflammatory response, cytokine receptor binding, TNF signaling pathway. As hub genes, CXCR4, IL1B, IL6, CXCL2, and MMP9 were detected based on the protein-protein interactions network. IL1B, CXCR4 mRNA were up-regulated in gingivitis samples compared with normal tissues (P < .05). Western blot indicated that the levels of TNF were enhanced in gingivitis of type 2 diabetes compared with normal tissues (P < .01). Hub gene and TNF signaling pathway are helpful to elucidate the molecular mechanism of the bidirectional relationship between periodontitis and diabetes.

Diabetes mellitus type 2 as an underlying, comorbid or consequent state of mental disorders

Somatic disturbances that occur in parallel with psychiatric diseases are a major challenge in clinical practice. Various factors contribute to the development of mental and somatic disorders. Type 2 diabetes mellitus (T2DM) is a significant health burden worldwide, and the prevalence of diabetes in adults is increasing. The comorbidity of diabetes and mental disorders is very common. By sharing a bidirectional link, both T2DM and mental disorders influence each other in various manners, but the exact mechanisms underlying this link are not yet elucidated. The potential mechanisms of both mental disorders and T2DM are related to immune and inflammatory system dysfunction, oxidative stress, endothelial dysfunction, and metabolic disturbances. Moreover, diabetes is also a risk factor for cognitive dysfunction that can range from subtle diabetes-associated cognitive decline to pre-dementia and dementia. A complex re-lationship between the gut and the brain also represents a new therapeutic approach since gut-brain signalling pathways regulate food intake and hepatic glucose production. The aim of this minireview is to summarize and present the latest data on mutual pathogenic pathways in these disorders, emphasizing their complexity and interweaving. We also focused on the cognitive performances and changes in neurodegenerative disorders. The importance of implementing integrated approaches in treating both of these states is highlighted, along with the need for individual therapeutic strategies.

Shared Genes of PPARG and NOS2 in Alzheimer’s Disease and Ulcerative Colitis Drive Macrophages and Microglia Polarization: Evidence from Bioinformatics Analysis and Following Validation

Emerging evidence shows that peripheral systemic inflammation, such as inflammatory bowel disease (IBD), has a close even interaction with central nervous disorders such as Alzheimer’s disease (AD). This study is designed to further clarify the relationship between AD and ulcerative colitis (UC, a subclass of IBD). The GEO database was used to download gene expression profiles for AD (GSE5281) and UC (GSE47908). Bioinformatics analysis included GSEA, KEGG pathway, Gene Ontology (GO), WikiPathways, PPI network, and hub gene identification. After screening the shared genes, qRT-PCR, Western blot, and immunofluorescence were used to verify the reliability of the dataset and further confirm the shared genes. GSEA, KEGG, GO, and WikiPathways suggested that PPARG and NOS2 were identified as shared genes and hub genes by cytoHubba in AD and UC and further validated via qRT-PCR and Western blot. Our work identified PPARG and NOS2 are shared genes of AD and UC. They drive macrophages and microglia heterogeneous polarization, which may be potential targets for treating neural dysfunction induced by systemic inflammation and vice versa.

Special Issue: Genetics of Psychiatric Disease and the Basics of Neurobiology

A psychiatric disorder is a mental illness involving significant disturbances in thinking, emotional regulation or behavior [...]

Meta-Analysis of Long-Term Efficacy of Proliferative Diabetic Retinopathy Based on Intelligent Medical Treatment

In order to solve some problems of subhealth and high chronic diseases, the diagnosis and treatment of value-added diabetic retinopathy are studied. In particular, diabetes, a high chronic disease, poses a great threat to people's health. With the continuous improvement of national health awareness, the medical field also begins to pay more attention to the diagnosis and treatment of value-added diabetic retinopathy. In order to improve the long-term treatment of value-added diabetic retinopathy through intelligent medical monitoring and systematic scientific efficacy analysis and evaluation, the purpose of this study is to explore how to effectively achieve the meta-analysis of long-term efficacy of proliferative diabetic retinopathy through intelligent medical treatment. Through the study of diabetic retinopathy, the system can help doctors to achieve unlimited further signs of parameter acquisition and transmission and build more mature after treatment of the results of the monitoring platform. At the same time, a conclusion based on vitrectomy was proposed to effectively improve the surgical efficacy of patients with proliferative diabetic retinopathy.

Crosstalk between Schizophrenia and Metabolic Syndrome: The Role of Oxytocinergic Dysfunction

The high prevalence of metabolic syndrome in persons with schizophrenia has spurred investigational efforts to study the mechanism beneath its pathophysiology. Early psychosis dysfunction is present across multiple organ systems. On this account, schizophrenia may be a multisystem disorder in which one organ system is predominantly affected and where other organ systems are also concurrently involved. Growing evidence of the overlapping neurobiological profiles of metabolic risk factors and psychiatric symptoms, such as an association with cognitive dysfunction, altered autonomic nervous system regulation, desynchrony in the resting-state default mode network, and shared genetic liability, suggest that metabolic syndrome and schizophrenia are connected via common pathways that are central to schizophrenia pathogenesis, which may be underpinned by oxytocin system dysfunction. Oxytocin, a hormone that involves in the mechanisms of food intake and metabolic homeostasis, may partly explain this piece of the puzzle in the mechanism underlying this association. Given its prosocial and anorexigenic properties, oxytocin has been administered intranasally to investigate its therapeutic potential in schizophrenia and obesity. Although the pathophysiology and mechanisms of oxytocinergic dysfunction in metabolic syndrome and schizophrenia are both complex and it is still too early to draw a conclusion upon, oxytocinergic dysfunction may yield a new mechanistic insight into schizophrenia pathogenesis and treatment.

Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology

Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures.

Evidence for Shared Genetic Aetiology Between Schizophrenia, Cardiometabolic, and Inflammation-Related Traits: Genetic Correlation and Colocalization Analyses

BACKGROUND

Schizophrenia commonly co-occurs with cardiometabolic and inflammation-related traits. It is unclear to what extent the comorbidity could be explained by shared genetic aetiology.

METHODS

We used GWAS data to estimate shared genetic aetiology between schizophrenia, cardiometabolic, and inflammation-related traits: fasting insulin (FI), fasting glucose, glycated haemoglobin, glucose tolerance, type 2 diabetes (T2D), lipids, body mass index (BMI), coronary artery disease (CAD), and C-reactive protein (CRP). We examined genome-wide correlation using linkage disequilibrium score regression (LDSC); stratified by minor-allele frequency using genetic covariance analyzer (GNOVA); then refined to locus-level using heritability estimation from summary statistics (ρ-HESS). Regions with local correlation were used in hypothesis prioritization multi-trait colocalization to examine for colocalisation, implying common genetic aetiology.

RESULTS

We found evidence for weak genome-wide negative correlation of schizophrenia with T2D (rg = -0.07; 95% C.I., -0.03,0.12; P = .002) and BMI (rg = -0.09; 95% C.I., -0.06, -0.12; P = 1.83 × 10-5). We found a trend of evidence for positive genetic correlation between schizophrenia and cardiometabolic traits confined to lower-frequency variants. This was underpinned by 85 regions of locus-level correlation with evidence of opposing mechanisms. Ten loci showed strong evidence of colocalization. Four of those (rs6265 (BDNF); rs8192675 (SLC2A2); rs3800229 (FOXO3); rs17514846 (FURIN)) are implicated in brain-derived neurotrophic factor (BDNF)-related pathways.

CONCLUSIONS

LDSC may lead to downwardly-biased genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. Common genetic aetiology for these traits could be confined to lower-frequency common variants and involve opposing mechanisms. Genes related to BDNF and glucose transport amongst others may partly explain the comorbidity between schizophrenia and cardiometabolic disorders.

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.