Brain imaging data and summary-data-based Mendelian randomization analysis reveal the impact of multiorgan aging on schizophrenia

Brain imaging data and summary-data-based Mendelian randomization analysis reveal the impact of multiorgan aging on schizophrenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Brain imaging data and summary-data-based Mendelian randomization analysis reveal the impact of multiorgan aging on schizophrenia Yan-Kun Han, Miao-Yan Liu, Ding-long Yang, Jia-Xin Xie, Xiao-Hui Wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7922183/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim The adverse health outcomes of schizophrenia (SZ) are largely driven by the high prevalence of other non-neurological diseases. In addition to accelerated brain aging, patients with SZ also exhibit signs of systemic aging. However, the potential causal or biological mechanisms between multisystem aging and schizophrenia remain unknown. Methods We obtained SZ-associated single Nucleotide Polymorphism (SNP) sets, aging gene data, and tissue-specific cis-expression Quantitative Trait Locus (cis-eQTL) data of the cerebral cortex and other tissues from a previous two-stage genome-wide association study (GWAS), Genecards database, and Genotype-Tissue Expression (GTEx) project. We employed tissue-specific Mendelian Randomization (MR) analysis to elucidate the tissue-specific expression patterns of aging-related genes, and used the Summary-data-based MR (SMR) approach to obtain tissue aging-related genes associated with the risk of SZ development. We identified the potential aging-related pathways through which these tissue-specific cis-eQTL may affect SZ using enrichment analyses. Finally, we explored the relationship between the identified crucial aging-related genes and predicted age difference (PAD) of brain in our clinical patients. Results We found that the expression of tissue-specific aging genes including NCA , ACE , BRCA1 , MLH1 , VEGFA , MAPT , and ARMS2 may affect SZ. The tissue-specific cis-eQTL may influence SZ through aging pathways. The brain PAD was significantly higher in the high-expression group of BRCA1 than in the low-expression group. Conclusions This study provides valuable clues to understand the link between SZ and multiorgan system aging and improves the current understanding of multiple tissue-specific aging-related genes with SZ. Schizophrenia Genome-Wide Association Study Aging Brain Neuroimaging Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Schizophrenia (SZ) is a highly heterogeneous disorder 1 . People with SZ show abnormalities in several organ systems in addition to the central nervous system (CNS) 2 . Negative health outcomes for SZ are to a large extent driven by the high rates of comorbid metabolic syndrome and related diseases 3 . Previous studies have uncovered a shared genetic etiology among cardiovascular disease, frailty, and SZ, as well as altered oral microbiota and systemic immune dysfunction in patients with SZ 4–6 . Whether SZ is a multi-system disorder or their high rates of comorbid may be triggered by different mechanisms but result from common risk factors still remains unknow. Evidence suggested that aging play an important role in SZ 7–10 . Five Case-Control Studies found SZ is accompanied by accelerated biological aging by midlife 11 . Previous studies have found that there is a common biological basis between SZ patients and normal elderly individuals with brain aging. In SZ and aging, astrocytes, glutamatergic, and GABAergic neurons show low synaptic neuron-astrocyte program expression, which is associated with cognitive flexibility and plasticity 12 , 13 .The cognitive impairment symptoms of SZ patients resemble those of the elderly, mainly involving decreased ability to process high-load information, episodic nonverbal memory impairment, slowed processing speed, and weakened motor coordination. These symptoms suggest that the pathological state of SZ patients is associated with accelerated brain aging 14 – 16 . Compared with the general population, early SZ is not only associated with alterations in brain structure and function, it is also associated with multiple changes in the body 2 . However, the association between brain and body health as well as the associated disease risk and physical multimorbidity across body systems hence remain poorly characterized. Mendelian randomization (MR) analysis is an emerging method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of an exposure on an outcome 17 . In order to be able to locate causality more precisely at the molecular level, we utilize the Summary-data-based Mendelian Randomization (SMR), which could effectively integrate multi-source data 18 . Due to the specificity of IVs, the MR estimates are not commonly subject to confounding bias and reverse causation. MR has also been applied to detect putative causal effects of tissue-specific gene expression and a wide range of diseases using expression quantitative trait loci (eQTLs) as instruments 19 , 20 . Generally, comorbidity-related studies use SMR, which is essential for studying the causal relationships between different organ systems and diseases, as it helps to avoid confounding factors and establish more reliable causal links 21 , 22 . The aim of this study was to investigate the causal effect of aging on SZ by using MR method. To identify potential target gene, the tissue- type-specific causal effects of aging on cognitive function were evaluated using cis-eQTL-based MR. MATERIALS AND METHODS Data acquisition SZ associated SNP sets derived from a previous two-stage genome-wide association study (GWAS) 23 . This is one of the largest available GWASs of SZ which report common variant associations at 287 distinct genomic loci, including up to 76,755 individuals with SZ and 243,649 control individuals. In the primary GWAS, they have analyzed up to 7,585,078 SNPs with MAF ≥ 1% in 175,799 individuals of whom 74.3% were European, 17.5% East Asian, 5.7% African-American, and 2.5% Latino. In the extended GWAS, they have meta-analysed the primary GWAS results with summary statistics from deCODE Genetics (1,979 cases, 142,626 controls) for index SNPs with P<1x10 −5 and identified 342 LD-independent significant SNPs located in 287 loci. The tissue-specific cis-eQTL data of brain cortex, brain hippocampus, brain hypothalamus, heart, liver, lung, kidney, pancreas, muscle, and adipose was obtained from the Genotype-Tissue Expression (GTEx) project (v8; https://gtexportal.org/home/). The GTEx Portal is a comprehensive public resource for researchers studying tissue and cell-specific gene expression and re gulation across individuals, development, and species, with data from 3 NIH projects. Ethical approval of all data was obtained in the original studies. The aging genes were obtained from the Genecards database, which is a comprehensive and authoritative compendium of human gene information. We selected the top 50 genes associated with aging to represent. We selected 50 genes to represent, and these 50 genes are the top 50 genes with the highest aging-related scores in the Genecards database. We recruited 43 patients with SZ from Xijing Hospital for brain MRI scanning to calculate brain age, and collected peripheral whole blood samples from the patients to measure gene expression using RNA-Seq technology. The diagnosis of SCZ was determined according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and confirmed by two experienced clinical psychologists after a comprehensive assessment of all available information. Detailed inclusion and exclusion criteria were previously documented. RNA sequencing (RNA-seq) data derived from peripheral blood samples were utilized in this study, as previously described in detail. Specifically, 2.5 mL of whole blood was collected into PAXgene Blood RNA Tubes and immediately stored at −80°C. Subsequent RNA sequencing was performed on the Illumina Novaseq 6000 platform. Raw sequencing data underwent quality control using Fastp (v.0.18.0; fastp: an ultra-fast all-in-one FASTQ preprocessor), with low-quality reads being excluded. The cleaned reads were then aligned to the human reference genome (hg19) using HISAT2.2.4 (HISAT: a fast spliced aligner with low memory requirements). Finally, the raw count data were normalized using DESeq2 (Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2). The study protocol was reviewed and approved by the Xijing Hospital Institutional Ethics Committee and conformed to the ethical standards for medical research involving human subjects, as laid out in the 1964 Declaration of Helsinki and its later amendments. Participants provided written informed consent prior to taking part in the study. Brain age calculation Imaging Data Acquisition and Preprocessing: Patients and healthy controls underwent 3D high - resolution structural and resting - state functional MRI scans, with raw images stored in DICOM format. Imaging data were acquired using a GE Architect 3.0 T scanner and a 48 - channel standard phased - array head coil. Brain Age Model: Leveraging a 3D - convolutional neural network (3D - CNN) algorithm and multimodal MRI data, this model accurately predicts brain age. Based on the classic VGGNet architecture, it is optimized into a simple fully convolutional neural network (SFCN) for efficient brain imaging data processing and analysis. The predicted age difference (PAD)=calculated age- chronological age. MR analysis We first assess different tissue-dependent effects of aging gene expression on SZ through tissue-specific MR analysis, we estimated the putative causal effects of 50 aging genes expression in 10 tissues based on the GTEx databased. MR analysis of aging genes expression in 8 tissues on SZ was then conducted. FDR correction for SMR P-value was applied using the Benjamini-Hochberg method. Tissue-dependent effects of aging genes expression on SZ are significant when FDR SMR 0.05. Then, we applied a tissue- MR analysis to determine all tissue-specific eQTL which have causal effect on SZ. The SMR selected tissue-specific cis-eQTL significantly associated with SZ with a genome-wide threshold of 5 × 10 -8 . We again performed MR of tissue-specific genes with genes obtained from the SNP dataset for SZ. Enrichment analysis Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to detect whether the tissue-specific cis-eQTL through the aging pathways to influence SZ. The "clusterProfiler" package in R software was utilized for the GO and KEGG enrichment analysis of cis-eQTL, with a selection criterion of q-value < 0.05. The comparison of brain PAD between high and low expression groups of aging genes. We divided the data of SZ patients into two groups based on the top 20% and bottom 20% of tissue-specific senescence gene expression, in order of high and low expression groups. Then we analyzed their data of brain age to verify whether the screened genes significantly affect the brain PAD of SZ patients. Statistical analyses Data are presented as mean ± standard deviation (S.D.). Comparisons between two groups were performed using the t-test and Wilcoxon test. All statistical analyses were conducted using SPSS software (version 30.0.0) A p-value of less than 0.05 was considered to indicate statistical significance. RESULTS Tissue-dependent effects of aging gene expression on SZ The MR analyses suggested putative causal effects of 7 aging gene expression in 7 tissues on SZ, among them ACE in lung, VEGFA in pancreas, MAPT in spleen, and SNCA in heart left ventricle associations with SZ passed FDR _SMR 0.05. Additionally, we detected several genes with suggestive associations, such as MLH1 in muscle, and BRCA1 in liver. The influence of tissue-specific aging eQTLs to SZ Among the seven tissues analyzed, we identified that the ACE gene in lung tissue, the VEGFA gene in pancreatic tissue, the MAPT gene in the spleen, and the SNCA gene in the heart exhibited causal relationships with SZ (Fig. 1). Enrichment Analysis of tissue-specific eQTLs To further elucidate the connections between peripheral organs and SZ, we conducted enrichment analyses of tissue-specific genes associated with SZ (Supplementary file 1). As shown by GO enrichment analysis, we found that organs like the liver, lungs, and heart may influence SZ through aging pathways. For instance, liver-related pathways involve antigen processing and presentation (e.g., peptide antigen via MHC class I), DNA strand elongation, and oligopeptide transport. In the lungs, the cAMP responsive element binding protein (CREB) pathway, isomerase activity pathway, and four-way junction DNA binding pathway are relevant. The pancreas-specific genes were associated with ATPase complexes and SWI/SNF superfamily type complexes pathways. KEGG pathway analysis revealed that liver-specific genes associated with SZ were mainly enriched in lysosomes, ABC transporter proteins, which are the essential roles in aging-related pathways. The details were shown in Fig. 2. Validation of the effects of selected genes on brain PAD in SZ patients The brain PAD was significantly higher in the high-expression group of BRCA1 than in the low-expression group (Fig. 3). Although statistically significant results were obtained only for the BRCA1 gene, the VEGFA , SNCA , and ARMS2 still showed a favorable trend, probably due to the limitation of the small sample size. DISCUSSION Previous studies have mostly focused on the interplay between SZ and brain aging, or the impact of a single organ or system on SZ. No research has yet examined how the body's multiple organ systems may influence the brain via aging pathways to affect SZ. In this study, we used genetic tools and found that organ-specific aging genes in peripheral organs— ACE in the lung, VEGFA in the pancreas, MAPT in the spleen, SNCA in the heart's left ventricle, MLH1 in muscle, BRCA1 in the liver, and ARMS2 in adipose tissue—might influence SZ through aging-related pathways. Expression data of these genes in whole blood and enrichment analysis results further confirmed this finding. The SNCA gene encodes α-synuclein, which is involved in synaptic transmission and neurotransmitter release. α-synuclein is highly expressed at the presynaptic terminal and is involved in a variety of cellular functions, including synaptic vesicle transport, membrane binding and signal transduction, and affecting the homeostasis of mitochondria and lysosomes 24 . Joung et al. found that, although the role of the SNCA gene is more pronounced in the brain, it also affects cardiac aging. The heart-brain axis is a bidirectional communication system that enables interaction between the heart and the brain through various pathways, including the autonomic nervous system and the immune system. Aging can lead to functional and structural changes in the heart, which in turn can affect brain function. Previous study found that cardiovascular diseases can result in reduced cerebral blood flow, potentially leading to cognitive decline symptoms observed in patients with SZ 25 . Additionally, the SNCA gene may influence monocyte metabolism by participating in the regulation of the unfolded protein response and endoplasmic reticulum stress. Abnormal aggregation of α-synuclein may also activate monocytes and macrophages, prompting them to secrete pro-inflammatory cytokines and exacerbate systemic inflammation. These factors may all contribute to the development and progression of SZ. Our research also found that monocyte count genes are associated with cortical thickness 24, 26, 27 . The liver-brain axis and the muscle-brain axis are also bidirectional communication systems. Aging can lead to changes in liver function and structure, which may promote the development of liver and muscle diseases and affect brain function through the liver-brain axis and muscle-brain axis 28, 29 . Although the primary functions of BRCA1 and MLH1 are related to DNA repair 30 , they can still influence the function and structure of the liver and muscles, which in turn can affect brain function 31, 32 . More importantly, in our clinical patients with SZ, the BRCA1 gene expression in the blood are associated with brain PAD, which further suggests the potential role of the aging-related gene BRCA1 in liver-brain axis and SZ. In lung-brain interaction, changes in ACE gene expression impacts pulmonary blood flow and oxygenation, which in turn affect brain function. ACE inhibitors are widely used to treat hypertension and heart failure. Cao et al. found that the effects of ACE inhibitors on blood pressure can indirectly influence the lung-brain axis, thereby impacting brain function. Over expression of ACE is associated with increased oxidative metabolism and heightened immune responses. This can affect the energy production and immune function of monocytes, leading to inflammation and subsequent brain changes, which is consistent with our study 27, 33 . ARMS 2 is a gene linked to age-related macular degeneration. It encodes a secreted protein functioning in the extracellular matrix, likely maintaining matrix homeostasis through interactions with various matrix proteins 34 .Adipose tissue undergoes significant changes during aging, including adipocyte volume reduction, decreased lipid storage capacity, and alterations in adipokine secretion levels 35 .Adipose tissue may upregulate ARMS2 to regulate and maintain its structure and function. ARMS2 may interact with matrix proteins to influence monocyte migration, phagocytosis, and immune responses. Its mitochondria-associated functions could indirectly affect monocyte energy metabolism and activity 36 , potentially causing chronic inflammation that impacts brain function 27 . The VEGFA gene is crucial for angiogenesis, inflammation, and oxidative stress. As pancreatic tissue ages, vascular regression and reduced blood supply occur. By promoting angiogenesis, VEGFA may improve pancreatic blood supply and slow its aging. VEGFA might also alter chromatin accessibility via the SWI/SNF complex, regulating genes linked to neuronal survival, differentiation, and synaptic plasticity. Moreover, VEGFA overexpression may activate pathways that influence monocyte and macrophage function, potentially leading to a pro-inflammatory microenvironment 27, 37, 38 . In conclusion, our study offers novel insights into the relationships between SZ and the aging-related genes SNCA , ACE , BRCA1 , MLH1 , VEGFA , MAPT , and ARMS2 in multiple organs. Notably, the BRCA1 gene may be associated with accelerated brain aging in individuals with SZ. Our findings provide valuable clues for understanding the link between peripheral organ aging and SZ. Declarations Acknowledgements Not applicable. Funding This study was supported by the "Sanxin" talent projects Xijing 986 Hospital Department, Air Force Medical University (986SX24XMPY03) and the State Key Laboratory of Neurology and Oncology Drug Development Open Project (SKLSIM-F-2024-66). Authors’ contributions Yan-Kun Han: Investigation, Writing - Original Draft, Data Curation; Miao-Yan Liu: Writing - Original Draft, Data Curation, Resources; Dinglong Yang: Visualization, Software, Writing - Review & Editing; Jia-Xin Xie: Data Curation, Investigation; Xiao-Hui Wang: Data Curation, Investigation; Dong-Bao Wang: Data Curation; Cui-Cui Wang: Investigation; Yun-Long Liang: Methodology; Long-Biao Cui: Conceptualization; Yu-Jing Chen: Conceptualization, Resources, Supervision, Software, Writing - Review & Editing; Hai-Jun Zhang: Methodology, Resources, Supervision, Conceptualization, Writing - Review & Editing. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The sample collection and experimental procedures were approved by the Xijing Hospital Institutional Ethics Committee, and informed consent was obtained from all donors. Competing interests The authors declared no conflict of interests. References Smeland OB, Frei O, Dale AM, Andreassen OA. The polygenic architecture of schizophrenia - rethinking pathogenesis and nosology. Nature reviews Neurology 2020;16(7):366-379. Pillinger T, D'Ambrosio E, McCutcheon R, Howes OD. Is psychosis a multisystem disorder? A meta-review of central nervous system, immune, cardiometabolic, and endocrine alterations in first-episode psychosis and perspective on potential models. Mol Psychiatry 2019;24(6):776-794. Taube C, Mentzel C, Glue P, Barak Y. Aging of persons with schizophrenia: analysis of a national dataset. International psychogeriatrics 2024;36(1):43-50. Rodevand L, Rahman Z, Hindley GFL, Smeland OB, Frei O, Tekin TF, et al. Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors. Am J Psychiatry 2023;180(11):815-826. Liu X, Ling Z, Cheng Y, Wu L, Shao L, Gao J, et al. Oral fungal dysbiosis and systemic immune dysfunction in Chinese patients with schizophrenia. Transl Psychiatry 2024;14(1):475. Deng MG, Wang K, Liu F, Zhou X, Nie JQ, Zhao ZH, et al. Shared genetic architecture and causal relationship between frailty and schizophrenia. Schizophrenia (Heidelb) 2025;11(1):24. Gerretsen P, Plitman E, Rajji TK, Graff-Guerrero A. The effects of aging on insight into illness in schizophrenia: a review. International journal of geriatric psychiatry 2014;29(11):1145-1161. Parlikar R, Shivakumar V. Accelerated aging and frailty in schizophrenia. International psychogeriatrics 2024;36(1):18-20. Okusaga OO. Accelerated aging in schizophrenia patients: the potential role of oxidative stress. Aging and disease 2014;5(4):256-262. Seeman MV. Subjective Overview of Accelerated Aging in Schizophrenia. International journal of environmental research and public health 2022;20(1). Caspi A, Shireby G, Mill J, Moffitt TE, Sugden K, Hannon E. Accelerated Pace of Aging in Schizophrenia: Five Case-Control Studies. Biological psychiatry 2024;95(11):1038-1047. Ling E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, et al. A concerted neuron-astrocyte program declines in ageing and schizophrenia. Nature 2024;627(8004):604-611. Hua JPY, Abram SV, Loewy RL, Stuart B, Fryer SL, Vinogradov S, et al. Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis. Schizophrenia bulletin 2024;50(5):1159-1170. Constantinides C, Han LKM, Alloza C, Antonucci LA, Arango C, Ayesa-Arriola R, et al. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium. Molecular psychiatry 2023;28(3):1201-1209. Kirkpatrick B, Messias E, Harvey PD, Fernandez-Egea E, Bowie CR. Is schizophrenia a syndrome of accelerated aging? Schizophrenia bulletin 2008;34(6):1024-1032. Nguyen TT, Eyler LT, Jeste DV. Systemic Biomarkers of Accelerated Aging in Schizophrenia: A Critical Review and Future Directions. Schizophrenia bulletin 2018;44(2):398-408. Evans DM, Davey Smith G. Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. Annual review of genomics and human genetics 2015;16:327-350. Dong XX, Chen DL, Chen HM, Li DL, Hu DN, Lanca C, et al. DNA methylation biomarkers and myopia: a multi-omics study integrating GWAS, mQTL and eQTL data. Clinical epigenetics 2024;16(1):157. Qiu X, Guo R, Wang Y, Zheng S, Wang B, Gong Y. Mendelian randomization reveals potential causal relationships between cellular senescence-related genes and multiple cancer risks. Communications biology 2024;7(1):1069. Bryois J, Calini D, Macnair W, Foo L, Urich E, Ortmann W, et al. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nature neuroscience 2022;25(8):1104-1112. Shi X, Li M, Yao J, Li MD, Yang Z. Alcohol drinking, DNA methylation and psychiatric disorders: A multi-omics Mendelian randomization study to investigate causal pathways. Addiction (Abingdon, England) 2024;119(7):1226-1237. Wang D, Li D, Dang X, Mu C, Liu C, Zeng Y, et al. Mendelian randomization reveals causalities between DNA methylation and schizophrenia. Biological psychiatry 2025. Trubetskoy V, Pardinas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 2022;604(7906):502-508. Joung J, Heo Y, Kim Y, Kim J, Choi H, Jeon T, et al. Cell enlargement modulated by GATA4 and YAP instructs the senescence-associated secretory phenotype. Nature communications 2025;16(1):1696. Shen J, Jiang C. Unraveling the heart-brain axis: shared genetic mechanisms in cardiovascular diseases and Schizophrenia. Schizophrenia (Heidelberg, Germany) 2024;10(1):113. Soda T, Pasqua T, De Sarro G, Moccia F. Cognitive Impairment and Synaptic Dysfunction in Cardiovascular Disorders: The New Frontiers of the Heart-Brain Axis. Biomedicines 2024;12(10). Cui LB, Wang XY, Fu YF, Liu XF, Wei Y, Zhao SW, et al. Transcriptional level of inflammation markers associates with short-term brain structural changes in first-episode schizophrenia. BMC medicine 2023;21(1):250. Duan L, Li H, Li S, Shi Y, Feng Y. Causal association between sarcopenia and cognitive impairment contributes to the muscle-brain axis: A bidirectional Mendelian randomization study. Geriatrics & gerontology international 2025;25(1):116-122. Cutuli D, Decandia D, Giacovazzo G, Coccurello R. Physical Exercise as Disease-Modifying Alternative against Alzheimer's Disease: A Gut-Muscle-Brain Partnership. International journal of molecular sciences 2023;24(19). Tomer G, Buermeyer AB, Nguyen MM, Liskay RM. Contribution of human mlh1 and pms2 ATPase activities to DNA mismatch repair. The Journal of biological chemistry 2002;277(24):21801-21809. Wang Z, Yin Z, Sun G, Zhang D, Zhang J. Genetic evidence for the liver-brain axis: lipid metabolism and neurodegenerative disease risk. Lipids in health and disease 2025;24(1):41. Wei C, Li X, Jin Y, Zhang Y, Yuan Q. Does the liver facilitate aging-related cognitive impairment: Conversation between liver and brain during exercise? Journal of cellular physiology 2024;239(8):e31287. Cao DY, Spivia WR, Veiras LC, Khan Z, Peng Z, Jones AE, et al. ACE overexpression in myeloid cells increases oxidative metabolism and cellular ATP. The Journal of biological chemistry 2020;295(5):1369-1384. Kortvely E, Hauck SM, Duetsch G, Gloeckner CJ, Kremmer E, Alge-Priglinger CS, et al. ARMS2 is a constituent of the extracellular matrix providing a link between familial and sporadic age-related macular degenerations. Investigative ophthalmology & visual science 2010;51(1):79-88. Nguyen TT, Corvera S. Adipose tissue as a linchpin of organismal ageing. Nature metabolism 2024;6(5):793-807. Fritsche LG, Loenhardt T, Janssen A, Fisher SA, Rivera A, Keilhauer CN, et al. Age-related macular degeneration is associated with an unstable ARMS2 (LOC387715) mRNA. Nature genetics 2008;40(7):892-896. Failla CM, Carbone ML, Ramondino C, Bruni E, Orecchia A. Vascular Endothelial Growth Factor (VEGF) Family and the Immune System: Activators or Inhibitors? Biomedicines 2024;13(1). Torres-Espin A, Radabaugh HL, Treiman S, Fitzsimons SS, Harvey D, Chou A, et al. Sexually dimorphic differences in angiogenesis markers are associated with brain aging trajectories in humans. Science translational medicine 2024;16(775):eadk3118. Table Table.1 Tissue-dependent association for aging genes expression on SZ. Tissue ProbeID ProbeChr Gene P _SMR FDR _SMR P _HEIDI Lung ENSG00000159640 17 ACE 2.28E-04 1.14E-03 6.05E-01 Pancreas ENSG00000112715 6 VEGFA 8.78E-03 4.39E-02 4.95E-01 Spleen ENSG00000186868 17 MAPT 2.27E-04 1.13E-03 1.77E-01 Heart ENSG00000145335 4 SNCA 8.07E-03 4.04E-02 3.49E-02 Muscle ENSG00000076242 3 MLH1 2.16E-02 1.08E-01 1.50E-01 Liver ENSG00000012048 17 BRCA1 3.53E-02 7.06E-02 9.81E-01 Adipose ENSG00000254636 10 ARMS2 7.26E-03 5.81E-02 8.38E-01 Additional Declarations No competing interests reported. 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GWAS, genome-wide association study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7922183/v1/3d7a5a71259e2c396efe223f.png"},{"id":101785272,"identity":"04f8b023-2ad0-4abd-aa49-7e633c70cf8f","added_by":"auto","created_at":"2026-02-03 15:34:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1060263,"visible":true,"origin":"","legend":"\u003cp\u003eThe GO and KEGG enrichment analysis of tissue-specific aging genes. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7922183/v1/2f8b97c4aea701a343f52176.png"},{"id":101785273,"identity":"340fd773-c420-4009-a392-0e19aadc86a1","added_by":"auto","created_at":"2026-02-03 15:34:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":955830,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differences in brain PAD between high and low expression groups of tissue-specific aging genes in patients with SZ. PAD, predicted age difference. SZ, schizophrenia.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7922183/v1/dd8eb403ab18bffc357021e2.png"},{"id":103358763,"identity":"d2e0bb26-4c35-4306-86ab-97b8b0bf6781","added_by":"auto","created_at":"2026-02-24 19:39:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1741601,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7922183/v1/2512c0e7-b546-47ce-9ea9-28d12154debd.pdf"},{"id":101785271,"identity":"7a2ada7d-1fae-4489-9f5b-f2624c4f5fe0","added_by":"auto","created_at":"2026-02-03 15:34:40","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41325,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7922183/v1/a80038a738d084a2b546776a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain imaging data and summary-data-based Mendelian randomization analysis reveal the impact of multiorgan aging on schizophrenia","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSchizophrenia (SZ) is a highly heterogeneous disorder \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. People with SZ show abnormalities in several organ systems in addition to the central nervous system (CNS) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Negative health outcomes for SZ are to a large extent driven by the high rates of comorbid metabolic syndrome and related diseases \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Previous studies have uncovered a shared genetic etiology among cardiovascular disease, frailty, and SZ, as well as altered oral microbiota and systemic immune dysfunction in patients with SZ \u003csup\u003e4\u0026ndash;6\u003c/sup\u003e. Whether SZ is a multi-system disorder or their high rates of comorbid may be triggered by different mechanisms but result from common risk factors still remains unknow.\u003c/p\u003e \u003cp\u003eEvidence suggested that aging play an important role in SZ \u003csup\u003e7\u0026ndash;10\u003c/sup\u003e. Five Case-Control Studies found SZ is accompanied by accelerated biological aging by midlife \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Previous studies have found that there is a common biological basis between SZ patients and normal elderly individuals with brain aging. In SZ and aging, astrocytes, glutamatergic, and GABAergic neurons show low synaptic neuron-astrocyte program expression, which is associated with cognitive flexibility and plasticity \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.The cognitive impairment symptoms of SZ patients resemble those of the elderly, mainly involving decreased ability to process high-load information, episodic nonverbal memory impairment, slowed processing speed, and weakened motor coordination. These symptoms suggest that the pathological state of SZ patients is associated with accelerated brain aging \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Compared with the general population, early SZ is not only associated with alterations in brain structure and function, it is also associated with multiple changes in the body \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the association between brain and body health as well as the associated disease risk and physical multimorbidity across body systems hence remain poorly characterized.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) analysis is an emerging method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of an exposure on an outcome \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In order to be able to locate causality more precisely at the molecular level, we utilize the Summary-data-based Mendelian Randomization (SMR), which could effectively integrate multi-source data \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Due to the specificity of IVs, the MR estimates are not commonly subject to confounding bias and reverse causation. MR has also been applied to detect putative causal effects of tissue-specific gene expression and a wide range of diseases using expression quantitative trait loci (eQTLs) as instruments \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Generally, comorbidity-related studies use SMR, which is essential for studying the causal relationships between different organ systems and diseases, as it helps to avoid confounding factors and establish more reliable causal links \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe aim of this study was to investigate the causal effect of aging on SZ by using MR method. To identify potential target gene, the tissue- type-specific causal effects of aging on cognitive function were evaluated using cis-eQTL-based MR.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eData acquisition\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSZ associated SNP sets derived from a previous two-stage genome-wide association study (GWAS) \u003csup\u003e23\u003c/sup\u003e. This is one of the largest available GWASs of SZ\u0026nbsp;which report common variant associations at 287 distinct genomic loci, including up to 76,755 individuals with SZ and 243,649 control individuals. In the primary GWAS, they have analyzed up to 7,585,078 SNPs with MAF \u0026ge; 1% in 175,799 individuals of whom 74.3% were European, 17.5% East Asian, 5.7% African-American, and 2.5% Latino. In the extended GWAS, they have meta-analysed the primary GWAS results with summary statistics from deCODE Genetics (1,979 cases, 142,626 controls) for index SNPs with P\u0026lt;1x10\u003csup\u003e\u0026minus;5\u003c/sup\u003e and identified 342 LD-independent significant SNPs\u0026nbsp;located in 287 loci. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe tissue-specific cis-eQTL data of brain cortex, brain hippocampus, brain hypothalamus, heart, liver, lung, kidney, pancreas, muscle, and adipose was obtained from the Genotype-Tissue Expression (GTEx) project (v8; https://gtexportal.org/home/). The GTEx Portal is a comprehensive public resource for researchers studying tissue and cell-specific gene expression and re gulation across individuals, development, and species, with data from 3 NIH projects. Ethical approval of all data was obtained in the original studies.\u003c/p\u003e\n\u003cp\u003eThe aging genes were obtained from the Genecards database, which is a comprehensive and authoritative compendium of human gene information. We selected the top 50 genes associated with aging to represent. We selected 50 genes to represent, and these 50 genes are the top 50 genes with the highest aging-related scores in the Genecards database.\u003c/p\u003e\n\u003cp\u003eWe recruited 43 patients with SZ from Xijing Hospital for brain MRI scanning to calculate brain age, and collected peripheral whole blood samples from the patients to measure gene expression using RNA-Seq technology. The diagnosis of SCZ was determined according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and confirmed by two experienced clinical psychologists after a comprehensive assessment of all available information. Detailed inclusion and exclusion criteria were previously documented. RNA sequencing (RNA-seq) data derived from peripheral blood samples were utilized in this study, as previously described in detail. Specifically, 2.5 mL of whole blood was collected into PAXgene Blood RNA Tubes and immediately stored at \u0026minus;80\u0026deg;C. Subsequent RNA sequencing was performed on the Illumina Novaseq 6000 platform. Raw sequencing data underwent quality control using Fastp (v.0.18.0; fastp: an ultra-fast all-in-one FASTQ preprocessor), with low-quality reads being excluded. The cleaned reads were then aligned to the human reference genome (hg19) using HISAT2.2.4 (HISAT: a fast spliced aligner with low memory requirements). Finally, the raw count data were normalized using DESeq2 (Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2). The study protocol was reviewed and approved by the Xijing Hospital Institutional Ethics Committee and conformed to the ethical standards for medical research involving human subjects, as laid out in the 1964 Declaration of Helsinki and its later amendments. Participants provided written informed consent prior to taking part in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrain age calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImaging Data Acquisition and Preprocessing: Patients and healthy controls underwent 3D high - resolution structural and resting - state functional MRI scans, with raw images stored in DICOM format. Imaging data were acquired using a GE Architect 3.0 T scanner and a 48 - channel standard phased - array head coil.\u003c/p\u003e\n\u003cp\u003eBrain Age Model: Leveraging a 3D - convolutional neural network (3D - CNN) algorithm and multimodal MRI data, this model accurately predicts brain age. Based on the classic VGGNet architecture, it is optimized into a simple fully convolutional neural network (SFCN) for efficient brain imaging data processing and analysis. The predicted age difference (PAD)=calculated age-\u0026nbsp;chronological age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first assess different tissue-dependent effects of aging gene expression on SZ through tissue-specific MR analysis, we estimated the putative causal effects of 50 aging genes expression in 10 tissues based on the GTEx databased. MR analysis of aging genes expression in 8 tissues on SZ was then conducted. FDR correction for SMR P-value was applied using the Benjamini-Hochberg method. Tissue-dependent effects of aging genes expression on SZ are significant when \u003cem\u003eFDR\u003csub\u003e\u0026nbsp;SMR\u003c/sub\u003e\u003c/em\u003e \u0026lt; 0.05 and \u003cem\u003eP\u003csub\u003eHEIDI\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05.\u003c/p\u003e\n\u003cp\u003eThen, we applied a tissue- MR analysis to determine all tissue-specific eQTL which have causal effect on SZ. The SMR selected tissue-specific cis-eQTL significantly associated with SZ with a genome-wide threshold of 5 \u0026times; 10\u003csup\u003e-8\u003c/sup\u003e. We again performed MR of tissue-specific genes with genes obtained from the SNP dataset for SZ.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to detect whether the tissue-specific cis-eQTL through the aging pathways to influence SZ.\u0026nbsp;The \u0026quot;clusterProfiler\u0026quot; package in R software was utilized for the GO and KEGG enrichment analysis of cis-eQTL, with a selection criterion of q-value \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe comparison of brain PAD between high and low expression groups of aging genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; We divided the data of SZ patients into two groups based on the top 20% and bottom 20% of tissue-specific senescence gene expression, in order of high and low expression groups. Then we analyzed their data of brain age to verify whether the screened genes significantly affect the brain PAD of SZ patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; standard deviation (S.D.). Comparisons between two groups were performed using the t-test and Wilcoxon test. All statistical analyses were conducted using SPSS software (version 30.0.0) A p-value of less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eTissue-dependent effects of aging gene expression on SZ\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MR analyses suggested putative causal effects of 7 aging gene expression in 7 tissues on SZ, among them\u0026nbsp;\u003cem\u003eACE\u0026nbsp;\u003c/em\u003ein lung, \u003cem\u003eVEGFA\u003c/em\u003e in pancreas, \u003cem\u003eMAPT\u003c/em\u003e in spleen, and \u003cem\u003eSNCA\u003c/em\u003e in\u0026nbsp;heart left ventricle\u0026nbsp;associations with SZ passed\u0026nbsp;\u003cem\u003eFDR\u003csub\u003e_SMR\u003c/sub\u003e\u003c/em\u003e \u0026lt; 0.05, and\u0026nbsp;\u003cem\u003eP\u003csub\u003e_HEIDI\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e\u0026gt; 0.05. Additionally, we detected several genes with suggestive associations, such as\u003cem\u003e\u0026nbsp;MLH1\u003c/em\u003e in muscle, and \u003cem\u003eBRCA1\u0026nbsp;\u003c/em\u003ein liver.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe influence of tissue-specific aging eQTLs to SZ\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the seven tissues analyzed, we identified that the \u003cem\u003eACE\u003c/em\u003e gene in lung tissue, the \u003cem\u003eVEGFA\u003c/em\u003e gene in pancreatic tissue, the \u003cem\u003eMAPT\u003c/em\u003e gene in the spleen, and the \u003cem\u003eSNCA\u003c/em\u003e gene in the heart exhibited causal relationships with SZ (Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment Analysis of tissue-specific eQTLs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further elucidate the connections between peripheral organs and SZ, we conducted enrichment analyses of tissue-specific genes associated with SZ (Supplementary file 1). As shown by GO \u003cem\u003eenrichment\u003c/em\u003e analysis, we found that organs like the liver, lungs, and heart may influence SZ through aging pathways. For instance, liver-related pathways involve antigen processing and presentation (e.g., peptide antigen via MHC class I), DNA strand elongation, and oligopeptide transport. In the lungs, the cAMP responsive element binding protein (CREB) pathway, isomerase activity pathway, and four-way junction DNA binding pathway are relevant. The pancreas-specific genes were associated with ATPase complexes and SWI/SNF superfamily type complexes pathways. KEGG pathway analysis revealed that liver-specific genes associated with SZ were mainly enriched in lysosomes, ABC transporter proteins, which are the essential roles in aging-related pathways. The details were shown in Fig. 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the effects of selected genes on brain PAD in SZ patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe brain PAD was significantly higher in the high-expression group of \u003cem\u003eBRCA1\u003c/em\u003e than in the low-expression group (Fig. 3). Although statistically significant results were obtained only for the \u003cem\u003eBRCA1\u003c/em\u003e gene, the \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eSNCA\u003c/em\u003e, and \u003cem\u003eARMS2\u003c/em\u003e still showed a favorable trend, probably due to the limitation of the small sample size.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003ePrevious studies have mostly focused on the interplay between SZ and brain aging, or the impact of a single organ or system on SZ. No research has yet examined how the body\u0026apos;s multiple organ systems may influence the brain via aging pathways to affect SZ. In this study, we used genetic tools and found that organ-specific aging genes in peripheral organs\u0026mdash;\u003cem\u003eACE\u0026nbsp;\u003c/em\u003ein the lung, \u003cem\u003eVEGFA\u003c/em\u003e in the pancreas, \u003cem\u003eMAPT\u003c/em\u003e in the spleen, \u003cem\u003eSNCA\u003c/em\u003e in the heart\u0026apos;s left ventricle, \u003cem\u003eMLH1\u003c/em\u003e in muscle, \u003cem\u003eBRCA1\u003c/em\u003e in the liver, and \u003cem\u003eARMS2\u0026nbsp;\u003c/em\u003ein adipose tissue\u0026mdash;might influence SZ through aging-related pathways. Expression data of these genes in whole blood and enrichment analysis results further confirmed this finding.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eSNCA\u003c/em\u003e gene encodes\u0026nbsp;\u0026alpha;-synuclein, which is involved in synaptic transmission and neurotransmitter release.\u0026nbsp;\u0026alpha;-synuclein is highly expressed at the presynaptic terminal and is involved in a variety of cellular functions, including synaptic vesicle transport, membrane binding and signal transduction, and affecting the homeostasis of mitochondria and lysosomes \u003csup\u003e24\u003c/sup\u003e. Joung et al. found that, although the role of the \u003cem\u003eSNCA\u003c/em\u003e gene is more pronounced in the brain, it also affects cardiac aging. The heart-brain axis is a bidirectional communication system that enables interaction between the heart and the brain through various pathways, including the autonomic nervous system and the immune system. Aging can lead to functional and structural changes in the heart, which in turn can affect brain function. Previous study found that cardiovascular diseases can result in reduced cerebral blood flow, potentially leading to cognitive decline symptoms observed in patients with SZ \u003csup\u003e25\u003c/sup\u003e. Additionally, the \u003cem\u003eSNCA\u003c/em\u003e gene may influence monocyte metabolism by participating in the regulation of the unfolded protein response and endoplasmic reticulum stress. Abnormal aggregation of\u0026nbsp;\u0026alpha;-synuclein may also activate monocytes and macrophages, prompting them to secrete pro-inflammatory cytokines and exacerbate systemic inflammation. These factors may all contribute to the development and progression of SZ. Our research also found that monocyte count genes are associated with cortical thickness\u0026nbsp;\u003csup\u003e24, 26, 27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe liver-brain axis and the muscle-brain axis are also bidirectional communication systems. Aging can lead to changes in liver function and structure, which may promote the development of liver and muscle diseases and affect brain function through the liver-brain axis and muscle-brain axis \u003csup\u003e28, 29\u003c/sup\u003e. Although the primary functions of BRCA1 and MLH1 are related to DNA repair\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e, they can still influence the function and structure of the liver and muscles, which in turn can affect brain function\u0026nbsp;\u003csup\u003e31, 32\u003c/sup\u003e.\u0026nbsp;More importantly, in our clinical patients with SZ, the \u003cem\u003eBRCA1\u003c/em\u003e gene expression in the blood are associated with brain PAD, which further suggests the potential role of the aging-related gene \u003cem\u003eBRCA1\u003c/em\u003e in liver-brain axis and SZ.\u003c/p\u003e\n\u003cp\u003eIn lung-brain interaction, changes in \u003cem\u003eACE\u003c/em\u003e gene expression impacts pulmonary blood flow and oxygenation, which in turn affect brain function. \u003cem\u003eACE\u003c/em\u003e inhibitors are widely used to treat hypertension and heart failure. Cao et al. found that the effects of ACE inhibitors on blood pressure can indirectly influence the lung-brain axis, thereby impacting brain function. Over expression of \u003cem\u003eACE\u003c/em\u003e is associated with increased oxidative metabolism and heightened immune responses. This can affect the energy production and immune function of monocytes, leading to inflammation and subsequent brain changes, which is consistent with our study \u003csup\u003e27, 33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eARMS\u003c/em\u003e2 is a gene linked to age-related macular degeneration. It encodes a secreted protein functioning in the extracellular matrix, likely maintaining matrix homeostasis through interactions with various matrix proteins \u003csup\u003e34\u003c/sup\u003e.Adipose tissue undergoes significant changes during aging, including adipocyte volume reduction, decreased lipid storage capacity, and alterations in adipokine secretion levels \u003csup\u003e35\u003c/sup\u003e.Adipose tissue may upregulate \u003cem\u003eARMS2\u003c/em\u003e to regulate and maintain its structure and function. \u003cem\u003eARMS2\u003c/em\u003e may interact with matrix proteins to influence monocyte migration, phagocytosis, and immune responses. Its mitochondria-associated functions could indirectly affect monocyte energy metabolism and activity \u003csup\u003e36\u003c/sup\u003e, potentially causing chronic inflammation that impacts brain function \u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eVEGFA\u003c/em\u003e gene is crucial for angiogenesis, inflammation, and oxidative stress. As pancreatic tissue ages, vascular regression and reduced blood supply occur. By promoting angiogenesis, \u003cem\u003eVEGFA\u003c/em\u003e may improve pancreatic blood supply and slow its aging. \u003cem\u003eVEGFA\u003c/em\u003e might also alter chromatin accessibility via the SWI/SNF complex, regulating genes linked to neuronal survival, differentiation, and synaptic plasticity. Moreover, \u003cem\u003eVEGFA\u003c/em\u003e overexpression may activate pathways that influence monocyte and macrophage function, potentially leading to a pro-inflammatory microenvironment \u003csup\u003e27, 37, 38\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study offers novel insights into the relationships between SZ and the aging-related genes \u003cem\u003eSNCA\u003c/em\u003e, \u003cem\u003eACE\u003c/em\u003e, \u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eMAPT\u003c/em\u003e, and \u003cem\u003eARMS2\u003c/em\u003e in multiple organs. Notably, the \u003cem\u003eBRCA1\u003c/em\u003e gene may be associated with accelerated brain aging in individuals with SZ. Our findings provide valuable clues for understanding the link between peripheral organ aging and SZ.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the \u0026quot;Sanxin\u0026quot; talent projects Xijing 986 Hospital Department, Air Force Medical University (986SX24XMPY03) and the State Key Laboratory of Neurology and Oncology Drug Development Open Project (SKLSIM-F-2024-66).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYan-Kun Han: Investigation, Writing - Original Draft, Data Curation; Miao-Yan Liu: Writing - Original Draft, Data Curation, Resources; Dinglong Yang: Visualization, Software, Writing - Review \u0026amp; Editing; Jia-Xin Xie: Data Curation, Investigation;\u003c/p\u003e\n\u003cp\u003eXiao-Hui Wang: Data Curation, Investigation; Dong-Bao Wang: Data Curation;\u003c/p\u003e\n\u003cp\u003eCui-Cui Wang: Investigation; Yun-Long Liang: Methodology; Long-Biao Cui: Conceptualization; Yu-Jing Chen: Conceptualization, Resources, Supervision, Software, Writing - Review \u0026amp; Editing; Hai-Jun Zhang: Methodology, Resources, Supervision, Conceptualization, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample collection and experimental procedures were approved by the Xijing Hospital Institutional Ethics Committee, and informed consent was obtained from all donors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no conflict of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmeland OB, Frei O, Dale AM, Andreassen OA. The polygenic architecture of schizophrenia - rethinking pathogenesis and nosology. Nature reviews Neurology 2020;16(7):366-379.\u003c/li\u003e\n\u003cli\u003ePillinger T, D\u0026apos;Ambrosio E, McCutcheon R, Howes OD. Is psychosis a multisystem disorder? A meta-review of central nervous system, immune, cardiometabolic, and endocrine alterations in first-episode psychosis and perspective on potential models. Mol Psychiatry 2019;24(6):776-794.\u003c/li\u003e\n\u003cli\u003eTaube C, Mentzel C, Glue P, Barak Y. Aging of persons with schizophrenia: analysis of a national dataset. International psychogeriatrics 2024;36(1):43-50.\u003c/li\u003e\n\u003cli\u003eRodevand L, Rahman Z, Hindley GFL, Smeland OB, Frei O, Tekin TF, et al. Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors. Am J Psychiatry 2023;180(11):815-826.\u003c/li\u003e\n\u003cli\u003eLiu X, Ling Z, Cheng Y, Wu L, Shao L, Gao J, et al. Oral fungal dysbiosis and systemic immune dysfunction in Chinese patients with schizophrenia. Transl Psychiatry 2024;14(1):475.\u003c/li\u003e\n\u003cli\u003eDeng MG, Wang K, Liu F, Zhou X, Nie JQ, Zhao ZH, et al. Shared genetic architecture and causal relationship between frailty and schizophrenia. Schizophrenia (Heidelb) 2025;11(1):24.\u003c/li\u003e\n\u003cli\u003eGerretsen P, Plitman E, Rajji TK, Graff-Guerrero A. The effects of aging on insight into illness in schizophrenia: a review. International journal of geriatric psychiatry 2014;29(11):1145-1161.\u003c/li\u003e\n\u003cli\u003eParlikar R, Shivakumar V. Accelerated aging and frailty in schizophrenia. International psychogeriatrics 2024;36(1):18-20.\u003c/li\u003e\n\u003cli\u003eOkusaga OO. Accelerated aging in schizophrenia patients: the potential role of oxidative stress. Aging and disease 2014;5(4):256-262.\u003c/li\u003e\n\u003cli\u003eSeeman MV. Subjective Overview of Accelerated Aging in Schizophrenia. International journal of environmental research and public health 2022;20(1).\u003c/li\u003e\n\u003cli\u003eCaspi A, Shireby G, Mill J, Moffitt TE, Sugden K, Hannon E. Accelerated Pace of Aging in Schizophrenia: Five Case-Control Studies. Biological psychiatry 2024;95(11):1038-1047.\u003c/li\u003e\n\u003cli\u003eLing E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, et al. A concerted neuron-astrocyte program declines in ageing and schizophrenia. Nature 2024;627(8004):604-611.\u003c/li\u003e\n\u003cli\u003eHua JPY, Abram SV, Loewy RL, Stuart B, Fryer SL, Vinogradov S, et al. Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis. Schizophrenia bulletin 2024;50(5):1159-1170.\u003c/li\u003e\n\u003cli\u003eConstantinides C, Han LKM, Alloza C, Antonucci LA, Arango C, Ayesa-Arriola R, et al. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium. Molecular psychiatry 2023;28(3):1201-1209.\u003c/li\u003e\n\u003cli\u003eKirkpatrick B, Messias E, Harvey PD, Fernandez-Egea E, Bowie CR. Is schizophrenia a syndrome of accelerated aging? Schizophrenia bulletin 2008;34(6):1024-1032.\u003c/li\u003e\n\u003cli\u003eNguyen TT, Eyler LT, Jeste DV. Systemic Biomarkers of Accelerated Aging in Schizophrenia: A Critical Review and Future Directions. Schizophrenia bulletin 2018;44(2):398-408.\u003c/li\u003e\n\u003cli\u003eEvans DM, Davey Smith G. Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. Annual review of genomics and human genetics 2015;16:327-350.\u003c/li\u003e\n\u003cli\u003eDong XX, Chen DL, Chen HM, Li DL, Hu DN, Lanca C, et al. DNA methylation biomarkers and myopia: a multi-omics study integrating GWAS, mQTL and eQTL data. Clinical epigenetics 2024;16(1):157.\u003c/li\u003e\n\u003cli\u003eQiu X, Guo R, Wang Y, Zheng S, Wang B, Gong Y. Mendelian randomization reveals potential causal relationships between cellular senescence-related genes and multiple cancer risks. Communications biology 2024;7(1):1069.\u003c/li\u003e\n\u003cli\u003eBryois J, Calini D, Macnair W, Foo L, Urich E, Ortmann W, et al. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nature neuroscience 2022;25(8):1104-1112.\u003c/li\u003e\n\u003cli\u003eShi X, Li M, Yao J, Li MD, Yang Z. Alcohol drinking, DNA methylation and psychiatric disorders: A multi-omics Mendelian randomization study to investigate causal pathways. Addiction (Abingdon, England) 2024;119(7):1226-1237.\u003c/li\u003e\n\u003cli\u003eWang D, Li D, Dang X, Mu C, Liu C, Zeng Y, et al. Mendelian randomization reveals causalities between DNA methylation and schizophrenia. Biological psychiatry 2025.\u003c/li\u003e\n\u003cli\u003eTrubetskoy V, Pardinas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 2022;604(7906):502-508.\u003c/li\u003e\n\u003cli\u003eJoung J, Heo Y, Kim Y, Kim J, Choi H, Jeon T, et al. Cell enlargement modulated by GATA4 and YAP instructs the senescence-associated secretory phenotype. Nature communications 2025;16(1):1696.\u003c/li\u003e\n\u003cli\u003eShen J, Jiang C. Unraveling the heart-brain axis: shared genetic mechanisms in cardiovascular diseases and Schizophrenia. Schizophrenia (Heidelberg, Germany) 2024;10(1):113.\u003c/li\u003e\n\u003cli\u003eSoda T, Pasqua T, De Sarro G, Moccia F. Cognitive Impairment and Synaptic Dysfunction in Cardiovascular Disorders: The New Frontiers of the Heart-Brain Axis. Biomedicines 2024;12(10).\u003c/li\u003e\n\u003cli\u003eCui LB, Wang XY, Fu YF, Liu XF, Wei Y, Zhao SW, et al. Transcriptional level of inflammation markers associates with short-term brain structural changes in first-episode schizophrenia. BMC medicine 2023;21(1):250.\u003c/li\u003e\n\u003cli\u003eDuan L, Li H, Li S, Shi Y, Feng Y. Causal association between sarcopenia and cognitive impairment contributes to the muscle-brain axis: A bidirectional Mendelian randomization study. Geriatrics \u0026amp; gerontology international 2025;25(1):116-122.\u003c/li\u003e\n\u003cli\u003eCutuli D, Decandia D, Giacovazzo G, Coccurello R. Physical Exercise as Disease-Modifying Alternative against Alzheimer\u0026apos;s Disease: A Gut-Muscle-Brain Partnership. International journal of molecular sciences 2023;24(19).\u003c/li\u003e\n\u003cli\u003eTomer G, Buermeyer AB, Nguyen MM, Liskay RM. Contribution of human mlh1 and pms2 ATPase activities to DNA mismatch repair. The Journal of biological chemistry 2002;277(24):21801-21809.\u003c/li\u003e\n\u003cli\u003eWang Z, Yin Z, Sun G, Zhang D, Zhang J. Genetic evidence for the liver-brain axis: lipid metabolism and neurodegenerative disease risk. Lipids in health and disease 2025;24(1):41.\u003c/li\u003e\n\u003cli\u003eWei C, Li X, Jin Y, Zhang Y, Yuan Q. Does the liver facilitate aging-related cognitive impairment: Conversation between liver and brain during exercise? Journal of cellular physiology 2024;239(8):e31287.\u003c/li\u003e\n\u003cli\u003eCao DY, Spivia WR, Veiras LC, Khan Z, Peng Z, Jones AE, et al. ACE overexpression in myeloid cells increases oxidative metabolism and cellular ATP. The Journal of biological chemistry 2020;295(5):1369-1384.\u003c/li\u003e\n\u003cli\u003eKortvely E, Hauck SM, Duetsch G, Gloeckner CJ, Kremmer E, Alge-Priglinger CS, et al. ARMS2 is a constituent of the extracellular matrix providing a link between familial and sporadic age-related macular degenerations. Investigative ophthalmology \u0026amp; visual science 2010;51(1):79-88.\u003c/li\u003e\n\u003cli\u003eNguyen TT, Corvera S. Adipose tissue as a linchpin of organismal ageing. Nature metabolism 2024;6(5):793-807.\u003c/li\u003e\n\u003cli\u003eFritsche LG, Loenhardt T, Janssen A, Fisher SA, Rivera A, Keilhauer CN, et al. Age-related macular degeneration is associated with an unstable ARMS2 (LOC387715) mRNA. Nature genetics 2008;40(7):892-896.\u003c/li\u003e\n\u003cli\u003eFailla CM, Carbone ML, Ramondino C, Bruni E, Orecchia A. Vascular Endothelial Growth Factor (VEGF) Family and the Immune System: Activators or Inhibitors? Biomedicines 2024;13(1).\u003c/li\u003e\n\u003cli\u003eTorres-Espin A, Radabaugh HL, Treiman S, Fitzsimons SS, Harvey D, Chou A, et al. Sexually dimorphic differences in angiogenesis markers are associated with brain aging trajectories in humans. Science translational medicine 2024;16(775):eadk3118.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable.1 Tissue-dependent association for aging genes expression on SZ.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTissue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProbeID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProbeChr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003csub\u003e_SMR\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFDR\u003csub\u003e_SMR\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003csub\u003e_HEIDI\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eENSG00000159640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eACE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.28E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.14E-03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6.05E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003ePancreas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eENSG00000112715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eVEGFA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.78E-03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Schizophrenia, Genome-Wide Association Study, Aging, Brain Neuroimaging","lastPublishedDoi":"10.21203/rs.3.rs-7922183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7922183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThe adverse health outcomes of schizophrenia (SZ) are largely driven by the high prevalence of other non-neurological diseases. In addition to accelerated brain aging, patients with SZ also exhibit signs of systemic aging. However, the potential causal or biological mechanisms between multisystem aging and schizophrenia remain unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe obtained SZ-associated single Nucleotide Polymorphism (SNP) sets, aging gene data, and tissue-specific cis-expression Quantitative Trait Locus (cis-eQTL) data of the cerebral cortex and other tissues from a previous two-stage genome-wide association study (GWAS), Genecards database, and Genotype-Tissue Expression (GTEx) project. We employed tissue-specific Mendelian Randomization (MR) analysis to elucidate the tissue-specific expression patterns of aging-related genes, and used the Summary-data-based MR (SMR) approach to obtain tissue aging-related genes associated with the risk of SZ development. We identified the potential aging-related pathways through which these tissue-specific cis-eQTL may affect SZ using enrichment analyses. Finally, we explored the relationship between the identified crucial aging-related genes and predicted age difference (PAD) of brain in our clinical patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that the expression of tissue-specific aging genes including \u003cem\u003eNCA\u003c/em\u003e, \u003cem\u003eACE\u003c/em\u003e, \u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eMAPT\u003c/em\u003e, and \u003cem\u003eARMS2\u003c/em\u003e may affect SZ. The tissue-specific cis-eQTL may influence SZ through aging pathways. The brain PAD was significantly higher in the high-expression group of \u003cem\u003eBRCA1\u003c/em\u003e than in the low-expression group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides valuable clues to understand the link between SZ and multiorgan system aging and improves the current understanding of multiple tissue-specific aging-related genes with SZ.\u003c/p\u003e","manuscriptTitle":"Brain imaging data and summary-data-based Mendelian randomization analysis reveal the impact of multiorgan aging on schizophrenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:34:35","doi":"10.21203/rs.3.rs-7922183/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9a72021-e4ef-4fd0-b9b9-57367c1ef2c9","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T19:39:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:34:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7922183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7922183","identity":"rs-7922183","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}