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Shen C, Tong X, Ran J, Sun S, Yang Q, Shen H, Yao XI. Associations between residential environments and late-onset schizophrenia in UK Biobank: Interaction with genetic risk factor. Schizophr Res 2024; 270:85-93. [PMID: 38885569 DOI: 10.1016/j.schres.2024.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 05/04/2024] [Accepted: 06/08/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Environment and genes both contribute to schizophrenia. However, the impact of different natural environments surrounding residential addresses on schizophrenia in urban settings remains unknown. This study aimed to investigate the association of urbanisation, measured by residential environments, with late-onset schizophrenia and explore whether genetic risk for schizophrenia modified the associations. METHODS We examined the associations between residential environments and late-onset schizophrenia and its interaction with genetic risk factors in UK Biobank, followed from 2006 to 2010 (baseline) to Dec 2021. Residential environments, including greenspace, domestic garden, blue space, and total natural environment, were evaluated using land use coverage percentage. The polygenic risk score (PRS) of schizophrenia was derived using a Bayesian approach and adjusted it against ancestry. Cox proportional hazard regression model was used to assess the associations between per interquartile (IQR) increase of each type of residential environments and late-onset schizophrenia. Interactive effects of PRS and residential environments on late-onset schizophrenia were assessed on both additive and multiplicative scales. RESULTS A total of 393,680 participants were included in the analysis, with 844 cases of late-onset schizophrenia being observed after 12.8 years of follow-up. Within 300 m buffer surrounding the residential addresses, per interquartile increase in greenspace (31.5 %) and total natural environment (34.4 %) were both associated with an 11 % (HR = 0.89, 95 % CI 0.80, 0.99) lower risk of late-onset schizophrenia. Domestic garden and blue space did not show significant protective effects on late-onset schizophrenia. A strong dose-response relationship between schizophrenia PRS and schizophrenia was found, while no additive or multiplicative interaction effects were present between residential environments and PRS on late-onset schizophrenia. CONCLUSION Residential greenspace and total natural environment may protect against late-onset schizophrenia in older people regardless of genetic risk. These findings shed light on the prevention of schizophrenia and urban planning to optimise ecosystem benefits linked to schizophrenia.
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Affiliation(s)
- Chen Shen
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK; National Institute for Health Research Health Protection Research Unit in Chemical and Radiation Threats and Hazards, Imperial College London, UK
| | - Xinning Tong
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-Sen University, China
| | - Jinjun Ran
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shengzhi Sun
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Qian Yang
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Huiyong Shen
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-Sen University, China; Department of Clinical Research, The Eighth Affiliated Hospital, Sun Yat-sen University, China
| | - Xiaoxin I Yao
- Department of Orthopaedics, The Eighth Affiliated Hospital, Sun Yat-Sen University, China; Department of Clinical Research, The Eighth Affiliated Hospital, Sun Yat-sen University, China.
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Grover S, Varadharajan N, Venu S. Urbanization and psychosis: an update of recent evidence. Curr Opin Psychiatry 2024; 37:191-201. [PMID: 38441163 DOI: 10.1097/yco.0000000000000931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
PURPOSE OF REVIEW Urbanization, a complex global phenomenon, has a significant bearing on schizophrenia/psychosis burden through various socioeconomic and environmental factors. This review focuses on recent evidence (2019-2023) linking urbanization, schizophrenia, and the role of green space. RECENT FINDINGS This review analyzed 43 articles that examined the correlation between urban birth or upbringing, urban living (urbanicity), and various schizophrenia/psychosis-related outcomes such as incidence, psychotic experiences, etc. The studies showed differing results across geographical locations. Socioeconomic factors like area deprivation, migrant status (ethnic density) and social fragmentation were independently associated with the risk of schizophrenia/psychosis irrespective of urbanicity. More recently, environmental factors such as green space reduction and air pollution have been explored in urban living conditions and were positively associated with an increased risk of schizophrenia/psychosis. SUMMARY There is a need for further investigation in low and middle-income countries. The impact of urbanization-related factors and green space on the risk of schizophrenia/psychosis calls for appropriate governmental commitments toward structured and healthy urban planning.
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Affiliation(s)
- Sandeep Grover
- Department of Psychiatry, Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh, Punjab
| | - Natarajan Varadharajan
- Department of Psychiatry, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER)
| | - Sandesh Venu
- Department of Psychiatry, Pondicherry Institute of Medical Sciences (PIMS), Kalapet, Puducherry, India
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Zhang Y, Wu T, Yu H, Fu J, Xu J, Liu L, Tang C, Li Z. Green spaces exposure and the risk of common psychiatric disorders: A meta-analysis. SSM Popul Health 2024; 25:101630. [PMID: 38405164 PMCID: PMC10885792 DOI: 10.1016/j.ssmph.2024.101630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 02/07/2024] [Accepted: 02/13/2024] [Indexed: 02/27/2024] Open
Abstract
Objective To explore the effects of green spaces exposure on common psychiatric disorders. Methods PubMed, Embase, Web of Science and MEDLINE were screened and articles published prior to November 15, 2023 were included. Analyses were performed on common psychiatric disorders, categorized into depression, anxiety, dementia, schizophrenia, and attention deficit hyperactivity disorder (ADHD). And the subgroup analyses were conducted for depression, anxiety, dementia, and schizophrenia. Results In total, 2,0064 studies were retrieved, 59 of which were included in our study; 37 for depression, 14 for anxiety, 8 for dementia, 7 for schizophrenia and 5 for ADHD. Green spaces were found to benefit the moderation of psychiatric disorders (OR = 0.91, 95% CI: 0.89 to 0.92). Green spaces positively influence depression (OR = 0.89, 95% CI: 0.86 to 0.93), regardless of the cross-sectional or cohort studies. Green spaces can also help mitigate the risk of anxiety (OR = 0.94, 95%CI:0.92 to 0.96). As an important index for measuring green spaces, a higher normalized difference vegetation index (NDVI) level related to a lower level of depression (OR = 0.95, 95%CI:0.91 to 0.98) and anxiety (OR = 0.95, 95%:0.92 to 0.98). The protection was also found in dementia (OR = 0.95, 95% CI: 0.93 to 0.96), schizophrenia (OR = 0.74, 95% CI: 0.67 to 0.82), and ADHD (OR = 0.89, 95% CI: 0.86 to 0.92) results. Conclusion Green spaces decrease the risk of psychiatric disorders, including depression, anxiety, dementia, schizophrenia, and ADHD. Further studies on green spaces and psychiatric disorders are needed, and more green spaces should be considered in city planning.
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Affiliation(s)
- Yimin Zhang
- School of Public Health, Health Science Center, Ningbo University, Ningbo, China
| | - Tongyan Wu
- School of Public Health, Health Science Center, Ningbo University, Ningbo, China
| | - Hao Yu
- Department of Urology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Urology, The Second Clinical Medical College of Nanjing Medical University, Nanjing, China
| | - Jianfei Fu
- Department of Medical Records and Statistics, Ningbo First Hospital, Ningbo, China
| | - Jin Xu
- School of Public Health, Health Science Center, Ningbo University, Ningbo, China
| | - Liya Liu
- School of Public Health, Health Science Center, Ningbo University, Ningbo, China
| | - Chunlan Tang
- School of Public Health, Health Science Center, Ningbo University, Ningbo, China
| | - Zhen Li
- School of Public Health, Health Science Center, Ningbo University, Ningbo, China
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Wei N, Wang S, Li X, Pan R, Yi W, Song J, Liu L, Liu J, Yuan J, Song R, Cheng J, Su H. The association between greenery type and gut microbiome in schizophrenia: did all greenspaces play the equivalent role? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:100006-100017. [PMID: 37624502 DOI: 10.1007/s11356-023-29419-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023]
Abstract
In recent years, attention has been focused on the benefit of greenspace on mental health, and it is suggested this link may vary with the type of greenspace. More and more studies have emphasized the influence of the gut microbiome on schizophrenia (SCZ). However, the effects of greenspaces on the gut microbiota in SCZ and the effect of different types of greenspaces on the gut microbiota remain unclear. We aim to examine if there were variations in the effects of various greenspace types on the gut microbiome in SCZ. Besides, we sink to explore important taxonomic compositions associated with different greenspace types. We recruited 243 objects with schizophrenia from Anhui Mental Health Center and collected fecal samples for 16Sr RNA gene sequencing. Three types of greenery coverage were calculated with different circular buffers (800, 1500, and 3000 m) corresponding to individual addresses. The association between greenspace and microbiome composition was analyzed with permutational analysis of variance (PERMANOVA). We conducted the linear regression to capture specific gut microbiome taxa associated with greenery coverage. Tree coverage was consistently associated with microbial composition in both 1500 m (R2 = 0.007, P = 0.030) and 3000 m (R2 = 0.007, P = 0.039). In contrast, there was no association with grass cover in any of the buffer zones. In the regression analysis, higher tree coverage was significantly correlated with the relative abundance of several taxa. Among them, tree coverage was positively associated with increased Bifidobacterium longum (β = 1.069, P = 0.004), which was the dominant composition in the gut microbiota. The relationship between greenspace and gut microbiome in SCZ differed by the type of greenspace. Besides, "tree coverage" may present a dominant effect on the important taxonomic composition. Our findings might provide instructive evidence for the design of urban greenspace to optimize health and well-being in SCZ as well as the whole people.
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Affiliation(s)
- Ning Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Shusi Wang
- Hefei Stomatological Hospital, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Xuanxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Li Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Jintao Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Jiajun Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Rong Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China.
- Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, 230032, Anhui, China.
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Song J, Liang Y, Xu Z, Wu Y, Yan S, Mei L, Sun X, Li Y, Jin X, Yi W, Pan R, Cheng J, Hu W, Su H. Built environment and schizophrenia re-hospitalization risk in China: A cohort study. ENVIRONMENTAL RESEARCH 2023; 227:115816. [PMID: 37003555 DOI: 10.1016/j.envres.2023.115816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Built environment exposure, characterized by ubiquity and changeability, has the potential to be the prospective target of public health policy. However, little research has been conducted to explore its impact on schizophrenia. This study aimed to investigate the association between built environmentand and schizophrenia rehospitalization by simultaneously considering substantial built environmental exposures. METHODS We recruited eligible schizophrenia patients from Hefei, Anhui Province, China between 2017 and 2019. The main outcome for this study was the time interval until the first recurrent hospital admission occurred within one year after discharge. For each included subject, we estimated the built environment exposures, including population density, walkability, land use mix, green and blue space, public transportation accessibility and road traffic indicator. Lasso (Least Absolute Shrinkage and Selection Operator) analysis was used to select the key variables. Multivariable Cox regression model was applied to obtain hazard ratio (HR) and its corresponding 95% confidence intervals (CI). Further, we also evaluated the joint effects of built environment characteristics on rehospitalization for schizophrenia by Quantile g-computation model. RESULTS A total of 1564 hospitalized schizophrenia patients were enrolled, with 347 patients (22.2%) had a rehospitalization within one-year after discharge. Multivariable Cox regression analysis indicated that the re-hospitalization rate for schizophrenia would be higher in areas with a high population density (HR: 1.10, 95%CI: 1.04-1.16). Nonetheless, compared to the reference (Q1), participants who lived in a neighborhood with the highest walkability and NDVI (Normalized Difference Vegetation Index) (Q4) had a 76% and 47% lower risk of re-hospitalization within one year (HR:0.24, 95%CI: 0.13-0.45; and 0.53, 95%CI:0.32-0.85), respectively. Moreover, quantile-based g-computation analyses revealed that increased walkability and green space significantly eliminated the adverse effects of population density increases on schizophrenia patients, with a HR ratio of 0.61 (95%CI:0.48,0.79) per one quartile change at the same time. CONCLUSION Our study provides scientific evidence for the significant role of built environment in schizophrenia rehospitalization, suggesting that optimizing the built environment is required in designing and building a healthy city.
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Affiliation(s)
- Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China; Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Australia
| | - Yunfeng Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Zhiwei Xu
- School of Medicine and Dentistry, Gold Coast Campus, Griffith University, QLD, 4222, Australia
| | - Yudong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Shuangshuang Yan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Lu Mei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Xiaoni Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Yuxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Xiaoyu Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Australia.
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan Road, Shushan District, Hefei, Anhui, 230031, China.
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Cianconi P, Hirsch D, Chiappini S, Martinotti G, Janiri L. Climate change, biodiversity loss and mental health: a global perspective. BJPsych Int 2022. [DOI: 10.1192/bji.2022.20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Climate change can have various psychopathological manifestations which have been more actively addressed by scientific research only in recent years. Indeed, extreme weather events and environmental changes have been shown to be associated with a range of mental health problems. Following the destruction of ecosystems, biodiversity loss can cause mental distress and emotional responses, including so-called ‘psychoterratic’ syndromes arising from negatively felt and perceived environmental change. Studies investigating relationships between biodiversity and mental health reveal a complex landscape of scientific evidence, calling for a better understanding of this challenging issue.
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Talking about Health: A Topic Analysis of Narratives from Individuals with Schizophrenia and Other Serious Mental Illnesses. Behav Sci (Basel) 2022; 12:bs12080286. [PMID: 36004857 PMCID: PMC9405157 DOI: 10.3390/bs12080286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
Individuals with schizophrenia have higher mortality and shorter lifespans. There are a multitude of factors which create these conditions, but one aspect is worse physical health, particularly cardiovascular and metabolic health. Many interventions to improve the health of individuals with schizophrenia have been created, but on the whole, there has been limited effectiveness in improving quality of life or lifespan. One potential new avenue for inquiry involves a more patient-centric perspective; understanding aspects of physical health most important, and potentially most amenable to change, for individuals based on their life narratives. This study used topic modeling, a type of Natural Language Processing (NLP) on unstructured speech samples from individuals (n = 366) with serious mental illness, primarily schizophrenia, in order to extract topics. Speech samples were drawn from three studies collected over a decade in two geographically distinct regions of the United States. Several health-related topics emerged, primarily centered around food, living situation, and lifestyle (e.g., routine, hobbies). The implications of these findings for how individuals with serious mental illness and schizophrenia think about their health, and what may be most effective for future health promotion policies and interventions, are discussed.
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