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Zhao X, Ruan Z, Tian Y, Du W, Fan L. Estimating the joint effect of household solid fuel use and social isolation on depression among middle-aged and older adults in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 901:166411. [PMID: 37611698 DOI: 10.1016/j.scitotenv.2023.166411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/28/2023] [Accepted: 08/16/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND Household solid fuel use and social isolation are reported to increase the risk of depressive symptoms, but their joint effect has not yet been examined. This study aimed to explore the separate and joint effects of household solid fuel use and social isolation on depression. METHODS We used data from the latest four waves (2011-2018) of the China Health and Retirement Longitudinal Study (CHARLS). Depression was defined as a score of ≥12 using the Center for Epidemiologic Studies Depression Scale (CES-D 10). Cox proportional hazards models were applied to explore the separate and joint associations of household solid fuel use and social isolation with incident depression. RESULTS During the seven-year follow-up, 2793 (30.25 %) out of the 9232 participants were identified with depressive symptoms. Solid fuel use for household heating or cooking was significantly associated with more hazards of depressive symptoms after adjusting for potential confounders (cooking: HR = 1.280, 95 % CI = 1.175-1.394; heating: HR = 1.142, 95 % CI = 1.054-1.238). High social isolation at baseline was also a significant predictor of incident depressive symptoms (HR = 1.139, 95 % CI = 1.053-1.231). Participants exposed to both solid fuel use and high social isolation were found to have higher hazards of experiencing depressive symptoms than those exposed to none or only one of these two risk factors (heating: HR for 'solid fuel use + high social isolation'=1.308 versus HR for other groups = 1-1.185; cooking: HR for 'solid fuel use + high social isolation' = 1.430 versus HR for other groups = 1-1.255). CONCLUSION Household solid fuel use and social isolation were separately and jointly associated with higher risks of incident depression. Appropriate interventions to reduce solid fuel use and social isolation are recommended to improve the psychological health among middle-aged and older adults in China.
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Affiliation(s)
- Xinyu Zhao
- School of Public Health, Southeast University, Nanjing 210009, China
| | - Zengliang Ruan
- School of Public Health, Southeast University, Nanjing 210009, China
| | - Yong Tian
- School of Public Health, Southeast University, Nanjing 210009, China
| | - Wei Du
- School of Public Health, Southeast University, Nanjing 210009, China
| | - Lijun Fan
- School of Public Health, Southeast University, Nanjing 210009, China.
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Fu Z, Liu Q, Liang J, Weng Z, Li W, Xu J, Zhang X, Xu C, Huang T, Gu A. Air pollution, genetic factors and the risk of depression. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:158001. [PMID: 35973541 DOI: 10.1016/j.scitotenv.2022.158001] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
Both genetics and ambient air pollutants contribute to depression, but the degree to which genetic susceptibility modifies the effect of air pollution on depression remains unknown. We aimed to investigate the effect of the modification of genetic susceptibility on depression. Notably, 490,780 participants who were free of depression at baseline in the UK Biobank study were recruited from 2006 to 2010. A land use regression (LUR) model was performed to estimate the concentrations of particulate matter with diameters ranging from ≤2.5-≤10 μm (PM2.5, PM2.5-10 and PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOx). The International Classification of Diseases 10th Revision (ICD-10) code was used to identify depression cases. Cox proportional hazard models adjusted for covariates were used to investigate the association between ambient air pollutants and depression. Moreover, the polygenic risk score (PRS) was calculated to evaluate cumulative genetic effects, and additive interaction models were established to explore whether genetic susceptibility modified the effects of air pollutants on depression. PM2.5, PM10, NO2 and NOx exposure were significantly positively associated with the risk of depression, and the hazard ratios and 95 % confidence intervals for a 10-μg/m3 increase in PM2.5, PM10, NO2 and NOx concentrations were 2.12 (1.82, 2.47), 1.12 (1.03, 1.23), 1.07 (1.05, 1.10) and 1.04 (1.03, 1.05), respectively. Air pollutants and genetic variants exerted significant additive effects on the risk of depression (relative excess risk due to the interaction [RERI]: 0.15 for PM2.5, 0.12 for PM10, 0.10 for NO2, and 0.12 for NOx; attributable proportion due to the interaction [AP]: 0.12 for PM2.5, 0.10 for PM10, 0.08 for NO2, and 0.09 for NOx). Air pollution exposure was significantly associated with the risk of depression, and participants with a higher genetic risk were more likely to develop depression when exposed to high levels of air pollution.
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Affiliation(s)
- Zuqiang Fu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China; School of Public Health, Southeast University, Nanjing, China
| | - Qian Liu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Jingjia Liang
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Zhenkun Weng
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Wenxiang Li
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Jin Xu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China; Department of Maternal, Child, and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xin Zhang
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Cheng Xu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China.
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
| | - Aihua Gu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China; School of Public Health, Southeast University, Nanjing, China.
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Zundel CG, Ryan P, Brokamp C, Heeter A, Huang Y, Strawn JR, Marusak HA. Air pollution, depressive and anxiety disorders, and brain effects: A systematic review. Neurotoxicology 2022; 93:272-300. [PMID: 36280190 PMCID: PMC10015654 DOI: 10.1016/j.neuro.2022.10.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/12/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022]
Abstract
Accumulating data suggest that air pollution increases the risk of internalizing psychopathology, including anxiety and depressive disorders. Moreover, the link between air pollution and poor mental health may relate to neurostructural and neurofunctional changes. We systematically reviewed the MEDLINE database in September 2021 for original articles reporting effects of air pollution on 1) internalizing symptoms and behaviors (anxiety or depression) and 2) frontolimbic brain regions (i.e., hippocampus, amygdala, prefrontal cortex). One hundred and eleven articles on mental health (76% human, 24% animals) and 92 on brain structure and function (11% human, 86% animals) were identified. For literature search 1, the most common pollutants examined were PM2.5 (64.9%), NO2 (37.8%), and PM10 (33.3%). For literature search 2, the most common pollutants examined were PM2.5 (32.6%), O3 (26.1%) and Diesel Exhaust Particles (DEP) (26.1%). The majority of studies (73%) reported higher internalizing symptoms and behaviors with higher air pollution exposure. Air pollution was consistently associated (95% of articles reported significant findings) with neurostructural and neurofunctional effects (e.g., increased inflammation and oxidative stress, changes to neurotransmitters and neuromodulators and their metabolites) within multiple brain regions (24% of articles), or within the hippocampus (66%), PFC (7%), and amygdala (1%). For both literature searches, the most studied exposure time frames were adulthood (48% and 59% for literature searches 1 and 2, respectively) and the prenatal period (26% and 27% for literature searches 1 and 2, respectively). Forty-three percent and 29% of studies assessed more than one exposure window in literature search 1 and 2, respectively. The extant literature suggests that air pollution is associated with increased depressive and anxiety symptoms and behaviors, and alterations in brain regions implicated in risk of psychopathology. However, there are several gaps in the literature, including: limited studies examining the neural consequences of air pollution in humans. Further, a comprehensive developmental approach is needed to examine windows of susceptibility to exposure and track the emergence of psychopathology following air pollution exposure.
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Affiliation(s)
- Clara G Zundel
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA.
| | - Patrick Ryan
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Cole Brokamp
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| | - Autumm Heeter
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA.
| | - Yaoxian Huang
- Department of Civil and Environmental Engineering, Wayne State University, 5050 Anthony Wayne Drive, Detroit, MI, USA.
| | - Jeffrey R Strawn
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Anxiety Disorders Research Program, Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA.
| | - Hilary A Marusak
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA; Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, MI, USA; Translational Neuroscience Program, Wayne State University, Detroit, MI, USA.
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Li N, Song Q, Su W, Guo X, Wang H, Liang Q, Liang M, Qu G, Ding X, Zhou X, Sun Y. Exposure to indoor air pollution from solid fuel and its effect on depression: a systematic review and meta-analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:49553-49567. [PMID: 35593981 DOI: 10.1007/s11356-022-20841-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
A growing body of research has investigated the relationship between indoor air pollution from solid fuel and depression risk. Our study aimed to elucidate the relationship between indoor air pollution from solid fuel and depression in observational studies. The effect of indoor air pollution on depression was estimated using pooled odds ratios (ORs) with 95% confidence intervals (CIs). Heterogeneity was evaluated by the I-squared value (I2), and the random-effects model was adopted as the summary method. We finalized nine articles with 70,214 subjects. The results showed a statistically positive relationship between the use of household solid fuel and depression (OR = 1.22, 95% CI = 1.09-1.36). Subgroup analysis based on fuel type groups demonstrated that indoor air pollution from solid fuel was a higher risk to depression (OR = 1.24, 95% CI = 1. 10-1.39; I2 = 67.0%) than that from biomass (OR = 1.18, 95% CI = 0.96-1.45; I2 = 66.5%). In terms of fuel use, the use of solid fuel for cooking and heating increased depression risk, and the pooled ORs were 1.21 (95% CI = 1.08-1.36) and 1.23 (95% CI = 1.13-1.34). Exposure to indoor air pollution from solid fuel might increase depression risk.
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Affiliation(s)
- Ning Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
| | - Qiuxia Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
| | - Wanying Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
| | - Xianwei Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
| | - Hao Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
| | - Qiwei Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
- Anhui Provincial Children's Hospital/Children's Hospital of Anhui Medical University, Hefei, 230051, People's Republic of China
| | - Mingming Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
| | - Guangbo Qu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
| | - Xiuxiu Ding
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China
| | - Xiaoqin Zhou
- Chaohu Hospital, Anhui Medical University, Hefei, 238000, Anhui, People's Republic of China
| | - Yehuan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, People's Republic of China.
- Chaohu Hospital, Anhui Medical University, Hefei, 238000, Anhui, People's Republic of China.
- Center for Evidence-Based Practice, Anhui Medical University, Hefei, 230032, Anhui, People's Republic of China.
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Petkus AJ, Resnick SM, Wang X, Beavers DP, Espeland MA, Gatz M, Gruenewald T, Millstein J, Chui HC, Kaufman JD, Manson JE, Wellenius GA, Whitsel EA, Widaman K, Younan D, Chen JC. Ambient air pollution exposure and increasing depressive symptoms in older women: The mediating role of the prefrontal cortex and insula. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 823:153642. [PMID: 35122843 PMCID: PMC8983488 DOI: 10.1016/j.scitotenv.2022.153642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/29/2022] [Accepted: 01/29/2022] [Indexed: 04/13/2023]
Abstract
Exposures to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) have been associated with the emergence of depressive symptoms in older adulthood, although most studies used cross-sectional outcome measures. Elucidating the brain structures mediating the adverse effects can strengthen the causal role between air pollution and increasing depressive symptoms. We evaluated whether smaller volumes of brain structures implicated in late-life depression mediate associations between ambient air pollution exposure and changes in depressive symptoms. This prospective study included 764 community-dwelling older women (aged 81.6 ± 3.6 in 2008-2010) from the Women's Health Initiative Memory Study (WHIMS) Magnetic Resonance Imaging study (WHIMS-MRI; 2005-06) and WHIMS-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO; 2008-16). Three-year average annual mean concentrations (scaled by interquartile range [IQR]) of ambient PM2.5 (in μg/m3; IQR = 3.14 μg/m3) and NO2 (in ppb; IQR = 7.80 ppb) before WHIMS-MRI were estimated at participants' addresses via spatiotemporal models. Mediators included structural brain MRI-derived grey matter volumes of the prefrontal cortex and structures of the limbic-cortical-striatal-pallidal-thalamic circuit. Depressive symptoms were assessed annually by the 15-item Geriatric Depression Scale. Structural equation models were constructed to estimate associations between exposure, structural brain volumes, and depressive symptoms. Increased exposures (by each IQR) were associated with greater annual increases in depressive symptoms (βPM2.5 = 0.022; 95% Confidence Interval (CI) = 0.003, 0.042; βNO2 = 0.019; 95% CI = 0.001, 0.037). The smaller volume of prefrontal cortex associated with exposures partially mediated the associations of increased depressive symptoms with NO2 (8%) and PM2.5 (13%), and smaller insula volume associated with NO2 contributed modestly (13%) to the subsequent increase in depressive symptoms. We demonstrate the first evidence that the smaller volumes of the prefrontal cortex and insula may mediate the subsequent increases in depressive symptoms associated with late-life exposures to NO2 and PM2.5.
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Affiliation(s)
- Andrew J Petkus
- University of Southern California, Department of Neurology, 1520 San Pablo St. Suite 3000, Los Angeles, CA 90033, United States
| | - Susan M Resnick
- National Institute on Aging, Laboratory of Behavioral Neuroscience, 251 Bayview Boulevard, Suite 100, Baltimore, MD 21224, United States
| | - Xinhui Wang
- University of Southern California, Department of Neurology, 1520 San Pablo St. Suite 3000, Los Angeles, CA 90033, United States
| | - Daniel P Beavers
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, One Medical Center Blvd, Winston-Salem, NC 27157, United States of American
| | - Mark A Espeland
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, One Medical Center Blvd, Winston-Salem, NC 27157, United States of American
| | - Margaret Gatz
- University of Southern California, Center for Economic and Social Research, 635 Downey Way, Los Angeles, CA 90089-3332, United States of America
| | - Tara Gruenewald
- Chapman University, Department of Psychology, 1 University Dr., Orange, CA 92866, United States of America
| | - Joshua Millstein
- University of Southern California, Department of Population and Public Health Sciences, 2001 North Soto Street, Los Angeles, CA 90033, United States of America
| | - Helena C Chui
- University of Southern California, Department of Neurology, 1520 San Pablo St. Suite 3000, Los Angeles, CA 90033, United States
| | - Joel D Kaufman
- University of Washington, Department of Environmental and Occupational Health Sciences, 1959 NE Pacific St., Box 257230, Seattle, WA 98105, United States of America
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 900 Commonwealth Avenue, Boston, MA 02215, United States of America
| | - Gregory A Wellenius
- Boston University, Boston, School of Public Health, Department of Environmental Health, 715 Albany St., Boston, MA 02118, United States of America
| | - Eric A Whitsel
- University of North Carolina, Gillings School of Public Health, Department of Epidemiology, 123 W. Franklin St., Suite 410, Chapel Hill, NC 27516-8050, United States of America
| | - Keith Widaman
- University of California, Riverside, Graduate School of Education, 900 University Ave, Riverside, CA 9251, United States of America
| | - Diana Younan
- University of Southern California, Department of Population and Public Health Sciences, 2001 North Soto Street, Los Angeles, CA 90033, United States of America
| | - Jiu-Chiuan Chen
- University of Southern California, Department of Neurology, 1520 San Pablo St. Suite 3000, Los Angeles, CA 90033, United States; University of Southern California, Department of Population and Public Health Sciences, 2001 North Soto Street, Los Angeles, CA 90033, United States of America.
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Abstract
PURPOSE OF REVIEW There is increasing interest in the links between exposure to air pollution and a range of health outcomes. The association with mental health however is much less established. This article reviews developments in the field over the past 12 months, highlighting the evidence for causation, associations between multiple air pollutants and mental health outcomes, and assesses the challenges of researching this topic. RECENT FINDINGS Increasingly rigorous methods are being applied to the investigation of a broader range of mental health outcomes. These methods include basic science, neuroimaging, and observational studies representing diverse geographical locations. Cohort studies with linked high-resolution air pollutant exposure data are common, facilitating advanced analytic methods. To date, meta-analyses have demonstrated small and significant positive associations between long-term exposure to fine particulate matter and depressive symptoms and cognitive decline. Methodological complexities in measuring exposure and outcome pose ongoing difficulties for the field. SUMMARY Literature on this topic has recently seen an appreciable expansion. Work that better estimates daily exposure, controls for complex confounders, and is driven by hypotheses founded in candidate causal mechanisms would help clarify associations, and inform targeted interventions and policymakers.
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