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Liu J, Fan Y, Song J, Song R, Li X, Liu L, Wei N, Yuan J, Yi W, Pan R, Jin X, Cheng J, Zhang X, Su H. Impaired thyroid hormone sensitivity exacerbates the effect of PM 2.5 and its components on dyslipidemia in schizophrenia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:174055. [PMID: 38889814 DOI: 10.1016/j.scitotenv.2024.174055] [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: 02/22/2024] [Revised: 06/06/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Dyslipidemia in schizophrenia causes a serious loss of healthy life expectancy, making it imperative to explore key environmental risk factors. We aimed to assess the effect of PM2.5 and its constituents on dyslipidemia in schizophrenia, identify the critical hazardous components, and investigate the role of impaired thyroid hormones (THs) sensitivity in this association. METHODS We collected disease data on schizophrenia from the Anhui Mental Health Center from 2019 to 2022. Logistic regression was constructed to explore the effect of average annual exposure to PM2.5 and its components [black carbon (BC), organic matter (OM), sulfate (SO42-), ammonium (NH4+), and nitrate (NO3-)] on dyslipidemia, with subgroup analyses for age and gender. The degree of impaired THs sensitivity in participants was reflected by the Thyroid Feedback Quantile-based Index (TFQI), and its role in the association of PM2.5 components with dyslipidemia was explored. RESULTS A total of 5125 patients with schizophrenia were included in this study. Exposure to PM2.5 and its components (BC, OM, SO42-, NH4+, and NO3-) were associated with dyslipidemia with the odds ratios and 95 % confidence interval of 1.13 (1.04, 1.23), 1.16 (1.07, 1.26), 1.15 (1.06, 1.25), 1.11 (1.03, 1.20), 1.09 (1.00, 1.18), 1.12 (1.04, 1.20), respectively. Mixed exposure modeling indicated that BC played a major role in the effects of the mixture. More significant associations were observed in males and groups <45 years. In addition, we found that the effect of PM2.5 and its components on dyslipidemia was exacerbated as impaired THs sensitivity in the patients. CONCLUSIONS Exposure to PM2.5 and its components is associated with an increased risk of dyslipidemia in schizophrenia, which may be exacerbated by impaired THs sensitivity. Our results suggest a new perspective for the management of ambient particulate pollution and the protection of thyroid function in schizophrenia.
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Affiliation(s)
- Jintao Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Yinguang Fan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Rong Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Xuanxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Li Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Ning Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Jiajun Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Xiaoyu Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Xulai Zhang
- Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China.
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China.
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Huang TY, Chen LC, Li XP, Li WH, Xu SX, Nagy C, Ibrahim P, Nie ZW, Yang NY, Zeng L, Huang HW, Turecki G, Xie XH. Elevated triglycerides and low triiodothyronine: Key risk factors for coronary artery calcification in patients with schizophrenia. Schizophr Res 2024; 264:113-121. [PMID: 38128342 DOI: 10.1016/j.schres.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 11/04/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVE Coronary artery calcification (CAC) is a well-established independent predictor of coronary heart disease, and patients with schizophrenia have significantly higher rates compared to the general population. We performed this study to examine the population-specific risk factors associated with CAC in patients with schizophrenia. METHODS In this cross-sectional study, patients with schizophrenia who underwent low-dose chest CT scans between January 2020 and December 2021 were analyzed. Ordinary CAC scores and results of routine blood tests were obtained. Logistic regression was used to calculate the odds ratio (OR) for potential risk factors in patients with and without CAC, while the negative binomial additive model was used to explore the dose-response relationship between risk factors and CAC score. RESULTS Of the 916 patients, 233 (25.4 %) had CAC, while 683 (74.6 %) did not. After adjusting for confounding factors, higher triglyceride levels (OR = 1.20, 95 % confidence interval (CI): 1.04 to 1.38, p = 0.013) and low triiodothyronine levels (OR = 0.50, 95 % CI: 0.29 to 0.84; p = 0.010) were identified as risk factors for CAC. Both triglycerides (p = 0.021) and triiodothyronine (p = 0.010) were also found to have significant dose-response relationships with CAC scores according to the negative binomial additive model in the exploratory analysis. CONCLUSIONS This study highlights elevated serum triglycerides and decreased triiodothyronine levels as population-specific risk factors for CAC in patients with schizophrenia, suggest the need for close monitoring of CAC in patients with schizophrenia and further prospective trials to provide additional evidence on this topic.
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Affiliation(s)
- Tan-Yu Huang
- Department of Radiology, Second People's Hospital of Huizhou, Huizhou, China
| | - Li-Chang Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Xiao-Ping Li
- Department of Psychiatry, Second People's Hospital of Huizhou, Huizhou, China
| | - Wu-Hao Li
- Department of Radiology, Second People's Hospital of Huizhou, Huizhou, China
| | - Shu-Xian Xu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Corina Nagy
- Department of Psychiatry, McGill University, Montreal, QC, Canada; McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Pascal Ibrahim
- Department of Psychiatry, McGill University, Montreal, QC, Canada; McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Zhao-Wen Nie
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Nai-Yan Yang
- Department of Psychiatry, Second People's Hospital of Huizhou, Huizhou, China
| | - Lun Zeng
- Department of Psychiatry, Second People's Hospital of Huizhou, Huizhou, China
| | - Hua-Wei Huang
- Department of Psychiatry, Second People's Hospital of Huizhou, Huizhou, China
| | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, QC, Canada; McGill Group for Suicide Studies, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Xin-Hui Xie
- Brain Function and Psychosomatic Medicine Institute, Second People's Hospital of Huizhou, Huizhou, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
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