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Zhang F, Armando I, Jose PA, Zeng C, Yang J. G protein-coupled receptor kinases in hypertension: physiology, pathogenesis, and therapeutic targets. Hypertens Res 2024; 47:2317-2336. [PMID: 38961282 PMCID: PMC11374685 DOI: 10.1038/s41440-024-01763-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 05/10/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
G protein-coupled receptors (GPCRs) mediate cellular responses to a myriad of hormones and neurotransmitters that play vital roles in the regulation of physiological processes such as blood pressure. In organs such as the artery and kidney, hormones or neurotransmitters, such as angiotensin II (Ang II), dopamine, epinephrine, and norepinephrine exert their functions via their receptors, with the ultimate effect of keeping normal vascular reactivity, normal body sodium, and normal blood pressure. GPCR kinases (GRKs) exert their biological functions, by mediating the regulation of agonist-occupied GPCRs, non-GPCRs, or non-receptor substrates. In particular, increasing number of studies show that aberrant expression and activity of GRKs in the cardiovascular system and kidney inhibit or stimulate GPCRs (e.g., dopamine receptors, Ang II receptors, and α- and β-adrenergic receptors), resulting in hypertension. Current studies focus on the effect of selective GRK inhibitors in cardiovascular diseases, including hypertension. Moreover, genetic studies show that GRK gene variants are associated with essential hypertension, blood pressure response to antihypertensive medicines, and adverse cardiovascular outcomes of antihypertensive treatment. In this review, we present a comprehensive overview of GRK-mediated regulation of blood pressure, role of GRKs in the pathogenesis of hypertension, and highlight potential strategies for the treatment of hypertension. Schematic representation of GPCR desensitization process. Activation of GPCRs begins with the binding of an agonist to its corresponding receptor. Then G proteins activate downstream effectors that are mediated by various signaling pathways. GPCR signaling is halted by GRK-mediated receptor phosphorylation, which causes receptor internalization through β-arrestin.
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Affiliation(s)
- Fuwei Zhang
- Research Center for Metabolic and Cardiovascular Diseases, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
- Department of Nutrition, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
- Department of Cardiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
| | - Ines Armando
- Division of Renal Diseases & Hypertension, Department of Medicine and Department of Physiology/Pharmacology, The George Washington University School of Medicine & Health Sciences, Washington, DC, USA
| | - Pedro A Jose
- Division of Renal Diseases & Hypertension, Department of Medicine and Department of Physiology/Pharmacology, The George Washington University School of Medicine & Health Sciences, Washington, DC, USA
| | - Chunyu Zeng
- Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, PR China
- Chongqing Key Laboratory for Hypertension Research, Chongqing Cardiovascular Clinical Research Center, Chongqing Institute of Cardiology, Chongqing, PR China
| | - Jian Yang
- Research Center for Metabolic and Cardiovascular Diseases, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, PR China.
- Department of Nutrition, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, PR China.
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Ai L, Li R, Wang X, Liu Z, Li Y. Tempol alleviates acute lung injury by affecting glutathione synthesis through Nrf2 and inhibiting ferroptosis in lung epithelial cells. J Biochem Mol Toxicol 2024; 38:e23674. [PMID: 38454815 DOI: 10.1002/jbt.23674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/12/2023] [Accepted: 02/23/2024] [Indexed: 03/09/2024]
Abstract
As a life-threatening disease, acute lung injury (ALI) may progress to chronic pulmonary fibrosis. For the treatment of lung injury, Tempol is a superoxide dismutase mimetic and intracellular redox agent that can be a potential drug. This study investigated the regulatory mechanism of Tempol in the treatment of ALI. A mouse model of ALI was established, and HE staining was used to examine histomorphology. The CCK-8 assay was used to measure cell viability, and oxidative stress was assessed by corresponding kits. Flow cytometry and dichlorodihydrofluorescein diacetate staining assays were used to detect reactive oxygen species (ROS) levels. Protein expression levels were measured by Western blot analysis and ELISA. Pulmonary vascular permeability was used to measure the lung wet/dry weight ratio. The level of oxidative stress was increased in ALI mice, and the level of ferroptosis was upregulated. Tempol inhibited this effect and alleviated ALI. The administration of Tempol alleviated the pathological changes in ALI, inhibited pulmonary vascular permeability, and improved lung injury in ALI mice. The upregulation of genes essential for glutathione (GSH) metabolism induced by lipopolysaccharide (LPS) was inhibited by Tempol. In addition, nuclear factor-related factor 2 (Nrf2) is activated by Tempol therapy to regulate the de novo synthesis pathway of GSH, thereby alleviating LPS-induced lung epithelial cell damage. The results showed that Tempol alleviated ALI by activating the Nrf2 pathway to inhibit oxidative stress and ferroptosis in lung epithelial cells. In conclusion, this study demonstrates that Tempol alleviates ALI by inhibiting ferroptosis in lung epithelial cells through the effect of Nrf2 on GSH synthesis.
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Affiliation(s)
- Li Ai
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Ran Li
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Xiaona Wang
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Zhijuan Liu
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yongxia Li
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
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Xue Y, Li J, Xu YN, Cui JS, Li Y, Lu YQ, Luo XZ, Liu DZ, Huang F, Zeng ZY, Huang RJ. Mediating effect of body fat percentage in the association between ambient particulate matter exposure and hypertension: a subset analysis of China hypertension survey. BMC Public Health 2023; 23:1897. [PMID: 37784103 PMCID: PMC10544618 DOI: 10.1186/s12889-023-16815-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 09/22/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND Hypertension caused by air pollution exposure is a growing concern in China. The association between air pollutant exposure and hypertension has been found to be potentiated by obesity, however, little is known about the processes mediating this association. This study investigated the association between fine particulate matter (aerodynamic equivalent diameter ≤ 2.5 microns, PM2.5) exposure and the prevalence of hypertension in a representative population in southern China and tested whether obesity mediated this association. METHODS A total of 14,308 adults from 48 communities/villages in southern China were selected from January 2015 to December 2015 using a stratified multistage random sampling method. Hourly PM2.5 measurements were collected from the China National Environmental Monitoring Centre. Restricted cubic splines were used to analyze the nonlinear dose-response relationship between PM2.5 exposure and hypertension risk. The mediating effect mechanism of obesity on PM2.5-associated hypertension was tested in a causal inference framework following the approach proposed by Imai and Keele. RESULTS A total of 20.7% (2966/14,308) of participants in the present study were diagnosed with hypertension. Nonlinear exposure-response analysis revealed that exposure to an annual mean PM2.5 concentration above 41.8 µg/m3 was associated with increased hypertension risk at an incremental gradient. 9.1% of the hypertension burden could be attributed to exposure to elevated annual average concentrations of PM2.5. It is noteworthy that an increased body fat percentage positively mediated 59.3% of the association between PM2.5 exposure and hypertension risk, whereas body mass index mediated 34.3% of this association. CONCLUSIONS This study suggests that a significant portion of the estimated effect of exposure to PM2.5 on the risk of hypertension appears to be attributed to its effect on alterations in body composition and the development of obesity. These findings could inform intersectoral actions in future studies to protect populations with excessive fine particle exposure from developing hypertension.
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Affiliation(s)
- Yan Xue
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China
| | - Jin Li
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China
| | - Yu-Nan Xu
- Department of Medical Research, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jia-Sheng Cui
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China
| | - Yue Li
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China
| | - Yao-Qiong Lu
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China
| | - Xiao-Zhi Luo
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China
| | - De-Zhao Liu
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China
| | - Feng Huang
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China.
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China.
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China.
| | - Zhi-Yu Zeng
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China.
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China.
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China.
| | - Rong-Jie Huang
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, China.
- Guangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, Nanning, China.
- Guangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, China.
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Shen W, Li X, Fang Q, Li G, Xiao W, Wu Y, Liu J, Hu W, Lu H, Huang F. The impact of ambient air pollutants on childhood respiratory system disease and the resulting disease burden: a time-series study. Int Arch Occup Environ Health 2023; 96:1087-1100. [PMID: 37338586 DOI: 10.1007/s00420-023-01991-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 06/13/2023] [Indexed: 06/21/2023]
Abstract
PURPOSE The effects of air pollution on human health have long been a hot topic of research. For respiratory diseases, a large number of studies have proved that air pollution is one of the main causes. The purpose of this study was to investigate the risk of hospitalization of children with respiratory system diseases (CRSD) caused by six pollutants (PM2.5, PM10, NO2, SO2, CO, and O3) in Hefei City, and further calculate the disease burden. METHOD In the first stage, the generalized additive models were combined with the distributed lag non-linear models to evaluate the impact of air pollution on the inpatients for CRSD in Hefei. In the second stage, this study used the cost-of-illness approach to calculate the attributable number of hospitalizations and the extra disease burden. RESULT Overall, all the six kinds of pollutants had the strongest effects on CRSD inpatients within lag10 days. SO2 and CO caused the highest and lowest harm, respectively, and the RR values were SO2 (lag0-5): 1.1 20 (1.053, 1.191), and CO (lag0-6): 1.002 (1.001, 1.003). During the study period (January 1, 2014 to December 30, 2020), the 7-year cumulative burden of disease was 36.19 million CNY under the WHO air pollution standards. CONCLUSION In general, we found that six air pollutants were risk factors for CRSD in Hefei City, and create a huge burden of disease.
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Affiliation(s)
- Wenbin Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China
| | - Xue Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China
| | - Qingfeng Fang
- Department of Infectious Diseases, Anhui Provincial Children's Hospital, Hefei, Anhui, China
| | - Guoao Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China
| | - Wei Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China
| | - Yueyang Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China
| | - Jianjun Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China
| | - Wenlei Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China
| | - Huanhuan Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China
| | - Fen Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei, 230032, Anhui, China.
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Li W, Huang G, Tang N, Lu P, Jiang L, Lv J, Qin Y, Lin Y, Xu F, Lei D. Effects of heavy metal exposure on hypertension: A machine learning modeling approach. CHEMOSPHERE 2023; 337:139435. [PMID: 37422210 DOI: 10.1016/j.chemosphere.2023.139435] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023]
Abstract
Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable predictive machine learning (ML) model for hypertension based on levels of heavy metal exposure, data from the NHANES (2003-2016) were employed. Random forest (RF), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), ridge regression (RR), AdaBoost (AB), gradient boosting decision tree (GBDT), voting classifier (VC), and K-nearest neighbour (KNN) algorithms were utilized to generate an optimal predictive model for hypertension. Three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) methods, were integrated into a pipeline and embedded in ML for model interpretation. A total of 9005 eligible individuals were randomly allocated into two distinct sets for predictive model training and validation. The results showed that among the predictive models, the RF model demonstrated the highest performance, achieving an accuracy rate of 77.40% in the validation set. The AUC and F1 score for the model were 0.84 and 0.76, respectively. Blood Pb, urinary Cd, urinary Tl, and urinary Co levels were identified as the main influencers of hypertension, and their contribution weights were 0.0504 ± 0.0482, 0.0389 ± 0.0256, 0.0307 ± 0.0179, and 0.0296 ± 0.0162, respectively. Blood Pb (0.55-2.93 μg/dL) and urinary Cd (0.06-0.15 μg/L) levels exhibited the most pronounced upwards trend with the risk of hypertension within a specific value range, while urinary Tl (0.06-0.26 μg/L) and urinary Co (0.02-0.32 μg/L) levels demonstrated a declining trend with hypertension. The findings on the synergistic effects indicated that Pb and Cd were the primary determinants of hypertension. Our findings underscore the predictive value of heavy metals for hypertension. By utilizing interpretable methods, we discerned that Pb, Cd, Tl, and Co emerged as noteworthy contributors within the predictive model.
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Affiliation(s)
- Wenxiang Li
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China.
| | - Guangyi Huang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Ningning Tang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Peng Lu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Li Jiang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Jian Lv
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Yuanjun Qin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Yunru Lin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China
| | - Fan Xu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China.
| | - Daizai Lei
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Nanning, 530021, China.
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Mo J, Liu X, Huang Y, He R, Zhang Y, Huang H. Developmental origins of adult diseases. MEDICAL REVIEW (BERLIN, GERMANY) 2022; 2:450-470. [PMID: 37724166 PMCID: PMC10388800 DOI: 10.1515/mr-2022-0027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/11/2022] [Indexed: 09/20/2023]
Abstract
The occurrence and mechanisms of developmental adult diseases have gradually attracted attention in recent years. Exposure of gametes and embryos to adverse environments, especially during plastic development, can alter the expression of certain tissue-specific genes, leading to increased susceptibility to certain diseases in adulthood, such as diabetes, cardiovascular disease, neuropsychiatric, and reproductive system diseases, etc. The occurrence of chronic disease in adulthood is partly due to genetic factors, and the remaining risk is partly due to environmental-dependent epigenetic information alteration, including DNA methylation, histone modifications, and noncoding RNAs. Changes in this epigenetic information potentially damage our health, which has also been supported by numerous epidemiological and animal studies in recent years. Environmental factors functionally affect embryo development through epimutation, transmitting diseases to offspring and even later generations. This review mainly elaborated on the concept of developmental origins of adult diseases, and revealed the epigenetic mechanisms underlying these events, discussed the theoretical basis for the prevention and treatment of related diseases.
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Affiliation(s)
- Jiaying Mo
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, Zhejiang Province, China
- The Key Laboratory of Reproductive Genetics (Zhejiang University), Ministry of Education, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Xuanqi Liu
- The Key Laboratory of Reproductive Genetics (Zhejiang University), Ministry of Education, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Yutong Huang
- The Key Laboratory of Reproductive Genetics (Zhejiang University), Ministry of Education, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Renke He
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, Zhejiang Province, China
- The Key Laboratory of Reproductive Genetics (Zhejiang University), Ministry of Education, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Yu Zhang
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
| | - Hefeng Huang
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, Zhejiang Province, China
- The Key Laboratory of Reproductive Genetics (Zhejiang University), Ministry of Education, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China
- Research Units of Embryo Original Diseases, Chinese Academy of Medical Sciences (No. 2019RU056), Shanghai, China
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Chen Z, Liu P, Xia X, Wang L, Li X. The underlying mechanism of PM2.5-induced ischemic stroke. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 310:119827. [PMID: 35917837 DOI: 10.1016/j.envpol.2022.119827] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/04/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Under the background of global industrialization, PM2.5 has become the fourth-leading risk factor for ischemic stroke worldwide, according to the 2019 GBD estimates. This highlights the hazards of PM2.5 for ischemic stroke, but unfortunately, PM2.5 has not received the attention that matches its harmfulness. This article is the first to systematically describe the molecular biological mechanism of PM2.5-induced ischemic stroke, and also propose potential therapeutic and intervention strategies. We highlight the effect of PM2.5 on traditional cerebrovascular risk factors (hypertension, hyperglycemia, dyslipidemia, atrial fibrillation), which were easily overlooked in previous studies. Additionally, the effects of PM2.5 on platelet parameters, megakaryocytes activation, platelet methylation, and PM2.5-induced oxidative stress, local RAS activation, and miRNA alterations in endothelial cells have also been described. Finally, PM2.5-induced ischemic brain pathological injury and microglia-dominated neuroinflammation are discussed. Our ultimate goal is to raise the public awareness of the harm of PM2.5 to ischemic stroke, and to provide a certain level of health guidance for stroke-susceptible populations, as well as point out some interesting ideas and directions for future clinical and basic research.
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Affiliation(s)
- Zhuangzhuang Chen
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Peilin Liu
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xiaoshuang Xia
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China
| | - Lin Wang
- Department of Geriatrics, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China
| | - Xin Li
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China.
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