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Ding E, Deng F, Fang J, Liu J, Yan W, Yao Q, Miao K, Wang Y, Sun P, Li C, Liu Y, Dong H, Dong L, Zhang X, Lu Y, Lin X, Ding C, Li T, Shi Y, Cai Y, Liu X, Godri Pollitt KJ, Ji JS, Tong S, Tang S, Shi X. Exposome-Wide Ranking to Uncover Environmental Chemicals Associated with Dyslipidemia: A Panel Study in Healthy Older Chinese Adults from the BAPE Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:97005. [PMID: 39240788 PMCID: PMC11379127 DOI: 10.1289/ehp13864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2024]
Abstract
BACKGROUND Environmental contaminants (ECs) are increasingly recognized as crucial drivers of dyslipidemia and cardiovascular disease (CVD), but the comprehensive impact spectrum and interlinking mechanisms remain uncertain. OBJECTIVES We aimed to systematically evaluate the association between exposure to 80 ECs across seven divergent categories and markers of dyslipidemia and investigate their underpinning biomolecular mechanisms via an unbiased integrative approach of internal chemical exposome and multi-omics. METHODS A longitudinal study involving 76 healthy older adults was conducted in Jinan, China, and participants were followed five times from 10 September 2018 to 19 January 2019 in 1-month intervals. A broad spectrum of seven chemical categories covering the prototypes and metabolites of 102 ECs in serum or urine as well as six serum dyslipidemia markers [total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoprotein (Apo)A1, ApoB, and ApoE4] were measured. Multi-omics, including the blood transcriptome, serum/urine metabolome, and serum lipidome, were profiled concurrently. Exposome-wide association study and the deletion/substitution/addition algorithms were applied to explore the associations between 80 EC exposures detection frequency > 50 % and dyslipidemia markers. Weighted quantile sum regression was used to assess the mixture effects and relative contributions. Multi-omics profiling, causal inference model, and pathway analysis were conducted to interpret the mediating biomolecules and underlying mechanisms. Examination of cytokines and electrocardiograms was further conducted to validate the observed associations and biomolecular pathways. RESULTS Eight main ECs [1-naphthalene, 1-pyrene, 2-fluorene, dibutyl phosphate, tri-phenyl phosphate, mono-(2-ethyl-5-hydroxyhexyl) phthalate, chromium, and vanadium] were significantly associated with most dyslipidemia markers. Multi-omics indicated that the associations were mediated by endogenous biomolecules and pathways, primarily pertinent to CVD, inflammation, and metabolism. Clinical measures of cytokines and electrocardiograms further cross-validated the association of these exogenous ECs with systemic inflammation and cardiac function, demonstrating their potential mechanisms in driving dyslipidemia pathogenesis. DISCUSSION It is imperative to prioritize mitigating exposure to these ECs in the primary prevention and control of the dyslipidemia epidemic. https://doi.org/10.1289/EHP13864.
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Affiliation(s)
- Enmin Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fuchang Deng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Juan Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Wenyan Yan
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qiao Yao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Ke Miao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Yu Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Peijie Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Chenfeng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Yuanyuan Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Haoran Dong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Li Dong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Xu Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Yifu Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Xiao Lin
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Changming Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yali Shi
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
| | - Yaqi Cai
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
| | - Xiaohui Liu
- National Protein Science Technology Center, Tsinghua University, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Krystal J Godri Pollitt
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Shilu Tong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Song Tang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NIEH, China CDC, Beijing, China
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Zhao H, Jia Y, Wang F, Chai Y, Zhang C, Xu J, Kang Q. Cobalt-Doped Mesoporous Silica Coated Magnetic Nanoparticles Promoting Accelerated Bone Healing in Distraction Osteogenesis. Int J Nanomedicine 2023; 18:2359-2370. [PMID: 37187997 PMCID: PMC10178404 DOI: 10.2147/ijn.s393878] [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: 12/14/2022] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
Introduction Large bone abnormalities are commonly treated using distraction osteogenesis (DO), but it is not suitable for a long-term application; therefore, there is an urgent need for adjuvant therapy that can accelerate bone repair. Methods We have synthesized mesoporous silica-coated magnetic nanoparticles doped with cobalt ions (Co-MMSNs) and assessed their capacity to quicken bone regrowth in a mouse model of DO. Furthermore, local injection of the Co-MMSNs significantly accelerated bone healing in DO, as demonstrated by X-ray imaging, micro-CT, mechanical tests, histological evaluation, and immunochemical analysis. Results In vitro, the Co-MMSNs exhibited good biocompatibility and induced angiogenic gene expression and osteogenic development in bone mesenchymal stem cells. And the Co-MMSNs can promote bone regeneration in a rat DO model. Discussion This study demonstrated the significant potential of Co-MMSNs to shorten the DO treatment duration and effectively reduce the incidence of complications.
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Affiliation(s)
- Haoyu Zhao
- Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Department of Orthopedic Surgery, Shanghai, People’s Republic of China
| | - Yachao Jia
- Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Department of Orthopedic Surgery, Shanghai, People’s Republic of China
| | - Feng Wang
- Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Department of Orthopedic Surgery, Shanghai, People’s Republic of China
| | - Yimin Chai
- Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Department of Orthopedic Surgery, Shanghai, People’s Republic of China
| | - Chunfu Zhang
- Shanghai Jiao Tong University, School of Biomedical Engineering, Shanghai, People’s Republic of China
| | - Jia Xu
- Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Department of Orthopedic Surgery, Shanghai, People’s Republic of China
- Correspondence: Jia Xu; Qinglin Kang, Email ;
| | - Qinglin Kang
- Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Department of Orthopedic Surgery, Shanghai, People’s Republic of China
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Chen W, Wu P, Yu F, Luo G, Qing L, Tang J. HIF-1α Regulates Bone Homeostasis and Angiogenesis, Participating in the Occurrence of Bone Metabolic Diseases. Cells 2022; 11:cells11223552. [PMID: 36428981 PMCID: PMC9688488 DOI: 10.3390/cells11223552] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 11/12/2022] Open
Abstract
In the physiological condition, the skeletal system's bone resorption and formation are in dynamic balance, called bone homeostasis. However, bone homeostasis is destroyed under pathological conditions, leading to the occurrence of bone metabolism diseases. The expression of hypoxia-inducible factor-1α (HIF-1α) is regulated by oxygen concentration. It affects energy metabolism, which plays a vital role in preventing bone metabolic diseases. This review focuses on the HIF-1α pathway and describes in detail the possible mechanism of its involvement in the regulation of bone homeostasis and angiogenesis, as well as the current experimental studies on the use of HIF-1α in the prevention of bone metabolic diseases. HIF-1α/RANKL/Notch1 pathway bidirectionally regulates the differentiation of macrophages into osteoclasts under different conditions. In addition, HIF-1α is also regulated by many factors, including hypoxia, cofactor activity, non-coding RNA, trace elements, etc. As a pivotal pathway for coupling angiogenesis and osteogenesis, HIF-1α has been widely studied in bone metabolic diseases such as bone defect, osteoporosis, osteonecrosis of the femoral head, fracture, and nonunion. The wide application of biomaterials in bone metabolism also provides a reasonable basis for the experimental study of HIF-1α in preventing bone metabolic diseases.
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Effect of Rehabilitation Nursing under the Guidance of the Health Action Process Approach Model on Perioperative Nursing Effect of Artificial Hip Arthroplasty: Effect on Promoting Quality of Life and Postoperative Rehabilitation. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1247002. [PMID: 35465014 PMCID: PMC9019436 DOI: 10.1155/2022/1247002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/03/2022] [Accepted: 03/14/2022] [Indexed: 02/03/2023]
Abstract
Objective To explore the influence of rehabilitation nursing under the guidance of Health Action Process Approach (HAPA) model on the perioperative nursing effect of artificial hip replacement and to analyze the effect of this nursing model on the quality of life and postoperative rehabilitation of patients undergoing artificial hip replacement. Methods A total of 200 patients with hip arthroplasty treated in our hospital from January 2019 to July 2021 were enrolled. The patients were randomly assigned into the control group and study group. The former received routine nursing, and the latter received rehabilitation nursing under the guidance of the HAPA model. Nursing satisfaction, pain score, Harris hip function score, timed stand-up-walk test, MBI score, and quality of life score were compared. Results First of all, we compared the nursing satisfaction. In the study group, 86 cases were very satisfied, 8 cases were satisfied, and 6 cases were general; the satisfaction rate was 100%. In the control group, 48 cases were very satisfied, 22 cases were satisfied, 12 cases were general, and 18 cases were dissatisfied; the satisfaction rate was 82.0%. The nursing satisfaction in the study group was higher compared to that in the control group (P < 0.05). Secondly, we compared the pain scores. Before nursing, there exhibited no significant difference (P > 0.05). After nursing, the pain score of the two groups increased. Moreover, the pain score of the study group at discharge and 1 month, 3 months, and 6 months after operation was lower compared to that of the control group (P < 0.05). Before nursing, there exhibited no significant difference in the Harris hip joint function score (P > 0.05). After nursing, the Harris hip function score increased. Furthermore, the Harris hip function score of the study group at discharge and 1 month, 3 months, and 6 months after operation was higher compared to that of the control group (P < 0.05). In terms of the timed stand-up-walking test, there exhibited no significant difference before nursing (P > 0.05). After nursing, the time of the timed stand-up-walk test in both groups decreased. And the timed stand-up-walk test at discharge and 1 month, 3 months, and 6 months after operation in the study group was lower compared to that in the control group (P < 0.05). Compared with the MBI scores, there exhibited no significant difference before nursing (P > 0.05). After nursing, the MBI scores increased. Of note, the MBI scores of the study group at discharge and 1 month, 3 months, and 6 months after operation were higher compared to those of the control group (P < 0.05). Finally, we compared the scores of life quality. Before nursing, there exhibited no significant difference (P > 0.05). After nursing, the scores of life quality decreased. The scores of physiological function, psychological function, social function, and health self-cognition in the study group were lower compared to those in the control group (P < 0.05). Conclusion The perioperative rehabilitation nursing program of artificial hip replacement for the elderly based on the HAPA model is feasible, which can effectively enhance the functional recovery of hip joint, promote the ability of self-care of daily life, relieve pain and anxiety, and help to achieve dynamic balance and gait stability in the early stage. The rehabilitation program is better than routine nursing. As a new social cognitive model, the HAPA model is applied to the rehabilitation nursing environment of hip replacement from the aspect of social cognitive behavior, which can help to enhance the rehabilitation behavior of elderly patients, playing an important role in the rehabilitation effect of perioperative nursing.
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