1
|
Xu S, Liu Y, Wang Q, Liu F, Xian Y, Xu F, Liu Y. Gut microbiota in combination with blood metabolites reveals characteristics of the disease cluster of coronary artery disease and cognitive impairment: a Mendelian randomization study. Front Immunol 2024; 14:1308002. [PMID: 38288114 PMCID: PMC10822940 DOI: 10.3389/fimmu.2023.1308002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 12/29/2023] [Indexed: 01/31/2024] Open
Abstract
Background The coexistence of coronary artery disease (CAD) and cognitive impairment has become a common clinical phenomenon. However, there is currently limited research on the etiology of this disease cluster, discovery of biomarkers, and identification of precise intervention targets. Methods We explored the causal connections between gut microbiota, blood metabolites, and the disease cluster of CAD combined with cognitive impairment through two-sample Mendelian randomization (TSMR). Additionally, we determine the gut microbiota and blood metabolites with the strongest causal associations using Bayesian model averaging multivariate Mendelian randomization (MR-BMA) analysis. Furthermore, we will investigate the mediating role of blood metabolites through a two-step Mendelian randomization design. Results We identified gut microbiota that had significant causal associations with cognitive impairment. Additionally, we also discovered blood metabolites that exhibited significant causal associations with both CAD and cognitive impairment. According to the MR-BMA results, the free cholesterol to total lipids ratio in large very low density lipoprotein (VLDL) was identified as the key blood metabolite significantly associated with CAD. Similarly, the cholesteryl esters to total lipids ratio in small VLDL emerged as the primary blood metabolite with a significant causal association with dementia with lewy bodies (DLB). For the two-step Mendelian randomization analysis, we identified blood metabolites that could potentially mediate the association between genus Butyricicoccus and CAD in the potential causal links. Conclusion Our study utilized Mendelian randomization (MR) to identify the gut microbiota features and blood metabolites characteristics associated with the disease cluster of CAD combined with cognitive impairment. These findings will provide a meaningful reference for the identification of biomarkers for the disease cluster of CAD combined with cognitive impairment as well as the discovery of targets for intervention to address the problems in the clinic.
Collapse
Affiliation(s)
- Shihan Xu
- The Second Department of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Key Laboratory of Disease and Syndrome Integration Prevention and Treatment of Vascular Aging, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanfei Liu
- The Second Department of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Key Laboratory of Disease and Syndrome Integration Prevention and Treatment of Vascular Aging, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Qing Wang
- The Second Department of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Key Laboratory of Disease and Syndrome Integration Prevention and Treatment of Vascular Aging, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Fenglan Liu
- School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yanfang Xian
- School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Fengqin Xu
- The Second Department of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Key Laboratory of Disease and Syndrome Integration Prevention and Treatment of Vascular Aging, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
- School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yue Liu
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Key Laboratory of Disease and Syndrome Integration Prevention and Treatment of Vascular Aging, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| |
Collapse
|
2
|
Lu Y, Zhu H, Hu Z, He F, Chen G. Epidemic Characteristics, Spatiotemporal Pattern, and Risk Factors of Other Infectious Diarrhea in Fujian Province From 2005 to 2021: Retrospective Analysis. JMIR Public Health Surveill 2023; 9:e45870. [PMID: 38032713 PMCID: PMC10722358 DOI: 10.2196/45870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 05/03/2023] [Accepted: 09/05/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Other infectious diarrhea (OID) continues to pose a significant public health threat to all age groups in Fujian Province. There is a need for an in-depth analysis to understand the epidemiological pattern of OID and its associated risk factors in the region. OBJECTIVE In this study, we aimed to describe the overall epidemic characteristics and spatiotemporal pattern of OID in Fujian Province from 2005 to 2021 and explore the linkage between sociodemographic and environmental factors and the occurrence of OID within the study area. METHODS Notification data for OID in Fujian were extracted from the China Information System for Disease Control and Prevention. The spatiotemporal pattern of OID was analyzed using Moran index and Kulldorff scan statistics. The seasonality of and short-term impact of meteorological factors on OID were examined using an additive decomposition model and a generalized additive model. Geographical weighted regression and generalized linear mixed model were used to identify potential risk factors. RESULTS A total of 388,636 OID cases were recorded in Fujian Province from January 2005 to December 2021, with an average annual incidence of 60.3 (SD 16.7) per 100,000 population. Children aged <2 years accounted for 50.7% (196,905/388,636) of all cases. There was a steady increase in OID from 2005 to 2017 and a clear seasonal shift in OID cases from autumn to winter and spring between 2005 and 2020. Higher maximum temperature, atmospheric pressure, humidity, and precipitation were linked to a higher number of deseasonalized OID cases. The spatial and temporal aggregations were concentrated in Zhangzhou City and Xiamen City for 17 study years. Furthermore, the clustered areas exhibited a dynamic spreading trend, expanding from the southernmost Fujian to the southeast and then southward over time. Factors such as densely populated areas with a large <1-year-old population, less economically developed areas, and higher pollution levels contributed to OID cases in Fujian Province. CONCLUSIONS This study revealed a distinct distribution of OID incidence across different population groups, seasons, and regions in Fujian Province. Zhangzhou City and Xiamen City were identified as the major hot spots for OID. Therefore, prevention and control efforts should prioritize these specific hot spots and highly susceptible groups.
Collapse
Affiliation(s)
- Yixiao Lu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Hansong Zhu
- Fujian Provincial Center for Disease Control and Prevention, The Practice Base on the School of Public Health, Fujian Medical University, Fuzhou, China
| | - Zhijian Hu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Fei He
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Guangmin Chen
- Fujian Provincial Center for Disease Control and Prevention, The Practice Base on the School of Public Health, Fujian Medical University, Fuzhou, China
| |
Collapse
|
3
|
Sha M, Shen C, Tong Y, Xia Q. Cluster of Donor-Derived Cryptococcosis after Liver and Kidney Transplantation. Emerg Infect Dis 2022; 28:2112-2114. [PMID: 36148992 PMCID: PMC9514340 DOI: 10.3201/eid2810.220522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Cryptococcosis infection after transplantation is easily overlooked or misdiagnosed. We report a cluster of donor-derived cryptococcosis infection in liver and kidney transplant recipients from the same donor in China. Infections occurred within 1 month after transplantation, and were confirmed by using biopsies and blood tests.
Collapse
|
4
|
Hong K, Yum S, Kim J, Yoo D, Chun BC. Epidemiology and Regional Predictors of COVID-19 Clusters: A Bayesian Spatial Analysis Through a Nationwide Contact Tracing Data. Front Med (Lausanne) 2021; 8:753428. [PMID: 34746188 PMCID: PMC8563697 DOI: 10.3389/fmed.2021.753428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: Revealing the clustering risks of COVID-19 and prediction is essential for effective quarantine policies, since clusters can lead to rapid transmission and high mortality in a short period. This study aimed to present which regional and social characteristics make COVID-19 cluster with high risk. Methods: By analyzing the data of all confirmed cases (14,423) in Korea between January 10 and August 3, 2020, provided by the Korea Disease Control and Prevention Agency, we manually linked each case and discovered clusters. After classifying the cases into clusters as nine types, we compared the duration and size of clusters by types to reveal high-risk cluster types. Also, we estimated odds for the risk factors for COVID-19 clustering by a spatial autoregressive model using the Bayesian approach. Results: Regarding the classified clusters (n = 539), the mean size was 19.21, and the mean duration was 9.24 days. The number of clusters was high in medical facilities, workplaces, and nursing homes. However, multilevel marketing, religious facilities, and restaurants/business-related clusters tended to be larger and longer when an outbreak occurred. According to the spatial analysis in COVID-19 clusters of more than 20 cases, the global Moran's I statistics value was 0.14 (p < 0.01). After adjusting for population size, the risks of COVID-19 clusters were related to male gender (OR = 1.29) and low influenza vaccination rate (OR = 0.87). After the spatial modeling, the predicted probability of forming clusters was visualized and compared with the actual incidence and local Moran's I statistics 2 months after the study period. Conclusions: COVID-19 makes different sizes of clusters in various contact settings; thus, precise epidemic control measures are needed. Also, when detecting and screening for COVID-19 clusters, regional risks such as vaccination rate should be considered for predicting risk to control the pandemic cost-effectively.
Collapse
Affiliation(s)
- Kwan Hong
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea
| | - Sujin Yum
- Graduate School of Public Health, Korea University, Seoul, South Korea
| | - Jeehyun Kim
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea.,Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea
| | - Daesung Yoo
- Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea.,Veterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, South Korea
| | - Byung Chul Chun
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea.,Graduate School of Public Health, Korea University, Seoul, South Korea.,Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea
| |
Collapse
|
5
|
Chau NVV, Hong NTT, Ngoc NM, Thanh TT, Khanh PNQ, Nguyet LA, Nhu LNT, Ny NTH, Man DNH, Hang VTT, Phong NT, Que NTH, Tuyen PT, Tu TNH, Hien TT, Minh NNQ, Hung LM, Truong NT, Yen LM, Rogier van Doorn H, Dung NT, Thwaites G, Dung NT, Van Tan L. Superspreading Event of SARS-CoV-2 Infection at a Bar, Ho Chi Minh City, Vietnam. Emerg Infect Dis 2020; 27. [PMID: 33063657 PMCID: PMC7774544 DOI: 10.3201/eid2701.203480] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
We report a superspreading event of severe acute respiratory syndrome coronavirus 2 infection initiated at a bar in Vietnam with evidence of symptomatic and asymptomatic transmission, based on ministry of health reports, patient interviews, and whole-genome sequence analysis. Crowds in enclosed indoor settings with poor ventilation may be considered at high risk for transmission.
Collapse
|
6
|
Islam MM, McRae IS, Yen L, Jowsey T, Valderas JM. Time spent on health-related activities by senior Australians with chronic diseases: what is the role of multimorbidity and comorbidity? Aust N Z J Public Health 2015; 39:277-83. [PMID: 25903449 DOI: 10.1111/1753-6405.12355] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2014] [Revised: 10/01/2014] [Accepted: 11/01/2014] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE To examine the effect of various morbidity clusters of chronic diseases on health-related time use and to explore factors associated with heavy time burden (more than 30 hours/month) of health-related activities. METHODS Using a national survey, data were collected from 2,540 senior Australians. Natural clusters were identified using cluster analysis and clinical clusters using clinical expert opinion. We undertook a set of linear regressions to model people's time use, and logistic regressions to model heavy time burden. RESULTS Time use increases with the number of chronic diseases. Six of the 12 diseases are significantly associated with higher time use, with the highest effect for diabetes followed by depression; 18% reported a heavy time burden, with diabetes again being the most significant disease. Clusters and dominant comorbid groupings do not contribute to predicting time use or time burden. CONCLUSIONS Total number of diseases and specific diseases are useful determinants of time use and heavy time burden. Dominant groupings and disease clusters do not predict time use. IMPLICATIONS In considering time demands on patients and the need for care co-ordination, care providers need to be aware of how many and what specific diseases the patient faces.
Collapse
Affiliation(s)
- M Mofizul Islam
- Australian Primary Health Care Research Institute, Australian National University, Australian Capital Territory
| | - Ian S McRae
- Australian Primary Health Care Research Institute, Australian National University, Australian Capital Territory
| | - Laurann Yen
- Australian Primary Health Care Research Institute, Australian National University, Australian Capital Territory
| | - Tanisha Jowsey
- Australian Primary Health Care Research Institute, Australian National University, Australian Capital Territory
| | - Jose M Valderas
- Health Services and Policy Research, University of Exeter Medical School, England, UK
| |
Collapse
|