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Huang X, Chen X, Liu Q, Zhang Z, Miao J, Lai Y, Wu J. The relationship between education attainment and gout, and the mediating role of modifiable risk factors: a Mendelian randomization study. Front Public Health 2024; 11:1269426. [PMID: 38259784 PMCID: PMC10800502 DOI: 10.3389/fpubh.2023.1269426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/14/2023] [Indexed: 01/24/2024] Open
Abstract
Objective To investigate the causal relationship between educational attainment (EA) and gout, as well as the potential mediating effects of individual physical status (IPS) such as body mass index (BMI) and systolic blood pressure (SBP) and lifestyle habits (LH) including alcohol intake frequency (drinking), current tobacco smoking (smoking), and time spent watching television (TV). Methods Utilizing two-sample Mendelian randomization (MR), we analyzed the causal effects of EA on gout risk, and of IPS (BMI and SBP) and LH (smoking, drinking, and TV time) on gout risk. Multivariable MR (MVMR) was employed to explore and quantify the mediating effects of IPS and LH on the causal relationship between EA and gout risk. Results An elevation of educational attainment by one standard deviation (4.2 years) exhibited a protective effect against gout (odds ratio 0.724, 95% confidence interval 0.552-0.950; p = 0.020). We did not observe a causal relationship between smoking and gout, but BMI, SBP, drinking, and TV time were found to be causal risk factors for gout. Moreover, BMI, SBP, drinking, and TV time acted as mediating factors in the causal relationship between EA and gout risk, explaining 27.17, 14.83, 51.33, and 1.10% of the causal effects, respectively. Conclusion Our study indicates that having a genetically predicted higher level of EA may provide protection against gout. We found that this relationship is influenced by IPS factors such as BMI and SBP, as well as LH including drinking and TV time.
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Affiliation(s)
- Xin Huang
- Department of Orthopedics, Mindong Hospital Affiliated to Fujian Medical University, Fuan, Fujian Province, China
| | - Xin Chen
- Department of Urology, Mindong Hospital Affiliated to Fujian Medical University, Fuan, Fujian Province, China
| | - Qixi Liu
- Department of Nursing, Mindong Hospital Affiliated to Fujian Medical University, Fuan, Fujian Province, China
| | - Zhiwei Zhang
- Department of Orthopedics, Mindong Hospital Affiliated to Fujian Medical University, Fuan, Fujian Province, China
| | - Juan Miao
- Department of Nursing, Mindong Hospital Affiliated to Fujian Medical University, Fuan, Fujian Province, China
| | - Yuchan Lai
- Department of Nursing, Mindong Hospital Affiliated to Fujian Medical University, Fuan, Fujian Province, China
| | - Jinqing Wu
- Department of Orthopedics, Mindong Hospital Affiliated to Fujian Medical University, Fuan, Fujian Province, China
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Schlueter DJ, Sulieman L, Mo H, Keaton JM, Ferrara TM, Williams A, Qian J, Stubblefield O, Zeng C, Tran TC, Bastarache L, Dai J, Babbar A, Ramirez A, Goleva SB, Denny JC. Systematic replication of smoking disease associations using survey responses and EHR data in the All of Us Research Program. J Am Med Inform Assoc 2023; 31:139-153. [PMID: 37885303 PMCID: PMC10746325 DOI: 10.1093/jamia/ocad205] [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: 01/06/2023] [Revised: 05/04/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
OBJECTIVE The All of Us Research Program (All of Us) aims to recruit over a million participants to further precision medicine. Essential to the verification of biobanks is a replication of known associations to establish validity. Here, we evaluated how well All of Us data replicated known cigarette smoking associations. MATERIALS AND METHODS We defined smoking exposure as follows: (1) an EHR Smoking exposure that used International Classification of Disease codes; (2) participant provided information (PPI) Ever Smoking; and, (3) PPI Current Smoking, both from the lifestyle survey. We performed a phenome-wide association study (PheWAS) for each smoking exposure measurement type. For each, we compared the effect sizes derived from the PheWAS to published meta-analyses that studied cigarette smoking from PubMed. We defined two levels of replication of meta-analyses: (1) nominally replicated: which required agreement of direction of effect size, and (2) fully replicated: which required overlap of confidence intervals. RESULTS PheWASes with EHR Smoking, PPI Ever Smoking, and PPI Current Smoking revealed 736, 492, and 639 phenome-wide significant associations, respectively. We identified 165 meta-analyses representing 99 distinct phenotypes that could be matched to EHR phenotypes. At P < .05, 74 were nominally replicated and 55 were fully replicated. At P < 2.68 × 10-5 (Bonferroni threshold), 58 were nominally replicated and 40 were fully replicated. DISCUSSION Most phenotypes found in published meta-analyses associated with smoking were nominally replicated in All of Us. Both survey and EHR definitions for smoking produced similar results. CONCLUSION This study demonstrated the feasibility of studying common exposures using All of Us data.
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Affiliation(s)
- David J Schlueter
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
- Department of Health and Society, University of Toronto, Scarborough, Toronto, ON, Canada
| | - Lina Sulieman
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Huan Mo
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
- The Cohort Analytics Core (CAC), Center for Precision Health Research, National Human Genome Research Institute, Bethesda, MD, USA
| | - Jacob M Keaton
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Tracey M Ferrara
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Ariel Williams
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Jun Qian
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Onajia Stubblefield
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Chenjie Zeng
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Tam C Tran
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
- The Cohort Analytics Core (CAC), Center for Precision Health Research, National Human Genome Research Institute, Bethesda, MD, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jian Dai
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Anav Babbar
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Andrea Ramirez
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Slavina B Goleva
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Joshua C Denny
- Precision Health Informatics Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
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Wang Y, Guo X, Chen B, Chen H, Chen Y, Ma L, Liu H. The Relationship Between Psychosocial Behavior and the Quality of Life of Male Gout Patients in Southwest China: A Cross-Sectional Study Based on an Information-Motivation-Behavioral Skills Model. Patient Prefer Adherence 2023; 17:3503-3514. [PMID: 38146501 PMCID: PMC10749546 DOI: 10.2147/ppa.s434875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/05/2023] [Indexed: 12/27/2023] Open
Abstract
Purpose Gout is more severe in men, leading to a poor quality of life. Previous studies did not sufficiently pay attention to the quality of life and related factors in gout patients in Southwest China. This study aimed to investigate the quality of life of men with gout in Southwest China and explore the relationship between psychosocial factors and health-related quality of life from the perspective of an information-motivation-behavioral skill model. Patients and Methods This was a cross-sectional study conducted in the West China Hospital of Sichuan University located in Southwest China. In total, 230 male patients with gout were enrolled. The health-related quality of life of patients was assessed using the gout impact scale. The gout knowledge questionnaire was used to assess patients' information. The positive psycap questionnaire was used to assess motivation. The gout patients' self-management assessment scale was used to assess behavioral skills. Multiple linear regression was used to identify the factors associated with the health-related quality of life of patients. Results The overall mean gout impact scale score was 52.7±15.3 (maximum possible = 100). Factors associated with the total gout impact scale score were tophi (β=0.138, P=0.050), pain (β=0.255, P<0.001), and resiliency (β=-0.282, P<0.001). In addition, demographic characteristics (educational level, smoking and marital status), clinical characteristics (tophi, pain, number of attacks over half a year, and number of affected joints) and psychosocial behavior variables (resiliency, hope, disease treatment management, diet management) were associated with several dimensions of the gout impact scale. Conclusion The health-related quality of life of male patients with gout in Southwest China was at a medium level. We found that demographic characteristics, clinical characteristics, and psychosocial factors were associated with health-related quality of life of patients with gout. These findings can be used as a reference to improve health-related quality of life of patients with gout.
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Affiliation(s)
- Ying Wang
- Department of Rheumatology and Immunology, West China Hospital of Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Xin Guo
- Department of Rheumatology and Immunology, West China Hospital of Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Bo Chen
- Department of Rheumatology and Immunology, West China Hospital of Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Hong Chen
- West China School of Nursing/West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Yanling Chen
- Department of Rheumatology and Immunology, West China Hospital of Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Ling Ma
- Department of Rheumatology and Immunology, West China Hospital of Sichuan University/West China School of Nursing, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Huan Liu
- Department of Rheumatology and Immunology, West China Hospital of Sichuan University, Chengdu, Sichuan, People’s Republic of China
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Wu ZD, Yang XK, He YS, Ni J, Wang J, Yin KJ, Huang JX, Chen Y, Feng YT, Wang P, Pan HF. Environmental factors and risk of gout. ENVIRONMENTAL RESEARCH 2022; 212:113377. [PMID: 35500858 DOI: 10.1016/j.envres.2022.113377] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/30/2022] [Accepted: 04/25/2022] [Indexed: 06/14/2023]
Abstract
Gout is a chronic disease with inflammatory arthritis caused by monosodium urate (MSU) crystals deposition, an elevated serum urate level (hyperuricaemia) is the critical factor leading to MSU crystals deposition and promoting the progression of gout. The onset and development of gout is generally the result of multiple factors, such as diet, heredity and environmental factors. Although genetics and diet are thought to play as major factors, a growing body of research evidence has highlighted that environmental factors also play a significant role in the onset and exacerbation of gout. Recent studies have shown that air pollutants such as particulate matter, sulfur dioxide (SO2) and carbon monoxide (CO) may increase the risk of hospitalizations for gout, and that the changes in temperature and humidity may affect uric acid (UA) levels. There is also seasonal trend in gout. It has been demonstrated that environmental factors may induce or accelerate the production and release of pro-inflammatory mediators, causing an unbalance oxidative stress and systemic inflammation, and then participating in the overall process or a certain link of gout. Moreover, several environmental factors have shown the ability to induce the production urate and regulate the innate immune pathways, involving in the pathogenesis of gout. Nevertheless, the role of environmental factors in the etiology of gout remains unclear. In this review, we summarized the recent literatures and aimed to discuss the relationship between environmental factors (such as microclimate, season, ambient/indoor air pollution and extreme weather) and gout. We further discussed the inflammatory mechanisms of environmental factors and gout and the comprehensive effects of environmental factors on gout. We also made a prospect of the management and treatment of gout, with special consideration to environmental factors associated with gout.
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Affiliation(s)
- Zheng-Dong Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Xiao-Ke Yang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yi-Sheng He
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Jing Ni
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Jie Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Kang-Jia Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Ji-Xiang Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Yue Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Ya-Ting Feng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Peng Wang
- Teaching Center of Preventive Medicine, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China.
| | - Hai-Feng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China.
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Thompson MD, Wu YY, Cooney RV, Wilkens LR, Haiman CA, Pirkle CM. Modifiable Factors and Incident Gout Across Ethnicity Within a Large Multiethnic Cohort of Older Adults. J Rheumatol 2022; 49:504-512. [PMID: 35105711 DOI: 10.3899/jrheum.210394] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2022] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Gout disproportionately affects older Pacific Islander and Black populations relative to White populations. However, the ethnic-specific determinants remain understudied within these groups, as well as within other ethnicities. We examined gout incidence and associations with behavioral factors, including diet, alcohol, and smoking, within a large multiethnic population of older adults from the Multiethnic Cohort Study, which linked prospective cohort data to Medicare gout claims between 1999-2016. METHODS Using samples of Black (n = 12,370), Native Hawaiian (n = 6459), Japanese (n = 29,830), Latino (n = 17,538), and White (n = 26,067) participants, we conducted multiple Cox regressions, producing hazard ratios (HRs) and 95% CIs. RESULTS Relative to White individuals, Native Hawaiians had the highest risk of gout (HR 2.21, 95% CI 2.06-2.38), followed successively by Black and Japanese participants, whereas Latino individuals had a lower risk of gout (HR 0.78, 95% CI 0.73-0.83). Alcohol use was associated with an increased risk, with significantly greater effects observed among Japanese participants drinking ≥ 3 drinks per day (HR 1.46, 95% CI 1.27-1.66), or > 5 beers per week (HR 1.29, 95% CI 1.17-1.43), compared to White individuals (Pinteraction < 0.001). Former smokers with ≥ 20 pack-years had an increased risk (HR 1.14, 95% CI 1.06-1.22). Higher dietary quality was associated with a decreased gout risk, with the largest effect observed among White participants (HRQ5vsQ1 0.84, 95% CI 0.79-0.90), whereas vitamin C was weakly associated with a decreased risk of gout only among Japanese individuals (HR 0.91, 95% CI 0.85-0.98). CONCLUSION Overall, notable ethnic differences were observed in both gout risk and associations with modifiable behavioral factors. Our findings offer crucial insights that may improve precision in preventing and managing gout.
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Affiliation(s)
- Mika D Thompson
- M.D. Thompson, MSc, Y.Y. Wu, PhD, R.V. Cooney, PhD, C.M. Pirkle, PhD, Office of Public Health Studies, Thompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, Hawaii
| | - Yan Yan Wu
- M.D. Thompson, MSc, Y.Y. Wu, PhD, R.V. Cooney, PhD, C.M. Pirkle, PhD, Office of Public Health Studies, Thompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, Hawaii
| | - Robert V Cooney
- M.D. Thompson, MSc, Y.Y. Wu, PhD, R.V. Cooney, PhD, C.M. Pirkle, PhD, Office of Public Health Studies, Thompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, Hawaii
| | - Lynne R Wilkens
- L.R. Wilkens, DrPH, University of Hawai'i Cancer Center, Honolulu, Hawaii
| | - Christopher A Haiman
- C.A. Haiman, ScD, Department of Preventative Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Catherine M Pirkle
- M.D. Thompson, MSc, Y.Y. Wu, PhD, R.V. Cooney, PhD, C.M. Pirkle, PhD, Office of Public Health Studies, Thompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, Hawaii
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Hansildaar R, Vedder D, Baniaamam M, Tausche AK, Gerritsen M, Nurmohamed MT. Cardiovascular risk in inflammatory arthritis: rheumatoid arthritis and gout. THE LANCET. RHEUMATOLOGY 2021; 3:e58-e70. [PMID: 32904897 PMCID: PMC7462628 DOI: 10.1016/s2665-9913(20)30221-6] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The increased risk of cardiovascular morbidity and mortality in rheumatoid arthritis and gout has been increasingly acknowledged in past decades, with accumulating evidence that gout, just as with rheumatoid arthritis, is an independent cardiovascular risk factor. Although both diseases have a completely different pathogenesis, the underlying pathophysiological mechanisms in systemic inflammation overlap to some extent. Following the recognition that systemic inflammation has an important causative role in cardiovascular disease, anti-inflammatory therapy in both conditions and urate-lowering therapies in gout are expected to lower the cardiovascular burden of patients. Unfortunately, much of the existing data showing that urate-lowering therapy has consistent beneficial effects on cardiovascular outcomes in patients with gout are of low quality and contradictory. We will discuss the latest evidence in this respect. Cardiovascular disease risk management for patients with rheumatoid arthritis and gout is essential. Clinical guidelines and implementation of cardiovascular risk management in daily clinical practice, as well as unmet needs and areas for further investigation, will be discussed.
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Affiliation(s)
- Romy Hansildaar
- Amsterdam Rheumatology and Immunology Center, Amsterdam University Medical Center, Vrije Universiteit Amsterdam and Reade, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam, Netherlands
| | - Daisy Vedder
- Amsterdam Rheumatology and Immunology Center, Amsterdam University Medical Center, Vrije Universiteit Amsterdam and Reade, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam, Netherlands
| | - Milad Baniaamam
- Amsterdam Rheumatology and Immunology Center, Amsterdam University Medical Center, Vrije Universiteit Amsterdam and Reade, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam, Netherlands
| | - Anne-Kathrin Tausche
- Department of Rheumatology, University Clinic Carl Gustav Carus at TU Dresden, Dresden, Germany
| | - Martijn Gerritsen
- Amsterdam Rheumatology and Immunology Center, Amsterdam University Medical Center, Vrije Universiteit Amsterdam and Reade, Amsterdam, Netherlands
| | - Michael T Nurmohamed
- Amsterdam Rheumatology and Immunology Center, Amsterdam University Medical Center, Vrije Universiteit Amsterdam and Reade, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam, Netherlands
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Kardeş S. Seasonal variation in the internet searches for gout: an ecological study. Clin Rheumatol 2018; 38:769-775. [DOI: 10.1007/s10067-018-4345-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 10/13/2018] [Accepted: 10/17/2018] [Indexed: 11/29/2022]
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