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Habudele Z, Chen G, Qian SE, Vaughn MG, Zhang J, Lin H. High Dietary Intake of Iron Might Be Harmful to Atrial Fibrillation and Modified by Genetic Diversity: A Prospective Cohort Study. Nutrients 2024; 16:593. [PMID: 38474722 DOI: 10.3390/nu16050593] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/03/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Some studies suggest an association between iron overload and cardiovascular diseases (CVDs). However, the relationship between dietary iron intake and atrial fibrillation (AF) remains uncertain, as does the role of genetic loci on this association. The study involved 179,565 participants from UK Biobank, tracking incident atrial fibrillation (AF) cases. Iron intake was categorized into low, moderate, and high groups based on dietary surveys conducted from 2009 to 2012. The Cox regression model was used to estimate the risk of AF in relation to iron intake, assessing the hazard ratio (HR) and 95% confidence interval (95% CI). It also examined the impact of 165 AF-related and 20 iron-related genetic variants on this association. Pathway enrichment analyses were performed using Metascape and FUMA. During a median follow-up period of 11.6 years, 6693 (3.97%) incident AF cases were recorded. A total of 35,874 (20.0%) participants had high iron intake. High iron intake was associated with increased risk of AF [HR: 1.13 (95% CI: 1.05, 1.22)] in a fully adjusted model. Importantly, there were 83 SNPs (11 iron-related SNPs) that could enhance the observed associations. These genes are mainly involved in cardiac development and cell signal transduction pathways. High dietary iron intake increases the risk of atrial fibrillation, especially when iron intake exceeds 16.95 mg. The association was particularly significant among the 83 SNPs associated with AF and iron, the individuals with these risk genes. Gene enrichment analysis revealed that these genes are significantly involved in cardiac development and cell signal transduction processes.
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Affiliation(s)
- Zierdi Habudele
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510275, China
| | - Ge Chen
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510275, China
| | - Samantha E Qian
- College of Arts and Sciences, Saint Louis University, St. Louis, MO 63108, USA
| | - Michael G Vaughn
- School of Social Work, Saint Louis University, St. Louis, MO 63103, USA
| | - Junguo Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510275, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510275, China
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Wu Y, Zhang S, Qian SE, Cai M, Li H, Wang C, Zou H, Chen L, Vaughn MG, McMillin SE, Lin H. Ambient air pollution associated with incidence and dynamic progression of type 2 diabetes: a trajectory analysis of a population-based cohort. BMC Med 2022; 20:375. [PMID: 36310158 PMCID: PMC9620670 DOI: 10.1186/s12916-022-02573-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/21/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Though the association between air pollution and incident type 2 diabetes (T2D) has been well documented, evidence on the association with development of subsequent diabetes complications and post-diabetes mortality is scarce. We investigate whether air pollution is associated with different progressions and outcomes of T2D. METHODS Based on the UK Biobank, 398,993 participants free of diabetes and diabetes-related events at recruitment were included in this analysis. Exposures to particulate matter with a diameter ≤ 10 μm (PM10), PM2.5, nitrogen oxides (NOx), and NO2 for each transition stage were estimated at each participant's residential addresses using data from the UK's Department for Environment, Food and Rural Affairs. The outcomes were incident T2D, diabetes complications (diabetic kidney disease, diabetic eye disease, diabetic neuropathy disease, peripheral vascular disease, cardiovascular events, and metabolic events), all-cause mortality, and cause-specific mortality. Multi-state model was used to analyze the impact of air pollution on different progressions of T2D. Cumulative transition probabilities of different stages of T2D under different air pollution levels were estimated. RESULTS During the 12-year follow-up, 13,393 incident T2D patients were identified, of whom, 3791 developed diabetes complications and 1335 died. We observed that air pollution was associated with different progression stages of T2D with different magnitudes. In a multivariate model, the hazard ratios [95% confidence interval (CI)] per interquartile range elevation in PM2.5 were 1.63 (1.59, 1.67) and 1.08 (1.03, 1.13) for transitions from healthy to T2D and from T2D to complications, and 1.50 (1.47, 1.53), 1.49 (1.36, 1.64), and 1.54 (1.35, 1.76) for mortality risk from baseline, T2D, and diabetes complications, respectively. Generally, we observed stronger estimates of four air pollutants on transition from baseline to incident T2D than those on other transitions. Moreover, we found significant associations between four air pollutants and mortality risk due to cancer and cardiovascular diseases from T2D or diabetes complications. The cumulative transition probability was generally higher among those with higher levels of air pollution exposure. CONCLUSIONS This study indicates that ambient air pollution exposure may contribute to increased risk of incidence and progressions of T2D, but to diverse extents for different progressions.
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Affiliation(s)
- Yinglin Wu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Samantha E Qian
- College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63104, USA
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Haitao Li
- Department of Social Medicine and Health Service Management, Health Science Center, Shenzhen University, Shenzhen, 518060, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Hongtao Zou
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Lan Chen
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Michael G Vaughn
- School of Social Work, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63103, USA
| | - Stephen Edward McMillin
- School of Social Work, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63103, USA
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
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