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Jin Y, Huang Y, Zhang T, Sun Q, Zhang Y, Zhang P, Wang G, Zhang J, Wu J. Associations of dietary total, heme, non-heme iron intake with diabetes, CVD, and all-cause mortality in men and women with diabetes. Heliyon 2024; 10:e38758. [PMID: 39430450 PMCID: PMC11490858 DOI: 10.1016/j.heliyon.2024.e38758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 09/29/2024] [Accepted: 09/29/2024] [Indexed: 10/22/2024] Open
Abstract
Background Iron metabolism disorders significantly increase the risk of diabetes and its related complications by inducing oxidative stress, inflammation, insulin resistance, and disturbances in glucose and lipid metabolism. However, whether dietary iron intake can influence progression of diabetes remains unclear. The present study aims to investigate the relationship between total iron, heme iron, and non-heme iron intake and diabetes, CVD, and all-cause mortality among men and women with diabetes in the U.S. population. Methods A total of 4416 adults with diabetes(2415 men and 2001 women) from the NHANES 2003-2014 were enrolled. Dietary information was collected by 24-h dietary recall during two nonconsecutive days. Dietary total iron intake was measured based on the dietary survey. Dietary heme iron intake was calculated based on its proportion in dietary total iron intake from each food. non-heme iron is the difference between total iron and heme iron. Diabetes, CVD, and all-cause mortality status were identified as main outcomes. Cox models and RCS analysis were performed to estimate the hazard ratios and 95%CIs. Results For men, the participants with a higher dietary heme iron intake were associated with higher risks of CVD (HRheme iron = 1.61,95%CI:1.03-2.51) and all-cause mortality (HRheme iron = 1.42,95%CI:1.10-1.83). For women, participants in the highest quartile of dietary total/heme/non-heme iron intake had a higher diabetes mortality risk ((HRtotal iron = 2.33,95%CI:1.24-4.38; HRheme iron = 1.87,95%CI:1.00-3.49; HRnon-heme iron = 2.28,95%CI:1.19-4.39), compared to those in the lowest quartile. Additionally, the dose-response curve for the relationship between dietary non-heme iron intake and CVD mortality followed a reverse J-shape in women with diabetes. Conclusions Higher dietary heme iron intake was associated with an increased CVD mortality risk in both men and women with diabetes. Additionally, higher dietary total, heme, and non-heme iron intake was linked to an increased risk of diabetes mortality among women with diabetes. Therefore, women with diabetes should pay more attention on the overconsumption of any type of dietary iron.
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Affiliation(s)
- Yimin Jin
- Department of General Practice, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yang Huang
- Wu Lian De Memorial Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tongshuai Zhang
- Department of Neurobiology, Harbin Medical University, Harbin, China
- Ministry of Education Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China, Harbin Medical University, Harbin, China
| | - Qixu Sun
- Department of Digestive System, YANTAI PENGLAI People's Hospital, Yan Tai, China
| | - Yao Zhang
- Department of Neurobiology, Harbin Medical University, Harbin, China
| | - Peiru Zhang
- School of Public Health, Harbin Medical University, Harbin, China
| | - Guangyou Wang
- Department of Neurobiology, Harbin Medical University, Harbin, China
- Ministry of Education Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China, Harbin Medical University, Harbin, China
| | - Jingyu Zhang
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jinrong Wu
- Department of Anaesthesiology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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Fawaz S, Martin Alonso A, Qiu Y, Ramnath R, Stowell-Connolly H, Gamez M, May C, Down C, Coward RJ, Butler MJ, Welsh GI, Satchell SC, Foster RR. Adiponectin Reduces Glomerular Endothelial Glycocalyx Disruption and Restores Glomerular Barrier Function in a Mouse Model of Type 2 Diabetes. Diabetes 2024; 73:964-976. [PMID: 38530908 DOI: 10.2337/db23-0455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/26/2024] [Indexed: 03/28/2024]
Abstract
Adiponectin has vascular anti-inflammatory and protective effects. Although adiponectin protects against the development of albuminuria, historically, the focus has been on podocyte protection within the glomerular filtration barrier (GFB). The first barrier to albumin in the GFB is the endothelial glycocalyx (eGlx), a surface gel-like barrier covering glomerular endothelial cells (GEnCs). In diabetes, eGlx dysfunction occurs before podocyte damage; hence, we hypothesized that adiponectin could protect from eGlx damage to prevent early vascular damage in diabetic kidney disease (DKD). Globular adiponectin (gAd) activated AMPK signaling in human GEnCs through AdipoR1. It significantly reduced eGlx shedding and the tumor necrosis factor-α (TNF-α)-mediated increase in syndecan-4 (SDC4) and MMP2 mRNA expression in GEnCs in vitro. It protected against increased TNF-α mRNA expression in glomeruli isolated from db/db mice and against expression of genes associated with glycocalyx shedding (namely, SDC4, MMP2, and MMP9). In addition, gAd protected against increased glomerular albumin permeability (Ps'alb) in glomeruli isolated from db/db mice when administered intraperitoneally and when applied directly to glomeruli (ex vivo). Ps'alb was inversely correlated with eGlx depth in vivo. In summary, adiponectin restored eGlx depth, which was correlated with improved glomerular barrier function, in diabetes. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Sarah Fawaz
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Aldara Martin Alonso
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Yan Qiu
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Raina Ramnath
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Holly Stowell-Connolly
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Monica Gamez
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Carl May
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Colin Down
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Richard J Coward
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Matthew J Butler
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Gavin I Welsh
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Simon C Satchell
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
| | - Rebecca R Foster
- Bristol Renal, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, U.K
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Guo S, Hua L, Liu W, Liu H, Chen Q, Li Y, Li X, Zhao L, Li R, Zhang Z, Zhang C, Zhu L, Sun H, Zhao H. Multiple metal exposure and metabolic syndrome in elderly individuals: A case-control study in an active mining district, Northwest China. CHEMOSPHERE 2023; 326:138494. [PMID: 36966925 DOI: 10.1016/j.chemosphere.2023.138494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 06/18/2023]
Abstract
The prevalence of metabolic syndrome (MetS) is increasing at an alarming rate worldwide, particularly among elderly individuals. Exposure to various metals has been linked to the development of MetS. However, limited studies have focused attention on the elderly population living in active mining districts. Participants with MetS (N = 292) were matched for age (±2 years old) and sex with a healthy subject (N = 292). We measured the serum levels of 14 metals in older people aged 65-85 years. Conditional logistic regression, restricted cubic spline model, multiple linear regression, and Bayesian Kernel Machine Regression (BKMR) were applied to estimate potential associations between multiple metals and the risk of MetS. Serum levels of Sb and Fe were significantly higher than the controls (0.58 μg/L vs 0.46 μg/L, 2167 μg/L vs 2042 μg/L, p < 0.05), while Mg was significantly lower (20035 μg/L vs 20,394 μg/L, p < 0.05). An increased risk of MetS was associated with higher serum Sb levels (adjusted odds ratio (OR) = 1.61 for the highest tertile vs. the lowest tertile, 95% CI = 1.08-2.40, p-trend = 0.018) and serum Fe levels (adjusted OR = 1.55 for the highest tertile, 95% CI = 1.04-2.33, p-trend = 0.032). Higher Mg levels in serum may have potential protective effects on the development of MetS (adjusted OR = 0.61 for the highest tertile, 95% CI = 0.41-0.91, p-trend = 0.013). A joint exposure analysis by the BKMR model revealed that the mixture of 12 metals (except Tl and Cd) was associated with increased risk of MetS. Our results indicated that exposure to Sb and Fe might increase the risk of MetS in an elderly population living in mining-intensive areas. Further work is needed to confirm the protective effect of Mg on MetS.
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Affiliation(s)
- Sai Guo
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Liting Hua
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Wu Liu
- Jingyuan County Center for Disease Control and Prevention, Baiyin, Gansu, 730699, China
| | - Hongxiu Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China
| | - Qiusheng Chen
- Institute of Agro-product Safety and Nutrition, Tianjin Academy of Agricultural Sciences, Tianjin, 300381, China
| | - Yongcheng Li
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Xiaoxiao Li
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Leicheng Zhao
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Ruoqi Li
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Zining Zhang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Chong Zhang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Lin Zhu
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Hongwen Sun
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Hongzhi Zhao
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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Deng Y, Feng Y, Lv Z, He J, Chen X, Wang C, Yuan M, Xu T, Gao W, Chen D, Zhu H, Hou D. Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer’s disease. Front Aging Neurosci 2022; 14:994130. [PMID: 36262887 PMCID: PMC9575464 DOI: 10.3389/fnagi.2022.994130] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s disease (AD) is a complex, and multifactorial neurodegenerative disease. Previous studies have revealed that oxidative stress, synaptic toxicity, autophagy, and neuroinflammation play crucial roles in the progress of AD, however, its pathogenesis is still unclear. Recent researches have indicated that ferroptosis, an iron-dependent programmed cell death, might be involved in the pathogenesis of AD. Therefore, we aim to screen correlative ferroptosis-related genes (FRGs) in the progress of AD to clarify insights into the diagnostic value. Interestingly, we identified eight FRGs were significantly differentially expressed in AD patients. 10,044 differentially expressed genes (DEGs) were finally identified by differential expression analysis. The following step was investigating the function of DEGs using gene set enrichment analysis (GSEA). Weight gene correlation analysis was performed to explore ten modules and 104 hub genes. Subsequently, based on machine learning algorithms, we constructed diagnostic classifiers to select characteristic genes. Through the multivariable logistic regression analysis, five features (RAF1, NFKBIA, MOV10L1, IQGAP1, FOXO1) were then validated, which composed a diagnostic model of AD. Thus, our findings not only developed genetic diagnostics strategy, but set a direction for further study of the disease pathogenesis and therapy targets.
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Affiliation(s)
- Yanyao Deng
- Department of Rehabilitation, The First Hospital of Changsha, Changsha, China
| | - Yanjin Feng
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Zhicheng Lv
- Department of Neurosurgery, The First People’s Hospital of Chenzhou, Chenzhou, China
| | - Jinli He
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Xun Chen
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Chen Wang
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Mingyang Yuan
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Ting Xu
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Wenzhe Gao
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Dongjie Chen
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Hongwei Zhu
- Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Deren Hou
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Deren Hou,
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High Iron Exposure from the Fetal Stage to Adulthood in Mice Alters Lipid Metabolism. Nutrients 2022; 14:nu14122451. [PMID: 35745181 PMCID: PMC9227341 DOI: 10.3390/nu14122451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 12/11/2022] Open
Abstract
Iron supplementation is recommended during pregnancy and fetal growth. However, excess iron exposure may increase the risk of abnormal fetal development. We investigated the potential side effects of high iron levels in fetuses and through their adult life. C57BL/6J pregnant mice from 2 weeks of gestation and their offspring until 30 weeks were fed a control (CTRL, FeSO4 0 g/1 kg) or high iron (HFe, FeSO4 9.9 g/1 kg) diets. HFe group showed higher iron accumulation in the liver with increased hepcidin, reduced TfR1/2 mRNAs, and lowered ferritin heavy chain (FTH) proteins in both liver and adipose tissues despite iron loading. HFe decreased body weight, fat weight, adipocyte size, and triglyceride levels in the blood and fat, along with downregulation of lipogenesis genes, including PPARγ, C/EBPα, SREBP1c, FASN, and SCD1, and fatty acid uptake and oxidation genes, such as CD36 and PPARα. UCP2, adiponectin, and mRNA levels of antioxidant genes such as GPX4, HO-1, and NQO1 were increased in the HFe group, while total glutathione was reduced. We conclude that prolonged exposure to high iron from the fetal stage to adulthood may decrease fat accumulation by altering ferritin expression, adipocyte differentiation, and triglyceride metabolism, resulting in an alteration in normal growth.
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DDAH1/ADMA Regulates Adiponectin Resistance in Cerebral Ischemia via the ROS/FOXO1/APR1 Pathway. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:2350857. [PMID: 35509834 PMCID: PMC9060971 DOI: 10.1155/2022/2350857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/14/2022] [Accepted: 03/30/2022] [Indexed: 11/27/2022]
Abstract
Dimethylarginine dimethylaminohydrolase 1 (DDAH1) protects against cerebral ischemia injury via regulating the level of asymmetric dimethylarginine (ADMA). This study is aimed at exploring the effect of adiponectin resistance on ADMA-induced neuronal loss in ischemic stroke (IS) and the underlying mechanism. DDAH1 knockout (DDAH1−/−) and wild-type (DDAH1+/+) rats underwent middle cerebral artery occlusion/reperfusion (MCAO/R). Plasma and brain adiponectin levels and the expressions of adiponectin receptor 1 (APR1), adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1 (APPL1), adenosine monophosphate-activated protein kinase (AMPK), and phosphorylated AMPK were determined after 24 h, 3 days, and 7 days. Neurological behavior, infarct volume, and adiponectin signaling were evaluated using adiponectin peptide or AdipoRon. The levels of reactive oxygen species (ROS) and Forkhead box O1 (FOXO1) (a transcription factor for APR1) were also assessed. An oxygen-glucose deprivation/reoxygenation (OGD/R) model was established in primary neurons. DDAH1 was overexpressed in neurons, after which FOXO1 expression, ROS production, adiponectin resistance, and cell viability were detected. DDAH1−/− rats showed no significant difference in adiponectin level in either plasma or brain after MCAO/R in DDAH1+/+ rats, but downregulated APR1 expression and suppressed adiponectin signaling were observed. AdipoRon, but not adiponectin peptide, attenuated the neurological deficits and adiponectin resistance in DDAH1−/− rats. ROS accumulation and phosphorylated FOXO1 expression also increased with DDAH1 depletion. Following DDAH1 overexpression, decreased cell viability and inhibited adiponectin signaling induced by OGD/R were alleviated in primary neurons, accompanied by reduced ROS production and phosphorylated FOXO1 expression. Our study elucidated that in IS, DDAH1 protected against adiponectin resistance in IS via the ROS/FOXO1/APR1 pathway.
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Wang Z, Fang S, Ding S, Tan Q, Zhang X. Research Progress on Relationship Between Iron Overload and Lower Limb Arterial Disease in Type 2 Diabetes Mellitus. Diabetes Metab Syndr Obes 2022; 15:2259-2264. [PMID: 35936055 PMCID: PMC9347475 DOI: 10.2147/dmso.s366729] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/01/2022] [Indexed: 01/07/2023] Open
Abstract
Iron is one of the most important trace elements in life activities. It participates in a variety of important physiological processes in the body through oxidation-reduction reaction. A large number of studies show that iron overload (IO) is closely related to the progression of diabetes and its various chronic complications. However, the mechanism of iron overload in the pathogenesis of diabetes and the mechanism of iron overload in atherosclerosis (AS) are still controversial, and the relationship between iron overload and diabetic lower extremity arterial disease (LEAD) remains still unclear. Some recent reviews and original research articles suggest further studies to explain the complex relationship between iron metabolism and atherosclerosis. This article reviews the relationship between iron overload and diabetes and its relationship with LEAD, and discusses its mechanisms from various aspects, such as lipid peroxidation induced by iron overload, so as to provide clinical diagnosis and treatment ideas for diabetic lower extremity arterial disease. It is hoped that early evaluation, diagnosis and treatment of LEAD will be inspired.
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Affiliation(s)
- Zhongjing Wang
- Department of Endocrinology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People’s Republic of China
| | - Shu Fang
- Department of Endocrinology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People’s Republic of China
- School of Medicine, Jianghan University, Wuhan, 430056, People’s Republic of China
| | - Sheng Ding
- Department of Endocrinology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People’s Republic of China
| | - Qin Tan
- Department of Endocrinology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People’s Republic of China
| | - Xuyan Zhang
- Department of Endocrinology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, People’s Republic of China
- Correspondence: Xuyan Zhang, Department of Endocrinology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, No. 26 of Shengli Street, Jiang’an District, Wuhan, 430014, People’s Republic of China, Tel +86 027 6569 6337, Fax +86 027 8276 1417, Email
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