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Wang YH, Pan W. Serum Bilirubin as a Novel Biomarker of Carotid Atherosclerosis. Angiology 2024; 75:998-999. [PMID: 37694686 DOI: 10.1177/00033197231202026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Affiliation(s)
- Yong-Hong Wang
- Department of Neurology, The Second People's Hospital of Liaocheng, Liaocheng, China
| | - Wei Pan
- Department of Neurology, The Second People's Hospital of Liaocheng, Liaocheng, China
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2
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Ling C, Liu Y, Yao M, Tian L. Positive association between serum bilirubin within the physiological range and serum testosterone levels. BMC Endocr Disord 2024; 24:119. [PMID: 39020370 PMCID: PMC11256393 DOI: 10.1186/s12902-024-01651-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 07/09/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUNDS Research has demonstrated that elevated serum total bilirubin (STB) levels have a beneficial impact on various diseases, particularly metabolic syndrome. This study aims to investigate the association between STB levels and serum testosterone (STT) in order to determine if bilirubin plays a protective role in relation to testosterone deficiency (TD) risk. METHODS In this study, a total of 6,526 eligible male participants aged 20 years or older were analyzed, all of whom took part in the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2016. To investigate the relationship between STB and STT levels, we employed weighted multivariate regression models along with restricted cubic splines (RCS). Additionally, a subgroup analysis was conducted to explore the heterogeneity of this relationship across different subpopulations. RESULTS Among the participants, 1,832 individuals (28.07%) were identified as having testosterone deficiency (TD), defined as an STT level below 300 ng/dL. A significant positive correlation between STB and STT levels was observed in both crude and adjusted models (all P < 0.0001). The association between STB and STT levels was found to be statistically significant up to a threshold of 17.1 µmol/L, after which it became statistically insignificant(P for non-linearity = 0.0035). Weighted logistic regression analysis indicated that a 1 µmol/L increase in STB was associated with a 4% decrease in the likelihood of TD (odds ratio = 0.96, P < 0.0001). Subgroup analysis showed that the inverse relationship was limited to individuals aged 60 and over, non-smokers/drinkers, and obese individuals. CONCLUSION STB within the physiological range(17.1 µmol/L) was positively associated with STT in adult males. The potential protective role of bilirubin regarding testosterone levels merits further exploration.
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Affiliation(s)
- Cunbao Ling
- School of Basic Medical Sciences, Jiangsu Vocational College of Medicine, Yancheng, Jiangsu Province, China
| | - Yadong Liu
- Department of Urology, The Yancheng School of Clinical Medicine of Nanjing Medical University, Yancheng, Jiangsu Province, China
- Department of Urology, The sixth affiliated hospital of Nantong University, Yancheng, Jiangsu Province, China
| | - Meiling Yao
- School of Public Health and Management, Jiangsu Vocational College of Medicine, Yancheng, Jiangsu Province, China
| | - Libing Tian
- School of Basic Medical Sciences, Jiangsu Vocational College of Medicine, Yancheng, Jiangsu Province, China.
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3
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Su Q, Chen H, Du S, Dai Y, Chen C, He 何天敏 T, Feng R, Tao T, Hu Z, Zhao H, Guo P, Ye W. Association Between Serum Bilirubin, Lipid Levels, and Prevalence of Femoral and Carotid Atherosclerosis: A Population-Based Cross-Sectional Study. Arterioscler Thromb Vasc Biol 2023; 43:136-145. [PMID: 36453272 DOI: 10.1161/atvbaha.122.318086] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
BACKGROUND Bilirubin may prevent lipid peroxidation and have important antiatherosclerotic effects. We determined associations of serum bilirubin and lipid with peripheral atherosclerosis. METHODS We included 4290 participants (35% men; median age, 60 years) from the southeast China who underwent B-mode ultrasound examination. Increased intima-media thickness or a focal structure encroaching into the arterial lumen by at least 0.5 mm or >50% of the surrounding intima-media thickness value was regarded as having atherosclerosis. Fasting serum bilirubin and lipid levels were measured. Cholesterol/(HDL [high-density lipoprotein] cholesterol+bilirubin), and LDL (low-density lipoprotein cholesterol)/(HDL+bilirubin) ratios were calculated. Unconditional and multinomial logistic regression models were used to examine associations of bilirubin or lipid with prevalence of peripheral atherosclerosis. Mediation analyses were performed to assess the effect of bilirubin on atherosclerosis risk mediated via lipid. RESULTS Compared with participants with the lowest levels of bilirubin, those with the highest tertile were less likely to have carotid or femoral atherosclerosis (odds ratios were 0.55-0.74). The highest levels of bilirubin significantly reduced the odds of concurrent carotid and femoral atherosclerosis by 35% to 45%. Participants with the highest levels of cholesterol, LDL, cholesterol/(HDL+bilirubin), and LDL/(HDL+bilirubin) ratios had 2.8- to 3.7-fold increased odds of concurrent carotid and femoral atherosclerosis. LDL accounted for 25.65% of the total bilirubin-atherosclerosis association. LDL and cholesterol mediated the associations between direct bilirubin and atherosclerosis (proportion: 20.40%, 9.67%, respectively). CONCLUSIONS Increased serum bilirubin levels are inversely associated with the prevalence of carotid or femoral atherosclerosis. LDL and cholesterol may mediate these associations.
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Affiliation(s)
- Qingling Su
- Department of Epidemiology and Health Statistics, School of Public Health (Q.S., S.D., R.F., T.T., Z.H., W.Y.), Fujian Medical University, Fuzhou, China
| | - Hongyu Chen
- Department of Vascular Surgery, the First Affiliated Hospital (H.C., Y.D., C.C., T.H., P.G.), Fujian Medical University, Fuzhou, China
| | - Shanshan Du
- Department of Epidemiology and Health Statistics, School of Public Health (Q.S., S.D., R.F., T.T., Z.H., W.Y.), Fujian Medical University, Fuzhou, China
| | - Yiquan Dai
- Department of Vascular Surgery, the First Affiliated Hospital (H.C., Y.D., C.C., T.H., P.G.), Fujian Medical University, Fuzhou, China
| | - Cheng Chen
- Department of Vascular Surgery, the First Affiliated Hospital (H.C., Y.D., C.C., T.H., P.G.), Fujian Medical University, Fuzhou, China
| | - Tianmin He 何天敏
- Department of Vascular Surgery, the First Affiliated Hospital (H.C., Y.D., C.C., T.H., P.G.), Fujian Medical University, Fuzhou, China
| | - Ruimei Feng
- Department of Epidemiology and Health Statistics, School of Public Health (Q.S., S.D., R.F., T.T., Z.H., W.Y.), Fujian Medical University, Fuzhou, China
| | - Tao Tao
- Department of Epidemiology and Health Statistics, School of Public Health (Q.S., S.D., R.F., T.T., Z.H., W.Y.), Fujian Medical University, Fuzhou, China
| | - Zhijian Hu
- Department of Epidemiology and Health Statistics, School of Public Health (Q.S., S.D., R.F., T.T., Z.H., W.Y.), Fujian Medical University, Fuzhou, China
| | - Hongwei Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, College Station (H.Z.)
| | - Pingfan Guo
- Department of Vascular Surgery, the First Affiliated Hospital (H.C., Y.D., C.C., T.H., P.G.), Fujian Medical University, Fuzhou, China
| | - Weimin Ye
- Department of Epidemiology and Health Statistics, School of Public Health (Q.S., S.D., R.F., T.T., Z.H., W.Y.), Fujian Medical University, Fuzhou, China.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (W.Y.)
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4
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Jang SA, Kim KM, Park SW, Kim CS. Risk of Carotid Atherosclerosis in Subjects with Prediabetes Overlapping Metabolic Syndrome. Metab Syndr Relat Disord 2022; 20:599-605. [PMID: 36251877 DOI: 10.1089/met.2022.0040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Background: While the number of individuals with prediabetes and metabolic syndrome (MetS) is increasing, only a few studies have reported differences in cardiovascular risk according to the presence or absence of MetS in individuals with prediabetes. Here, we examined differences in carotid intima-media thickness (CIMT) and carotid plaques in individuals with prediabetes with or without MetS among subjects who visited a single center in Seoul (Huh Diabetes Center). Methods: A total of 328 participants aged ≥20 years, including the group with normoglycemia, were enrolled in the analysis, of which 273 had prediabetes. Individuals with prediabetes were defined as those who met one or more of the following two criteria: fasting plasma glucose of 100-125 mg/dL and/or HbA1c level of 5.7%-6.4%. Carotid atherosclerosis was determined by mean and maximal CIMT and by the presence of carotid plaques. Results: Eighty-nine subjects (32.6% of prediabetes group) were categorized as having MetS. Those with MetS had significantly higher mean CIMT and maximal CIMT than those without (P < 0.05). Moreover, the group with MetS had a significantly higher prevalence of carotid plaques than the group without MetS [odds ratio (OR): 2.45, 95% confidence interval (CI): 1.43-4.19; P = 0.001]. After adjusting for age, sex, body mass index, and low-density lipoprotein cholesterol, individuals with MetS still had greater mean and maximal CIMT than individuals without MetS (P < 0.05), and the presence of MetS was significantly associated with a higher risk of carotid plaques (OR: 2.55, 95% CI: 1.06-6.15; P = 0.037). Conclusion: These results suggest that MetS is independently associated with increased CIMT and the presence of carotid plaques in prediabetes. Our study indicates that the risk of cardiovascular disease (CVD) is high in prediabetic individuals with MetS, and that more attention is needed on the risk of CVD in these individuals.
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Affiliation(s)
- Seol A Jang
- Division of Endocrinology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Kyoung Min Kim
- Division of Endocrinology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Seok Won Park
- Division of Endocrinology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Chul Sik Kim
- Division of Endocrinology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
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5
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Kim KS, Hong S, Hwang YC, Ahn HY, Park CY. Evaluating Triglyceride and Glucose Index as a Simple and Easy-to-Calculate Marker for All-Cause and Cardiovascular Mortality. J Gen Intern Med 2022; 37:4153-4159. [PMID: 35676587 PMCID: PMC9708968 DOI: 10.1007/s11606-022-07681-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/20/2022] [Indexed: 01/04/2023]
Abstract
OBJECTIVE The triglyceride and glucose (TyG) index is a useful marker of insulin resistance and is a predictor of several metabolic diseases. The aim of this study was to evaluate the association between the TyG index and all-cause or cardiovascular mortality using a large population-based cohort study database. METHODS A total of 255,508 subjects in the Kangbuk Samsung Health Study cohort were enrolled. Cox proportional hazards models were used to analyze the risk of mortality. RESULTS During a median 5.7-year follow-up, the cumulative all-cause and cardiovascular mortality was 0.47% and 0.07%. There was a nonlinear relationship between the TyG index and death, and moving from moderate to high, the TyG index levels were associated with an increase in the risk of death. The hazard ratio (HR) for all-cause and cardiovascular mortality of the TyG index was 1.21 [95% confidence interval (CI) 1.14-1.28] and 1.45 (95% CI 1.26-1.66) in the unadjusted model, respectively. After adjustment for covariates, the association between the TyG index and all-cause and cardiovascular mortality was attenuated. In the multivariable-adjusted model, the TyG index was associated with an elevated risk of all-cause mortality in women (HR 1.13, 95% CI 1.02-1.26) and a decreased risk in men (HR 0.92, 95% CI 0.85-0.99). The association between cardiovascular mortality and the TyG index was not statistically significant among either men or women in the multivariable-adjusted model. CONCLUSIONS The TyG index in a young, relatively healthy, population is associated with an elevated risk of all-cause and cardiovascular mortality. This association between the TyG index and all-cause mortality persists in women after multivariable adjustment.
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Affiliation(s)
- Kyung-Soo Kim
- Department of Internal Medicine, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Sangmo Hong
- Department of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Republic of Korea
| | - You-Cheol Hwang
- Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University School of Medicine, Seoul, Republic of Korea
| | - Hong-Yup Ahn
- Department of Statistics, Dongguk University-Seoul, Seoul, Republic of Korea
| | - Cheol-Young Park
- Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea.
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6
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Qin L, Li L, Fan H, Gu Y, He W, Zhang K, Sun Y, Zhao W, Niu X, Wei C, Li L, Wang H. Longitudinal Associations Between Serum Bilirubin Level and Carotid Atherosclerosis Plaque in a Health Screening Population. Angiology 2022; 74:452-460. [PMID: 35759358 DOI: 10.1177/00033197221110966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This study aimed to determine the relationship between bilirubin levels and carotid atherosclerosis (CAS) in the health screening population. After propensity score matching, this retrospective cohort study included 4360 subjects who underwent health examinations regularly in Hebei General Hospital between January 2010 and December 2019 and had no carotid plaque at baseline. After an average follow-up of 26.76 months, the main endpoint Cox regression analysis of carotid plaques was performed. After adjusting the confounding factors, Cox regression analysis showed that when serum total bilirubin (TBIL) and unconjugated bilirubin (UCB) increased by 1 standard deviation (SD), the risk of carotid plaque decreased by 7.30% (95% confidence interval (CI): 2.80-11.60%) and 15.70% (95% CI: 11.40-19.80%), respectively. When conjugated bilirubin (CB) increased by 1 SD, the risk of carotid plaques increased by 24.3% (95% CI: 19.7-29.0%). TBIL and UCB levels were negatively associated with CAS, and CB levels were positively associated with CAS.
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Affiliation(s)
- Lu Qin
- Graduate school, 12553Hebei Medical University, Shijiazhuang, China.,Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Lin Li
- Graduate school, 12553Hebei Medical University, Shijiazhuang, China.,Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Hongzhen Fan
- Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Yongsheng Gu
- Graduate school, 12553Hebei Medical University, Shijiazhuang, China.,Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Weiliang He
- Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Kaihua Zhang
- Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Yingru Sun
- Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Wannian Zhao
- Graduate school, 12553Hebei Medical University, Shijiazhuang, China.,Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Xiaoli Niu
- Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Ci Wei
- Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Litao Li
- Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
| | - Hebo Wang
- Graduate school, 12553Hebei Medical University, Shijiazhuang, China.,Department of Neurology, 117872Hebei General Hospital, Shijiazhuang, China
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7
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Li RJ, Jie ZY, Feng Q, Fang RL, Li F, Gao Y, Xia HH, Zhong HZ, Tong B, Madsen L, Zhang JH, Liu CL, Xu ZG, Wang J, Yang HM, Xu X, Hou Y, Brix S, Kristiansen K, Yu XL, Jia HJ, He KL. Network of Interactions Between Gut Microbiome, Host Biomarkers, and Urine Metabolome in Carotid Atherosclerosis. Front Cell Infect Microbiol 2021; 11:708088. [PMID: 34692558 PMCID: PMC8529068 DOI: 10.3389/fcimb.2021.708088] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/21/2021] [Indexed: 01/06/2023] Open
Abstract
Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers concerning distinct clinical features. We integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite feature were covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. In a specific note about clinical parameters of liver function, we identified Eubacteriumeligens, Faecalibacteriumprausnitzii and Ruminococcuslactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerellanexills, Ruminococcusgnavus, Blautiahansenii, and Atopobiumparvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases. Deciphering the complexity of human physiology and disease requires a holistic and trans-omics approach integrating multi-layer data sets, including the gut microbiome and profiles of biological fluids. By studying the gut microbiome on carotid atherosclerosis, we identified microbial features associated with clinical parameters, and we observed that groups of urine metabolites correlated with groups of clinical parameters. Combining the three data sets, we revealed correlations of entities across the three systems, suggesting that physiological changes are reflected in each of the omics. Our findings provided insights into the interactive network between the gut microbiome, blood clinical parameters and the urine metabolome concerning physiological variations, and showed the promise of trans-omics study for biomarker discovery.
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Affiliation(s)
- Rui-Jun Li
- Department of Geriatric Cardiology, the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.,Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Zhu-Ye Jie
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Qiang Feng
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, Shenzhen, China.,Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark.,Department of Human Microbiome, School of Stomatology, Shandong University, Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Jinan, China
| | - Rui-Ling Fang
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Fei Li
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Yuan Gao
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Hui-Hua Xia
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Huan-Zi Zhong
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark
| | - Bin Tong
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Lise Madsen
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark.,Institute Marine Research (IMR), Bergen, Norway
| | - Jia-Hao Zhang
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Chun-Lei Liu
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Zhen-Guo Xu
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China
| | - Jian Wang
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Huan-Ming Yang
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Xun Xu
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Yong Hou
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Susanne Brix
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Karsten Kristiansen
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Department of Biology, Laboratory of Genomics and Molecular Biomedicine, University of Copenhagen, Copenhagen, Denmark
| | - Xin-Lei Yu
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China
| | - Hui-Jue Jia
- Department of Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,China National GeneBank, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.,Macau University of Science and Technology, Macau, China
| | - Kun-Lun He
- Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, China.,Analysis Center of Big Data, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
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