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Zhao Z, Yang X, Dorn S, Miao J, Barcellos SH, Fletcher JM, Lu Q. Controlling for polygenic genetic confounding in epidemiologic association studies. Proc Natl Acad Sci U S A 2024; 121:e2408715121. [PMID: 39432782 DOI: 10.1073/pnas.2408715121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 09/20/2024] [Indexed: 10/23/2024] Open
Abstract
Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.
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Affiliation(s)
- Zijie Zhao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
| | - Xiaoyu Yang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
| | - Stephen Dorn
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
| | - Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
| | - Silvia H Barcellos
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA 90089
- Department of Economics, University of Southern California, Los Angeles, CA 90089
| | - Jason M Fletcher
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI 53706
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706
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2
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Zhang L, Mao H, Zhou R, Zhu J, Wang H, Miao Z, Chen X, Yan J, Jiang H. Low blood S-methyl-5-thioadenosine is associated with postoperative delayed neurocognitive recovery. Commun Biol 2024; 7:1356. [PMID: 39428444 PMCID: PMC11491466 DOI: 10.1038/s42003-024-07086-5] [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: 01/19/2024] [Accepted: 10/15/2024] [Indexed: 10/22/2024] Open
Abstract
Elderly individuals display metabolite alterations that may contribute to development of cognitive impairment following surgery and anesthesia. However, these relationships remain largely unexplored. The study aims to assess the S-methyl-5-thioadenosine (MTA) is associated with postoperative delayed neurocognitive recovery (dNCR). We assess altered metabolites following anesthesia/surgery in both mice and patients to identify blood biomarkers of dNCR. Preoperative and postoperative plasma metabolites are determined by widely targeted metabolomics. The brains of mice with anesthesia/surgery show decreased MTA and activated MTA phosphorylase. Mice also show that preoperative administration of MTA can prevent inflammation and cognitive decline. In clinical patients, we detect lower preoperative serum MTA levels in those who developed dNCR. Both low preoperative and postoperative blood MTA levels are associated with increased risk of postoperative dNCR. These results suggest that anesthesia/surgery induces cognitive decline through methionine synthesis pathways and that MTA could be a perioperative predictor of dNCR.
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Affiliation(s)
- Lei Zhang
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China.
| | - Haoli Mao
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Ren Zhou
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiao Zhu
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Wang
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhengjie Miao
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao Chen
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Yan
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong Jiang
- Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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3
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Lu C, Liu D, Wu Q, Zeng J, Xiong Y, Luo T. EphA2 blockage ALW-II-41-27 alleviates atherosclerosis by remodeling gut microbiota to regulate bile acid metabolism. NPJ Biofilms Microbiomes 2024; 10:108. [PMID: 39426981 PMCID: PMC11490535 DOI: 10.1038/s41522-024-00585-7] [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: 06/13/2024] [Accepted: 10/10/2024] [Indexed: 10/21/2024] Open
Abstract
Coronary artery disease (CAD), a critical condition resulting from systemic inflammation, metabolic dysfunction, and gut microbiota dysbiosis, poses a global public health challenge. ALW-II-41-27, a specific inhibitor of the EphA2 receptor, has shown anti-inflammatory prosperities. However, the impact of ALW-II-41-27 on atherosclerosis has not been elucidated. This study aimed to examine the roles of pharmacologically inhibiting EphA2 and the underlying mechanism in ameliorating atherosclerosis. ALW-II-41-27 was administered to apoE-/- mice fed a high-fat diet via intraperitoneal injection. We first discovered that ALW-II-41-27 led to a significant reduction in atherosclerotic plaques, evidenced by reduced lipid and macrophage accumulation, alongside an increase in collagen and smooth muscle cell content. ALW-II-41-27 also significantly lowered plasma and hepatic cholesterol levels, as well as the colonic inflammation. Furthermore, gut microbiota was analyzed by metagenomics and plasma metabolites by untargeted metabolomics. ALW-II-41-27-treated mice enriched Enterococcus, Akkermansia, Eggerthella and Lactobaccilus, accompanied by enhanced secondary bile acids production. To explore the causal link between ALW-II-41-27-associated gut microbiota and atherosclerosis, fecal microbiota transplantation was employed. Mice that received ALW-II-41-27-treated mouse feces exhibited the attenuated atherosclerotic plaque. In clinical, lower plasma DCA and HDCA levels were determined in CAD patients using quantitative metabolomics and exhibited a negative correlation with higher monocytes EphA2 expression. Our findings underscore the potential of ALW-II-41-27 as a novel therapeutic agent for atherosclerosis, highlighting its capacity to modulate gut microbiota composition and bile acid metabolism, thereby offering a promising avenue for CAD.
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Affiliation(s)
- Cong Lu
- Department of Cardiology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Dan Liu
- Department of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Qiao Wu
- Department of Cardiology, Huiqiao Medical Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Jie Zeng
- Department of Cardiology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Xiong
- Department of Cardiology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Tiantian Luo
- Department of Cardiology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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4
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Mo Y, Bier R, Li X, Daniels M, Smith A, Yu L, Kan J. Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning. Commun Biol 2024; 7:1349. [PMID: 39424928 PMCID: PMC11489707 DOI: 10.1038/s42003-024-07059-8] [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: 03/27/2024] [Accepted: 10/11/2024] [Indexed: 10/21/2024] Open
Abstract
Agricultural practices affect soil microbes which are critical to soil health and sustainable agriculture. To understand prokaryotic and fungal assembly under agricultural practices, we use machine learning-based methods. We show that fertility source is the most pronounced factor for microbial assembly especially for fungi, and its effect decreases with soil depths. Fertility source also shapes microbial co-occurrence patterns revealed by machine learning, leading to fungi-dominated modules sensitive to fertility down to 30 cm depth. Tillage affects soil microbiomes at 0-20 cm depth, enhancing dispersal and stochastic processes but potentially jeopardizing microbial interactions. Cover crop effects are less pronounced and lack depth-dependent patterns. Machine learning reveals that the impact of agricultural practices on microbial communities is multifaceted and highlights the role of fertility source over the soil depth. Machine learning overcomes the linear limitations of traditional methods and offers enhanced insights into the mechanisms underlying microbial assembly and distributions in agriculture soils.
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Affiliation(s)
- Yujie Mo
- Sino-French Engineer School, Beihang University, Beijing, China
| | - Raven Bier
- Stroud Water Research Center, Avondale, PA, USA
- Savannah River Ecology Laboratory, University of Georgia, Aiken, SC, USA
| | - Xiaolin Li
- Zibo Vocational Institute, Zibo, Shandong, China
| | | | | | - Lei Yu
- Sino-French Engineer School, Beihang University, Beijing, China.
| | - Jinjun Kan
- Stroud Water Research Center, Avondale, PA, USA.
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5
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Zhang S, Wang Z, Wang Y, Zhu Y, Zhou Q, Jian X, Zhao G, Qiu J, Xia K, Tang B, Mutz J, Li J, Li B. A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank. Nat Commun 2024; 15:8081. [PMID: 39278973 PMCID: PMC11402978 DOI: 10.1038/s41467-024-52310-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 09/02/2024] [Indexed: 09/18/2024] Open
Abstract
The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate "biomarker - disease" causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.
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Affiliation(s)
- Shiyu Zhang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China
| | - Zheng Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yijing Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Yixiao Zhu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Qiao Zhou
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Xingxing Jian
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Guihu Zhao
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
| | - Jian Qiu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Kun Xia
- MOE Key Laboratory of Pediatric Rare Diseases & Hunan Key Laboratory of Medical Genetics, Central South University, Changsha, Hunan, 410008, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Department of Neurology & Multi-omics Research Center for Brain Disorders, The First Affiliated Hospital University of South China, Hengyang, Hunan, China
| | - Julian Mutz
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Jinchen Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China.
- Key Laboratory of Hunan Province in Neurodegenerative Disorders, Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
- Bioinformatics Center, Xiangya Hospital & Furong Laboratory, Changsha, Hunan, 410008, China.
| | - Bin Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital & Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, China.
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6
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Li Y, Han S. Metabolomic Applications in Gut Microbiota-Host Interactions in Human Diseases. Gastroenterol Clin North Am 2024; 53:383-397. [PMID: 39068001 DOI: 10.1016/j.gtc.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The human gut microbiota, consisting of trillions of microorganisms, encodes diverse metabolic pathways that impact numerous aspects of host physiology. One key way in which gut bacteria interact with the host is through the production of small metabolites. Several of these microbiota-dependent metabolites, such as short-chain fatty acids, have been shown to modulate host diseases. In this review, we examine how disease-associated metabolic signatures are identified using metabolomic platforms, and where metabolomics is applied in gut microbiota-disease interactions. We further explore how integration of metagenomic and metabolomic data in human studies can facilitate biomarkers discoveries in precision medicine.
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Affiliation(s)
- Yuxin Li
- Biochemistry Graduate Program, Duke University School of Medicine, Durham, NC 27710, USA
| | - Shuo Han
- Department of Biochemistry, Duke University School of Medicine, Durham, NC 27710, USA; Duke Microbiome Center, Duke University School of Medicine, Durham, NC 27710, USA; Department of Molecular Genetics and Microbiology, Duke University School of Medicine, NC 27710, USA.
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7
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Tian S, Ding T, Li H. Oral microbiome in human health and diseases. MLIFE 2024; 3:367-383. [PMID: 39359681 PMCID: PMC11442140 DOI: 10.1002/mlf2.12136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 04/13/2024] [Accepted: 05/05/2024] [Indexed: 10/04/2024]
Abstract
The oral cavity contains the second-largest microbiota in the human body. The cavity's anatomically and physiologically diverse niches facilitate a wide range of symbiotic bacteria living at distinct oral sites. Consequently, the oral microbiota exhibits site specificity, with diverse species, compositions, and structures influenced by specific aspects of their placement. Variations in oral microbiota structure caused by changes in these influencing factors can impact overall health and lead to the development of diseases-not only in the oral cavity but also in organs distal to the mouth-such as cancer, cardiovascular disease, and respiratory disease. Conversely, diseases can exacerbate the imbalance of the oral microbiota, creating a vicious cycle. Understanding the heterogeneity of both the oral microbiome and individual humans is important for investigating the causal links between the oral microbiome and diseases. Additionally, understanding the intricacies of the oral microbiome's composition and regulatory factors will help identify the potential causes of related diseases and develop interventions to prevent and treat illnesses in this domain. Therefore, turning to the extant research in this field, we systematically review the relationship between oral microbiome dynamics and human diseases.
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Affiliation(s)
- Siqi Tian
- Department of Immunology and Microbiology, Zhongshan School of Medicine Sun Yat-Sen University Guangzhou China
- Key Laboratory of Tropical Diseases Control (Sun Yat-Sen University) Ministry of Education Guangzhou China
| | - Tao Ding
- Department of Immunology and Microbiology, Zhongshan School of Medicine Sun Yat-Sen University Guangzhou China
- Key Laboratory of Tropical Diseases Control (Sun Yat-Sen University) Ministry of Education Guangzhou China
- Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-sen University) Ministry of Education, China Guangzhou China
| | - Hui Li
- Department of Immunology and Microbiology, Zhongshan School of Medicine Sun Yat-Sen University Guangzhou China
- Key Laboratory of Tropical Diseases Control (Sun Yat-Sen University) Ministry of Education Guangzhou China
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024; 24:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [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: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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Byndloss M, Devkota S, Duca F, Niess JH, Nieuwdorp M, Orho-Melander M, Sanz Y, Tremaroli V, Zhao L. The gut microbiota and diabetes: research, translation, and clinical applications - 2023 Diabetes, Diabetes Care, and Diabetologia Expert Forum. Diabetologia 2024; 67:1760-1782. [PMID: 38910152 PMCID: PMC11410996 DOI: 10.1007/s00125-024-06198-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/23/2024] [Indexed: 06/25/2024]
Abstract
This article summarises the state of the science on the role of the gut microbiota (GM) in diabetes from a recent international expert forum organised by Diabetes, Diabetes Care, and Diabetologia, which was held at the European Association for the Study of Diabetes 2023 Annual Meeting in Hamburg, Germany. Forum participants included clinicians and basic scientists who are leading investigators in the field of the intestinal microbiome and metabolism. Their conclusions were as follows: (1) the GM may be involved in the pathophysiology of type 2 diabetes, as microbially produced metabolites associate both positively and negatively with the disease, and mechanistic links of GM functions (e.g. genes for butyrate production) with glucose metabolism have recently emerged through the use of Mendelian randomisation in humans; (2) the highly individualised nature of the GM poses a major research obstacle, and large cohorts and a deep-sequencing metagenomic approach are required for robust assessments of associations and causation; (3) because single time point sampling misses intraindividual GM dynamics, future studies with repeated measures within individuals are needed; and (4) much future research will be required to determine the applicability of this expanding knowledge to diabetes diagnosis and treatment, and novel technologies and improved computational tools will be important to achieve this goal.
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Affiliation(s)
- Mariana Byndloss
- Vanderbilt University Medical Center, Nashville, TN, USA
- Howard Hughes Medical Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Suzanne Devkota
- Cedars-Sinai Medical Center, Human Microbiome Research Institute, Los Angeles, CA, USA
| | | | - Jan Hendrik Niess
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Department of Gastroenterology and Hepatology, University Digestive Healthcare Center, Clarunis, Basel, Switzerland
| | - Max Nieuwdorp
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
- Amsterdam Diabeter Center, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Marju Orho-Melander
- Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Yolanda Sanz
- Institute of Agrochemistry and Food Technology, Spanish National Research Council (IATA-CSIC), Valencia, Spain.
| | - Valentina Tremaroli
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Liping Zhao
- Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ, USA
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10
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Uche-Anya E, Ha J, Balasubramanian R, Rexrode KM, Chan AT. Metabolomic profiles of incident gallstone disease. BMJ Open Gastroenterol 2024; 11:e001417. [PMID: 39209332 PMCID: PMC11367368 DOI: 10.1136/bmjgast-2024-001417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/26/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND AND AIMS Gallstone disease affects ≥40 million people in the USA and accounts for health costs of ≥$4 billion a year. Risk factors such as obesity and metabolic syndrome are well established. However, data are limited on relevant metabolomic alterations that could offer mechanistic and predictive insights into gallstone disease. This study prospectively identifies and externally validates circulating prediagnostic metabolites associated with incident gallstone disease. METHODS Female participants in Nurses' Health Study (NHS) and Nurses' Health Study II (NHS II) who were free of known gallstones (N=9960) were prospectively followed up after baseline metabolomic profiling with liquid chromatography-tandem mass spectrometry. Multivariable logistic regression and enrichment analysis were used to identify metabolites and metabolite groups associated with incident gallstone disease at PFDR<0.05. Findings were validated in 1866 female participants in the Women's Health Initiative and a comparative analysis was performed with 2178 male participants in the Health Professionals Follow-up Study. RESULTS After multivariate adjustment for lifestyle and putative risk factors, we identified and externally validated 17 metabolites associated with incident gallstone disease in women-nine triacylglycerols (TAGs) and diacylglycerols (DAGs) were positively associated, while eight plasmalogens and cholesterol ester (CE) were negatively associated. Enrichment analysis in male and female cohorts revealed positive class associations with DAGs, TAGs (≤56 carbon atoms and ≤3 double bonds) and de novo TAG biosynthesis pathways, as well as inverse associations with CEs. CONCLUSIONS This study highlights several metabolites (TAGs, DAGs, plasmalogens and CE) that could be implicated in the aetiopathogenesis of gallstone disease and serve as clinically relevant markers.
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Affiliation(s)
- Eugenia Uche-Anya
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jane Ha
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Kathryn M Rexrode
- Division of Women's Health, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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Adolph TE, Tilg H. Western diets and chronic diseases. Nat Med 2024; 30:2133-2147. [PMID: 39085420 DOI: 10.1038/s41591-024-03165-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/28/2024] [Indexed: 08/02/2024]
Abstract
'Westernization', which incorporates industrial, cultural and dietary trends, has paralleled the rise of noncommunicable diseases across the globe. Today, the Western-style diet emerges as a key stimulus for gut microbial vulnerability, chronic inflammation and chronic diseases, affecting mainly the cardiovascular system, systemic metabolism and the gut. Here we review the diet of modern times and evaluate the threat it poses for human health by summarizing recent epidemiological, translational and clinical studies. We discuss the links between diet and disease in the context of obesity and type 2 diabetes, cardiovascular diseases, gut and liver diseases and solid malignancies. We collectively interpret the evidence and its limitations and discuss future challenges and strategies to overcome these. We argue that healthcare professionals and societies must react today to the detrimental effects of the Western diet to bring about sustainable change and improved outcomes in the future.
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Affiliation(s)
- Timon E Adolph
- Department of Internal Medicine I, Gastroenterology, Hepatology, Endocrinology and Metabolism, Medical University of Innsbruck, Innsbruck, Austria.
| | - Herbert Tilg
- Department of Internal Medicine I, Gastroenterology, Hepatology, Endocrinology and Metabolism, Medical University of Innsbruck, Innsbruck, Austria.
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12
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Nam Y, Kim J, Jung SH, Woerner J, Suh EH, Lee DG, Shivakumar M, Lee ME, Kim D. Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. Annu Rev Biomed Data Sci 2024; 7:225-250. [PMID: 38768397 DOI: 10.1146/annurev-biodatasci-102523-103801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.
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Affiliation(s)
- Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Jaesik Kim
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Erica H Suh
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Dong-Gi Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Matthew E Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Dokyoon Kim
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
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He S, Wang J, Zhou L, Mao Z, Zhang X, Cai J, Huang P. Enhanced hepatic metabolic perturbation of polystyrene nanoplastics by UV irradiation-induced hydroxyl radical generation. J Environ Sci (China) 2024; 142:259-268. [PMID: 38527891 DOI: 10.1016/j.jes.2023.06.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/24/2023] [Accepted: 06/25/2023] [Indexed: 03/27/2024]
Abstract
The environmental behavior of and risks associated with nanoplastics (NPs) have attracted considerable attention. However, compared to pristine NPs, environmental factors such as ultraviolet (UV) irradiation that lead to changes in the toxicity of NPs have rarely been studied. We evaluated the changes in morphology and physicochemical properties of polystyrene (PS) NPs before and after UV irradiation, and compared their hepatotoxicity in mice. The results showed that UV irradiation caused particle size reduction and increased the carbonyl index (CI) and negative charge on the particle surface. UV-aged PS NPs (aPS NPs) could induce the generation of hydroxyl radicals (·OH), but also further promoted the generation of ·OH in the Fenton reaction system. Hepatic pathological damage was more severe in mice exposed to aPS NPs, accompanied by a large number of vacuoles and hepatocyte balloon-like changes and more marked perturbations in blood glucose and serum lipoprotein, alanine aminotransferase and aspartate aminotransferase levels. In addition, exposure to PS NPs and aPS NPs, especially aPS NPs, triggered oxidative stress and significantly damaged the antioxidant capacity of mice liver. Compared with PS NPs, exposure to aPS NPs increased the number of altered metabolites in hepatic and corresponding metabolic pathways, especially glutathione metabolism. Our research suggests that UV irradiation can disrupt the redox balance in organisms by promoting the production of ·OH, enhancing PS NPs-induced liver damage and metabolic disorders. This study will help us understand the health risks of NPs and to avoid underestimation of the risks of NPs in nature.
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Affiliation(s)
- Shiyu He
- School of Public Health and Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China
| | - Jingran Wang
- School of Public Health and Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China
| | - Lihong Zhou
- School of Public Health and Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China
| | - Zhen Mao
- School of Public Health and Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China
| | - Xiaodan Zhang
- School of Public Health and Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China
| | - Jin Cai
- School of Public Health and Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China
| | - Peili Huang
- School of Public Health and Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, China.
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Tan L, She H, Wang Y, Du Y, Zhang J, Du Y, Wu Y, Chen W, Huang B, Long D, Peng X, Li Q, Mao Q, Li T, Hu Y. The New Nano-Resuscitation Solution (TPP-MR) Attenuated Myocardial Injury in Hemorrhagic Shock Rats by Inhibiting Ferroptosis. Int J Nanomedicine 2024; 19:7567-7583. [PMID: 39081897 PMCID: PMC11287375 DOI: 10.2147/ijn.s463121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/21/2024] [Indexed: 08/02/2024] Open
Abstract
Background Hemorrhagic shock was a leading cause of death worldwide, with myocardial injury being a primary affected organ. As commonly used solutions in fluid resuscitation, acetated Ringer's (AR) and Lactate Ringer's solution (LR) were far from perfect for their adverse reactions such as lactic acidosis and electrolyte imbalances. In previous studies, TPP@PAMAM-MR (TPP-MR), a novel nanocrystal resuscitation fluid has been found to protect against myocardial injury in septic rats. However, its role in myocardial injury in rats with hemorrhagic shock and underlying mechanism is unclear. Methods The hemorrhagic shock rats and hypoxia-treated cardiomyocytes (H9C2) were utilized to investigate the impact of TPP-MR on cardiac function, mitochondrial function, and lipid peroxidation. The expressions of ferritin-related proteins glutathione peroxidase 4 (GPX4), Acyl CoA Synthase Long Chain Family Member 4 (ACSL4), and Cyclooxygenase-2(COX2) were analyzed through Western blotting to explore the mechanism of TPP-MR on hemorrhagic myocardial injury. Results TPP-MR, a novel nanocrystalline resuscitation fluid, was synthesized using TPP@PAMAM@MA as a substitute for L-malic acid. We found that TPP-MR resuscitation significantly reduced myocardial injury reflected by enhancing cardiac output, elevating mean arterial pressure (MAP), and improving perfusion. Moreover, TPP-MR substantially prolonged hemorrhagic shock rats' survival time and survival rate. Further investigations indicated that TPP-MR improved the mitochondrial function of myocardial cells, mitigated the production of oxidative stress agents (ROS) and increased the glutathione (GSH) content. Additionally, TPP-MR inhibited the expression of the ferroptosis-associated GPX4 protein, ACSL4 and COX2, thereby enhancing the antioxidant capacity. Conclusion The results showed that TPP-MR had a protective effect on myocardial injury in rats with hemorrhagic shock, and its mechanism might be related to improving the mitochondrial function of myocardial cells and inhibiting the process of ferroptosis.
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Affiliation(s)
- Lei Tan
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Han She
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Yi Wang
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Yuanlin Du
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Jun Zhang
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Yunxia Du
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Yinyu Wu
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Wei Chen
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Bingqiang Huang
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Duanyang Long
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Xiaoyong Peng
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Qinghui Li
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Qingxiang Mao
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Tao Li
- State Key Laboratory of Trauma, Burns and Combined Injury, Shock and Transfusion Department, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
| | - Yi Hu
- Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
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Wu G, Liao J, Zhu X, Zhang Y, Lin Y, Zeng Y, Zhao J, Zhang J, Yao T, Shen X, Li H, Hu L, Zhang W. Shexiang Baoxin Pill enriches Lactobacillus to regulate purine metabolism in patients with stable coronary artery disease. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 130:155727. [PMID: 38781732 DOI: 10.1016/j.phymed.2024.155727] [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: 12/07/2023] [Revised: 04/29/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND It has been clinically confirmed that the Shexiang Baoxin Pill (SBP) dramatically reduces the frequency of angina in patients with stable coronary artery disease (SCAD). However, potential therapeutic mechanism of SBP has not been fully explored. PURPOSE The study explored the therapeutic mechanism of SBP in the treatment of SCAD patients. METHODS We examined the serum metabolic profiles of patients with SCAD following SBP treatment. A rat model of acute myocardial infarction (AMI) was established, and the potential therapeutic mechanism of SBP was explored using metabolomics, transcriptomics, and 16S rRNA sequencing. RESULTS SBP decreased inosine production and improved purine metabolic disorders in patients with SCAD and in animal models of AMI. Inosine was implicated as a potential biomarker for SBP efficacy. Furthermore, SBP inhibited the expression of genes involved in purine metabolism, which are closely associated with thrombosis, inflammation, and platelet function. The regulation of purine metabolism by SBP was associated with the enrichment of Lactobacillus. Finally, the effects of SBP on inosine production and vascular function could be transmitted through the transplantation of fecal microbiota. CONCLUSION Our study reveals a novel mechanism by which SBP regulates purine metabolism by enriching Lactobacillus to exert cardioprotective effects in patients with SCAD. The data also provide previously undocumented evidence indicating that inosine is a potential biomarker for evaluating the efficacy of SBP in the treatment of SCAD.
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Affiliation(s)
- Gaosong Wu
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Jingyu Liao
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Xiaoyan Zhu
- School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Yuhao Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Yuan Lin
- School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Yuanyuan Zeng
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Jing Zhao
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Jingfang Zhang
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Tingting Yao
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, 100700, China
| | - Xiaoxu Shen
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, 100700, China.
| | - Houkai Li
- School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
| | - Liang Hu
- School of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
| | - Weidong Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; School of Pharmacy, Naval Medical University, Shanghai, 200433, China; Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100193, China.
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16
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Tian D, Xu Y, Wang Y, Zhu X, Huang C, Liu M, Li P, Li X. Causal factors of cardiovascular disease in end-stage renal disease with maintenance hemodialysis: a longitudinal and Mendelian randomization study. Front Cardiovasc Med 2024; 11:1306159. [PMID: 39091361 PMCID: PMC11291196 DOI: 10.3389/fcvm.2024.1306159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 07/08/2024] [Indexed: 08/04/2024] Open
Abstract
Background The risk factors of cardiovascular disease (CVD) in end-stage renal disease (ESRD) with hemodialysis remain not fully understood. In this study, we developed and validated a clinical-longitudinal model for predicting CVD in patients with hemodialysis, and employed Mendelian randomization to evaluate the causal 6study included 468 hemodialysis patients, and biochemical parameters were evaluated every three months. A generalized linear mixed (GLM) predictive model was applied to longitudinal clinical data. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the model. Kaplan-Meier curves were applied to verify the effect of selected risk factors on the probability of CVD. Genome-wide association study (GWAS) data for CVD (n = 218,792,101,866 cases), end-stage renal disease (ESRD, n = 16,405, 326 cases), diabetes (n = 202,046, 9,889 cases), creatinine (n = 7,810), and uric acid (UA, n = 109,029) were obtained from the large-open GWAS project. The inverse-variance weighted MR was used as the main analysis to estimate the causal associations, and several sensitivity analyses were performed to assess pleiotropy and exclude variants with potential pleiotropic effects. Results The AUCs of the GLM model was 0.93 (with accuracy rates of 93.9% and 93.1% for the training set and validation set, sensitivity of 0.95 and 0.94, specificity of 0.87 and 0.86). The final clinical-longitudinal model consisted of 5 risk factors, including age, diabetes, ipth, creatinine, and UA. Furthermore, the predicted CVD response also allowed for significant (p < 0.05) discrimination between the Kaplan-Meier curves of each age, diabetes, ipth, and creatinine subclassification. MR analysis indicated that diabetes had a causal role in risk of CVD (β = 0.088, p < 0.0001) and ESRD (β = 0.26, p = 0.007). In turn, ESRD was found to have a causal role in risk of diabetes (β = 0.027, p = 0.013). Additionally, creatinine exhibited a causal role in the risk of ESRD (β = 4.42, p = 0.01). Conclusions The results showed that old age, diabetes, and low level of ipth, creatinine, and UA were important risk factors for CVD in hemodialysis patients, and diabetes played an important bridging role in the link between ESRD and CVD.
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Affiliation(s)
- Dandan Tian
- Department of Hypertension, Henan Provincial People’s Hospital & Zhengzhou University People’s Hospital, Zhengzhou, China
| | - You Xu
- Department of Clinical Laboratory, The Third Affifiliated Hospital, Southern Medical University, Guangzhou, China
| | - Ying Wang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Xirui Zhu
- Department of Medical Imaging, Henan Provincial People’s Hospital & Zhengzhou University People’s Hospital, Zhengzhou, China
| | - Chun Huang
- Department of Medical Imaging, Henan Provincial People’s Hospital & Zhengzhou University People’s Hospital, Zhengzhou, China
| | - Min Liu
- Department of Hypertension, Henan Provincial People’s Hospital & Zhengzhou University People’s Hospital, Zhengzhou, China
| | - Panlong Li
- Department of Medical Imaging, Henan Provincial People’s Hospital & Zhengzhou University People’s Hospital, Zhengzhou, China
- The School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Xiangyong Li
- Department of Infectious Disease, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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17
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Bermingham KM, Linenberg I, Polidori L, Asnicar F, Arrè A, Wolf J, Badri F, Bernard H, Capdevila J, Bulsiewicz WJ, Gardner CD, Ordovas JM, Davies R, Hadjigeorgiou G, Hall WL, Delahanty LM, Valdes AM, Segata N, Spector TD, Berry SE. Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial. Nat Med 2024; 30:1888-1897. [PMID: 38714898 PMCID: PMC11271409 DOI: 10.1038/s41591-024-02951-6] [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: 10/18/2023] [Accepted: 03/26/2024] [Indexed: 05/15/2024]
Abstract
Large variability exists in people's responses to foods. However, the efficacy of personalized dietary advice for health remains understudied. We compared a personalized dietary program (PDP) versus general advice (control) on cardiometabolic health using a randomized clinical trial. The PDP used food characteristics, individual postprandial glucose and triglyceride (TG) responses to foods, microbiomes and health history, to produce personalized food scores in an 18-week app-based program. The control group received standard care dietary advice (US Department of Agriculture Guidelines for Americans, 2020-2025) using online resources, check-ins, video lessons and a leaflet. Primary outcomes were serum low-density lipoprotein cholesterol and TG concentrations at baseline and at 18 weeks. Participants (n = 347), aged 41-70 years and generally representative of the average US population, were randomized to the PDP (n = 177) or control (n = 170). Intention-to-treat analysis (n = 347) between groups showed significant reduction in TGs (mean difference = -0.13 mmol l-1; log-transformed 95% confidence interval = -0.07 to -0.01, P = 0.016). Changes in low-density lipoprotein cholesterol were not significant. There were improvements in secondary outcomes, including body weight, waist circumference, HbA1c, diet quality and microbiome (beta-diversity) (P < 0.05), particularly in highly adherent PDP participants. However, blood pressure, insulin, glucose, C-peptide, apolipoprotein A1 and B, and postprandial TGs did not differ between groups. No serious intervention-related adverse events were reported. Following a personalized diet led to some improvements in cardiometabolic health compared to standard dietary advice. ClinicalTrials.gov registration: NCT05273268 .
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Affiliation(s)
- Kate M Bermingham
- Department of Nutritional Sciences, King's College London, London, UK
- Zoe Ltd, London, UK
| | - Inbar Linenberg
- Department of Nutritional Sciences, King's College London, London, UK
- Zoe Ltd, London, UK
| | | | - Francesco Asnicar
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | | | | | | | | | | | | | | | - Jose M Ordovas
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
- IMDEA Food Institute, Campus of International Excellence, Universidad Autónoma de Madrid, Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Universidad Camilo José Cela, Madrid, Spain
| | | | | | - Wendy L Hall
- Department of Nutritional Sciences, King's College London, London, UK
| | - Linda M Delahanty
- Diabetes Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ana M Valdes
- School of Medicine, University of Nottingham, Nottingham, UK
- Nottingham National Institute for Health and Care Research Biomedical Research Centre, Nottingham, UK
| | - Nicola Segata
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Tim D Spector
- Department of Nutritional Sciences, King's College London, London, UK
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, UK.
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Wang Q, Chen S, Wang G, Zhang T, Gao Y. Integrated mendelian randomization analyses highlight AFF3 as a novel eQTL-mediated susceptibility gene in renal cancer and its potential mechanisms. BMC Cancer 2024; 24:739. [PMID: 38886730 PMCID: PMC11181572 DOI: 10.1186/s12885-024-12513-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUNDS A growing number of expression quantitative trait loci (eQTLs) have been found to be linked with tumorigenesis. In this article, we employed integrated Mendelian randomization (MR) analyses to identify novel susceptibility genes in renal cancer (RC) and reveal their potential mechanisms. METHODS Two-sample MR analyses were performed to infer causal relationships between eQTLs, metabolites, and RC risks through the "TwoSampleMR" R package. Sensitivity analyses, such as heterogeneity, pleiotropy, and leave-one-out analysis, were used to assess the stability of our outcomes. Summary-data-based MR (SMR) analyses were used to verify the causal relationships among cis-eQTLs and RC risks via the SMR 1.3.1 software. RESULTS Our results provided the first evidence for AFF3 eQTL elevating RC risks, suggesting its oncogenic roles (IVW method; odds ratio (OR) = 1.0005; 95% confidence interval (CI) = 1.0001-1.0010; P = 0.0285; heterogeneity = 0.9588; pleiotropy = 0.8397). Further SMR analysis validated the causal relationships among AFF3 cis-eQTLs and RC risks (P < 0.05). Moreover, the TCGA-KIRC, the ICGC-RC, and the GSE159115 datasets verified that the AFF3 gene was more highly expressed in RC tumors than normal control via scRNA-sequencing and bulk RNA-sequencing (P < 0.05). Gene set enrichment analysis (GSEA) analysis identified six potential biological pathways of AFF3 involved in RC. As for the potential mechanism of AFF3 in RC, we concluded in this article that AFF3 eQTL could negatively modulate the levels of the X-11,315 metabolite (IVW method; OR = 0.9127; 95% CI = 0.8530-0.9765; P = 0.0081; heterogeneity = 0.4150; pleiotropy = 0.8852), exhibiting preventive effects against RC risks (IVW method; OR = 0.9987; 95% CI = 0.9975-0.9999; P = 0.0380; heterogeneity = 0.5362; pleiotropy = 0.9808). CONCLUSIONS We concluded that AFF3 could serve as a novel eQTL-mediated susceptibility gene in RC and reveal its potential mechanism of elevating RC risks via negatively regulating the X-11,315 metabolite levels.
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Affiliation(s)
- Qiming Wang
- Department of Urology, Jianhu Clinical Medical College of Yangzhou University, No. 666 South Ring Road, Yancheng, Jiangsu Province, 224700, China
| | - Shaopeng Chen
- Department of Urology, Jianhu Clinical Medical College of Yangzhou University, No. 666 South Ring Road, Yancheng, Jiangsu Province, 224700, China
| | - Gang Wang
- Department of Urology, Jianhu Clinical Medical College of Yangzhou University, No. 666 South Ring Road, Yancheng, Jiangsu Province, 224700, China
| | - Tielong Zhang
- Department of Urology, Jianhu Clinical Medical College of Yangzhou University, No. 666 South Ring Road, Yancheng, Jiangsu Province, 224700, China
| | - Yulong Gao
- Department of Urology, Jianhu Clinical Medical College of Yangzhou University, No. 666 South Ring Road, Yancheng, Jiangsu Province, 224700, China.
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19
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Ross PA, Xu W, Jalomo-Khayrova E, Bange G, Gumerov VM, Bradley PH, Sourjik V, Zhulin IB. Framework for exploring the sensory repertoire of the human gut microbiota. mBio 2024; 15:e0103924. [PMID: 38757952 PMCID: PMC11237719 DOI: 10.1128/mbio.01039-24] [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: 04/09/2024] [Accepted: 04/17/2024] [Indexed: 05/18/2024] Open
Abstract
Bacteria sense changes in their environment and transduce signals to adjust their cellular functions accordingly. For this purpose, bacteria employ various sensors feeding into multiple signal transduction pathways. Signal recognition by bacterial sensors is studied mainly in a few model organisms, but advances in genome sequencing and analysis offer new ways of exploring the sensory repertoire of many understudied organisms. The human gut is a natural target of this line of study: it is a nutrient-rich and dynamic environment and is home to thousands of bacterial species whose activities impact human health. Many gut commensals are also poorly studied compared to model organisms and are mainly known through their genome sequences. To begin exploring the signals human gut commensals sense and respond to, we have designed a framework that enables the identification of sensory domains, prediction of signals that they recognize, and experimental verification of these predictions. We validate this framework's functionality by systematically identifying amino acid sensors in selected bacterial genomes and metagenomes, characterizing their amino acid binding properties, and demonstrating their signal transduction potential.IMPORTANCESignal transduction is a central process governing how bacteria sense and respond to their environment. The human gut is a complex environment with many living organisms and fluctuating streams of nutrients. One gut inhabitant, Escherichia coli, is a model organism for studying signal transduction. However, E. coli is not representative of most gut microbes, and signaling pathways in the thousands of other organisms comprising the human gut microbiota remain poorly understood. This work provides a foundation for how to explore signals recognized by these organisms.
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Affiliation(s)
- Patricia A. Ross
- Department of Microbiology, The Ohio State University, Columbus, Ohio, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, Ohio, USA
| | - Wenhao Xu
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
| | - Ekaterina Jalomo-Khayrova
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
- Department of Chemistry, Philipps-University Marburg, Marburg, Germany
| | - Gert Bange
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
- Department of Chemistry, Philipps-University Marburg, Marburg, Germany
| | - Vadim M. Gumerov
- Department of Microbiology, The Ohio State University, Columbus, Ohio, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, Ohio, USA
| | - Patrick H. Bradley
- Department of Microbiology, The Ohio State University, Columbus, Ohio, USA
- Infectious Diseases Institute, The Ohio State University, Columbus, Ohio, USA
| | - Victor Sourjik
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
| | - Igor B. Zhulin
- Department of Microbiology, The Ohio State University, Columbus, Ohio, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, Ohio, USA
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20
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Lin W, Ji J, Su KJ, Qiu C, Tian Q, Zhao LJ, Luo Z, Wu C, Shen H, Deng H. omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data. NAR Genom Bioinform 2024; 6:lqae071. [PMID: 38881578 PMCID: PMC11177553 DOI: 10.1093/nargab/lqae071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 04/25/2024] [Accepted: 05/30/2024] [Indexed: 06/18/2024] Open
Abstract
Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.
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Affiliation(s)
- Weiqiang Lin
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China
| | - Kuan-Jui Su
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Chuan Qiu
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Qing Tian
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Lan-Juan Zhao
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Zhe Luo
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hongwen Deng
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
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21
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Cui P, Li X, Huang C, Lin D. Metabolomics-driven discovery of therapeutic targets for cancer cachexia. J Cachexia Sarcopenia Muscle 2024; 15:781-793. [PMID: 38644205 PMCID: PMC11154780 DOI: 10.1002/jcsm.13465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 12/07/2023] [Accepted: 01/09/2024] [Indexed: 04/23/2024] Open
Abstract
Cancer cachexia (CC) is a devastating metabolic syndrome characterized by skeletal muscle wasting and body weight loss, posing a significant burden on the health and survival of cancer patients. Despite ongoing efforts, effective treatments for CC are still lacking. Metabolomics, an advanced omics technique, offers a comprehensive analysis of small-molecule metabolites involved in cellular metabolism. In CC research, metabolomics has emerged as a valuable tool for identifying diagnostic biomarkers, unravelling molecular mechanisms and discovering potential therapeutic targets. A comprehensive search strategy was implemented to retrieve relevant articles from primary databases, including Web of Science, Google Scholar, Scopus and PubMed, for CC and metabolomics. Recent advancements in metabolomics have deepened our understanding of CC by uncovering key metabolic signatures and elucidating underlying mechanisms. By targeting crucial metabolic pathways including glucose metabolism, amino acid metabolism, fatty acid metabolism, bile acid metabolism, ketone body metabolism, steroid metabolism and mitochondrial energy metabolism, it becomes possible to restore metabolic balance and alleviate CC symptoms. This review provides a comprehensive summary of metabolomics studies in CC, focusing on the discovery of potential therapeutic targets and the evaluation of modulating specific metabolic pathways for CC treatment. By harnessing the insights derived from metabolomics, novel interventions for CC can be developed, leading to improved patient outcomes and enhanced quality of life.
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Affiliation(s)
- Pengfei Cui
- College of Food and PharmacyXuchang UniversityXuchangChina
| | - Xiaoyi Li
- Xuchang Central HospitalXuchangChina
| | - Caihua Huang
- Research and Communication Center of Exercise and HealthXiamen University of TechnologyXiamenChina
| | - Donghai Lin
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical EngineeringXiamen UniversityXiamenChina
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22
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Fan Y, Li F, Tan X, Ren L, Peng X, Yu J, Chen W, Jia L, Zhu F, Yin W, Du J, Wang Y. Abnormal circulating steroids refine risk of progression to heart failure in ischemic heart disease. Eur J Clin Invest 2024; 54:e14156. [PMID: 38214411 DOI: 10.1111/eci.14156] [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: 11/21/2023] [Revised: 12/26/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
BACKGROUND Patients with ischemic heart disease (IHD) experience a high incidence of progression to heart failure (HF) despite current therapies. We speculated that steroid hormone metabolic disorders distinct adverse phenotypes and contribute to HF. METHODS We measured 18 steroids using liquid chromatography with tandem mass spectrometry in 2023 patients from the Registry Study of Biomarkers in Ischemic Heart Disease (BIOMS-IHD), including 1091 patients with IHD in a retrospective discovery set and 932 patients with IHD in a multicentre validation set. Our outcomes included incident HF after a median follow-up of 4 years. RESULTS We demonstrated steroid-based signatures of inflammation, coronary microvascular dysfunction and left ventricular hypertrophy that were associated with subsequent HF events in patients with IHD. In both cohorts, patients with a high steroid-heart failure score (SHFS) (>1) exhibited a greater risk of incident HF than patients with a low SHFS (≤1). The SHFS further improved the prognostic accuracy beyond clinical variables (net reclassification improvement of 0.628 in the discovery set and 0.299 in the validation set) and demonstrated the maximal effect of steroid signatures in patients with IHD who had lower B-type natriuretic peptide levels (pinteraction = 0.038). CONCLUSIONS A steroid-based strategy can simply and effectively identify individuals at higher HF risk who may derive benefit from more intensive follow-ups.
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Affiliation(s)
- Yangkai Fan
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
| | - Fengjuan Li
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
| | - Xin Tan
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
| | - Lu Ren
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
| | - Xueyan Peng
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
| | - Jiaqi Yu
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
| | - Weiyao Chen
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
| | - Lixin Jia
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Fuli Zhu
- Department of Cardiology, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Wenjie Yin
- Department of Hypertension, The First Hospital of Shanxi Medical University, Shanxi, China
| | - Jie Du
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
| | - Yuan Wang
- Beijing Collaborative Innovation Centre for Cardiovascular Disorders, The Key Laboratory of Remodeling-Related Cardiovascular Disease, Ministry of Education, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing, China
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23
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Deehan EC, Mocanu V, Madsen KL. Effects of dietary fibre on metabolic health and obesity. Nat Rev Gastroenterol Hepatol 2024; 21:301-318. [PMID: 38326443 DOI: 10.1038/s41575-023-00891-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/18/2023] [Indexed: 02/09/2024]
Abstract
Obesity and metabolic syndrome represent a growing epidemic worldwide. Body weight is regulated through complex interactions between hormonal, neural and metabolic pathways and is influenced by numerous environmental factors. Imbalances between energy intake and expenditure can occur due to several factors, including alterations in eating behaviours, abnormal satiation and satiety, and low energy expenditure. The gut microbiota profoundly affects all aspects of energy homeostasis through diverse mechanisms involving effects on mucosal and systemic immune, hormonal and neural systems. The benefits of dietary fibre on metabolism and obesity have been demonstrated through mechanistic studies and clinical trials, but many questions remain as to how different fibres are best utilized in managing obesity. In this Review, we discuss the physiochemical properties of different fibres, current findings on how fibre and the gut microbiota interact to regulate body weight homeostasis, and knowledge gaps related to using dietary fibres as a complementary strategy. Precision medicine approaches that utilize baseline microbiota and clinical characteristics to predict individual responses to fibre supplementation represent a new paradigm with great potential to enhance weight management efficacy, but many challenges remain before these approaches can be fully implemented.
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Affiliation(s)
- Edward C Deehan
- Department of Food Science and Technology, University of Nebraska, Lincoln, NE, USA
- Nebraska Food for Health Center, Lincoln, NE, USA
| | - Valentin Mocanu
- Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Karen L Madsen
- Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
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24
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Chen B, Zeng G, Sun L, Jiang C. When smoke meets gut: deciphering the interactions between tobacco smoking and gut microbiota in disease development. SCIENCE CHINA. LIFE SCIENCES 2024; 67:854-864. [PMID: 38265598 DOI: 10.1007/s11427-023-2446-y] [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: 05/23/2023] [Accepted: 09/09/2023] [Indexed: 01/25/2024]
Abstract
Tobacco smoking is a prevalent and detrimental habit practiced worldwide, increasing the risk of various diseases, including chronic obstructive pulmonary disease (COPD), cardiovascular disease, liver disease, and cancer. Although previous research has explored the detrimental health effects of tobacco smoking, recent studies suggest that gut microbiota dysbiosis may play a critical role in these outcomes. Numerous tobacco smoke components, such as nicotine, are found in the gastrointestinal tract and interact with gut microbiota, leading to lasting impacts on host health and diseases. This review delves into the ways tobacco smoking and its various constituents influence gut microbiota composition and functionality. We also summarize recent advancements in understanding how tobacco smoking-induced gut microbiota dysbiosis affects host health. Furthermore, this review introduces a novel perspective on how changes in gut microbiota following smoking cessation may contribute to withdrawal syndrome and the degree of health improvements in smokers.
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Affiliation(s)
- Bo Chen
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, 100191, China
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, 100191, China
- Center for Obesity and Metabolic Disease Research, School of Basic Medical Sciences, Peking University, Beijing, 100191, China
| | - Guangyi Zeng
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, 100191, China
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, 100191, China
- Center for Obesity and Metabolic Disease Research, School of Basic Medical Sciences, Peking University, Beijing, 100191, China
| | - Lulu Sun
- State Key Laboratory of Women's Reproductive Health and Fertility Promotion, Peking University, Beijing, 100191, China.
- Department of Endocrinology and Metabolism, Peking University Third Hospital, Beijing, 100191, China.
| | - Changtao Jiang
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, 100191, China.
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, 100191, China.
- Center for Obesity and Metabolic Disease Research, School of Basic Medical Sciences, Peking University, Beijing, 100191, China.
- State Key Laboratory of Women's Reproductive Health and Fertility Promotion, Peking University, Beijing, 100191, China.
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25
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Nakamura M. Lipotoxicity as a therapeutic target in obesity and diabetic cardiomyopathy. JOURNAL OF PHARMACY & PHARMACEUTICAL SCIENCES : A PUBLICATION OF THE CANADIAN SOCIETY FOR PHARMACEUTICAL SCIENCES, SOCIETE CANADIENNE DES SCIENCES PHARMACEUTIQUES 2024; 27:12568. [PMID: 38706718 PMCID: PMC11066298 DOI: 10.3389/jpps.2024.12568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 04/09/2024] [Indexed: 05/07/2024]
Abstract
Unhealthy sources of fats, ultra-processed foods with added sugars, and a sedentary lifestyle make humans more susceptible to developing overweight and obesity. While lipids constitute an integral component of the organism, excessive and abnormal lipid accumulation that exceeds the storage capacity of lipid droplets disrupts the intracellular composition of fatty acids and results in the release of deleterious lipid species, thereby giving rise to a pathological state termed lipotoxicity. This condition induces endoplasmic reticulum stress, mitochondrial dysfunction, inflammatory responses, and cell death. Recent advances in omics technologies and analytical methodologies and clinical research have provided novel insights into the mechanisms of lipotoxicity, including gut dysbiosis, epigenetic and epitranscriptomic modifications, dysfunction of lipid droplets, post-translational modifications, and altered membrane lipid composition. In this review, we discuss the recent knowledge on the mechanisms underlying the development of lipotoxicity and lipotoxic cardiometabolic disease in obesity, with a particular focus on lipotoxic and diabetic cardiomyopathy.
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Affiliation(s)
- Michinari Nakamura
- Department of Cell Biology and Molecular Medicine, Rutgers New Jersey Medical School, Rutgers Biomedical and Health Sciences, Newark, United States
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26
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Wang JT, Hu W, Xue Z, Cai X, Zhang SY, Li FQ, Lin LS, Chen H, Miao Z, Xi Y, Guo T, Zheng JS, Chen YM, Lin HL. Mapping multi-omics characteristics related to short-term PM 2.5 trajectory and their impact on type 2 diabetes in middle-aged and elderly adults in Southern China. JOURNAL OF HAZARDOUS MATERIALS 2024; 468:133784. [PMID: 38382338 DOI: 10.1016/j.jhazmat.2024.133784] [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: 12/14/2023] [Revised: 01/29/2024] [Accepted: 02/12/2024] [Indexed: 02/23/2024]
Abstract
The relationship between PM2.5 and metabolic diseases, including type 2 diabetes (T2D), has become increasingly prominent, but the molecular mechanism needs to be further clarified. To help understand the mechanistic association between PM2.5 exposure and human health, we investigated short-term PM2.5 exposure trajectory-related multi-omics characteristics from stool metagenome and metabolome and serum proteome and metabolome in a cohort of 3267 participants (age: 64.4 ± 5.8 years) living in Southern China. And then integrate these features to examine their relationship with T2D. We observed significant differences in overall structure in each omics and 193 individual biomarkers between the high- and low-PM2.5 groups. PM2.5-related features included the disturbance of microbes (carbohydrate metabolism-associated Bacteroides thetaiotaomicron), gut metabolites of amino acids and carbohydrates, serum biomarkers related to lipid metabolism and reducing n-3 fatty acids. The patterns of overall network relationships among the biomarkers differed between T2D and normal participants. The subnetwork membership centered on the hub nodes (fecal rhamnose and glycylproline, serum hippuric acid, and protein TB182) related to high-PM2.5, which well predicted higher T2D prevalence and incidence and a higher level of fasting blood glucose, HbA1C, insulin, and HOMA-IR. Our findings underline crucial PM2.5-related multi-omics biomarkers linking PM2.5 exposure and T2D in humans.
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Affiliation(s)
- Jia-Ting Wang
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Wei Hu
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhangzhi Xue
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China
| | - Xue Cai
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China
| | - Shi-Yu Zhang
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Fan-Qin Li
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Li-Shan Lin
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Hanzu Chen
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zelei Miao
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China
| | - Yue Xi
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China
| | - Ju-Sheng Zheng
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, 310030, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, 310030, China.
| | - Yu-Ming Chen
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Hua-Liang Lin
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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27
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Li C, Stražar M, Mohamed AMT, Pacheco JA, Walker RL, Lebar T, Zhao S, Lockart J, Dame A, Thurimella K, Jeanfavre S, Brown EM, Ang QY, Berdy B, Sergio D, Invernizzi R, Tinoco A, Pishchany G, Vasan RS, Balskus E, Huttenhower C, Vlamakis H, Clish C, Shaw SY, Plichta DR, Xavier RJ. Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria. Cell 2024; 187:1834-1852.e19. [PMID: 38569543 PMCID: PMC11071153 DOI: 10.1016/j.cell.2024.03.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 01/23/2024] [Accepted: 03/11/2024] [Indexed: 04/05/2024]
Abstract
Accumulating evidence suggests that cardiovascular disease (CVD) is associated with an altered gut microbiome. Our understanding of the underlying mechanisms has been hindered by lack of matched multi-omic data with diagnostic biomarkers. To comprehensively profile gut microbiome contributions to CVD, we generated stool metagenomics and metabolomics from 1,429 Framingham Heart Study participants. We identified blood lipids and cardiovascular health measurements associated with microbiome and metabolome composition. Integrated analysis revealed microbial pathways implicated in CVD, including flavonoid, γ-butyrobetaine, and cholesterol metabolism. Species from the Oscillibacter genus were associated with decreased fecal and plasma cholesterol levels. Using functional prediction and in vitro characterization of multiple representative human gut Oscillibacter isolates, we uncovered conserved cholesterol-metabolizing capabilities, including glycosylation and dehydrogenation. These findings suggest that cholesterol metabolism is a broad property of phylogenetically diverse Oscillibacter spp., with potential benefits for lipid homeostasis and cardiovascular health.
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Affiliation(s)
- Chenhao Li
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Ahmed M T Mohamed
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Tina Lebar
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Shijie Zhao
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Lockart
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrea Dame
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Eric M Brown
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Qi Yan Ang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Dallis Sergio
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rachele Invernizzi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Antonio Tinoco
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | | | - Ramachandran S Vasan
- Boston University and NHLBI's Framingham Heart Study, Framingham, MA, USA; Sections of Preventive Medicine and Epidemiology and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA; University of Texas School of Public Health, San Antonio, TX, USA
| | - Emily Balskus
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA; Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
| | - Curtis Huttenhower
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hera Vlamakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stanley Y Shaw
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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28
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Li Y, Liu Y, Cui J, Zhu M, Wang W, Chen K, Huang L, Liu Y. Oral-gut microbial transmission promotes diabetic coronary heart disease. Cardiovasc Diabetol 2024; 23:123. [PMID: 38581039 PMCID: PMC10998415 DOI: 10.1186/s12933-024-02217-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 03/27/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND Diabetes is a predominant driver of coronary artery disease worldwide. This study aims to unravel the distinct characteristics of oral and gut microbiota in diabetic coronary heart disease (DCHD). Simultaneously, we aim to establish a causal link between the diabetes-driven oral-gut microbiota axis and increased susceptibility to diabetic myocardial ischemia-reperfusion injury (MIRI). METHODS We comprehensively investigated the microbial landscape in the oral and gut microbiota in DCHD using a discovery cohort (n = 183) and a validation chohort (n = 68). Systematically obtained oral (tongue-coating) and fecal specimens were subjected to metagenomic sequencing and qPCR analysis, respectively, to holistically characterize the microbial consortia. Next, we induced diabetic MIRI by administering streptozotocin to C57BL/6 mice and subsequently investigated the potential mechanisms of the oral-gut microbiota axis through antibiotic pre-treatment followed by gavage with specific bacterial strains (Fusobacterium nucleatum or fecal microbiota from DCHD patients) to C57BL/6 mice. RESULTS Specific microbial signatures such as oral Fusobacterium nucleatum and gut Lactobacillus, Eubacterium, and Roseburia faecis, were identified as potential microbial biomarkers in DCHD. We further validated that oral Fusobacterium nucleatum and gut Lactobacillus are increased in DCHD patients, with a positive correlation between the two. Experimental evidence revealed that in hyperglycemic mice, augmented Fusobacterium nucleatum levels in the oral cavity were accompanied by an imbalance in the oral-gut axis, characterized by an increased coexistence of Fusobacterium nucleatum and Lactobacillus, along with elevated cardiac miRNA-21 and a greater extent of myocardial damage indicated by TTC, HE, TUNEL staining, all of which contributed to exacerbated MIRI. CONCLUSION Our findings not only uncover dysregulation of the oral-gut microbiota axis in diabetes patients but also highlight the pivotal intermediary role of the increased abundance of oral F. nucleatum and gut Lactobacillus in exacerbating MIRI. Targeting the oral-gut microbiota axis emerges as a potent strategy for preventing and treating DCHD. Oral-gut microbial transmission constitutes an intermediate mechanism by which diabetes influences myocardial injury, offering new insights into preventing acute events in diabetic patients with coronary heart disease.
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Affiliation(s)
- Yiwen Li
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, 100078, China
| | - Yanfei Liu
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Jing Cui
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Mengmeng Zhu
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Wenting Wang
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Keji Chen
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Luqi Huang
- China Academy of Chinese Medical Sciences, Beijing, 100078, China
| | - Yue Liu
- National Clinical Research Center for TCM Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China.
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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Park JK, Petrazzini BO, Bafna S, Duffy Á, Forrest IS, Vy HM, Marquez-Luna C, Verbanck M, Narula J, Rosenson RS, Jordan DM, Rocheleau G, Do R. Muesli Intake May Protect Against Coronary Artery Disease: Mendelian Randomization on 13 Dietary Traits. JACC. ADVANCES 2024; 3:100888. [PMID: 38737007 PMCID: PMC11087059 DOI: 10.1016/j.jacadv.2024.100888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/12/2023] [Indexed: 05/14/2024]
Abstract
BACKGROUND Diet is a key modifiable risk factor of coronary artery disease (CAD). However, the causal effects of specific dietary traits on CAD risk remain unclear. With the expansion of dietary data in population biobanks, Mendelian randomization (MR) could help enable the efficient estimation of causality in diet-disease associations. OBJECTIVES The primary goal was to test causality for 13 common dietary traits on CAD risk using a systematic 2-sample MR framework. A secondary goal was to identify plasma metabolites mediating diet-CAD associations suspected to be causal. METHODS Cross-sectional genetic and dietary data on up to 420,531 UK Biobank and 184,305 CARDIoGRAMplusC4D individuals of European ancestry were used in 2-sample MR. The primary analysis used fixed effect inverse-variance weighted regression, while sensitivity analyses used weighted median estimation, MR-Egger regression, and MR-Pleiotropy Residual Sum and Outlier. RESULTS Genetic variants serving as proxies for muesli intake were negatively associated with CAD risk (OR: 0.74; 95% CI: 0.65-0.84; P = 5.385 × 10-4). Sensitivity analyses using weighted median estimation supported this with a significant association in the same direction. Additionally, we identified higher plasma acetate levels as a potential mediator (OR: 0.03; 95% CI: 0.01-0.12; P = 1.15 × 10-4). CONCLUSIONS Muesli, a mixture of oats, seeds, nuts, dried fruit, and milk, may causally reduce CAD risk. Circulating levels of acetate, a gut microbiota-derived short-chain fatty acid, could be mediating its cardioprotective effects. These findings highlight the role of gut flora in cardiovascular health and help prioritize randomized trials on dietary interventions for CAD.
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Affiliation(s)
- Joshua K. Park
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ben Omega Petrazzini
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shantanu Bafna
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Áine Duffy
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Iain S. Forrest
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ha My Vy
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carla Marquez-Luna
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Jagat Narula
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Cardiovascular Imaging Program, Zena and Michael A. Wiener Cardiovascular Institute, Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert S. Rosenson
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Metabolism and Lipids Unit, Zena and Michael A. Wiener Cardiovascular Institute, Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Daniel M. Jordan
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ghislain Rocheleau
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ron Do
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Wang M, Yin F, Kong L, Yang L, Sun H, Sun Y, Yan G, Han Y, Wang X. Chinmedomics: a potent tool for the evaluation of traditional Chinese medicine efficacy and identification of its active components. Chin Med 2024; 19:47. [PMID: 38481256 PMCID: PMC10935806 DOI: 10.1186/s13020-024-00917-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
As an important part of medical science, Traditional Chinese Medicine (TCM) attracts much public attention due to its multi-target and multi-pathway characteristics in treating diseases. However, the limitations of traditional research methods pose a dilemma for the evaluation of clinical efficacy, the discovery of active ingredients and the elucidation of the mechanism of action. Therefore, innovative approaches that are in line with the characteristics of TCM theory and clinical practice are urgently needed. Chinmendomics, a newly emerging strategy for evaluating the efficacy of TCM, is proposed. This strategy combines systems biology, serum pharmacochemistry of TCM and bioinformatics to evaluate the efficacy of TCM with a holistic view by accurately identifying syndrome biomarkers and monitoring their complex metabolic processes intervened by TCM, and finding the agents associated with the metabolic course of pharmacodynamic biomarkers by constructing a bioinformatics-based correlation network model to further reveal the interaction between agents and pharmacodynamic targets. In this article, we review the recent progress of Chinmedomics to promote its application in the modernisation and internationalisation of TCM.
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Affiliation(s)
- Mengmeng Wang
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Fengting Yin
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Ling Kong
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Le Yang
- State Key Laboratory of Dampness Syndrome, The Second Affiliated Hospital Guangzhou University of Chinese Medicine, Dade Road 111, Guangzhou, China
| | - Hui Sun
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China.
| | - Ye Sun
- State Key Laboratory of Dampness Syndrome, The Second Affiliated Hospital Guangzhou University of Chinese Medicine, Dade Road 111, Guangzhou, China
| | - Guangli Yan
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Ying Han
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China
| | - Xijun Wang
- State Key Laboratory of Integration and Innovation of Classical Formula and Modern Chinese Medicines, National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin, 150040, China.
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
- State Key Laboratory of Dampness Syndrome, The Second Affiliated Hospital Guangzhou University of Chinese Medicine, Dade Road 111, Guangzhou, China.
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Jayakrishnan T, Mariam A, Farha N, Rotroff DM, Aucejo F, Barot SV, Conces M, Nair KG, Krishnamurthi SS, Schmit SL, Liska D, Khorana AA, Kamath SD. Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer. Sci Rep 2024; 14:4294. [PMID: 38383634 PMCID: PMC10881959 DOI: 10.1038/s41598-024-54560-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/14/2024] [Indexed: 02/23/2024] Open
Abstract
Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.
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Affiliation(s)
- Thejus Jayakrishnan
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
| | - Arshiya Mariam
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, USA
| | - Nicole Farha
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, USA
| | - Federico Aucejo
- Department of Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, USA
| | - Shimoli V Barot
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
| | - Madison Conces
- Case Comprehensive Cancer Center, Cleveland, USA
- Department of Hematology-Oncology, University Hospital Seidman Cancer Center, Cleveland, USA
| | - Kanika G Nair
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Smitha S Krishnamurthi
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Stephanie L Schmit
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, USA
| | - David Liska
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
- Department of Colorectal Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, USA
| | - Alok A Khorana
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Suneel D Kamath
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
- Case Comprehensive Cancer Center, Cleveland, USA.
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA.
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA.
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Yadav SK, Khan SA, Tiwari M, Kumar A, Kumar V, Akhter Y. Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces. Spat Spatiotemporal Epidemiol 2024; 48:100634. [PMID: 38355258 DOI: 10.1016/j.sste.2024.100634] [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: 06/15/2023] [Revised: 11/15/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
SARS-CoV-2, the virus responsible for COVID-19, posed a significant threat to the world. We analyzed COVID-19 dissemination data in the top ten Indian provinces by infection incidences using the Susceptible-Infectious-Removed (SIR) model, an Autoregressive Integrated Moving Average (ARIMA) time series model, a machine learning model based on the Random Forest, and distribution fitting. Outbreaks are expected to continue if the Basic Reproduction Number (R0) > 1, and infection waves are anticipated to end if the R0 < 1, as determined by the SIR model. Different parametric probability distributions are also fitted. Data collected from December 12, 2021, to March 31, 2022, encompassing data from both before and during the implementation of strict control measures. Based on the estimates of the model parameters, health agencies and government policymakers can develop strategies to combat the spread of the disease in the future, and the most effective technique can be recommended for real-world application for other outbreaks of COVID-19. The best method out of these could be also implemented further on the epidemiological data of other similar infectious agents.
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Affiliation(s)
- Subhash Kumar Yadav
- Department of Statistics, School of Physical and Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India
| | - Saif Ali Khan
- Department of Statistics, School of Physical and Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India
| | - Mayank Tiwari
- Department of Statistics, School of Physical and Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India
| | - Arun Kumar
- Department of Statistics, School of Physical and Decision Science, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India
| | - Vinit Kumar
- Department of Library & Information Science, School of Information Science & Technology, Babasaheb Bhimrao Ambedkar University, Lucknow 226025, India
| | - Yusuf Akhter
- Department of Biotechnology, School of Life Sciences, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India.
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Song Y, Wei H, Zhou Z, Wang H, Hang W, Wu J, Wang DW. Gut microbiota-dependent phenylacetylglutamine in cardiovascular disease: current knowledge and new insights. Front Med 2024; 18:31-45. [PMID: 38424375 DOI: 10.1007/s11684-024-1055-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/04/2023] [Indexed: 03/02/2024]
Abstract
Phenylacetylglutamine (PAGln) is an amino acid derivate that comes from the amino acid phenylalanine. There are increasing studies showing that the level of PAGln is associated with the risk of different cardiovascular diseases. In this review, we discussed the metabolic pathway of PAGln production and the quantitative measurement methods of PAGln. We summarized the epidemiological evidence to show the role of PAGln in diagnostic and prognostic value in several cardiovascular diseases, such as heart failure, coronary heart disease/atherosclerosis, and cardiac arrhythmia. The underlying mechanism of PAGln is now considered to be related to the thrombotic potential of platelets via adrenergic receptors. Besides, other possible mechanisms such as inflammatory response and oxidative stress could also be induced by PAGln. Moreover, since PAGln is produced across different organs including the intestine, liver, and kidney, the cross-talk among multiple organs focused on the function of this uremic toxic metabolite. Finally, the prognostic value of PAGln compared to the classical biomarker was discussed and we also highlighted important gaps in knowledge and areas requiring future investigation of PAGln in cardiovascular diseases.
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Affiliation(s)
- Yaonan Song
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China
| | - Haoran Wei
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China
| | - Zhitong Zhou
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China
| | - Huiqing Wang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China
| | - Weijian Hang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China
| | - Junfang Wu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China.
| | - Dao Wen Wang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, 430030, China
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Yan Y, Du X, Dou X, Li J, Zhang W, Yang S, Meng W, Tian G. Effects of Ninjurin 2 polymorphisms on susceptibility to coronary heart disease. Cell Cycle 2024; 23:328-337. [PMID: 38512812 PMCID: PMC11057668 DOI: 10.1080/15384101.2024.2330225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 02/29/2024] [Indexed: 05/01/2024] Open
Abstract
OBJECTIVE The aim of this study was to explore the effects of Ninjurin 2 (NINJ2) polymorphisms on susceptibility to coronary heart disease (CHD). METHODS We conducted a case-control study with 499 CHD cases and 505 age and gender-matched controls. Five single nucleotide polymorphisms (SNPs) in NINJ2 (rs118050317, rs75750647, rs7307242, rs10849390, and rs11610368) were genotyped by the Agena MassARRAY platform. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression analysis to assess the association of NINJ2 polymorphisms and CHD risk-adjusted for age and gender. What's more, risk genes and molecular functions were screened via protein-protein interaction (PPI) network and functional enrichment analysis. RESULTS Rs118050317 in NINJ2 significantly increased CHD risk in people aged more than 60 years and women. Rs118050317 and rs7307242 had strong relationships with hypertension risk in CHD patients. Additionally, rs75750647 exceedingly raised diabetes risk in cases under multiple models, whereas rs10849390 could protect CHD patients from diabetes in allele, homozygote, and additive models. We also observed two blocks in NINJ2. Further interaction network and enrichment analysis showed that NINJ2 played a greater role in the pathogenesis and progression of CHD. CONCLUSION Our results suggest that NINJ2 polymorphisms are associated with CHD risk.
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Affiliation(s)
- Yuping Yan
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Department of Cardiovascular Medicine, Xi’an Daxing Hospital, Xi’an, Shaanxi, China
| | - Xiaoyan Du
- Department of Cardiovascular Medicine, First Hospital of Yulin City, Yulin, Shaanxi, China
| | - Xia Dou
- Ministry of Education, Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Xi’an, Shaanxi, China
| | - Jingjie Li
- Ministry of Education, Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Xi’an, Shaanxi, China
| | - Wenjie Zhang
- Ministry of Education, Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Xi’an, Shaanxi, China
| | - Shuangyu Yang
- Ministry of Education, Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Xi’an, Shaanxi, China
| | - Wenting Meng
- Ministry of Education, Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Xi’an, Shaanxi, China
| | - Gang Tian
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
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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] [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.
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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
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Gong Y, Ding W, Wang P, Wu Q, Yao X, Yang Q. Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics. J Chem Inf Model 2023; 63:7628-7641. [PMID: 38079572 DOI: 10.1021/acs.jcim.3c01525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.
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Affiliation(s)
- Yaguo Gong
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Wei Ding
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Qibiao Wu
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Fu M, He R, Zhang Z, Ma F, Shen L, Zhang Y, Duan M, Zhang Y, Wang Y, Zhu L, He J. Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome. Sci Rep 2023; 13:20535. [PMID: 37996510 PMCID: PMC10667512 DOI: 10.1038/s41598-023-47783-5] [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: 08/25/2023] [Accepted: 11/18/2023] [Indexed: 11/25/2023] Open
Abstract
A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.
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Affiliation(s)
- Meijiao Fu
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Ruhua He
- Department of Cardiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Zhihan Zhang
- Department of Cardiology, Hanzhong Central Hospital, Hanzhong, 723200, Shanxi, China
| | - Fuqing Ma
- Department of Cardiology, The Fifth People's Hospital of Ningxia, Shizuishan, 753000, Ningxia, China
| | - Libo Shen
- Center for Cardiovascular Diseases, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750002, Ningxia, China
| | - Yu Zhang
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Mingyu Duan
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Yameng Zhang
- Department of Cardiology, The Second Affiliated Hospital of Henan University of Science and Technology, Luoyang, 471000, Henan, China
| | - Yifan Wang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Li Zhu
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
| | - Jun He
- Department of Cardiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
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Tu KJ, Diplas BH, Regal JA, Waitkus MS, Pirozzi CJ, Reitman ZJ. Mining cancer genomes for change-of-metabolic-function mutations. Commun Biol 2023; 6:1143. [PMID: 37950065 PMCID: PMC10638295 DOI: 10.1038/s42003-023-05475-w] [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: 04/20/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
Enzymes with novel functions are needed to enable new organic synthesis techniques. Drawing inspiration from gain-of-function cancer mutations that functionally alter proteins and affect cellular metabolism, we developed METIS (Mutated Enzymes from Tumors In silico Screen). METIS identifies metabolism-altering cancer mutations using mutation recurrence rates and protein structure. We used METIS to screen 298,517 cancer mutations and identify 48 candidate mutations, including those previously identified to alter enzymatic function. Unbiased metabolomic profiling of cells exogenously expressing a candidate mutant (OGDHLp.A400T) supports an altered phenotype that boosts in vitro production of xanthosine, a pharmacologically useful chemical that is currently produced using unsustainable, water-intensive methods. We then applied METIS to 49 million cancer mutations, yielding a refined set of candidates that may impart novel enzymatic functions or contribute to tumor progression. Thus, METIS can be used to identify and catalog potentially-useful cancer mutations for green chemistry and therapeutic applications.
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Affiliation(s)
- Kevin J Tu
- Department of Radiation Oncology, Duke University, Durham, NC, 27710, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 21044, USA
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Bill H Diplas
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Joshua A Regal
- Department of Radiation Oncology, Duke University, Durham, NC, 27710, USA
| | | | | | - Zachary J Reitman
- Department of Radiation Oncology, Duke University, Durham, NC, 27710, USA.
- Department of Neurosurgery, Duke University, Durham, NC, 27710, USA.
- Department of Pathology, Duke University, Durham, NC, 27710, USA.
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Chen HC, Liu YW, Chang KC, Wu YW, Chen YM, Chao YK, You MY, Lundy DJ, Lin CJ, Hsieh ML, Cheng YC, Prajnamitra RP, Lin PJ, Ruan SC, Chen DHK, Shih ESC, Chen KW, Chang SS, Chang CMC, Puntney R, Moy AW, Cheng YY, Chien HY, Lee JJ, Wu DC, Hwang MJ, Coonen J, Hacker TA, Yen CLE, Rey FE, Kamp TJ, Hsieh PCH. Gut butyrate-producers confer post-infarction cardiac protection. Nat Commun 2023; 14:7249. [PMID: 37945565 PMCID: PMC10636175 DOI: 10.1038/s41467-023-43167-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023] Open
Abstract
The gut microbiome and its metabolites are increasingly implicated in several cardiovascular diseases, but their role in human myocardial infarction (MI) injury responses have yet to be established. To address this, we examined stool samples from 77 ST-elevation MI (STEMI) patients using 16 S V3-V4 next-generation sequencing, metagenomics and machine learning. Our analysis identified an enriched population of butyrate-producing bacteria. These findings were then validated using a controlled ischemia/reperfusion model using eight nonhuman primates. To elucidate mechanisms, we inoculated gnotobiotic mice with these bacteria and found that they can produce beta-hydroxybutyrate, supporting cardiac function post-MI. This was further confirmed using HMGCS2-deficient mice which lack endogenous ketogenesis and have poor outcomes after MI. Inoculation increased plasma ketone levels and provided significant improvements in cardiac function post-MI. Together, this demonstrates a previously unknown role of gut butyrate-producers in the post-MI response.
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Affiliation(s)
- Hung-Chih Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Yen-Wen Liu
- Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, 704, Taiwan
| | - Kuan-Cheng Chang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, 40447, Taiwan
- School of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Yen-Wen Wu
- Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City, 220, Taiwan
| | - Yi-Ming Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Yu-Kai Chao
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Min-Yi You
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - David J Lundy
- Graduate Institute of Biomedical Materials and Tissue Engineering, Taipei Medical University, Taipei, 110, Taiwan
| | - Chen-Ju Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Marvin L Hsieh
- Model Organisms Research Core, Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Yu-Che Cheng
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Ray P Prajnamitra
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Po-Ju Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Shu-Chian Ruan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | | | - Edward S C Shih
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Ke-Wei Chen
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shih-Sheng Chang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, 40447, Taiwan
- School of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Cindy M C Chang
- Model Organisms Research Core, Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Riley Puntney
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Amy Wu Moy
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Yuan-Yuan Cheng
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Hsin-Yuan Chien
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Jia-Jung Lee
- Division of Nephrology, Department of Medicine, Kaohsiung Medical University & Hospital, Kaohsiung, 807, Taiwan
| | - Deng-Chyang Wu
- Division of Gastroenterology, Department of Medicine, Kaohsiung Medical University & Hospital, Kaohsiung, 807, Taiwan
| | - Ming-Jing Hwang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Jennifer Coonen
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Timothy A Hacker
- Model Organisms Research Core, Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - C-L Eric Yen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
- Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Federico E Rey
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Timothy J Kamp
- Department of Medicine and Stem Cell and Regenerative Medicine Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Patrick C H Hsieh
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan.
- Department of Medicine and Stem Cell and Regenerative Medicine Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Institute of Medical Genomics and Proteomics and Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei, 100, Taiwan.
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Lin W, Ji J, Su KJ, Qiu C, Tian Q, Zhao LJ, Luo Z, Shen H, Wu C, Deng H. omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass Spectrometry-based Omics Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557189. [PMID: 37745599 PMCID: PMC10515867 DOI: 10.1101/2023.09.12.557189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics, and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive and systematic comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to simulate and evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. OmicsMIC is freely available at https://github.com/WQLin8/omicsMIC.
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Affiliation(s)
- Weiqiang Lin
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan 250100, China
| | - Kuan-Jui Su
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Chuan Qiu
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Qing Tian
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Lan-Juan Zhao
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Zhe Luo
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hongwen Deng
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
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Wang Y, Zhou J, Ye J, Sun Z, He Y, Zhao Y, Ren S, Zhang G, Liu M, Zheng P, Wang G, Yang J. Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder. MICROBIOME 2023; 11:195. [PMID: 37641148 PMCID: PMC10464022 DOI: 10.1186/s40168-023-01635-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 07/30/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND There is a growing body of evidence suggesting that disturbance of the gut-brain axis may be one of the potential causes of major depressive disorder (MDD). However, the effects of antidepressants on the gut microbiota, and the role of gut microbiota in influencing antidepressant efficacy are still not fully understood. RESULTS To address this knowledge gap, a multi-omics study was undertaken involving 110 MDD patients treated with escitalopram (ESC) for a period of 12 weeks. This study was conducted within a cohort and compared to a reference group of 166 healthy individuals. It was found that ESC ameliorated abnormal blood metabolism by upregulating MDD-depleted amino acids and downregulating MDD-enriched fatty acids. On the other hand, the use of ESC showed a relatively weak inhibitory effect on the gut microbiota, leading to a reduction in microbial richness and functions. Machine learning-based multi-omics integrative analysis revealed that gut microbiota contributed to the changes in plasma metabolites and was associated with several amino acids such as tryptophan and its gut microbiota-derived metabolite, indole-3-propionic acid (I3PA). Notably, a significant correlation was observed between the baseline microbial richness and clinical remission at week 12. Compared to non-remitters, individuals who achieved remission had a higher baseline microbial richness, a lower dysbiosis score, and a more complex and well-organized community structure and bacterial networks within their microbiota. These findings indicate a more resilient microbiota community in remitters. Furthermore, we also demonstrated that it was not the composition of the gut microbiota itself, but rather the presence of sporulation genes at baseline that could predict the likelihood of clinical remission following ESC treatment. The predictive model based on these genes revealed an area under the curve (AUC) performance metric of 0.71. CONCLUSION This study provides valuable insights into the role of the gut microbiota in the mechanism of ESC treatment efficacy for patients with MDD. The findings represent a significant advancement in understanding the intricate relationship among antidepressants, gut microbiota, and the blood metabolome. Additionally, this study offers a microbiota-centered perspective that can potentially improve antidepressant efficacy in clinical practice. By shedding light on the interplay between these factors, this research contributes to our broader understanding of the complex mechanisms underlying the treatment of MDD and opens new avenues for optimizing therapeutic approaches. Video Abstract.
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Affiliation(s)
- Yaping Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Junbin Ye
- Beijing WeGenome Paradigm Co., Ltd, Beijing, China
| | - Zuoli Sun
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Yi He
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Yingxin Zhao
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Siyu Ren
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Guofu Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Min Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Peng Zheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
- NHC Key Laboratory of Diagnosis and Treatment On Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Gang Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.
| | - Jian Yang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.
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Wu X, Tang Z, Zhao R, Wang Y, Wang X, Liu S, Zou H. Taxonomic and functional profiling of fecal metagenomes for the early detection of colorectal cancer. Front Oncol 2023; 13:1218056. [PMID: 37601681 PMCID: PMC10436198 DOI: 10.3389/fonc.2023.1218056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
Objectives This study aimed to identify colorectal cancer (CRC)-associated phylogenetic and functional bacterial features by a large-scale metagenomic sequencing and develop a binomial classifier to accurately distinguish between CRC patients and healthy individuals. Methods We conducted shotgun metagenomic analyses of fecal samples from a ZhongShanMed discovery cohort of 121 CRC and 52 controls and SouthernMed validation cohort of 67 CRC and 44 controls. Taxonomic profiling and quantification were performed by direct sequence alignment against genome taxonomy database (GTDB). High-quality reads were also aligned to IGC datasets to obtain functional profiles defined by Kyoto Encyclopedia of Genes and Genomes (KEGG). A least absolute shrinkage and selection operator (LASSO) classifier was constructed to quantify risk scores of probability of disease and to discriminate CRC from normal for discovery, validation, Fudan, GloriousMed, and HongKong cohorts. Results A diverse spectrum of bacterial and fungi species were found to be either enriched (368) or reduced (113) in CRC patients (q<0.05). Similarly, metabolic functions associated with biosynthesis and metabolism of amino acids and fatty acids were significantly altered (q<0.05). The LASSO regression analysis of significant changes in the abundance of microbial species in CRC achieved areas under the receiver operating characteristic curve (AUROCs) of 0.94 and 0.91 in the ZhongShanMed and SouthernMed cohorts, respectively. A further analysis of Fudan, GloriousMed, and HK cohorts using the same classification model also demonstrated AUROC of 0.80, 0.78, and 0.91, respectively. Moreover, major CRC-associated bacterial biomarkers identified in this study were found to be coherently enriched or depleted across 10 metagenomic sequencing studies of gut microbiota. Conclusion A coherent signature of CRC-associated bacterial biomarkers modeled on LASSO binomial classifier maybe used accurately for early detection of CRC.
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Affiliation(s)
- Xudong Wu
- Creative Biosciences (Guangzhou) CO., Ltd, Guangzhou, Guangdong, China
| | - Zhimin Tang
- Creative Biosciences (Guangzhou) CO., Ltd, Guangzhou, Guangdong, China
| | - Rongsong Zhao
- Creative Biosciences (Guangzhou) CO., Ltd, Guangzhou, Guangdong, China
| | - Yusi Wang
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Xianshu Wang
- Creative Biosciences (Guangzhou) CO., Ltd, Guangzhou, Guangdong, China
| | - Side Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Hongzhi Zou
- Creative Biosciences (Guangzhou) CO., Ltd, Guangzhou, Guangdong, China
- Department of Colorectal Surgery, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
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Ben-Yacov O, Godneva A, Rein M, Shilo S, Lotan-Pompan M, Weinberger A, Segal E. Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes. Gut 2023; 72:1486-1496. [PMID: 37137684 PMCID: PMC10359530 DOI: 10.1136/gutjnl-2022-329201] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 04/17/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVE To explore the interplay between dietary modifications, microbiome composition and host metabolic responses in a dietary intervention setting of a personalised postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet in pre-diabetes. DESIGN In a 6-month dietary intervention, adults with pre-diabetes were randomly assigned to follow an MED or PPT diet (based on a machine-learning algorithm for predicting postprandial glucose responses). Data collected at baseline and 6 months from 200 participants who completed the intervention included: dietary data from self-recorded logging using a smartphone application, gut microbiome data from shotgun metagenomics sequencing of faecal samples, and clinical data from continuous glucose monitoring, blood biomarkers and anthropometrics. RESULTS PPT diet induced more prominent changes to the gut microbiome composition, compared with MED diet, consistent with overall greater dietary modifications observed. Particularly, microbiome alpha-diversity increased significantly in PPT (p=0.007) but not in MED arm (p=0.18). Post hoc analysis of changes in multiple dietary features, including food-categories, nutrients and PPT-adherence score across the cohort, demonstrated significant associations between specific dietary changes and species-level changes in microbiome composition. Furthermore, using causal mediation analysis we detect nine microbial species that partially mediate the association between specific dietary changes and clinical outcomes, including three species (from Bacteroidales, Lachnospiraceae, Oscillospirales orders) that mediate the association between PPT-adherence score and clinical outcomes of hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C) and triglycerides. Finally, using machine-learning models trained on dietary changes and baseline clinical data, we predict personalised metabolic responses to dietary modifications and assess features importance for clinical improvement in cardiometabolic markers of blood lipids, glycaemic control and body weight. CONCLUSIONS Our findings support the role of gut microbiome in modulating the effects of dietary modifications on cardiometabolic outcomes, and advance the concept of precision nutrition strategies for reducing comorbidities in pre-diabetes. TRIAL REGISTRATION NUMBER NCT03222791.
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Affiliation(s)
- Orly Ben-Yacov
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Anastasia Godneva
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Rein
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- School of Public Health, University of Haifa, Haifa, Israel
| | - Smadar Shilo
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center, Petah Tikva, Israel
| | - Maya Lotan-Pompan
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Adina Weinberger
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
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45
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Yin L, Liu L, Tang Y, Chen Q, Zhang D, Lin Z, Wang Y, Liu Y. The Implications in Meat Quality and Nutrition by Comparing the Metabolites of Pectoral Muscle between Adult Indigenous Chickens and Commercial Laying Hens. Metabolites 2023; 13:840. [PMID: 37512547 PMCID: PMC10384229 DOI: 10.3390/metabo13070840] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 06/30/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Aged chickens are often a secondary dietary choice, owing to the poor organoleptic qualities of their meat. With regard to the meat quality of chickens, the metabolic profiles of pectoral muscle in Guangyuan grey chickens (group G) and Hy-Line grey hens (group H) aged 55 weeks were compared via ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). A total of 74 metabolites were identified with differential changes in the ion model. Lipids and lipid-like molecules comprised the largest proportion among the different metabolites. The content of myristic acid and palmitic acid were found to be higher in the pectoral muscle of group G, while group H showed significantly higher levels of glycerophospholipid molecules, such as LPC(18:2/0:0), Pi(38:5), Pc(16:0/16:0), and Pe(16:1e/14-hdohe). KEGG pathway analysis indicated that the abundant metabolites in group G were mainly involved in energy metabolism and fatty acid biosynthesis and metabolism, whereas those of group H were mainly attributed to the metabolism of unsaturated fatty acids and amino acids. Overall, the differences in lipid and amino acid metabolism in pectoral muscle appear to be responsible for the difference in meat quality between indigenous chickens and commercial laying hens.
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Affiliation(s)
- Lingqian Yin
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Li Liu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Yuan Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Qian Chen
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Donghao Zhang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Zhongzhen Lin
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Yan Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
| | - Yiping Liu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
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Muhamadali H, Winder CL, Dunn WB, Goodacre R. Unlocking the secrets of the microbiome: exploring the dynamic microbial interplay with humans through metabolomics and their manipulation for synthetic biology applications. Biochem J 2023; 480:891-908. [PMID: 37378961 PMCID: PMC10317162 DOI: 10.1042/bcj20210534] [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: 04/06/2023] [Revised: 06/12/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023]
Abstract
Metabolomics is a powerful research discovery tool with the potential to measure hundreds to low thousands of metabolites. In this review, we discuss the application of GC-MS and LC-MS in discovery-based metabolomics research, we define metabolomics workflows and we highlight considerations that need to be addressed in order to generate robust and reproducible data. We stress that metabolomics is now routinely applied across the biological sciences to study microbiomes from relatively simple microbial systems to their complex interactions within consortia in the host and the environment and highlight this in a range of biological species and mammalian systems including humans. However, challenges do still exist that need to be overcome to maximise the potential for metabolomics to help us understanding biological systems. To demonstrate the potential of the approach we discuss the application of metabolomics in two broad research areas: (1) synthetic biology to increase the production of high-value fine chemicals and reduction in secondary by-products and (2) gut microbial interaction with the human host. While burgeoning in importance, the latter is still in its infancy and will benefit from the development of tools to detangle host-gut-microbial interactions and their impact on human health and diseases.
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Affiliation(s)
- Howbeer Muhamadali
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, U.K
| | - Catherine L. Winder
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, U.K
| | - Warwick B. Dunn
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, U.K
| | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, U.K
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Sciarra F, Franceschini E, Campolo F, Venneri MA. The Diagnostic Potential of the Human Blood Microbiome: Are We Dreaming or Awake? Int J Mol Sci 2023; 24:10422. [PMID: 37445600 DOI: 10.3390/ijms241310422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/01/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
Human blood has historically been considered a sterile environment. Recently, a thriving microbiome dominated by Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes phyla was detected in healthy blood. The localization of these microbes is restricted to some blood cell populations, particularly the peripheral blood mononuclear cells and erythrocytes. It was hypothesized that the blood microbiome originates from the skin-oral-gut axis. In addition, many studies have evaluated the potential of blood microbiome dysbiosis as a prognostic marker in cardiovascular diseases, cirrhosis, severe liver fibrosis, severe acute pancreatitis, type 2 diabetes, and chronic kidney diseases. The present review aims to summarize current findings and most recent evidence in the field.
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Affiliation(s)
- Francesca Sciarra
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | - Edoardo Franceschini
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | - Federica Campolo
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | - Mary Anna Venneri
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy
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48
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Guiducci L, Nicolini G, Forini F. Dietary Patterns, Gut Microbiota Remodeling, and Cardiometabolic Disease. Metabolites 2023; 13:760. [PMID: 37367916 DOI: 10.3390/metabo13060760] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/28/2023] Open
Abstract
The cardiovascular and metabolic disorders, collectively known as cardiometabolic disease (CMD), are high morbidity and mortality pathologies associated with lower quality of life and increasing health-care costs. The influence of the gut microbiota (GM) in dictating the interpersonal variability in CMD susceptibility, progression and treatment response is beginning to be deciphered, as is the mutualistic relation established between the GM and diet. In particular, dietary factors emerge as pivotal determinants shaping the architecture and function of resident microorganisms in the human gut. In turn, intestinal microbes influence the absorption, metabolism, and storage of ingested nutrients, with potentially profound effects on host physiology. Herein, we present an updated overview on major effects of dietary components on the GM, highlighting the beneficial and detrimental consequences of diet-microbiota crosstalk in the setting of CMD. We also discuss the promises and challenges of integrating microbiome data in dietary planning aimed at restraining CMD onset and progression with a more personalized nutritional approach.
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Affiliation(s)
- Letizia Guiducci
- CNR Institute of Clinical Physiology, Via Moruzzi 1, 56124 Pisa, Italy
| | | | - Francesca Forini
- CNR Institute of Clinical Physiology, Via Moruzzi 1, 56124 Pisa, Italy
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Yang R, Wang Y, Yuan C, Shen X, Cai M, Wang L, Hu J, Song H, Wang H, Zhang L. The combined analysis of urine and blood metabolomics profiles provides an accurate prediction of the training and competitive status of Chinese professional swimmers. Front Physiol 2023; 14:1197224. [PMID: 37398904 PMCID: PMC10307620 DOI: 10.3389/fphys.2023.1197224] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/31/2023] [Indexed: 07/04/2023] Open
Abstract
Objective: The purpose of this study was to employ metabolomics for the analysis of urine metabolites in swimmers, with the aim of establishing models for assessing their athletic status and competitive potential. Furthermore, the study sought to compare the identification efficacy of multi-component (urine and blood) model versus single-component (urine or blood) models, in order to determine the optimal approach for evaluating training and competitive status. Methods: A total of 187 Chinese professional swimmers, comprising 103 elite and 84 sub-elite level athletes, were selected as subjects for this study. Urine samples were obtained from each participant and subjected to nuclear magnetic resonance (NMR) metabolomics analysis. Significant urine metabolites were screened through multivariable logistic regression analysis, and an identification model was established. Based on the previously established model of blood metabolites, this study compared the discriminative and predictive performance of three models: either urine or blood metabolites model and urine + blood metabolites model. Results: Among 39 urine metabolites, 10 were found to be significantly associated with the athletic status of swimmers (p < 0.05). Of these, levels of 2-KC, cis-aconitate, formate, and LAC were higher in elite swimmers compared to sub-elite athletes, while levels of 3-HIV, creatinine, 3-HIB, hippurate, pseudouridine, and trigonelline were lower in elite swimmers. Notably, 2-KC and 3-HIB exhibited the most substantial differences. An identification model was developed to estimate physical performance and athletic level of swimmers while adjusting for different covariates and including 2-KC and 3-HIB. The urine metabolites model showed an area under the curve (AUC) of 0.852 (95% CI: 0.793-0.912) for discrimination. Among the three identification models tested, the combination of urine and blood metabolites showed the highest performance than either urine or blood metabolites, with an AUC of 0.925 (95% CI: 0.888-0.963). Conclusion: The two urine metabolites, 2-KC and 3-HIV, can serve as significant urine metabolic markers to establish a discrimination model for identifying the athletic status and competitive potential of Chinese elite swimmers. Combining two screened urine metabolites with four metabolites reported exhibiting significant differences in blood resulted in improved predictive performance compared to using urine metabolites alone. These findings indicate that combining blood and urine metabolites has a greater potential for identifying and predicting the athletic status and competitive potential of Chinese professional swimmers.
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Affiliation(s)
- Ruoyu Yang
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Yi Wang
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Chunhua Yuan
- Surgery Ward, Shanghai Health Rehabilitation Hospital, Shanghai, China
| | - Xunzhang Shen
- Shanghai Research Institute of Sports Science (Shanghai Anti-Doping Center), Shanghai, China
| | - Ming Cai
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Liyan Wang
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Jingyun Hu
- Central Lab, Shanghai Key Laboratory of Pathogenic Fungi Medical Testing, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Haihan Song
- Central Lab, Shanghai Key Laboratory of Pathogenic Fungi Medical Testing, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Hongbiao Wang
- Department of Physical Education, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Lei Zhang
- Department of Pediatrics, Shanghai Pudong New Area People’s Hospital, Shanghai, China
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50
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Zhong P, Tan S, Zhu Z, Bulloch G, Long E, Huang W, He M, Wang W. Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases. J Transl Med 2023; 21:384. [PMID: 37308902 DOI: 10.1186/s12967-023-04244-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND We aimed to evaluate the impacts of metabolomic body mass index (metBMI) phenotypes on the risks of cardiovascular and ocular diseases outcomes. METHODS This study included cohorts in UK and Guangzhou, China. By leveraging the serum metabolome and BMI data from UK Biobank, this study developed and validated a metBMI prediction model using a ridge regression model among 89,830 participants based on 249 metabolites. Five obesity phenotypes were obtained by metBMI and actual BMI (actBMI): normal weight (NW, metBMI of 18.5-24.9 kg/m2), overweight (OW, metBMI of 25-29.9 kg/m2), obesity (OB, metBMI ≥ 30 kg/m2), overestimated (OE, metBMI-actBMI > 5 kg/m2), and underestimated (UE, metBMI-actBMI < - 5 kg/m2). Additional participants from the Guangzhou Diabetes Eye Study (GDES) were included for validating the hypothesis. Outcomes included all-cause and cardiovascular (CVD)-cause mortality, as well as incident CVD (coronary heart disease, heart failure, myocardial infarction [MI], and stroke) and age-related eye diseases (age-related macular degeneration [AMD], cataracts, glaucoma, and diabetic retinopathy [DR]). RESULTS In the UKB, although OE group had lower actBMI than NW group, the OE group had a significantly higher risk of all-cause mortality than those in NW prediction group (HR, 1.68; 95% CI 1.16-2.43). Similarly, the OE group had a 1.7-3.6-fold higher risk than their NW counterparts for cardiovascular mortality, heart failure, myocardial infarction, and coronary heart disease (all P < 0.05). In addition, risk of age-related macular denegation (HR, 1.96; 95% CI 1.02-3.77) was significantly higher in OE group. In the contrast, UE and OB groups showed similar risks of mortality and of cardiovascular and age-related eye diseases (all P > 0.05), though the UE group had significantly higher actBMI than OB group. In the GDES cohort, we further confirmed the potential of metabolic BMI (metBMI) fingerprints for risk stratification of cardiovascular diseases using a different metabolomic approach. CONCLUSIONS Gaps of metBMI and actBMI identified novel metabolic subtypes, which exhibit distinctive cardiovascular and ocular risk profiles. The groups carrying obesity-related metabolites were at higher risk of mortality and morbidity than those with normal health metabolites. Metabolomics allowed for leveraging the future of diagnosis and management of 'healthily obese' and 'unhealthily lean' individuals.
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Affiliation(s)
- Pingting Zhong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Shaoying Tan
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Hong Kong, China
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
| | - Gabriella Bulloch
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
| | - Erping Long
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Hong Kong, China.
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China.
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC, 3002, Australia.
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
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