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Coskun A, Ertaylan G, Pusparum M, Van Hoof R, Kaya ZZ, Khosravi A, Zarrabi A. Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167339. [PMID: 38986819 DOI: 10.1016/j.bbadis.2024.167339] [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: 04/04/2024] [Revised: 06/25/2024] [Accepted: 07/03/2024] [Indexed: 07/12/2024]
Abstract
Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.
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Affiliation(s)
- Abdurrahman Coskun
- Acibadem University, School of Medicine, Department of Medical Biochemistry, Istanbul, Turkey.
| | - Gökhan Ertaylan
- Unit Health, Environmental Intelligence, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium
| | - Murih Pusparum
- Unit Health, Environmental Intelligence, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium; I-Biostat, Data Science Institute, Hasselt University, Hasselt 3500, Belgium
| | - Rebekka Van Hoof
- Unit Health, Environmental Intelligence, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium
| | - Zelal Zuhal Kaya
- Nisantasi University, School of Medicine, Department of Medical Biochemistry, Istanbul, Turkey
| | - Arezoo Khosravi
- Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul 34959, Turkey
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Turkey; Graduate School of Biotehnology and Bioengeneering, Yuan Ze University, Taoyuan 320315, Taiwan; Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600 077, India
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2
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Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nat Biotechnol 2024; 42:1133-1149. [PMID: 37679543 PMCID: PMC11252085 DOI: 10.1038/s41587-023-01934-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.
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Affiliation(s)
- Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | | | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Feng Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Ling Lin
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Jingwei Lou
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Ruiqiang Li
- Novogene Bioinformatics Institute, Beijing, China
| | - Jingchao Lin
- Metabo-Profile Biotechnology (Shanghai) Co. Ltd., Shanghai, China
| | | | | | - Depeng Wang
- Nextomics Biosciences Institute, Wuhan, China
| | | | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Yinbo Huo
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Gang Liu
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chengming Cao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Li Shao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xiaozhen Liang
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weida Tong
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
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3
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Ferreira CR, Lima Gomes PCFD, Robison KM, Cooper BR, Shannahan JH. Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure. Mol Omics 2024; 20:296-321. [PMID: 38623720 PMCID: PMC11163948 DOI: 10.1039/d3mo00214d] [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: 10/19/2023] [Accepted: 03/22/2024] [Indexed: 04/17/2024]
Abstract
Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.
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Affiliation(s)
- Christina R Ferreira
- Purdue Metabolite Profiling Facility, Purdue University, West Lafayette, IN 47907, USA.
| | | | - Kiley Marie Robison
- School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Bruce R Cooper
- Purdue Metabolite Profiling Facility, Purdue University, West Lafayette, IN 47907, USA.
| | - Jonathan H Shannahan
- School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN 47907, USA
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4
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Minvielle Moncla LH, Briend M, Sokhna Sylla M, Mathieu S, Rufiange A, Bossé Y, Mathieu P. Mendelian randomization reveals interactions of the blood proteome and immunome in mitral valve prolapse. COMMUNICATIONS MEDICINE 2024; 4:108. [PMID: 38844506 PMCID: PMC11156961 DOI: 10.1038/s43856-024-00530-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Mitral valve prolapse (MVP) is a common heart disorder characterized by an excessive production of proteoglycans and extracellular matrix in mitral valve leaflets. Large-scale genome-wide association study (GWAS) underlined that MVP is heritable. The molecular underpinnings of the disease remain largely unknown. METHODS We interrogated cross-modality data totaling more than 500,000 subjects including GWAS, 4809 molecules of the blood proteome, and genome-wide expression of mitral valves to identify candidate drivers of MVP. Data were investigated through Mendelian randomization, network analysis, ligand-receptor inference and digital cell quantification. RESULTS In this study, Mendelian randomization identify that 33 blood proteins, enriched in networks for immunity, are associated with the risk of MVP. MVP- associated blood proteins are enriched in ligands for which their cognate receptors are differentially expressed in mitral valve leaflets during MVP and enriched in cardiac endothelial cells and macrophages. MVP-associated blood proteins are involved in the renewal-polarization of macrophages and regulation of adaptive immune response. Cytokine activity profiling and digital cell quantification show in MVP a shift toward cytokine signature promoting M2 macrophage polarization. Assessment of druggability identify CSF1R, CX3CR1, CCR6, IL33, MMP8, ENPEP and angiotensin receptors as actionable targets in MVP. CONCLUSIONS Hence, integrative analysis identifies networks of candidate molecules and cells involved in immune control and remodeling of the extracellular matrix, which drive the risk of MVP.
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Affiliation(s)
| | - Mewen Briend
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Mame Sokhna Sylla
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Samuel Mathieu
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Anne Rufiange
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada
| | - Yohan Bossé
- Department of Molecular Medicine, Laval University, Quebec City, QC, Canada
| | - Patrick Mathieu
- Genomic Medicine Laboratory, Quebec Heart and Lung Institute, Laval University, Quebec City, QC, Canada.
- Department of Surgery, Laval University, Quebec City, QC, Canada.
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5
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Hariharan R, Hood L, Price ND. A data-driven approach to improve wellness and reduce recurrence in cancer survivors. Front Oncol 2024; 14:1397008. [PMID: 38665952 PMCID: PMC11044254 DOI: 10.3389/fonc.2024.1397008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
For many cancer survivors, toxic side effects of treatment, lingering effects of the aftermath of disease and cancer recurrence adversely affect quality of life (QoL) and reduce healthspan. Data-driven approaches for quantifying and improving wellness in healthy individuals hold great promise for improving the lives of cancer survivors. The data-driven strategy will also guide personalized nutrition and exercise recommendations that may help prevent cancer recurrence and secondary malignancies in survivors.
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Affiliation(s)
- Ramkumar Hariharan
- College of Engineering, Northeastern University, Seattle, WA, United States
- Institute for Experiential Artificial Intelligence, Northeastern University, Boston, MA, United States
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, United States
- Buck Institute for Research on Aging, Novato, CA, United States
- Phenome Health, Seattle, WA, United States
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, WA, United States
- Thorne HealthTech, New York, NY, United States
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Rose SMSF, Tran TDB, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas PB, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe 2024; 32:506-526.e9. [PMID: 38479397 PMCID: PMC11022754 DOI: 10.1016/j.chom.2024.02.012] [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: 12/05/2023] [Revised: 01/23/2024] [Accepted: 02/20/2024] [Indexed: 03/26/2024]
Abstract
To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.
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Affiliation(s)
- Xin Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Jethro S Johnson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Oxford Centre for Microbiome Studies, Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7FY, UK
| | - Daniel J Spakowicz
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Division of Medical Oncology, Ohio State University Wexner Medical Center, James Cancer Hospital and Solove Research Institute, Columbus, OH 43210, USA
| | | | - Wenyu Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Monica Avina
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexander Honkala
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Faye Chleilat
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shirley Jingyi Chen
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kexin Cha
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shana Leopold
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chenchen Zhu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lei Chen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Lin Lyu
- Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Si Wu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xinyue Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Chao Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Liuyiqi Jiang
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Lihua Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruiqi Jian
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Andrew W Brooks
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Peng Gao
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | - Hoan Nguyen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Alessandra Celli
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bo-Young Hong
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Woody L Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX 79905, USA
| | - Eddy J Bautista
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Headquarters-Mosquera, Cundinamarca 250047, Colombia
| | - Yair Dorsett
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Paula B Kavathas
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Yanjiao Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Erica Sodergren
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA.
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7
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Anwar MA, Keshteli AH, Yang H, Wang W, Li X, Messier HM, Cullis PR, Borchers CH, Fraser R, Wishart DS. Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:182-192. [PMID: 38634790 DOI: 10.1089/omi.2023.0278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.
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Affiliation(s)
| | | | - Haiyan Yang
- Molecular You Corporation, Vancouver, British Columbia, Canada
| | - Windy Wang
- Molecular You Corporation, Vancouver, British Columbia, Canada
| | - Xukun Li
- Molecular You Corporation, Vancouver, British Columbia, Canada
| | - Helen M Messier
- Molecular You Corporation, Vancouver, British Columbia, Canada
- Fountain Life, Naples, Florida, USA
| | - Pieter R Cullis
- Molecular You Corporation, Vancouver, British Columbia, Canada
- Life Sciences Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Christoph H Borchers
- Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada
- Department of Pathology, McGill University, Montreal, Quebec, Canada
| | - Robert Fraser
- Molecular You Corporation, Vancouver, British Columbia, Canada
| | - David S Wishart
- Molecular You Corporation, Vancouver, British Columbia, Canada
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
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8
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Huang Q, Hu Z, Zheng Q, Mao X, Lv W, Wu F, Fu D, Lu C, Zeng C, Wang F, Zeng Q, Fang Q, Hood L. A Proactive Intervention Study in Metabolic Syndrome High-Risk Populations Using Phenome-Based Actionable P4 Medicine Strategy. PHENOMICS (CHAM, SWITZERLAND) 2024; 4:91-108. [PMID: 38884061 PMCID: PMC11169348 DOI: 10.1007/s43657-023-00115-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/13/2023] [Accepted: 06/16/2023] [Indexed: 06/18/2024]
Abstract
The integration of predictive, preventive, personalized, and participatory (P4) healthcare advocates proactive intervention, including dietary supplements and lifestyle interventions for chronic disease. Personal profiles include deep phenotypic data and genetic information, which are associated with chronic diseases, can guide proactive intervention. However, little is known about how to design an appropriate intervention mode to precisely intervene with personalized phenome-based data. Here, we report the results of a 3-month study on 350 individuals with metabolic syndrome high-risk that we named the Pioneer 350 Wellness project (P350). We examined: (1) longitudinal (two times) phenotypes covering blood lipids, blood glucose, homocysteine (HCY), and vitamin D3 (VD3), and (2) polymorphism of genes related to folic acid metabolism. Based on personalized data and questionnaires including demographics, diet and exercise habits information, coaches identified 'actionable possibilities', which combined exercise, diet, and dietary supplements. After a 3-month proactive intervention, two-thirds of the phenotypic markers were significantly improved in the P350 cohort. Specifically, we found that dietary supplements and lifestyle interventions have different effects on phenotypic improvement. For example, dietary supplements can result in a rapid recovery of abnormal HCY and VD3 levels, while lifestyle interventions are more suitable for those with high body mass index (BMI), but almost do not help the recovery of HCY. Furthermore, although people who implemented only one of the exercise or diet interventions also benefited, the effect was not as good as the combined exercise and diet interventions. In a subgroup of 226 people, we examined the association between the polymorphism of genes related to folic acid metabolism and the benefits of folate supplementation to restore a normal HCY level. We found people with folic acid metabolism deficiency genes are more likely to benefit from folate supplementation to restore a normal HCY level. Overall, these results suggest: (1) phenome-based data can guide the formulation of more precise and comprehensive interventions, and (2) genetic polymorphism impacts clinical responses to interventions. Notably, we provide a proactive intervention example that is operable in daily life, allowing people with different phenome-based data to design the appropriate intervention protocol including dietary supplements and lifestyle interventions. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00115-z.
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Affiliation(s)
- Qiongrong Huang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, CAS Center for Excellence in Nanoscience, Beijing, 100190 China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049 China
| | - Zhiyuan Hu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, CAS Center for Excellence in Nanoscience, Beijing, 100190 China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049 China
- Beijing P4 Healthcare Institute, 316 Wanfeng Road, Beijing, 100161 China
- Health Management Institute, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853 China
- School of Nanoscience and Technology, Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049 China
- Fujian Provincial Key Laboratory of Brain Aging and Neurodegenerative Diseases, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350108 Fujian China
- School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, 430205 Hubei China
| | - Qiwen Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
| | - Xuemei Mao
- Beijing P4 Healthcare Institute, 316 Wanfeng Road, Beijing, 100161 China
| | - Wenxi Lv
- Beijing P4 Healthcare Institute, 316 Wanfeng Road, Beijing, 100161 China
| | - Fei Wu
- Beijing P4 Healthcare Institute, 316 Wanfeng Road, Beijing, 100161 China
| | - Dapeng Fu
- Beijing Zhongguancun Hospital, No. 12, Zhongguancun South Road, Haidian District, Beijing, 100190 China
| | - Cuihong Lu
- Beijing Zhongguancun Hospital, No. 12, Zhongguancun South Road, Haidian District, Beijing, 100190 China
| | - Changqing Zeng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
| | - Fei Wang
- Health Management Institute, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853 China
| | - Qiang Zeng
- Health Management Institute, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853 China
| | - Qiaojun Fang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Key Laboratory of Standardization and Measurement for Nanotechnology, National Center for Nanoscience and Technology, CAS Center for Excellence in Nanoscience, Beijing, 100190 China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049 China
- School of Nanoscience and Technology, Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Leroy Hood
- Health Management Institute, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853 China
- Institute for Systems Biology, Seattle, WA 98109 USA
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9
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Yurkovich JT, Evans SJ, Rappaport N, Boore JL, Lovejoy JC, Price ND, Hood LE. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 2024; 25:286-302. [PMID: 38093095 DOI: 10.1038/s41576-023-00674-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 03/21/2024]
Abstract
Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
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Affiliation(s)
- James T Yurkovich
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Simon J Evans
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Noa Rappaport
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Jeffrey L Boore
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Jennifer C Lovejoy
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy E Hood
- Phenome Health, Seattle, WA, USA.
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.
- Institute for Systems Biology, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
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10
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Qiao N, Lyu Y, Liu F, Zhang Y, Ma X, Lin X, Wang J, Xie Y, Zhang R, Qiao J, Zhu H, Chen L, Fang H, Yin T, Chen Z, Tian Q, Chen S. Cross-sectional network analysis of plasma proteins/metabolites correlated with pathogenesis and therapeutic response in acute promyelocytic leukemia. Front Med 2024; 18:327-343. [PMID: 38151667 DOI: 10.1007/s11684-023-1022-x] [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: 02/17/2023] [Accepted: 07/20/2023] [Indexed: 12/29/2023]
Abstract
The treatment of PML/RARA+ acute promyelocytic leukemia (APL) with all-trans-retinoic acid and arsenic trioxide (ATRA/ATO) has been recognized as a model for translational medicine research. Though an altered microenvironment is a general cancer hallmark, how APL blasts shape their plasma composition is poorly understood. Here, we reported a cross-sectional correlation network to interpret multilayered datasets on clinical parameters, proteomes, and metabolomes of paired plasma samples from patients with APL before or after ATRA/ATO induction therapy. Our study revealed the two prominent features of the APL plasma, suggesting a possible involvement of APL blasts in modulating plasma composition. One was characterized by altered secretory protein and metabolite profiles correlating with heightened proliferation and energy consumption in APL blasts, and the other featured APL plasma-enriched proteins or enzymes catalyzing plasma-altered metabolites that were potential trans-regulatory targets of PML/RARA. Furthermore, results indicated heightened interferon-gamma signaling characterizing a tumor-suppressing function of the immune system at the first hematological complete remission stage, which likely resulted from therapy-induced cell death or senescence and ensuing supraphysiological levels of intracellular proteins. Overall, our work sheds new light on the pathophysiology and treatment of APL and provides an information-rich reference data cohort for the exploratory and translational study of leukemia microenvironment.
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Affiliation(s)
- Niu Qiao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yizhu Lyu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- Department of Hematology, Second Hospital of Dalian Medical University, Dalian, 116021, China
| | - Feng Liu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Yuliang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaolin Ma
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaojing Lin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Junyu Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yinyin Xie
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ruihong Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jing Qiao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Hongming Zhu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Li Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tong Yin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Zhu Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qiang Tian
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Saijuan Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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11
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Andorra M, Freire A, Zubizarreta I, de Rosbo NK, Bos SD, Rinas M, Høgestøl EA, de Rodez Benavent SA, Berge T, Brune-Ingebretse S, Ivaldi F, Cellerino M, Pardini M, Vila G, Pulido-Valdeolivas I, Martinez-Lapiscina EH, Llufriu S, Saiz A, Blanco Y, Martinez-Heras E, Solana E, Bäcker-Koduah P, Behrens J, Kuchling J, Asseyer S, Scheel M, Chien C, Zimmermann H, Motamedi S, Kauer-Bonin J, Brandt A, Saez-Rodriguez J, Alexopoulos LG, Paul F, Harbo HF, Shams H, Oksenberg J, Uccelli A, Baeza-Yates R, Villoslada P. Predicting disease severity in multiple sclerosis using multimodal data and machine learning. J Neurol 2024; 271:1133-1149. [PMID: 38133801 PMCID: PMC10896787 DOI: 10.1007/s00415-023-12132-z] [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: 06/24/2023] [Revised: 10/28/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. METHODS We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. RESULTS We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. CONCLUSION Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.
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Affiliation(s)
- Magi Andorra
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Ana Freire
- School of Management, Pompeu Fabra University, Barcelona, Spain
- UPF Barcelona School of Management, Balmes 132, 08008, Barcelona, Spain
| | - Irati Zubizarreta
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Nicole Kerlero de Rosbo
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Steffan D Bos
- University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Melanie Rinas
- Institute for Computational Biomedicine, Heidelberg University Hospital, and Heidelberg University, Heidelberg, Germany
| | - Einar A Høgestøl
- University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | | | - Tone Berge
- Oslo University Hospital, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | | | - Federico Ivaldi
- Department of Internal Medicine, University of Genoa, Genoa, Italy
| | - Maria Cellerino
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Matteo Pardini
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Gemma Vila
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Irene Pulido-Valdeolivas
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Elena H Martinez-Lapiscina
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Albert Saiz
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Eloy Martinez-Heras
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain
| | | | | | | | - Susanna Asseyer
- Charité Universitaetsmedizin Berlin, Berlin, Germany
- Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | | | - Claudia Chien
- Charité Universitaetsmedizin Berlin, Berlin, Germany
- Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Hanna Zimmermann
- Charité Universitaetsmedizin Berlin, Berlin, Germany
- Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | | | | | - Alex Brandt
- Charité Universitaetsmedizin Berlin, Berlin, Germany
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University Hospital, and Heidelberg University, Heidelberg, Germany
| | - Leonidas G Alexopoulos
- ProtATonce Ltd, Athens, Greece
- School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece
| | - Friedemann Paul
- Charité Universitaetsmedizin Berlin, Berlin, Germany
- Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Hanne F Harbo
- University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Hengameh Shams
- Department of Neurology, University of California, San Francisco, USA
| | - Jorge Oksenberg
- Department of Neurology, University of California, San Francisco, USA
| | - Antonio Uccelli
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Pablo Villoslada
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.
- Hospital del Mar Research Institute, Barcelona, Spain.
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12
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Kennedy KE, Kerlero de Rosbo N, Uccelli A, Cellerino M, Ivaldi F, Contini P, De Palma R, Harbo HF, Berge T, Bos SD, Høgestøl EA, Brune-Ingebretsen S, de Rodez Benavent SA, Paul F, Brandt AU, Bäcker-Koduah P, Behrens J, Kuchling J, Asseyer S, Scheel M, Chien C, Zimmermann H, Motamedi S, Kauer-Bonin J, Saez-Rodriguez J, Rinas M, Alexopoulos LG, Andorra M, Llufriu S, Saiz A, Blanco Y, Martinez-Heras E, Solana E, Pulido-Valdeolivas I, Martinez-Lapiscina EH, Garcia-Ojalvo J, Villoslada P. Multiscale networks in multiple sclerosis. PLoS Comput Biol 2024; 20:e1010980. [PMID: 38329927 PMCID: PMC10852301 DOI: 10.1371/journal.pcbi.1010980] [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: 02/25/2023] [Accepted: 12/12/2023] [Indexed: 02/10/2024] Open
Abstract
Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.
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Affiliation(s)
- Keith E. Kennedy
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nicole Kerlero de Rosbo
- Department of Neurology, Ospedale Policlinico San Martino-IRCCS and Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa Italy
- TomaLab, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
| | - Antonio Uccelli
- Department of Neurology, Ospedale Policlinico San Martino-IRCCS and Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa Italy
| | - Maria Cellerino
- Department of Neurology, Ospedale Policlinico San Martino-IRCCS and Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa Italy
| | - Federico Ivaldi
- Department of Neurology, Ospedale Policlinico San Martino-IRCCS and Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa Italy
| | - Paola Contini
- Department of Neurology, Ospedale Policlinico San Martino-IRCCS and Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa Italy
| | - Raffaele De Palma
- Department of Neurology, Ospedale Policlinico San Martino-IRCCS and Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa Italy
| | - Hanne F. Harbo
- Department of Neurology, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Tone Berge
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | - Steffan D. Bos
- Department of Neurology, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Einar A. Høgestøl
- Department of Neurology, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Synne Brune-Ingebretsen
- Department of Neurology, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Sigrid A. de Rodez Benavent
- Department of Neurology, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Friedemann Paul
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Alexander U. Brandt
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
- Department of Neurology, University of California, Irvine, California, United States of America
| | - Priscilla Bäcker-Koduah
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Janina Behrens
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Joseph Kuchling
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Susanna Asseyer
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Michael Scheel
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Claudia Chien
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Hanna Zimmermann
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Seyedamirhosein Motamedi
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Josef Kauer-Bonin
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, University of Heidelberg, Heidelberg, Germany
| | - Melanie Rinas
- Institute for Computational Biomedicine, University of Heidelberg, Heidelberg, Germany
| | - Leonidas G. Alexopoulos
- ProtATonce Ltd, Athens, Greece
- School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece
| | - Magi Andorra
- Center of Neuroimmunology, Hospital Clinic Barcelona, and Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain
| | - Sara Llufriu
- Center of Neuroimmunology, Hospital Clinic Barcelona, and Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain
| | - Albert Saiz
- Center of Neuroimmunology, Hospital Clinic Barcelona, and Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain
| | - Yolanda Blanco
- Center of Neuroimmunology, Hospital Clinic Barcelona, and Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Hospital Clinic Barcelona, and Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain
| | - Elisabeth Solana
- Center of Neuroimmunology, Hospital Clinic Barcelona, and Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain
| | - Irene Pulido-Valdeolivas
- Center of Neuroimmunology, Hospital Clinic Barcelona, and Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain
| | - Elena H. Martinez-Lapiscina
- Center of Neuroimmunology, Hospital Clinic Barcelona, and Institut d’Investigacions Biomediques August Pi i Sunyer, Barcelona, Spain
| | - Jordi Garcia-Ojalvo
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Pablo Villoslada
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Neurology, Hospital del Mar Research Institute, Barcelona, Spain
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13
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Schüssler-Fiorenza Rose SM, Binh Tran TD, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas P, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.577565. [PMID: 38352363 PMCID: PMC10862915 DOI: 10.1101/2024.02.01.577565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.
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14
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Babu M, Lautman Z, Lin X, Sobota MHB, Snyder MP. Wearable Devices: Implications for Precision Medicine and the Future of Health Care. Annu Rev Med 2024; 75:401-415. [PMID: 37983384 DOI: 10.1146/annurev-med-052422-020437] [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: 11/22/2023]
Abstract
Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.
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Affiliation(s)
- Mohan Babu
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Ziv Lautman
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
- Department of Bioengineering, Stanford University School of Medicine, Stanford, California, USA
| | - Xiangping Lin
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Milan H B Sobota
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
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15
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Shen X, Kellogg R, Panyard DJ, Bararpour N, Castillo KE, Lee-McMullen B, Delfarah A, Ubellacker J, Ahadi S, Rosenberg-Hasson Y, Ganz A, Contrepois K, Michael B, Simms I, Wang C, Hornburg D, Snyder MP. Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nat Biomed Eng 2024; 8:11-29. [PMID: 36658343 PMCID: PMC10805653 DOI: 10.1038/s41551-022-00999-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/14/2022] [Indexed: 01/21/2023]
Abstract
Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.
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Affiliation(s)
- Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Ryan Kellogg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Daniel J Panyard
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Nasim Bararpour
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Kevin Erazo Castillo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Brittany Lee-McMullen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Alireza Delfarah
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Jessalyn Ubellacker
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sara Ahadi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Yael Rosenberg-Hasson
- Human Immune Monitoring Center, Microbiology and Immunology, Stanford University Medical Center, Stanford, CA, USA
| | - Ariel Ganz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Basil Michael
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Ian Simms
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Chuchu Wang
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA.
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16
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Green S, Prainsack B, Sabatello M. The roots of (in)equity in precision medicine: gaps in the discourse. Per Med 2024; 21:5-9. [PMID: 38088178 PMCID: PMC10784620 DOI: 10.2217/pme-2023-0097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Affiliation(s)
- Sara Green
- Department of Science Education, Section for History & Philosophy of Science, University of Copenhagen, 2100 Copenhagen, Denmark
- Department of Public Health, Centre for Medical Science & Technology Studies, University of Copenhagen, 1014 Copenhagen, Denmark
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, 1010 Vienna, Austria
- School of Social & Political Sciences, Faculty of Arts & Social Sciences, University of Sydney, 2006 NSW, Australia
| | - Maya Sabatello
- Department of Medicine, Center for Precision Medicine & Genomics, Columbia University, 10032 New York, USA
- Department of Medical Humanities & Ethics, Division of Ethics, Columbia University, 10032 New York, USA
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17
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Sun L, Li Z, Hu C, Ding J, Zhou Q, Pang G, Wu Z, Yang R, Li S, Li J, Cai J, Sun Y, Li R, Zhen H, Sun S, Zhang J, Fang M, Chen Z, Lv Y, Cao Q, Sun Y, Gong R, Huang Z, Duan Y, Liu H, Dong J, Li J, Ruan J, Lu H, He B, Li N, Li T, Xue W, Li Y, Shen J, Yang F, Zhao C, Liang Q, Zhang M, Chen C, Gong H, Hou Y, Wang J, Zhang Y, Yang H, Zhu S, Xiao L, Jin Z, Guo H, Zhao P, Brix S, Xu X, Jia H, Kristiansen K, Yang Z, Nie C. Age-dependent changes in the gut microbiota and serum metabolome correlate with renal function and human aging. Aging Cell 2023; 22:e14028. [PMID: 38015106 PMCID: PMC10726799 DOI: 10.1111/acel.14028] [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: 03/15/2023] [Revised: 10/16/2023] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Human aging is invariably accompanied by a decline in renal function, a process potentially exacerbated by uremic toxins originating from gut microbes. Based on a registered household Chinese Guangxi longevity cohort (n = 151), we conducted comprehensive profiling of the gut microbiota and serum metabolome of individuals from 22 to 111 years of age and validated the findings in two independent East Asian aging cohorts (Japan aging cohort n = 330, Yunnan aging cohort n = 80), identifying unique age-dependent differences in the microbiota and serum metabolome. We discovered that the influence of the gut microbiota on serum metabolites intensifies with advancing age. Furthermore, mediation analyses unveiled putative causal relationships between the gut microbiota (Escherichia coli, Odoribacter splanchnicus, and Desulfovibrio piger) and serum metabolite markers related to impaired renal function (p-cresol, N-phenylacetylglutamine, 2-oxindole, and 4-aminohippuric acid) and aging. The fecal microbiota transplantation experiment demonstrated that the feces of elderly individuals could influence markers related to impaired renal function in the serum. Our findings reveal novel links between age-dependent alterations in the gut microbiota and serum metabolite markers of impaired renal function, providing novel insights into the effects of microbiota-metabolite interplay on renal function and healthy aging.
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Affiliation(s)
- Liang Sun
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Zhiming Li
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Shenzhen Key Laboratory of Neurogenomics, BGI ResearchShenzhenChina
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan UniversityShanghaiChina
| | | | - Jiahong Ding
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Shenzhen Key Laboratory of Neurogenomics, BGI ResearchShenzhenChina
| | - Qi Zhou
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | | | - Zhu Wu
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Ruiyue Yang
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Shenghui Li
- Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and HealthChina Agricultural UniversityBeijingChina
| | - Jian Li
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Jianping Cai
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Yuzhe Sun
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Shenzhen Key Laboratory of Neurogenomics, BGI ResearchShenzhenChina
| | - Rui Li
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Hefu Zhen
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Shenzhen Key Laboratory of Neurogenomics, BGI ResearchShenzhenChina
| | - Shuqin Sun
- School of GerontologyBinzhou Medical UniversityYantaiChina
| | - Jianmin Zhang
- School of GerontologyBinzhou Medical UniversityYantaiChina
| | - Mingyan Fang
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Zhihua Chen
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Yuan Lv
- Jiangbin HospitalNanningChina
| | - Qizhi Cao
- School of GerontologyBinzhou Medical UniversityYantaiChina
| | - Yanan Sun
- School of GerontologyBinzhou Medical UniversityYantaiChina
| | - Ranhui Gong
- Office of Longevity Cultural, People's Government of Yongfu CountyGuilinChina
| | - Zezhi Huang
- Office of Longevity Cultural, People's Government of Yongfu CountyGuilinChina
| | - Yong Duan
- Yunnan Key Laboratory of Laboratory MedicineKunmingChina
- Yunnan Institute of Experimental DiagnosisKunmingChina
| | - Hengshuo Liu
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Jun Dong
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Junchun Li
- Office of Longevity Cultural, People's Government of Yongfu CountyGuilinChina
| | - Jie Ruan
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Haorong Lu
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | | | | | - Tao Li
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Wenbin Xue
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Yan Li
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Shenzhen Key Laboratory of Neurogenomics, BGI ResearchShenzhenChina
| | - Juan Shen
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Fan Yang
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Cheng Zhao
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | | | - Mingrong Zhang
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Chen Chen
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Huan Gong
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Yong Hou
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Jian Wang
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Ying Zhang
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Huanming Yang
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Shida Zhu
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Shenzhen Engineering Laboratory for Innovative Molecular Diagnostics, BGI ResearchShenzhenChina
| | - Liang Xiao
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, BGI ResearchShenzhenChina
| | - Zhen Jin
- Yunnan Key Laboratory of Laboratory MedicineKunmingChina
- Yunnan Institute of Experimental DiagnosisKunmingChina
| | - Haiyun Guo
- Yunnan Key Laboratory of Laboratory MedicineKunmingChina
| | - Peng Zhao
- Yunnan Key Laboratory of Laboratory MedicineKunmingChina
| | - Susanne Brix
- Department of Biotechnology and BiomedicineTechnical University of DenmarkLyngbyDenmark
| | - Xun Xu
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI ResearchShenzhenChina
| | - Huijue Jia
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
| | - Karsten Kristiansen
- BGI ResearchShenzhenChina
- Laboratory of Genomics and Molecular Biomedicine, Department of BiologyUniversity of CopenhagenCopenhagenDenmark
- Qingdao‐Europe Advanced Institute for Life SciencesQingdaoShandongChina
| | - Ze Yang
- The NHC Key Laboratory of GeriatricsInstitute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health CommissionBeijingChina
| | - Chao Nie
- BGI ResearchShenzhenChina
- China National GeneBank, BGI ResearchShenzhenChina
- Shenzhen Key Laboratory of Neurogenomics, BGI ResearchShenzhenChina
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18
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Shastry A, Dunham-Snary K. Metabolomics and mitochondrial dysfunction in cardiometabolic disease. Life Sci 2023; 333:122137. [PMID: 37788764 DOI: 10.1016/j.lfs.2023.122137] [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: 08/01/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/05/2023]
Abstract
Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.
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Affiliation(s)
- Abhishek Shastry
- Department of Medicine, Queen's University, Kingston, ON, Canada
| | - Kimberly Dunham-Snary
- Department of Medicine, Queen's University, Kingston, ON, Canada; Department of Biomedical & Molecular Sciences, Queen's University, Kingston, ON, Canada.
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19
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Mallin M, Hall J, Herlihy M, Gelman EJ, Stone MB. A pilot retrospective study of a physician-directed and genomics-based model for precision lifestyle medicine. Front Med (Lausanne) 2023; 10:1239737. [PMID: 37942418 PMCID: PMC10629614 DOI: 10.3389/fmed.2023.1239737] [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: 06/13/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
Precision lifestyle medicine is a relatively new field in primary care, based on the hypothesis that genetic predispositions influence an individual's response to specific interventions such as diet, exercise, and prescription medications. Despite the increase in commercially available genomic testing, few studies have investigated effects of a physician-directed program to optimize chronic disease using genomics-based precision medicine. We performed an pilot, observational cohort study to evaluate effects of the Wild Health program, a physician and health coach service offering genomics-based lifestyle and medical interventions, on biomarkers indicative of chronic disease. 871 patients underwent genomic testing, biomarker testing, and ongoing health coaching after initial medical consultation by a physician. Improvements in several clinically relevant out-of-range biomarkers at baseline were identified in a large proportion of patients treated through lifestyle intervention without the use of prescription medication. Notably, normalization of several biomarkers associated with chronic disease occurred in 47.5% (hemoglobin A1c [HbA1c]), 33.3% (low density lipoprotein particle number [LDL-P]), and 33.2% (C-reactive protein [CRP]). However, due to the inherent limitations of our observational study design and use of retrospective data, ongoing work will be crucial for continuing to shed light on the effectiveness of physician-led, genomics-based lifestyle coaching programs. Future studies would benefit from implementing a randomized controlled study design, tracking specific interventions, and evaluating physiological data, such as BMI.
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Affiliation(s)
| | - Jane Hall
- Jane Hall Biomed, LLC., Seattle, WA, United States
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20
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Chang D, Gupta VK, Hur B, Cobo-López S, Cunningham KY, Han NS, Lee I, Kronzer VL, Teigen LM, Karnatovskaia LV, Longbrake EE, Davis JM, Nelson H, Sung J. Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.30.560294. [PMID: 37873265 PMCID: PMC10592848 DOI: 10.1101/2023.09.30.560294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level α-diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individual's unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science.
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Affiliation(s)
- Daniel Chang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Vinod K Gupta
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Benjamin Hur
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Sergio Cobo-López
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
| | - Kevin Y Cunningham
- Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nam Soo Han
- Brain Korea 21 Center for Bio-Health Industry, Department of Food Science and Biotechnology, Chungbuk National University, Cheongju, South Korea
| | - Insuk Lee
- Department of Biotechnology, Yonsei University, Seoul 03722, South Korea
| | - Vanessa L Kronzer
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Levi M Teigen
- Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108, USA
| | | | - Erin E Longbrake
- Department of Neurology, Yale University, New Haven, CT 06510, USA
| | - John M Davis
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Heidi Nelson
- Emeritus, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Jaeyun Sung
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Surgery Research, Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA
- Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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21
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Wei H, Wu J, Wang H, Huang J, Li C, Zhang Y, Song Y, Zhou Z, Sun Y, Xiao L, Peng L, Chen C, Zhao C, Wang DW. Increased circulating phenylacetylglutamine concentration elevates the predictive value of cardiovascular event risk in heart failure patients. J Intern Med 2023; 294:515-530. [PMID: 37184278 DOI: 10.1111/joim.13653] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Phenylacetylglutamine (PAGln)-a newly discovered microbial metabolite produced by phenylalanine metabolism-is reportedly associated with cardiovascular events via adrenergic receptors. Nonetheless, its association with cardiovascular outcomes in heart failure (HF) patients remains unknown. OBJECTIVES This study aimed to prospectively investigate the prognostic value of PAGln for HF. METHODS Plasma PAGln levels were quantified by liquid chromatography-tandem mass spectrometry. We first assessed the association between plasma PAGln levels and the incidence of adverse cardiovascular events in 3152 HF patients (including HF with preserved and reduced ejection fraction) over a median follow-up period of 2 years. The primary endpoint was the composite of cardiovascular death or heart transplantation. We then assessed the prognostic role of PAGln in addition to the classic biomarker N-terminal pro-B-type natriuretic peptide (NT-proBNP). The correlation between PAGln levels and β-blocker use was also investigated. RESULTS In total, 520 cardiovascular deaths or heart transplantations occurred in the HF cohort. Elevated PAGln levels were independently associated with a higher risk of the primary endpoint in a dose-response manner, regardless of HF subtype. Concurrent assessment of PAGln and NT-proBNP levels enhanced risk stratification among HF patients. PAGln further showed prognostic value at low NT-proBNP levels. Additionally, the interaction effects between PAGln and β-blocker use were not significant. CONCLUSIONS Plasma PAGln levels are an independent predictor of an increased risk of adverse cardiovascular events in HF. Our work could provide joint and complementary prognostic value to NT-proBNP levels in HF patients.
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Affiliation(s)
- Haoran Wei
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junfang Wu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huiqing Wang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Jin Huang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Chenze Li
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Institute of Myocardial Injury and Repair, Wuhan University, Wuhan, China
| | - Yuxuan Zhang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Yaonan Song
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Zhitong Zhou
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Yang Sun
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Lei Xiao
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Liyuan Peng
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Chen Chen
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Chunxia Zhao
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
| | - Dao Wen Wang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China
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22
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Feng J, Liu H, Mai S, Su J, Sun J, Zhou J, Zhang Y, Wang Y, Wu F, Zheng G, Zhu Z. Protocol of a parallel, randomized controlled trial on the effects of a novel personalized nutrition approach by artificial intelligence in real world scenario. BMC Public Health 2023; 23:1700. [PMID: 37660022 PMCID: PMC10474697 DOI: 10.1186/s12889-023-16434-9] [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: 07/07/2023] [Accepted: 08/01/2023] [Indexed: 09/04/2023] Open
Abstract
BACKGROUND Nutrition service needs are huge in China. Previous studies indicated that personalized nutrition (PN) interventions were effective. The aim of the present study is to identify the effectiveness and feasibility of a novel PN approach supported by artificial intelligence (AI). METHODS This study is a two-arm parallel, randomized, controlled trial in real world scenario. The participants will be enrolled among who consume lunch at a staff canteen. In Phase I, a total of 170 eligible participants will be assigned to either intervention or control group on 1:1 ratio. The intervention group will be instructed to use the smartphone applet to record their lunches and reach the real-time AI-based information of dish nutrition evaluation and PN evaluation after meal consumption for 3 months. The control group will receive no nutrition information but be asked to record their lunches though the applet. Dietary pattern, body weight or blood pressure optimizing is expected after the intervention. In phase II, the applet will be free to all the diners (about 800) at the study canteen for another one year. Who use the applet at least 2 days per week will be regarded as the intervention group while the others will be the control group. Body metabolism normalization is expected after this period. Generalized linear mixed models will be used to identify the dietary, anthropometric and metabolic changes. DISCUSSION This novel approach will provide real-time AI-based dish nutrition evaluation and PN evaluation after meal consumption in order to assist users with nutrition information to make wise food choice. This study is designed under a real-life scenario which facilitates translating the trial intervention into real-world practice. TRIAL REGISTRATION This trial has been registered with the Chinese Clinical Trial Registry (ChiCTR2100051771; date registered: 03/10/2021).
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Affiliation(s)
- Jingyuan Feng
- School of Public Health, Fudan University, Shanghai, China
| | - Hongwei Liu
- School of Public Health, Fudan University, Shanghai, China
| | - Shupeng Mai
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jin Su
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jing Sun
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Jianjie Zhou
- Basebit (Shanghai) Information Technology Co., Ltd, Shanghai, China
| | - Yingyao Zhang
- Basebit (Shanghai) Information Technology Co., Ltd, Shanghai, China
| | - Yinyi Wang
- Department of Nutrition and Food Science, Education, and Human Development, Steinhardt School of Culture, New York University, New York, USA
| | - Fan Wu
- School of Public Health, Fudan University, Shanghai, China.
| | - Guangyong Zheng
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Zhenni Zhu
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
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23
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Spiekerkoetter U, Bick D, Scott R, Hopkins H, Krones T, Gross ES, Bonham JR. Genomic newborn screening: Are we entering a new era of screening? J Inherit Metab Dis 2023; 46:778-795. [PMID: 37403863 DOI: 10.1002/jimd.12650] [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: 02/28/2023] [Revised: 06/01/2023] [Accepted: 06/13/2023] [Indexed: 07/06/2023]
Abstract
Population newborn screening (NBS) for phenylketonuria began in the United States in 1963. In the 1990s electrospray ionization mass spectrometry permitted an array of pathognomonic metabolites to be identified simultaneously, enabling up to 60 disorders to be recognized with a single test. In response, differing approaches to the assessment of the harms and benefits of screening have resulted in variable screening panels worldwide. Thirty years on and another screening revolution has emerged with the potential for first line genomic testing extending the range of screening conditions recognized after birth to many hundreds. At the annual SSIEM conference in 2022 in Freiburg, Germany, an interactive plenary discussion on genomic screening strategies and their challenges and opportunities was conducted. The Genomics England Research project proposes the use of Whole Genome Sequencing to offer extended NBS to 100 000 babies for defined conditions with a clear benefit for the child. The European Organization for Rare Diseases seeks to include "actionable" conditions considering also other types of benefits. Hopkins Van Mil, a private UK research institute, determined the views of citizens and revealed as a precondition that families are provided with adequate information, qualified support, and that autonomy and data are protected. From an ethical standpoint, the benefits ascribed to screening and early treatment need to be considered in relation to asymptomatic, phenotypically mild or late-onset presentations, where presymptomatic treatment may not be required. The different perspectives and arguments demonstrate the unique burden of responsibility on those proposing new and far-reaching developments in NBS programs and the need to carefully consider both harms and benefits.
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Affiliation(s)
- Ute Spiekerkoetter
- Department of Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, University Children's Hospital, Freiburg, Germany
| | | | | | | | - Tanja Krones
- URPP Human Reproduction Reloaded - H2R and Institute of Biomedical Ethics and History of Medicine, University Hospital/University of Zurich, Zurich, Switzerland
| | | | - James R Bonham
- International Society of Neonatal Screening, Maarssen, The Netherlands
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24
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Sawicki C, Haslam D, Bhupathiraju S. Utilising the precision nutrition toolkit in the path towards precision medicine. Proc Nutr Soc 2023; 82:359-369. [PMID: 37475596 DOI: 10.1017/s0029665123003038] [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] [Indexed: 07/22/2023]
Abstract
The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.
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Affiliation(s)
- Caleigh Sawicki
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Danielle Haslam
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Shilpa Bhupathiraju
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
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25
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Green S, Prainsack B, Sabatello M. Precision medicine and the problem of structural injustice. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2023; 26:433-450. [PMID: 37231234 PMCID: PMC10212228 DOI: 10.1007/s11019-023-10158-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/24/2023] [Indexed: 05/27/2023]
Abstract
Many countries currently invest in technologies and data infrastructures to foster precision medicine (PM), which is hoped to better tailor disease treatment and prevention to individual patients. But who can expect to benefit from PM? The answer depends not only on scientific developments but also on the willingness to address the problem of structural injustice. One important step is to confront the problem of underrepresentation of certain populations in PM cohorts via improved research inclusivity. Yet, we argue that the perspective needs to be broadened because the (in)equitable effects of PM are also strongly contingent on wider structural factors and prioritization of healthcare strategies and resources. When (and before) implementing PM, it is crucial to attend to how the organisation of healthcare systems influences who will benefit, as well as whether PM may present challenges for a solidaristic sharing of costs and risks. We discuss these issues through a comparative lens of healthcare models and PM-initiatives in the United States, Austria, and Denmark. The analysis draws attention to how PM hinges on-and simultaneously affects-access to healthcare services, public trust in data handling, and prioritization of healthcare resources. Finally, we provide suggestions for how to mitigate foreseeable negative effects.
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Affiliation(s)
- Sara Green
- Section for History and Philosophy of Science, Department of Science Education, University of Copenhagen, Niels Bohr Building (NBB), Universitetsparken 5, 2100 Copenhagen Ø, Denmark
- Centre for Medical Science and Technology Studies, Department of Public Health, University of Copenhagen, Oester Farimagsgade 5, 1014 Copengagen, Denmark
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, Universitätsstraße 7, 1010 Vienna, Austria
- School of Social and Political Sciences, Faculty of Arts and Social Sciences, University of Sydney, Camperdown, NSW 2006 Australia
| | - Maya Sabatello
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University, New York, USA
- Division of Ethics, Department of Medical Humanities and Ethics, Columbia University, New York, USA
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26
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Rundle M, Fiamoncini J, Thomas EL, Wopereis S, Afman LA, Brennan L, Drevon CA, Gundersen TE, Daniel H, Perez IG, Posma JM, Ivanova DG, Bell JD, van Ommen B, Frost G. Diet-induced Weight Loss and Phenotypic Flexibility Among Healthy Overweight Adults: A Randomized Trial. Am J Clin Nutr 2023; 118:591-604. [PMID: 37661105 PMCID: PMC10517213 DOI: 10.1016/j.ajcnut.2023.07.002] [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: 12/16/2022] [Revised: 06/28/2023] [Accepted: 07/03/2023] [Indexed: 09/05/2023] Open
Abstract
BACKGROUND The capacity of an individual to respond to changes in food intake so that postprandial metabolic perturbations are resolved, and metabolism returns to its pre-prandial state, is called phenotypic flexibility. This ability may be a more important indicator of current health status than metabolic markers in a fasting state. AIM In this parallel randomized controlled trial study, an energy-restricted healthy diet and 2 dietary challenges were used to assess the effect of weight loss on phenotypic flexibility. METHODS Seventy-two volunteers with overweight and obesity underwent a 12-wk dietary intervention. The participants were randomized to a weight loss group (WLG) with 20% less energy intake or a weight-maintenance group (WMG). At weeks 1 and 12, participants were assessed for body composition by MRI. Concurrently, markers of metabolism and insulin sensitivity were obtained from the analysis of plasma metabolome during 2 different dietary challenges-an oral glucose tolerance test (OGTT) and a mixed-meal tolerance test. RESULTS Intended weight loss was achieved in the WLG (-5.6 kg, P < 0.0001) and induced a significant reduction in total and regional adipose tissue as well as ectopic fat in the liver. Amino acid-based markers of insulin action and resistance such as leucine and glutamate were reduced in the postprandial phase of the OGTT in the WLG by 11.5% and 28%, respectively, after body weight reduction. Weight loss correlated with the magnitude of changes in metabolic responses to dietary challenges. Large interindividual variation in metabolic responses to weight loss was observed. CONCLUSION Application of dietary challenges increased sensitivity to detect metabolic response to weight loss intervention. Large interindividual variation was observed across a wide range of measurements allowing the identification of distinct responses to the weight loss intervention and mechanistic insight into the metabolic response to weight loss.
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Affiliation(s)
- Milena Rundle
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Jarlei Fiamoncini
- Food Research Center, Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Suzan Wopereis
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research, Hague, The Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin, Ireland
| | - Christian A Drevon
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway; Vitas Ltd, Oslo Science Park, Oslo, Norway
| | | | - Hannelore Daniel
- Hannelore Daniel, Molecular Nutrition Unit, Technische Universität München, München, Germany
| | - Isabel Garcia Perez
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Joram M Posma
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Diana G Ivanova
- Department of Biochemistry, Molecular Medicine and Nutrigenomics, Faculty of Pharmacy, Medical University, Varna, Bulgaria
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Ben van Ommen
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research, Hague, The Netherlands
| | - Gary Frost
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom.
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27
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Limeta A, Gatto F, Herrgård MJ, Ji B, Nielsen J. Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors. Comput Struct Biotechnol J 2023; 21:3912-3919. [PMID: 37602228 PMCID: PMC10432706 DOI: 10.1016/j.csbj.2023.07.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 07/17/2023] [Accepted: 07/22/2023] [Indexed: 08/22/2023] Open
Abstract
A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.
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Affiliation(s)
- Angelo Limeta
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Francesco Gatto
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
- Department of Oncology-Pathology, Karolinska Institute, 171 64 Stockholm, Sweden
| | | | - Boyang Ji
- BioInnovation Institute, 2200 Copenhagen N, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
- BioInnovation Institute, 2200 Copenhagen N, Denmark
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28
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Zheng M, Charvat J, Zwart SR, Mehta SK, Crucian BE, Smith SM, He J, Piermarocchi C, Mias GI. Time-resolved molecular measurements reveal changes in astronauts during spaceflight. Front Physiol 2023; 14:1219221. [PMID: 37520819 PMCID: PMC10376710 DOI: 10.3389/fphys.2023.1219221] [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: 05/08/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts' wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n = 27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts' physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.
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Affiliation(s)
- Minzhang Zheng
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | | | - Sara R. Zwart
- University of Texas Medical Branch, Galveston, TX, United States
| | | | | | | | - Jin He
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
| | - George I. Mias
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
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29
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Affiliation(s)
- Bruna Gomes
- From the Departments of Medicine, Genetics, and Biomedical Data Science, Stanford University, Stanford, CA (B.G., E.A.A.); and the Department of Cardiology, Pneumology, and Angiology, Heidelberg University Hospital, Heidelberg, Germany (B.G.)
| | - Euan A Ashley
- From the Departments of Medicine, Genetics, and Biomedical Data Science, Stanford University, Stanford, CA (B.G., E.A.A.); and the Department of Cardiology, Pneumology, and Angiology, Heidelberg University Hospital, Heidelberg, Germany (B.G.)
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30
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Diamandis EP. Please do not call it Theranos. Clin Chem Lab Med 2023; 61:e103-e104. [PMID: 36794522 DOI: 10.1515/cclm-2023-0110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/17/2023]
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31
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Lokhov PG, Balashova EE, Trifonova OP, Maslov DL, Plotnikova OA, Sharafetdinov KK, Nikityuk DB, Tutelyan VA, Ponomarenko EA, Archakov AI. Clinical Blood Metabogram: Application to Overweight and Obese Patients. Metabolites 2023; 13:798. [PMID: 37512504 PMCID: PMC10386708 DOI: 10.3390/metabo13070798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/23/2023] [Accepted: 06/24/2023] [Indexed: 07/30/2023] Open
Abstract
Recently, the concept of a mass spectrometric blood metabogram was introduced, which allows the analysis of the blood metabolome in terms of the time, cost, and reproducibility of clinical laboratory tests. It was demonstrated that the components of the metabogram are related groups of the blood metabolites associated with humoral regulation; the metabolism of lipids, carbohydrates, and amines; lipid intake into the organism; and liver function, thereby providing clinically relevant information. The purpose of this work was to evaluate the relevance of using the metabogram in a disease. To do this, the metabogram was used to analyze patients with various degrees of metabolic alterations associated with obesity. The study involved 20 healthy individuals, 20 overweight individuals, and 60 individuals with class 1, 2, or 3 obesity. The results showed that the metabogram revealed obesity-associated metabolic alterations, including changes in the blood levels of steroids, amino acids, fatty acids, and phospholipids, which are consistent with the available scientific data to date. Therefore, the metabogram allows testing of metabolically unhealthy overweight or obese patients, providing both a general overview of their metabolic alterations and detailing their individual characteristics. It was concluded that the metabogram is an accurate and clinically applicable test for assessing an individual's metabolic status in disease.
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Affiliation(s)
- Petr G Lokhov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Elena E Balashova
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Oxana P Trifonova
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Dmitry L Maslov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Oksana A Plotnikova
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia
| | - Khaider K Sharafetdinov
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia
| | - Dmitry B Nikityuk
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia
| | - Victor A Tutelyan
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia
| | - Elena A Ponomarenko
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Alexander I Archakov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
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32
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Sonin J, Becker A, Nipp K. Designing health outcomes through patient data ownership. J Hosp Med 2023. [PMID: 37321927 DOI: 10.1002/jhm.13148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/17/2023]
Affiliation(s)
- Juhan Sonin
- University of Illinois at Champaign-Urbana, Champaign, Illinois, USA
- GoInvo.com (LLC), Arlington, Massachusetts, USA
- Mechanical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States
| | - Annie Becker
- University of North Carolina Gillings School for Global Public Health, Chapel Hill, North Carolina, USA
- University of Washington School of Public Health, Seattle, Washington, USA
| | - Kim Nipp
- University of Toronto, Toronto, Ontario, Canada
- University of British Columbia, Vancouver, University of British Columbia, Canada
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33
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Babu M, Snyder M. Multi-Omics Profiling for Health. Mol Cell Proteomics 2023; 22:100561. [PMID: 37119971 PMCID: PMC10220275 DOI: 10.1016/j.mcpro.2023.100561] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/20/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023] Open
Abstract
The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.
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Affiliation(s)
- Mohan Babu
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
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Pan T, Yu Z, Huang F, Yao H, Hu G, Tang C, Gu J. Flexible Humidity Sensor with High Sensitivity and Durability for Respiratory Monitoring Using Near-Field Electrohydrodynamic Direct-Writing Method. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37262400 DOI: 10.1021/acsami.3c04283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The humidity of breath can serve as an important health indicator, providing crucial clinical information about human physiology. Significant progress had been made in the development of flexible humidity sensors. However, improving its humidity sensing performance (sensitivity and durability) is still facing many challenges. In this work, near-field electrohydrodynamic direct writing (NFEDW) was proposed to fabricate humidity sensors with high sensitivity and durability for respiration monitoring. Due to the applied electric field, dense carbon nanotube/cellulose nanofiber (CNT/CNF) networks formed during the printing process that enhance the sensitivity of the sensor. The prepared sensor showed excellent humidity responses, with a maximum response value of 61.5% (ΔR/R0) at 95% relative humidity (RH). Additionally, the sensitivity film prepared by the NFEDW method closely fits the poly(ethylene terephthalate) (PET) substrate, endowing the sensor with outstanding bending (with a maximum curvature of 4.7 cm-1) and folding durability (up to 50 times). The sensitivity of the prepared sensor under different simulated conditions, namely, nose breathing, mouth breathing, coughing, yawning, breath holding, and speaking, was excellent, demonstrating the potential of the sensor for the real-time monitoring of human breath humidity. Thus, the high-performance flexible humidity sensor is suitable for human respiration and health monitoring.
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Affiliation(s)
- Taiyao Pan
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
- Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, Jiaxing 341000, China
| | - Zhiheng Yu
- College of Mechanical and Electrical Engineering, Jiaxing Nanhu University, Jiaxing 314000, China
| | - Fengli Huang
- Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, Jiaxing 341000, China
| | - Haoyang Yao
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Guohong Hu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Chengli Tang
- Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, Jiaxing 341000, China
| | - Jinmei Gu
- Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, Jiaxing 341000, China
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Shen K, Din AU, Sinha B, Zhou Y, Qian F, Shen B. Translational informatics for human microbiota: data resources, models and applications. Brief Bioinform 2023; 24:7152256. [PMID: 37141135 DOI: 10.1093/bib/bbad168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/07/2023] [Accepted: 04/11/2023] [Indexed: 05/05/2023] Open
Abstract
With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.
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Affiliation(s)
- Ke Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Ahmad Ud Din
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Baivab Sinha
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Yi Zhou
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
| | - Fuliang Qian
- Center for Systems Biology, Suzhou Medical College of Soochow University, Suzhou 215123, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou 215123, China
| | - Bairong Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, China
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Omenn GS, Lane L, Overall CM, Pineau C, Packer NH, Cristea IM, Lindskog C, Weintraub ST, Orchard S, Roehrl MH, Nice E, Liu S, Bandeira N, Chen YJ, Guo T, Aebersold R, Moritz RL, Deutsch EW. The 2022 Report on the Human Proteome from the HUPO Human Proteome Project. J Proteome Res 2023; 22:1024-1042. [PMID: 36318223 PMCID: PMC10081950 DOI: 10.1021/acs.jproteome.2c00498] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".
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Affiliation(s)
- Gilbert S. Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, 1015 Lausanne, Switzerland
| | | | - Charles Pineau
- French Institute of Health and Medical Research, 35042 RENNES Cedex, France
| | - Nicolle H. Packer
- Macquarie University, Sydney, NSW 2109, Australia
- Griffith University’s Institute for Glycomics, Sydney, NSW 2109, Australia
| | | | | | - Susan T. Weintraub
- University of Texas Health Science Center-San Antonio, San Antonio, Texas 78229-3900, United States
| | - Sandra Orchard
- EMBL-EBI, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom
| | - Michael H.A. Roehrl
- Memorial Sloan Kettering Cancer Center, New York, New York, 10065, United States
| | | | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | - Yu-Ju Chen
- National Taiwan University, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Tiannan Guo
- Westlake University Guomics Laboratory of Big Proteomic Data, Hangzhou 310024, Zhejiang Province, China
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology in ETH Zurich, 8092 Zurich, Switzerland
| | - Robert L. Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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37
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Watanabe K, Wilmanski T, Diener C, Earls JC, Zimmer A, Lincoln B, Hadlock JJ, Lovejoy JC, Gibbons SM, Magis AT, Hood L, Price ND, Rappaport N. Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention. Nat Med 2023; 29:996-1008. [PMID: 36941332 PMCID: PMC10115644 DOI: 10.1038/s41591-023-02248-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 02/02/2023] [Indexed: 03/23/2023]
Abstract
Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte-analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine.
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Affiliation(s)
| | | | | | - John C Earls
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
| | - Anat Zimmer
- Institute for Systems Biology, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | | | | | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | | | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Phenome Health, Seattle, WA, USA
- Department of Immunology, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
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38
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Scott RT, Sanders LM, Antonsen EL, Hastings JJA, Park SM, Mackintosh G, Reynolds RJ, Hoarfrost AL, Sawyer A, Greene CS, Glicksberg BS, Theriot CA, Berrios DC, Miller J, Babdor J, Barker R, Baranzini SE, Beheshti A, Chalk S, Delgado-Aparicio GM, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Kalantari J, Khezeli K, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Garcia Martin H, Mason CE, Matar M, Mias GI, Myers JG, Nelson C, Oribello J, Parsons-Wingerter P, Prabhu RK, Qutub AA, Rask J, Saravia-Butler A, Saria S, Singh NK, Snyder M, Soboczenski F, Soman K, Van Valen D, Venkateswaran K, Warren L, Worthey L, Yang JH, Zitnik M, Costes SV. Biomonitoring and precision health in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00617-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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39
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Sun H, Wang Y, Xiao Z, Huang X, Wang H, He T, Jiang X. multiMiAT: an optimal microbiome-based association test for multicategory phenotypes. Brief Bioinform 2023; 24:7005163. [PMID: 36702753 DOI: 10.1093/bib/bbad012] [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: 09/05/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/28/2023] Open
Abstract
Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.
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Affiliation(s)
- Han Sun
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
- School of Computer Science, Central China Normal University, Wuhan 430079, China
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
| | - Yue Wang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
- School of Computer Science, Central China Normal University, Wuhan 430079, China
| | - Zhen Xiao
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
- School of Computer Science, Central China Normal University, Wuhan 430079, China
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
| | - Xiaoyun Huang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
- School of Computer Science, Central China Normal University, Wuhan 430079, China
- Collaborative & Innovative Center for Educational Technology, Central China Normal University, Wuhan 430079, China
| | - Haodong Wang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
- School of Computer Science, Central China Normal University, Wuhan 430079, China
| | - Tingting He
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
- School of Computer Science, Central China Normal University, Wuhan 430079, China
- National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan 430079, China
| | - Xingpeng Jiang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China
- School of Computer Science, Central China Normal University, Wuhan 430079, China
- National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan 430079, China
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40
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Zheng M, Charvat J, Zwart SR, Mehta S, Crucian BE, Smith SM, He J, Piermarocchi C, Mias GI. Time-resolved molecular measurements reveal changes in astronauts during spaceflight. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.17.530234. [PMID: 36993537 PMCID: PMC10055136 DOI: 10.1101/2023.03.17.530234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts' wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n=27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts' physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.
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Arafah A, Khatoon S, Rasool I, Khan A, Rather MA, Abujabal KA, Faqih YAH, Rashid H, Rashid SM, Bilal Ahmad S, Alexiou A, Rehman MU. The Future of Precision Medicine in the Cure of Alzheimer's Disease. Biomedicines 2023; 11:biomedicines11020335. [PMID: 36830872 PMCID: PMC9953731 DOI: 10.3390/biomedicines11020335] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
This decade has seen the beginning of ground-breaking conceptual shifts in the research of Alzheimer's disease (AD), which acknowledges risk elements and the evolving wide spectrum of complicated underlying pathophysiology among the range of diverse neurodegenerative diseases. Significant improvements in diagnosis, treatments, and mitigation of AD are likely to result from the development and application of a comprehensive approach to precision medicine (PM), as is the case with several other diseases. This strategy will probably be based on the achievements made in more sophisticated research areas, including cancer. PM will require the direct integration of neurology, neuroscience, and psychiatry into a paradigm of the healthcare field that turns away from the isolated method. PM is biomarker-guided treatment at a systems level that incorporates findings of the thorough pathophysiology of neurodegenerative disorders as well as methodological developments. Comprehensive examination and categorization of interrelated and convergent disease processes, an explanation of the genomic and epigenetic drivers, a description of the spatial and temporal paths of natural history, biological markers, and risk markers, as well as aspects about the regulation, and the ethical, governmental, and sociocultural repercussions of findings at a subclinical level all require clarification and realistic execution. Advances toward a comprehensive systems-based approach to PM may finally usher in a new era of scientific and technical achievement that will help to end the complications of AD.
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Affiliation(s)
- Azher Arafah
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
- Correspondence: (A.A.); (A.K.); (M.U.R.)
| | - Saima Khatoon
- Department of Medical Elementology and Toxicology, School of Chemical and Life Sciences, Jamia Hamdard, New Delhi 110062, India
| | - Iyman Rasool
- Department of Pathology, Government Medical College (GMC-Srinagar), Karan Nagar, Srinagar 190010, India
| | - Andleeb Khan
- Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence: (A.A.); (A.K.); (M.U.R.)
| | - Mashoque Ahmad Rather
- Department of Molecular Pharmacology & Physiology, Bryd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33620, USA
| | | | | | - Hina Rashid
- Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan 45142, Saudi Arabia
| | - Shahzada Mudasir Rashid
- Division of Veterinary Biochemistry, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST-K), Srinagar 190006, India
| | - Sheikh Bilal Ahmad
- Division of Veterinary Biochemistry, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST-K), Srinagar 190006, India
| | - Athanasios Alexiou
- Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
- AFNP Med, Haidingergasse 29, 1030 Vienna, Austria
| | - Muneeb U. Rehman
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
- Correspondence: (A.A.); (A.K.); (M.U.R.)
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Schork NJ, Beaulieu-Jones B, Liang WS, Smalley S, Goetz LH. Exploring human biology with N-of-1 clinical trials. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e12. [PMID: 37255593 PMCID: PMC10228692 DOI: 10.1017/pcm.2022.15] [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: 09/26/2022] [Revised: 12/12/2022] [Accepted: 12/26/2022] [Indexed: 06/01/2023]
Abstract
Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole-person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real-time care to the very individuals who are seeking attention for their health concerns in the first place.
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Affiliation(s)
- N. J. Schork
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Net.bio Inc., Los Angeles, CA, USA
| | - B. Beaulieu-Jones
- Net.bio Inc., Los Angeles, CA, USA
- University of Chicago, Chicago, IL, USA
| | | | - S. Smalley
- Net.bio Inc., Los Angeles, CA, USA
- The University of California Los Angeles, Los Angeles, CA, USA
| | - L. H. Goetz
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Net.bio Inc., Los Angeles, CA, USA
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Niranjan B, de Courten MP, Iyngkaran P, Battersby M. Malthusian Trajectory for Heart Failure and Novel Translational Ambulatory Technologies. Curr Cardiol Rev 2023; 19:e240522205193. [PMID: 35611782 PMCID: PMC10280992 DOI: 10.2174/1573403x18666220524145646] [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: 10/18/2021] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION It has been estimated that congestive heart failure (CHF) will reach epidemic proportions and contribute to large unsustainable impacts on health budgets for any cardiovascular condition. Against other major trends in cardiovascular outcomes, readmission and disease burden continue to rise as the demographics shift. METHODS The rise in heart failure with preserved ejection fraction (HFpEF) among elderly women will present new challenges. Gold standard care delivers sustainable and cost-effective health improvements using organised care programs. When coordinated with large hospitals, this can be replicated universally. RESULTS A gradient of outcomes and ambulatory care needs to be shifted from established institutions and shared with clients and community health services, being a sizeable proportion of CHF care. CONCLUSION In this review, we explore health technologies as an emerging opportunity to address gaps in CHF management.
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Affiliation(s)
- Bidargaddi Niranjan
- Digital Health at College of Medicine and Public Health Flinders University & SAHMRI, Adelaide, Australia
| | - Maximilian P de Courten
- Mitchell Institute for Education and Health Policy, Victoria University, 300 Queen St, Melbourne, Australia
| | - Pupalan Iyngkaran
- Mitchell Institute, Victoria University, Melbourne, Australia and Werribee Mercy Sub School, School of Medicine Sydney, The University of Notre Dame Australia, Werribee, Australia
| | - Malcolm Battersby
- College of Medicine and Public Health, South Australian Health and Medical Research Institute, Southern Adelaide Local Health Network, Mental Health Division, Flinders Medical Centre, Flinders University, Adelaide, Australia
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44
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Huang Z, Li Y, Park H, Ho M, Bhardwaj K, Sugimura N, Lee HW, Meng H, Ebert MP, Chao K, Burgermeister E, Bhatt AP, Shetty SA, Li K, Wen W, Zuo T. Unveiling and harnessing the human gut microbiome in the rising burden of non-communicable diseases during urbanization. Gut Microbes 2023; 15:2237645. [PMID: 37498052 PMCID: PMC10376922 DOI: 10.1080/19490976.2023.2237645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023] Open
Abstract
The world is witnessing a global increase in the urban population, particularly in developing Asian and African countries. Concomitantly, the global burden of non-communicable diseases (NCDs) is rising, markedly associated with the changing landscape of lifestyle and environment during urbanization. Accumulating studies have revealed the role of the gut microbiome in regulating the immune and metabolic homeostasis of the host, which potentially bridges external factors to the host (patho-)physiology. In this review, we discuss the rising incidences of NCDs during urbanization and their links to the compositional and functional dysbiosis of the gut microbiome. In particular, we elucidate the effects of urbanization-associated factors (hygiene/pollution, urbanized diet, lifestyles, the use of antibiotics, and early life exposure) on the gut microbiome underlying the pathogenesis of NCDs. We also discuss the potential and feasibility of microbiome-inspired and microbiome-targeted approaches as novel avenues to counteract NCDs, including fecal microbiota transplantation, diet modulation, probiotics, postbiotics, synbiotics, celobiotics, and precision antibiotics.
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Affiliation(s)
- Ziyu Huang
- Key Laboratory of Human Microbiome and Chronic Diseases, Sun Yat-Sen University, Ministry of Education, Guangzhou, China
- Guangdong Institute of Gastroenterology, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Biomedical Innovation Centre, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yue Li
- Key Laboratory of Human Microbiome and Chronic Diseases, Sun Yat-Sen University, Ministry of Education, Guangzhou, China
- Guangdong Institute of Gastroenterology, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Biomedical Innovation Centre, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Heekuk Park
- Department of Medicine, Division of Infectious Diseases, Columbia University Irving Medical Centre, New York, NY, USA
| | - Martin Ho
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Kanchan Bhardwaj
- Department of Biotechnology, Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Haryana, India
| | - Naoki Sugimura
- Gastrointestinal Centre and Institute of Minimally-Invasive Endoscopic Care (iMEC), Sano Hospital, Kobe, Japan
| | - Hye Won Lee
- Institute of Gastroenterology and Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Huicui Meng
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou, China
- Guangdong Province Engineering Laboratory for Nutrition Translation, Guangzhou, China
| | - Matthias P. Ebert
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- DKFZ-Hector Cancer Institute, Mannheim, Germany
- Mannheim Cancer Centre (MCC), University Medical Centre Mannheim, Mannheim, Germany
| | - Kang Chao
- The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Department of Gastroenterology, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Elke Burgermeister
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Aadra P. Bhatt
- Department of Medicine, Centre for Gastrointestinal Biology and Disease, and the Lineberger Comprehensive Cancer Centre, University of North Carolina, Chapel Hill, NC, USA
| | - Sudarshan A. Shetty
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Groningen, Groningen, The Netherlands
| | - Kai Li
- Key Laboratory of Human Microbiome and Chronic Diseases, Sun Yat-Sen University, Ministry of Education, Guangzhou, China
- Guangdong Institute of Gastroenterology, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Weiping Wen
- Key Laboratory of Human Microbiome and Chronic Diseases, Sun Yat-Sen University, Ministry of Education, Guangzhou, China
- Biomedical Innovation Centre, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Tao Zuo
- Key Laboratory of Human Microbiome and Chronic Diseases, Sun Yat-Sen University, Ministry of Education, Guangzhou, China
- Guangdong Institute of Gastroenterology, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Biomedical Innovation Centre, the Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
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45
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Trifonova OP, Maslov DL, Balashova EE, Lokhov PG. Current State and Future Perspectives on Personalized Metabolomics. Metabolites 2023; 13:metabo13010067. [PMID: 36676992 PMCID: PMC9863827 DOI: 10.3390/metabo13010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.
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46
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Shomorony I. Data-driven precision medicine through the analysis of biological functional modules. Cell Rep Med 2022; 3:100876. [PMID: 36543115 PMCID: PMC9798076 DOI: 10.1016/j.xcrm.2022.100876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Data-driven methods are expected to enable a next generation of personalized, preventative medicine. Zhang and colleagues1 demonstrate how biological functional modules (BFMs) derived from the analysis of multimodal data can provide detailed quantitative health assessments and inform medical interventions.
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Affiliation(s)
- Ilan Shomorony
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA,Corresponding author
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47
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Zhang W, Wan Z, Li X, Li R, Luo L, Song Z, Miao Y, Li Z, Wang S, Shan Y, Li Y, Chen B, Zhen H, Sun Y, Fang M, Ding J, Yan Y, Zong Y, Wang Z, Zhang W, Yang H, Yang S, Wang J, Jin X, Wang R, Chen P, Min J, Zeng Y, Li T, Xu X, Nie C. A population-based study of precision health assessments using multi-omics network-derived biological functional modules. Cell Rep Med 2022; 3:100847. [PMID: 36493776 PMCID: PMC9798030 DOI: 10.1016/j.xcrm.2022.100847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 10/05/2022] [Accepted: 11/11/2022] [Indexed: 12/13/2022]
Abstract
Recent technological advances in multi-omics and bioinformatics provide an opportunity to develop precision health assessments, which require big data and relevant bioinformatic methods. Here we collect multi-omics data from 4,277 individuals. We calculate the correlations between pairwise features from cross-sectional data and then generate 11 biological functional modules (BFMs) in males and 12 BFMs in females using a community detection algorithm. Using the features in the BFM associated with cardiometabolic health, carotid plaques can be predicted accurately in an independent dataset. We developed a model by comparing individual data with the health baseline in BFMs to assess health status (BFM-ash). Then we apply the model to chronic patients and modify the BFM-ash model to assess the effects of consuming grape seed extract as a dietary supplement. Finally, anomalous BFMs are identified for each subject. Our BFMs and BFM-ash model have huge prospects for application in precision health assessment.
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Affiliation(s)
- Wei Zhang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Ziyun Wan
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Xiaoyu Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,BGI Education Center, University of the Chinese Academy of Sciences, Shenzhen 518083, China
| | - Rui Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Lihua Luo
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,BGI Education Center, University of the Chinese Academy of Sciences, Shenzhen 518083, China
| | - Zijun Song
- The First Affiliated Hospital, Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yu Miao
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,BGI Education Center, University of the Chinese Academy of Sciences, Shenzhen 518083, China
| | - Zhiming Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Shiyu Wang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,BGI Education Center, University of the Chinese Academy of Sciences, Shenzhen 518083, China
| | - Ying Shan
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Yan Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Bangwei Chen
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Hefu Zhen
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Yuzhe Sun
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Mingyan Fang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Jiahong Ding
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Yizhen Yan
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Yang Zong
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Zhen Wang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Wenwei Zhang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Shuang Yang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Xin Jin
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Ru Wang
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai, China
| | - Peijie Chen
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai, China
| | - Junxia Min
- The First Affiliated Hospital, Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Zeng
- Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, China
| | - Tao Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Chao Nie
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,Corresponding author
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48
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Su Z, Bai X, Wang H, Wang S, Chen C, Xiao F, Guo H, Gao H, Leng L, Li H. Identification of biomarkers associated with the feed efficiency by metabolomics profiling: results from the broiler lines divergent for high or low abdominal fat content. J Anim Sci Biotechnol 2022; 13:122. [PMID: 36352447 PMCID: PMC9647982 DOI: 10.1186/s40104-022-00775-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/05/2022] [Indexed: 11/11/2022] Open
Abstract
Background Improving feed efficiency (FE) is one of the main objectives in broiler breeding. It is difficult to directly measure FE traits, and breeders hence have been trying to identify biomarkers for the indirect selection and improvement of FE traits. Metabolome is the "bridge" between genome and phenome. The metabolites may potentially account for more of the phenotypic variation and can suitably serve as biomarkers for selecting FE traits. This study aimed to identify plasma metabolite markers for selecting high-FE broilers. A total of 441 birds from Northeast Agricultural University broiler lines divergently selected for abdominal fat content were used to analyze plasma metabolome and estimate the genetic parameters of differentially expressed metabolites. Results The results identified 124 differentially expressed plasma metabolites (P < 0.05) between the lean line (high-FE birds) and the fat line (low-FE birds). Among these differentially expressed plasma metabolites, 44 were found to have higher positive or negative genetic correlations with FE traits (|rg| ≥ 0.30). Of these 44 metabolites, 14 were found to display moderate to high heritability estimates (h2 ≥ 0.20). However, among the 14 metabolites, 4 metabolites whose physiological functions have not been reported were excluded. Ultimately, 10 metabolites were suggested to serve as the potential biomarkers for breeding the high-FE broilers. Based on the physiological functions of these metabolites, reducing inflammatory and improving immunity were proposed to improve FE and increase production efficiency. Conclusions According to the pipeline for the selection of the metabolite markers established in this study, it was suggested that 10 metabolites including 7-ketocholesterol, dimethyl sulfone, epsilon-(gamma-glutamyl)-lysine, gamma-glutamyltyrosine, 2-oxoadipic acid, L-homoarginine, testosterone, adenosine 5'-monophosphate, adrenic acid, and calcitriol could be used as the potential biomarkers for breeding the "food-saving broilers".
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49
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Chowdhury R, Bouatta N, Biswas S, Floristean C, Kharkar A, Roy K, Rochereau C, Ahdritz G, Zhang J, Church GM, Sorger PK, AlQuraishi M. Single-sequence protein structure prediction using a language model and deep learning. Nat Biotechnol 2022; 40:1617-1623. [PMID: 36192636 PMCID: PMC10440047 DOI: 10.1038/s41587-022-01432-w] [Citation(s) in RCA: 121] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 07/15/2022] [Indexed: 12/30/2022]
Abstract
AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents Cα backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.
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Affiliation(s)
- Ratul Chowdhury
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Nazim Bouatta
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
| | - Surojit Biswas
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Nabla Bio, Inc., Boston, MA, USA
| | | | - Anant Kharkar
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Koushik Roy
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Charlotte Rochereau
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | - Gustaf Ahdritz
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Joanna Zhang
- Department of Computer Science, Columbia University, New York, NY, USA
| | - George M Church
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
| | - Mohammed AlQuraishi
- Department of Computer Science, Columbia University, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA.
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50
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van den Brink WJ, van den Broek TJ, Palmisano S, Wopereis S, de Hoogh IM. Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies. Nutrients 2022; 14:4465. [PMID: 36364728 PMCID: PMC9654068 DOI: 10.3390/nu14214465] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/13/2022] [Accepted: 10/20/2022] [Indexed: 09/26/2023] Open
Abstract
Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2-96%) and 76.8% (74.3-81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/-0.04) mmol/L for the training data and 0.62 (+/-0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.
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Affiliation(s)
- Willem J. van den Brink
- Netherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The Netherlands
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