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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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2
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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: 18] [Impact Index Per Article: 18.0] [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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3
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Chamoso-Sanchez D, Rabadán Pérez F, Argente J, Barbas C, Martos-Moreno GA, Rupérez FJ. Identifying subgroups of childhood obesity by using multiplatform metabotyping. Front Mol Biosci 2023; 10:1301996. [PMID: 38174068 PMCID: PMC10761426 DOI: 10.3389/fmolb.2023.1301996] [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: 09/25/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.
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Affiliation(s)
- David Chamoso-Sanchez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
| | | | - Jesús Argente
- Department of Pediatrics and Pediatric Endocrinology, Hospital Infantil Universitario Niño Jesús, Instituto de Investigación Sanitaria La Princesa, Universidad Autónoma de Madrid, Madrid, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- IMDEA Food Institute, Madrid, Spain
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
| | - Gabriel A. Martos-Moreno
- Department of Pediatrics and Pediatric Endocrinology, Hospital Infantil Universitario Niño Jesús, Instituto de Investigación Sanitaria La Princesa, Universidad Autónoma de Madrid, Madrid, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Francisco J. Rupérez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
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Kortesniemi M, Noerman S, Kårlund A, Raita J, Meuronen T, Koistinen V, Landberg R, Hanhineva K. Nutritional metabolomics: Recent developments and future needs. Curr Opin Chem Biol 2023; 77:102400. [PMID: 37804582 DOI: 10.1016/j.cbpa.2023.102400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/21/2023] [Accepted: 09/07/2023] [Indexed: 10/09/2023]
Abstract
Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.
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Affiliation(s)
- Maaria Kortesniemi
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland.
| | - Stefania Noerman
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Anna Kårlund
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Jasmin Raita
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Topi Meuronen
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Ville Koistinen
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland; Institute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Kati Hanhineva
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland; Institute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
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5
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Maculewicz E, Leońska-Duniec A, Mastalerz A, Szarska E, Garbacz A, Lepionka T, Łakomy R, Anyżewska A, Bertrandt J. The Influence of FTO, FABP2, LEP, LEPR, and MC4R Genes on Obesity Parameters in Physically Active Caucasian Men. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19106030. [PMID: 35627568 PMCID: PMC9141290 DOI: 10.3390/ijerph19106030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/09/2022] [Accepted: 05/12/2022] [Indexed: 02/01/2023]
Abstract
Obesity is a complex multifactorial abnormality that has a well-confirmed genetic basis. However, the problem still lies in identifying the polymorphisms linked to body mass and composition. Therefore, this study aimed to analyze associations between FTO (rs9939609), FABP2 (rs1799883), and LEP (rs2167270), LEPR (rs1137101), and MC4R (rs17782313) polymorphisms and obesity-related parameters. Unrelated Caucasian males (n = 165) were recruited. All participants had similar physical activity levels. The participants were divided into two groups depending on their body mass index (BMI) and fat mass index (FMI). All samples were genotyped using real-time polymerase chain reaction (real-time PCR). When tested individually, only one statistically significant result was found. The FTO A/T polymorphism was significantly associated with FMI (p = 0.01). The chance of having increased FMI was >2-fold higher for the FTO A allele carriers (p < 0.01). Gene−gene interaction analyses showed the additional influence of all investigated genes on BMI and FMI. In summary, it was demonstrated that harboring the FTO A allele might be a risk factor for elevated fat mass. Additionally, this study confirmed that all five polymorphisms are involved in the development of common obesity in the studied population and the genetic risk of obesity is linked to the accumulation of numerous variants.
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Affiliation(s)
- Ewelina Maculewicz
- Faculty of Physical Education, Jozef Pilsudski University of Physical Education in Warsaw, 00-809 Warsaw, Poland;
- Correspondence:
| | - Agata Leońska-Duniec
- Faculty of Physical Education, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland;
| | - Andrzej Mastalerz
- Faculty of Physical Education, Jozef Pilsudski University of Physical Education in Warsaw, 00-809 Warsaw, Poland;
| | - Ewa Szarska
- Military Institute of Hygiene and Epidemiology, 01-163 Warsaw, Poland; (E.S.); (T.L.); (R.Ł.)
| | - Aleksandra Garbacz
- Institute of Animal Sciences, Faculty of Animal Breeding, Bioengineering and Conservation, Warsaw University of Life Sciences—SGGW, 02-787 Warsaw, Poland;
| | - Tomasz Lepionka
- Military Institute of Hygiene and Epidemiology, 01-163 Warsaw, Poland; (E.S.); (T.L.); (R.Ł.)
| | - Roman Łakomy
- Military Institute of Hygiene and Epidemiology, 01-163 Warsaw, Poland; (E.S.); (T.L.); (R.Ł.)
| | - Anna Anyżewska
- University of Economics and Human Sciences in Warsaw, Okopowa 59, 01-043 Warsaw, Poland;
| | - Jerzy Bertrandt
- Faculty of Health Sciences, Pope John Paul II State School of Higher Education in Biala Podlaska, 21-500 Biala Podlaska, Poland;
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Powell J, Li X. Integrated, data-driven health management: A step closer to personalized and predictive healthcare. Cell Syst 2022; 13:201-203. [PMID: 35298911 DOI: 10.1016/j.cels.2022.02.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Integrated, data-driven health management provides a roadmap to personalized and predictive healthcare. In this issue of Cell Systems, Marabita et al. showcase the application of data-driven, individualized lifestyle coaching to promote health and present an interpretable view of human health by integrating deep molecular, digital health, and clinical data.
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Affiliation(s)
- Joseph Powell
- Department of Biochemistry, Case Western University, Cleveland, OH 44106, USA; Center for RNA Science and Therapeutics, Case Western University, Cleveland, OH 44106, USA; Department of Computer and Data Sciences, Case Western University, Cleveland, OH 44106, USA
| | - Xiao Li
- Department of Biochemistry, Case Western University, Cleveland, OH 44106, USA; Center for RNA Science and Therapeutics, Case Western University, Cleveland, OH 44106, USA; Department of Computer and Data Sciences, Case Western University, Cleveland, OH 44106, USA.
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Kataria S, Ravindran V. Musculoskeletal care - at the confluence of data science, sensors, engineering, and computation. BMC Musculoskelet Disord 2022; 23:169. [PMID: 35193536 PMCID: PMC8863295 DOI: 10.1186/s12891-022-05126-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 02/17/2022] [Indexed: 12/27/2022] Open
Abstract
Data has always been integral to modern medicine in almost all aspects of patient care and the recent proliferation of data has opened up innumerable opportunities for all the stakeholders in trying to improve the quality of care and health outcomes including quality of life and rehabilitation. Greater usage and adoption of digital technologies have led to the convergence of health data in different forms – clinical, self-reported, electronic health records social media, etc. The application and utilization of patient data set continue to get broadened each day with greater availability and access. These are empowering newer cutting-edge solutions such as connected care and artificial intelligence, 3D printing and real-life mimicking prosthetics. The availability of data at micro and macro levels has the potential to act as a catalyst for personalized care based on behavioral, cultural, genetic, and psychological needs for patients with musculoskeletal disorders. Realistic algorithms coupled with biomarkers which can identify relevant interventions and alert the care providers regarding any deterioration. Although in the nascent stage currently, 3D printing, exoskeletons, and virtual rehabilitation hold tremendous potential of cost-effective, precise interventions for the patients.
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