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Halama A, Zaghlool S, Thareja G, Kader S, Al Muftah W, Mook-Kanamori M, Sarwath H, Mohamoud YA, Stephan N, Ameling S, Pucic Baković M, Krumsiek J, Prehn C, Adamski J, Schwenk JM, Friedrich N, Völker U, Wuhrer M, Lauc G, Najafi-Shoushtari SH, Malek JA, Graumann J, Mook-Kanamori D, Schmidt F, Suhre K. A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes. Nat Commun 2024; 15:7111. [PMID: 39160153 PMCID: PMC11333501 DOI: 10.1038/s41467-024-51134-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: 08/01/2023] [Accepted: 07/26/2024] [Indexed: 08/21/2024] Open
Abstract
In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.
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Affiliation(s)
- Anna Halama
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
| | - Shaza Zaghlool
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Sara Kader
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Wadha Al Muftah
- Qatar Genome Program, Qatar Foundation, Qatar Science and Technology Park, Innovation Center, Doha, Qatar
- Department of Genetic Medicine, Weill Cornell Medicine, Doha, Qatar
| | | | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | | | - Nisha Stephan
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Sabine Ameling
- German Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine Greifswald, Greifswald, Germany
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | | | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Nele Friedrich
- German Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- German Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine Greifswald, Greifswald, Germany
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - S Hani Najafi-Shoushtari
- MicroRNA Core Laboratory, Division of Research, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Cell and Developmental Biology, Weill Cornell Medicine, New York, NY, USA
| | - Joel A Malek
- Department of Genetic Medicine, Weill Cornell Medicine, Doha, Qatar
- Genomics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Johannes Graumann
- Institute of Translational Proteomics, Department of Medicine, Philipps-Universität Marburg, Marburg, Germany
| | - Dennis Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Biochemistry, Weill Cornell Medicine, New York, NY, USA
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
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Bossi E, Limo E, Pagani L, Monza N, Serrao S, Denti V, Astarita G, Paglia G. Revolutionizing Blood Collection: Innovations, Applications, and the Potential of Microsampling Technologies for Monitoring Metabolites and Lipids. Metabolites 2024; 14:46. [PMID: 38248849 PMCID: PMC10818866 DOI: 10.3390/metabo14010046] [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: 12/14/2023] [Revised: 01/03/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
Blood serves as the primary global biological matrix for health surveillance, disease diagnosis, and response to drug treatment, holding significant promise for personalized medicine. The diverse array of lipids and metabolites in the blood provides a snapshot of both physiological and pathological processes, with many routinely monitored during conventional wellness checks. The conventional method involves intravenous blood collection, extracting a few milliliters via venipuncture, a technique limited to clinical settings due to its dependence on trained personnel. Microsampling methods have evolved to be less invasive (collecting ≤150 µL of capillary blood), user-friendly (enabling self-collection), and suitable for remote collection in longitudinal studies. Dried blood spot (DBS), a pioneering microsampling technique, dominates clinical and research domains. Recent advancements in device technology address critical limitations of classical DBS, specifically variations in hematocrit and volume. This review presents a comprehensive overview of state-of-the-art microsampling devices, emphasizing their applications and potential for monitoring metabolites and lipids in blood. The scope extends to diverse areas, encompassing population studies, nutritional investigations, drug discovery, sports medicine, and multi-omics research.
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Affiliation(s)
- Eleonora Bossi
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy; (E.B.); (E.L.); (L.P.); (N.M.); (V.D.)
| | - Elena Limo
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy; (E.B.); (E.L.); (L.P.); (N.M.); (V.D.)
| | - Lisa Pagani
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy; (E.B.); (E.L.); (L.P.); (N.M.); (V.D.)
| | - Nicole Monza
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy; (E.B.); (E.L.); (L.P.); (N.M.); (V.D.)
| | - Simone Serrao
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy; (E.B.); (E.L.); (L.P.); (N.M.); (V.D.)
| | - Vanna Denti
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy; (E.B.); (E.L.); (L.P.); (N.M.); (V.D.)
| | - Giuseppe Astarita
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, DC 20057, USA;
| | - Giuseppe Paglia
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, 20854 Vedano al Lambro, Italy; (E.B.); (E.L.); (L.P.); (N.M.); (V.D.)
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Wang ZB, Tan L, Gao PY, Ma YH, Fu Y, Sun Y, Yu JT. Associations of the A/T/N profiles in PET, CSF, and plasma biomarkers with Alzheimer's disease neuropathology at autopsy. Alzheimers Dement 2023; 19:4421-4435. [PMID: 37506291 DOI: 10.1002/alz.13413] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
INTRODUCTION To examine the extent to which positron emission tomography (PET)-, cerebrospinal fluid (CSF)-, and plasma-related amyloid-β/tau/neurodegeneration (A/T/N) biomarkers are associated with Alzheimer's disease (AD) neuropathology at autopsy. METHODS A total of 100 participants who respectively underwent antemortem biomarker measurements and postmortem neuropathology were included in the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined the associations of PET-, CSF-, and plasma-related A/T/N biomarkers in combinations or alone with AD neuropathological changes (ADNC). RESULTS PET- and CSF-related A/T/N biomarkers in combination showed high concordance with the ADNC stage and alone showed high accuracy in discriminating autopsy-confirmed AD. However, the plasma-related A/T/N biomarkers alone showed better discriminative performance only when combined with apolipoprotein E (APO)E ε4 genotype. DISCUSSION This study supports that PET- and CSF-related A/T/N profiles can be used to predict accurately the stages of AD neuropathology. For diagnostic settings, PET-, CSF-, and plasma-related A/T/N biomarkers are all useful diagnostic tools to detect the presence of AD neuropathology. HIGHLIGHTS PET- and CSF-related A/T/N biomarkers in combination can accurately predict the specific stages of AD neuropathology. PET- and CSF-related A/T/N biomarkers alone may serve as a precise diagnostic tool for detecting AD neuropathology at autopsy. Plasma-related A/T/N biomarkers may need combined risk factors when used as a diagnostic tool. Aβ PET and CSF p-tau181/Aβ42 were most consistent with Aβ pathology, while tau PET and CSF p-tau181/Aβ42 were most consistent with tau pathology.
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Affiliation(s)
- Zhi-Bo Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Pei-Yang Gao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ya-Hui Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yan Sun
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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