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Sattar N, Taheri S, Astling DP, Chadwick J, Hinterberg MA, Holmes MV, Troth EV, Welsh P, Zaghloul H, Chagoury O, Lean M, Taylor R, Williams S. Prediction of Cardiometabolic Health Through Changes in Plasma Proteins With Intentional Weight Loss in the DiRECT and DIADEM-I Randomized Clinical Trials of Type 2 Diabetes Remission. Diabetes Care 2023; 46:1949-1957. [PMID: 37756566 PMCID: PMC10628468 DOI: 10.2337/dc23-0602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/04/2023] [Indexed: 09/29/2023]
Abstract
OBJECTIVE To determine the extent to which changes in plasma proteins, previously predictive of cardiometabolic outcomes, predict changes in two diabetes remission trials. RESEARCH DESIGN AND METHODS We applied SomaSignal predictive tests (each derived from ∼5,000 plasma protein measurements using aptamer-based proteomics assay) to baseline and 1-year samples of trial intervention (Diabetes Remission Clinical Trial [DiRECT], n = 118, and Diabetes Intervention Accentuating Diet and Enhancing Metabolism [DIADEM-I], n = 66) and control (DiRECT, n = 144, DIADEM-I, n = 76) group participants. RESULTS Mean (SD) weight loss in DiRECT (U.K.) and DIADEM-I (Qatar) was 10.2 (7.4) kg and 12.1 (9.5) kg, respectively, vs. 1.0 (3.7) kg and 4.0 (5.4) kg in control groups. Cardiometabolic SomaSignal test results showed significant improvement (Bonferroni-adjusted P < 0.05) in DiRECT and DIADEM-I (expressed as relative difference, intervention minus control) as follows, respectively: liver fat (-26.4%, -37.3%), glucose tolerance (-36.6%, -37.4%), body fat percentage (-8.6%, -8.7%), resting energy rate (-8.0%, -5.1%), visceral fat (-34.3%, -26.1%), and cardiorespiratory fitness (9.5%, 10.3%). Cardiovascular risk (measured with SomaSignal tests) also improved in intervention groups relative to control, but this was significant only in DiRECT (DiRECT, -44.2%, and DIADEM-I, -9.2%). However, weight loss >10 kg predicted significant reductions in cardiovascular risk, -19.1% (95% CI -33.4 to -4.91) in DiRECT and -33.4% (95% CI -57.3, -9.6) in DIADEM-I. DIADEM-I also demonstrated rapid emergence of metabolic improvements at 3 months. CONCLUSIONS Intentional weight loss in recent-onset type 2 diabetes rapidly induces changes in protein-based risk models consistent with widespread cardiometabolic improvements, including cardiorespiratory fitness. Protein changes with greater (>10 kg) weight loss also predicted lower cardiovascular risk, providing a positive outlook for relevant ongoing trials.
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Affiliation(s)
- Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, U.K
| | - Shahrad Taheri
- Qatar Metabolic Institute, Hamad Medical Corporation, Doha, Qatar
- Weill Cornell Medicine-Qatar, Doha, Qatar
- Weill Cornell Medicine, New York, NY
| | | | | | | | - Michael V. Holmes
- Medical Research Council, Integrative Epidemiology Unit, University of Bristol, Bristol, U.K
| | | | - Paul Welsh
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, U.K
| | - Hadeel Zaghloul
- Weill Cornell Medicine-Qatar, Doha, Qatar
- Weill Cornell Medicine, New York, NY
| | - Odette Chagoury
- Qatar Metabolic Institute, Hamad Medical Corporation, Doha, Qatar
- Weill Cornell Medicine-Qatar, Doha, Qatar
- Weill Cornell Medicine, New York, NY
| | - Mike Lean
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, U.K
| | - Roy Taylor
- Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, U.K
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Su CY, Zhou S, Gonzalez-Kozlova E, Butler-Laporte G, Brunet-Ratnasingham E, Nakanishi T, Jeon W, Morrison DR, Laurent L, Afilalo J, Afilalo M, Henry D, Chen Y, Carrasco-Zanini J, Farjoun Y, Pietzner M, Kimchi N, Afrasiabi Z, Rezk N, Bouab M, Petitjean L, Guzman C, Xue X, Tselios C, Vulesevic B, Adeleye O, Abdullah T, Almamlouk N, Moussa Y, DeLuca C, Duggan N, Schurr E, Brassard N, Durand M, Del Valle DM, Thompson R, Cedillo MA, Schadt E, Nie K, Simons NW, Mouskas K, Zaki N, Patel M, Xie H, Harris J, Marvin R, Cheng E, Tuballes K, Argueta K, Scott I, Greenwood CMT, Paterson C, Hinterberg MA, Langenberg C, Forgetta V, Pineau J, Mooser V, Marron T, Beckmann ND, Kim-Schulze S, Charney AW, Gnjatic S, Kaufmann DE, Merad M, Richards JB. Circulating proteins to predict COVID-19 severity. Sci Rep 2023; 13:6236. [PMID: 37069249 PMCID: PMC10107586 DOI: 10.1038/s41598-023-31850-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 03/17/2023] [Indexed: 04/19/2023] Open
Abstract
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.
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Affiliation(s)
- Chen-Yang Su
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Computer Science, McGill University, Montréal, QC, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | | | - Guillaume Butler-Laporte
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | | | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Graduate School of Medicine, McGill International Collaborative School in Genomic Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Wonseok Jeon
- Department of Computer Science, McGill University, Montréal, QC, Canada
| | - David R Morrison
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Laetitia Laurent
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Jonathan Afilalo
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Marc Afilalo
- Department of Emergency Medicine, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Danielle Henry
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Julia Carrasco-Zanini
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Yossi Farjoun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Maik Pietzner
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nofar Kimchi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Zaman Afrasiabi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Nardin Rezk
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Meriem Bouab
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Louis Petitjean
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Charlotte Guzman
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Xiaoqing Xue
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Chris Tselios
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Branka Vulesevic
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Olumide Adeleye
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Tala Abdullah
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Noor Almamlouk
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Yara Moussa
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Chantal DeLuca
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Naomi Duggan
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Erwin Schurr
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, QC, Canada
| | - Nathalie Brassard
- Research Centre of the Centre Hospitalier de L'Université de Montréal, Montreal, QC, Canada
| | - Madeleine Durand
- Research Centre of the Centre Hospitalier de L'Université de Montréal, Montreal, QC, Canada
| | - Diane Marie Del Valle
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan Thompson
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mario A Cedillo
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric Schadt
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Nie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole W Simons
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Konstantinos Mouskas
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicolas Zaki
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manishkumar Patel
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hui Xie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jocelyn Harris
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Marvin
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Esther Cheng
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kevin Tuballes
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kimberly Argueta
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ieisha Scott
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | | | | | - Claudia Langenberg
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Joelle Pineau
- Department of Computer Science, McGill University, Montréal, QC, Canada
| | - Vincent Mooser
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Thomas Marron
- Immunotherapy and Phase 1 Trials, Mount Sinai Hospital, New York, NY, USA
| | - Noam D Beckmann
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel E Kaufmann
- Research Centre of the Centre Hospitalier de L'Université de Montréal, Montreal, QC, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, Canada
- Division of Infectious Diseases, Department of Medicine, University Hospital of Lausanne and University of Lausanne, Lausanne, Switzerland
| | - Miriam Merad
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
- Department of Human Genetics, McGill University, Montreal, QC, Canada.
- Department of Twin Research, King's College London, London, UK.
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Williams SA, Ostroff R, Hinterberg MA, Coresh J, Ballantyne CM, Matsushita K, Mueller CE, Walter J, Jonasson C, Holman RR, Shah SH, Sattar N, Taylor R, Lean ME, Kato S, Shimokawa H, Sakata Y, Nochioka K, Parikh CR, Coca SG, Omland T, Chadwick J, Astling D, Hagar Y, Kureshi N, Loupy K, Paterson C, Primus J, Simpson M, Trujillo NP, Ganz P. A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk. Sci Transl Med 2022; 14:eabj9625. [PMID: 35385337 DOI: 10.1126/scitranslmed.abj9625] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c-statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a "universal" surrogate end point for cardiovascular risk.
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Affiliation(s)
| | | | | | - Josef Coresh
- Johns Hopkins University, Baltimore, MD 21218, USA
| | | | | | - Christian E Mueller
- Cardiovascular Research Institute, University of Basel, Basel 4001, Switzerland
| | - Joan Walter
- Cardiovascular Research Institute, University of Basel, Basel 4001, Switzerland.,Institute of Diagnostic and Interventional Radiology, University Hospital Zürich, University of Zürich, Zürich 7491, Switzerland
| | - Christian Jonasson
- Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Rury R Holman
- Diabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
| | - Svati H Shah
- Division of Cardiology, Duke Department of Medicine, and Duke Molecular Physiology Institute, Duke University, Durham, NC 27710, USA
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - Roy Taylor
- Newcastle Magnetic Resonance Centre, University of Newcastle upon Tyne, Newcastle upon Tyne NE1 7RU, UK
| | - Michael E Lean
- School of Medicine, Nursing and Dentistry, University of Glasgow, Glasgow G12 8QQ, UK
| | | | - Hiroaki Shimokawa
- Tohoku University Graduate School of Medicine, Sendai 980-8576, Japan.,Graduate School, International University of Health and Welfare, Narita 286-8686, Japan
| | - Yasuhiko Sakata
- Tohoku University Graduate School of Medicine, Sendai 980-8576, Japan
| | - Kotaro Nochioka
- Tohoku University Graduate School of Medicine, Sendai 980-8576, Japan
| | | | - Steven G Coca
- Mt Sinai Clinical and Translational Science Research Unit, Icahn School of Medicine at Mount Sinai, New York, NY 11766, USA
| | - Torbjørn Omland
- Department of Cardiology, Akershus University Hospital and University of Oslo, Oslo 1478, Norway
| | | | | | | | | | | | | | | | | | | | - Peter Ganz
- Zuckerberg San Francisco General Hospital, University of California, San Francisco, San Francisco, CA 94110, USA
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