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Ngo D, Benson MD, Long JZ, et al. Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk. JCI Insight. 2021;6(5)doi: 10.1172/jci.insight.144392.
Ngo, D., Benson, M. D., Long, J. Z., Chen, Z. Z., Wang, R., Nath, A. K., Keyes, M. J., Shen, D., Sinha, S., Kuhn, E., Morningstar, J. E., Shi, X., Peterson, B. D., Chan, C., Katz, D. H., Tahir, U. A., Farrell, L. A., Melander, O., Mosley, J. D., Carr, S. A., Vasan, R. S., Larson, M. G., Smith, J. G., Wang, T. J., Yang, Q., & Gerszten, R. E. (2021). Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk. JCI insight, 6(5), . https://doi.org/10.1172/jci.insight.144392
Ngo, Debby, et al. "Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk." JCI insight vol. 6,5 (2021). doi: https://doi.org/10.1172/jci.insight.144392
Ngo D, Benson MD, Long JZ, Chen ZZ, Wang R, Nath AK, Keyes MJ, Shen D, Sinha S, Kuhn E, Morningstar JE, Shi X, Peterson BD, Chan C, Katz DH, Tahir UA, Farrell LA, Melander O, Mosley JD, Carr SA, Vasan RS, Larson MG, Smith JG, Wang TJ, Yang Q, Gerszten RE. Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk. JCI Insight. 2021 Mar 08;6(5). doi: 10.1172/jci.insight.144392. PMID: 33591955; PMCID: PMC8021115.
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JCI Insight. 2021 Mar 08;6(5). doi: 10.1172/jci.insight.144392.

Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk.

JCI insight

Debby Ngo, Mark D Benson, Jonathan Z Long, Zsu-Zsu Chen, Ruiqi Wang, Anjali K Nath, Michelle J Keyes, Dongxiao Shen, Sumita Sinha, Eric Kuhn, Jordan E Morningstar, Xu Shi, Bennet D Peterson, Christopher Chan, Daniel H Katz, Usman A Tahir, Laurie A Farrell, Olle Melander, Jonathan D Mosley, Steven A Carr, Ramachandran S Vasan, Martin G Larson, J Gustav Smith, Thomas J Wang, Qiong Yang, Robert E Gerszten

Affiliations

  1. Cardiovascular Institute.
  2. Division of Pulmonary, Critical Care and Sleep Medicine, and.
  3. Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA.
  4. Department of Pathology, Stanford University, Stanford, California, USA.
  5. Division of Endocrinology, Diabetes and Metabolism, BIDMC, Boston, Massachusetts, USA.
  6. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
  7. Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
  8. Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden.
  9. Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  10. Department of Medicine, Divisions of Preventive Medicine and Cardiology, Boston University School of Medicine, Boston, Massachusetts, USA.
  11. The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA.
  12. Wallenberg Center for Molecular Medicine and Diabetes Center, Lund University, Lund, Sweden.
  13. Department of Cardiology and Wallenberg Laboratory, Gothenburg University and Sahlgrenska University Hospital, Gothenburg, Sweden.
  14. Department of Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA.

PMID: 33591955 PMCID: PMC8021115 DOI: 10.1172/jci.insight.144392

Abstract

Recent advances in proteomic technologies have made high-throughput profiling of low-abundance proteins in large epidemiological cohorts increasingly feasible. We investigated whether aptamer-based proteomic profiling could identify biomarkers associated with future development of type 2 diabetes (T2DM) beyond known risk factors. We identified dozens of markers with highly significant associations with future T2DM across 2 large longitudinal cohorts (n = 2839) followed for up to 16 years. We leveraged proteomic, metabolomic, genetic, and clinical data from humans to nominate 1 specific candidate to test for potential causal relationships in model systems. Our studies identified functional effects of aminoacylase 1 (ACY1), a top protein association with future T2DM risk, on amino acid metabolism and insulin homeostasis in vitro and in vivo. Furthermore, a loss-of-function variant associated with circulating levels of the biomarker WAP, Kazal, immunoglobulin, Kunitz, and NTR domain-containing protein 2 (WFIKKN2) was, in turn, associated with fasting glucose, hemoglobin A1c, and HOMA-IR measurements in humans. In addition to identifying potentially novel disease markers and pathways in T2DM, we provide publicly available data to be leveraged for insights about gene function and disease pathogenesis in the context of human metabolism.

Keywords: Diabetes; Endocrinology; Proteomics

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