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The Cardiometabolic Biobank catalogs patient samples obtained by metabolic disease researchers across the UofU. These include blood and tissue samples collected over the past 30 years, comprising numerous clinical studies and thousands of biospecimens relating to diabetes, obesity, extreme familial thinness, and renal and cardiovascular disease.

Collections are listed below, and described in a number of publications, including landmark papers on the outcomes of roux-en-Ygastric bypass surgery (RYGB)1,2  and mechanistic studies on the links between altered glucose and lipid metabolism, coronary artery disease, and heart failure,3-6 as well as genetic implications of diabetes and diabetic kidney disease.7-8

Biospecimen collections relevant to diabetes and comorbidities are stored within a freezer farm and made available to UDMRC investigators for studies of novel metabolites, genes, or proteins involved in diabetes and its related complications (obesity, heart disease, and kidney disease). Clinical data are integrated into the Utah Population Database (UPDB), which allows UDMRC investigators to leverage the data-mining platforms to identify unique biospecimens using cohort assembly tools.

Request access to samples.

UDMRC Cardiometabolic Biobank Biospecimen Archive Table

 References:

  1. Adams, T.D., Davidson, L.E. & Hunt, S.C. Weight and Metabolic Outcomes 12 Years after Gastric Bypass. N Engl J Med 378, 93-96 (2018).
  2. Adams, T.D., Gress, R.E., Smith, S.C., Halverson, R.C., Simper, S.C., Rosamond, W.D., Lamonte, M.J., Stroup, A.M. & Hunt, S.C. Long-term mortality after gastric bypass surgery. N Engl J Med 357, 753-761 (2007).
  3. Badolia, R., Ramadurai, D.K.A., Abel, E.D., Ferrin, P., Taleb, I., Shankar, T.S., Krokidi, A.T., Navankasattusas, S., McKellar, S.H., Yin, M., Kfoury, A.G., Wever-Pinzon, O., Fang, J.C., Selzman, C.H., Chaudhuri, D., Rutter, J. & Drakos, S.G. The Role of Nonglycolytic Glucose Metabolism in Myocardial Recovery Upon Mechanical Unloading and Circulatory Support in Chronic Heart Failure. Circulation 142, 259-274 (2020). PMC7380956
  4. Cluntun, A.A., Badolia, R., Lettlova, S., Parnell, K.M., Shankar, T.S., Diakos, N.A., Olson, K.A., Taleb, I., Tatum, S.M., Berg, J.A., Cunningham, C.N., Van Ry, T., Bott, A.J., Krokidi, A.T., Fogarty, S., Skedros, S., Swiatek, W.I., Yu, X., Luo, B., Merx, S., Navankasattusas, S., Cox, J.E., Ducker, G.S., Holland, W.L., McKellar, S.H., Rutter, J. & Drakos, S.G. The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure. Cell Metab 33, 629-648 e610 (2021). PMC7933116
  5. Diakos, N.A., Navankasattusas, S., Abel, E.D., Rutter, J., McCreath, L., Ferrin, P., McKellar, S.H., Miller, D.V., Park, S.Y., Richardson, R.S., Deberardinis, R., Cox, J.E., Kfoury, A.G., Selzman, C.H., Stehlik, J., Fang, J.C., Li, D.Y. & Drakos, S.G. Evidence of Glycolysis Up-Regulation and Pyruvate Mitochondrial Oxidation Mismatch During Mechanical Unloading of the Failing Human Heart: Implications for Cardiac Reloading and Conditioning. JACC Basic Transl Sci 1, 432-444 (2016). PMC5422992
  6. Poss, A.M., Maschek, J.A., Cox, J.E., Hauner, B.J., Hopkins, P.N., Hunt, S.C., Holland, W.L., Summers, S.A. & Playdon, M.C. Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease. J Clin Invest 130, 1363-1376 (2020). PMC7269567
  7. Frodsham, S.G., Yu, Z., Lyons, A.M., Agarwal, A., Pezzolesi, M.H., Dong, L., Srinivas, T.R., Ying, J., Greene, T., Raphael, K.L., Smith, K.R. & Pezzolesi, M.G. The Familiality of Rapid Renal Decline in Diabetes. Diabetes 68, 420-429 (2019). PMC6341306
  8. Lazaro-Guevara, J., Fierro-Morales, J., Wright, A.H., Gunville, R., Simeone, C., Frodsham, S.G., Pezzolesi, M.H., Zaffino, C.A., Al-Rabadi, L., Ramkumar, N. & Pezzolesi, M.G. Targeted Next-Generation Sequencing Identifies Pathogenic Variants in Diabetic Kidney Disease. Am J Nephrol, 1-11 (2021).

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