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Structural and Functional Imaging of Muscle, Heart, Endocrine Pancreas and Kidneys in Cardiometabolic Drug Development

  • Olof Eriksson
  • Paul Hockings
  • Edvin Johansson
  • Lars JohanssonEmail author
  • Joel Kullberg
Chapter

Abstract

In the context of diabetes and related metabolic disorders imaging is well suited to studying the effects of new drugs on relevant clinical outcomes including atherosclerotic cardiovascular events, heart failure, and renal function. Accordingly, imaging is becoming more widely used in aspects of cardiometabolic drug development. Key techniques include computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). Hybrid imaging systems, e.g. PET/CT and PET/MRI which are becoming more available offer opportunities to combine detailed morphological imaging with functional and molecular imaging. Combining imaging with other investigative techniques, e.g. hyperinsulinemic euglycemic clamps, can provide additional functional assessments that may be of value in drug development. This chapter reviews recent innovations in structural and functional imaging of major metabolically active organs including the pancreas, skeletal muscle, adipose tissue, kidney, and heart. Each method has advantages and limitations with respect to applications in drug development. These include the pathophysiological relevance of the imaging, accuracy, suitability for repeated clinical studies, cost, and availability of expertise in application and interpretation.

Keywords

Medical imaging Computed tomography Magnetic resonance imaging Positron emission tomography Glucose clamp Pancreas Muscle Adipose tissue Kidney Heart 

References

  1. 1.
    Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373:2117–28.CrossRefGoogle Scholar
  2. 2.
    Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016;375(4):311–22.PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Rahier J, Guiot Y, Goebbels RM, Sempoux C, Henquin JC. Pancreatic beta-cell mass in European subjects with type 2 diabetes. Diabetes Obes Metab. 2008;10 Suppl 4:32–42.PubMedCrossRefPubMedCentralGoogle Scholar
  4. 4.
    Krogvold L, Edwin B, Buanes T, et al. Pancreatic biopsy by minimal tail resection in live adult patients at the onset of type 1 diabetes: experiences from the DiViD study. Diabetologia. 2014;57(4):841–3.PubMedCrossRefPubMedCentralGoogle Scholar
  5. 5.
    Pisania A, Weir GC, O’Neil JJ, et al. Quantitative analysis of cell composition and purity of human pancreatic islet preparations. Lab Investig. 2010;90(11):1661–75.Google Scholar
  6. 6.
    Macauley M, Percival K, Thelwall PE, Hollingsworth KG, Taylor R. Altered volume, morphology and composition of the pancreas in type 2 diabetes. PLoS One. 2015;10(5):e0126825.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Garcia TS, Rech TH, Leitao CB. Pancreatic size and fat content in diabetes: a systematic review and meta-analysis of imaging studies. PLoS One. 2017;12(7):e0180911.PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Eriksson O, Espes D, Selvaraju RK, et al. Positron emission tomography ligand [11C]5-hydroxy-tryptophan can be used as a surrogate marker for the human endocrine pancreas. Diabetes. 2014;63(10):3428–37.PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Staaf J, Labmayr V, Paulmichl K, et al. Pancreatic fat is associated with metabolic syndrome and visceral fat but not Beta-cell function or body mass index in pediatric obesity. Pancreas. 2017;46(3):358–65.PubMedCrossRefPubMedCentralGoogle Scholar
  10. 10.
    Guglielmi V, Sbraccia P. Type 2 diabetes: does pancreatic fat really matter? Diabetes Metab Res Rev. 2018;34(2)Google Scholar
  11. 11.
    Nowotny B, Kahl S, Kluppelholz B, et al. Circulating triacylglycerols but not pancreatic fat associate with insulin secretion in healthy humans. Metabolism. 2018;81:113–25.PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Al-Mrabeh A, Hollingsworth KG, Steven S, Taylor R. Morphology of the pancreas in type 2 diabetes: effect of weight loss with or without normalisation of insulin secretory capacity. Diabetologia. 2016;59(8):1753–9.PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Kalliokoski T, Nuutila P, Virtanen KA, et al. Pancreatic glucose uptake in vivo in men with newly diagnosed type 1 diabetes. J Clin Endocrinol Metab. 2008;93(5):1909–14.PubMedCrossRefPubMedCentralGoogle Scholar
  14. 14.
    Gaglia JL, Harisinghani M, Aganj I, et al. Noninvasive mapping of pancreatic inflammation in recent-onset type-1 diabetes patients. Proc Natl Acad Sci U S A. 2015;112(7):2139–44.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Lifson N, Kramlinger KG, Mayrand RR, Lender EJ. Blood flow to the rabbit pancreas with special reference to the islets of Langerhans. Gastroenterology. 1980;79(3):466–73.PubMedPubMedCentralGoogle Scholar
  16. 16.
    Jansson L, Hellerstrom C. Stimulation by glucose of the blood flow to the pancreatic islets of the rat. Diabetologia. 1983;25(1):45–50.PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Carlbom L, Espes D, Lubberink M, et al. Pancreatic perfusion and subsequent response to glucose in healthy individuals and patients with type 1 diabetes. Diabetologia. 2016;59(9):1968–72.PubMedCrossRefPubMedCentralGoogle Scholar
  18. 18.
    Honka H, Hannukainen JC, Tarkia M, et al. Pancreatic metabolism, blood flow, and beta-cell function in obese humans. J Clin Endocrinol Metab. 2014;99(6):E981–90.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Xu G, Stoffers DA, Habener JF, Bonner-Weir S. Exendin-4 stimulates both beta-cell replication and neogenesis, resulting in increased beta-cell mass and improved glucose tolerance in diabetic rats. Diabetes. 1999;48(12):2270–6.PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    Eriksson O, Laughlin M, Brom M, et al. In vivo imaging of beta cells with radiotracers: state of the art, prospects and recommendations for development and use. Diabetologia. 2016;59(7):1340–9.PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Sweet IR, Cook DL, Lernmark A, et al. Systematic screening of potential beta-cell imaging agents. Biochem Biophys Res Commun. 2004;314(4):976–83.PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Karlsson F, Antonodimitrakis PC, Eriksson O. Systematic screening of imaging biomarkers for the Islets of Langerhans, among clinically available positron emission tomography tracers. Nucl Med Biol. 2015;42(10):762–9.PubMedCrossRefPubMedCentralGoogle Scholar
  23. 23.
    Maffei A, Liu Z, Witkowski P, et al. Identification of tissue-restricted transcripts in human islets. Endocrinology. 2004;145(10):4513–21.PubMedCrossRefPubMedCentralGoogle Scholar
  24. 24.
    Kung MP, Hou C, Lieberman BP, et al. In vivo imaging of beta-cell mass in rats using 18F-FP-(+)-DTBZ: a potential PET ligand for studying diabetes mellitus. J Nucl Med. 2008;49(7):1171–6.PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    Eriksson O, Jahan M, Johnstrom P, et al. In vivo and in vitro characterization of [18F]-FE-(+)-DTBZ as a tracer for beta-cell mass. Nucl Med Biol. 2010;37(3):357–63.PubMedCrossRefPubMedCentralGoogle Scholar
  26. 26.
    Normandin MD, Petersen KF, Ding YS, et al. In vivo imaging of endogenous pancreatic beta-cell mass in healthy and type 1 diabetic subjects using 18F-fluoropropyl-dihydrotetrabenazine and PET. J Nucl Med. 2012;53(6):908–16.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Freeby MJ, Kringas P, Goland RS, et al. Cross-sectional and test-retest characterization of PET with [(18)F]FP-(+)-DTBZ for beta cell mass estimates in diabetes. Mol Imaging Biol. 2016;18(2):292–301.PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Ohta Y, Kosaka Y, Kishimoto N, et al. Convergence of the insulin and serotonin programs in the pancreatic beta-cell. Diabetes. 2011;60(12):3208–16.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Eriksson O, Selvaraju RK, Johansson L, et al. Quantitative imaging of serotonergic biosynthesis and degradation in the endocrine pancreas. J Nucl Med. 2014;55(3):460–5.PubMedCrossRefPubMedCentralGoogle Scholar
  30. 30.
    Ekholm R, Ericson LE, Lundquist I. Monoamines in the pancreatic islets of the mouse. Subcellular localization of 5-hydroxytryptamine by electron microscopic autoradiography. Diabetologia. 1971;7(5):339–48.PubMedCrossRefPubMedCentralGoogle Scholar
  31. 31.
    Carlbom L, Espes D, Lubberink M, et al. [(11)C]5-hydroxy-tryptophan PET for assessment of islet mass during progression of type 2 diabetes. Diabetes. 2017;66(5):1286–92.PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Talchai C, Xuan S, Lin HV, Sussel L, Accili D. Pancreatic beta cell dedifferentiation as a mechanism of diabetic beta cell failure. Cell. 2012;150(6):1223–34.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Spijker HS, Song H, Ellenbroek JH, et al. Loss of beta-cell identity occurs in type 2 diabetes and is associated with islet amyloid deposits. Diabetes. 2015;64(8):2928–38.PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Goke R, Fehmann HC, Linn T, et al. Exendin-4 is a high potency agonist and truncated exendin-(9-39)-amide an antagonist at the glucagon-like peptide 1-(7-36)-amide receptor of insulin-secreting beta-cells. J Biol Chem. 1993;268(26):19650–5.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Tornehave D, Kristensen P, Romer J, Knudsen LB, Heller RS. Expression of the GLP-1 receptor in mouse, rat, and human pancreas. J Histochem Cytochem. 2008;56(9):841–51.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Brom M, Oyen WJ, Joosten L, Gotthardt M, Boerman OC. 68Ga-labelled exendin-3, a new agent for the detection of insulinomas with PET. Eur J Nucl Med Mol Imaging. 2010;37(7):1345–55.PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Brom M, Woliner-van der Weg W, Joosten L, et al. Non-invasive quantification of the beta cell mass by SPECT with (1)(1)(1)In-labelled exendin. Diabetologia. 2014;57(5):950–9.PubMedCrossRefPubMedCentralGoogle Scholar
  38. 38.
    Eriksson O, Rosenstrom U, Selvaraju RK, Eriksson B, Velikyan I. Species differences in pancreatic binding of DO3A-VS-Cys(40)-Exendin4. Acta Diabetol. 2017;54(11):1039–45.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Bouckenooghe T, Flamez D, Ortis F, Goldman S, Eizirik DL. Identification of new pancreatic beta cell targets for in vivo imaging by a systems biology approach. Curr Pharm Des. 2010;16(14):1609–18.PubMedCrossRefPubMedCentralGoogle Scholar
  40. 40.
    Flamez D, Roland I, Berton A, et al. A genomic-based approach identifies FXYD domain containing ion transport regulator 2 (FXYD2)gammaa as a pancreatic beta cell-specific biomarker. Diabetologia. 2010;53(7):1372–83.PubMedCrossRefPubMedCentralGoogle Scholar
  41. 41.
    Burtea C, Laurent S, Crombez D, et al. Development of a peptide-functionalized imaging nanoprobe for the targeting of (FXYD2)gammaa as a highly specific biomarker of pancreatic beta cells. Contrast Media Mol Imaging. 2015;10(5):398–412.PubMedCrossRefPubMedCentralGoogle Scholar
  42. 42.
    Lindskog C, Korsgren O, Ponten F, et al. Novel pancreatic beta cell-specific proteins: antibody-based proteomics for identification of new biomarker candidates. J Proteome. 2012;75(9):2611–20.CrossRefGoogle Scholar
  43. 43.
    Hellstrom-Lindahl E, Danielsson A, Ponten F, et al. GPR44 is a pancreatic protein restricted to the human beta cell. Acta Diabetol. 2016;53(3):413–21.PubMedCrossRefPubMedCentralGoogle Scholar
  44. 44.
    Jahan M. Development of novel PET radioligands for visualizing beta cell mass and amyloid plaques (PhD thesis). Stockholm: Karolinska Institute; 2016Google Scholar
  45. 45.
    Eriksson O, Johnstrom P, Cselenyi Z, et al. In vivo visualization of beta-cells by targeting of GPR44. Diabetes. 2018;67(2):182–92.PubMedCrossRefPubMedCentralGoogle Scholar
  46. 46.
    Nalin L, Selvaraju RK, Velikyan I, et al. Positron emission tomography imaging of the glucagon-like peptide-1 receptor in healthy and streptozotocin-induced diabetic pigs. Eur J Nucl Med Mol Imaging. 2014;41(9):1800–10.PubMedCrossRefPubMedCentralGoogle Scholar
  47. 47.
    Selvaraju RK, Velikyan I, Johansson L, et al. In vivo imaging of the glucagonlike peptide 1 receptor in the pancreas with 68Ga-labeled DO3A-exendin-4. J Nucl Med. 2013;54(8):1458–63.PubMedCrossRefPubMedCentralGoogle Scholar
  48. 48.
    Sanchez-Garrido MA, Brandt SJ, Clemmensen C, et al. GLP-1/glucagon receptor co-agonism for treatment of obesity. Diabetologia. 2017;60(10):1851–61.PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    Eriksson O, Laitinen I, Johansson L, Bossart M, Wagner M, Plettenburg O, Larsen P, Pierrou S, Haack T. First-in-class PET tracer for the glucagon receptor. Lisbon: European Association for the Study of Diabetes; 2017.Google Scholar
  50. 50.
    DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Phys. 1979;237(3):E214–23.Google Scholar
  51. 51.
    Ng JM, Bertoldo A, Minhas DS, et al. Dynamic PET imaging reveals heterogeneity of skeletal muscle insulin resistance. J Clin Endocrinol Metab. 2014;99(1):E102–6.PubMedCrossRefPubMedCentralGoogle Scholar
  52. 52.
    Koffert JP, Mikkola K, Virtanen KA, et al. Metformin treatment significantly enhances intestinal glucose uptake in patients with type 2 diabetes: results from a randomized clinical trial. Diabetes Res Clin Pract. 2017;131:208–16.PubMedCrossRefPubMedCentralGoogle Scholar
  53. 53.
    Hallsten K, Virtanen KA, Lonnqvist F, et al. Rosiglitazone but not metformin enhances insulin- and exercise-stimulated skeletal muscle glucose uptake in patients with newly diagnosed type 2 diabetes. Diabetes. 2002;51(12):3479–85.PubMedCrossRefPubMedCentralGoogle Scholar
  54. 54.
    Williams KV, Bertoldo A, Kinahan P, Cobelli C, Kelley DE. Weight loss-induced plasticity of glucose transport and phosphorylation in the insulin resistance of obesity and type 2 diabetes. Diabetes. 2003;52(7):1619–26.PubMedCrossRefPubMedCentralGoogle Scholar
  55. 55.
    Johansson E, Lubberink M, Heurling K, et al. Whole-body imaging of tissue-specific insulin sensitivity and body composition by using an integrated PET/MR system: a feasibility study. Radiology. 2018;286(1):271–8.PubMedCrossRefPubMedCentralGoogle Scholar
  56. 56.
    Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983;3(1):1–7.PubMedCrossRefPubMedCentralGoogle Scholar
  57. 57.
    Strand R, Malmberg F, Johansson L, et al. A concept for holistic whole body MRI data analysis, Imiomics. PLoS One. 2017;12(2):e0169966.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Kaul S, Rothney MP, Peters DM, et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring). 2012;20(6):1313–8.CrossRefGoogle Scholar
  59. 59.
    Frossing S, Nylander MC, Chabanova E, et al. Quantification of visceral adipose tissue in polycystic ovary syndrome: dual-energy X-ray absorptiometry versus magnetic resonance imaging. Acta Radiol. 2018;59(1):13–7.PubMedCrossRefPubMedCentralGoogle Scholar
  60. 60.
    Meredith-Jones K, Haszard J, Stanger N, Taylor R. Precision of DXA-derived visceral fat measurements in a large sample of adults of varying body size. Obesity (Silver Spring). 2018;26(3):505–12.CrossRefGoogle Scholar
  61. 61.
    Thomas EL, Saeed N, Hajnal JV, et al. Magnetic resonance imaging of total body fat. J Appl Physiol (1985). 1998;85(5):1778–85.CrossRefGoogle Scholar
  62. 62.
    Ross R. Advances in the application of imaging methods in applied and clinical physiology. Acta Diabetol. 2003;40 Suppl 1:S45–50.PubMedCrossRefPubMedCentralGoogle Scholar
  63. 63.
    Dixon WT. Simple proton spectroscopic imaging. Radiology. 1984;153(1):189–94.PubMedCrossRefPubMedCentralGoogle Scholar
  64. 64.
    Hu HH, Chen J, Shen W. Segmentation and quantification of adipose tissue by magnetic resonance imaging. MAGMA. 2016;29(2):259–76.PubMedCrossRefPubMedCentralGoogle Scholar
  65. 65.
    Belaroussi B, Milles J, Carme S, Zhu YM, Benoit-Cattin H. Intensity non-uniformity correction in MRI: existing methods and their validation. Med Image Anal. 2006;10(2):234–46.PubMedCrossRefPubMedCentralGoogle Scholar
  66. 66.
    Kullberg J, Johansson L, Ahlstrom H, et al. Automated assessment of whole-body adipose tissue depots from continuously moving bed MRI: a feasibility study. J Magn Reson Imaging. 2009;30(1):185–93.PubMedCrossRefPubMedCentralGoogle Scholar
  67. 67.
    Kullberg J, Karlsson AK, Stokland E, Svensson PA, Dahlgren J. Adipose tissue distribution in children: automated quantification using water and fat MRI. J Magn Reson Imaging. 2010;32(1):204–10.PubMedCrossRefPubMedCentralGoogle Scholar
  68. 68.
    Ranefall P, Bidar AW, Hockings PD. Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI. J Magn Reson Imaging. 2009;30(3):554–60.PubMedCrossRefPubMedCentralGoogle Scholar
  69. 69.
    Kullberg J, Hedstrom A, Brandberg J, et al. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci Rep. 2017;7(1):10425.PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Borga M, Thomas EL, Romu T, et al. Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies. NMR Biomed. 2015;28(12):1747–53.PubMedCrossRefPubMedCentralGoogle Scholar
  71. 71.
    Joshi AA, Hu HH, Leahy RM, Goran MI, Nayak KS. Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI. J Magn Reson Imaging. 2013;37(2):423–30.PubMedCrossRefPubMedCentralGoogle Scholar
  72. 72.
    Middleton MS, Haufe W, Hooker J, et al. Quantifying abdominal adipose tissue and thigh muscle volume and hepatic proton density fat fraction: repeatability and accuracy of an MR imaging-based, semiautomated analysis method. Radiology. 2017;283(2):438–49.PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Eriksson JW, Jansson PA, Carlberg B, et al. Hydrochlorothiazide, but not Candesartan, aggravates insulin resistance and causes visceral and hepatic fat accumulation: the mechanisms for the diabetes preventing effect of Candesartan (MEDICA) Study. Hypertension. 2008;52(6):1030–7.PubMedCrossRefPubMedCentralGoogle Scholar
  74. 74.
    Bolinder J, Ljunggren O, Kullberg J, et al. Effects of dapagliflozin on body weight, total fat mass, and regional adipose tissue distribution in patients with type 2 diabetes mellitus with inadequate glycemic control on metformin. J Clin Endocrinol Metab. 2012;97(3):1020–31.PubMedCrossRefPubMedCentralGoogle Scholar
  75. 75.
    Lundkvist P, Sjostrom CD, Amini S, et al. Dapagliflozin once-daily and exenatide once-weekly dual therapy: a 24-week randomized, placebo-controlled, phase II study examining effects on body weight and prediabetes in obese adults without diabetes. Diabetes Obes Metab. 2017;19(1):49–60.PubMedCrossRefPubMedCentralGoogle Scholar
  76. 76.
    Merlotti C, Ceriani V, Morabito A, Pontiroli AE. Subcutaneous fat loss is greater than visceral fat loss with diet and exercise, weight-loss promoting drugs and bariatric surgery: a critical review and meta-analysis. Int J Obes. 2017;41(5):672–82.CrossRefGoogle Scholar
  77. 77.
    Webster AC, Nagler EV, Morton RL, Masson P. Chronic kidney disease. Lancet. 2017;389(10075):1238–52.PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Nakazato T, Ikehira H, Imasawa T. An equation to estimate the renal cortex volume in chronic kidney disease patients. Clin Exp Nephrol. 2018;22(3):603–12.PubMedCrossRefPubMedCentralGoogle Scholar
  79. 79.
    Beland MD, Walle NL, Machan JT, Cronan JJ. Renal cortical thickness measured at ultrasound: is it better than renal length as an indicator of renal function in chronic kidney disease? AJR Am J Roentgenol. 2010;195(2):W146–9.PubMedCrossRefPubMedCentralGoogle Scholar
  80. 80.
    Emamian SA, Nielsen MB, Pedersen JF. Intraobserver and interobserver variations in sonographic measurements of kidney size in adult volunteers. A comparison of linear measurements and volumetric estimates. Acta Radiol. 1995;36(4):399–401.PubMedCrossRefPubMedCentralGoogle Scholar
  81. 81.
    Bellenger NG, Davies LC, Francis JM, Coats AJ, Pennell DJ. Reduction in sample size for studies of remodeling in heart failure by the use of cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2000;2(4):271–8.PubMedCrossRefPubMedCentralGoogle Scholar
  82. 82.
    Wang X, Vrtiska TJ, Avula RT, et al. Age, kidney function, and risk factors associate differently with cortical and medullary volumes of the kidney. Kidney Int. 2014;85(3):677–85.PubMedCrossRefPubMedCentralGoogle Scholar
  83. 83.
    Meinel FG, De Cecco CN, Schoepf UJ, Katzberg R. Contrast-induced acute kidney injury: definition, epidemiology, and outcome. Biomed Res Int. 2014;2014:859328.PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Dalla-Palma L, Panzetta G, Pozzi-Mucelli RS, et al. Dynamic magnetic resonance imaging in the assessment of chronic medical nephropathies with impaired renal function. Eur Radiol. 2000;10(2):280–6.PubMedCrossRefPubMedCentralGoogle Scholar
  85. 85.
    Lee VS, Kaur M, Bokacheva L, et al. What causes diminished corticomedullary differentiation in renal insufficiency? J Magn Reson Imaging. 2007;25(4):790–5.PubMedCrossRefPubMedCentralGoogle Scholar
  86. 86.
    Otsuka T, Kaneko Y, Sato Y, et al. Kidney morphological parameters measured using noncontrast-enhanced steady-state free precession MRI with spatially selective inversion recovery pulse correlate with eGFR in patients with advanced CKD. Clin Exp Nephrol. 2018;22(1):45–54.PubMedCrossRefPubMedCentralGoogle Scholar
  87. 87.
    Cakmak P, Yagci AB, Dursun B, Herek D, Fenkci SM. Renal diffusion-weighted imaging in diabetic nephropathy: correlation with clinical stages of disease. Diagn Interv Radiol. 2014;20(5):374–8.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Zhao J, Wang ZJ, Liu M, et al. Assessment of renal fibrosis in chronic kidney disease using diffusion-weighted MRI. Clin Radiol. 2014;69(11):1117–22.PubMedCrossRefPubMedCentralGoogle Scholar
  89. 89.
    Liu Z, Xu Y, Zhang J, et al. Chronic kidney disease: pathological and functional assessment with diffusion tensor imaging at 3T MR. Eur Radiol. 2015;25(3):652–60.PubMedCrossRefPubMedCentralGoogle Scholar
  90. 90.
    Xu X, Palmer SL, Lin X, et al. Diffusion-weighted imaging and pathology of chronic kidney disease: initial study. Abdom Radiol (NY). 2018;43(7):1749–55.CrossRefGoogle Scholar
  91. 91.
    Chen X, Xiao W, Li X, et al. In vivo evaluation of renal function using diffusion weighted imaging and diffusion tensor imaging in type 2 diabetics with normoalbuminuria versus microalbuminuria. Front Med. 2014;8(4):471–6.PubMedCrossRefPubMedCentralGoogle Scholar
  92. 92.
    Cox EF, Buchanan CE, Bradley CR, et al. Multiparametric renal magnetic resonance imaging: validation, interventions, and alterations in chronic kidney disease. Front Physiol. 2017;8:696.PubMedPubMedCentralCrossRefGoogle Scholar
  93. 93.
    Rapacchi S, Smith RX, Wang Y, et al. Towards the identification of multi-parametric quantitative MRI biomarkers in lupus nephritis. Magn Reson Imaging. 2015;33(9):1066–74.PubMedCrossRefPubMedCentralGoogle Scholar
  94. 94.
    Skeoch S, Hubbard Cristinacce PL, Dobbs M, et al. Evaluation of non-contrast MRI biomarkers in lupus nephritis. Clin Exp Rheumatol. 2017;35(6):954–8.PubMedPubMedCentralGoogle Scholar
  95. 95.
    Koinuma M, Ohashi I, Hanafusa K, Shibuya H. Apparent diffusion coefficient measurements with diffusion-weighted magnetic resonance imaging for evaluation of hepatic fibrosis. J Magn Reson Imaging. 2005;22(1):80–5.PubMedCrossRefPubMedCentralGoogle Scholar
  96. 96.
    Buisson A, Joubert A, Montoriol PF, et al. Diffusion-weighted magnetic resonance imaging for detecting and assessing ileal inflammation in Crohn’s disease. Aliment Pharmacol Ther. 2013;37(5):537–45.Google Scholar
  97. 97.
    Leung G, Kirpalani A, Szeto SG, et al. Could MRI be used to image kidney fibrosis? A review of recent advances and remaining barriers. Clin J Am Soc Nephrol. 2017;12(6):1019–28.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Fine LG, Norman JT. Chronic hypoxia as a mechanism of progression of chronic kidney diseases: from hypothesis to novel therapeutics. Kidney Int. 2008;74(7):867–72.PubMedCrossRefPubMedCentralGoogle Scholar
  99. 99.
    Niendorf T, Pohlmann A, Arakelyan K, et al. How bold is blood oxygenation level-dependent (BOLD) magnetic resonance imaging of the kidney? Opportunities, challenges and future directions. Acta Physiol (Oxf). 2015;213(1):19–38.CrossRefGoogle Scholar
  100. 100.
    Pruijm M, Hofmann L, Piskunowicz M, et al. Determinants of renal tissue oxygenation as measured with BOLD-MRI in chronic kidney disease and hypertension in humans. PLoS One. 2014;9(4):e95895.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    van der Bel R, Coolen BF, Nederveen AJ, et al. Magnetic resonance imaging-derived renal oxygenation and perfusion during continuous, steady-state angiotensin-II infusion in healthy humans. J Am Heart Assoc. 2016;5(3):e003185.PubMedPubMedCentralGoogle Scholar
  102. 102.
    Milani B, Ansaloni A, Sousa-Guimaraes S, et al. Reduction of cortical oxygenation in chronic kidney disease: evidence obtained with a new analysis method of blood oxygenation level-dependent magnetic resonance imaging. Nephrol Dial Transplant. 2017;32(12):2097–105.PubMedPubMedCentralGoogle Scholar
  103. 103.
    Pruijm M, Milani B, Pivin E, et al. Reduced cortical oxygenation predicts a progressive decline of renal function in patients with chronic kidney disease. Kidney Int. 2018;93(4):932–40.PubMedCrossRefPubMedCentralGoogle Scholar
  104. 104.
    Hall ME, Rocco MV, Morgan TM, et al. Beta-blocker use is associated with higher renal tissue oxygenation in hypertensive patients suspected of renal artery stenosis. Cardiorenal Med. 2016;6(4):261–8.PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Khatir DS, Pedersen M, Jespersen B, Buus NH. Evaluation of renal blood flow and oxygenation in CKD using magnetic resonance imaging. Am J Kidney Dis. 2015;66(3):402–11.PubMedCrossRefPubMedCentralGoogle Scholar
  106. 106.
    Snowdon VK, Lachlan NJ, Hoy AM, et al. Serelaxin as a potential treatment for renal dysfunction in cirrhosis: preclinical evaluation and results of a randomized phase 2 trial. PLoS Med. 2017;14(2):e1002248.PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Cutajar M, Thomas DL, Hales PW, et al. Comparison of ASL and DCE MRI for the non-invasive measurement of renal blood flow: quantification and reproducibility. Eur Radiol. 2014;24(6):1300–8.PubMedCrossRefPubMedCentralGoogle Scholar
  108. 108.
    Gillis KA, McComb C, Patel RK, et al. Non-contrast renal magnetic resonance imaging to assess perfusion and corticomedullary differentiation in health and chronic kidney disease. Nephron. 2016;133(3):183–92.PubMedCrossRefPubMedCentralGoogle Scholar
  109. 109.
    Pecoits-Filho R, Perkovic V. Are SGLT2 inhibitors ready for prime time for CKD? Clin J Am Soc Nephrol. 2018;13(2):318–20.PubMedCrossRefPubMedCentralGoogle Scholar
  110. 110.
    Ray KK, Seshasai SR, Wijesuriya S, et al. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials. Lancet. 2009;373(9677):1765–72.PubMedCrossRefPubMedCentralGoogle Scholar
  111. 111.
    Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377:644–57.CrossRefGoogle Scholar
  112. 112.
    Inzucchi SE, Zinman B, Wanner C, et al. SGLT-2 inhibitors and cardiovascular risk: proposed pathways and review of ongoing outcome trials. Diab Vasc Dis Res. 2015;12(2):90–100.PubMedPubMedCentralCrossRefGoogle Scholar
  113. 113.
    Stanley WC, Recchia FA, Lopaschuk GD. Myocardial substrate metabolism in the normal and failing heart. Physiol Rev. 2005;85(3):1093–129.PubMedCrossRefPubMedCentralGoogle Scholar
  114. 114.
    How OJ, Aasum E, Severson DL, et al. Increased myocardial oxygen consumption reduces cardiac efficiency in diabetic mice. Diabetes. 2006;55(2):466–73.PubMedCrossRefPubMedCentralGoogle Scholar
  115. 115.
    Clarke GD, Solis-Herrera C, Molina-Wilkins M, et al. Pioglitazone improves left ventricular diastolic function in subjects with diabetes. Diabetes Care. 2017;40(11):1530–6.PubMedPubMedCentralCrossRefGoogle Scholar
  116. 116.
    Witte KK, Byrom R, Gierula J, et al. Effects of vitamin D on cardiac function in patients with chronic HF: the VINDICATE study. J Am Coll Cardiol. 2016;67(22):2593–603.PubMedPubMedCentralCrossRefGoogle Scholar
  117. 117.
    Shimada YJ, Passeri JJ, Baggish AL, et al. Effects of losartan on left ventricular hypertrophy and fibrosis in patients with nonobstructive hypertrophic cardiomyopathy. JACC Heart Fail. 2013;1(6):480–7.PubMedPubMedCentralCrossRefGoogle Scholar
  118. 118.
    Chen WR, Chen YD, Tian F, et al. Effects of liraglutide on reperfusion injury in patients with ST-segment-elevation myocardial infarction. Circ Cardiovasc Imaging. 2016;9(12)Google Scholar
  119. 119.
    Lunning MA, Kutty S, Rome ET, et al. Cardiac magnetic resonance imaging for the assessment of the myocardium after doxorubicin-based chemotherapy. Am J Clin Oncol. 2015;38(4):377–81.PubMedCrossRefPubMedCentralGoogle Scholar
  120. 120.
    Scatteia A, Baritussio A, Bucciarelli-Ducci C. Strain imaging using cardiac magnetic resonance. Heart Fail Rev. 2017;22(4):465–76.PubMedPubMedCentralCrossRefGoogle Scholar
  121. 121.
    Hor KN, Baumann R, Pedrizzetti G, et al. Magnetic resonance derived myocardial strain assessment using feature tracking. J Vis Exp. 2011;(48)Google Scholar
  122. 122.
    Stokke TM, Hasselberg NE, Smedsrud MK, et al. Geometry as a confounder when assessing ventricular systolic function: comparison between ejection fraction and strain. J Am Coll Cardiol. 2017;70(8):942–54.PubMedCrossRefPubMedCentralGoogle Scholar
  123. 123.
    Abbasi SA, Shah RV, McNulty SE, et al. Left atrial structure and function in heart failure with preserved ejection fraction: a RELAX substudy. PLoS One. 2016;11(11):e0164914.PubMedPubMedCentralCrossRefGoogle Scholar
  124. 124.
    Caudron J, Fares J, Bauer F, Dacher JN. Evaluation of left ventricular diastolic function with cardiac MR imaging. Radiographics. 2011;31(1):239–59.PubMedCrossRefPubMedCentralGoogle Scholar
  125. 125.
    Kellman P, Arai AE. Cardiac imaging techniques for physicians: late enhancement. J Magn Reson Imaging. 2012;36(3):529–42.PubMedPubMedCentralCrossRefGoogle Scholar
  126. 126.
    Phelps ME, Huang SC, Hoffman EJ, et al. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-D-glucose: validation of method. Ann Neurol. 1979;6(5):371–88.PubMedCrossRefPubMedCentralGoogle Scholar
  127. 127.
    Herrero P, Kisrieva-Ware Z, Dence CS, et al. PET measurements of myocardial glucose metabolism with 1-11C-glucose and kinetic modeling. J Nucl Med. 2007;48(6):955–64.PubMedCrossRefPubMedCentralGoogle Scholar
  128. 128.
    Mather KJ, DeGrado TR. Imaging of myocardial fatty acid oxidation. Biochim Biophys Acta. 2016;1861(10):1535–43.PubMedCrossRefPubMedCentralGoogle Scholar
  129. 129.
    Weiss ES, Hoffman EJ, Phelps ME, et al. External detection and visualization of myocardial ischemia with 11C-substrates in vitro and in vivo. Circ Res. 1976;39(1):24–32.PubMedCrossRefPubMedCentralGoogle Scholar
  130. 130.
    DeGrado TR, Coenen HH, Stocklin G. 14(R,S)-[18F]fluoro-6-thia-heptadecanoic acid (FTHA): evaluation in mouse of a new probe of myocardial utilization of long chain fatty acids. J Nucl Med. 1991;32(10):1888–96.PubMedPubMedCentralGoogle Scholar
  131. 131.
    Blomqvist G, Thorell JO, Ingvar M, et al. Use of R-beta-[1-11C]hydroxybutyrate in PET studies of regional cerebral uptake of ketone bodies in humans. Am J Phys. 1995;269(5 Pt 1):E948–59.Google Scholar
  132. 132.
    Thorell JO, Stone-Elander S, Halldin C, Widen L. Synthesis of [1-11C]-beta-hydroxybutyric acid. Acta Radiol Suppl. 1991;376:94.PubMedPubMedCentralGoogle Scholar
  133. 133.
    Gormsen LC, Svart M, Thomsen HH, et al. Ketone body infusion with 3-hydroxybutyrate reduces myocardial glucose uptake and increases blood flow in humans: a positron emission tomography study. J Am Heart Assoc. 2017;6(3)Google Scholar
  134. 134.
    Herrero P, Dence CS, Coggan AR, et al. L-3-11C-lactate as a PET tracer of myocardial lactate metabolism: a feasibility study. J Nucl Med. 2007;48(12):2046–55.PubMedCrossRefPubMedCentralGoogle Scholar
  135. 135.
    Sun KT, Yeatman LA, Buxton DB, et al. Simultaneous measurement of myocardial oxygen consumption and blood flow using [1-carbon-11]acetate. J Nucl Med. 1998;39(2):272–80.PubMedPubMedCentralGoogle Scholar
  136. 136.
    Bing RJ, Hammond MM, et al. The measurement of coronary blood flow, oxygen consumption, and efficiency of the left ventricle in man. Am Heart J. 1949;38(1):1–24.PubMedCrossRefPubMedCentralGoogle Scholar
  137. 137.
    Knaapen P, Germans T, Knuuti J, et al. Myocardial energetics and efficiency: current status of the noninvasive approach. Circulation. 2007;115(7):918–27.PubMedCrossRefPubMedCentralGoogle Scholar
  138. 138.
    Hansson NH, Tolbod L, Harms HJ, et al. Evaluation of ECG-gated [(11)C]acetate PET for measuring left ventricular volumes, mass, and myocardial external efficiency. J Nucl Cardiol. 2016;23(4):670–9.PubMedCrossRefPubMedCentralGoogle Scholar
  139. 139.
    Nesterov SV, Turta O, Han C, et al. C-11 acetate has excellent reproducibility for quantification of myocardial oxidative metabolism. Eur Heart J Cardiovasc Imaging. 2015;16(5):500–6.PubMedCrossRefPubMedCentralGoogle Scholar
  140. 140.
    Murthy VL, Naya M, Foster CR, et al. Improved cardiac risk assessment with noninvasive measures of coronary flow reserve. Circulation. 2011;124(20):2215–24.Google Scholar
  141. 141.
    Schindler TH. Positron-emitting myocardial blood flow tracers and clinical potential. Prog Cardiovasc Dis. 2015;57(6):588–606.PubMedCrossRefPubMedCentralGoogle Scholar
  142. 142.
    Engblom H, Xue H, Akil S, et al. Fully quantitative cardiovascular magnetic resonance myocardial perfusion ready for clinical use: a comparison between cardiovascular magnetic resonance imaging and positron emission tomography. J Cardiovasc Magn Reson. 2017;19(1):78.PubMedPubMedCentralCrossRefGoogle Scholar
  143. 143.
    Rijzewijk LJ, van der Meer RW, Lamb HJ, et al. Altered myocardial substrate metabolism and decreased diastolic function in nonischemic human diabetic cardiomyopathy: studies with cardiac positron emission tomography and magnetic resonance imaging. J Am Coll Cardiol. 2009;54(16):1524–32.PubMedCrossRefPubMedCentralGoogle Scholar
  144. 144.
    Mather KJ, Hutchins GD, Perry K, et al. Assessment of myocardial metabolic flexibility and work efficiency in human type 2 diabetes using 16-[18F]fluoro-4-thiapalmitate, a novel PET fatty acid tracer. Am J Physiol Endocrinol Metab. 2016;310(6):E452–60.PubMedCrossRefPubMedCentralGoogle Scholar
  145. 145.
    Taylor M, Wallhaus TR, Degrado TR, et al. An evaluation of myocardial fatty acid and glucose uptake using PET with [18F]fluoro-6-thia-heptadecanoic acid and [18F]FDG in Patients with Congestive Heart Failure. J Nucl Med. 2001;42(1):55–62.PubMedPubMedCentralGoogle Scholar
  146. 146.
    Lin CH, Kurup S, Herrero P, et al. Myocardial oxygen consumption change predicts left ventricular relaxation improvement in obese humans after weight loss. Obesity (Silver Spring). 2011;19(9):1804–12.CrossRefGoogle Scholar
  147. 147.
    Maki MT, Haaparanta M, Nuutila P, et al. Free fatty acid uptake in the myocardium and skeletal muscle using fluorine-18-fluoro-6-thia-heptadecanoic acid. J Nucl Med. 1998;39(8):1320–7.PubMedPubMedCentralGoogle Scholar
  148. 148.
    Tuunanen H, Engblom E, Naum A, et al. Free fatty acid depletion acutely decreases cardiac work and efficiency in cardiomyopathic heart failure. Circulation. 2006;114(20):2130–7.PubMedCrossRefPubMedCentralGoogle Scholar
  149. 149.
    Conway MA, Allis J, Ouwerkerk R, et al. Detection of low phosphocreatine to ATP ratio in failing hypertrophied human myocardium by 31P magnetic resonance spectroscopy. Lancet. 1991;338(8773):973–6.PubMedCrossRefPubMedCentralGoogle Scholar
  150. 150.
    Fragasso G, Perseghin G, De Cobelli F, et al. Effects of metabolic modulation by trimetazidine on left ventricular function and phosphocreatine/adenosine triphosphate ratio in patients with heart failure. Eur Heart J. 2006;27(8):942–8.PubMedCrossRefPubMedCentralGoogle Scholar
  151. 151.
    Spoladore R, Fragasso G, Perseghin G, et al. Beneficial effects of beta-blockers on left ventricular function and cellular energy reserve in patients with heart failure. Fundam Clin Pharmacol. 2013;27(4):455–64.PubMedCrossRefPubMedCentralGoogle Scholar
  152. 152.
    Golman K, Ardenkjaer-Larsen JH, Petersson JS, Mansson S, Leunbach I. Molecular imaging with endogenous substances. Proc Natl Acad Sci U S A. 2003;100(18):10435–9.PubMedPubMedCentralCrossRefGoogle Scholar
  153. 153.
    Ardenkjaer-Larsen JH, Fridlund B, Gram A, et al. Increase in signal-to-noise ratio of > 10,000 times in liquid-state NMR. Proc Natl Acad Sci U S A. 2003;100(18):10158–63.PubMedPubMedCentralCrossRefGoogle Scholar
  154. 154.
    Tyler DJ, Neubauer S. Science to practice: hyperpolarized metabolic MR imaging--the light at the end of the tunnel for clinical (13)C MR spectroscopy? Radiology. 2016;278(3):639–41.PubMedCrossRefPubMedCentralGoogle Scholar
  155. 155.
    Le Page LM, Rider OJ, Lewis AJ, et al. Increasing pyruvate dehydrogenase flux as a treatment for diabetic cardiomyopathy: a combined 13C hyperpolarized magnetic resonance and echocardiography study. Diabetes. 2015;64(8):2735–43.PubMedPubMedCentralCrossRefGoogle Scholar
  156. 156.
    Cunningham CH, Lau JY, Chen AP, et al. Hyperpolarized 13C metabolic MRI of the human heart: initial experience. Circ Res. 2016;119(11):1177–82.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Olof Eriksson
    • 1
    • 2
  • Paul Hockings
    • 1
  • Edvin Johansson
    • 1
  • Lars Johansson
    • 1
    Email author
  • Joel Kullberg
    • 1
    • 2
  1. 1.Antaros MedicalMölndalSweden
  2. 2.Uppsala UniversityUppsalaSweden

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