Advertisement

European Radiology

, Volume 28, Issue 8, pp 3326–3334 | Cite as

Renal fat fraction and diffusion tensor imaging in patients with early-stage diabetic nephropathy

  • Yuan-Cheng Wang
  • Yinglian Feng
  • Chun-Qiang Lu
  • Shenghong Ju
Magnetic Resonance
First Online:

Abstract

Objective

To investigate the renal fat fraction and water molecular diffusion features in patients with early-stage DN using Dixon imaging and diffusion tensor imaging (DTI).

Methods

Sixty-one type 2 diabetics (normoalbuminuria: n = 40; microalbuminuria: n = 21) and 34 non-diabetic volunteers were included. All participants received three-point Dixon imaging and DTI using a 3.0-T magnetic resonance imager. The fat fraction [FF] and DTI features [fractional anisotropy (FA), apparent diffusion coefficient (ADC), tract counts and length from DTI tractography] were collected. All image features were compared between cohorts using one-way ANOVA with Bonferroni post-hoc analysis.

Results

Renal FF in the microalbuminuric group was significantly higher than in the normoalbuminuric and control groups (5.6% ± 1.3%, 4.7% ± 1.1% and 4.3% ± 0.5%, respectively; p < 0.001). Medullary FA in the microalbuminuric group was the lowest (0.31 ± 0.06) in all cohorts. The tract counts and length in the renal medulla were significantly lower in the microalbuminuric group than in the other two groups.

Conclusions

Dixon imaging and DTI are able to detect renal lipid deposition and water molecule diffusion abnormalities in patients with early-stage DN. Both techniques have the potential to noninvasively evaluate early renal impairment in type 2 diabetes.

Key points

• Dixon imaging demonstrated renal fat deposition in early-stage DN;

• Renal fractional anisotropy decreased in patients with early-stage DN;

• Renal tractography demonstrated reduced track counts and length in early-stage DN.

Keywords

Adipose tissue Diffusion tensor imaging Magnetic resonance imaging Observational study Diabetic nephropathies 

Abbreviations

ADC

Apparent Diffusion Coefficient

BMI

Body Mass Index

DN

Diabetic Nephropathy

DTI

Diffusion Tensor Imaging

eGFR

Estimated Glomerular Filtration Rate

FA

Fractional Anisotropy

FF

Fat Fraction

fMRI

Functional Magnetic Resonance Imaging

FOV

Field of View

ROI

Region of Interest

TE

Echo Time

TR

Repetition Time

This is a preview of subscription content, log in to check access.

Notes

Funding

This research was supported by the National Nature Science Foundation of China (NSFC, no. 81525014), the Jiangsu Provincial Special Program of Medical Science (BL2013029) and the Key Research and Development Program of Jiangsu Province (BE2016782).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Shenghong Ju.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• observational

• performed at one institution

References

  1. 1.
    Jha V, Garcia-Garcia G, Iseki K et al (2013) Chronic kidney disease: global dimension and perspectives. Lancet 382:260–272CrossRefPubMedGoogle Scholar
  2. 2.
    Jones CA, Krolewski AS, Rogus J, Xue JL, Collins A, Warram JH (2005) Epidemic of end-stage renal disease in people with diabetes in the United States population: do we know the cause? Kidney Int 67:1684–1691CrossRefPubMedGoogle Scholar
  3. 3.
    Macisaac RJ, Ekinci EI, Jerums G (2014) Markers of and risk factors for the development and progression of diabetic kidney disease. Am J Kidney Dis 63:S39–S62CrossRefPubMedGoogle Scholar
  4. 4.
    Wahba IM, Mak RH (2007) Obesity and obesity-initiated metabolic syndrome: mechanistic links to chronic kidney disease. Clin J Am Soc Nephrol 2:550–562CrossRefPubMedGoogle Scholar
  5. 5.
    Peng XG, Bai YY, Fang F et al (2013) Renal lipids and oxygenation in diabetic mice: noninvasive quantification with MR imaging. Radiology 269:748–757CrossRefPubMedGoogle Scholar
  6. 6.
    Wang Z, Jiang T, Li J et al (2005) Regulation of renal lipid metabolism, lipid accumulation, and glomerulosclerosis in FVBdb/db mice with type 2 diabetes. Diabetes 54:2328–2335CrossRefPubMedGoogle Scholar
  7. 7.
    Yokoo T, Clark HR, Pedrosa I et al (2016) Quantification of renal steatosis in type II diabetes mellitus using Dixon-based MRI. J Magn Reson Imaging 44:1312–1319CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Herman-Edelstein M, Scherzer P, Tobar A, Levi M, Gafter U (2014) Altered renal lipid metabolism and renal lipid accumulation in human diabetic nephropathy. J Lipid Res 55:561–572CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Dominguez JH, Tang N, Xu W et al (2000) Studies of renal injury III: lipid-induced nephropathy in type II diabetes. Kidney Int 57:92–104CrossRefPubMedGoogle Scholar
  10. 10.
    Bobulescu IA (2010) Renal lipid metabolism and lipotoxicity. Curr Opin Nephrol Hypertens 19:393–402CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Abrass CK (2004) Cellular lipid metabolism and the role of lipids in progressive renal disease. Am J Nephrol 24:46–53CrossRefPubMedGoogle Scholar
  12. 12.
    Bagby SP (2004) Obesity-initiated metabolic syndrome and the kidney: a recipe for chronic kidney disease? J Am Soc Nephrol 15:2775–2791CrossRefPubMedGoogle Scholar
  13. 13.
    Moorhead JF, Chan MK, El-Nahas M, Varghese Z (1982) Lipid nephrotoxicity in chronic progressive glomerular and tubulo-interstitial disease. Lancet 2:1309–1311CrossRefPubMedGoogle Scholar
  14. 14.
    Hojs R, Ekart R, Bevc S, Hojs N (2015) Biomarkers of renal disease and progression in patients with diabetes. J Clin Med 4:1010–1024CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Baxmann AC, Ahmed MS, Marques NC et al (2008) Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol 3:348–354CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Peng XG, Ju S, Qin Y et al (2011) Quantification of liver fat in mice: comparing dual-echo Dixon imaging, chemical shift imaging, and 1H-MR spectroscopy. J Lipid Res 52:1847–1855CrossRefPubMedGoogle Scholar
  17. 17.
    Reeder SB, Hu HH, Sirlin CB (2012) Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 36:1011–1014CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Li G, Xu Z, Chen Y et al (2016) Longitudinal assessment of marrow fat content using three-point Dixon technique in osteoporotic rabbits. Menopause 23:1339–1344CrossRefPubMedGoogle Scholar
  19. 19.
    Mann LW, Higgins DM, Peters CN et al (2016) Accelerating MR imaging liver steatosis measurement using combined compressed sensing and parallel imaging: a quantitative evaluation. Radiology 278:247–256CrossRefPubMedGoogle Scholar
  20. 20.
    Gaudiano C, Clementi V, Busato F et al (2013) Diffusion tensor imaging and tractography of the kidneys: assessment of chronic parenchymal diseases. Eur Radiol 23:1678–1685CrossRefPubMedGoogle Scholar
  21. 21.
    Hueper K, Gutberlet M, Rodt T et al (2011) Diffusion tensor imaging and tractography for assessment of renal allograft dysfunction-initial results. Eur Radiol 21:2427–2433CrossRefPubMedGoogle Scholar
  22. 22.
    Hueper K, Hartung D, Gutberlet M et al (2012) Magnetic resonance diffusion tensor imaging for evaluation of histopathological changes in a rat model of diabetic nephropathy. Invest Radiol 47:430–437CrossRefPubMedGoogle Scholar
  23. 23.
    Liu Z, Xu Y, Zhang J et al (2015) Chronic kidney disease: pathological and functional assessment with diffusion tensor imaging at 3T MR. Eur Radiol 25:652–660CrossRefPubMedGoogle Scholar
  24. 24.
    Zheng Z, Shi H, Zhang J, Zhang Y (2014) Renal water molecular diffusion characteristics in healthy native kidneys: assessment with diffusion tensor MR imaging. PLoS One 9:e113469CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Sigmund EE, Vivier PH, Sui D et al (2012) Intravoxel incoherent motion and diffusion-tensor imaging in renal tissue under hydration and furosemide flow challenges. Radiology 263:758–769CrossRefPubMedGoogle Scholar
  26. 26.
    Thoeny HC, De Keyzer F (2011) Diffusion-weighted MR imaging of native and transplanted kidneys. Radiology 259:25–38CrossRefPubMedGoogle Scholar
  27. 27.
    Iima M, Le Bihan D (2016) Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology 278:13–32CrossRefPubMedGoogle Scholar
  28. 28.
    Leung WK, Gao L, Siu PM, Lai CW (2016) Diabetic nephropathy and endothelial dysfunction: current and future therapies, and emerging of vascular imaging for preclinical renal-kinetic study. Life Sci 166:121–130CrossRefPubMedGoogle Scholar
  29. 29.
    Notohamiprodjo M, Glaser C, Herrmann KA et al (2008) Diffusion tensor imaging of the kidney with parallel imaging: initial clinical experience. Invest Radiol 43:677–685CrossRefPubMedGoogle Scholar
  30. 30.
    Dixon WT (1984) Simple proton spectroscopic imaging. Radiology 153:189–194CrossRefPubMedGoogle Scholar
  31. 31.
    Atchley DH, Lopes-Virella MF, Zheng D, Kenny D, Virella G (2002) Oxidized LDL-anti-oxidized LDL immune complexes and diabetic nephropathy. Diabetologia 45:1562–1571CrossRefPubMedGoogle Scholar
  32. 32.
    Wang XX, Jiang T, Shen Y et al (2009) The farnesoid X receptor modulates renal lipid metabolism and diet-induced renal inflammation, fibrosis, and proteinuria. Am J Physiol Renal Physiol 297:F1587–F1596CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Wang ZJ, Kumar R, Banerjee S, Hsu CY (2011) Blood oxygen level-dependent (BOLD) MRI of diabetic nephropathy: preliminary experience. J Magn Reson Imaging 33:655–660CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Li Q, Wu X, Qiu L, Zhang P, Zhang M, Yan F (2013) Diffusion-weighted MRI in the assessment of split renal function: comparison of navigator-triggered prospective acquisition correction and breath-hold acquisition. AJR Am J Roentgenol 200:113–119CrossRefPubMedGoogle Scholar
  35. 35.
    Wang YC, Tang A, Chang D, Zhang SJ, Ju S (2014) Significant perturbation in renal functional magnetic resonance imaging parameters and contrast retention for iodixanol compared with iopromide: an experimental study using blood-oxygen-level-dependent/diffusion-weighted magnetic resonance imaging and computed tomography in rats. Invest Radiol 49:699–706CrossRefPubMedGoogle Scholar
  36. 36.
    Tonneijck L, Muskiet MH, Smits MM et al (2017) Glomerular hyperfiltration in diabetes: mechanisms, clinical significance, and treatment. J Am Soc Nephrol 28:1023–1039CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Helal I, Fick-Brosnahan GM, Reed-Gitomer B, Schrier RW (2012) Glomerular hyperfiltration: definitions, mechanisms and clinical implications. Nat Rev Nephrol 8:293–300CrossRefPubMedGoogle Scholar
  38. 38.
    Hostetter TH (2001) Hypertrophy and hyperfunction of the diabetic kidney. J Clin Invest 107:161–162CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Inoue T, Kozawa E, Okada H et al (2011) Noninvasive evaluation of kidney hypoxia and fibrosis using magnetic resonance imaging. J Am Soc Nephrol 22:1429–1434CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional ImagingZhongda Hospital, Medical School of Southeast UniversityNanjingChina

Personalised recommendations

Cite article

Buy options