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Abdominal Radiology

, Volume 42, Issue 5, pp 1493–1500 | Cite as

Diffusion tensor imaging of the renal cortex in diabetic patients: correlation with urinary and serum biomarkers

  • Ahmed Abdel Khalek Abdel Razek
  • Mohammad Alsayed Abd Alhamid Al-Adlany
  • Alhadidy Mohammed Alhadidy
  • Mohammed Ali Atwa
  • Naglaa Elsayed Abass Abdou
Article

Abstract

Purpose

To demonstrate role of diffusion tensor imaging of the kidney in diabetic patients and to correlate renal fractional anisotropy (FA) and apparent diffusion coefficient (ADC) of the renal cortex with urinary and serum biomarkers of diabetes.

Material and methods

Prospective study was conducted upon 42 diabetic patients (28 males, 14 females; mean age = 33 years) and 17 age- and sex-matched volunteers. Diabetic patients were micro-normoalbuminuric (n = 27) and macroalbuminuric (n = 15). Patients and volunteers underwent diffusion tensor imaging of the kidney. The FA and ADC of the renal cortex were calculated from 3 regions of interests of both kidneys.

Results

The mean FA and ADC of the renal cortex in diabetic patients (0.36 ± 0.10 and 1.74 ± 0.16 × 10−3 mm2/s) was significantly different (p = 0.001) from that of volunteers (0.26 ± 0.02 and 1.88 ± 0.03 × 10−3 mm2/s). The cut-off renal FA and ADC used to differentiate diabetic patients from volunteers were 0.28 and 1.89 × 10−3 mm2/s with AUC of 0.791 and 0.773 and accuracy of 71% and 76%. The FA and ADC calculated in the renal cortex in patients with macroalbuminuria (0.43 ± 0.10 and 1.63 ± 0.19 × 10−3 mm2/s) was significantly different (p = 0.001) from that of patients with micro-normoalbuminuria (0.35 ± 0.12 and 1.80 ± 0.18 × 10−3 mm2/s). The FA and ADC of the renal cortex in diabetic patients correlated with urinary albumin (r = 0.530; p = 0.001, r = −0.421; p = 0.006), urinary NAG (r = 0.376; p = 0.014, r = −0.245; p = 0.01), urinary TGF-β1 (r = 0.287; p = 0.065, r = −0.214; p = 0.175), and serum creatinine (r = 0.381; p = 0.013, r = −0.349; p = 0.023).

Conclusion

The FA and ADC of the renal cortex may help in differentiation of diabetic kidney from volunteers and prediction of the presence of macroalbuminuria in diabetic patients and correlated with some of the urinary and serum biomarkers of diabetes.

Keywords

Diffusion Tensor MR imaging Diabetic Kidney 

Abbreviations

ADC

Apparent diffusion coefficient

FA

Fractional anisotropy

NAG

N-acetyl-β-d-glucosaminidase

TGF-β1

Transforming growth factor beta-1

Notes

Compliance with ethical standards

Funding

No funding was received for this study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Kim SS, Kim JH, Kim IJ (2016) Current challenges in diabetic nephropathy: early diagnosis and ways to improve outcomes. Endocrinol Metab 31:245–253CrossRefGoogle Scholar
  2. 2.
    Pofi R, Di Mario F, Gigante A, et al. (2016) Diabetic nephropathy: focus on current and future therapeutic strategies. Curr Drug Metab 17:497–502CrossRefGoogle Scholar
  3. 3.
    Bjornstad P, Cherney DZ, Maahs DM, Nadeau KJ (2016) Diabetic kidney disease in adolescents with type 2 diabetes: new insights and potential therapies. Curr Diab Rep 16:11CrossRefGoogle Scholar
  4. 4.
    Ahmad J (2015) Management of diabetic nephropathy: recent progress and future perspective. Diabetes Metab Syndr 9:343–358CrossRefGoogle Scholar
  5. 5.
    Fiseha T, Tamir Z (2016) Urinary markers of tubular injury in early diabetic nephropathy. Int J Nephrol 2016:4647685CrossRefGoogle Scholar
  6. 6.
    Chang AS, Hathaway CK, Smithies O, Kakoki M (2015) Transforming growth factor β1 and diabetic nephropathy. Am J Physiol Renal Physiol 310:F689–F696CrossRefGoogle Scholar
  7. 7.
    Sheira G, Noreldin N, Tamer A, Saad M (2015) Urinary biomarker N-acetyl-β-d-glucosaminidase can predict severity of renal damage in diabetic nephropathy. J Diabetes Metab Disord 14:4CrossRefGoogle Scholar
  8. 8.
    Granata A, Zanoli L, Clementi S, et al. (2014) Resistive intrarenal index: myth or reality? Br J Radiol 87:20140004CrossRefGoogle Scholar
  9. 9.
    Bruno RM, Salvati A, Barzacchi M, et al. (2015) Predictive value of dynamic renal resistive index (DRIN) for renal outcome in type 2 diabetes and essential hypertension: a prospective study. Cardiovasc Diabetol 14:63CrossRefGoogle Scholar
  10. 10.
    Goya C, Kilinc F, Hamidi C, et al. (2015) Acoustic radiation force impulse imaging for evaluation of renal parenchyma elasticity in diabetic nephropathy. AJR Am J Roentgenol 204:324–329CrossRefGoogle Scholar
  11. 11.
    Abdel Razek AA, Sadek AG, Gaballa G (2010) Diffusion-weighed MR of the thyroid gland in Graves’ disease: assessment of disease activity and prediction of outcome. Acad Radiol 17:779–783CrossRefGoogle Scholar
  12. 12.
    Razek AA, Abdalla A, Fathy A, Megahed A (2013) Apparent diffusion coefficient of the vertebral bone marrow in children with Gaucher’s disease type I and III. Skeletal Radiol 42:283–287CrossRefGoogle Scholar
  13. 13.
    Razek AA, Khashaba M, Abdalla A, Bayomy M, Barakat T (2014) Apparent diffusion coefficient value of hepatic fibrosis and inflammation in children with chronic hepatitis. Radiol Med 119:903–909CrossRefGoogle Scholar
  14. 14.
    Razek AA, Farouk A, Mousa A, Nabil N (2011) Role of diffusion-weighted magnetic resonance imaging in characterization of renal tumors. J Comput Assist Tomogr 35:332–336CrossRefGoogle Scholar
  15. 15.
    Thoeny HC, De Keyzer F (2011) Diffusion-weighted MR imaging of native and transplanted kidneys. Radiology 259:25–38CrossRefGoogle Scholar
  16. 16.
    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:e113469CrossRefGoogle Scholar
  17. 17.
    Wang WJ, Pui MH, Guo Y, et al. (2014) MR diffusion tensor imaging of normal kidneys. J Magn Reson Imaging 40:1099–1102CrossRefGoogle Scholar
  18. 18.
    Fan WJ, Ren T, Li Q, et al. (2016) Assessment of renal allograft function early after transplantation with isotropic resolution diffusion tensor imaging. Eur Radiol 26:567–575CrossRefGoogle Scholar
  19. 19.
    Palmucci S, Cappello G, Attinà G, et al. (2015) Diffusion weighted imaging and diffusion tensor imaging in the evaluation of transplanted kidneys. Eur J Radiol Open 2:71–80CrossRefGoogle Scholar
  20. 20.
    Zhao J, Wang ZJ, Liu M, et al. (2014) Assessment of renal fibrosis in chronic kidney disease using diffusion-weighted MRI. Clin Radiol 69:1117–1122CrossRefGoogle Scholar
  21. 21.
    Feng Q, Ma Z, Wu J, Fang W (2015) DTI for the assessment of disease stage in patients with glomerulonephritis–correlation with renal histology. Eur Radiol 25:92–98CrossRefGoogle Scholar
  22. 22.
    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 25:652–660CrossRefGoogle Scholar
  23. 23.
    Gaudiano C, Clementi V, Busato F, et al. (2013) Diffusion tensor imaging and tractography of the kidneys: assessment of chronic parenchymal diseases. Eur Radiol 23:1678–1685CrossRefGoogle Scholar
  24. 24.
    Xu X, Fang W, Ling H, Chai W, Chen K (2010) Diffusion-weighted MR imaging of kidneys in patients with chronic kidney disease: initial study. Eur Radiol 20:978–983CrossRefGoogle Scholar
  25. 25.
    Cakmak P, Yağcı AB, Dursun B, Herek D, Fenkci SM (2014) Renal diffusion-weighted imaging in diabetic nephropathy: correlation with clinical stages of disease. Diagn Interv Radiol 20:374–378CrossRefGoogle Scholar
  26. 26.
    Lu L, Sedor JR, Gulani V, et al. (2011) Use of diffusion tensor MRI to identify early changes in diabetic nephropathy. Am J Nephrol 34:476–482CrossRefGoogle Scholar
  27. 27.
    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 47:430–437CrossRefGoogle Scholar
  28. 28.
    American Diabetes Association (2015) Standards of medical care in diabetes—2015. Diabetes Care 38:S1–S93Google Scholar
  29. 29.
    El-Serougy L, Abdel Razek AA, Ezzat A, Eldawoody H, El-Morsy A (2016) Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas. Neuroradiol J 29:400–407CrossRefGoogle Scholar
  30. 30.
    Huang Y, Chen X, Zhang Z, et al. (2015) MRI quantification of non-Gaussian water diffusion in normal human kidney: a diffusional kurtosis imaging study. NMR Biomed 28:154–161CrossRefGoogle Scholar
  31. 31.
    Gürses B, Kılıçkesmez O, Tadelen N, Fırat Z, Gürmen N (2011) Diffusion tensor imaging of the kidney at 3 Tesla MRI: normative values and repeatability of measurements in healthy volunteers. Diagn Interv Radiol 17:317–322PubMedGoogle Scholar
  32. 32.
    Friedli I, Crowe LA, Viallon M, et al. (2015) Improvement of renal diffusion-weighted magnetic resonance imaging with readout-segmented echo-planar imaging at 3T. Magn Reson Imaging 33:701–708CrossRefGoogle Scholar
  33. 33.
    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 263:758–769CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ahmed Abdel Khalek Abdel Razek
    • 1
  • Mohammad Alsayed Abd Alhamid Al-Adlany
    • 2
  • Alhadidy Mohammed Alhadidy
    • 2
  • Mohammed Ali Atwa
    • 3
  • Naglaa Elsayed Abass Abdou
    • 2
  1. 1.Department of Diagnostic RadiologyMansoura Faculty of MedicineMansouraEgypt
  2. 2.Internal Medicine HospitalMansoura Faculty of MedicineMansouraEgypt
  3. 3.Department of Clinical PathologyMansoura Faculty of MedicineMansouraEgypt

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