Deteriorated functional and structural brain networks and normally appearing functional–structural coupling in diabetic kidney disease: a graph theory-based magnetic resonance imaging study

  • Yun Fei Wang
  • Ping Gu
  • Jiong Zhang
  • Rongfeng Qi
  • Michael de Veer
  • Gang Zheng
  • Qiang Xu
  • Ya Liu
  • Guang Ming Lu
  • Long Jiang ZhangEmail author



This study was conducted in order to investigate the topological organization of functional and structural brain networks in diabetic kidney disease (DKD) and its potential clinical relevance.


Two hundred two subjects (62 DKD patients, 60 diabetes mellitus [DM] patients, and 80 healthy controls) underwent laboratory examination, neuropsychological test, and magnetic resonance imaging (MRI). Large-scale functional and structural brain networks were constructed and graph theoretical network analyses were performed. The effect of renal function on brain functional and structural networks in DKD patients was further evaluated. Correlations were performed between network properties and neuropsychological scores and clinical variables.


Progressing deteriorated global and local network topology organizations (especially for functional network) were observed for DKD patients compared with control subjects (all p < 0.05, Bonferroni-corrected), with intermediate values for the patients with DM. DKD patients showed normally appearing functional–structural coupling compared with controls, while DM patients manifested functional–structural decoupling (p < 0.05, Bonferroni-corrected). Impaired kidney function markedly affected functional and structural network organization in DKD patients (all p < 0.05). Urea nitrogen correlated with global and local efficiency in the structural networks (r = − 0.551, p < 0.001; r = − 0.476, p < 0.001, respectively). Global and local efficiency in the structural networks and normalized characteristic path length in the functional networks were associated with information processing speed and/or psychomotor speed.


DKD patients showed enhanced functional and structural brain network disruption and normally appearing functional–structural coupling compared with DM patients, which correlated with kidney function, renal toxins, and cognitive performance.

Key Points

• DKD patients showed markedly disrupted functional and structural brain network efficiency measures compared with DM patients and healthy controls.

• Reduced kidney function clearly deteriorated functional and structural brain networks in DKD patients.

• DKD patients displayed normally appearing functional–structural coupling compared with DM patients.


Diabetic nephropathies Diabetes mellitus Diffusion tensor imaging Magnetic resonance imaging 



Normalized clustering coefficient


Normalized characteristic path length




Automated anatomical labeling


American Diabetes Association


Analysis of covariance


Blood urea nitrogen


Clustering coefficient

Cyst C

Cystatin C


Diabetic kidney disease


Diabetes mellitus


Data Processing Assistant for Resting-State fMRI


Deep subcortical


Diffusion tensor imaging


Global efficiency


Estimated glomerular filtration rate


Local efficiency


Fasting blood glucose


Hemoglobin A1c


High-density lipoprotein cholesterol


Low-density lipoprotein cholesterol


Characteristic path length


Line Tracing Test


Mini-Mental State Examination


Montreal Cognitive Assessment


Magnetic resonance imaging


Number Connection Test type-A




Resting-state functional magnetic resonance imaging


Self-Rating Anxiety Scale


Serum creatinine


Self-Rating Depression Scale


Serial Dotting Test


Total cholesterol


Total intracranial volume




Uric acid


White matter lesions



This study was funded by grants from the National Natural Science Foundation of China (81322020, 81230032, 81471644).

Compliance with ethical standards


The scientific guarantor of this publication is Long Jiang Zhang.

Conflict of interest

The authors declare that they have no conflicts of interest.

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.

Study subjects or cohorts overlap

Some study subjects or cohorts have not been previously reported.


• prospective

• observational

• performed at one institution

Supplementary material

330_2019_6164_MOESM1_ESM.docx (64 kb)
ESM 1 (DOCX 63 kb)


  1. 1.
    Ingelfinger JR, Jarcho JA (2017) Increase in the incidence of diabetes and its implications. N Engl J Med 376:1473–1474CrossRefGoogle Scholar
  2. 2.
    Polonsky KS (2012) The past 200 years in diabetes. N Engl J Med 367:1332–1340CrossRefGoogle Scholar
  3. 3.
    Yang W, Lu J, Weng J et al (2010) Prevalence of diabetes among men and women in China. N Engl J Med 362:1090–1101CrossRefGoogle Scholar
  4. 4.
    Gregg EW, Li Y, Wang J et al (2014) Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med 370:1514–1523CrossRefGoogle Scholar
  5. 5.
    [No authors listed] (2015) Diabetic kidney disease: what does the next era hold? Lancet Diabetes Endocrinol 3:665CrossRefGoogle Scholar
  6. 6.
    de Boer IH (2017) A new chapter for diabetic kidney disease. N Engl J Med 377:885–887CrossRefGoogle Scholar
  7. 7.
    Reijmer YD, Leemans A, Brundel M, Kappelle LJ, Biessels GJ, Utrecht Vascular Cognitive Impairment Study Group (2013) Disruption of the cerebral white matter network is related to slowing of information processing speed in patients with type 2 diabetes. Diabetes 62:2112–2115CrossRefGoogle Scholar
  8. 8.
    van Bussel FC, Backes WH, van Veenendaal TM et al (2016) Functional brain networks are altered in type 2 diabetes and prediabetes: signs for compensation of cognitive decrements? The Maastricht study. Diabetes 65:2404–2413CrossRefGoogle Scholar
  9. 9.
    Sink KM, Divers J, Whitlow CT et al (2015) Cerebral structural changes in diabetic kidney disease: African American-Diabetes Heart Study MIND. Diabetes Care 38:206–212CrossRefGoogle Scholar
  10. 10.
    Murea M, Hsu FC, Cox AJ et al (2015) Structural and functional assessment of the brain in European Americans with mild-to-moderate kidney disease: Diabetes Heart Study-MIND. Nephrol Dial Transplant 30:1322–1329CrossRefGoogle Scholar
  11. 11.
    Bressler SL, Menon V (2010) Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci 14:277–290CrossRefGoogle Scholar
  12. 12.
    Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198CrossRefGoogle Scholar
  13. 13.
    Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13:336–349CrossRefGoogle Scholar
  14. 14.
    Vaessen MJ, Jansen JF, Vlooswijk MC et al (2012) White matter network abnormalities are associated with cognitive decline in chronic epilepsy. Cereb Cortex 22:2139–2147CrossRefGoogle Scholar
  15. 15.
    Bassett DS, Bullmore ET (2009) Human brain networks in health and disease. Curr Opin Neurol 22:340–347CrossRefGoogle Scholar
  16. 16.
    Zheng G, Wen J, Zhang L et al (2014) Altered brain functional connectivity in hemodialysis patients with end-stage renal disease: a resting-state functional MR imaging study. Metab Brain Dis 29:777–786CrossRefGoogle Scholar
  17. 17.
    Zhang LJ, Zheng G, Zhang L et al (2012) Altered brain functional connectivity in patients with cirrhosis and minimal hepatic encephalopathy: a functional MR imaging study. Radiology 265:528–536CrossRefGoogle Scholar
  18. 18.
    Zhang LJ, Zheng G, Zhang L et al (2014) Disrupted small world networks in patients without overt hepatic encephalopathy: a resting state fMRI study. Eur J Radiol 83:1890–1899CrossRefGoogle Scholar
  19. 19.
    American Diabetes Association (2018) 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2018. Diabetes Care 41:S13–S27CrossRefGoogle Scholar
  20. 20.
    KDOQI (2007) KDOQI clinical practice guidelines and clinical practice recommendations for diabetes and chronic kidney disease. Am J Kidney Dis 49:S12–S154Google Scholar
  21. 21.
    Van der Elst W, Van Boxtel MP, Van Breukelen GJ, Jolles J (2006) The Stroop color-word test: influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment 13:62–79CrossRefGoogle Scholar
  22. 22.
    Zung WW (1971) A rating instrument for anxiety disorders. Psychosomatics 12:371–379CrossRefGoogle Scholar
  23. 23.
    Faravelli C, Albanesi G, Poli E (1986) Assessment of depression: a comparison of rating scales. J Affect Disord 11:245–253CrossRefGoogle Scholar
  24. 24.
    Wang YF, Kong X, Lu GM, Zhang LJ (2017) Diabetes mellitus is associated with more severe brain spontaneous activity impairment and gray matter loss in patients with cirrhosis. Sci Rep 7:7775CrossRefGoogle Scholar
  25. 25.
    Zhang LJ, Wen J, Liang X et al (2016) Brain default mode network changes after renal transplantation: a diffusion-tensor imaging and resting-state functional MR imaging study. Radiology 278:485–495CrossRefGoogle Scholar
  26. 26.
    Chao-Gan Y, Yu-Feng Z (2010) DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13Google Scholar
  27. 27.
    Cui Z, Zhong S, Xu P, He Y, Gong G (2013) PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci 7:42Google Scholar
  28. 28.
    Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289CrossRefGoogle Scholar
  29. 29.
    Wang J, Wang X, Xia M, Liao X, Evans A, He Y (2015) GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 9:386Google Scholar
  30. 30.
    Fornito A, Zalesky A, Bullmore ET (2010) Network scaling effects in graph analytic studies of human resting-state fMRI data. Front Syst Neurosci 4:22Google Scholar
  31. 31.
    Braun U, Plichta MM, Esslinger C et al (2012) Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. Neuroimage 59:1404–1412CrossRefGoogle Scholar
  32. 32.
    Zhang J, Wang J, Wu Q et al (2011) Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry 70:334–342CrossRefGoogle Scholar
  33. 33.
    Fazekas F, Chawluk JB, Alavi A et al (1987) MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 149:351–356CrossRefGoogle Scholar
  34. 34.
    Bournonville C, Hénon H, Dondaine T et al (2018) Identification of a specific functional network altered in poststroke cognitive impairment. Neurology 90:e1879–e1888CrossRefGoogle Scholar
  35. 35.
    Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442CrossRefGoogle Scholar
  36. 36.
    Sporns O, Chialvo DR, Kaiser M, Hilgetag CC (2004) Organization, development and function of complex brain networks. Trends Cogn Sci 8:418–425CrossRefGoogle Scholar
  37. 37.
    Lynall ME, Bassett DS, Kerwin R et al (2010) Functional connectivity and brain networks in schizophrenia. J Neurosci 30:9477–9487CrossRefGoogle Scholar
  38. 38.
    Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS et al (2010) Loss of ‘small-world’ networks in Alzheimer’s disease: graph analysis of FMRI resting-state functional connectivity. PLoS One 5:e13788CrossRefGoogle Scholar
  39. 39.
    Zhang J, Liu Z, Li Z et al (2016) Disrupted white matter network and cognitive decline in type 2 diabetes patients. J Alzheimers Dis 53:185–195CrossRefGoogle Scholar
  40. 40.
    Hill CJ, Cardwell CR, Patterson CC et al (2014) Chronic kidney disease and diabetes in the National Health Service: a cross-sectional survey of the U.K. National Diabetes Audit. Diabet Med 31:448–454CrossRefGoogle Scholar
  41. 41.
    Doshi SM, Friedman AN (2017) Diagnosis and management of type 2 diabetic kidney disease. Clin J Am Soc Nephrol 12:1366–1373CrossRefGoogle Scholar
  42. 42.
    Tong L, Adler SG (2018) Diabetic kidney disease. Clin J Am Soc Nephrol 13:335–338CrossRefGoogle Scholar
  43. 43.
    Luo S, Qi RF, Wen JQ et al (2016) Abnormal intrinsic brain activity patterns in patients with end-stage renal disease undergoing peritoneal dialysis: a resting-state functional MR imaging study. Radiology 278(1):181–189CrossRefGoogle Scholar
  44. 44.
    Kong X, Wen JQ, Qi RF et al (2014) Diffuse interstitial brain edema in patients with end-stage renal disease undergoing hemodialysis: a tract-based spatial statistics study. Medicine (Baltimore) 93:e313CrossRefGoogle Scholar
  45. 45.
    Bugnicourt JM, Godefroy O, Chillon JM, Choukroun G, Massy ZA (2013) Cognitive disorders and dementia in CKD: the neglected kidney-brain axis. J Am Soc Nephrol 24:353–363CrossRefGoogle Scholar
  46. 46.
    Zhang Z, Liao W, Chen H et al (2011) Altered functional–structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain 134:2912–2928CrossRefGoogle Scholar
  47. 47.
    Zalesky A, Fornito A, Harding IH et al (2010) Whole-brain anatomical networks: does the choice of nodes matter. Neuroimage 50:970–983CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Medical Imaging, Jinling HospitalMedical School of Nanjing UniversityNanjingChina
  2. 2.Department of Endocrinology, Jinling HospitalMedical School of Nanjing UniversityNanjingChina
  3. 3.National Clinical Research Center of Kidney Disease, Jinling HospitalMedical School of Nanjing UniversityNanjingChina
  4. 4.Monash Biomedical ImagingMonash UniversityClaytonAustralia

Personalised recommendations