Download PDF
Exp Clin Endocrinol Diabetes 2013; 121(06): 354-360
DOI: 10.1055/s-0033-1345120
Article
© J. A. Barth Verlag in Georg Thieme Verlag KG Stuttgart · New York

Macrostructural Brain Changes in Patients with Longstanding Type 1 Diabetes Mellitus – a Cortical Thickness Analysis Study

J. B. Frøkjær
1   Mech-Sense, Department of Radiology, Aalborg University Hospital, Aalborg, Denmark
2   Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark
,
C. Brock
2   Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark
,
E. Søfteland
3   Department of Medicine, Haukeland University Hospital and Institute of Medicine, University of Bergen, Bergen, Norway
,
G. Dimcevski
3   Department of Medicine, Haukeland University Hospital and Institute of Medicine, University of Bergen, Bergen, Norway
,
H. Gregersen
4   GIOME and Sino-Danish Centre for Education and Research, Aarhus, Denmark and Beijing, China
,
M. Simrén
5   Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
,
A. M. Drewes
2   Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Aalborg, Denmark
› Author Affiliations
Further Information

Publication History

received 20 January 2013
first decision 31 March 2013

accepted 12 April 2013

Publication Date:
06 June 2013 (online)

Also available at
    Permissions and Reprints

Abstract

Aims/hypothesis:

Longstanding diabetes mellitus (DM) is associated with the risk of complications affecting the central nervous system. The aims were to study brain volume and cortical thickness in regional brain areas in DM patients and to correlate the findings with relevant clinical data.

Methods:

15 patients with longstanding (average 24.6 years) type 1 DM and 20 healthy controls were studied in a 3T magnetic resonance scanner. Using an automated surface based cortical segmentation method, cortical thickness was assessed in anatomical regions including total and lobe-wise grey and white matter volumes. Also morphological changes were evaluated.

Results:

No differences between patients and controls were found in regard to number of white matter lesions (P=0.50), grey and white matter volumes (P=0.25) and overall cortical thickness (P=0.64). Subanalysis revealed exclusively reduced cortical thickness of the postcentral (P=0.03) and superior parietal gyrus (P=0.008) in patients. The cortical thickness of these regions was not associated with diabetes duration, age at diabetes onset or to HbA1c (all P>0.08). Patients with peripheral neuropathy showed reduced right postcentral gyrus cortical thickness compared to patients without peripheral neuropathy (P=0.02).

Conclusions:

Patients with longstanding type 1 diabetes showed cortical thinning involving sensory related areas, even though no overall macrostructural brain alterations were detected. This could possibly have underlying functional significance since cortical thinning was associated to presence of peripheral neuropathy. The absence of universal macrostructural changes might illustrate that more pronounced brain pathology is likely to be preceded by more subtle microstructural changes as reported in other studies.

Key words

magnetic resonance imaging - cortical atrophy - diabetes mellitus - neuropathy - CNS complications - nerve damage
 

  • References

  • 1 Wild S, Roglic G, Green A et al. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 2004; 27: 1047-1053
    Google Scholar
  • 2 Horowitz MEd, Samsom MEd. Gastrointestinal Function in Diabetes Mellius. Chichester: John Wiley & Sons Ltd; 2004
    CrossrefGoogle Scholar
  • 3 Selvarajah D, Wilkinson ID, Davies J et al. Central nervous system involvement in diabetic neuropathy. Curr Diab Rep 2011; 11: 310-322
    CrossrefPubMedGoogle Scholar
  • 4 van den Berg E, Kessels RP, Kappelle LJ et al. Type 2 diabetes, cognitive function and dementia: vascular and metabolic determinants. Timely Top Med Cardiovasc Dis 2007; 11: E7
    PubMedGoogle Scholar
  • 5 Kodl CT, Franc DT, Rao JP et al. Diffusion tensor imaging identifies deficits in white matter microstructure in subjects with type 1 diabetes that correlate with reduced neurocognitive function. Diabetes 2008; 57: 3083-3089
    CrossrefPubMedGoogle Scholar
  • 6 Franc DT, Kodl CT, Mueller BA et al. High connectivity between reduced cortical thickness and disrupted white matter tracts in long-standing type 1 diabetes. Diabetes 2011; 60: 315-319
    CrossrefPubMedGoogle Scholar
  • 7 Hsu JL, Chen YL, Leu JG et al. Microstructural white matter abnormalities in type 2 diabetes mellitus: a diffusion tensor imaging study. Neuroimage 2012; 59: 1098-1105
    CrossrefPubMedGoogle Scholar
  • 8 Yau PL, Javier DC, Ryan CM et al. Preliminary evidence for brain complications in obese adolescents with type 2 diabetes mellitus. Diabetologia 2010; 53: 2298-2306
    CrossrefPubMedGoogle Scholar
  • 9 Yau PL, Javier D, Tsui W et al. Emotional and neutral declarative memory impairments and associated white matter microstructural abnormalities in adults with type 2 diabetes. Psychiatry Res 2009; 174: 223-230
    CrossrefPubMedGoogle Scholar
  • 10 Fiorina P, Vezzulli P, Bassi R et al. Near normalization of metabolic and functional features of the central nervous system in type 1 diabetic patients with end-stage renal disease after kidney-pancreas transplantation. Diabetes Care 2012; 35: 367-374
    CrossrefPubMedGoogle Scholar
  • 11 Musen G, Lyoo IK, Sparks CR et al. Effects of type 1 diabetes on gray matter density as measured by voxel-based morphometry. Diabetes 2006; 55: 326-333
    CrossrefPubMedGoogle Scholar
  • 12 Wessels AM, Simsek S, Remijnse PL et al. Voxel-based morphometry demonstrates reduced grey matter density on brain MRI in patients with diabetic retinopathy. Diabetologia 2006; 49: 2474-2480
    CrossrefPubMedGoogle Scholar
  • 13 Last D, Alsop DC, Abduljalil AM et al. Global and regional effects of type 2 diabetes on brain tissue volumes and cerebral vasoreactivity. Diabetes Care 2007; 30: 1193-1199
    CrossrefPubMedGoogle Scholar
  • 14 Jongen C, Biessels GJ. Structural brain imaging in diabetes: a methodological perspective. Eur J Pharmacol 2008; 585: 208-218
    CrossrefPubMedGoogle Scholar
  • 15 Frokjaer JB, Andersen LW, Brock C et al. Altered Brain Microstructure Assessed by Diffusion Tensor Imaging in Patients With Diabetes Mellitus and Gastrointestinal Symptoms. Diabetes Care 2013; 36: 662-668
    CrossrefPubMedGoogle Scholar
  • 16 Reijmer YD, Brundel M, de BJ et al. Microstructural White Matter Abnormalities and Cognitive Functioning in Type 2 Diabetes: A diffusion tensor imaging study. Diabetes Care 2013; 36: 137-144
    CrossrefPubMedGoogle Scholar
  • 17 Eskildsen SF, Ostergaard LR. Active surface approach for extraction of the human cerebral cortex from MRI. Med Image Comput Comput Assist Interv 2006; 9 (Pt 2) 823-830
    PubMedGoogle Scholar
  • 18 Eskildsen SF, Ostergaard LR, Rodell AB et al. Cortical volumes and atrophy rates in FTD-3 CHMP2B mutation carriers and related non-carriers. Neuroimage 2009; 45: 713-721
    CrossrefPubMedGoogle Scholar
  • 19 Eskildsen SF, Ostergaard LR. Quantitative comparison of two cortical surface extraction methods using MRI phantoms. Med Image Comput Comput Assist Interv 2007; 10 (Pt 1) 409-416
    PubMedGoogle Scholar
  • 20 Frokjaer JB, Bouwense SA, Olesen SS et al. Reduced cortical thickness of brain areas involved in pain processing in patients with chronic pancreatitis. Clin Gastroenterol Hepatol 2012; 10: 434-438
    CrossrefPubMedGoogle Scholar
  • 21 Spjuth M, Gravesen F, Eskildsen SF et al. Early detection of AD using cortical thickness measurements. Progr Biomed Opt Imaging – Proc SPIE 2007; 65120L-1-65120L-9
    PubMedGoogle Scholar
  • 22 Jarnum H, Eskildsen SF, Steffensen EG et al. Longitudinal MRI study of cortical thickness, perfusion, and metabolite levels in major depressive disorder. Acta Psychiatr Scand 2011; 124: 435-446
    CrossrefPubMedGoogle Scholar
  • 23 Boulton AJ, Vinik AI, Arezzo JC et al. Diabetic neuropathies: a statement by the American Diabetes Association. Diabetes Care 2005; 28: 956-962
    CrossrefPubMedGoogle Scholar
  • 24 Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15: 273-289
    CrossrefPubMedGoogle Scholar
  • 25 Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imag 1998; 17: 87-97
    CrossrefPubMedGoogle Scholar
  • 26 Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17: 143-155
    CrossrefPubMedGoogle Scholar
  • 27 Eskildsen SF, Østergaard LR. Evaluation of five algorithms for mapping brain cortical surfaces. Proceedings of XXI Brazilian Symposium on Computer Graphics and Image Processing, SIBGRAPI 2008; 137-144
    PubMedGoogle Scholar
  • 28 Perneger TV. What’s wrong with Bonferroni adjustments. BMJ 1998; 316: 1236-1238
    CrossrefPubMedGoogle Scholar
  • 29 Glaser N, Ngo C, Anderson S et al. Effects of hyperglycemia and effects of ketosis on cerebral perfusion, cerebral water distribution, and cerebral metabolism. Diabetes 2012; 61: 1831-1837
    CrossrefPubMedGoogle Scholar
  • 30 Streitburger DP, Moller HE, Tittgemeyer M et al. Investigating structural brain changes of dehydration using voxel-based morphometry. PLoS One 2012; 7: e44195
    CrossrefPubMedGoogle Scholar
  • 31 Said G, Slama G, Selva J. Progressive centripetal degeneration of axons in small fibre diabetic polyneuropathy. Brain 1983; 106 (Pt 4) 791-807
    CrossrefPubMedGoogle Scholar
  • 32 Malik RA, Newrick PG, Sharma AK et al. Microangiopathy in human diabetic neuropathy: relationship between capillary abnormalities and the severity of neuropathy. Diabetologia 1989; 32: 92-102
    CrossrefPubMedGoogle Scholar
  • 33 Tesfaye S, Malik R, Ward JD. Vascular factors in diabetic neuropathy. Diabetologia 1994; 37: 847-854
    CrossrefPubMedGoogle Scholar
  • 34 Malik RA, Tesfaye S, Thompson SD et al. Endoneurial localisation of microvascular damage in human diabetic neuropathy. Diabetologia 1993; 36: 454-459
    CrossrefPubMedGoogle Scholar
  • 35 Malik RA, Tesfaye S, Thompson SD et al. Transperineurial capillary abnormalities in the sural nerve of patients with diabetic neuropathy. Microvasc Res 1994; 48: 236-245
    CrossrefPubMedGoogle Scholar
  • 36 Giannini C, Dyck PJ. Ultrastructural morphometric abnormalities of sural nerve endoneurial microvessels in diabetes mellitus. Ann Neurol 1994; 36: 408-415
    CrossrefPubMedGoogle Scholar
  • 37 Smith KJ, Kapoor R, Felts PA. Demyelination: the role of reactive oxygen and nitrogen species. Brain Pathol 1999; 9: 69-92
    PubMedGoogle Scholar
  • 38 Li X, Pan W, Yang GZ et al. Proteome analysis of differential protein expression in brain of rats with type 1 diabetes mellitus. Exp Clin Endocrinol Diabetes 2011; 119: 265-270
    Article in Thieme ConnectPubMedGoogle Scholar
  • 39 Pantoni L, Garcia JH. Pathogenesis of leukoaraiosis: a review. Stroke 1997; 28: 652-659
    CrossrefPubMedGoogle Scholar
  • 40 Brands AM, Biessels GJ, Kappelle LJ et al. Cognitive functioning and brain MRI in patients with type 1 and type 2 diabetes mellitus: a comparative study. Dement Geriatr Cogn Disord 2007; 23: 343-350
    CrossrefPubMedGoogle Scholar