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, 50 (7), 1509-16

Automated Measurement of Brain and White Matter Lesion Volume in Type 2 Diabetes Mellitus


Automated Measurement of Brain and White Matter Lesion Volume in Type 2 Diabetes Mellitus

C Jongen et al. Diabetologia.


Aims/hypothesis: Type 2 diabetes mellitus has been associated with brain atrophy and cognitive decline, but the association with ischaemic white matter lesions is unclear. Previous neuroimaging studies have mainly used semiquantitative rating scales to measure atrophy and white matter lesions (WMLs). In this study we used an automated segmentation technique to investigate the association of type 2 diabetes, several diabetes-related risk factors and cognition with cerebral tissue and WML volumes.

Subjects and methods: Magnetic resonance images of 99 patients with type 2 diabetes and 46 control participants from a population-based sample were segmented using a k-nearest neighbour classifier trained on ten manually segmented data sets. White matter, grey matter, lateral ventricles, cerebrospinal fluid not including lateral ventricles, and WML volumes were assessed. Analyses were adjusted for age, sex, level of education and intracranial volume.

Results: Type 2 diabetes was associated with a smaller volume of grey matter (-21.8 ml; 95% CI -34.2, -9.4) and with larger lateral ventricle volume (7.1 ml; 95% CI 2.3, 12.0) and with larger white matter lesion volume (56.5%; 95% CI 4.0, 135.8), whereas white matter volume was not affected. In separate analyses for men and women, the effects of diabetes were only significant in women.

Conclusions/interpretation: The combination of atrophy with larger WML volume indicates that type 2 diabetes is associated with mixed pathology in the brain. The observed sex differences were unexpected and need to be addressed in further studies.


Fig. 1
Fig. 1
MR FLAIR (a) and inversion recovery image (b) of a diabetes patient with relatively severe WMLs. On the MR FLAIR image, the WMLs are clearly visible as white areas, whereas on the inversion recovery image the boundary between grey and white matter is much better defined. (c) The result of segmentation using the automated KNN-based algorithm. The colours indicate the different tissue classes: grey matter (yellow), white matter (dark blue), lateral ventricles (green), CSF (red) and WML (light blue)
Fig. 2
Fig. 2
Cumulative distribution of WML volume (control men, closed squares; men with type 2 diabetes, closed triangles; control women, open squares; women with type 2 diabetes, open triangles). Very small lesion volumes (<0.5 ml) were significantly more frequent among controls (p = 0.014) than participants with type 2 diabetes

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