Structural alterations in deep brain structures in type 1 diabetes

Pavel Filip, Antonietta Canna, Amir Moheet, Petr Bednarik, Heidi Grohn, Xiufeng Li, Anjali F. Kumar, Evan Olawsky, Lynn E. Eberly, Elizabeth R. Seaquist, Silvia Mangia

Research output: Contribution to journalArticlepeer-review

Abstract

Even though well known in type 2 diabetes, the existence of brain changes in type 1 diabetes (T1D) and both their neuroanatomical and clinical features are less well char-acterized. To fill the void in the current understanding of this disease, we sought to determine the possible neural correlate in long-duration T1D at several levels, including macrostructural, microstructural cerebral damage, and blood flow alterations. In this cross-sectional study, we compared a cohort of 61 patients with T1D with an aver-age disease duration of 21 years with 54 well-matched control subjects without diabetes in a multimodal MRI protocol providing macrostructural metrics (cortical thickness and structural volumes), microstructural mea-sures (T1-weighted/T2-weighted [T1w/T2w] ratio as a marker of myelin content, inflammation, and edema), and cerebral blood flow. Patients with T1D had higher T1w/T2w ratios in the right parahippocampal gyrus, the executive part of both putamina, both thalami, and the cerebellum. These alterations were reflected in lower putaminal and thalamic volume bilaterally. No cerebral blood flow differences between groups were found in any of these structures, suggesting nonvascular etiologies of these changes. Our findings implicate a marked nonvascular disruption in T1D of several essential neural nodes engaged in both cognitive and motor processing.

Original languageEnglish (US)
Pages (from-to)2458-2466
Number of pages9
JournalDiabetes
Volume69
Issue number11
DOIs
StatePublished - Nov 2020

Bibliographical note

Funding Information:
Acknowledgments. The authors are grateful to the volunteers who participated in the study and thereby made this research possible. The authors are also grateful to the MRI support available at the Center for Magnetic Resonance Research (namely, Erik Solheid and Wendy Elvendahl) and the support of the nurses (Michelle Snyder and Kathryn France). In addition, the authors thank Dr. Alena Svatkova for helpful suggestions on data processing and Nathan Rubin for help with data entry. The authors acknowledge the Minnesota Supercomputing Institute at the University of Minnesota for providing resources that contributed to the research results reported within this article (https://www.msi.umn.edu). Funding. Research reported in this article was supported by the National Institutes of Health (award numbers P41-EB-015894, P30-NS-076408, R01-DK-099137, and R56-DK-099137) and by the National Center for Advancing Translational Sciences of the National Institutes of Health (award numbers KL2-TR-000113 and UL1-TR-000114). Research reported in this publication was also supported by the European Union H2020 Marie Skłodowska-Curie Actions RISE project #691110 (MICROBRADAM).

Funding Information:
Research reported in this article was supported by the National Institutes of Health (award numbers P41-EB-015894, P30-NS-076408, R01-DK-099137, and R56-DK-099137) and by the National Center for Advancing Translational Sciences of the National Institutes of Health (award numbers KL2-TR-000113 and UL1-TR-000114). Research reported in this publication was also supported by the European Union H2020 Marie Sk?odowska-Curie Actions RISE project #691110 (MICROBRADAM).

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