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Analysis of the relationships between type 2 diabetes status, glycemic control, and neuroimaging measures in the Diabetes Heart Study Mind

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Abstract

Aims

To examine the relationships between type 2 diabetes (T2D) status, glycemic control, and T2D duration with magnetic resonance imaging (MRI)-derived neuroimaging measures in European Americans from the Diabetes Heart Study (DHS) Mind cohort.

Methods

Relationships were examined using marginal models with generalized estimating equations in 784 participants from 514 DHS Mind families. Fasting plasma glucose, glycated hemoglobin, and diabetes duration were analyzed in 682 participants with T2D. Models were adjusted for potential confounders, including age, sex, history of cardiovascular disease, smoking, educational attainment, and use of statins or blood pressure medications. Association was tested with gray and white matter volume, white matter lesion volume, gray matter cerebral blood flow, and white and gray matter fractional anisotropy and mean diffusivity.

Results

Adjusting for multiple comparisons, T2D status was associated with reduced white matter volume (p = 2.48 × 10−6) and reduced gray and white matter fractional anisotropy (p ≤ 0.001) in fully adjusted models, with a trend toward increased white matter lesion volume (p = 0.008) and increased gray and white matter mean diffusivity (p ≤ 0.031). Among T2D-affected participants, neither fasting glucose, glycated hemoglobin, nor diabetes duration were associated with the neuroimaging measures assessed (p > 0.05).

Conclusions

While T2D was significantly associated with MRI-derived neuroimaging measures, differences in glycemic control in T2D-affected individuals in the DHS Mind study do not appear to significantly contribute to variation in these measures. This supports the idea that the presence or absence of T2D, not fine gradations of glycemic control, may be more significantly associated with age-related changes in the brain.

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References

  1. Lu F-P, Lin K-P, Kuo H-K (2009) Diabetes and the risk of multi-system aging phenotypes: a systematic review and meta-analysis. PLoS ONE 4(1):e4144. doi:10.1371/journal.pone.0004144

    Article  PubMed  PubMed Central  Google Scholar 

  2. Reijmer Y, van den Berg E, Ruis C, Kappelle L, Biessels G (2010) Cognitive dysfunction in patients with type 2 diabetes. Diabetes/Metab Res Rev 26(7):507–519. doi:10.1002/dmrr.1112

    Article  Google Scholar 

  3. Palta P, Schneider AL, Biessels GJ, Touradji P, Hill-Briggs F (2014) Magnitude of cognitive dysfunction in adults with type 2 diabetes: a meta-analysis of six cognitive domains and the most frequently reported neuropsychological tests within domains. J Int Neuropsychol Soc 20(3):278–291. doi:10.1017/s1355617713001483

    Article  PubMed  PubMed Central  Google Scholar 

  4. Biessels GJ, Reijmer YD (2014) Brain changes underlying cognitive dysfunction in diabetes: what can we learn from MRI? Diabetes 63(7):2244–2252. doi:10.2337/db14-0348

    Article  PubMed  Google Scholar 

  5. Falvey C, Rosano C, Simonsick E et al (2013) Macro- and microstructural magnetic resonance imaging indices associated with diabetes among community-dwelling older adults. Diabetes Care 36(3):677–682. doi:10.2337/dc12-0814

    Article  PubMed  PubMed Central  Google Scholar 

  6. Moran C, Phan T, Chen J et al (2013) Brain atrophy in type 2 diabetes: regional distribution and influence on cognition. Diabetes Care 36(12):4036–4042. doi:10.2337/dc13-0143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cukierman-Yaffe T, Gerstein HC, Williamson JD et al (2009) Relationship between baseline glycemic control and cognitive function in individuals with type 2 diabetes and other cardiovascular risk factors: the action to control cardiovascular risk in diabetes-memory in diabetes (ACCORD-MIND) trial. Diabetes Care 32(2):221–226. doi:10.2337/dc08-1153

    Article  PubMed  PubMed Central  Google Scholar 

  8. Geijselaers SL, Sep SJ, Stehouwer CD, Biessels GJ (2014) Glucose regulation, cognition, and brain MRI in type 2 diabetes: a systematic review. Lancet Diabetes Endocrinol. doi:10.1016/s2213-8587(14)70148-2

    PubMed  Google Scholar 

  9. Yaffe K, Blackwell T, Whitmer R, Krueger K, Barrett-Connor E (2006) Glycosylated hemoglobin level and development of mild cognitive impairment or dementia in older women. J Nutr Health Aging 10(4):292–295

    CAS  Google Scholar 

  10. van Elderen S, de Roos A, de Craen A et al (2010) Progression of brain atrophy and cognitive decline in diabetes mellitus: a 3-year follow-up. Neurology 75(11):997–1002. doi:10.1212/WNL.0b013e3181f25f06

    Article  PubMed  Google Scholar 

  11. Bryan RN, Bilello M, Davatzikos C et al (2014) Effect of diabetes on brain structure: the action to control cardiovascular risk in diabetes MR imaging baseline data. Radiology 272(1):210–216. doi:10.1148/radiol.14131494

    Article  PubMed  PubMed Central  Google Scholar 

  12. Weinstein G, Maillard P, Himali JJ et al (2015) Glucose indices are associated with cognitive and structural brain measures in young adults. Neurology 84(23):2329–2337. doi:10.1212/wnl.0000000000001655

    Article  CAS  PubMed  Google Scholar 

  13. Bowden D, Cox A, Freedman B et al (2010) Review of the Diabetes Heart Study (DHS) family of studies: a comprehensively examined sample for genetic and epidemiological studies of type 2 diabetes and its complications. Rev Diabetic Stud RDS 7(3):188–201. doi:10.1900/rds.2010.7.188

    PubMed  Google Scholar 

  14. Hugenschmidt C, Hsu F-C, Hayasaka S et al (2013) The influence of subclinical cardiovascular disease and related risk factors on cognition in type 2 diabetes mellitus: the DHS-Mind study. J Diabetes Complications 27(5):422–428. doi:10.1016/j.jdiacomp.2013.04.004

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cox AJ, Hugenschmidt CE, Raffield LM et al (2014) Heritability and genetic association analysis of cognition in the Diabetes Heart Study. Neurobiol Aging 35(8):1953–1958. doi:10.1016/j.neurobiolaging.2014.03.005

    Article  Google Scholar 

  16. Raffield LM, Cox AJ, Hugenschmidt CE et al (2015) Heritability and genetic association analysis of neuroimaging measures in the Diabetes Heart Study. Neurobiol Aging 36(3):1602–1615. doi:10.1016/j.neurobiolaging.2014.11.008

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hsu FC, Raffield LM, Hugenschmidt CE et al (2015) Relationships between cognitive performance, neuroimaging and vascular disease: the DHS-MIND Study. Neuroepidemiology 45(1):1–11. doi:10.1159/000435775

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jenkinson M, Beckmann C, Behrens T, Woolrich M, Smith S (2012) Fsl. Neuroimage 62(2):782–790. doi:10.1016/j.neuroimage.2011.09.015

    Article  PubMed  Google Scholar 

  19. Maldjian JA, Laurienti PJ, Burdette JH, Kraft RA (2008) Clinical implementation of spin-tag perfusion magnetic resonance imaging. J Comput Assist Tomogr 32(3):403–406. doi:10.1097/RCT.0b013e31816b650b

    Article  PubMed  Google Scholar 

  20. Schmidt P, Gaser C, Arsic M et al (2012) An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59(4):3774–3783. doi:10.1016/j.neuroimage.2011.11.032

    Article  PubMed  Google Scholar 

  21. Maldjian J, Whitlow C, Saha B et al (2013) Automated white matter total lesion volume segmentation in diabetes. AJNR Am J Neuroradiol 34(12):2265–2270. doi:10.3174/ajnr.A3590

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. van Harten B, de Leeuw F-E, Weinstein H, Scheltens P, Biessels G (2006) Brain imaging in patients with diabetes: a systematic review. Diabetes Care 29(11):2539–2548. doi:10.2337/dc06-1637

    Article  PubMed  Google Scholar 

  23. de Leeuw FE, de Groot JC, Oudkerk M et al (2002) Hypertension and cerebral white matter lesions in a prospective cohort study. Brain 125(Pt 4):765–772. doi:10.1093/brain/125.4.765

    Article  PubMed  Google Scholar 

  24. Longstreth WT Jr, Arnold AM, Beauchamp NJ Jr et al (2005) Incidence, manifestations, and predictors of worsening white matter on serial cranial magnetic resonance imaging in the elderly: the Cardiovascular Health Study. Stroke 36(1):56–61. doi:10.1161/01.str.0000149625.99732.69

    Article  PubMed  Google Scholar 

  25. Biessels GJ, Strachan MW, Visseren FL, Kappelle LJ, Whitmer RA (2014) Dementia and cognitive decline in type 2 diabetes and prediabetic stages: towards targeted interventions. Lancet Diabetes Endocrinol 2(3):246–255. doi:10.1016/s2213-8587(13)70088-3

    Article  PubMed  Google Scholar 

  26. Schmidt R, Launer L, Nilsson L-G et al (2004) Magnetic resonance imaging of the brain in diabetes: the Cardiovascular Determinants of Dementia (CASCADE) Study. Diabetes 53(3):687–692. doi:10.2337/diabetes.53.3.687

    Article  CAS  PubMed  Google Scholar 

  27. Geerlings M, Appelman A, Vincken K et al (2010) Brain volumes and cerebrovascular lesions on MRI in patients with atherosclerotic disease: the SMART-MR Study. Atherosclerosis 210(1):130–136. doi:10.1016/j.atherosclerosis.2009.10.039

    Article  CAS  PubMed  Google Scholar 

  28. Vidal JS, Sigurdsson S, Jonsdottir MK et al (2010) Coronary artery calcium, brain function and structure: the AGES-Reykjavik Study. Stroke 41(5):891–897. doi:10.1161/strokeaha.110.579581

    Article  PubMed  PubMed Central  Google Scholar 

  29. Debette S, Beiser A, Hoffmann U et al (2010) Visceral fat is associated with lower brain volume in healthy middle-aged adults. Ann Neurol 68(2):136–144. doi:10.1002/ana.22062

    PubMed  PubMed Central  Google Scholar 

  30. Verstynen T, Weinstein A, Erickson K, Sheu L, Marsland A, Gianaro P (2013) Competing physiological pathways link individual differences in weight and abdominal adiposity to white matter microstructure. NeuroImage 79:129–137. doi:10.1016/j.neuroimage.2013.04.075

    Article  PubMed  PubMed Central  Google Scholar 

  31. He XS, Wang ZX, Zhu YZ et al (2015) Hyperactivation of working memory-related brain circuits in newly diagnosed middle-aged type 2 diabetics. Acta Diabetol 52(1):133–142. doi:10.1007/s00592-014-0618-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors thank the other investigators, the staff, and the Diabetes Heart Study participants for their valuable contributions.

Funding

This study was supported in part by the National Institutes of Health through R01 HL67348, R01 HL092301, R01 NS058700 (to DWB), R01 NS075107 (to BIF, JAM), F32 DK083214-01 (to CEH), and F31 AG044879 (to LMR).

Author information

Authors and Affiliations

  1. Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Laura M. Raffield

  2. Center for Human Genomics, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Laura M. Raffield, Jianzhao Xu & Donald W. Bowden

  3. Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Laura M. Raffield, Jianzhao Xu & Donald W. Bowden

  4. Molecular Basis of Disease, Griffith University, Southport, QLD, Australia

    Amanda J. Cox

  5. Department of Internal Medicine-Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Barry I. Freedman

  6. Department of Gerontology and Geriatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Christina E. Hugenschmidt

  7. Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Fang-Chi Hsu

  8. Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Benjamin C. Wagner & Joseph A. Maldjian

  9. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Donald W. Bowden

  10. Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA

    Donald W. Bowden

Authors
  1. Laura M. Raffield
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  2. Amanda J. Cox
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  3. Barry I. Freedman
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  4. Christina E. Hugenschmidt
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  5. Fang-Chi Hsu
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  6. Benjamin C. Wagner
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  7. Jianzhao Xu
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  8. Joseph A. Maldjian
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  9. Donald W. Bowden
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Corresponding author

Correspondence to Donald W. Bowden.

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All authors report no conflicts of interest.

Human and animal rights statement

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.

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

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Raffield, L.M., Cox, A.J., Freedman, B.I. et al. Analysis of the relationships between type 2 diabetes status, glycemic control, and neuroimaging measures in the Diabetes Heart Study Mind. Acta Diabetol 53, 439–447 (2016). https://doi.org/10.1007/s00592-015-0815-z

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  • DOI: https://doi.org/10.1007/s00592-015-0815-z

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