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May 23, 2005

Risk factors for progression of brain atrophy in aging
Six-year follow-up of normal subjects

May 24, 2005 issue
64 (10) 1704-1711

Abstract

Objectives: To determine the rate of brain atrophy in neurologically asymptomatic elderly and to investigate the impact of baseline variables including conventional cerebrovascular risk factors, APOE ε4, and white matter hyperintensity (WMH) on its progression.
Methods: We assessed the brain parenchymal fraction at baseline and subsequent annual brain volume changes over 6 years for 201 participants (F/M = 96/105; 59.8 ± 5.9 years) in the Austrian Stroke Prevention Study from 1.5-T MRI scans using SIENA (structural image evaluation using normalization of atrophy)/SIENAX (an adaptation of SIENA for cross-sectional measurement)(www.fmrib.ox.ac.uk/fsl). Hypertension, cardiac disease, diabetes mellitus, smoking, and regular alcohol intake were present in 64 (31.8%), 60 (29.9%), 5 (2.5%), 70 (39.3%), and 40 (20.7%) subjects, respectively. Plasma levels of fasting glucose (93.7 ± 18.6 mg/dL), glycated hemoglobin A (HbA1c; 5.6 ± 0.7%), total cholesterol (228.3 ± 40.3 mg/dL), and triglycerides (127.0 ± 75.2 mg/dL) were determined. WMH was rated as absent (n = 56), punctate (n = 120), early confluent (n = 14), and confluent (n = 11).
Results: The baseline brain parenchymal fraction of the entire cohort was 0.80 ± 0.02 with a mean annual brain volume change of −0.40 ± 0.29%. Univariate analysis demonstrated a higher rate of brain atrophy in older subjects (p = 0.0001), in those with higher HbA1c (p = 0.0001), higher body mass index (p = 0.02), high alcohol intake (p = 0.04), severe WMH (p = 0.03), and in APOE ε4 carriers (p = 0.07). Multivariate analysis suggested that baseline brain parenchymal fraction, HbA1c, and WMH score explain a major proportion of variance in the rates of brain atrophy in the cohort (corrected R2 = 0.27; p = 0.0001).
Conclusions: Neurologically asymptomatic elderly experience continuing brain volume loss, which appears to accelerate with age. Glycated hemoglobin A (HbA1c) was identified as a risk factor for a greater rate of brain atrophy. Clustering of factors associated with the so-called metabolic syndrome in subjects with high HbA1c suggests a link between this syndrome and late-life brain tissue loss.

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References

1.
Araki Y, Nomura M, Tanaka H, et al. MRI of the brain in diabetes mellitus. Neuroradiology 1994;36:101–103.
2.
Blatter D, Bigler E, Gale S, et al. Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life. Am J Neuroradiol 1995;16:241–251.
3.
Resnick SM, Goldszal AF, Davatzikos C, et al. One-year age changes in MRI brain volumes in older adults. Cereb Cortex 2000;10:464–472.
4.
Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001;14:21–36.
5.
Coffey CE, Wilkinson WE, Parashos IA, et al. Quantitative cerebral anatomy of the aging human brain: a cross-sectional study using magnetic resonance imaging. Neurology 1992;42:527–536.
6.
Longstreth WT Jr, Arnold AM, Manolio TA, et al. Clinical correlates of ventricular and sulcal size on cranial magnetic resonance imaging of 3,301 elderly people. Neuroepidemiology 2000;19:30–42.
7.
Strassburger TL, Hing-Chung L, Daly EM, et al. Interactive effects of age and hypertension on volumes of brain structures. Stroke 1997;28:1410–1417.
8.
Meyer JS, Rauch GM, Crawford K, et al. Risk factors accelerating cerebral degenerative changes, cognitive decline and dementia. Int J Geriatr Psychiatry 1999;14:1050–1061.
9.
Schmidt R, Launer LJ, Nilsson L-G, et al. Magnetic resonance imaging of the brain in diabetes: the Cardiovascular Determinants of Dementia (CASCADE) study. Diabetes 2004;53:687–692.
10.
Lunetta M, Damanti AR, Fabbri G, Lombardo M, Di Mauro M, Mughini L. Evidence by magnetic resonance imaging of cerebral alterations of atrophy type in young insulin-dependent diabetic patients. J Endocrinol Invest 1994;17:241–245.
11.
Sabri O, Hellwig D, Schreckenberger M, et al. Influence of diabetes mellitus on regional cerebral glucose metabolism and regional cerebral blood flow. Nucl Med Commun 2000;21:19–29.
12.
Mukamal KJ, Longstreth WT Jr, Mittleman MA, Crum RM, Siscovick DS, Bereczki D. Alcohol consumption and subclinical findings on magnetic resonance imaging of the brain in older adults: the Cardiovascular Health Study. Stroke 2001;32:1939–1946.
13.
Sachdev PS, Valenzuela M, Wang XL, Looi JCL, Brodaty H. Relationship between plasma homocysteine levels and brain atrophy in healthy elderly individuals. Neurology 2002;58:1539–1541.
14.
Gunter JL, Shiung MM, Manduca A, Jack CR Jr. Methodological considerations for measuring rates of brain atrophy. J Magn Reson Imaging 2003;18:16–24.
15.
Fox NC, Schott JM. Imaging cerebral atrophy: normal ageing to Alzheimer’s disease. Lancet 2004;31:392–394.
16.
Cardenas VA, Du AT, Hardin D, et al. Comparison of methods for measuring longitudinal brain change in cognitive impairment and dementia. Neurobiol Aging 2003;24:537–544.
17.
Jack CR Jr, Shiung MM, Gunter JL, et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 2004;62:591–600.
18.
Smith SM, De Stefano N, Jenkinson M, Matthews PM. Normalized accurate measurement of longitudinal brain change. J Comput Assist Tomogr 2001;25:466–475.
19.
Schmidt R, Fazekas F, Kapeller P, Schmidt H, Hartung HP. MRI white matter hyperintensities: three-year follow-up of the Austrian Stroke Prevention Study. Neurology 1999;53:132–139.
20.
Schmidt R, Schmidt H, Fazekas F, et al. Apolipoprotein E polymorphism and silent microangiopathy-related cerebral damage. Results of the Austrian Stroke Prevention Study. Stroke 1997;28:951–956.
21.
Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 1987;149:351–356.
22.
Schmidt R, Enzinger C, Ropele S, Schmidt H, Fazekas F, the Austrian Stroke Prevention Study. Progression of cerebral white matter lesions: 6-year results of the Austrian Stroke Prevention Study. Lancet 2003;14:2046–2048.
23.
Fazekas F, Kleinert R, Offenbacher H, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993;43:1683–1689.
24.
O’Sullivan M, Lythgoe DJ, Pereira AC, et al. Patterns of cerebral blood flow reduction in patients with ischemic leukoaraiosis. Neurology 2002;59:321–326.
25.
Smith SM, Zhang Y, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002;17:479–489.
26.
Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C. Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci 2003;23:3295–3301.
27.
Fox NC, Warrington EK, Rossor MN. Serial magnetic resonance imaging of cerebral atrophy in preclinical Alzheimer’s disease. Lancet 1999;353:2125.
28.
Scahill RI, Frost C, Jenkins R, Whitwell JL, Rossor MN, Fox NC. A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch Neurol 2003;60:989–994.
29.
Liu RSN, Lemieux L, Bell GS, et al. A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis. Neuroimage 2003;20:22–33.
30.
Mueller EA, Moore MM, Kerr DC, et al. Brain volume preserved in healthy elderly through the eleventh decade. Neurology 1998;51:1555–1562.
31.
Raz N, Gunning FM, Head D, et al. Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cereb Cortex 1997;7:268–282.
32.
Enzinger C, Ropele S, Smith S, et al. Accelerated evolution of brain atrophy and “black holes” in MS patients with APOE-epsilon4. Ann Neurol 2004;55:563–569.
33.
Wang D, Chalk JB, Rose SE, et al. MR image-based measurement of rates of change in volumes of brain structures. Part II: application to a study of Alzheimer’s disease and normal aging. Magn Reson Imaging 2002;20:41–48.
34.
Biessels GJ, van der Heide LP, Kamal A, Bleys RLAW, Gispen WH. Ageing and diabetes: implications for brain function. Eur J Pharmacol 2002;441:1–14.
35.
Jeffcoate SL. Diabetes control and complications: the role of glycated haemoglobin, 25 years on. Diabetes Med 2004;21:657–665.
36.
Porte D Jr, Seeley RJ, Woods SC, Baskin DG, Figlewicz DP, Schwartz MW. Obesity, diabetes and the central nervous system. Diabetologia 1998;41:863–881.
37.
Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C, for the Conference Participants. Definition of Metabolic Syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 2004;109:433–438.
38.
Craft S, Watson GS. Insulin and neurodegenerative disease: shared and specific mechanisms. Lancet Neurol 2004;3:169–178.
39.
Sima AAF, Kamiya H, Guo Li Z. Insulin, C-peptide, hyperglycemia, and central nervous system complications in diabetes. Eur J Pharmacol 2004;490:187–197.
40.
Sormani MP, Rovaris M, Valsasina P, Wolinsky JS, Comi G, Filippi M. Measurement error of two different techniques for brain atrophy assessment in multiple sclerosis. Neurology 2004;62:1432–1434.
41.
Matthews FE, Chatfield M, Freeman C, McCracken C, Brayne C, MRC CFAS. Attrition and bias in the MRC cognitive function and ageing study: an epidemiological investigation. BMC Public Health 2004;27:12.
42.
Hedden T, Gabrieli JD. Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci 2004;5:87–96.
43.
Carmelli D, DeCarli C, Swan GE, et al. Evidence for genetic variance in white matter hyperintensity volume in normal elderly male twins. Stroke 1998;29:1177–1181.
44.
Atwood LD, Wolf PA, Heard-Costa NL, et al. Genetic variation in white matter hyperintensity volume in the Framingham study. Stroke 2004;35:1609–1613.
45.
Staff RT, Murray AD, Deary IJ, Whalley LJ. What provides cerebral reserve? Brain 2004;127:1191–1199.
46.
Hu G, Qiao Q, Tuomilehto J, Balkau B, Borch-Johnsen K, Pyorala K. Prevalence of the metabolic syndrome and its relation to all-cause and cardiovascular mortality in nondiabetic European men and women. Arch Intern Med 2004;164:1066–1076.

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Published In

Neurology®
Volume 64Number 10May 24, 2005
Pages: 1704-1711
PubMed: 15911795

Publication History

Published online: May 23, 2005
Published in print: May 24, 2005

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Authors

Affiliations & Disclosures

C. Enzinger, MD
From the Departments of Neurology (Drs. Enzinger, Fazekas, Ropele, and R. Schmidt) and Radiology, Section of Neuroradiology (Dr. Fazekas), MR Research Unit (Dr. Ropele), and the Institute of Medical Biochemistry and Molecular Genetics (Dr. H. Schmidt), Medical University of Graz, Austria; and the Centre for Functional MRI of the Brain (Drs. Matthews and Smith), John Radcliffe Hospital, University of Oxford, UK.
F. Fazekas, MD
From the Departments of Neurology (Drs. Enzinger, Fazekas, Ropele, and R. Schmidt) and Radiology, Section of Neuroradiology (Dr. Fazekas), MR Research Unit (Dr. Ropele), and the Institute of Medical Biochemistry and Molecular Genetics (Dr. H. Schmidt), Medical University of Graz, Austria; and the Centre for Functional MRI of the Brain (Drs. Matthews and Smith), John Radcliffe Hospital, University of Oxford, UK.
P. M. Matthews, MD, DPhil, FRCP
From the Departments of Neurology (Drs. Enzinger, Fazekas, Ropele, and R. Schmidt) and Radiology, Section of Neuroradiology (Dr. Fazekas), MR Research Unit (Dr. Ropele), and the Institute of Medical Biochemistry and Molecular Genetics (Dr. H. Schmidt), Medical University of Graz, Austria; and the Centre for Functional MRI of the Brain (Drs. Matthews and Smith), John Radcliffe Hospital, University of Oxford, UK.
S. Ropele, PhD
From the Departments of Neurology (Drs. Enzinger, Fazekas, Ropele, and R. Schmidt) and Radiology, Section of Neuroradiology (Dr. Fazekas), MR Research Unit (Dr. Ropele), and the Institute of Medical Biochemistry and Molecular Genetics (Dr. H. Schmidt), Medical University of Graz, Austria; and the Centre for Functional MRI of the Brain (Drs. Matthews and Smith), John Radcliffe Hospital, University of Oxford, UK.
H. Schmidt, MD
From the Departments of Neurology (Drs. Enzinger, Fazekas, Ropele, and R. Schmidt) and Radiology, Section of Neuroradiology (Dr. Fazekas), MR Research Unit (Dr. Ropele), and the Institute of Medical Biochemistry and Molecular Genetics (Dr. H. Schmidt), Medical University of Graz, Austria; and the Centre for Functional MRI of the Brain (Drs. Matthews and Smith), John Radcliffe Hospital, University of Oxford, UK.
S. Smith, DPhil
From the Departments of Neurology (Drs. Enzinger, Fazekas, Ropele, and R. Schmidt) and Radiology, Section of Neuroradiology (Dr. Fazekas), MR Research Unit (Dr. Ropele), and the Institute of Medical Biochemistry and Molecular Genetics (Dr. H. Schmidt), Medical University of Graz, Austria; and the Centre for Functional MRI of the Brain (Drs. Matthews and Smith), John Radcliffe Hospital, University of Oxford, UK.
R. Schmidt, MD
From the Departments of Neurology (Drs. Enzinger, Fazekas, Ropele, and R. Schmidt) and Radiology, Section of Neuroradiology (Dr. Fazekas), MR Research Unit (Dr. Ropele), and the Institute of Medical Biochemistry and Molecular Genetics (Dr. H. Schmidt), Medical University of Graz, Austria; and the Centre for Functional MRI of the Brain (Drs. Matthews and Smith), John Radcliffe Hospital, University of Oxford, UK.

Notes

Address correspondence and reprint requests to Dr. Christian Enzinger, Department of Neurology, Medical University Graz, Auenbruggerplatz 22, A-8036 Graz, Austria; e-mail: [email protected]

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