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Global cerebrovascular burden and long-term clinical outcomes in Asian elderly across the spectrum of cognitive impairment

Published online by Cambridge University Press:  18 April 2018

Xin Xu ,
Yiong Huak Chan ,
Qun Lin Chan ,
Bibek Gyanwali ,
Saima Hilal ,
Boon Yeow Tan ,
Mohammad Kamran Ikram ,
Narayanaswamy Venketasubramanian  and
Christopher Li-Hsian Chen
Xin Xu*
Affiliation:
Department of Pharmacology, National University of Singapore, Singapore Memory Aging and Cognition Centre, National University Health System, Singapore Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
Yiong Huak Chan
Affiliation:
Biostatistics Unit, Yong Loo Lin School of Medicine, National University Health System, Singapore
Qun Lin Chan
Affiliation:
Department of Pharmacology, National University of Singapore, Singapore Memory Aging and Cognition Centre, National University Health System, Singapore
Bibek Gyanwali
Affiliation:
Department of Pharmacology, National University of Singapore, Singapore Memory Aging and Cognition Centre, National University Health System, Singapore
Saima Hilal
Affiliation:
Department of Epidemiology & Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands
Boon Yeow Tan
Affiliation:
St. Luke Hospital, Singapore
Mohammad Kamran Ikram
Affiliation:
Department of Epidemiology & Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands
Narayanaswamy Venketasubramanian
Affiliation:
Department of Pharmacology, National University of Singapore, Singapore Raffles Neuroscience Centre, Raffles Hospital, Singapore
Christopher Li-Hsian Chen
Affiliation:
Department of Pharmacology, National University of Singapore, Singapore Memory Aging and Cognition Centre, National University Health System, Singapore
*
Correspondence should be addressed to: Dr. Xin Xu, Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore. Phone: +65-6592-3962. Email: xu.xin@ntu.edu.sg.
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Abstract

Background/Aim:

To investigate the predictive ability of the previously established global cerebrovascular disease (CeVD) burden scale on long-term clinical outcomes in a longitudinal study of Asian elderly participants across the spectrum of cognitive impairment.

Methods:

A case-control study was conducted over a 2-year period involving participants with no cognitive impairment, cognitive impairment-no dementia (CIND), and Alzheimer's disease (AD). Annually, cognitive function was assessed with a comprehensive neuropsychological battery and the clinical dementia rating (CDR) scale was used to stage disease severity.

Results:

Of 314 participants, 102 had none/very mild CeVD, 31 mild CeVD, 94 moderate CeVD, and 87 severe CeVD at baseline. There was a 1.14 and 1.42 units decline per year on global cognitive z-scores in moderate and severe CeVD groups, respectively, compared to none/very mild CeVD. Moderate-severe CeVD predicted significant functional deterioration at year 2 (HR = 2.0, 95% CI = 1.2–3.4), and conversion to AD (HR = 6.3, 95% CI = 1.7–22.5), independent of medial temporal atrophy.

Conclusion:

The global CeVD burden scale predicts poor long-term clinical outcome independent of neurodegenerative markers. Furthermore, CeVD severity affects the rate of cognitive and functional deterioration. Hence, cerebrovascular burden, which is potentially preventable, is a strong prognostic indicator, both at preclinical and clinical stages of AD, independent of neurodegenerative processes.

Keywords

longitudinal studiescognitive assessmentcognitive impairmentcerebrovascular disease
Type
Original Research Article
Information
International Psychogeriatrics , Volume 30 , Issue 9: Issue Theme: Quality of Care for Frail Older Adults , September 2018 , pp. 1355 - 1363
Copyright
Copyright © International Psychogeriatric Association 2018 

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References

Association, A. P. and Association, A. P. (1994). Diagnostic and Statistical Manual of Mental Disorders (DSM) (pp. 143–147). Washington, DC: American Psychiatric Association.Google Scholar
Brandt, J. (1991). The hopkins verbal learning test: development of a new memory test with six equivalent forms. Clinical Neuropsychologist, 5, 125–142.Google Scholar
Carmichael, O. et al. (2010). Longitudinal changes in white matter disease and cognition in the first year of the Alzheimer disease neuroimaging initiative. Archives of Neurology, 67, 1370–1378.Google Scholar
Cummings, J. L. (1997). The neuropsychiatric inventory assessing psychopathology in dementia patients. Neurology, 48, 10S–16S.Google Scholar
DeCarli, C. et al. (2004). Memory impairment, but not cerebrovascular disease, predicts progression of MCI to dementia. Neurology, 63, 220–227.Google Scholar
D'Elia, L., Satz, P., Uchiyama, C. and White, T. (1996). Color Trails Test. Professional Manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
Fazekas, F. et al. (1993). Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology, 43, 1683–1689.Google Scholar
Greenberg, S. M. et al. (2009). Cerebral microbleeds: a guide to detection and interpretation. Lancet Neurology, 8, 165–174.Google Scholar
Hachinski, V. et al. (2006). National institute of neurological disorders and stroke–Canadian stroke network vascular cognitive impairment harmonization standards. Stroke, 37, 2220–2241.Google Scholar
Hilal, S. et al. (2015a). Markers of cardiac dysfunction in cognitive impairment and dementia. Medicine, 94, e297.Google Scholar
Hilal, S. et al. (2015b). Intracranial stenosis, cerebrovascular diseases, and cognitive impairment in Chinese. Alzheimer Disease Associated Disorders, 29, 12–17.Google Scholar
Huijts, M., Duits, A., Van Oostenbrugge, R. J., Kroon, A. A., De Leeuw, P. W. and Staals, J. (2013). Accumulation of MRI markers of cerebral small vessel disease is associated with decreased cognitive function. A study in first-ever lacunar stroke and hypertensive patients. Frontiers in Aging Neuroscience, 5, 72.Google Scholar
Iadecola, C. (2010). The overlap between neurodegenerative and vascular factors in the pathogenesis of dementia. Acta Neuropathologica, 120, 287–296.Google Scholar
Kalaria, R. N. (2012). Cerebrovascular disease and mechanisms of cognitive impairment evidence from clinicopathological studies in humans. Stroke, 43, 2526–2534.Google Scholar
Kalaria, R. N., Akinyemi, R. and Ihara, M. (2012). Does vascular pathology contribute to Alzheimer changes? Journal of the Neurological Sciences, 322, 141–147.Google Scholar
Kim, H. J. et al. (2016). Relative impact of amyloid-β, lacunes, and downstream imaging markers on cognitive trajectories. Brain, 139, 2516–2527.Google Scholar
Knopman, D. S. (2007). Cerebrovascular disease and dementia. The British Journal of Radiology, 80, 121–127.Google Scholar
Luchsinger, J. et al. (2009). Subclinical cerebrovascular disease in mild cognitive impairment. Neurology, 73, 450–456.Google Scholar
Mack, W. J., Freed, D. M., Williams, B. W. and Henderson, V. W. (1992). Boston naming test: shortened versions for use in Alzheimer's disease. Journal of Gerontology, 47, 154–158.Google Scholar
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D. and Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's disease. Neurology, 34, 939–939.Google Scholar
Meyers, J. E. and Meyers, K. R. (1994). Rey Complex Figure Test and Recognition Trial. Odessa, FL: Psychological Assessment Resources.Google Scholar
Mungas, D. et al. (2002). Volumetric MRI predicts rate of cognitive decline related to AD and cerebrovascular disease. Neurology, 59, 867–873.Google Scholar
Narasimhalu, K. et al. (2009). Severity of CIND and MCI predict incidence of dementia in an ischemic stroke cohort. Neurology, 73, 1866–1872.Google Scholar
Narasimhalu, K. et al. (2011). The prognostic effects of poststroke cognitive impairment no dementia and domain-specific cognitive impairments in nondisabled ischemic stroke patients. Stroke, 42, 883–888.Google Scholar
Patel, B. et al. (2013). Cerebral microbleeds and cognition in patients with symptomatic small vessel disease. Stroke, 44, 356–361.Google Scholar
Prins, N. D. et al. (2005). Cerebral small-vessel disease and decline in information processing speed, executive function and memory. Brain, 128, 2034–2041.Google Scholar
Riba-Llena, I. et al. (2015). Small cortical infarcts: prevalence, determinants, and cognitive correlates in the general population. International Journal of Stroke, 10, 18–24.Google Scholar
Schmidt, R. et al. (2012). White matter lesion progression in LADIS frequency, clinical effects, and sample size calculations. Stroke, 43, 2643–2647.Google Scholar
Silvestrini, M. et al. (2006). Cerebrovascular reactivity and cognitive decline in patients with Alzheimer disease. Stroke, 37, 1010–1015.Google Scholar
Smith, A. (1973). Symbol Digit Modalities Test. Los Angeles, CA: Services WP.Google Scholar
Staals, J., Makin, S. D., Doubal, F. N., Dennis, M. S. and Wardlaw, J. M. (2014). Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden. Neurology, 83, 1228–1234.Google Scholar
Staekenborg, S. S., Van Straaten, E. C., Van der Flier, W. M., Lane, R., Barkhof, F. and Scheltens, P. (2008). Small vessel versus large vessel vascular dementia. Journal of Neurology, 255, 1644–1651.Google Scholar
Tham, W. et al. (2002). Progression of cognitive impairment after stroke: one year results from a longitudinal study of Singaporean stroke patients. Journal of the Neurological Sciences, 203, 49–52.Google Scholar
Wardlaw, J. et al. (2013). Standards for reporting vascular changes on neuroimaging (STRIVE v1). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurology, 12, 822–838.Google Scholar
Wechsler, D. (2009). Subtest Administration and Scoring. WAIS-IV: Administration and Scoring Manual. San Antonio, TX: The Psychological Corporation.Google Scholar
Xu, X. et al. (2015). Association of magnetic resonance imaging markers of cerebrovascular disease burden and cognition. Stroke, 46, 2808–2814.Google Scholar
Xu, X. et al. (2016a). The diagnostic utility of the NINDS-CSN neuropsychological battery in memory clinics. Dementia and Geriatric Cognitive Disorders Extra, 6, 276–282.Google Scholar
Xu, X. et al. (2016b). Validation of the total cerebrovascular disease burden scale in a community sample. Journal of Alzheimer's Disease, 52, 1021–1028.Google Scholar
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