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| Principal Investigator | |
| Principal Investigator's Name: | Lisa Kakinami |
| Institution: | Concordia University |
| Department: | Mathematics and Statistics |
| Country: | |
| Proposed Analysis: | Naturally, brain volume changes during a lifetime. In fact, it increases until early adolescence then starts to decline beginning in early adulthood (Courchesne et al, 2000). These changes in brain tissues vary in different parts, and the atrophy rate is not constant over the life course and accelerates over time. Moreover, brain deterioration in the elderly with normal health, mild cognitive impairment (MCI), and Alzheimer disease (AD) is diverse. Driscoll et al. (2009) conducted a ten-year longitudinal study to explore the age-related regional volume loss in normal controls and the MCI group. The authors found that in normal individuals, natural changes in specific regions of the brain such as ventricular Cerebrospinal fluid (vCSF) were due to aging. However, evidence suggested that the rate of change in both total brain volume, and other brain regions (such as temporal gray matter) not normally affected by aging may be accelerated in the MCI group. In addition to age- related brain atrophy, some studies revealed that other factors such as carrying the APOE-ε4 allele (Manning et al., 2014) or physical activity (Alskog et al., 2009) might affect the decline in brain volume in late life. Beside brain volume changes, cognitive changes are also a normal process of aging, and declines in mental abilities are inevitable as people age. While some cognitive abilities (such as vocabulary) are resilient to brain aging and may even improve with age, other abilities (such as conceptual reasoning, memory, and processing speed) decline gradually over time (Harada et al., 2013). Sluimer et al. (2008) reported that the brain atrophy rate was significantly associated with a decline in cognitive abilities. Similarly, a study by Royle et al. (2013) indicated that brain tissue deterioration contributed significantly to lower cognitive ability in later life, controlling for prior brain volume and prior cognitive performance. Adding to the association between brain loss volume and decrease in cognitive intelligence, some studies tried to explore factors which can be considered as a moderator to slow down the decline in cognitive abilities. Some researchers found some evidence of associations of health factors such as disease comorbidities (diabetes or hypertension; Kuo et al. (2005)), and lifestyle behavior (current smoking status; Sabia et al. (2012)) with cognitive decline. Further exploring these associations, Karp et al. (2003) reported that a low level of education and a low socioeconomic status (based on occupation) were individually associated with increased risk of AD and dementia. In addition, when both education and low socioeconomic status were included in the same model, education remained as a significant predictor of AD. However, a study by Wilson et al. (2009) reported that while education was associated with level of cognitive function, it was not associated with the rate of cognitive decline. As a result, drawing conclusions from these studies is difficult due to their variability in study quality and covariates (Plassman et al., 2010). Thus, despite the great development in the longitudinal study of age- related decline in brain volume and cognitive abilities, notable limitations of the literature exist. For instance, most longitudinal studies have only a small number of follow-up visits (Sluimer et al., 2008), which not only decreases the power and sensitivity of the results but also limit testing for non-linear trajectories (Ritchie et al., 2015). In addition, inadequate covariate adjustment for important factors such as education or health factors highlights the need to study the association between brain volume loss and cognitive abilities while adjusting for potential predictors and risk factors. Therefore, we propose to analyze the longitudinal association between brain atrophy and cognitive decline controlling for potential predictors to address some these previous limitations. In particular, in recognition of the better exploration of the relationship as compared to a basic linear change score, we propose to investigate the relationship through a mixed effects model and to explore potential moderators for this association. Our first objective is to investigate the association between global and regional brain atrophy and change (decline) in cognitive abilities and to compare their rates of decline across the three groups previously described (normal controls, MCI, AD). Our second objective is to examine whether education can play a moderating role for the changes in brain volume and cognitive abilities, since it may represent a lifetime trajectory (Schoenhofen Sharp and Gatz, 2011). Materials and methods Our response variable is cognitive abilities, defined by the cognitive subscale of the extended Alzheimer's Disease Assessment-Scale (ADAS-cog 13). It includes 13 items to measure key areas of cognition such as verbal episodic memory, language, comprehension and ideomotor praxis, visual attention and concentration and delayed verbal recall (Dowling & Johnson, 2015); moreover, we will consider ADAS-cog 13 as a continuous variable in our study. Total or regional brain volume will be our main predictor to explore the association between decline in cognition and brain tissue deterioration. As a preliminary analysis, we will analyze the change in cognitive abilities over time separately in three different groups: healthy (normal) controls, and subjects with MCI and AD, and we will adjust for clinical groups to study the changes in cognitive decline between groups. Since normal aging is associated with the decline of cognitive abilities, we will assess whether the age is multicollinear with brain volume loss or whether it should be included as a covariate in our model. Other potential covariates to be considered for inclusion are: gender, lifestyle (smoking status and alcohol), carriers of APOE-ε4 allele, and whether the participant has suffered from a stroke, diabetes or hypertension. In addition, intracranial volume as a measure of prior maximal brain size will be included in our model, controlling for prior biological characteristic that has been associated with cognitive abilities (Royle et al., 2013). First, using univariate tests at baseline, we will test for the bivariate associations with potential covariates. Based on a conservative p-value of <0.20 with either our predictor or outcome, covariates will be considered for inclusion in our multilevel, adjusted analyses. In addition, based on the distribution of these data, and in preliminary analyses, we will explore the level of complexity in our multilevel analyses that is necessary to properly model the association. Specifically, we will assess for the necessity of random intercepts, fixed and random effects, and whether covariates are time-varying or time-invariant. Different covariance structures will also be tested to see which one will best fit our data. Thus, a mixed-effects model will be applied to analyze the effect of brain volume deterioration and other potential factors on cognitive abilities and to explore any differences in trajectories of decline in three different groups. Importantly, since we have multiple follow-up visits, we will be able to test for non-linear mixed-effects while adjusting for important covariates. Finally, to investigate whether years of education or level of education is a moderator, we will add interaction terms of education with brain volume in our model. References Ahlskog, J.E., Geda, Y.E., Graff-Radford, N.R.,&Petersen, R.C.(2011), Physical exercise as a preventive or disease- modifying treatment of dementia and brain aging, Mayo Clin. Proc. 86, 876-884. Courchesne, E., Chisum, H. J., Townsend, J., Cowles, A., Covington, J., Egaas, B., Harwood, M., Hinds, S., & Press, G.A. (2000), Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers, Radiology, 216:672–682. Driscoll, I., Davatzikos, C., An, Y., Wu, X., Shen, D., Kraut, M., & Resnick, S.M. (2009), Longitudinal pattern of regional brain volume change differentiates normal aging from MCI, Neurology,72:1906–1913 Dowling, N.M., &Johnson, S.C. (2015), The mediational effects of FDG hypometabolism on the association between cerebrospinal fluid biomarkers and neurocognitive function, NeuroImage, 105:357-368. Haradaa, C. N., Natelson Love, M. C., &Triebeld, K. (2013), Normal cognitive aging, ClinGeriatr Med, 29(4): 737–752. doi: 10.1016/j.cger.2013.07.002. Karp, A., Kåreholt, I., Qiu, C., Bellander, T., Winblad, B., &Fratiglioni,L. (2004), Relation of education and occupation-based socioeconomic status to incidentAlzheimer’s Disease, American Journal of Epidemiology, 159(2):175–183 Kuo, H.K., Jones, R.N., Milberg, W.P., Tennstedt, S., Talbot, L., Morris, J.N., &Lipsitz, L.A. (2005), Effect of blood pressure and diabetes mellitus on cognitive and physical functions in older adults: a longitudinal analysis of the advanced cognitive training for independent and vital elderly cohort, Journal of the American Geriatrics Society, 53(7): 1154–1161. doi:10.1111/j.1532-5415.2005.53368.x Manning, E.N., Barnes, J., Cash, D.M., Bartlett, J.W., Leung, K.K., Ourselin, S., & Fox, N.C. (2014), APOE ε4 is associated with disproportionate progressive Hippocampial atrophy in AD, PLoS ONE, 9(5): e97608. doi: 10.1371/journal.pone.0097608. Plassman, B.L.,Williams, J.R., Holsinger, T.,& Benjamin, S. (2010). Systematic review: factors associated with risk for and possible prevention of cognitive decline in later life. Ann. Intern. Med. 153,182-193. Ritchie, S.J., Dikie, D.A., Cox, S.R., Valdes Hernandez, M.C., Corley, J., Royle, N.A., Pattie, A., Aribisala, B.S., Redmond,P., Munoz Maniega, S., Taylor, A.M., Sibett, R., Gow, A.J., Starr, J.M., Bastin, M.E., Wardlaw, J.M., &Deary, I.J. (2015), Brain volumetric changes and cognitive aging during eight decades of life, Human Brain Mapping, 36: 4910-4925. Sabia, S.,Elbaz, A., Dugravot, A., Head, J., Shipley, M., Hagger-Johnson, G., Kivimaki, M.,&Singh-Manoux, A. (2012), Impact of smoking on cognitive declinein early old age, Archives of General Psychiatry, 69(6): 627-633. Schoenhofen Sharp, E. and Gatz, (2011), The relationship between education and dementia an updated systematic review, Alzheimer Disease and Associated Disorders,25(4): 289–304. doi:10.1097/WAD.0b013e318211c83c. Sluimer, J. D., van der Flier, W. M., Karas, G. B., Fox, N. C., Scheltens, P., Barkhof F., &Vrenken, H. (2008), Whole-Brain atrophy rate and cognitive decline: longitudinal MR study of memory clinic patients, Radiology: 248(2): 590-598 Wilson, R.S., Hebert, L.E., Scherr, P.A., Barnes, L.L., Mendes de Leon, C. F., &Evans, D.A.(2009), Educational attainment and cognitive decline in old age, Neurology, 72(5): 460–465. |
| Additional Investigators |
