There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Francis Hane |
| Institution: | Lakehead University |
| Department: | Chemistry |
| Country: | |
| Proposed Analysis: | Alzheimer’s disease (AD) is a devastating age-related neurodegenerative disease. Its devastation lies in its ability to impair the memory and cognitive function of people who have lived to an elderly age by having avoided or conquered many of the other mortal diseases such as cancer, cardio- and cerebro-vascular diseases, and respiratory infections. This cognitive impairment requires the allocation of extensive familial and societal resources to care for the afflicted individual [1]. Many people are interested in obtaining genetic testing to determine their possibility of suffering from AD and a variety of corporations have begun to offer these genetic testing services to fulfil the demand. The results of these tests have not been demonstrated to cause significant emotional harm to subjects [2]. Genetic testing involves the collection of blood or other genetic material to detect the presence of the APP, PSEN1, or PSEN2 genes in the case of early onset AD (EOAD) or the APOE-ε4 (APOE4) allele in the case late onset AD (LOAD) [3]. While these genetic tests are very accurate in determining genotype, these tests suffer cost drawbacks as well as a general failure to predict the age of onset of AD by correcting for other factors such as history of Type 2 Diabetes Mellitus (T2DM) and traumatic brain injury (TBI). A number of factors have been demonstrated to affect the probability of AD and the age of onset. Major factors that influence age of AD onset include T2DM [4,5], TBI [6], education level [7], and race [8]. Additionally, previous studies have found observational links between age of onset and a variety of foods, mental activity, and physical exercise [9]. A number of other reports have successfully demonstrated the use of various data to predict the onset of AD. Tierney et al used neuropsychological tests to predict AD with a sensitivity and specificity above 70% for predicting AD within 5 years [10]. Callahan et al used clinical memory test scores and imaging biomarkers to derive a predictive model for AD [11]. Macdonald et al derived a mathematical model of AD based on APOE status [12] based largely on the earlier work by Farrar et al. [13]. In this work, we propose a theoretical framework to predicting AD onset by applying a hierarchical Bayesian model to a subject’s familial history to predict their APOE4 genotype, the major genetic risk factor for LOAD. Firstly, this model considers the familial history of the subject dating back two generations, i.e. parents and grandparents. Following the APOE4 genotype estimate, known hazard ratios are applied to determine a mathematical model of the probability of AD as a function of age correcting for covariates sex, race, diabetic history, traumatic brain injury, physical activity, diet, and education level. Our novel AD prediction model provides a theoretical predictive tool that corrects for various factors not considered in existing genetic tests without the costs associated with genetic tests. ADNI data will provide important validation of this model by allowing us to look at actual subjects and compare their age of AD onset with the age of AD onset predicted in our model. Research Questions • Does the theoretical Bayesian model correlate to actual age of AD onset? • What is the sensitivity and specificity of our model to actual AD onset? • Do the risk factors for AD act in a complementary or additive fashion? Data We will use existing MESA data to validate our theoretical model. We will analyze the following variables: sex, race, APOE4 genotype, age of AD diagnosis, history of type 2 diabetes mellitus, diet, exercise, education, and history of traumatic brain injury. Data Analysis We will randomly select approximately 1000 MESA data points (i.e. human participants) and input their variables into our model. We will then compare our model’s prediction of the participant’s age of AD onset with the actual age of the participant’s AD onset by calculating the root mean square error between the predicted values and actual values. References [1] Alzheimer’s Association, 2011 Alzheimer’s Disease Facts and Figures, Alzheimers Dement. 7 (2011) 1–63. [2] R. Green, J. Roberts, L. Cupples, N. Relkin, P. Whitehouse, T. Brown, et al., Disclosure of APOE genotype for risk of Alzheimer’s disease., N Engl J Med. 361 (2009) 245–254. [3] E. Corder, A. Saunders, W. Strittmatter, D. Schmechel, P. Gaskell, G. Small, et al., Gene Dose of Apolipoprotein E Type 4 Allele and the Risk of Alzheimer’s Disease in Late Onset Families, Science. 261 (1993) 921–923. [4] S. Croxson, C. Jagger, Diabetes and cognative impairment: a community based study of elderly subjects, Age Aging. 24 (1995) 421–424. [5] Z. Arvanitakis, R. Wilson, J. Bienias, D. Evans, D. Bennett, Diabetes Mellitus and the risk of and risk of Alzheimer’s disease and decline in cognative function, Arch Neurol. 61 (2004) 661–666. [6] P.N. Nemetz, C. Leibson, J. Naessens, M. Beard, E. Kokmen, J.F. Annegers, et al., Traumatic Brain Injury and Time to Onset of Alzheimer’s Disease: A Population-based Study, J Epidemiol. 149 (1999) 32–40. [7] A. Ott, M.M.B. Breteler, F. Van Harskamp, J.J. Claus, V.D.C. Tischa, D.E. Grobbee, et al., Prevalence Of Alzheimer’s Disease And Vascular Dementia: Association With Education. The Rotterdam Study, Br Med J. 310 (1995) 970–973. [8] L.A. Farrer, A. Cupples, J. Haynes, B. Hyman, W.A. Kukull, R. Mayeux, et al., Effects of Age, Sex, and Ethnicity on the Association Between Apolipoprotein E Genotype and Alzheimer Disease, J Am Med Assoc. 278 (1997) 1349–1356. [9] N. Scarmeas, J.A. Luchsinger, N. Schupf, S. Cosentino, Physical Activity, Diet, and Risk of Alzheimer Disease, J Am Med Assoc. 302 (2009) 627–637. [10] M.C. Tierney, C. Yao, A. Kiss, I. Mcdowell, Neuropsychological tests accurately predict incident Alzheimer disease after 5 and 10 years, Neurology. 64 (2005) 1853–1860. [11] B.L. Callahan, J. Ramirez, C. Berezuk, S. Duchesne, S.E. Black, Predicting Alzheimer’s disease development: a comparison of cognitive criteria and associated neuroimaging biomarkers, Alzheimers Res Ther. 7 (2015) 68–77. doi:10.1186/s13195-015-0152-z. [12] A. MacDonald, D. Prichard, A mathematical model of Alzheimer’s disease and the APOE gene, Astin Bull. 30 (2000) 69–110. [13] L.A. Farrer, A. Cupples, J.L. Haines, B. Hyman, W.A. Kukull, R. Mayeux, et al., Effects of Age , Sex , and Ethnicity on the Association Between Apolipoprotein E Genotype and Alzheimer Disease, J Am Med Assoc. 278 (1997) 1349–1356. |
| Additional Investigators |
