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: | Yoo Bin Lee |
| Institution: | Ottawa Hospital Research Institute |
| Department: | Neuroscience Research Institute |
| Country: | |
| Proposed Analysis: | We recently designed and began validating a hypothesis-driven formula to calculate the incidence of typical Parkinson’s disease (PrEDIGT Score). The model calculates the cumulative incidence rate using five factors, but without a motor exam: Environmental exposure history (E); DNA variants (D); initiation of lasting changes due to gene-environment interactions (I); gender (G); and time (T). Of note, risk factors for both AD and PD largely fall under the same 5 categories, albeit with varying parameters except for progression in age (under T). Using ADNI, we hope to build a predictive model for AD that could help test the specificity of the PrEDIGT score and identify the future incidence rate for dementia in healthy adults as well as those with MCI. Specifically, we'd like to analyze the medical history (ie. stroke, hypertension, cholesterol, diabetes, etc.), family history, and various other demographic variables in order to calculate a score of relative AD risk (ArEDIGT). In our prototype, we seek to add coefficients for variables under factors E, D and I to multiply their sum by a coefficient for gender (that is higher in females than males given the sexual dimorphism of AD), which is further multiplied by a person’s age, i.e., Ar=(E+D+I)xGxT. Where the nature of a variable is unknown (ie. genetic variants under D), we employ surrogates (ie. family history). If validated, we plan to revise this mathematical prototype in its performance of discriminating AD from controls (and from PD) using both curated nested control studies and population cohorts. Furthermore, we could use this calculated risk score to compare it to the population of amyloid-beta-positive participants in ADNI to determine its sensitivity. |
| Additional Investigators | |
| Investigator's Name: | Michael Schlossmacher |
| Proposed Analysis: | We recently designed and began validating a hypothesis-driven formula to calculate the incidence of typical Parkinson’s disease (PrEDIGT Score). The model calculates the cumulative incidence rate using five factors, but without a motor exam: Environmental exposure history (E); DNA variants (D); initiation of lasting changes due to gene-environment interactions (I); gender (G); and time (T). Of note, risk factors for both AD and PD largely fall under the same 5 categories, albeit with varying parameters except for progression in age (under T). Using ADNI, we hope to build a predictive model for AD that could help test the specificity of the PrEDIGT score, and also identify the future incidence rate for dementia in healthy adults as well as those with MCI. Specifically, we plan to analyze medical history (ie. heart disease, hypertension, cholesterol, stroke, etc.), family history, and various other risk factors to calculate a score of relative AD risk (ArEDIGT). In our prototype, we seek to add coefficients for variables under factors E, D and I to multiply their sum by a coefficient for gender (that is higher in females than males given the sexual dimorphism of AD), which is further multiplied by a person’s age, i.e., Ar=(E+D+I)xGxT. If validated, we plan to revise this mathematical prototype in its performance of discriminating AD from controls (and from PD) using both curated nested control studies and population cohorts. Furthermore, we hope to compare the risk calculated with the amyloid beta-positive participants in ADNI to determine the sensitivity of our proposed model. |
| Investigator's Name: | Juan Li |
| Proposed Analysis: | We recently designed and began validating a hypothesis-driven formula to calculate the incidence of typical Parkinson’s disease (PrEDIGT Score). The model calculates the cumulative incidence rate using five factors, but without a motor exam: Environmental exposure history (E); DNA variants (D); initiation of lasting changes due to gene-environment interactions (I); gender (G); and time (T). Of note, risk factors for both AD and PD largely fall under the same 5 categories, albeit with varying parameters except for progression in age (under T). Using ADNI, we hope to build a predictive model for AD that could help test the specificity of the PrEDIGT score, and also identify the future incidence rate for dementia in healthy adults as well as those with MCI. Specifically, we plan to analyze medical history (ie. heart disease, hypertension, cholesterol, stroke, etc.), family history, and various other risk factors to calculate a score of relative AD risk (ArEDIGT). In our prototype, we seek to add coefficients for variables under factors E, D and I to multiply their sum by a coefficient for gender (that is higher in females than males given the sexual dimorphism of AD), which is further multiplied by a person’s age, i.e., Ar=(E+D+I)xGxT. If validated, we plan to revise this mathematical prototype in its performance of discriminating AD from controls (and from PD) using both curated nested control studies and population cohorts. Furthermore, we hope to compare the risk calculated with the amyloid beta-positive participants in ADNI to determine the sensitivity of our proposed model. |
| Investigator's Name: | Julianna Tomlinson |
| Proposed Analysis: | We recently designed and began validating a hypothesis-driven formula to calculate the incidence of typical Parkinson’s disease (PrEDIGT Score). The model calculates the cumulative incidence rate using five factors, but without a motor exam: Environmental exposure history (E); DNA variants (D); initiation of lasting changes due to gene-environment interactions (I); gender (G); and time (T). Of note, risk factors for both AD and PD largely fall under the same 5 categories, albeit with varying parameters except for progression in age (under T). Using ADNI, we hope to build a predictive model for AD that could help test the specificity of the PrEDIGT score, and also identify the future incidence rate for dementia in healthy adults as well as those with MCI. Specifically, we plan to analyze medical history (ie. heart disease, hypertension, cholesterol, stroke, etc.), family history, and various other risk factors to calculate a score of relative AD risk (ArEDIGT). In our prototype, we seek to add coefficients for variables under factors E, D and I to multiply their sum by a coefficient for gender (that is higher in females than males given the sexual dimorphism of AD), which is further multiplied by a person’s age, i.e., Ar=(E+D+I)xGxT. If validated, we plan to revise this mathematical prototype in its performance of discriminating AD from controls (and from PD) using both curated nested control studies and population cohorts. Furthermore, we hope to compare the risk calculated with the amyloid beta-positive participants in ADNI to determine the sensitivity of our proposed model. |
| Investigator's Name: | Kelsey Grimes |
| Proposed Analysis: | We recently designed and began validating a hypothesis-driven formula to calculate the incidence of typical Parkinson’s disease (PrEDIGT Score). The model calculates the cumulative incidence rate using five factors, but without a motor exam: Environmental exposure history (E); DNA variants (D); initiation of lasting changes due to gene-environment interactions (I); gender (G); and time (T). Of note, risk factors for both AD and PD largely fall under the same 5 categories, albeit with varying parameters except for progression in age (under T). Using ADNI, we hope to build a predictive model for AD that could help test the specificity of the PrEDIGT score, and also identify the future incidence rate for dementia in healthy adults as well as those with MCI. Specifically, we plan to analyze medical history (ie. heart disease, hypertension, cholesterol, stroke, etc.), family history, and various other risk factors to calculate a score of relative AD risk (ArEDIGT). In our prototype, we seek to add coefficients for variables under factors E, D and I to multiply their sum by a coefficient for gender (that is higher in females than males given the sexual dimorphism of AD), which is further multiplied by a person’s age, i.e., Ar=(E+D+I)xGxT. If validated, we plan to revise this mathematical prototype in its performance of discriminating AD from controls (and from PD) using both curated nested control studies and population cohorts. Furthermore, we hope to compare the risk calculated with the amyloid beta-positive participants in ADNI to determine the sensitivity of our proposed model. |
