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: | Jessie Nicodemus Johnson |
| Institution: | Pentara Corporation Inc |
| Department: | Biostatistics |
| Country: | |
| Proposed Analysis: | Alzheimer’s disease (AD) composite scores (ADCOMS, APCC, PACC, PACC-R, etc) predict change in clinical AD measures with a varying degrees of efficiency. As each composite utilizes different metrics to measure progression of AD over time, composites have different sensitivities to cognitive decline, functional decline and biomarker change. These may differ based on common AD comorbidities such as diabetes, osteoporosis, BMI, and hypertension. For example, different composites may be more sensitive to brain amyloid deposition associated with hypertension-related ischemia or dopaminergic degenerative mechanisms shared between AD and osteoporosis. We propose to use the ADNI datasets to investigate the association of clinical AD measures and composite scores and the association with diabetes, osteoporosis, BMI, and hypertension in individuals with and without AD. The association between change in clinical and composite measures over time with baseline comorbidities and measures of progression of comorbidities over time will be assessed and validated across the ADNI datasets (ADNI1, ADNI GO, ADNI2, and ADNI3). A clear understanding of how varying metrics and composites perform in relation to common comorbidities holds the potential to increase awareness of possible population confounders that are not generally accounted for in clinical studies. |
| Additional Investigators | |
| Investigator's Name: | Suzanne Hendrix |
| Proposed Analysis: | Alzheimer’s disease (AD) composite scores (ADCOMS, APCC, PACC, PACC-R, etc) predict change in clinical AD measures with a varying degrees of efficiency. As each composite utilizes different metrics to measure progression of AD over time, composites have different sensitivities to cognitive decline, functional decline and biomarker change. These may differ based on common AD comorbidities such as diabetes, osteoporosis, BMI, and hypertension. For example, different composites may be more sensitive to brain amyloid deposition associated with hypertension-related ischemia or dopaminergic degenerative mechanisms shared between AD and osteoporosis. We propose to use the ADNI datasets to investigate the association of clinical AD measures and composite scores and the association with diabetes, osteoporosis, BMI, and hypertension in individuals with and without AD. The association between change in clinical and composite measures over time with baseline comorbidities and measures of progression of comorbidities over time will be assessed and validated across the ADNI datasets (ADNI1, ADNI GO, ADNI2, and ADNI3). A clear understanding of how varying metrics and composites perform in relation to common comorbidities holds the potential to increase awareness of possible population confounders that are not generally accounted for in clinical studies. |
