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: | Davide Viggiano |
| Institution: | Univ. Molise |
| Department: | Medicine and Health Sciences |
| Country: | |
| Proposed Analysis: | Background. Numerous studies have underlined the cognitive effects of chronic kidney disease (CKD). Due to the widespread diffusion of CKD, it is important to understand how the worsening kidney filtration impacts on brain functions (Viggiano et al 2020a, 2020b). Furthermore, it is possibile that patients with Alzheimer disease (AD) and CKD have poorer cognitive functions and greater brain damage compared to AD patients without CKD. Aim of the study. Using a retrospective transversal approach we aim at identifying modifications in brain extracellular fluid diffusion due to an alteration in the glomerular filtration rate and proteinuria in patients with AD and controls. To this aim, we plan to compare cognitive and brain imaging data from four cohorts: (i) healthy control patients (ii) patients with AD (iii) patients with AD and CKD stage II-IV or proteinuria (iv) patients with CKD stage II-IV or proteinuria (without AD). By analyzing available renal biomarkers and brain structure in these cohorts, scanned with MRI, we hypothesize that the cognitive impairment induced by a decreased eGFR or proteinuria sums up to that induced by AD, thus resulting in a different clinical scenario. Methods: Patients selection. We plan to compare cognitive and brain imaging data from four cohorts: (i) healthy control patients (ii) patients with AD (iii) patients with AD and CKD stage II-IV or proteinuria (iv) patients with CKD stage II-IV or proteinuria (without AD). Male and female patients will be included in the study. Selection of subjects in the control population will be pursued using propensity score matching in R environment. Exclusion criteria. We will exclude patients with diabetes, systemic illness, patients outside the range 20-70years old, patients with large brain infarcts. Type of data needed for the study: age, gender, weight, arterial blood pressure, creatinine, BUN, glycemia, proteinuria, uric acid (if available), red blood cells count and hemoglobin, education (yrs), MMSE, ADAS-cog. eGFR will be derived using the CKD-Epi formula. Imaging data necessary for the study: Diffusion tensor imaging (DTI) sets (mean, radial and axial diffusivity maps) and conventional MRI images Image analysis. We will consider ROIs pertaining a slice at the level of the lateral ventricle body. The mean, radial and axial diffusivity maps will then be used to evaluate the the diffusivity along the direction of the perivascular space compared with those of projection fibers and association fibers as described elsewhere in detail (Taoka 2017) Statistical methods Baseline characteristics will be compared using Chi-square tests and one-way ANOVA. Path analysis will be used to compare the role of varous factors mediating the effects of renal function on brain parameters and cognition. The model will incorporate eGFR, proteinuria, MMSE. To model eGFR and proteinuria as predictors on brain data we will use multiple regression models using as outcome either (i) ADC at each voxel or (ii) ALPS-Index at each voxel or (iii) MMSE scores. Considerations on sample size. By considering, in the control population (Taoka 2017), typical values of directional diffusivity of 1±0.5 m2/sec (mean±SD), an effect size of 30% over the mean, an alpha rejection level of 0.05 and a statistical power of 80%, a minimum sample size would be 44 patients per group. This is feasible because, the analysis of previous publications shows more than 200 control patients and a similar number of patients with CKD without AD. Based on Rajagopalan et al. (2013) the database comprises more than 150 AD patients, of which we estimate about 30 patients with additional CKD. Expected results. We expect to demonstrate (or reject) the hyothesis that AD with CKD is a specific subtype of the disease, with different cognitive performances and neuroimaging features. Main Outcomes. The results will be published on international journals with impact factor, according to the guidelines of ADNI. Essential References Viggiano D, Wagner CA, Martino G, et al. Mechanisms of cognitive dysfunction in CKD [published online ahead of print, 2020 Mar 31]. Nat Rev Nephrol. 2020;10.1038/s41581-020-0266-9 Viggiano D, Wagner CA, Blankestijn PJ, et al. Mild cognitive impairment and kidney disease: clinical aspects. Nephrol Dial Transplant. 2020;35(1):10-17 Rajagopalan P, Refsum H, Hua X, et al. Mapping creatinine- and cystatin C-related white matter brain deficits in the elderly. Neurobiol Aging. 2013;34(4):1221-1230 Taoka T, Masutani Y, Kawai H, et al. Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer's disease cases. Jpn J Radiol. 2017;35(4):172-178 |
| Additional Investigators |
