Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease

Abstract

Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond APOE. Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months (p = 1.07e-5), and PHS was significantly more predictive than APOE alone (p = 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model p = 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating—Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both APOE and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

“…A more complete picture of brain changes in aging and AD would include imaging metrics from other modalities, such as perfusion imaging, resting state functional MRI (Wang et al, 2017), and radiotracer methods such as FDG-PET (Popuri et al, 2018), or amyloid-and tau-sensitive PET (Grothe et al, 2017;Phillips et al, 2018). Genetic and other 'omics' data could be analyzed as well, and may help to predict diagnostic classification and brain aging, when combined with other neuroimaging markers (Ding et al, 2018;Kauppi et al, 2018). While these data are all being collected as part of ADNI3 and other studies of brain aging, our focus here was on the variety of available dMRI measures, calculated using different protocols.…”

Polygenic Risk Scores in Neurodegenerative Diseases: a Review

“…The PRS predicted longitudinal clinical decline in older individuals that showed moderate to high depositions of amyloid beta and or tau [62] and in clinically diagnosed AD individuals [63]. Moreover, the AD PRS was found to predict the level and rate of memory loss in a sample of non-Hispanic whites from the Health and Retirement Study [64] and in the ADNI (Alzheimer's Disease Neuroimaging Initiative) cohort [65]. In the same work, the authors evaluated the AD PRS in non-Hispanic black participants and found similar results but with a weaker association [64].…”

Whole mouse brain connectomics

“…The majority of the methods and applications have come in the clinical domain. The human connectome project (Ugurbil et al, ; Van Essen et al, ), the ENIGMA project (Kochunov et al, ; Thompson et al, ; Thompson, Ge, Glahn, Jahanshad, & Nichols, ) and the ADNI consortium (Jack, Bernstein, et al, ; Jack, Barnes, et al, ; Kauppi et al, ) have all exploited dMRI to reveal new insights into brain structure, genetics, and pathology. Multiple groups are working at higher magnetic field (>7.0 T).…”