Petersen, Ron

The Canadian Consortium on Neurodegeneration in Aging (CCNA) was created by the Canadian federal government through its health research funding agency, the Canadian Institutes for Health Research (CIHR), in 2014, as a response to the G7 initiative to fight dementia. Two five-year funding cycles (2014–2019; 2019–2024) have occurred following peer review, and a third cycle (Phase 3) has just begun. A unique construct was mandated, consisting of 20 national teams in Phase I and 19 teams in Phase II (with research topics spanning from basic to clinical science to health resource systems) along with cross-cutting programs to support them. Responding to the needs of researchers within the CCNA teams, a unique sample of 1173 deeply phenotyped patients with various forms of dementia was accrued and studied over eight years (COMPASS-ND). In the second phase of funding (2019–2024), a national dementia prevention program (CAN-THUMBS UP) was set up. In a short time, this prevention program became a member of the World Wide FINGERS prevention consortium. In this article, the challenges, successes, and impacts of CCNA in Canada and internationally are discussed. Short-term deliverables have occurred, along with considerable promise of impacts in the longer term. The creation of synergy, networking, capacity building, engagement of people with lived experience, and economies of scale have contributed to the considerable success of CCNA by all measures. CCNA is evidence that an organized “centrally-organized” approach to dementia research can catalyze important progress nationally and yield significant and measurable results.

AbstractBackgroundThe Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a longitudinal observational study of individuals across the dementia spectrum that validates biomarkers for clinical trials. The newest phase of the study, ADNI4, aims to substantially increase the inclusion of underrepresented populations to improve generalizability of ADNI results. Novel biomarkers, including a speech‐based cognitive assessment and blood‐based plasma biomarkers, are integrated into the study design as key participant screening measures.MethodTo help achieve recruitment goals, ADNI4 uses a new, multi‐phase approach to remotely enroll and screen participants. A large study cohort (aiming for 20,000+ participants) provide initial screening data via an online study platform including demographics, medical history related to study exclusionary criteria, and results from a speech‐based cognitive memory assessment (Novoic Ltd. Storyteller test) and a measure of subjective cognitive/functional decline (Everyday Cognition (ECog) 12‐item). Based on screening results, a subset of online study participants is referred to a Quest Diagnostics location to provide a blood sample for plasma biomarker analysis (aiming for 4,000+ participants). Finally, select participants are referred to ADNI clinical sites for in‐depth characterization (goal of up to 1,500 in‐clinic participants).ResultWe report ADNI’s initial approach for selecting participants from the online screening cohort to undergo plasma biomarker phenotyping, and subsequent selection to a clinical site. Participants from diverse demographic backgrounds (especially for race, ethnicity, and/or education) and those with a higher estimated likelihood of cognitive impairment are given priority in the referral selection process. Likelihood of cognitive impairment is assessed using results from subjective memory complaint questions, responses to the ECog 12‐item questionnaire, and the Novoic Storyteller results. Individuals with results showing high concordance across these measures are prioritized, as well as those who have subsequent plasma results indicating biomarker positivity.ConclusionWeighing the various study requirements and inclusivity goals increases the complexity of participant selection decision making, and is a time‐intensive process. As the ADNI study progresses we aim to automate the participant selection/referral process by developing algorithms that account and encode for changing study prioritizations and dynamics across numerous dimensions. Learnings from ADNI4’s new remote processes will help inform future clinical trial design.

AbstractBackgroundSmall vessel disease (SVD) is a common vascular contributor to cognitive impairment and dementia. In this work, we evaluated and compared non‐imaging measures that can predict variability in SVD related white matter (WM) damage in a population‐based sample.MethodWe identified 752 participants in the population‐based sample of Mayo Clinic Study of Aging (≥50 years of age), who had at least 2 diffusion tensor imaging (DTI) scans and white matter hyperintensity (WMH) assessments along with baseline demographics, systemic vascular health, lifestyle, motor impairment, behavioral symptoms, and plasma markers (Summarized in Figure 1). We computed the WM markers of SVD: fractional anisotropy of genu of the corpus callosum (Genu‐FA) representative of early WM changes, WMH representative of late WM changes, and Vascular WM score (combination of Genu‐FA and WMH). Using all available data, we fit linear mixed effect models and evaluated predictors of WM markers by considering each category of features independently (with demographics) as well as by combining all categories of featuresResultPlasma, gait, systemic vascular, and life‐style measures were significant predictors of all three WM markers of SVD (Figure 2). The predictors of early and late WM slightly differed such that there were no additional significant contributions from behavioral and plasma measures to Genu‐FA. In the final composite models shown in Table 1, higher gait speed, lower number of cardiovascular and metabolic conditions (CMC) and lower Unified Parkinson’s disease rating scale scores were associated with better Genu‐FA whereas higher CMC, Beck Anxiety Inventory scores (BAI), and glial acidic protein (GFAP) levels were associated with higher burden of WMH.ConclusionOur observations evaluated the non‐imaging markers of greater WM damage that are relevant to vascular contributions to cognitive impairment to dementia (VCID). We found that in addition to higher vascular risk, worse plasma GFAP, gait measures, and BAI scores were predictors of greater WM damage. Some non‐imaging markers were more associated with early WM changes and others with late WM changes.

Abstract We hypothesized that poorer cerebral perfusion could impair mobility, particularly at lower systolic blood pressure (SBP). Cerebral perfusion was measured via arterial spin-labelled (ASL)-MRI in community-dwelling adults of the Mayo Clinic Study of Aging. Usual-pace gait speed was assessed over 5.6 meters on an electronic mat. Linear regression models estimated cross-sectional gait speed associations with ASL and modifying effects of age and SBP, adjusting for sex and body mass index. Results report differences in gait speed (meter/second) per standard deviation (SD) lower ASL or SBP (mean=139.1 mmHg, SD=19.7 mmHg). Among 479 participants (aged 31-94 years; mean age 67.6 years; 44% women; mean gait speed 1.17m/s), relationships of ASL with gait speed varied by age and SBP (ASL-x-age interaction: p<.007; ASL-x-SBP interaction: p<.007). At age 65, 75 or 85 years and a SBP of 120mmHg, each SD lower ASL was associated with a 0.04 m/s (0.01,0.07), 0.07 m/s (0.04,0.10), and 0.09 m/s (0.05,0.13) slower gait speed at respective ages. At age 65, 75 or 85 years with low ASL (1 SD below the mean), each SD lower SBP was associated with 0.06 (0.02,0.09) m/s, 0.03 (0.01,0.06) m/s or .01 (-0.02,0.04) m/s slower gait speed, respectively. With high ASL (mean ASL+1SD), lower SBP trended towards faster gait at older age, e.g. estimated gait speed was 0.04 m/s (0.00,0.09) faster for each SD lower SBP at age 85years. Poorer cerebral perfusion may contribute to slower gait, particularly with lower SBP. The interrelations of cerebral perfusion, SBP, and age with mobility merit further study.

Abstract Background This study aimed to examine if the association of cerebral perfusion with gait speed differs across systolic blood pressure (SBP) and age. Methods Cerebral perfusion was measured via arterial spin labeled (ASL)-MRI among community-dwelling adults aged 31–94 years in the population-based Mayo Clinic Study of Aging. Usual gait speed was assessed over 5.6 meters on an electronic mat. Sex- and body mass index (BMI)-adjusted linear regression models estimated cross-sectional gait speed associations with ASL and modifying effects of age and SBP using 3-way and 2-way interaction terms between continuous age, SBP, and ASL. Results report estimated differences in gait speed per standard deviation (SD) lower ASL for exemplar SBPs and ages. Results Among 479 participants (mean age 67.6 years; 44% women; mean gait speed 1.17 m/s), ASL relations to gait speed varied by age (ASL-x–age interaction: p = .001) and SBP (ASL-x–SBP interaction: p = .009). At an SBP of 120 mmHg, each SD lower ASL was associated with a 0.04 m/s (95% confidence interval [CI]: 0.01, 0.07) slower gait speed at 65 years, 0.07 m/s (0.04, 0.10) at 75 years, and 0.09 m/s (0.05, 0.13) at 85 years. At an SBP of 140 mmHg, ASL associations with gait speed were attenuated to 0.01 (−0.01, 0.04), 0.04 (0.02, 0.06), and 0.06 (0.04, 0.09) m/s slower gait speed at ages 65, 75, and 85, respectively. Conclusion Poorer cerebral perfusion is associated with clinically meaningful slower gait speeds, particularly with older age, while higher perfusion markedly attenuates age differences in gait speed.

Abstract Few studies examined race-regional influences on mild cognitive impairment (MCI) and dementia risk factors. We examined 14 risk factors in 5 race-regions, e.g. cross-sectional age- and sex-adjusted relative prevalence ratios (RPR) from multinomial regression comparing associations of low education (<12yrs) with MCI and dementia (versus normal cognition) across 3 race-region groups, average age 76yrs: whites in the Mayo Clinic Study of Aging[MCSA, Olmsted County, MN—MCSA-MN-Whites); Atherosclerosis Risk in Communities(ARIC) whites in suburban Minneapolis, MN—ARIC-MN-Whites); ARIC-African Americans in Jackson, MS—ARIC-MS-AA. Among 3,787 MCSA-Whites, low education exerted a 2-fold risk for MCI, RPR=2.09(95%CI: 1.57,2.78). Conversely, low education was not a supported MCI risk factor for ARIC-MN-Whites (n=1,901—RPR=0.63(0.31,1.28) or ARIC-MS-AA, (n=1,416—RPR=.0.81 (0.60,1.10)), with substantially differential race-region effects. Low education RPRs for dementia also differed by race-region: RPR=4.43(2.68,7.31-MCSA-MN-W), RPR=0.70 (0.16,2.99-ARIC-MN-Whites), and RPR=2.54 (1.74,3.72-ARIC-MS-AA). Understanding why risk factors differ by race and region likely requires diverse samples and harmonized methods using culturally-appropriate assessments.

BackgroundThe majority of studies relating amyloid pathology with brain volumes have been cross‐sectional. Apolipoprotein ɛ4 (APOE ɛ4), a genetic risk factor for Alzheimer's disease, is also known to be associated with hippocampal volume loss. No studies have considered the effects of amyloid pathology and APOE ɛ4 together on longitudinal volume loss.MethodsWe evaluated whether an abnormal level of cerebrospinal fluid beta‐amyloid (CSF Aβ) and APOE ɛ4 carrier status were independently associated with greater hippocampal volume loss over 1 year. We then assessed whether APOE ɛ4 status and CSF Aβ acted synergistically, testing the significance of an interaction term in the regression analysis. We included 297 participants: 77 cognitively normal, 144 with mild cognitive impairment (MCI), and 76 with Alzheimer's disease.ResultsAn abnormal CSF Aβ level was found to be associated with greater hippocampal volume loss over 1 year in each group. APOE ɛ4 was associated with hippocampal volume loss only in the cognitively normal and MCI groups. APOE ɛ4 carriers with abnormal CSF Aβ in the MCI group acted synergistically to produce disproportionately greater volume loss than noncarriers.ConclusionBaseline CSF Aβ predicts progression of hippocampal volume loss. APOE ɛ4 carrier status amplifies the degree of neurodegeneration in MCI. Understanding the effect of interactions between genetic risk and amyloid pathology will be important in clinical trials and our understanding of the disease process.