Jack, Clifford

Abstract Introduction The relationship between sleep and brain metabolism remains unclear. This study aimed to examine whether overnight sleep features, using a home sleep apnea system based on peripheral arterial tonometry, are associated with regional FDG-PET changes in older adults. Methods We analyzed cognitively unimpaired adults (≥60 years) from the Mayo Clinic Study of Aging (MCSA) cohort who were not receiving sleep apnea treatment. Participants underwent overnight sleep assessment with the WatchPAT system, followed by FDG-PET imaging the next morning. FDG-PET data were co-registered to 3T MRI and automatically segmented into 122 anatomical regions. PET signal was referenced to the pons (SUVR). We computed voxel-weighted median FDG signal across 47 bilateral cortical and subcortical regions of interest (ROIs), excluding brainstem and cerebellum. Associations between sleep architecture and sleep apnea/hypoxemia burden and regional metabolism were assessed using partial Spearman’s rank correlation, adjusting for age. Results Thirty-seven participants were included (70.3% female; median [IQR] age: 72.0 [68.0–78.0] years). Wake after sleep onset (WASO) showed widespread negative associations with FDG uptake in cortical regions (p< 0.05), particularly the ventromedial/ventrolateral (rs=–0.47 to –0.33) prefrontal cortex, lateral temporal (rs=–0.38 to –0.33), and occipital (rs=–0.47 to –0.37) cortices. Total sleep time (TST) was positively associated with medial structures (p< 0.05), including the posterior cingulate (rs=0.39), retrosplenial cortex (rs=0.48), hippocampus/parahippocampal (rs=0.34 to 0.36), calcarine cortex (ρ=0.38), thalamus (rs=0.37), and caudate (rs=0.43). Hypoxic burden was positively associated with medial occipital regions (rs=0.35 to 0.39) and AHI-4% with cuneus (rs=0.33). Regional gray matter volume did not mediate these associations. WASO correlated with Insomnia Severity Index (rs=0.43, p=0.008) and self-reported refreshing sleep (rs=0.046). Conclusion Overnight sleep macrostructure features (WASO and TST) and hypoxic burden are significantly associated with brain metabolism, potentially underlying symptoms of sleep disturbance and cognitive aging. Support (if any) Mayo Clinic Alzheimer’s Disease Research Center Development Award (P30 AG062677), R01 AG056366, U01 AG006786, R01 AG056366, R01 AG034676, R37 AG011378, R01 AG041851, R01 NS097495, R01 AG068206, P30 AG062677, R01 HL065176. CLARA project work with funding from the European Union’s HORIZON EUROPE research and innovation programme (No 101136607).

Abstract Introduction Sleep-disordered breathing (SDB) is a potentially modifiable risk factor for the prevention of dementia, of which Alzheimer’s disease (AD) is the most common cause. However, there is a lack of longitudinal evidence relating SDB to AD pathology >20 years later. We examined the association between late-midlife SDB and late-life brain β-amyloid (Aβ) deposition—a hallmark of AD—assessed approximately 23 years later. Methods The Atherosclerosis Risk in Communities (ARIC) Study is an ongoing prospective cohort study initiated in 1987. Participants at two ARIC sites (Washington County, Maryland; Minneapolis, Minnesota) with primarily White participants also completed polysomnography (PSG) in the Sleep Heart Health Study (SHHS) in 1995-98. SDB measures included apnea-hypopnea index (AHI; number of apneas + hypopneas with ≥3% oxygen desaturation/hour of sleep), arousal index, and percentage of total sleep time in apnea, in hypopnea, and with oxygen saturation < 90%. A subset of participants completed florbetapir positron emission tomography (PET) scans through several ARIC ancillary studies (2011-2025 including the ARIC Sleep-PET study). We defined Aβ deposition as a global standardized uptake value ratio (SUVR) >1.2 (vs. SUVR ≤1.2). We used Poisson regression with robust standard errors to estimate relative risk (RR) of Aβ deposition by SDB, adjusting for demographics and vascular risk factors, and applied inverse probability attrition weighting to account for selective drop-out. Results Among 186 dementia-free participants (mean age=59 years at PSG and 82 years at PET scan, 58% female, 98% White), every 10% increase in total sleep time in hypopnea was associated with higher risk of Aβ deposition (RR=1.40, 95% confidence interval [CI]: 1.03, 1.91). When compared to no SDB (AHI < 5), moderate to severe SDB (AHI ≥15) was associated with elevated Aβ deposition (RR=2.36, 95% CI: 1.22, 4.57); mild SDB (AHI 5 to < 15) was not (RR=1.05, 95% CI: 0.53, 2.07). Conclusion SDB in late midlife was associated with greater risk of Aβ deposition approximately 23 years later. SDB may play a role in the early stages of AD pathogenesis. Future longitudinal studies are needed to optimize strategies for screening and treating SDB for dementia prevention. Support (if any) Supported in part by RF1AG050745.

Objective: Epidemiological data have suggested that late-life depression (LLD) occurs as a consequence of cerebrovascular and neurodegenerative disorders, but there is also some evidence suggesting that the relationship may occur in the other direction, namely that LLD may be a risk factor for cerebrovascular and neurodegenerative disorders. However, the neurobiological basis of these associations remain unclear. This study investigated the associations of LLD with markers of cerebrovascular pathology, brain volumes, and cortical thickness from 3-Tesla brain magnetic resonance imaging (MRI). Methods: Participants from the Atherosclerosis Risk in Communities Study received brain MRI scans in late-life (ages 67-90). The following neuroimaging outcomes were assessed: cortical infarcts, lacunar infarcts, any microhemorrhages, lobar microhemorrhages, subcortical microhemorrhages, white matter hyperintensity volume, total and regional brain volumes (z-scores), and total average and regional cortical thickness (z-scores). At the same visit, LLD was evaluated with the 11-item Center for Epidemiological Studies Depression Scale (CES-D) and defined as CES-D score≥9. Associations between LLD and neuroimaging outcomes were examined using logistic and linear regression models, and were adjusted for age, race-center, sex, education level, vascular risk factors, stroke, cognitive status, APOE e4 carrier status, and total intracranial volume when assessing volumes. Results: Among 1,857 participants, the median age was 76 years, 59.5% were female, 28.3% were Black, and 6.8% had LLD. LLD was not associated with infarcts or microhemorrhages. There was no evidence of an association between LLD and total white matter hyperintensity volume or total average cortical thickness. However, LLD was independently associated with smaller volume (SD) of the rostral anterior cingulate (b 1 : -0.18; 95% CI: -0.33, -0.04) and greater cortical thickness (SD) in the medial orbitofrontal cortex (b 1 : 0.19; 95% CI: 0.01, 0.38), though effect sizes were small and did not survive correction for multiple comparisons. Conclusion: Findings from this study suggest LLD is not cross-sectionally associated with neuroimaging markers of cerebrovascular disease. While LLD may be linked to subtle volume and cortical thickness changes in specific regions of interest, future longitudinal research is warranted to validate and clarify these associations and explore their potentially bidirectional relationship.

Background: Cerebral microbleeds (CMBs) are associated with incident dementia, but the impact of specific CMB patterns is unclear. CMBs in lobar regions suggest cerebral amyloid angiopathy (CAA), which is sometimes accompanied by superficial siderosis (SS), while subcortical CMBs indicate hypertensive origins. This study investigates the association between CMB patterns and dementia risk in the community-based longitudinal Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). Methods: All ARIC-NCS participants with a 3T research MRI at visit 5 (2011-13; aged 67-90) without intracerebral hemorrhage or dementia were included. CMB and SS presence and location were coded from T2* GRE sequences. Individuals were classified into one of four patterns: no CMBs, only subcortical, mixed (lobar and/or SS + subcortical), and only lobar and/or SS. Incident dementia diagnoses were defined by cognitive testing (in-person and telephone), informant interviews, and hospital discharge codes or death certificates. Cox proportional-hazards models assessed the association between the presence, patterns, and frequency of CMBs (0,1,2,3+), presence of SS, and incident dementia from visit 5 through 2020. Model covariates included demographics, vascular risk factors, and imaging markers of small vessel disease. Results: Among 1609 participants, 364 had CMBs (Table 1). Participants with CMBs tended to be older and female compared to those without CMBs. Compared to individuals with no CMBs, presence of any CMBs was associated with an increased risk of dementia (Table 2). Compared to individuals without CMBs, individuals with only lobar CMBs and/or SS had an increased risk of incident dementia, as did individuals with mixed CMBs, but individuals with subcortical-only did not. Participants with ≥3 CMBs of any variant had an increased risk of incident dementia vs no CMBs. Although SS was infrequent, its presence (vs no SS) was associated with an elevated risk of dementia. Conclusion: CMBs, particularly in a mixed or lobar and/or SS-only pattern, are linked to an increased risk of incident dementia. The number of recurrent CMBs and any SS also showed an increased dementia risk. These data support that dementia risk is high in individuals with a CAA-type CMB pattern, but also emphasize that a mixed pattern, not typical of CAA alone, is associated with a high risk of dementia. Further studies should evaluate mechanisms by which these different patterns contribute to dementia.

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.