Knopman, David S.

Abstract Background Some data suggest that neuroinflammation may be associated with pro‐inflammatory states in the body. Systemic, proinflammatory cytokines (TNFα, IL‐1, IL‐6, etc.) are produced by a variety of comorbidities. Neuroinflammation has been shown to be associated with the accumulation of amyloid in the brain (Becher, Heneka). We hypothesized that systemic cytokine production could be associated with neuroinflammation and thereby be related to brain amyloid accumulation. This study aims to evaluate the relationship between neuroinflammation, amyloid accumulation and systemic cytokines. Method 90 participants from the Mayo Clinic Study of Aging (MCSA) received a fasting cytokine panel over a 5‐year span where IL‐1, IL‐6, IL‐10, TNFa levels were measured. Participants were divided into four groups: cognitively unimpaired with normal levels of amyloid (CU A‐, n = 25), cognitively unimpaired with elevated amyloid (CU A+, n = 25), and mild cognitively impaired (MCI) with amyloid positive (MCI A+, n = 25), and Alzheimer's disease (AD) with elevated amyloid (AD A+, n = 15). To compare regional standardized uptake value ratio (SUVr) from neuroinflammation PET based on amyloid status (A+ vs A‐) and the four cytokines, Spearman's rank correlation and Fishers r‐to‐z transformations were used to assess pairwise group differences. Result Using Spearman's rank correlation and Fishers r‐to‐z transformations, no strong relationship was found between systemic inflammation and neuroinflammation. While comparing CUA‐ vs CUA+ in Figure 1, three regions (supplementary motor area, frontal superior medial lobe, and middle cingulate cortex) showed a significant difference ( p < 0.05). However, Figure 2 showed an overarching trend bias towards significance across most regions in IL10, IL6, and TNFa. Conclusion Although significant relationships between systemic inflammation, brain amyloid and neuroinflammation were limited, the data show an overarching trend bias in broad brain regions, suggesting that further analyses are needed, possibly with larger sample sizes, to assess the relationship of regional neuroinflammation and amyloid to systemic inflammation. Future analyses could also regress out factors like sex, age, and genotype to further proinflammatory cytokine research.

Abstract Background Cognitive resilience is the discrepancy in individuals with intermediate to high Alzheimer's disease neuropathologic change (ADNC) and normal cognition. Currently, the role of microglial activation in cognitive resilience is understudied. Method In the MCSA brain bank, high‐cognitively unimpaired (high‐CU) participants were defined as those performing in the upper quartile of their respective age groups based upon antemortem global cognition z‐scores, computed from memory, language, executive function, and visuospatial domain z‐scores. From the high‐CU group ( N = 118, global z‐scores from 1.55 to 1.78), cognitively resilient (CR) individuals were identified as those with intermediate to high ADNC upon neuropathologic evaluation. First, regression models were fit to evaluate global cognition as a function of age, sex, amyloid PET, and microglial‐specific genes of interest, including MS4A gene cluster SNPs. Second, a subset of CR participants was analyzed for postmortem ARM expression in the dorsolateral prefrontal cortex. Single immunohistochemistry was performed on formalin‐fixed, paraffin‐embedded sections using antibodies against beta‐amyloid (6F3D) and CD163 (10D6). Digital image analysis was performed on scanned sections (40x, Leica Aperio GT 450), and ARM (CD163%area) and beta‐amyloid (%area) measurements were obtained using QuPath. Cognitively impaired (CI) control group was matched on age, sex, ADNC level, and APOE4 status. Spearman correlations were performed between postmortem ARM and antemortem MRI, PET, plasma, and global cognition metrics. Result A third (39/118) of high‐CU cases showed intermediate/high ADNC (CR group). Furthermore, 28% of CR individuals harbored an MS4A6A SNP, which showed a protective effect on cognition via amyloid‐PET ( p = 0.043, Figure 1). Moreover, CR individuals harbored significantly higher ARM:amyloid than the CI group ( p <0.001, Figure 2) and fewer neuritic plaques ( p = 0.03, Figure 3). Antemortem white matter hyperintensities and global cognition were significantly correlated to ARM expression (r=0.556 [p=0.042] and r=0.648 [p=0.02], respectively). Conclusion Microglial specific MS4A6A SNP, which modulates microglial activation, was seen in a third of CR individuals. Postmortem analyses in CR individuals revealed increased ARM:amyloid, which was also associated with higher antemortem cognitive performance. These findings underscore the role of microglial activation in cognitive resilience. Future work is needed to investigate microglial protective mechanisms and links between vascular disease and microglial activation.

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.

Abstract Background The Aging and Cognitive Health Evaluation in Elders (ACHIEVE) randomized trial ( n = 977;ClinicalTrials.gov:NCT03243422) demonstrated that hearing intervention slowed 3‐year cognitive decline by 48% among a subgroup of participants. To further investigate differences by treatment with respect to cognitive benefit, we tested the hypothesis that hearing intervention is associated with improved neurodegeneration blood biomarkers 3‐years post‐randomization and assessed whether combining biomarkers and cognitive results improved power in the trial design. Method The ACHIEVE study is a multicenter, parallel‐arm, randomized trial (hearing intervention vs health education control) on 3‐year cognitive decline among adults 70–84years with untreated hearing loss and without substantial cognitive impairment. Participants were recruited from two populations: (1) a long‐standing cardiovascular health observational study (Atherosclerosis Risk in Communities [ARIC]), and (2) de novo community volunteers. Plasma was collected in year 3 in a subsample ( n = 540) while ARIC participants ( n = 164) also had baseline plasma. Glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) neurodegenerative biomarkers were derived using the Alamar CNS protein panel. Regression models estimated the association of treatment with 3‐year biomarkers and global cognitive differences in the combined sample and by recruitment source and annualized change from baseline in the ARIC sample. We included a simple composite score of the standardized outcomes (Table 2). Result Among the subsample, baseline characteristics were balanced by treatment. There were no significant differences on 3‐year neurodegeneration biomarker levels by treatment in the combined (Table 1) or de novo groups (Table 2). Among ARIC participants, intervention resulted in lower 3‐year GFAP (mean:‐0.415;95%CI:‐0.712,‐0.118) and NfL (mean:‐0.349;95%CI:‐0.652,‐0.046) (Table 2). Annualized change from baseline analyses among ARIC participants (Table 3) revealed intervention was associated with a slower rate of cognitive decline (difference‐in‐means:0.059;95%CI:0.018,0.100) and a slower rate of increase in GFAP (difference‐in‐means: ‐0.060;95%CI:‐0.112,‐0.009). Differences in estimates suggest combined score, relative to cognition alone, may increase power in clinical trials that show a positive effect (ARIC subgroup: p = 0.02vs. 0.08[Table 2] and p = 0.0009vs.0.048[Table 3]). Conclusion Hearing intervention was associated with positive 3‐year effects on neurodegeneration blood biomarkers in ARIC participants which parallels cognitive decline results. Results provides objective evidence of brain changes following hearing intervention and potential signal of a more powerful combined outcome approach for future brain health clinical trials.

Abstract The Atherosclerosis Risk in Communities (ARIC) study started in 1987‐1989 (visit 1), when participants from four U.S. communities were 45‐64yo. Participants have been seen at up to 10 additional in‐person visits with annual/semi‐annual telephone calls to ascertain clinical events, hospitalizations, and other health outcomes. In 2011‐2013, at ARIC visit 5, the ARIC Neurocognitive Study (ARIC‐NCS) was initiated for surviving participants, incorporating neuropsychological assessments at multiple in‐person visits and informant interviews to support cognitive adjudication. A subset of participants have undergone brain MRI with measurement of brain volumes, cortical thickness, arterial disease and vascular lesions, with a smaller subset having brain florbetapir PET. Blood‐based Alzheimer's disease (AD) biomarkers are available at multiple visits along with extensive measurement of vascular risk factors, vascular markers, and ‐omic (e.g.genomic and proteomic) data. This presentation will focus on the available data in ARIC, the methods underlying its collection and subsequent analysis, and some of the primary contributions of this study to the field. With 37 years of follow‐up, a great strength of ARIC is the ability to evaluate risk factors in midlife; ARIC studies have supported an association of midlife hypertension, diabetes, physical inactivity, and other vascular risk factors with reduced dementia risk, even among those with increased genetic risk for dementia. ARIC studies have demonstrated associations between stroke and dementia, and have linked vascular risk factors with blood‐based and imaging biomarkers of AD‐related dementias. ARIC has demonstrated the importance of hearing loss in dementia risk, and a subset of ARIC participants were recruited into (and were those who most benefited from the intervention of) the ACHIEVE study of hearing aid use for reduction of dementia. Proteomic studies have identified markers of dementia risk when evaluated in midlife. Recently, ARIC data informed updated estimates on lifetime risk of dementia, where it was estimated that among individuals surviving beyond age 55, 42% would develop dementia over their lifetime through age 95. ARIC studies continue to focus on risk factors for dementia, particularly now in the oldest old, and on contributors to cognitive reserve and resilience. ARIC follows a model of data sharing and broad collaboration.

Abstract Background Alzheimer’s Disease (AD) progression is conceptualized as a cascade of sigmoidal (S‐shaped) curves, with biomarkers shifting sequentially from normal to abnormal states. Estimating these curves requires sophisticated modeling, with challenges including (1) selecting appropriate sigmoidal‐type functions, (2) achieving algorithmic convergence, and (3) producing clinically useful parameters. We compare commonly used sigmoidal‐functions and effects on novel estimates of early and late AD‐biomarker progression ages. Method Using 3,999 Amyloid PET (PiB SUVR) measurements from 1,367 Mayo Clinic Study of Aging participants, we compared model fits (AIC, BIC) between logistic and hyperbolic functions using Non‐Linear Mixed Effect (NLME) models with subject‐specific age‐shifts. We defined novel “initial” and “maximum” acceleration points (IAP and MAP) to compare clinically “earlier” and “later” AD‐progression ages between sigmoidal‐curves. The MAP uses the maximum second‐derivative point, reflecting the age of fastest acceleration in biomarker accumulation. The IAP is defined as the first point where the second derivative of the sigmoidal‐curve exceeds a clinical threshold (here, 5% velocity increase per year), indicating onset age of early accelerated biomarker accumulation. Estimates for C 2 N plasma p ‐tau217 were also attempted (1,306 measurements on 457 participants). Models adjusted for sex and APOE4‐positivity. Result In the larger PET sample, the hyperbola had lower AIC/BIC (better fit), but showed a decreasing early PiB‐SUVR trend (Table 1A, Figure 1). Ages of late MAP were comparable, as were sex and APOE4+ related PiB‐SUVR‐curve shifts. Age of early AD‐onset was estimated 1.5 years earlier for logistic (IAP=63.6) versus hyperbola (IAP=65.1) functions. The logistic function always converged, while the hyperbolic function did not converge for p ‐tau217 in the smaller C 2 N sample. Early‐onset and late‐progression ages were 7‐8 years later for p ‐tau217 versus PiB‐SUVR using logistic‐curves. Conclusion Late AD‐progression and covariate‐related shifts in progression were comparable across models, suggesting robustness to sigmoid choice. In contrast, estimates of early AD‐onset may be more dependent on functional form. While logistic‐sigmoids inherently increase monotonically, hyperbolas do not, here estimating a decline in initial amyloid trend. Future work may explore adding biologically informed constraints to flexible sigmoidal‐functions, and alternative Bayesian approaches, to optimize both model fit and estimation of early AD‐onset age.

Abstract Background Use of software to “de‐face” research brain images has grown as advances in automated face recognition have made it increasingly possible to re‐identify participants. Imaging studies of Alzheimer’s Disease and related diseases have included more participants from historically underrepresented groups, but de‐facing software have primarily been validated using images from mostly non‐Hispanic White participants, and its performance has not been directly compared across ethnoracial groups. Method From The Health and Aging Brain Study: Health Disparities (HABS‐HD) study, we constructed a sample of 305 T1‐weighted MRI from HABS‐HD baseline visits, matched across three ethnoracial groups (non‐Hispanic White (NHW, n = 104), non‐Hispanic African American (AA, n = 104), Mexican American (MA, n = 97)) according to age‐decade, sex, cognitive status (impaired/unimpaired), and MRI scanner model. We ran mri_reface 0.3.5 on each image, and we ran FreeSurfer 7.4.1 on each unmodified image and on each de‐faced output. We then compared regional gray matter (GM) volume and cortical thickness measurements by subtracting each de‐faced image measurement from the corresponding unmodified image measurement. We compared these differences between de‐faced and unmodified images across the three matched groups using GLM analyses adjusted for age, sex, and cognitive status, and Bonferroni corrected. Result When aggregating across all brain regions, differences in cortical thickness measures due to de‐facing were significantly different between the NHW and MA groups, and between AA and MA, but not between NHW and AA (Figure 1). Differences between these group means were <0.005mm. For GM volume, differences were significant between AA and MA (magnitude < 0.2%), but not between NHW and AA or NHW and MA (Figure 2). Examining each region individually (Figure 3), cortical thickness differences were significant for only 4 regions (all with magnitudes <0.03mm), and GM volume differences were significant for only 6 regions (all with magnitudes <2%). Conclusion Effects of mri_reface on FreeSurfer brain measurements were significantly different across ethnoracial groups. Our future work will optimize mri_reface to eliminate these differences. However, the magnitudes of these differences are sufficiently small that they are unlikely to have a practical effect on analyses of data using current versions.

Abstract Background Plasma neurofilament light chains (NfL) and glial fibrillary acidic protein (GFAP) are blood‐based biomarkers of axonal injury and astrocytic activation, respectively, that are elevated in those with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) relative to cognitively unimpaired (CU) individuals. Neurite orientation dispersion and density imaging (NODDI) is a multishell diffusion MRI method capable of evaluating tissue microstructure at the axonal and dendritic level. We evaluated associations between temporal cortex orientation dispersion index (ODI), neurite density index (NDI) and isotropic volume fraction (ISOVF) and plasma NfL and GFAP concentrations. Method We included Mayo Clinic Study of Aging (MCSA) participants (ages 60‐95 years) with diagnoses or CU ( n = 356) or MCI ( n = 60), and Alzheimer’s Dementia Research Center (ADRC) participants with dementia ( n = 51). NODDI imaging was obtained on a 3T Siemens Prisma scanner with 2 mm isotropic resolution. NfL and GFAP were measured using the Simoa® Neurology 4‐Plex E Advantage kit. We fit separate linear regression models within each MCALT_ADIR122 cortical ROI with regional NODDI as the primary predictor and log‐transformed plasma concentration as the outcome. Models were adjusted for age, sex, BMI, cortical thickness (or hippocampal & amygdalar volume) and total intracranial volume. Result The Table displays demographic data. Figures 1 & 2 show the relationship of NDI and ISOVF measures to plasma NfL concentrations across participants in individual ROIs (similar findings were noted with GFAP). A significant inverse relationship between NfL concentrations and NDI was observed in hippocampus, amygdala, entorhinal cortex, parahippocampal gyrus as well as all temporal neocortical regions except the superior temporal gyrus ( p <0.001) (Figure 1), whereas higher NfL concentrations were associated with higher ISOVF in all regions ( p <0.001) (Figure 2). Fewer significant associations were also seen with ODI. After adjusting for age, cortical thickness/volume and other confounds, significant associations with NDI remain, particularly in hippocampal and amygdalar regions. Conclusion NODDI‐derived measures (NDI and ISOVF) within the temporal lobe cortical regions demonstrate significant associations with plasma biomarkers of axonal and astrocytic dysfunction that are elevated during cognitive decline. This association provides preliminary validation of the utility of NODDI in assessing cortical microstructural changes associated with cognitive decline.

Abstract BACKGROUND Understanding long-term sequelae of cancer care is critical. Cognitive impairment is a common and disabling survivorship issue. Because sex influences tumor biology, therapy exposure, and dementia risk, we examined whether cancer care for a prior malignancy alters late-life cognitive outcomes differently in males and females. METHODS We assessed adults ≥50 years, cognitively normal at study entry, from a longitudinal, population-based cohort (Mayo Clinic Study of Aging). Participants were linked to our institutional cancer registry (excluding non-melanoma skin cancers). Mild cognitive impairment (MCI) and dementia were adjudicated by consensus. We fitted multivariable Cox proportional-hazards models to estimate how prevalent cancer diagnosis influenced the risk of developing MCI/dementia. Sex-specific effects were determined by incorporating a cancer×sex interaction term and by generating separate hazard ratios. Analyses were controlled for age, APOE4 status, education, and presence of vascular/metabolic conditions. RESULTS 5793 MCSA participants (female: 49.4%, APOE4 positive: 27.5%) were included in the study, with a mean age of 73 years at study entry. Incident MCI was recorded in 866 participants, and 737 were diagnosed with incident dementia. About 20% (n=1176) of the cohort had a cancer diagnosis, most commonly prostate (n=423) and breast (n=277). There was no independent relationship between history of cancer and risk for MCI or dementia. We found that males with cancer had an increased risk for MCI (HR 1.28, 95% CI 1.04-1.57, p=0.003) and dementia (HR 1.29, 95% CI 1.04-1.6, p=0.053). Conversely, females with cancer had a lower risk for MCI (HR 0.79, 95% CI 0.61-1.01, p=0.063), although this was not statistically significant. CONCLUSIONS Our data suggests male cancer survivors may be more susceptible to cognitive impairment, independent of age, APOE4 status, education, and vascular/metabolic burden. Elucidating the hormonal, treatment-related, and systemic pathways driving this should be a priority for future mechanistic studies.