Day, Gregory S

Abstract Background Prion diseases are universally fatal causes of rapid progressive dementia owing to the formation and spread of prions throughout the brain. Palliative care focuses on improving quality of life for patients with serious or advanced medical conditions. Timely access to palliative care is central to the care of patients with prion disease and other progressive neurodegenerative diseases. We sought to evaluate palliative care utilization and mode of delivery in patients with prion disease. Method Clinical data were extracted from the electronic medical records of patients with definite or probable prion disease diagnosed and managed across the Mayo Clinic Enterprise from January 2012 to August 2023. We systematically evaluated the frequency of symptoms and signs likely to benefit from palliative care and considered the relationship between these features and palliative care resource utilization, method of delivery, and outcomes. Result 172 patients with symptomatic prion disease were identified. 165 (94.8%) patients experienced ≥1 symptom/sign likely to benefit from palliative care, yet only 113 (65.7%) patients accessed palliative care resources during the illness course (hospice care: 39.7%; palliative care consultation: 14.9% inpatient, 6.9% outpatient; both: 3.4%). Lack of referral by the diagnosing clinician was the most common reason (55.9%) patients did not receive palliative care services. Early and predominant language dysfunction, behavioral changes, constipation, and emergency department visits or hospital admissions were independently associated with greater odds of referral for palliative care services. Goals of care (i.e., living environment, nutritional and hospitalization plans) and advanced care plans (i.e., healthcare surrogates, living will, code status, hospice) were more likely to be documented in patients receiving palliative care consultations (88.6% and 93.2%, respectively) versus hospice care alone (7.8% and 14.5%, respectively; p <0.001). Conclusion More than a third of patients with prion disease did not receive palliative care resources despite a high prevalence of symptoms/signs likely to benefit from palliative care. These findings highlight an opportunity to enhance the care of patients with prion disease by promoting early access to palliative care resources that may support symptom management, enhance caregiver support, and reinforce patient autonomy by clarifying goals of care and advance care planning.

Abstract Background Alzheimer Disease (AD) pathology evolves over decades, and understanding this progression is critical to the understanding of the disease and timing therapeutic interventions. Since individuals with Autosomal Dominant AD (ADAD) develop symptoms around the same age as their parent, it is possible to predict symptom onset and stage individuals by their estimated years to symptom onset (EYO). This approach does not generalize to other forms of AD, thus there is a pressing need for the timecourse of ADAD to be defined in broadly relevant terms. The objective of this project is to validate the Sampled Iterative Local Approximation (SILA) algorithm in a cohort with a known disease timecourse. SILA generates an estimate of time from amyloid positivity (A time ) based on longitudinal PET data. Method We evaluated A time in a longitudinal ADAD sample ( N = 316) with PET PiB data in three ways. First, we compared predicted age at amyloid positive (A+) to observed age at A+ for individuals who became A+ during enrollment. Next, using linear regression, we compared estimated age at A+ to estimated age at symptom onset (EYO=0). Finally, we used generalized additive models to compare the amount of variance in concurrent cognitive performance explained both A time and EYO. Result We observed a mean average error of 1.15 years between actual age at A+ ( N = 26) and the SILA‐predicted A time . Across all participants, SILA‐estimated age at A+ explained 39% of the variance in estimated age at symptom onset ( β = 0.918, p < 0.0001). Finally, we observed a nonlinear association between cognition and both A time and EYO. A time explained 19% of the variance in the general cognitive composite while EYO explained 43% of the variance. Conclusion SILA produces a valid estimate of time‐from‐amyloid positivity in ADAD. This work allows for disease stage in ADAD to be compared to staging for broad forms of AD, which was not previously possible using EYO. However, this work also illustrates that there is a high degree of heterogeneity in preclinical disease duration that is not explained by amyloid alone.

Abstract Background Bedside cognitive screening tools intend to reliably and efficiently detect cognitive impairment in research, clinical, and community settings. Black American (BA) and Hispanic/Latino (H/L) adults obtain lower performance on widely used screeners, largely driven by demographics and structural and social determinants of health (SSDoH). This study characterized the performance of the Montreal Cognitive Assessment (MoCA) performance in a cohort of cognitively normal ethnoracially diverse adults while considering cohort‐specific factors that contributed to misclassification of impairment. Method Cognitively normal BA and H/L participants completed a comprehensive neurological assessment, neuropsychological testing, and MRI and amyloid‐ and tau‐PET imaging as part of a longitudinal study of memory and aging at Mayo Clinic in Florida. Demographically adjusted MoCA cutoffs were applied to BA (MoCA ≤ 22) and WH/L (MoCA ≤ 24) subgroups. Hierarchical binary logistic regression assessed whether amyloid burden (Centiloid value) improved prediction of scoring below demographically adjusted MoCA cutoffs beyond demographic/SSDoH factors, including age, education, and Area Deprivation Index (ADI). Result Participants included 105 (47 WH/L, 58 BA/AA) cognitively normal adults (mean age = 64.71 years, SD = 8.91; 62.9% female) with average of 16.07 years of education (SD=2.35) and mild‐to‐moderate neighborhood disadvantage (mean ADI=47.60, SD=23.77). 40% of H/L participants (MoCA (≤ 24) and 17% of BA participants (MoCa ≤22) scored below demographically adjusted MoCA cut‐offs. For the H/L subgroup, the best fitting model included age, education, ADI, and amyloid burden ( R 2 = 0 .43, χ 2 = 10.98, p = 0.03). Age (OR=1.21, 95% CI: 0.986 – 1.487) and amyloid burden (OR = 0.83, 95% CI: 0.67 – 1.03) approached statistical significance as individual predictors ( p = 0.06, p = 0.09). In the BA/AA subgroup, the best fitting model included age, education and ADI only ( R 2 = 0.47, χ 2 = 9.06, p = 0.02), with education approaching significance as an individual predictor (OR=0.46, 95% CI: 0.21 – 1.01, p = 0.05). Conclusion Sociodemographic factors continued to drive low MoCA performance in cognitively normal individuals despite applying demographically adjusted cut‐off scores by subgroup. These factors differentially impacted performance among BA/AA and WH/L participants. Careful consideration of these factors is warranted to mitigate risk of overdiagnosing impairment based on cognitive screening across clinical and research settings.

Abstract Background African Americans (AA) are twice as likely to develop dementia than non‐Hispanic Whites but remain underrepresented in Alzheimer's Disease (AD) research. Recent studies have identified AA specific risk factors for AD. Therefore, targeted biomarkers and/or therapies may be needed to effectively treat AD in AA. In this study, we aimed to determine if genetic variants that show association with AD‐risk, plasma transcript or protein levels in AA, may serve as accurate and accessible AD biomarkers. Method In 189 clinically diagnosed AA AD cases and 183 AA cognitively unimpaired (CU) controls, we performed targeted DNA sequencing of 10 AD‐associated loci. Genetic variants at these loci were tested for association with corresponding plasma transcripts and total tau protein levels previously measured in this cohort using a custom nanoString panel and Simoa assays, respectively. Utilizing phased haplotypes (SHAPEIT4) from target sequencing, we inferred local ancestry (RFMix v2) using five superpopulations from the 1000 Genomes Project as anchors. Subsequently, we tested the association of each ancestry specific allelic dosages with endophenotypes using Tractor. In receiver operating characteristic (ROC) analyses, the most significant e/pQTLs were added sequentially to a base model that included age and sex to identify e/pQTLs that improved the accuracy to correctly classify AD cases and CU controls. Result Of the 5,112 variants that were tested, 70 were nominally associated with AD‐risk and with plasma transcript or protein levels. Seven of these 70 variants also showed nominally significant associations of AD‐risk and transcript levels with either African or East Asian local ancestry. ROC analysis of age, sex, 33 e/pQTLs in APOE , SORL1 , TLR4 , EPHA1 , ABCA7 , CLU , and CR1 , and plasma levels of APP, ABCA7, AKAP9, CD14, CLU, and APOE ‐ɛ4 dosage achieved 88.2% area under the curve to discriminate AD vs. CU, a 30.4% improvement over the base model that only included age and sex. Conclusion Plasma transcript levels and e/pQTLs are promising diagnostic biomarkers for AD that may improve accessibility and reduce costs for a more accurate diagnosis of AD.