Stroke Incidence in Patients With Hypertension According to Cardiorespiratory Fitness.

BACKGROUND: Hypertension and physical inactivity are risk factors for stroke. The effect of cardiorespiratory fitness (CRF) on stroke risk in patients with hypertension has not been assessed. We evaluated stroke incidence in patients with hypertension according to CRF and changes in CRF. METHODS: We included 483 379 patients with hypertension (mean age±SD; 59.4±9.0 years) and no evidence of unstable cardiovascular disease as indicated by a standardized exercise treadmill test. Patients were assigned to 5 age- and sex-specific CRF categories based on peak metabolic equivalents achieved at the initial exercise treadmill test and in 4 categories based on metabolic equivalent changes over time (n=110 576). Multivariable Cox models, adjusted for age, and comorbidities were used to estimate hazard ratios and 95% CIs for stroke risk. RESULTS: During a median follow-up of 10.6 (interquartile range, 6.6-14.6) years, 15 925 patients developed stroke with an average yearly rate of 3.1 events/1000 person-years. Stroke risk declined progressively with higher CRF and was 55% lower for the High-fit individuals (hazard ratio, 0.45 [95% CI, 0.42-0.48]) compared with the Least-fit. Similar associations were observed across the race, sex, and age spectra. Poor CRF was the strongest predictor of stroke risk of all comorbidities studied (hazard ratio, 2.24 [95% CI, 2.10-2.40]). Changes in CRF reflected inverse and proportional changes in stroke risk. CONCLUSIONS: Poor CRF carried a greater risk than any of the cardiac risk factors in patients with hypertension, regardless of age, race, or sex. The lower stroke risk associated with improved CRF suggests that increasing physical activity, even later in life, may reduce stroke risk.

Relationship between Cerebral Microinfarcts and Dementia by Sex: Findings from a community-based Autopsy Study.

Cerebral microinfarcts are common in older adults and are associated with cognitive impairment. Less is known about sex-related variation in the relationship between cerebral microinfarcts and dementia in older adults, the examination of which was the objective of this study. This case-control study was based on the 727 participants (419 women) in the Adult Changes in Thought (ACT) autopsy data. Microinfarcts were ascertained by blinded board-certified neuropathologists, and dementia diagnoses were made by the ACT Consensus Diagnosis Conference per DSM-IV. Multivariable logistic regression models were used to estimate adjusted odds ratio (aOR) and 95% confidence interval (CI). Microinfarcts were present in 49% (356/727) of the participants, which was numerically higher in women: 51% (213/419) vs 46% (143/308). aOR (95% CI) for dementia associated with any microinfarct for female and male participants were 1.45 (0.91-2.30) and 1.24 (0.75-2.06), respectively (p for interaction, 0.34). Respective aORs (95%CIs) associated with ≥2 microinfarcts were 1.37 (0.79-2.36) and 1.53 (0.84-2.78), with interaction p, 0.84. Subcortical microinfarcts were present in 36% (138/381) and 23% (78/346) of patients with and without dementia (aOR, 1.65; 95% CI, 1.14-2.38). Respective aOR (95% CI) in female and male participants were 1.70 (1.03-2.82) and 1.59 (0.90-2.80), (p for interaction, 0.55). There was no association with cortical microinfarcts (aOR, 1.19; 95% CI, 0.83-1.69). These findings suggest that association between microinfarcts and dementia is primarily mediated by subcortical microinfarcts, but we found no evidence of sex-related variation. Future studies with greater power are needed to determine if the associations we found are replicable.

A Novel Explainable AI Method to Assess Associations between Temporal Patterns in Patient Trajectories and Adverse Outcome Risks: Analyzing Fitness as a Risk Factor of ADRD.

We present a novel explainable artificial intelligence (XAI) method to assess the associations between the temporal patterns in the patient trajectories recorded in longitudinal clinical data and the adverse outcome risks, through explanations for a type of deep neural network model called Hybrid Value-Aware Transformer (HVAT) model. The HVAT models can learn jointly from longitudinal and non-longitudinal clinical data, and in particular can leverage the time-varying numerical values associated with the clinical codes or concepts within the longitudinal data for outcome prediction. The key component of the XAI method is the definitions of two derived variables, the temporal mean and the temporal slope, which are defined for the clinical concepts with associated time-varying numerical values. The two variables represent the overall level and the rate of change over time, respectively, in the trajectory formed by the values associated with the clinical concept. Two operations on the original values are designed for changing the values of the two derived variables separately. The effects of the two variables on the outcome risks learned by the HVAT model are calculated in terms of impact scores and impacts. Interpretations of the impact scores and impacts as being similar to those of odds ratios are also provided. We applied the XAI method to the study of cardiorespiratory fitness (CRF) as a risk factor of Alzheimer's disease and related dementias (ADRD). Using a retrospective case-control study design, we found that each one-unit increase in the overall CRF level is associated with a 5% reduction in ADRD risk, while each one-unit increase in the changing rate of CRF over time is associated with a 1% reduction. A closer investigation revealed that the association between the changing rate of CRF level and the ADRD risk is nonlinear, or more specifically, approximately piecewise linear along the axis of the changing rate on two pieces: the piece of negative changing rates and the piece of positive changing rates.

Anti-Amyloid Therapy, AD, and ARIA: Untangling the Role of CAA.

Anti-amyloid therapies (AATs), such as anti-amyloid monoclonal antibodies, are emerging treatments for people with early Alzheimer's disease (AD). AATs target amyloid ß plaques in the brain. Amyloid-related imaging abnormalities (ARIA), abnormal signals seen on magnetic resonance imaging (MRI) of the brain in patients with AD, may occur spontaneously but occur more frequently as side effects of AATs. Cerebral amyloid angiopathy (CAA) is a major risk factor for ARIA. Amyloid ß plays a key role in the pathogenesis of AD and of CAA. Amyloid ß accumulation in the brain parenchyma as plaques is a pathological hallmark of AD, whereas amyloid ß accumulation in cerebral vessels leads to CAA. A better understanding of the pathophysiology of ARIA is necessary for early detection of those at highest risk. This could lead to improved risk stratification and the ultimate reduction of symptomatic ARIA. Histopathological confirmation of CAA by brain biopsy or autopsy is the gold standard but is not clinically feasible. MRI is an available in vivo tool for detecting CAA. Cerebrospinal fluid amyloid ß level testing and amyloid PET imaging are available but do not offer specificity for CAA vs amyloid plaques in AD. Thus, developing and testing biomarkers as reliable and sensitive screening tools for the presence and severity of CAA is a priority to minimize ARIA complications.

Relationships between Late-Life Blood Pressure and Cerebral Microinfarcts in Octogenarians: An Observational Autopsy Study.

Mid-life high blood pressure (BP) is a risk factor for cerebral microinfarcts. Less is known about the relationship between late-life BP and cerebral microinfarcts, the examination of which is the objective of the current study. This case-control study analyzed data from 551 participants (94.6% aged ≥80 years; 58.6% women) in the Adult Changes in Thought (ACT) study who had autopsy data on microinfarcts and four values of systolic and diastolic blood pressure (SBP and DBP) before death. Using the average of four values, SBP was categorized using 10 mmHg intervals; a trend was defined as a ≥10 mmHg rise or fall from the first to fourth values (average gap of 6.5 years). Multivariable-adjusted regression models were used to examine the associations of BP and microinfarcts, adjusting for age, sex, last BP-to-death time, APOE genotype, and antihypertensive medication use. Microinfarcts were present in 274 (49.7%) participants; there were multiple in 51.8% of the participants, and they were located in cortical areas in 40.5%, subcortical areas in 29.6%, and both areas in 29.9% of the participants. All SBP categories (reference of 100-119 mmHg) and both SBP trends were associated with higher odds of both the presence and number of microinfarcts. The magnitude of these associations was numerically greater for subcortical than cortical microinfarcts. Similar associations were observed with DBP. These hypothesis-generating findings provide new information about the overall relationship between BP and cerebral microinfarcts in octogenarians.

Hybrid Value-Aware Transformer Architecture for Joint Learning from Longitudinal and Non-Longitudinal Clinical Data.

Transformer is the latest deep neural network (DNN) architecture for sequence data learning, which has revolutionized the field of natural language processing. This success has motivated researchers to explore its application in the healthcare domain. Despite the similarities between longitudinal clinical data and natural language data, clinical data presents unique complexities that make adapting Transformer to this domain challenging. To address this issue, we have designed a new Transformer-based DNN architecture, referred to as Hybrid Value-Aware Transformer (HVAT), which can jointly learn from longitudinal and non-longitudinal clinical data. HVAT is unique in the ability to learn from the numerical values associated with clinical codes/concepts such as labs, and in the use of a flexible longitudinal data representation called clinical tokens. We have also trained a prototype HVAT model on a case-control dataset, achieving high performance in predicting Alzheimer's disease and related dementias as the patient outcome. The results demonstrate the potential of HVAT for broader clinical data-learning tasks.

Relationships between Cerebral Vasculopathies and Microinfarcts in a Community-Based Cohort of Older Adults.

Cerebral microinfarcts are associated with cognitive impairment and dementia. Small vessel diseases such as cerebral arteriolosclerosis and cerebral amyloid angiography (CAA) have been found to be associated with microinfarcts. Less is known about the associations of these vasculopathies with the presence, numbers, and location of microinfarcts. These associations were examined in the clinical and autopsy data of 842 participants in the Adult Changes in Thought (ACT) study. Both vasculopathies were categorized by severity (none, mild, moderate, and severe) and region (cortical and subcortical). Odds ratios (OR) and 95% CIs for microinfarcts associated with arteriolosclerosis and CAA adjusted for possible modifying covariates such as age at death, sex, blood pressure, APOE genotype, Braak, and CERAD were estimated. 417 (49.5%) had microinfarcts (cortical, 301; subcortical, 249), 708 (84.1%) had cerebral arteriolosclerosis, 320 (38%) had CAA, and 284 (34%) had both. Ors (95% CI) for any microinfarct were 2.16 (1.46-3.18) and 4.63 (2.90-7.40) for those with moderate (n = 183) and severe (n = 124) arteriolosclerosis, respectively. Respective Ors (95% CI) for the number of microinfarcts were 2.25 (1.54-3.30) and 4.91 (3.18-7.60). Similar associations were observed for cortical and subcortical microinfarcts. Ors (95% Cis) for the number of microinfarcts associated with mild (n = 75), moderate (n = 73), and severe (n = 15) amyloid angiopathy were 0.95 (0.66-1.35), 1.04 (0.71-1.52), and 2.05 (0.94-4.45), respectively. Respective Ors (95% Cis) for cortical microinfarcts were 1.05 (0.71-1.56), 1.50 (0.99-2.27), and 1.69 (0.73-3.91). Respective Ors (95% Cis) for subcortical microinfarcts were 0.84 (0.55-1.28), 0.72 (0.46-1.14), and 0.92 (0.37-2.28). These findings suggest a significant association of cerebral arteriolosclerosis with the presence, number, and location (cortical and subcortical) of microinfarcts, and a weak and non-significant association of CAA with each microinfarct, highlighting the need for future research to better understand the role of small vessel diseases in the pathogenesis of cerebral microinfarcts.

Characteristics and Predictors of Alzheimer's Disease Resilience Phenotype.

Alzheimer's disease (AD) is characterized by cognitive impairment in the presence of cerebral amyloid plaques and neurofibrillary tangles. Less is known about the characteristics and predictors of resilience to cognitive impairment in the presence of neuropathological evidence of AD, the focus of this study. Of 3170 adults age ≥65 years in the National Alzheimer's Coordinating Center (NACC) brain autopsy cohort, 1373 had evidence of CERAD level moderate to frequent neuritic plaque density and Braak stage V-VI neurofibrillary tangles. Resilience was defined by CDR-SOB and CDR-Global scores of 0-2.5 and 0-0.5, respectively, and non-resilience, CDR-SOB and CDR-Global scores >2.5 and >0.5, respectively. Multivariable logistic regression models were used to examine the independent associations of patient characteristics with resilience. There were 62 participants (4.8%) with resilience. Those with resilience were older (mean age, 88.3 vs. 82.4 years), more likely to be women (61.3% vs. 47.3%) and had a lower prevalence of the APOE-e4 carrier (41.9% vs. 56.2%). They also had a higher prevalence of hypertension, heart failure, atrial fibrillation, diuretic use, beta-blocker use, and APOE-e2 carrier status. Greater age at death, diuretic use, and APOE-e2 were the only characteristics independently associated with higher odds of the AD resilience phenotype (adjusted OR, 1.09; 95% CI, 1.05-1.13; p < 0.01; 2.00 (1.04-3.87), p = 0.04, 2.71 (1.31-5.64), p < 0.01, respectively). The phenotype of resilience to cognitive impairment is uncommon in older adults who have neuropathological evidence of AD.

Cardiorespiratory fitness and risk of Alzheimer's disease and related dementias among American veterans.

INTRODUCTION: Cardiorespiratory fitness (CRF) is associated with improved health and survival. Less is known about its association with Alzheimer's disease and related dementias (ADRD). METHODS: We identified 649,605 US veterans 30 to 95 years of age and free of ADRD who completed a standardized exercise tolerance test between 2000 and 2017 with no evidence of ischemia. We examined the association between five age- and sex-specific CRF categories and ADRD incidence using multivariate Cox regression models. RESULTS: During up to 20 (median 8.3) years of follow-up, incident ADRD occurred in 44,105 (6.8%) participants, with an incidence rate of 7.7/1000 person-years. Compared to the least-fit, multivariable-adjusted hazard ratios (95% confidence intervals) for incident ADRD were: 0.87 (0.85-0.90), 0.80 (0.78-0.83), 0.74 (0.72-0.76), and 0.67 (0.65-0.70), for low-fit, moderate-fit, fit, and high-fit individuals, respectively. DISSCUSSION: These findings demonstrate an independent, inverse, and graded association between CRF and incident ADRD. Future studies may determine the amount and duration of physical activity needed to optimize ADRD risk reduction.

Hybrid Value-Aware Transformer Architecture for Joint Learning from Longitudinal and Non-Longitudinal Clinical Data.

Transformer is the latest deep neural network (DNN) architecture for sequence data learning that has revolutionized the field of natural language processing. This success has motivated researchers to explore its application in the healthcare domain. Despite the similarities between longitudinal clinical data and natural language data, clinical data presents unique complexities that make adapting Transformer to this domain challenging. To address this issue, we have designed a new Transformer-based DNN architecture, referred to as Hybrid Value-Aware Transformer (HVAT), which can jointly learn from longitudinal and non-longitudinal clinical data. HVAT is unique in the ability to learn from the numerical values associated with clinical codes/concepts such as labs, and also the use of a flexible longitudinal data representation called clinical tokens. We trained a prototype HVAT model on a case-control dataset, achieving high performance in predicting Alzheimer’s disease and related dementias as the patient outcome. The result demonstrates the potential of HVAT for broader clinical data learning tasks.

Enhancing Clinical Data Analysis by Explaining Interaction Effects between Covariates in Deep Neural Network Models.

Deep neural network (DNN) is a powerful technology that is being utilized by a growing number and range of research projects, including disease risk prediction models. One of the key strengths of DNN is its ability to model non-linear relationships, which include covariate interactions. We developed a novel method called interaction scores for measuring the covariate interactions captured by DNN models. As the method is model-agnostic, it can also be applied to other types of machine learning models. It is designed to be a generalization of the coefficient of the interaction term in a logistic regression; hence, its values are easily interpretable. The interaction score can be calculated at both an individual level and population level. The individual-level score provides an individualized explanation for covariate interactions. We applied this method to two simulated datasets and a real-world clinical dataset on Alzheimer's disease and related dementia (ADRD). We also applied two existing interaction measurement methods to those datasets for comparison. The results on the simulated datasets showed that the interaction score method can explain the underlying interaction effects, there are strong correlations between the population-level interaction scores and the ground truth values, and the individual-level interaction scores vary when the interaction was designed to be non-uniform. Another validation of our new method is that the interactions discovered from the ADRD data included both known and novel relationships.

Medication-Wide Association Study Plus (MWAS+): A Proof of Concept Study on Drug Repurposing.

The high cost and time for developing a new drug or repositioning a partially-developed drug has fueled interest in "repurposing" drugs. Drug repurposing is particularly of interest for Alzheimer's disease (AD) or AD-related dementias (ADRD) because there are no unrestricted disease-modifying treatments for ADRD. We have designed and pilot tested a 3-Step Medication-Wide Association Study Plus (MWAS+) approach to rigorously accelerate the identification of drugs with a high potential to be repurposed for delaying and preventing AD/ADRD: Step 1 is a hypothesis-free exploration; Step 2 is mechanistic filtering; And Step 3 is hypothesis testing using observational data and prospective cohort design. Our results demonstrated the feasibility of the MWAS+ approach. The Step 1 analysis identified potential candidate drugs including atorvastatin and GLP1. The literature search in Step 2 found evidence supporting the mechanistic plausibility of the statin-ADRD association. Finally, Step 3 confirmed our hypothesis that statin may lower the risk of incident ADRD, which was statistically significant using a target trial design that emulated randomized controlled trials.

Alzheimer's Disease and Alzheimer's Disease-Related Dementias in Older African American and White Veterans.

BACKGROUND: Racial disparity in the epidemiology of Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD) has been reported. However, less is known about this disparity among Veterans. OBJECTIVE: To estimate the racial disparity in AD/ADRD among the Veterans. METHODS: Of the 5,413,418 Veterans≥65 years receiving care at the Veterans Health Administration (1999-2016), 4,045,269 were free of prevalent AD/ADRD, schizophrenia, or bipolar disorder at baseline. Of these, 432,469 were African American. Race was self-identified and incident AD/ADRD during 20 (median 6.7) years of follow-up was ascertained using International Classification of Diseases codes. RESULTS: Patients had a mean age of 70.4 (±6.6) years and 97.8% were men. Age-sex-adjusted incidence of AD/ADRD per 1,000 person-year was 19.3 and 10.8 for African American and white Veterans, respectively (age-sex-adjusted hazard ratio associated with African American race, 1.77; 95% confidence interval, 1.75-1.79; p < 0.0001). This association remained essentially unchanged after multivariable adjustment (hazard ratio, 1.67; 95% confidence interval, 1.65-1.69; p < 0.0001). Among the key baseline characteristics that were significant predictors of AD/ADRD in both races, stroke was a significantly stronger predictor among African Americans, and Hispanic ethnicity and depression among whites (p-value for all interaction,<0.0001). CONCLUSION: The findings of a higher incidence of AD/ADRD among African American Veterans is consistent with the findings in the general population reported in the literature, although the overall incidence appears to be lower than that in the general population. Future studies need to examine this disparity in incidence as well as the between-race heterogeneity in AD/ADRD risk.

Can motor function uncertainty and local instability within upper-extremity dual-tasking predict amnestic mild cognitive impairment and early-stage Alzheimer's disease?

In this study, we examined the uncertainty and local instability of motor function for cognitive impairment screening using a previously validated upper-extremity function (UEF). This approach was established based upon the fact that elders with an impaired executive function have trouble in the simultaneous execution of a motor and a cognitive task (dual-tasking). Older adults aged 65 years and older were recruited and stratified into 1) cognitive normal (CN), 2) amnestic MCI of the Alzheimer's type (aMCI), and 3) early-stage Alzheimer's Disease (AD). Participants performed normal-paced repetitive elbow flexion without counting and while counting backward by ones and threes. The influence of cognitive task on motor function was measured using uncertainty (measured by Shannon entropy), and local instability (measured by the largest Lyapunov exponent) of elbow flexion and compared between cognitive groups using ANOVAs, while adjusting for age, sex, and BMI. We developed logistic ordinal regression models for predicting cognitive groups based on these nonlinear measures. A total of 81 participants were recruited, including 35 CN (age = 83.8 ± 6.9), 30 aMCI (age = 83.9 ± 6.9), and 16 early AD (age = 83.2 ± 6.6). Uncertainty of motor function demonstrated the strongest associations with cognitive impairment, with an effect size of 0.52, 0.88, and 0.51 for CN vs. aMCI, CN vs. AD, and aMCI vs. AD comparisons, respectively. Ordinal logistic models predicted cognitive impairment (aMCI and AD combined) with a sensitivity and specificity of 0.82. The findings accentuate the potential of employing nonlinear dynamical features of motor functions during dual-tasking, especially uncertainty, in detecting cognitive impairment.

Cardiac sympathetic denervation and synucleinopathy in Alzheimer's disease with brain Lewy body disease.

Comorbid Lewy body pathology is very common in Alzheimer's disease and may confound clinical trial design, yet there is no in vivo test to identify patients with this. Tissue (and/or radioligand imaging) studies have shown cardiac sympathetic denervation in Parkinson's disease and dementia with Lewy bodies, but this has not been explored in Alzheimer's subjects with Lewy bodies not meeting dementia with Lewy bodies clinicopathological criteria. To determine if Alzheimer's disease with Lewy bodies subjects show sympathetic cardiac denervation, we analysed epicardial and myocardial tissue from autopsy-confirmed cases using tyrosine hydroxylase and neurofilament immunostaining. Comparison of tyrosine hydroxylase fibre density in 19 subjects with Alzheimer's disease/dementia with Lewy bodies, 20 Alzheimer's disease with Lewy bodies, 12 Alzheimer's disease subjects without Lewy body disease, 19 Parkinson's disease, 30 incidental Lewy body disease and 22 cognitively normal without Alzheimer's disease or Lewy body disease indicated a significant group difference (P < 0.01; Kruskal-Wallis analysis of variance) and subsequent pair-wise Mann-Whitney U tests showed that Parkinson's disease (P < 0.05) and Alzheimer's disease/dementia with Lewy bodies (P < 0.01) subjects, but not Alzheimer's disease with Lewy bodies subjects, had significantly reduced tyrosine hydroxylase fibre density as compared with cognitively normal. Both Parkinson's disease and Alzheimer's disease/dementia with Lewy bodies subjects also showed significant epicardial losses of neurofilament protein-immunoreactive nerve fibre densities within the fibre bundles as compared with cognitively normal subjects (P < 0.01) and both groups showed high pathologic alpha-synuclein densities (P < 0.0001). Cardiac alpha-synuclein densities correlated significantly with brain alpha-synuclein (P < 0.001), while cardiac tyrosine hydroxylase and neurofilament immunoreactive nerve fibre densities were negatively correlated with the densities of both brain and cardiac alpha-synuclein, as well as Unified Parkinson's Disease Rating Scale scores (P < 0.05). The clear separation of Alzheimer's disease/dementia with Lewy bodies subjects from Alzheimer's disease and cognitively normal, based on cardiac tyrosine hydroxylase fibre density, is the first report of a statistically significant difference between these groups. Our data do not show significant sympathetic cardiac denervation in Alzheimer's disease with Lewy bodies, but strongly confirm that cardiac nuclear imaging with a noradrenergic radioligand is worthy of further study as a potential means to separate Alzheimer's disease from Alzheimer's disease/dementia with Lewy bodies during life.

Do Parkinson disease subject and caregiver-reported Epworth sleepiness scale reponses correlate?

OBJECTIVE: Subjective excessive daytime sleepiness, commonly measured with the Epworth Sleepiness Scale (ESS), is associated with cognitive impairment in Parkinson disease (PD). Significant correlation between subject and informant responses has been reported in neurologically healthy individuals. We sought to assess this correlation in patients with PD. PATIENTS AND METHODS: 854 individuals in the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) had subject as well as informant-completed ESS completed within one year of a movement disorder exam and cognitive assessment. Correlations were evaluated using Spearman's rank correlation coefficients. RESULTS: Overall, 397/854(46.5 %) were female with mean age of 77.5 (SD 8.3). 572 (67 %) were cognitively normal (CogNL), 135 (15.8 %) had mild cognitive impairment (MCI) and 147 (17.2 %) dementia. Spearman R correlations (all with p < 0.001) between subject and informant ESS responses were 0.73 overall, 0.67 for the CogNL group, 0.79 for the MCI group, 0.79 for those with dementia. Of 175 with clinically probable PD, 115 (65.7 %) were CogNL, 38 had MCI, and 22 (12.6 %) dementia. For subjects with PD correlations (all with p < 0.001) were 0.65 for PD-CogNL, 0.83 for PD-MCI, and 0.70 for those with PD-dementia. CONCLUSION: These significant correlations between subject and informant-completed ESS can be useful in guiding clinical trials designed to assess efficacy of potential treatments for excessive daytime sleepiness for the general population and for patients with PD, even those having cognitive impairment.

Neuropathological Findings in Parkinson's Disease With Mild Cognitive Impairment.

OBJECTIVE: There are few neuropathological studies on Parkinson's disease with mild cognitive impairment (PD-MCI). Those published reveal coexisting Lewy body and Alzheimer's disease pathology. Our objective is to determine the pathology that underlies PD-MCI. METHODS: We used data from the Arizona Study of Aging and Neurodegenerative Disorders, a longitudinal clinicopathological study. Of 736 autopsied subjects with standardized movement and cognitive assessments, 25 had PD-MCI. Neuropathological findings, including Lewy body and Alzheimer's disease pathology, were compared in PD subjects with amnestic MCI (A-MCI) and nonamnestic MCI (NA-MCI). RESULTS: Significant pathological heterogeneity within PD-MCI was found. This included varying Lewy body stages, Alzheimer's disease pathology, and cerebral amyloid angiopathy. There was a significant increase in the severity of Lewy body pathology (meeting The Unified Staging System for Lewy Body disorders neocortical stage) in nonamnestic MCI (7/1, 63%) when compared with amnestic MCI (3/14, 21%, P = 0.032). CONCLUSION: Although a small study, distinct pathological changes may contribute to PD-MCI phenotype. © 2020 International Parkinson and Movement Disorder Society.

Unified Staging System for Lewy Body Disorders: Clinicopathologic Correlations and Comparison to Braak Staging.

This study was designed to correlate clinical findings with the extent of pathologic a-synuclein (aSyn) in the brain using the Unified Staging System for Lewy Body disorders (USSLB). Data from 280 cases from the Arizona Study of Aging and Neurodegenerative Disorders are presented. Each case had a complete USSLB staging and at least 1 full research clinical assessment, including subspecialty neurologist-administered movement and cognitive evaluation. Of the 280, 25.7% were cognitively normal, 8.6% had mild cognitive impairment, and 65.7% had dementia. All cases could be categorized into 1 of 5 USSLB stages (8.6% stage I-olfactory bulb only; 15.4% IIa-brainstem predominant; 13.6% IIb-limbic predominant; 31.8% III-brainstem and limbic; and 30.7% IV-neocortical) yet using the Braak staging system 70 cases (25.3%) could not be classified. Those with USSLB stages III and IV died at a younger age. Multiple measures of motor parkinsonism, cognitive impairment, hyposmia, and probable RBD were significantly correlated with increasing USSLB stage. We conclude that the USSLB is the most comprehensive staging system for all Lewy body disorders and allows for categorization and ranking of all brains with significant correlations to many motor and nonmotor clinical signs and symptoms.

Brain Lewy-Type Synucleinopathy Density Is Associated with a Lower Prevalence of Atherosclerotic Cardiovascular Disease Risk Factors in Patients with Parkinson's Disease1.

BACKGROUND: Some epidemiology studies suggest that atherosclerotic cardiovascular disease (ASCVD) risk factors increase the risk of developing Parkinson's disease (PD). However, conflicting data suggest lower rates of ASCVD in PD. OBJECTIVE: The objective of this study is to determine, with data from a longitudinal clinicopathological study, whether ASCVD risk factors are associated with a PD diagnosis and/or increased brain or peripheral load of Lewy-type synucleinopathy (LTS). METHODS: All subjects were followed to autopsy and neuropathological examination in the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND). Multivariable regression models, including age, gender, and smoking history, were used to investigate the association of a PD diagnosis or brain or submandibular gland LTS load with ASCVD risk factors. RESULTS: 150 subjects were included (PD n = 60, controls n = 90). Univariable comparisons and regression models showed a general trend to inverse associations. The multivariable odds ratio (OR) of brain LTS load for carotid artery disease was 0.93 (95% CI: 0.86 to 0.98; p = 0.02), for anticoagulant use 0.95 (95% CI: 0.90 to 0.99; p = 0.04) and for abnormal heart weight 0.96 (95% CI: 0.92 to 0.99; p = 0.01). Composite clinical and overall (clinical + pathology composite risk scores) composite risk scores were also significantly lower in the PD subjects (p = 0.0164 and 0.0187, respectively). Submandibular gland LTS load was not significantly related to ASCVD conditions. CONCLUSIONS: This study shows associations of higher brain LTS with lower prevalence of both clinical and pathological indices of ASCVD in PD subjects versus age-similar controls. We suggest that this is due to α-synuclein pathology-induced sympathetic denervation in PD.

Screening older adults for amnestic mild cognitive impairment and early-stage Alzheimer's disease using upper-extremity dual-tasking.

The purpose of the current study was to develop an objective tool based on dual-task performance for screening early-stage Alzheimer's disease (AD) and mild cognitive impairment (MCI of the Alzheimer's type). Dual-task involved a simultaneous execution of a sensor-based upper-extremity function (UEF) motor task (normal or rapid speed) and a cognitive task of counting numbers backward (by ones or threes). Motor function speed and variability were recorded and compared between cognitive groups using ANOVAs, adjusted for age, gender, and body mass index. Cognitive indexes were developed using multivariable ordinal logistic models to predict the cognitive status using UEF parameters. Ninety-one participants were recruited; 35 cognitive normal (CN, age = 83.8 ± 6.9), 34 MCI (age = 83.9 ± 6.6), and 22 AD (age = 84.1 ± 6.1). Flexion number and sensor-based motion variability parameters, within the normal pace elbow flexion, showed significant between-group differences (maximum effect size of 1.10 for CN versus MCI and 1.39 for CN versus AD, p < 0.0001). Using these parameters, the cognitive status (both MCI and AD) was predicted with a receiver operating characteristic area under curve of 0.83 (sensitivity = 0.82 and specificity = 0.72). Findings suggest that measures of motor function speed and accuracy within a more practical upper-extremity test (instead of walking) may provide enough complexity for cognitive impairment assessment.