Journal of Alzheimer's Disease最新文献

Dance or rhythmic movement-based training has demonstrated significant efficacy in addressing a range of motor and cognitive deficits associated with neurodegenerative diseases like Parkinson's and Alzheimer's diseases. Leveraging both human and non-human animal behavioral and neurobiological evidence, I hypothesize a possible untapped role of dance training in mitigating impairments in the motor control of speech, a complex sensorimotor behavior affected in these conditions. Here, this hypothesis is supported by an in-depth examination of motor speech deficits in Parkinson's and Alzheimer's diseases, at a behavioral, physiological, and neural level. Additionally, literature on the impact of dance training on behaviors and brain pathways possibly relevant to speech motor control in populations with neurodegenerative diseases is thoroughly reviewed. Synthesizing these findings, I propose repurposing dance as a novel treatment for motor speech deficits and outline specific experiments to test this hypothesis. By comprehensively investigating the full spectrum of the effects of a motor-based training, i.e., dance, on often overlooked motor-based behaviors, such as speech, we may uncover novel therapeutic avenues of a practice that has already shown promising implications.

Background: Mild cognitive impairment (MCI) represents a stage between cognitively normal and Alzheimer's disease. Despite much published research on MCI, there continues to be a knowledge gap of volumetric brain changes in MCI versus cognitively normal (CN) in racially diverse, community-based samples.

Objective: The study aimed to understand differences in volume of selected brain regions in individuals with MCI versus those who are cognitively normal.

Methods: This was a cross-sectional study with 1835 participants, which sampled all cognitively impaired participants (n = 667) and a subsample of cognitively normal participants from the ARIC neurocognitive study (ARIC-NCS). All individuals underwent a brain MRI. Two models (5 versus 22 regions of interest [ROI]) were built to analyze differences in brain volume between cognitively normal and MCI, and among 3 cognitive domains (memory, language, executive function). Using previous visits data, we estimated the standard deviations of 20-year cognitive decline equivalent to the difference in brain volume between MCI and CN.

Results: Every lobe was significantly smaller in individuals with MCI, with the largest difference observed in the temporal lobe. Moreover, there was a significant difference between MCI and CN in every subregion within the temporal lobe. The difference in volume between CN and MCI was equivalent to the total brain volume difference associated with a 1.24 standard deviation greater long-term cognitive decline.

Conclusions: Loss of volume in all cortical lobes, but particularly in the temporal lobe, was associated with MCI. Additionally, significant volume differences were observed in the temporal lobe in all three cognitive domains.

Background: Declining physical functionality is an indicator of cognitive impairment, distinguishing normal cognition (NC) from dementia. Whether this extends to pre-dementia stages is unclear.

Objective: Assess physical performance patterns, evaluate relationships with imaging biomarkers, and identify specific measures distinguishing NC, subjective cognitive decline (SCD) and mild cognitive impairment (MCI).

Methods: Group differences (78 NC, 35 SCD, and 41 MCI) in physical function (global function, balance, gait speed, step length, single leg support) were evaluated with logistic regression while distinguishing between MCI due-to-AD and MCI due-to-vascular etiology. Relationships with imaging biomarkers (cortical atrophy score, white matter hyperintensities volumes) were analyzed with ANCOVA.

Results: Participants were 68.6 ± 9.3 years old, had 16.2 ± 3.0 years of education, and 23% were ethnoracial minorities. Physical performance distinguished MCI from NC and SCD. Greater performance on the Mini Physical Performance Test (mini PPT) and balance were associated with lower odds of being SCD versus NC (OR_{mini PPT} = 0.73; 95% CI:0.56-0.97; OR_balance = 0.35, 95%CI:0.16-0.80). AD etiology accounted for most group differences in physical performance versus vascular etiology. Consistent associations between biomarkers, physical performance, and cognition were found.

Conclusions: Findings suggest that: 1) changes in mini PPT performance and balance may help detect cognitive impairments, as early as the SCD stage; 2) changes in gait speed, gait cycle parameters, and Timed Up-and-Go may indicate more significant cognitive impairment; 3) neuronal loss is linked to subtle changes in physical functionality as early as SCD. Physical performance may be a valuable tool in early dementia detection in clinical settings and could identify targets for early intervention.

The VA Million Veteran Program (MVP) is a nationwide initiative that seeks to examine how genes influence health and behaviors in military veterans. An article by Merritt et al. analyzing data from the MVP, developing and testing algorithms to query dementia and Alzheimer's disease (AD) diagnoses from the VA's electronic health record system and examining genetic factors, provides an extraordinarily important contribution to the dementia and AD fields. The analyses presented in the article show how large databases can be used to further understand dementia and AD, pointing the way for many more important advances for this field.

Background: Cognivue Clarity^® is an FDA-cleared computerized cognitive test to screen for cognitive impairment included in the Bio-Hermes Study to test blood-based and digital biomarkers' ability to screen for mild cognitive impairment (MCI) and Alzheimer's disease (AD). A subset of cognitively normal individuals have amyloid deposition (Preclinical AD) but no current assessment can identify these individuals in the absence of expensive biomarkers.

Objective: We examined differences in Cognivue Clarity performance between amyloid positive and amyloid negative individuals and whether Cognivue Clarity could differentiate True Controls (cognitively normal/amyloid negative), Preclinical AD (cognitively normal/amyloid positive), and MCI due to AD (MCI-AD, cognitively impaired/amyloid positive).

Methods: Cognivue Clarity was administered to all participants in the Bio-Hermes Study who also had amyloid PET and blood-based biomarkers. Performance was compared between biomarker-defined groups: True Controls (n = 297), Preclinical AD (n = 95), and MCI-AD (n = 113).

Results: Cognivue Clarity global scores distinguished amyloid positive individuals from amyloid negative individuals (p < 0.001) and differentiated True Controls versus Preclinical AD (p = 0.014) and Preclinical AD versus MCI-AD (p < 0.001). Three subtests [Shape Discrimination (p = 0.004), Visual Salience (p = 0.008), Adaptive Motor Control (p = 0.004)] and the 3-test mean (p < 0.001) differentiated True Controls from Preclinical AD. The 3-test composite correlated with Amyloid PET (r = -0.433) and pTau217 (r = -0.400). The 3-test mean identified Preclinical AD in both White and Black participants.

Conclusions: Cognivue Clarity, a 10-min computerized battery, screens for individuals with cognitive impairment, characterizes amyloid positive individuals, and identifies Preclinical AD. This has great potential as a cost- and time-effective strategy to screen and enroll in AD prevention trials.

Background: Understanding the sequential progression of cognitive decline in autosomal dominant Alzheimer's disease (ADAD) in the Latino population is crucial for enhancing early identification for targeted interventions. Given the tablet-based administration and increasing frequency of use in epidemiological research, validating this progression within the NIH Toolbox cognitive battery (NIHTB-CB) is important.

Objective: The first aim was to utilize an innovative Event-Based Modeling (EBM) analytic approach to estimate the sequence of cognitive declines in persons at risk for ADAD enriched for being of Latino origin. The second aim was to examine associations between EBM-derived estimates of cognitive disease severity and independent cognitive outcomes within carriers and noncarriers.

Methods: This cross-sectional observational study (N = 30) included 16 ADAD mutation carriers and 14 noncarriers who completed the NIHTB-CB in their primary language (n = 8 Spanish; n = 22 English). An EBM was constructed to compare ADAD mutation carriers and noncarriers on NIHTB-CB performance. We utilized linear regression to examine the associations between the EBM-derived cognitive-decline disease stage and independent outcomes (e.g., performance on the Cognitive Abilities Screening Instrument (CASI) and estimated years to dementia diagnosis).

Results: The EBM estimated that tests assessing episodic memory were the first to become abnormal in the sequence of ADAD-related cognitive decline. Each higher estimated cognitive-decline disease stage was associated with approximately a three-point decline in the CASI and two years closer to dementia diagnosis.

Conclusions: Findings support the EBM applied to the tablet-based NIHTB-CB to estimate the likely progression of cognitive decline in Latinos with ADAD.

Background: The prevalence of Alzheimer's disease or dementia in the elderly population has been increasing both nationally and globally. Males and females are impacted differently when it comes to cognitive health, and this can be influenced by various risk factors.

Objective: This study highlights the sociodemographic, chronic disease, and genetic biomarker risk factors associated with gender differences and cognitive impairments in the elderly population living in Cochran, Parmer, and Bailey counties of rural West Texas.

Methods: Cross tabulation, Pearson's chi-squared, two sample proportions, binary logistic regression, and multinomial logistic regression were utilized to analyze data. SPSS software was used to detect significant risk factors.

Results: Using a bivariate logistic regression, the age group 70 and above of males and females for the Cochran and Parmer counties was found significantly associated with cognitive impairment. Anxiety, depression, diabetes, and cardiovascular disease were found to be significantly associated with an increased risk of cognitive impairment in females in Parmer County. Gender differences were observed in Cochran County for smoking but females in Bailey County were found to be more tobacco-dependent compared to other counties. However, in Cochran County the prevalence of cognitive impairment with rates of 66% for males and 70% for females was observed to be significantly lower in hypertensive group who consumed modified diet.

Conclusions: Gender-based disparities in cognitive impairment are essential for gaining more insights into Alzheimer's disease or dementia prevention and advancement of healthcare and medical approaches in the underserved rural communities of West Texas.

This study evaluated the diagnostic performance of plasma biomarkers for Alzheimer's disease (AD) using an automated platform. In a cohort of 74 consecutive patients, plasma p-Tau181 levels were significantly higher in AD compared to non-AD groups and showed correlation with cerebrospinal fluid biomarkers. Plasma p-Tau181 demonstrated high diagnostic accuracy for AD, with an area under the curve of 0.854. The findings suggest that plasma biomarkers, particularly p-Tau181, could improve the accessibility and efficiency of AD diagnostics in clinical settings, offering a less invasive and cost-effective alternative to traditional methods.

Background: Survival after an Alzheimer's disease (AD) diagnosis is vital for patients, their families, caregivers, and healthcare providers. Hawaii, known for its diverse ethnic population, exhibits significant racial health disparities.

Objective: This study examined racial/ethnic and socioeconomic disparities in AD survival in Hawaii and developed machine learning models to predict overall survival using Hawaii Medicare data.

Methods: Nine years of Hawaii Medicare data were utilized to gather information on AD development after age 65, following patients to capture all-cause survival or until censoring. The study examined the effects of race/ethnicity and socioeconomic status (SES) on mortality risk. Cox regression analysis was conducted on overall survival, accounting for covariates. A Survival Random Forest was employed to model survival, incorporating K years of longitudinal health profiles.

Results: The study included 9393 AD subjects. Analysis revealed that Asian Americans (AA) had a later age at AD diagnosis (p < 0.001), with an average age of 85.9, compared to 82.7 and 83.3 years for whites and Native Hawaiians and Pacific Islanders (NHPI), respectively. Low SES showed a marginal increase in hazard (Hazard Ratio [HR] = 1.36, p < 0.001). After covariate adjustment, compared to AAs with better SES, increased hazards were found for their white counterpart (HR = 1.18, p < 0.001) and groups with low SES: AA (HR = 1.28, p < 0.001), white (HR = 1.51, p < 0.001), and NHPI (HR = 1.39, p < 0.001). The predictive model had a Concordance-Index of 0.82, showing reasonable predictability.

Conclusions: Racial/ethnic and SES disparities significantly influence AD onset and survival. Combined with longitudinal health status data, machine learning demonstrates reasonable predictability of survival.

Background: The introduction of therapeutics for Alzheimer's disease has led to increased interest in precisely quantifying amyloid-β (Aβ) burden for diagnosis, treatment monitoring, and further clinical research. Recent positron emission tomography (PET) hardware innovations including digital detectors have led to superior resolution and sensitivity, improving quantitative accuracy. However, the effect of PET scanner on Centiloid remains relatively unexplored and is assumed to be minimized by harmonizing PET resolutions.

Objective: To quantify the differences in Centiloid between scanners in a paired cohort.

Methods: 36 participants from the Australian Imaging, Biomarker and Lifestyle study (AIBL) cohort were scanned within a year on two scanners. Each participant underwent ¹⁸F-NAV4694 imaging on two of the three scanners investigated, the Siemens Vision, the Siemens mCT and the Philips Gemini. We compared Aβ Centiloid quantification between scanners and assessed the effectiveness of post-reconstruction PET resolution harmonization. We further compared the scanner differences in target sub-regions and with different reference regions to assess spatial variability.

Results: Centiloid from the Vision camera was found to be significantly higher compared to the Gemini and mCT; the difference was greater at high-Centiloid levels. Post-reconstruction resolution harmonization only accounted for and corrected ∼20% of the Centiloid (CL) difference between scanners. We further demonstrated that residual differences have effects that vary spatially between different subregions of the Centiloid mask.

Conclusions: We have demonstrated that the type of PET scanner that a participant is scanned on affects Centiloid quantification, even when scanner resolution is harmonized. We conclude by highlighting the need for further investigation into harmonization techniques that consider scanner differences.