bluuebunny/crossref_metadata_2025 huggingface dataset:
White matter myelination is associated with longitudinal changes in processing speed in normative aging
Authors: Gong Zhaoyuan , Bilgel Murat , Resnick Susan M. , An Yang , Bouhrara Mustapha
Published: December 2023

Background

Alterations in myelin may result in progressive disconnection of cerebral neural networks underlying age‐related cognitive decline and AD. Understanding the role of myelination in cognition may provide insights into the neurobiology of aging, help nominate new MRI biomarkers for longitudinal assessment, and identify targets for intervention in disease. Recently, we showed that low myelin content was associated with steeper longitudinal declines in memory, attention, verbal fluency, and executive function. However, its impact on changes in processing speed has not been established. We hypothesized that myelin content is associated with processing speed, given the central role of myelin in the speed of action potential propagation.

Method

121 cognitively unimpaired participants from the Baltimore Longitudinal Study of Aging and GESTALT studies were administered the Digit Symbol Substitution Test to measure processing speed over a mean follow‐up duration of 4.34±6.34 years (339 visits total) (Table 1). Myelin content was quantified cross‐sectionally using our advanced MRI method of myelin water fraction (MWF), a specific measure of myelin content, or longitudinal relaxation rate (R1
), a measure highly sensitive to myelin. Linear mixed‐effects models were used to assess associations between cross‐sectional myelin content in lobar white matter and retrospective longitudinal change in processing scores. Age, sex, race, time from MRI and its interaction with MRI measure were included as independent variables.

Result

Lower MWF or R1
values, that is, lower myelin content, in the temporal WM had trend‐level cross‐sectional associations with lower processing speed (P<0.06) (Table 2). Lower MWF or R1
values were associated with steeper declines in processing speed in all brain regions investigated (P<0.015), with all regions except for occipital WM in the MWF analysis meeting the Bonferroni threshold (P<0.01) (Fig.1, Table 2). Finally, as expected, age and time from MRI were negatively associated with processing speed (P<10−7).

Conclusion

We demonstrated statistically significant relationships between myelin content and the rate of change in processing speed among cognitively unimpaired adults. These results highlight the importance of white matter, specifically myelin integrity, in cognitive functioning, and suggest MWF and R1
as potential sensitive imaging biomarkers for predicting cognitive changes.
Neuron‐derived extracellular vesicles blood‐based diagnosis of synuclein TDP43 autophagy and synaptic dysfunction pathologies
Authors: Eitan Erez , Elgart Katya E , Rau Sara , Van Debrake Emma , Volpert Olga
Published: December 2023

Background

Stratifying dementia into different subgroups based on specific pathologies can facilitate the development of more effective treatments for subgroups of individuals with dementia. Effective stratification relies on reliable biomarkers. Neuron‐derived extracellular vesicles (NDEs) are lipid structures released by neurons into the extracellular space, and some get into the bloodstream. NDEs carry proteins and RNA that reflect their cell of origin and thus provide a promising biomarker platform.

Method

NeuroDex developed two methods. ExoSORT is an optimized NDE isolation platform by immunoaffinity, where NDEs are captured with antibodies against the neuronal‐specific markers GAP43 and NLGN3. LuminEV is an intact extracellular vesicle (EV) immunoassay based on the Luminex platform. Antibodies against a potential biomarker, general EV marker, and IgG (negative control) are multiplexed to generate a robust assay.

Result

The vesicular nature and size of NDEs were confirmed by electron microscopy and nanoparticle tracking. EV markers were ascertained by western blot and proteomic analyses, and capture specificity was demonstrated by significantly elevated neuron‐specific proteins and mRNA levels compared to procedural control (IgG). Capture efficiency (∼80%) and precision (CV < 20%) demonstrated recovery of spiked NDEs. Measurements of αSYN following ExoSORT with an MSD assay and on EVs with the LuminEV generated a separation with 83% sensitivity and 86% specificity (45 Parkinson and 30 healthy controls plasma samples). LC3, an autophagy marker, was also measured following ExoSORT and was significantly (P<0.03) elevated in PD and Alzheimer’s disease (AD) (P<0.02, N = 30) plasma samples. TDP43 was also significantly (P<0.004) higher in AD plasma samples following ExoSORT. The synaptic marker GLUR2 was measured by the LuminEV assays and was significantly (P<0.001) reduced in AD and PD plasma samples. Its level was also significantly (P<0.03) correlated with the patient’s cognitive score.

Conclusion

Immunoaffinity isolation of EVs from plasma samples according to their cell of origin is a promising biomarker platform. We demonstrate the NDEs isolation selectivity, specificity, and precision. We also show the platform’s potential to identify pathologies like αSYN, TDP43, autophagy, and synaptic dysfunction; this reflects the potential for patient stratification.
Predicting dementia onset in a US memory clinic using EHR data and machine learning
Authors: Adams Roy , Hu Jiazhen , Liu Anqi
Published: December 2023

Background

Predicting which patients are most likely to progress to dementia from clinical data remains an open problem with large implications for dementia care. Several predictive models have been developed using volunteer‐based study cohorts; however, practical implementations developed on high‐volume clinical data remain uncommon.

Method

We developed a model for predicting whether a patient would receive a dementia diagnosis in one or three years using electronic health record (EHR) data from patients of a US memory clinic.

Result

On a held out set of patient data, the developed model had an area under the receiver operating characteristic curve (AUROC) of 0.82 (95% CI: 0.73 – 0.88) and 0.81 (95% CI: 0.68 – 0.91) for predicting dementia within one and three years, respectively. The most predictive clinical features were prior neuropsychiatric and non‐dementia cognitive impairment diagnoses, hospital utilization, and cognitive test scores.

Conclusion

While the model’s predictive performance is roughly in line with previous studies using longitudinal data, our subsequent analyses highlight the important role of censoring in the development of dementia risk models and the importance of tailoring a model to specific predictive environments.
Cognitive performance and plasma ADAM10 levels in low‐educated older adults with normal cognition, mild cognitive impairment, and Alzheimer’s disease
Authors: de Carvalho Pelegrini Lucas Nogueira , Manzine Patricia Regina , Vatanabe Izabela Pereira , Grigoli Marina Mantellatto , Cominetti Marcia Regina
Published: December 2023

Background

ADAM10 has been investigated as a potential biomarker for Alzheimer’s disease (AD) (Monteiro et al., 2021). However, most of the production in the literature comes from high‐income countries, where the population usually has high educational attainment (Mukadam et al., 2019). Evidence in the literature suggests an effect of education on cognitive performance; thus, low‐educated older adults tend to have worse scores on cognitive tests. Also, low education has been associated with an increased risk of dementia (Mukadam et al., 2019; Suemoto et al., 2022). However, little evidence is available on plasma ADAM10 levels in a sample of older adults with low educational attainment.

Method

Participants were divided into three groups: Healthy (n = 33), MCI (n = 27), and AD (n = 30). Cognitive performance was assessed through MMSE and ACE‐R. SDS‐PAGE and Western‐Blotting were performed to obtain ADAM10 levels in plasma.

Result

Participants were primarily female (62.1%), the mean age was 74.5 (8.2), and the average years of education was 4.6 (4.3). Healthy participants performed better than the MCI group on MMSE (p = 0.04). Compared to the healthy participants, the AD group was older (p = 0.00); had more comorbidities (p = 0.02), was prescribed more medications (p = 0.00); had worse performance both on MMSE (p = 0.00) and on ACE‐R (p = 0.003); and had higher levels of plasma ADAM10 (p = 0.033). Significant differences between MCI and AD groups for age (p = 0.01), years of education (p = 0.008), medication use (p = 0.02), and MMSE score (p = 0.00) were observed. The logistic regression analysis indicated that having lower levels of plasma ADAM10 decreases the chances of an older adult having a diagnosis of AD by 82.3% (Exp(B) = 0.177; p = 0.005; 95%CI [0.053‐0.593]).

Conclusion

Our results suggested healthy low‐educated older adults had less plasma ADAM10 compared to individuals with AD. Also, despite low education, cognitive test scores differ for each group.
AI‐based histological analysis in Alzheimer’s disease : the point of view of the biologist
Authors: Delatour Benoit
Published: December 2023

Background

Tauopathies in Alzheimer’s disease are characterized by occurrence of neurofibrillary tangles (NFTs) and neuritic plaques (NPs) that are closely associated to cognitive decline and are key elements for neuropathological diagnosis and staging. The automated detection and segmentation of NFTs and NPs has not been fully implemented but thanks to deep learning (DL) based approaches could be assessed today.

Method

Whole‐slide images of phosphotau‐immunostained brain sections (frontal cortex) from AD patients presenting discrete to florid lesion loads were generated. Using a commercial software (©Visiopharm) we developed an iterative “human‐in‐the‐loop” pipeline to improve quality of annotations of NFTs and NPs and create an exhaustive and accurate ground truth database that was used to train DL algorithms for the different objects morphologies to be detected and segmented in brain parenchyma.

Result

A U‐Net based CNN was trained, and the obtained algorithm was precise in NP detection (F1 score: 0.86) and segmentation (Dice score: 0.77). A second algorithm was precise in NFT detection (F1 score: 0.85) and segmentation (Dice score: 0.82).

Conclusion

Two main objectives were reached : 1) optimization of human annotations thanks to iterative AI‐expert feedbacks, 2) implementation of methods to automatically accelerate, refine and robustify histological analysis of discrete objects. Such approaches may help to uncover and quantify subtle morphological differences between AD variants that otherwise could not be neuropathologically segregated. The biological relevance of dichotomizing NFTs and NPs will be discussed in terms of clinico‐pathological correlations and stratification needs. Also, the scope and limits of user‐friendly and commercially available DL‐learning image analysis softwares, directly accessible to researchers and pathologists with limited IT skills, will be reviewed.
iTRAQ‐based proteomic analysis reveals myeloperoxidase (MPO) as a diagnostic and prognostic biomarker of cognitive decline in Parkinson’s disease
Authors: Tan Yi Jayne , Ng Ebonne YL , Ng Samuel YE , Chia Nicole SY , Choi Xinyi , Heng Dede , Neo Shermyn XM , Xu Zheyu , Tay Kay Yaw , Au Wing Lok , Tan Louis CS , Tan Eng‐King , Ng Adeline Su Lyn
Published: December 2023

Background

Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder and is characterized by loss of dopaminergic neurons, leading to debilitating motor, cognitive, and behavioral symptoms. At present, there are no biological markers for antemortem PD diagnosis.

Methods

To identify potential blood biomarkers of PD, Isobaric Tags for Relative and Absolute Quantitation (iTRAQ)‐based mass spectrometry (MS) was performed to characterize alterations of serum proteins amongst PD patients of varying disease severity according to their Hoehn and Yahr (HY) stages. Sera of mild PD (HY 1.5‐2.0, n = 10), severe PD (HY 3.0‐4.0, n = 10) patients and healthy controls (n = 10) were enriched and subjected to 2D LC‐MS/MS for proteomic analysis. Candidate protein biomarkers were identified based on the relative abundance of the identified tryptic peptides, and were validated using ELISA in a validation set consisting of 103 early PD and 48 healthy controls (HC). Association with clinical outcomes were analyzed at cross‐sectional and longitudinal timepoints.

Results

A total of 96 proteins in the mild PD group and 78 proteins in the severe PD group were found to differ significantly between PD patients and healthy controls. Thirteen proteins were common between the mild PD and severe PD groups and were chosen for further analysis. These dysregulated proteins represent a diversity of biological processes, including innate immune response, complement activation, cytoskeleton reorganization and response to oxidative stress. Of which, myeloperoxidase showed higher serum levels in mild and severe PD. In the validation PD cohort, higher MPO was significantly associated with worse executive function over 4 years.

Conclusion

Using an unbiased proteomic approach, we identified serum MPO (a marker of oxidative stress) as a promising biomarker for PD. Our results suggest that MPO could also potentially be an early marker for cognitive decline in PD. Further studies with larger cohorts are needed to validate these findings.
Sleep disturbances in the memory clinic: Associations with clinical features and neuropsychological performance
Authors: Naismith Sharon L , D'Rozario Angela , Hoyos Camilla M , Ireland Catriona , Michaelian Johannes C , Kong Shawn Dexiao , Mowszowski Loren , Grunstein Ron , Lam Aaron Kin Fu
Published: December 2023

Background

The prevalence of sleep disturbances increases in ageing and may further increase in neurodegeneration. In a memory and cognition (MC) clinic setting, our study aims to: 1) determine the rates of various sleep disturbances including obstructive sleep apnoea (OSA), short and long sleep duration, insomnia, sleep quality, and rapid eye movement sleep behaviour disorder (RBD); and 2) examine whether these sleep disturbances are associated with neuropsychological performance in older adults without dementia.

Method

We captured data between 2009 and 2022 from a MC clinic for older adults aged over 50 with new cognitive and/or mood concerns. Self‐reported history, pulse oximetry, and polysomnography (PSG) captured rates of OSA. Actigraphy (>7 days) was used to measure sleep duration (defined short duration as <6 hours and long duration as >9 hours). Validated self‐report questionnaires were used to capture insomnia, sleep quality, and RBD. We examined group differences between those with and without sleep disturbances in the following cognitive composites: verbal learning and memory, processing speed, executive function, and language skills.

Results

We recruited 1016 participants (mean age = 66.9 (SD = 9.0), mean MMSE = 28.3 (SD = 2.2), male = 436 (43%)). For rates of OSA, 15% (n = 152/1016) self‐reported history of OSA, 76% (n = 383/504) had OSA as classified by PSG, 84% (n = 115/137) had OSA as classified by pulse oximetry. From actigraphy data, 10% (n = 55/566) had short sleep and 5% (n = 29/566) had long sleep duration. From self‐report data, 15% (n = 85/579) had moderate or severe clinical insomnia, 61% (n = 544/893) reported having poor sleep quality, 10% (n = 56/579) had RBD. Individuals with OSA (from PSG) performed worse in verbal learning compared to those without OSA (p<0.05). Individuals who had long sleep duration performed worse on both verbal learning (p<0.05) and memory (p<0.05) than those with short sleep and normal sleep duration. There were no other significant group differences in cognition outcomes, amongst the other sleep disturbance groups (p>0.05).

Conclusion

In MC clinics, there are very high rates of sleep disturbance in this at‐risk population. The large discrepancy in rates of OSA between self‐reported history and PSG suggests OSA is under‐diagnosed. Sleep disturbances may be associated with cognitive outcomes, in particular learning and memory.
Striatal Amyloid deposition in sporadic Alzheimer’s Disease
Authors: Arena Julieta , Urrutia Leandro , de Leon Magdalena Ponce , Seminara Emiliana , Falasco German , Bérgamo Yanina , Allegri Ricardo Francisco , Sevlever Gustavo , Vazquez Silvia
Published: December 2023

Background

It has been shown that disorders associated with early‐life alterations in amyloid production or processing such as in autosomal dominant Alzheimer’s disease (AD) and down syndrome‐associated dementia have a different pattern of Aβ deposition, with early striatal accumulation, as compared to sporadic late‐onset cases, in which striatal deposition has been suggested to be a marker of late and advanced amyloidosis. It is not clear, however, if the pattern of amyloid accumulation is related to any other factors or if it has any impact on brain metabolism. We aimed to determine if there are any clinical or demographic predictors of striatal tau accumulation in sporadic AD and whether its presence affects the subjects´ brain metabolism.

Method

Subjects that underwent positron emission tomography (PET) scans with 18F‐FDG and 11C‐PIB were analyzed retrospectively. Only those with clinical diagnosis of AD and mild cognitive impairment (MCI) due to AD were considered for the analysis. Visual analyses were performed to exclude subjects without cortical amyloid accumulation. Mean values from cortical volumes of interest (VOIs) and striatum were obtained. Amyloid deposition was correlated with age and disease duration via Pearson’s correlations. Amyloid deposition was also correlated to brain metabolism as assessed by FDG PET.

Result

150 subjects were included in this study. Neither age nor disease duration was correlated with striatal amyloid accumulation. Subjects with striatal Pib accumulation showed higher cortical amyloid burden. Moreover, higher striatal amyloid burden was positively correlated with higher frontal amyloid burden specifically. Amyloid burden in the striatum did not seem to have an impact on the glucose metabolism in the striatum itself. However, variations in cortical glucose metabolism could be observed. A higher striatal amyloid burden was correlated with cortical glucose hypometabolism, specifically in the precuneus and temporal areas (Figure 1).

Conclusion

There seems to be a stage of sporadic Alzheimer’s disease in which amyloid accumulates in the striatum. This, in turn, appears to have an impact on brain functioning. Whether this has a real impact on cognitive performance remains to be elucidated.
MRI‐Based Water Exchange Rate Across the Blood‐Brain‐Barrier is Associated with Diffusion MRI Free Water in Older Adults
Authors: Bauer Christopher E. , Pappas Colleen , Zachariou Valentinos , Maillard Pauline , Arvind Caprihan , Shao Xingfeng , Wang Danny J.J. , Gold Brian T.
Published: December 2023

Background

The blood brain barrier (BBB) tightly regulates the clearance of waste from the central nervous system. Changes in BBB function may disrupt such clearance mechanisms, resulting in increased waste and unrestricted fluid, or free water (FW), which can be measured using diffusion‐weighted imaging. Here, we explored whether MRI‐based BBB water exchange rate (kw; measure of BBB function) is associated with FW within white matter tracts connecting the executive control network (ECN). We further explored whether FW within the ECN would likewise be associated with executive function performance.

Method

Fifty‐three older adults (ages 65‐85) without dementia were scanned using a 3‐Tesla MRI Siemens Magnetom Prisma with a 64‐channel head coil. A 3D gradient‐and‐spin‐echo diffusion‐prepared arterial spin labeling (DP‐ASL) sequence and a 126‐direction main diffusion MRI sequence using 4 b‐values (0, 500, 1000, and 2000 s/mm2) were acquired. Each participant’s mean kw values averaged over the entire brain were calculated from the DP‐ASL data. FW maps were calculated, registered to FMRIB space, and skeletonized using FSL’s tract‐based spatial statistics (TBSS). Skeletonized FW values were averaged across the white matter tracts connecting the ECN using a previously defined fMRI template. All participants completed the cognitive assessment from the Uniform Data Set Version 3 within one year of the scan date. Executive function was assessed for each participant based on a composite measure including digit span backwards and trails making part B test minus part A. A multivariate linear regression model first tested the association between whole brain kw values (predictor) and FW in the ECN as the dependent variable. A second multivariate linear regression model then tested the association between FW in the ECN (predictor) and EF performance as the dependent variable. Models controlled for age and sex.

Results

Whole brain kw values were negatively associated with FW in the ECN (r = ‐0.338, p = 0.031). FW in the ECN was negatively associated with EF performance (r = ‐0.318, p = 0.024).

Conclusion

Our results suggest that BBB dysfunction is associated with free water in large‐scale cognitive networks. Subsequent free water changes in these networks are linked with associated decreases in cognitive performance.
Population attributable fractions for risk factors for dementia in Argentina: sex and socioeconomic differences during ten years
Authors: Calandri Ismael Luis , Elgani Sofía Alejandra , Crivelli Lucía , Allegri Ricardo Francisco , Suemoto Claudia Kimie
Published: December 2023

Background

The twelve potentially modifiable risk factors(RF) for dementia are estimated to account for 40% of dementia cases worldwide based on data from high‐income countries. However, population attributable fraction (PAF) distribution and impact may be affected by the socioeconomic context. We aimed: a) to calculate PAFsin Argentina (a low‐income country) using census data, b) to compare differences between sex and socioeconomic strata, and c) their evolution across a ten‐year period.

Method

We collected cross‐sectional data from three Argentinian census between 2009 and 2018 to estimate risk factor prevalences. We calculated PAFs for each risk factor using relative risk estimates from previous meta‐analyses. Next, we adjusted PAF for communality between risk factors, and used these values to calculate overall weighted PAFs. Finally, we analyzed the differences in PAFs for sex and socioeconomic strata defined as income quintiles and compared the overall PAFs between years.

Result

We could identify 8 of the 12 RF in census data; the rest were estimated using other sources. We estimated an overall weighted PAF of 39.9% 95%CI [38.29%‐39.89%] (Table 1). We found no significant differences between the overall PAF of women and men (p = 0.12) (Figure 2). However, women presented more social isolation (4.9% CI95%[4.2%,5.7%]) than men (2.2% CI95%[2.1%,4.1%]), and greater smoking PAF (p = .027). Significant differences were found in the overall PAF of the two lower economic strata (Q1 37.4% CI95% [35.3%,39.5%]; Q2 40.3% CI95% [38.6%,42.1%]) compared with the upper stratum (Q5 29.8% CI95% [28.2%,31.5%]) (Figure 3). There were no significant changes in weight PAF over the course of 10 years. However, while some factors, such as education, improved, alcohol use and social isolation worsened.

Conclusion

We found a higher prevalence of risk factors, even though not all of them could be assessed. Also, the distribution of risk factors varied according to sex and socioeconomic level, suggesting that public policies should consider working on the most important RF.
Physical activity attenuates the effect of poor sleep quality on cognitive function in adults with chronic stroke
Authors: Stein Ryan G , Falck Ryan S , Tai Daria , Best John R. , Davis Jennifer C , Hsiung Ging‐Yuek Robin , Eng Janice J , Middleton Laura E , Hall Peter A , Liu‐Ambrose Teresa
Published: December 2023

Background

Good sleep quality is important for cognitive health. Individuals with chronic stroke are at an increased risk for dementia and about half have insomnia. Stroke survivors are a target population in need of intervention strategies to promote sleep quality to preserve cognitive function. Physical activity is a promising approach. Hence, in this cross‐sectional analysis, we examined whether physical activity moderates the effect of poor sleep quality on cognitive function in adults with chronic stroke.

Method

This is a cross‐sectional analysis of baseline data acquired from 121 community‐dwelling older adults with chronic stroke (age: 70.73 ± 8.56 years; 38.30% female) enrolled in a 6‐month randomized control trial. Sleep quality was assessed with the MotionWatch8 (MW8) and physical activity was measured with the Life Space Questionnaire (LSQ; 0‐120). Participants were split into higher or lower physical activity by the median LSQ score (median = 64.00). Cognitive performance was measured with the 13‐item Alzheimer’s Disease Assessment Scale (ADAS‐Cog; 0‐85), with higher scores indicating worse cognitive performance. A linear regression was performed to first assess the independent association of MW8‐measures of sleep quality (i.e., sleep duration, fragmentation, efficiency, latency, and awakenings) and ADAS‐Cog score, after accounting for age and Montreal Cognitive Assessment score (MoCA) score. Based on the results of this linear regression, we performed a second linear regression with ADAS‐Cog score as the dependent variable, and sleep latency (minutes), physical activity level, and the interaction of sleep latency * physical activity level as independent variables, controlling for age and MoCA score.

Result

Greater sleep latency (β1 = 0.22, p = 0.017) was significantly associated with greater ADAS‐Cog score, after controlling for age and MoCA (R2 = .50, F(3, 105) = 34.74, p<.01). Physical activity significantly moderated the association between sleep latency (β1 = 0.38, p = 0.033) and ADAS‐Cog score (R2 = .53, F(5, 103) = 22.93, p<.01). Specifically, those in the higher physical activity group showed lower ADAS‐Cog scores with higher sleep latency (r = .67, p = .627) and those in the lower physical activity group showed higher ADAS‐Cog scores with higher sleep latency (r = .75, p = .007).

Conclusion

Physical activity attenuates the effect of poor sleep quality on cognitive function in adults with chronic stroke.
Indexing the Predictive Utility of Real‐World Sleep to Discriminate Early Alzheimer’s Disease
Authors: Chang Jun Ha , Wu Ruiqian , Zhang Ying , Rizzo Matthew , Merickel Jennifer
Published: December 2023

Background

Sleep dysfunction is one of earliest symptoms of Alzheimer’s disease (AD) and can accelerate cognitive decline, increasing risk of AD. Emerging technologies, like actigraphy, allow for continuous monitoring of real‐world sleep. Our goal is to determine the predictive utility of real‐world sleep dysfunction features for indexing warning signs of incipient dementia like mild cognitive impairment (MCI).

Method

Subjects were classed as cognitively neurotypical (N = 31) or impaired: MCI (n = 51) or AD (N = 7) based on NIA‐AA diagnostic criteria, as evaluated by a neurologist from clinical exam, neuropsychological, and demographic assessments. (Table 1) Each subject was tracked for 3 months with wrist‐worn actigraphy. Self‐reported sleep diaries validated real‐world sleep data. Sleep duration and quality were estimated across means and standard deviations (SD) of: Total Sleep Time (TST), Sleep Latency (SL), Sleep Efficiency (SE), Wakefulness After Sleep Onset (WASO), and Awakening Count (AC). An optimal logistic regression model, adjusting for demographics (age, gender, education), was quantified and used to rank the explanatory value of sleep covariates to discriminate impaired vs. typical aging using stepwise selection.

Result

Across 6,530 validated, nightly sleep periods (mean = 73.4 ± 17.0 sleep periods per subject), we found best discrimination of impaired vs. typical aging, ranked from best to least discrimination (Table 2), for average sleep efficiency [SE] (b = 3.58, p = 0.03), nightly wakefulness [WASO] (b = 3.42, p = 0.04), variability of daily sleep quality [SE] (b = 1.65, p = 0.001), and awakening frequency [AC] (b = ‐0.79, p = .03). TST did not discriminate impaired from typical aging.

Conclusion

Results align with prior literature, demonstrating that sleep quality is an optimal feature to track early warning signs of dementia from real‐world sleep. Critically, SE shows the most promise for discriminating early decline while TST did not show predictive utility in this study. Digital fingerprints of sleep behavior in the real world offer unique added value to neurobiological biomarker evidence of preclinical disease, improving early detection of AD risk to bolster interventions aimed at treating AD—‐even its earliest stages.
Utilizing Respondent Driven Sampling to assess neurocognitive functioning in the indigenous American Samoa population 60 years or older.
Authors: Tofaeono Vaatausili
Published: December 2023

Background

Respondent Driven Sampling (RDS) is a cost‐efficient, chain referral sampling method, where respondent seeds recruit others who are eligible within their social networks; those recruited by the seeds then recruit others (Heckathorn, 1997). In American Samoa recruitment of a random population‐based sample is difficult due to systematic error and biases in sample characteristics arising from the hierarchical relationships prominent in American Samoan society (Aitaoto, et al. 2007; Cassel 2010; Cassel, 2014). The INdigenous Samoan Partnership to Initiate Research Excellence (INSPIRE) program recruited 718 participants age 45 and over during a 51‐day period with 20% age 60 or higher. For this reason, RDS methods were utilized to assess neurocognitive functioning in the Samoan population age 60 or over.

Method

INSPIRE program staff recruited 60 participants as RDS seeds (30 men and 30 women). Once the seeds completed the data collection process, they were given coupons to recruit a maximum of 3 community members. In addition, they were provided a printed reminder of the eligibility criteria. The patterned continued to create consecutive recruitment waves (Wave 1, n = 60; Wave 2, n = 180; Wave 3, n = 540) until the sample size was reached. Participants received a total $25 incentive for participating in the survey. Additionally, participants will be compensated as RDS recruiters and receive an additional $15 for each recruit who participates, up to a maximum of 3. The RDS recruitment proposed to yield 780 participants, with 390 women and 390 men with a 12% loss to follow up.

Result

Preliminary data analysis reveals target sample size was collected in 126 days (41% Male, 59% Female). Referral chain analysis reports only 25% of seeds recruited 80% of participants although social networking sizes varied for the seeds. Some participants with higher social networks did not participate in recruiting.

Conclusion

Intercept promotions complimented the use of RDS to help identify seeds and include faith‐based institutions (churches), senior centers, civics clubs, village‐based and other networks to recruit men and women, ages 60 and over. Maintaining a central location for study implementation and coupon payment supported ease of access for the study population.
Greater sensitivity of the hippocampus than the entorhinal cortex in response to western diet, manifested by accelerated neuroinflammation and neurodegeneration in the progression of Alzheimer’s disease
Authors: Więckowska‐Gacek Angelika , Mietelska‐Porowska Anna , Domańska Justyna Marta , Wojda Urszula
Published: December 2023

Background

The western diet (WD), characterized by a combination of simple carbohydrates, saturated fatty acids and cholesterol, is known to induce metabolic syndrome and systemic inflammation. These peripheral disorders induce neuroinflammation and affect brain function, suggesting that WD may have a significant impact on Alzheimer’s disease (AD) progression (Więckowska‐Gacek et al., 2021). In this study we aimed to verify the hypothesis that WD feeding accelerates neuroinflammation, resulting in accelerated deposition of Aβ plaques, accelerated appearance of neurodegeneration markers and destabilization of neuronal cytoskeleton.

Methods

Transgenic male mice expressing human APP with the Swedish mutation (APPswe) were divided into three age groups: 4‐ and 8‐month old animals (4M, 8M) represent early pre‐symptomatic stages of AD, while 12‐month old animals (12M) represent later stages of AD with visible amyloid pathology. The APPswe mice were fed standard or western diet from the 3rd month of age. The effect of WD on selected markers was analyzed in the hippocampus and entorhinal cortex by Western blotting and immunohistochemistry.

Results

Compared to control mice fed standard diet, WD feeding induced astrogliosis (GFAP) in the hippocampus as early as in 4M, followed by several changes in 8M such as activation of the pro‐inflammatory microglia (↑Iba1, ↓CD68, ↓P2RY12), a decrease in the level of TLR4, and the fluctuations of levels of pro‐inflammatory cytokines (IL‐1β, IL‐6). These changes in the hippocampus at 8M co‐occurred with enhanced production of Aβ plaques. Moreover, WD accelerated the processes associated with age‐related neurodegeneration (lower levels of PSD95 in 8M, and of NeuN in 12M). In the entorhinal cortex, the examined molecular changes appeared with a delay in time or not at all.

Conclusion

These results indicate that the westernized pattern of nourishment is an important modifiable risk factor of AD which can accelerate development of AD pathology. Furthermore, the data demonstrate that, compared to the entorhinal cortex, the hippocampus is a brain structure that is generally more sensitive to the macronutrient composition characteristic of WD, manifested by accelerated neuroinflammation, amyloidopathy and neurodegeneration.

Polish National Science Center grants: 2018/29/N/NZ7/01724, 2014/15/D/NZ4/04361
Design of a Bilingual and Bicultural Dementia Screening Toolkit Integrated in the Electronic Health Record
Authors: Shah Samantha , Diaz‐Santos Mirella , Eliseo Stephanie G Ovalle , Moreno Gerado , Bholat Michelle , Vossel Keith , Chang Timothy S
Published: December 2023

Background

Based predominantly on longitudinal cohort studies, the prevalence of Alzheimer’ disease (AD) is estimated to be 3‐10% in individuals over 65 years old and has increased risk (1.5‐2.0 times) in Black and Hispanic/Latinx individuals. In our prior analyses of the University of California Los Angeles (UCLA) electronic health record (EHR), we found that only 1% of patients over 65 years old are diagnosed with AD, and AD is less likely to be diagnosed in Black and Hispanic/Latinx patients compared to white patients (relative risk 0.86‐0.9). One solution to the underdiagnosis of AD in real‐world clinical care, is to screen for dementia.

Methods

Recent work by several University California AD Centers and the California Department of Public Health developed a short (<5 min) dementia screening tool (DST). The DST includes a three‐question questionnaire for the patient and an informant if present, followed by the Mini‐Cog if applicable (Figure 1). The questions ask about changes in language, memory, and personality (Figure 2). The DST is considered positive if any question is answered “Yes” by the patient or informant, or if the Mini‐Cog score is <3 out of 5 points. To improve care for non‐English speaking patients, we developed the questionnaire in Spanish, which was adapted to Hispanic/Latinx culture.

Results

The DST is integrated in the EHR. The questionnaires are sent to patients prior to the visit via the messaging system. Answers to the questionnaire and the Mini‐Cog are entered into a flowsheet in an easily accessible EHR tab. We also created a documentation link that automatically loads the DST results, final score, and recommendation into provider notes. The DST is live at a diverse UCLA family medicine clinic, which sees HL and Black patients. In the first three weeks, 178 patients were screened with the toolkit and eight patients were referred for specialty care.

Conclusions

We have developed a cultural and linguistic responsive DST that is integrated in the EHR. We will continue evaluating its ability to increase appropriate diagnosis of dementia in diverse racial and ethnic groups, and share our DST including EHR integration so it can be implemented elsewhere.
pTau181 plasma biomarker performance as an inclusion criterion for Alzheimer’s Disease clinical trials
Authors: Mammel Anna , Kumar Pankaj , Fortna Ryan , Biehl Donald , Cruz Anna , Encarnacion Mary , Burns Lindsay H , Kupiec James , Hsiung Ging‐Yuek Robin , Mousavi Ali , MacKenzie Ian R , Hirsch‐Reinshagen Veronica , Frykman Hans
Published: December 2023

Background

Advances in ultrasensitive detection techniques for blood biomarkers allow the quantification of AD‐specific phosphorylated Tau proteins, including Tau phosphorylated at threonine 181 (pTau181) in patient plasma. pTau blood‐based biomarker have shown great promise as inclusion criteria and secondary endpoint evaluation in clinical trials.

Method

The University of British Columbia (UBC) CARD biobank plasma samples from clinically diagnosed AD and non‐AD patients were used to establish clinical and analytical validity of the pTau181 plasma assay per FDA fit‐for‐purpose guildelines (Neurcode USA, Inc.). We assessed the analytical measurement interval, clinical reportable range, linearity, intra‐laboratory precision, specimen stability, interference, and clinical performance.

Result

The pTau181 plasma assay provides a robust and accurate biomarker approach for independent determination of AD, with an AUC of 0.9 in our validation study cohort. The cut‐point (≥ 30 ng/L) had 100% sensitivity and 88% specificity for AD diagnosis in autopsy‐confirmed samples. The plasma pTau181 assay appears to be performing well as additional screening metric for inclusion of mild‐to‐moderate AD subjects in Phase 3 clinical trials (i.e. RETHINK‐ALZ and REFOCUS‐ALZ). 89% of the clinical sites participating in the REFOCUS‐ALZ study and 80% of sites participating in the RETHINK‐ALZ had at least 70% of screened subjects meeting this criterion.

Conclusion

AD plasma biomarker (pTau181) quantified using an RUO assay has great potential both as a diagnostic tool and to streamline clinical trials in AD.
Implementation outcomes of the ACT Trial in older adults with amnestic Mild Cognitive Impairment
Authors: Cervantes Hector , Verhoef Nicholas , Spafford Russell , Han SeungYong , Salisbury Dereck , Lin Feng Vankee , Yu Fang
Published: December 2023

Background

Preventing Alzheimer’s disease is a public health priority to address its substantial social and healthcare impacts. Exercise and cognitive training interventions are promising but show mixed effects on cognition in older adults with and without mild cognitive impairment (MCI), which is at least partially attributable to varied quality in trial implementation. The purpose of this study was to evaluate the implementation outcomes of the 3‐site Aerobic exercise and Cognitive training (ACT) trial that tests the cognitive effects and underlying mechanisms.

Method

The ACT Trial enrolled 146 older adults with amnestic MCI (aMCI). Participants were randomized equally to ACT, aerobic exercise only, cognitive training only, or attention control ed for six months and followed for another 12 months. Participants completed a 3‐step screening process that included a 30‐minute phone screen (step 1), a 120‐minute in‐person screening (step 2), medical clearance (step 3), and a 60‐minute graded exercise test and MRI (steps 4a & 4b). The implementation outcomes included durations between each screening step, from phone screen to enrollment, from enrollment to the first intervention section. Implementation outcomes were compared across sites using ANOVA.

Result

The mean age was 73.57±5.49 years, with 47.95% female, 91.78% white, 91.09% had a college degree or higher, and a mean Montreal Cognitive Assessment (MoCA) score of 23.38±2.16. The duration from step 1 to step 2 was 20.23±31.21 days, step 2 to step 3 was 24.42±41.27 days, step 3 to step 4a was 36.84±76.86 days, step 4a to step 4b was 25.84±60.75 days, and the duration from step 4b to enrollment was 25.60±60.17 days. The duration from step 1 to enrollment is 121.16±109.16 days. ANOVA results indicate no significant differences in implementation outcomes across the three sites.

Conclusion

The screening took longer than projected regarding completing a participant’s in‐person screening (step 2) and obtaining medical clearance from the participant’s provider (step 3). The major contributors to longer screen duration were delays in receiving faxed documentation from medical providers. Future trials need to build in adequate screening duration to ensure screening success.
Predictive Modeling Leveraging Electronic Health Records Identifies Early Predictors of Alzheimer’s Disease
Authors: Tang Alice S , Rankin Katherine P , Cerano Gabriel , Miramontes Silvia , Mills Hunter , Oskotsky Tomiko , Zeng Billy , Nelson Charlotte , Soman Karthik , Woldemariam Sarah , Li Yaqiao , Lee Albert M , Miller Zachary , Allen Isabel Elaine , Sanders Stephan , Baranzini Sergio , Sirota Marina
Published: December 2023

Background

Alzheimer’s Disease (AD) is a neurodegenerative disorder of aging that is difficult to diagnose. Electronic health records (EHR) are a valuable source of longitudinal data for disease phenotyping and prediction. Knowledge graphs, such as SPOKE (Scalable Precision Medicine Open Knowledge Engine), help derive biological meaning from phenotypic data. Here we utilize machine learning on EHR to predict and better understand AD.

Method

AD patients and controls were identified from the UCSF EHR. The index time was identified as the first dementia diagnosis or drug record among the AD patients, and 1 year before the last visit among controls. Controls matched on demographics and visit‐related factors were identified at five time points relative to the index time (‐7years, ‐5years, ‐3years, ‐1year, and ‐1day) at a 1:8 ratio. Random forest (RF) classification models for AD were trained utilizing clinical data (conditions, drugs, abnormal measurements) and evaluated with bootstrapped AUROCs on a 30% held‐out set. Top features mapped to phecodes were ranked based on average impurity decrease. Networks were created from SPOKE knowledge graph by identifying shortest paths between top 25 model features and AD, and associations were validated with genetic colocalization analysis on shared variants in the UK Biobank.

Result

From the UCSF EHR, 749 AD patients and 250,545 Controls were identified with clinical concepts 7 years prior to each individual’s index time. RF trained on AD and matched control individuals’ data performed with AUROC of 0.58 (‐7years) to 0.77 (‐1day). Top phecode features from models include those that are important earlier (osteoarthritis, allergic rhinitis), progress in importance closer to index time (dizziness, vitamin D deficiency, memory loss), and persist in importance throughout time (hyperlipidemia) (Figure 1). Knowledge networks highlight shared biological relationships between top features (e.g., hyperlipidemia, osteoporosis), including identification of relevant genes (e.g., APOE, PSEN 1/2, INS, AKT1, HFE) (Figure 2). Colocalization analysis of LDL cholesterol and AD confirms a commonly associated SNP in APOE.

Conclusion

EHR together with knowledge networks provide an opportunity to identify early clinical predictors of AD diagnosis, with utility for early intervention, hypothesis generation and biological insight.
Aberrant DJ‐1 expression underlies L‐type calcium channel hypoactivity in dendrites in Alzheimer’s disease (AD) and tuberous sclerosis complex (TSC)
Authors: Niere Farr
Published: December 2023

Background

L‐type voltage‐dependent Ca2+ channels (L‐VGCC) dysfunction is implicated in several neurological and psychiatric diseases. While a popular therapeutic target, it is unknown if molecular mechanisms leading to disrupted L‐VGCC across neurodegenerative disorders is conserved. Importantly, L‐VGCC integrate synaptic signals to facilitate a plethora of cellular mechanisms; however, mechanisms that regulate L‐VGCC channel density and subcellular compartmentalization are understudied.

Method

The following methods were used: bioinformatics; RNA immunoprecipitation; Surface Sensing of Translation–Proximity Ligation Assay (SUnSET‐PLA); calcium imaging, immunocytochemistry; Western blots, dissociated hippocampal neurons and acute hippocampal slices from APP/PS1 mouse mode of AD and Tsc1 conditionally knockout mice; Postmortem human prefrontal cortex tissue.

Result

Herein, we report that in disease models with overactive mammalian target of rapamycin complex 1 (mTORC1) signaling (or mTORopathies), deficits in dendritic L‐VGCC activity are associated with increased expression of the RNA‐binding protein (RBP) DJ‐1. DJ‐1 binds the mRNA coding for the alpha and auxiliary Ca2+ channel subunits CaV1.2 and alpha2delta2 (α2δ2), and represses their mRNA translation, only in the disease states, specifically preclinical models of Alzheimer’s disease (AD) and tuberous sclerosis complex (TSC). In agreement, DJ‐1 mediated repression of CaV1.2/α2δ2 protein synthesis in dendrites is exaggerated in mouse models of AD and TSC, resulting in deficits in dendritic L‐VGCC calcium activity. These preclinical findings are recapitulated in the synaptic and dendritic regions of prefrontal cortices of humans diagnosed with AD.

Conclusion

Discovery of DJ‐1 regulated L‐VGCC activity in dendrites in TSC and AD provides a new signaling pathway that can be targeted in clinical mTORopathies.