bluuebunny/crossref_metadata_2025 huggingface dataset:
Alzheimer drug candidate NA‐831 for the treatment of Dementia caused by Covid‐19‐ A Phase 2/3 clinical trial methodology
Authors: Tran Lloyd , Kurkinen Markku
Published: December 2022

Background

More than one third of Covid‐19 patients at the time of discharge from hospitalization have evidence of cognitive impairment and motor deficits. Patients surviving COVID‐19 are at high risk for subsequent development of neurological disease, particularly Alzheimer’s disease.

Method

The clinical Phase 2/3 evaluates the safety and efficacy of NA‐831 alone, and a combination therapy comprises NA‐831 with an anti‐viral drug Atazanavir, NA‐831 with an anti‐inflammatory drug, and a potential synergy between Atazanavir and Dexamethasone.

The Clinical Protocol:

Study Type: Interventional Phase 2/3

Estimated Enrollment: 525 participants

Allocation: Randomized

There will be daily symptom surveys for 14 days, then weekly thereafter for 3 weeks resulting in a total duration of follow up of 36 days.

Status: is recruiting and the study is expected to be completed by December 15, 2023

Result

Primary Outcome Measures

Time from randomization to recovery defined as

1) absence of fever, as defined as at least 48 hours since last temperature ≥ 38.0°C without the use of fever‐reducing medications

2) absence of symptoms of greater than mild severity for 24 hours

3) not requiring supplemental oxygen beyond pre‐COVID baseline

AND 4) freedom from mechanical ventilation or death

Secondary Outcome Measures

1) Brief Cognitive Rating Scale (BCRS) and

2) Clinician’s Interview‐Based Impression of Change plus caregiver input (CIBIC‐plus) after 24 weeks.

Conclusion

The progress of the clinical trials will be presented discussed.
Asymmetric degeneration of the hippocampus predicts risk of Alzheimer’s disease
Authors: Liu Xi , Li Hongming , Fan Yong
Published: December 2022

Background

Asymmetry of the hippocampus is an important finding in normal aging and Alzheimer disease (AD). However, it is largely unknown how asymmetric degeneration of the hippocampus is associated with an individual's risk of AD.

Method

A deep‐learning (DL) classification model was developed to quantify degeneration of the hippocampus based on its magnetic resonance imaging (MRI) scan and binary mask. Different from most extant studies, the DL model was trained to distinguish AD patients from cognitively normal (CN) controls with their bilateral hippocampi as two separate instances for generating two classification scores (positive indicating AD‐like and negative indicating CN‐like), one for each hippocampus. An integration of the two scores of an individual’s MRI data was utilized to classify AD patients and CN controls, to predict individual mild cognitive impairment (MCI) subjects’ progression to AD dementia in a time‐to‐event analysis setting, as well as to identify subgroups of MCI subjects with distinct risk of AD dementia. Particularly, the DL classification model was trained on the ADNI 1 cohort (n=420), and then validated for distinguishing AD subjects from CN controls on the ADNI 2&GO (n=469) and AIBL (n=396) cohorts and for predicting MCI subjects’ progression to AD dementia and identifying MCI subgroups on the ADNI 2&GO cohort (n=439).

Result

The DL model achieved accurate classification performance with classification rates of 0.923 and 0.927 and AUC values of 0.963 and 0.961 on the ADNI‐GO&2 and AIBL cohorts, respectively. The integration of the bilateral hippocampal classification scores achieved a concordance index of 0.765 for predicting individual MCI subjects’ progression to AD dementia. The MCI subgroups with bilateral AD‐like, bilateral CN‐like, and one AD‐like and one CN‐like scores were significantly different in risk of progression to AD dementia and a variety of biomarkers and cognitive scores, including Apoe4, Amyloid, tau, and ADAS‐cog score (p < 0.0004).

Conclusion

The present study demonstrated that the asymmetric hippocampal degeneration patterns quantified using a DL model on MRI data could achieve promising classification performance for distinguishing AD patients from CN controls and may aid the clinicians to early prediction of MCI subjects’ progression to AD dementia to facilitate personalized medicine.
Intraindividual variability of cognitive performance and associations with magnetic resonance imaging in aging American Indians: Data from the Strong Heart Study
Authors: Fonseca Luciana Mascarenhas , Chaytor Naomi S , Olufadi Yunusa M , Buchwald Dedra , Galvin James E , Schmitter‐Edgecombe Maureen , Suchy‐Dicey Astrid M
Published: December 2022

Background

American Indians present with increased risk factors for Alzheimer’s disease and related dementias (ADRDs). Intraindividual cognitive variability (IICV), which measures the variability in performance on neuropsychological tests within a person at a single time, has been proposed as a noninvasive marker of neurodegeneration and early dementia. In this study, we investigated the hypothesis that higher IICV was related to more features of neurodegeneration on magnetic resonance imaging (MRI) measurements among older American Indians.

Method

American Indian participants over 64 years of age who underwent MRI and completed a battery of neuropsychological tests were included in the study (N = 750). Data from three neuropsychological tests were used to compute a multidomain IICV measure of cognition along with IICV measures representing the cognitive domains of memory, executive function and processing speed. Regression models estimated IICV associations with total brain volume, hippocampal volume, and graded white matter disease, adjusting for age, sex, education, BMI, and intracranial volume.

Result

Memory, executive function and processing speed, and multidomain IICV measures were associated with total brain volume before correction for multiple comparisons. After correction with Bonferroni, only higher IICV of memory was associated with lower total brain volume (Beta = −0.20; 95%CI −0.332, −0.068; p = 0.009). Furthermore, additional adjustment of mean scores from the tests used for the variability measure resulted in loss of significance for all IICV measures, with only mean scores remaining significant. Measures of IICV were not associated with hippocampal volume or white matter disease.

Conclusion

These findings suggest that IICV of memory may be associated with imaging measures related to neurodegenerative injury in older American Indians. However, these associations representing variability in cognition are smaller than those representing mean cognition. More research is needed to understand the clinical and etiologic associations for IICV measures.
Dissecting cell‐subtype specific regulatory variants and their target genes underpinning the pathogenesis of late onset Alzheimer’s disease
Authors: Gamache Julia , Gingerich Daniel , Garrett Melanie E , Barrera Julio , Ashley‐Koch Allison , Crawford Gregory , Chiba‐Falek Ornit
Published: December 2022

Background

The genetic architecture of late‐onset Alzheimer’s disease (LOAD) is yet to be fully explored. While GWAS discovered numerous LOAD‐associated loci, the causal variants and their target genes remain largely unknown. We aim to advance our understanding of the genetic architecture of LOAD by systematic interrogation of elements and variants underlying gene dysregulation in LOAD in a cell‐subtype resolution.

Methods

We conducted a single‐nucleus (sn)multi‐omics study on the 10X Genomics platform using 24 frozen brain tissues, 12 normal and 12 LOAD. The parallel snRNA‐seq and snATAC‐seq collected from the same nuclei samples and at the same time were used in multimodal analysis to profile in gene expression and chromatin accessibility.

Results

snRNA‐seq data from a total of 202,223 nuclei were grouped into 33 clusters and snATAC‐seq from 79,120 nuclei were separated into 25 clusters. We identified multiple cell subtype specific, LOAD‐associated differential expressed genes (DEGs), differential accessible peaks (DAPs) and cis co‐accessibility networks (CCANs) in LOAD. Integrative analysis focused on 110 LOAD CCANs out of ∼15,000 with at least one peak overlap promoter/intron 1 of DEG that contain >1 DAPs, identified 331 functional candidate cis regulatory elements (cCRE) and 118 linked genes across all cell subtypes. Out of which 12 CCANs encompassing a total of 54 cCREs and their 13 target genes overlap LOAD‐GWAS regions. Finally, we catalogue putative functional SNPs within cell subtype‐specific cCREs that interrupt with transcription factor (TF) motifs. For example, in subtypes of excitatory neurons we identified SNPs in cCREs linked to RPS15, FKBP5 that strongly affect the TF motifs of GSC2 and TBX3; and found in microglia multiple SNPs within APOE region with weaker effects on four TFs.

Conclusion

To our knowledge this study represents the most comprehensive integrative single‐cell genomics study in LOAD. We provide new insights into the interactions between the genome, epigenome, and transcriptome in LOAD brains in an unprecedented cell‐subtype specific resolution. The outcomes of this work prioritize CREs, genetic variants and their linked genes for experimental validation in disease model systems using genome editing technologies. In summary, our findings enhance the translation of LOAD‐genetic risk into mechanistic understanding of causation.
Towards Discovering Digital Twins of Dementia Patients: Matching the Phases of Cognitive Decline
Authors: Wickramasinghe Nilmini , Ulapane Nalika , Nguyen Tuan Anh , Andargoli Amir , Ossai Chinedu , Shuakat Nadeem , Zelcer John
Published: December 2022

Background

Digital twins in healthcare is an emerging area. Twins are created from data of past patients. The objective is to facilitate data‐driven decision support for clinicians for more precise and personalized dementia care. The project has three phases: (1) Twin discovering algorithm development from synthetic data; (2) Algorithm validation with real patient data; (3) Clinical implementation. The current work is related to Phase 1.

Method

Past subjects (n=30) showing cognitive decline (based on MMSE scores) mimicking a sample monitored over three years (i.e., 365 x 3 = 1,095 days) were synthetically generated. All past subjects show monotonically decreasing MMSE scores, and have had varying numbers of cognitive tests (between Three and Nine) across three years, unevenly spaced in time. Fig. 1 shows Three examples. Five present subjects (n=5) were also synthetically generated. Present subjects have had only their first two cognitive tests, unevenly spaced in time (i.e., 90 or 180 days—see Fig. 2). Since cognitive decline is traditionally modeled linearly, linear interpolation was used over present and past subjects to fill out missing data between cognitive tests. Post interpolation, an MMSE score is available for every day between a subject’s first and last cognitive assessment. Then, for each present subject, the best matching past subject and their phase of cognitive decline—i.e., a digital twin from history that is closest to the present subject—were determined via Mean Absolute Percentage Error (MAPE) calculation between MMSE scores of a present subject and past subjects.

Results

Digital twin segments showing MAPE < 2%, i.e., 100 ‐ MAPE > 98%, were identifiable for all Five present subjects. Table 1 shows the Five best matching digital twin segments for each present subject. Fig 3 graphically shows a matching.

Conclusion

Results to discover digital twins via matching phases of cognitive decline are positive. This study looked at only the MMSE score as a one‐dimensional metric, for proof of concept. The validity for such one‐dimensional arrays, indicate generalizability for multi‐dimensional data (i.e., a collection of multiple streams of one‐dimensional data) collected over time, for discovering more comprehensive digital twins.
Sex hormones and Alzheimer’s disease: a Mendelian randomization approach
Authors: Kusters Cynthia Diana Johanna , Ritz Beate R
Published: December 2022

Background

To assess the association between sex steroid hormones with Alzheimer’s disease (AD). Sex hormones have been hypothesized to influence AD risk. However, studies of AD have been inconclusive. Here, we used a Mendelian randomization approach to assess the association between sex steroid hormones with Alzheimer’s disease (AD).

Methods

We performed inverse‐variance weighting (IVW) MR analysis using external GWAS summary to retrieve the effect estimates between genetic variants and sex hormone levels and GWAS summary statistics for Alzheimer’s disease from the ADGC, stratified by sex. We included independent variants (linkage disequilibrium R2<0.001) and a P‐value of 5x10‐8 in the GWAS for sex hormones.

Results

Our preliminary findings indicate an association between certain sex hormone concentrations and AD risk. Specifically, an increase in testosterone appears to be associated with a decreased AD risk among men (OR: 0.57; 95%CI: 0.33 – 0.99; P‐value 0.04), a decreased AD risk with an increase in estrone among women (OR: 0.46; 95%CI: 0.27 – 0.79; P‐value 0.005). Among the total population an increase in androstenedione and DHEAS were associated with a decrease in AD risk (OR: 0.83; 95%CI: 0.71 – 0.96; P‐value 0.02; OR: 0.48; 95%CI:0.24 – 0.95; P‐value: 0.03).

Conclusions

We found an indication that various sex hormones may have a protective effect for AD. Some findings indicate sex‐specific associations. Though further analysis should be done, these preliminary findings indicate sex hormones may influence AD risk, further supporting the hypothesis that sex hormones may be neuroprotective.
The SuperAging Research Initiative: A multisite consortium focused on identifying factors promoting extraordinary cognitive aging
Authors: Rogalski Emily J. , Huentelman Matthew J , Roberts Angela C , Maher Amanda Cook , McIlroy Bill , Van Ooteghem Karen , Finger Elizabeth , Lim Andrew , Okonkwo Ozioma C. , Goldstein Felicia C , Hajjar Ihab , Parrish Todd , Scholtens Denise , Gutierrez Stephanie , Weintraub Sandra , Geula Changiz , Mesulam M.‐Marsel
Published: December 2022

Background

The designation of SuperAger is reserved for individuals age 80+ who have episodic memory capacity that would be considered at least average for those 2‐3 decades younger. The presence of such outliers raises questions of fundamental importance to the neurobiology of brain aging. Have these superior memory performers resisted age‐related changes, or have they simply started from a much higher baseline? Do they have identifiable peculiarities of genetic background? Is there something special about their brain structure or perhaps their resistance to age‐related processes such as neurofibrillary degeneration and amyloid deposition? These are the questions that were initially addressed by the Northwestern SuperAging Project, which identified unique results encompassing cognitive, psychosocial, molecular, and neuropathologic markers that characterize SuperAgers. Obstacles to further progress have been the relative rarity of this phenotype and, consequently, the barriers to racial diversity in the cohort.

Methods

To address these challenges, we established the SuperAging Research Initiative, a multicenter study focused on increased minority representation, to identify behavioral, health, biologic, genetic, environmental, socioeconomic, psychosocial, neuroanatomic, and neuropathologic factors associated with SuperAging.

Results

Here we provide the organizational structure and progress to date of the SuperAging Research Initiative, which includes three Cores (Administrative/Biostatistics, Clinical/Imaging, and Biospecimen/Neuropathology) and two Research Projects. Enrollment (n = 500) is planned across four US Sites located in Illinois, Wisconsin, Michigan, and Georgia, and a Canadian Site in Southwest Ontario, with a focus on enrollment of Black SuperAgers and Cognitively Average Elderly Controls with similar demographics. Project 1 uses state‐of‐the‐art wearable technology to obtain quantitative everyday measurements of life sleep, physical activity, autonomic responsivity, and social engagement to determine whether SuperAgers have relatively preserved physiologic and behavioral ‘complexity’ compared to Controls. Project 2 focuses on transcriptomic, genetic, and protein profiling to examine central and peripheral immune and inflammatory system parameters of SuperAgers.

Conclusions

By identifying factors contributing to superior memory performance in old age, outcomes may help isolate modifiable factors that promote healthspan and perhaps also prevent age‐related brain diseases such as Alzheimer’s disease.
Head‐to‐Head comparison between Philips Gemini TF64 and Siemens Biograph Vision 600 for brain amyloid Centiloid quantitation
Authors: Li Shenpeng , Bourgeat Pierrick , Bozinovski Svetlana , Huang Kun , Guzman Rodney , Williams Robert , Fripp Jurgen , Villemagne Victor L , Rowe Christopher , Dore Vincent
Published: December 2022

Background

The Centiloid (CL) scale calibrates the beta‐amyloid (Aβ) deposition from different PET tracers to a standardised 0‐100 CL unit scale. As imaging sites update their PET cameras, most are switching to digital detector systems with superior resolution and sensitivity that may affect quantitation. This has significant implications for dementia clinical trials. In this study, we examine the impact on CL quantification between Philips Gemini TF64 and Siemens Biograph Vision 600.

Method

Seven subjects (76.4±2.2 yo) were imaged with 18F‐NAV4694 on both Gemini TF64 and Biograph Vision consecutively with an average scan interval of 25.1±11.2 weeks. The injected doses were 200MBq and 100MBq, respectively. On the Gemini TF64, the PET images were reconstructed by LOR‐RAMLA algorithm with smoothing parameter setup as ‘SHARP’. On Biograph Vision, the PET images were reconstructed by OSEM‐3D (8 iterations and 5 subsets, TOF enabled) with 3mm post Gaussian smoothing. A T1 MRI image was acquired for each subject. As per the standard Centiloid method the whole cerebellum was used as the reference in SUVR images, and all images were processed using CapAIBL to calculate the CL using both MR‐based and MR‐Less spatial normalisation.

Result

Figure 1 shows the CL images of a subject scanned on Gemini TF64 and Biograph Vision within sixteen weeks. The Biograph Vision images have higher contrast and higher spatial resolution despite using half of the dose. Figure 2 shows the linear regression plot of the scanner comparison. Biograph Vision CL are progressively higher than those obtained from the Gemini TF64 as the CL value rises (Table 1). There were no significant differences between the MR‐based and MR‐less results.

Conclusion

Biograph Vision yields higher SUVR and therefore CL values compared to Gemini TF64 in a head‐to‐head comparison. These results show that the selection of PET camera has a significant impact on CL quantification, which needs to be considered when merging cohorts from different studies or changing cameras during longitudinal studies or trials. These initial results indicate that the CL difference could be corrected by a linear transform.
ADSP Functional Genomics: from gene, to function to mechanisms and targets
Authors: Cruchaga Carlos
Published: December 2022

Background

Genetic studies have been highly successful in identifying genetic regions associated with Alzheimer’s disease, and we are now at the exciting juncture of applying this knowledge to understanding disease mechanisms. The ability to generate and mine clinical and omicdata is advancing our understanding of neurodegeneration. The Alzheimer’s Disease Sequencing Project (ADSP) Functional Genomics Consortium (FunGen‐AD; https://adsp‐fgc.niagads.org/) aims to apply cutting‐edge genomics technologies, high‐throughput genetic screening and cutting edge disease modeling to understand the functional consequences of genetic susceptibility and resilience to Alzheimer’s Disease, and to identify genetics‐guided targets for the prevention, diagnosis, and treatment of Alzheimer’s disease and related dementias (AD/ADRD).

Method

The investigators of the FunGen‐AD consortium are using multiple approaches, including brain single cell data acquisition, human stem cell modeling and high through put CRISPR‐based screens, to understand how AD variants lead to changes across molecular networks, and understand how specific risk variants affect disease in diverse populations. Multi‐omic data generated in well characterized cohorts are being used to identify systems‐level alterations in and provide insight into the mechanisms underlying genetic variants.

Result

The FunGen‐AD consortium is organizing and facilitating large collaborative projects. Currently we are working on a xQTL project putting together data from multi‐tissue (brain, myeloid cells, CSF and plasma), and multi‐omic layers (transcriptomic in bulk and at single cell level, epigenetic, proteomic, and metabolomic). In order to fully understand the biology of AD we recognize that multiple tissues, and ‐omic layers need to be studied. All the omic data are being processed, harmonized and analyzed using standard pipelines, and mapping on a reference genome and annotation developed by the group. This QTL atlas will be used to perform colocalization and identify the functional genes within AD risk loci, and Mendelian Randomization to identify novel causal genes, proteins and druggable targets. This xQTL project will provide a rich resource of multi‐omics datasets, analysis pipelines, and an xQTL atlas for the research community studying AD/ADRD and other complex neurodegenerative traits.

Conclusions

In summary, the FunGen‐AD consortium is using multidisciplinary and collaborative research approaches to identify genetics‐guided targets for the prevention, diagnosis, and treatment of AD/ADRD.
Neuropsychologically derived errors as predictors of neurocognitive phenotypes: The Framingham Heart Study
Authors: De Anda‐Duran Ileana , Drabick Deborah AG , Hwang Phillip H , Emrani Sheina , Matusz Emily , Wasserman Victor , Swenson Rodney , Andersen Stacy L. , Liu Chunyu , Au Rhoda , Libon David
Published: December 2022

Background

Data‐driven approaches are powerful for identifying impaired neurocognitive constructs and aid in characterizing neurocognitive impairment patterns. There is, however, a significant clinical need for time‐efficient diagnostic tools compared to traditional more time‐consuming neuropsychological (NP) assessments. We evaluated how NP errors can compress the number of tests needed to differentiate among neurocognitive phenotypes.

Method

We studied 1,915 Framingham Heart Study (FHS) Offspring participants (age: 70 ± 8 years, 56% women). Three neurocognitive phenotypes were identified by Latent Profile Analysis (LPA) using nine NP summary scores: Cognitively normal (CN; n = 1060, 88.7%); Moderate‐Mixed (n = 72, 6.0%); and Dysexecutive (n = 63, 5.3%). Receiver operating characteristic (ROC) analysis compared the predictive accuracy of NP errors in identifying the distinct neurocognitive phenotypes. Regression models evaluated associations between significant NP errors (i.e., with an area under the curve (AUC) ≥ 0.64) and neurocognitive phenotypes, adjusting for age, sex, and education. The CN group was used as reference for comparisons.

Result

Dysexecutive subgroup participants were older (77 ± 8 years) with less education (8 ± 3 years). Individuals producing any of the errors displayed in Figure 1 were 1.3 times more likely to be in the moderate/mixed and dysexecutive group [OR‐range = 1.3 ‐ 3.4, all p’s< 0.001]. Participants generating Trail Making Test‐Part B (TMT‐B) examiner‐corrected non‐perceptual errors were 3 times more likely to be classified into the dysexecutive [OR = 3.44, 95% CI (2.7, 4.4), p< 0.001] and moderate/mixed phenotypes [OR = 3.21, 95% CI (2.13, 4.85), p< 0.001]. Participants were more likely to have a moderate/mixed phenotype with Boston Naming Test (BNT) perceptual [OR = 2.1, 95% CI (1.7, 2.5), p< 0.001] and semantic errors [OR = 1.6, 95% CI (1.4, 21.9), p< 0.001].

Conclusion

An analysis of errors derived from the TMT‐B and BNT provided evidence for the presence of two distinct but complementary and impaired neurocognitive constructs: dysexecutive/ inhibition impairment (TMT‐B) and struggling to access lexical information (BNT). Since cognitive screeners (e.g., MoCA) incorporate briefer versions of these tests, these errors may aid in characterizing brain‐behavior relationships and identifying persons at risk for the emergence of dementia‐related syndromes in a less costly and burdensome manner than standard NP protocols.
Development of iPSC‐derived AD platforms for phenotypic screening
Authors: Poon Wayne , Zhou Peng , Murchison Angela , Noel Christopher , Haag Daniel , Wu Ji , Huang Tao
Published: December 2022

Background

Human iPSC‐derived in vitro neural co‐culture systems have been used increasingly in drug discovery for neurological and neurodegenerative disorders.

Method

Here we describe the development of iPSC‐derived platforms of AD that can be used to screen for preclinical targets and drug candidates. The foundation of these platforms utilizes a proprietary method for the generation of pure populations of NGN2‐based excitatory and ASCL1/DLX2 inhibitory neurons.

Result

Transgenic overexpression of tau isoforms leads to prion‐like tau spreading that can be used to screen for compounds to mitigate tau propagation. Using this platform, we investigated how tau overexpression leads to neuron‐neuron tau transmission, alters neuron electrophysiology as assessed by MEA, and leads to the accumulation of key neuropathological tau biomarkers. In addition, we have developed co‐culture platforms that facilitate examination of the role of neuroinflammation on the development of key neuropathological hallmarks of AD. This platform can be used in conjunction with isogenic iPSCs to interrogate the mechanism by which genetic risk factors confer increased or decreased AD risk.

Conclusion

Taken together, these phenotypic assays demonstrate the utility of NeuCyte platforms that can be applied to high‐throughput drug screening efforts for AD.
Modifiable dementia risk factors associated with higher CSF tau and poorer cognition in middle‐aged adults
Authors: Bransby Lisa , Rosenich Emily , Buckley Rachel F. , Maruff Paul , Yassi Nawaf , Lim Yen Ying
Published: December 2022

Background

Modifiable factors in domains such as mood, lifestyle behaviours, cardiovascular health, cognitive/social engagement, and sleep are associated with increased risk for dementia. These modifiable risk factors could promote Alzheimer’s disease (AD) related biological processes, such as beta‐amyloid (Aβ) or tau accumulation, but these associations remain poorly understood. This study aimed to determine associations between modifiable dementia risk factors with Aβ, tau, and cognition in cognitively unimpaired middle‐aged adults.

Method

Middle‐aged adults (n = 82) (age range 40‐70) enrolled in a biomarker sub‐study of the Healthy Brain Project completed self‐report questionnaires about their physical and psychological health and lifestyle. Cerebrospinal fluid (CSF) levels of Aβ42, total tau (t‐tau), and phosphorylated tau (p‐tau) (Roche Elecsys) were obtained. A comprehensive neuropsychological battery was administered to measure cognition, and composite scores were derived for memory, executive function, and the Preclinical Alzheimer’s Cognitive Composite (PACC). Participants were classified according to reporting normal modifiable risk (NMR) (risk in ≤1 domains; n = 34) or high modifiable risk (HMR) (risk in ≥2 domains; n = 48) across five domains (mood, lifestyle behaviours, cardiovascular health, cognitive and social engagement, and sleep).

Result

Compared to those with NMR, those with HMR had increased t‐tau (d = 0.54, p = .021) and p‐tau (d = 0.47, p = .044) levels but did not differ on Aβ42 (d = 0.23, p = .319) after adjusting for age and sex. With age, sex, and education adjusted for, HMR participants showed worse performance on PACC (d = 0.69, p = .003), executive function (d = 0.55, p = .016), and episodic memory (d = 0.40, p = 0.076) compared to NMR participants. Differences in cognitive performance remained when levels of Aβ42, t‐tau and p‐tau levels were controlled in the models [PACC (d = 0.59, p = .011), memory (d = 0.64, p = .005) and executive function (d = 0.45, p = .049)].

Conclusion

Modifiable dementia risk factors across multiple domains are related to higher total and phosphorylated tau levels and with subtle cognitive deficits, but not with Aβ, in middle‐aged adults. Together with the observation that differences in cognition across modifiable risk groups remained when Aβ and tau levels were controlled statistically, this suggests that modifiable dementia risk factors may increase risk through neurodegenerative processes non‐specific to AD, such as increased cerebrovascular burden, although this needs to be confirmed in future neuroimaging studies.
Impact of left censoring on longitudinal analyses of dementia using electronic health record data
Authors: Jiang Luohua , Shi Yuxi , Conniff Kyle R , Corrada Maria M. , Manson Spero , O'Connell Joan
Published: December 2022

Background

Longitudinal electronic health record (EHR) data provides an opportunity to evaluate associations between certain risk factors and dementia incidence. In practice, researchers using EHR usually extract data for multiple years and use the first 2‐4 years as a washout period to exclude baseline prevalent cases. However, identifying dementia via clinical diagnostic codes during a washout period likely under‐detect some cases, with time to dementia diagnosis for these cases left censored and estimates for the associations of interest potentially biased. This study aims to assess the amount of left censoring when analyzing time to dementia using EHR data from the Indian Health Service (IHS).

Methods

We extracted data from the IHS National Data Warehouse and related EHR databases between fiscal year (FY) 2007‐2013. Adults aged 45+ years were identified as having dementia if they had at least one qualifying ICD‐9 diagnostic code for all‐cause dementia. A total of 1,117 AI/AN adults were identified as dementia patients in FY2007. Among these, we calculated the proportions of patients with 1+ ICD‐9 code for dementia within 1‐5 years after FY2007. We used diabetes, identified in a similar manner, as a comparison condition.

Results

Among the FY2007 dementia patients who used IHS services in FY2008, only 63.7% had 1+ qualifying ICD‐9 code for dementia in FY2008. In contrast, 93.7% of FY2007 diabetes patients had an ICD‐9 code for diabetes in FY2008 (Fig1). If FY2007 data are not included in the analytical dataset and only one year of data (FY2008) is used as the washout period, the remaining 36.3% prevalent dementia cases are classified as incidence after FY2008 with their time to dementia left censored. Even using a 5‐year washout period, 25.0% prevalent dementia patients in FY2007 are classified as incident cases after FY2012 (Fig2a) while 4.6% prevalent diabetes patients in FY2007 are classified as incidence (Fig2b).

Conclusions

Longitudinal analysis of dementia risk using EHR data might be subject to relatively high amount of left censoring. It is important to recognize the potential biases associated with relatively high proportion of left censored observations when estimating longitudinal associations of risk factors with dementia incidence.
A Cluster‐Based Machine Learning Approach for Evaluating Factors Associated with Tau Protein in Plasma
Authors: Hu Yi‐Han , Meirelles Osorio , Shrioma Eric , Satizabal Claudia L , Tracy Russell P. , Launer Lenore J.
Published: December 2022

Background

Plasma total tau (t‐tau) has shown acceptable diagnostic accuracy for Alzheimer’s disease (AD), but other factors than levels of t‐tau in the brain may influence peripheral levels. While little is known about peripheral factors that may influence plasma levels of t‐tau, such factors may be similar to those associated with AD, such as cardiovascular risk factors, which are often correlated. We have developed a machine learning pipeline to identify individual and clusters of correlated variables that might explain a proportion of the variance of plasma t‐tau levels in a well‐phenotyped population‐based cohort.

Method

We studied 992 dementia‐free older adults (mean age 76 years, 44% men) in the Age, Gene/Environment Susceptibility‐Reykjavik Study. We used agglomerative hierarchical clustering along with principal component (PC) analysis to group factors with moderate‐to‐high similarity from 290 potential predictors including socio‐demographic, anthropometric, laboratory, cardiometabolic and lifestyle factors, diseases and medications. We then calculated the PCs of the clusters and examined their relationship to plasma t‐tau. We trained two nonlinear models using t‐tau as the dependent outcome variable ‐ the extreme gradient boosting model (GBM) and support vector machine (SVM) ̶ on 80% of data using a ten‐fold cross‐validation approach; the models were evaluated on the remaining 20% of data. Model performance was assessed using the root mean square error (RMSE) and correlation. SHapley Additive exPlanations (SHAP) was used to rank and interpret the contribution of the clusters to the best‐performed model.

Result

We identified 25 distinct clusters. The best performing model was a SVM model including age, sex and the first PC from all 25 clusters(Figure 1). Based on the SHAP values in this model, we found the top 10 ranked clusters associated with p‐tau(Figure 2): platelets(5 variables), body size(33), physical activity‐walk(6), and tobacco(9) were negatively associated with t‐tau level, while body fat(8), sex, cognitive function(27), inflammation(7), blood pressure(6), and red blood cells(11) were positively associated with t‐tau.

Conclusion

Our novel approach suggests additional factors explain some of the variance in peripheral tau levels in older adults. Additional research is needed to confirm these findings and investigate whether these factors modulate or mediate tau‐related pathology.
Reducing misdiagnosis of Alzheimer’s Disease pathology utilizing CSF and amyloid PET
Authors: Esquivel Rianne N , De Simone Francesca , Benina Natalya , Gannon Sara , Bastard Nathalie Le , Calabro Amanda , Hawkins Douglas M , Vandijck Manu , Latham Jessica , Dickson Diana , Radwan Rachel R.
Published: December 2022

Background

Utilizing cognitive tests alone, including the Mini‐Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), cannot detect the presence of amyloid plaques and tangles in Alzheimer’s disease (AD). While in late stages of AD clinical diagnosis relying primarily on cognitive testing is correct 70‐90% of the time, in MCI accuracy is reduced to 50‐60%. Although autopsy is the gold standard for AD diagnosis, amyloid PET imaging and more recently the Lumipulse Gβ‐Amyloid Ratio (Aβ1‐42/Aβ1‐40) are validated to determine amyloid pathology. With the upcoming availability of disease modifying therapies targeting amyloid in MCI and early AD, and anti‐tau drugs in the pipeline, it is necessary to have true measures of AD pathology for a clinical evaluation early in the disease process. Here we examine the performance of cognitive testing alone for identification of amyloid positivity in MCI patients from the ADNI study when compared to amyloid PET and CSF testing.

Method

A subset of 170 CSF samples from the ADNI biobank (all MCI; ≥ 50 years old; amyloid PET negative (n=74) or amyloid PET positive (n=96)) was used to examine correlation of PET, CSF and MMSE/MoCA scores. Amyloid PET images were derived from an FDA approved tracer (Florbetapir F18) and method. CSF Aβ1‐42 and Aβ1‐40 were measured using the LUMIPULSE G1200 (Fujirebio).

Result

At a set cutoff of 0.058, Aβ1‐42/Aβ1‐40 demonstrated a PPA and NPA of 85.9% and 93.5% respectively for identifying amyloid PET status in individuals suffering from cognitive complaints. When examining the Aβ1‐42/Aβ1‐40 ratio of the MCI patients compared to amyloid PET in ADNI, Aβ1‐42/Aβ1‐40 achieved an AUC of 0.90. Alternatively, the MMSE score and MOCA score had AUCs with amyloid PET of 0.61 and 0.63 respectively and exhibited poor correlation with CSF.

Conclusion

CSF measurements or amyloid PET provide greater accuracy in determining amyloid status than cognitive screening tests. MMSE or MoCA as standalone tests give low confidence in a correct pathological diagnosis for MCI patients. Moving forward it will be important to consider PET or CSF results to determine underlying pathology in patients with MCI to ensure proper treatments and avoid unwanted side effects.
Population‐based Discovery and Ethnically Mixed Mendelian Randomization Validation Identifies Telmisartan as a Candidate Drug for Alzheimer’s Disease in African Americans
Authors: Cheng Feixiong , Zhang Pengyue , Hou Yuan , Pieper Andrew A. , Leverenz James B. , Cummings Jeffrey
Published: December 2022

Background

Elevated incidences of Alzheimer’s disease (AD) and disease progression may be associated with African American (AA)‐specific disease comorbidities, such as hypertension, diabetes, and chronic kidney disease. Treatments and management of these comorbidities — such as antihypertensive angiotensin‐II receptor blockers (ARBs) — may reduce risk of AD in AA people, while conferring additional beneficial effects on renal function and diabetes control.

Method

We conducted a race‐specific pharmacoepidemiologic study to investigate the association between telmisartan (an ARB having PPAR‐gamma (PPARG) agonistic properties and beneficial anti‐diabetic effects) use and AD using Cox analysis, Kaplan‐Meier analysis, and propensity score‐matched log‐rank test. We further conducted ethnically mixed Mendelian randomization analysis to identify likely causal relationships between telmisartan use and AD in AA people. All models were adjusted for age, sex, underlying health, and comorbidities.

Result

In Cox analyses of ARB users in insurance claim data, telmisartan use was significantly associated with reduced risks of AD in AA people (Hazard ratio [HR] = 0.77, P‐value = 0.002), but was not associated with AD reduction in non‐Hispanic White Americans (HR = 0.97, P‐value = 0.411). Compared to lisinopril (an angiotensin‐converting‐enzyme inhibitor) use, telmisartan use was associated with stronger reduced risk of AD in AA people (HR = 0.68, P‐value < 0.001). In all sensitivity and secondary analyses, telmisartan use was associated with stronger protective effects (including AD and dementia) in AA people. Using ethnically mixed Mendelian randomization analysis from large genome‐wide association studies (over 2 million individuals) across AD, hypertension, and diabetes, we demonstrated that telmisartan has greater beneficial effect on AD in AA people compared to European populations.

Conclusion

We identified that telmisartan is a candidate drug for potential prevention and treatment of AD in AA people, but not in non‐Hispanic White Americans. Our findings indicate that early comorbidity management (hypertension, diabetes, and kidney dysfunction) by telmisartan will significantly reduce AD incidence for aged individuals, in particular for aged AA people. The association between telmisartan use and decreased incidence of AD in AAs will require a randomized controlled trial with an ethnically diverse population to establish causality and explore therapeutic outcomes.
A Network‐based Deep Learning Framework Translates GWAS and Multi‐Omics Findings to Pathobiology and Drug Repurposing for Alzheimer’s Disease
Authors: Xu Jielin , Hou Yuan , Zhou Yadi , Bekris Lynn M. , Pieper Andrew A. , Cummings Jeffrey , Leverenz James B. , Cheng Feixiong
Published: December 2022

Background

Human genome sequencing studies have identified numerous loci associated with complex diseases, including Alzheimer’s disease (AD). However, translating human genetic and genomic findings (i.e., genome‐wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge. Today, artificial intelligence and deep learning approaches that identify new risk genes and drug targets from human genome sequencing findings are enabling a more complete mechanistic understanding of disease biology. This is facilitating more rapid development of targeted therapeutic interventions for AD.

Method

We presented a network topology‐based deep learning framework to identify disease‐associated genes (NETTAG). NETTAG integrates multi‐genomics data and the human protein‐protein interactome to infer putative risk genes and drug targets impacted by GWAS loci. Specifically, we leveraged non‐coding GWAS loci effects on expression quantitative trait loci (eQTLs), histone‐QTLs, transcription factor binding‐QTLs, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions. The fundamental premise of NETTAG is that disease risk genes exhibit distinct functional characteristics compared to non‐risk genes, and can therefore be distinguished by their aggregated genomic features under the human protein interactome.

Result

Applying NETTAG to the latest AD GWAS data, we identified 156 putative AD‐risk genes (i.e., APOE, BIN1, GSK3B, MARK4, and PICALM). We showed that predicted risk genes are: 1) significantly enriched in AD‐related pathobiological pathways, 2) more likely to be differentially expressed in transcriptomes and proteomes of AD brains, and 3) enriched in druggable targets with approved medicines. Specifically, we showed that NETTAG‐predicted genes (e.g., MEF2D, CPLX2, KLF4, ACTL6B, P2RX7 and etc.) are differentially expressed in AD‐associated microglia and astrocytes from single‐nuclei RNA‐sequencing data of human postmortem brains with varying degrees of AD neurobiology. Via network‐based prediction, we identified multiple repurposable drug candidates (i.e., choline, deferoxamine and ibudilast) for potential treatment of AD.

Conclusion

Our findings suggest that understanding human pathobiology and therapeutic development could benefit from network‐based deep learning methodology that utilizes GWAS findings with multimodal genomic analyses.
BAG3 attenuates tau hyperphosphorylation and gliosis induced by traumatic brain injury
Authors: Morrison Cody , Li Liangping , Liang Jiawen , Chen Shuo , Acosta Diana M , Fitzgerald Julie A , Karelina Kate , Bray Chelsea E , Weil Zachary M , Godbout Jonathan , Kokiko‐cochran Olga N , Saito Takashi , Saido Takaomi C. , Fu Hongjun
Published: December 2022

Background

Alzheimer disease (AD) and traumatic brain injury (TBI) are two devastating brain disorders with complex relationships. Growing evidence supports that early or middle life of TBI may be a risk factor for developing late‐life AD and AD‐related dementias (ADRD). Tau hyperphosphorylation and gliosis may serve as a causative link between TBI and AD as well as ADRD. We have recently identified BCL2 associated athanogene 3 (BAG3) as a hub gene of regulating tau protein homeostasis. The protein level of BAG3 is significantly reduced in neurons, but it is increased in astrocytes of human AD compared to controls. We hypothesized that overexpression of BAG3 in neurons would attenuate tau hyperphosphorylation and gliosis induced by TBI.

Method

We injected 500 nL AAV9‐hSYN1‐eGFP‐2A‐hBAG3‐WPRE or AAV9‐hSYN1‐eGFP‐WPRE (control AAV9) into the hippocampal CA1 and DG regions of C57BL6/J and htau knock‐in mice. These mice were subjected to TBI surgery (controlled cortical impact, velocity: 3.00 m/s, depth: 0.8 mm, dwell time: 200 ms) three months post the injection. We then performed behavioral tests (open‐field test, Y‐maze, and Morris Water Maze) on these mice one month after the CCI surgery. Following the behavioral tests, we collected the brain tissues and performed the immunofluorescent staining of ptau (PHF1, pS396/404 tau), BAG3, IBA‐1 (microglia/macrophage marker) and GFAP (astrocyte marker) on fixed mouse brain floating sections.

Result

TBI increased the immunoreactivity of PHF1, IBA‐1, GFAP and astrocytic BAG3, whereas reducing neuronal BAG3 compared to sham C57BL6/J and htau knock‐in mice. Furthermore, the immunoreactivity of BAG3 increased, while PHF1, IBA‐1 and GFAP decreased in the ipsilateral hippocampus after the injection of AAV9‐BAG3 compared to those TBI mice injected with control AAVs. Continued immunofluorescent staining and quantification for AAV9‐BAG3‐ and control AAV9‐injected mice is currently ongoing, as is behavior data quantification.

Conclusion

TBI induces tau hyperphosphorylation, microglia activation and reactive astrocytes, while reducing neuronal BAG3 in C57BL6/J and htau knock‐in mice. Neuronal overexpression of BAG3 can significantly attenuate tau hyperphosphorylation and gliosis induced by TBI. Our data suggests that targeting neuronal BAG3 may be a therapeutic strategy for preventing or reducing intra‐neuronal tau aggregates and gliosis found in TBI and TBI‐associated AD and ADRD.
Combined segmentation and classification of Alzheimer’s disease pathology distribution within human hippocampal subregions
Authors: Stephen Terri‐Leigh , Korobkova Laura , Jones Kymry T , Cabeen Ryan P , Pachicano Maricarmen , Hawes Debra , Miller Carol A , Bienkowski Michael S
Published: December 2022

Background

Alzheimer’s disease (AD) is a complex neurodegenerative disease characterized by progressive amyloid and tau pathology. Quantifying and classifying AD pathology across the human brain is challenging due to its large size and expertise needed for critical evaluation of the morphological variety in structure types (e.g. diffuse vs dense plaques, neurofibrillary tangles, neuropil threads, etc.). High‐throughput microscopy imaging and artificial intelligence allow for large‐scale, quantitative ‘digital pathology’ surveys. Due to their structural differences, distinct pathological aggregates in microscopy images can be identified and quantified by trained artificial neural networks (Tang et al 2019). However, current approaches do not incorporate anatomical segmentation to map the distribution of AD pathology across whole tissue sections. Understanding where and when AD pathology is distributed across different brain regions is critical to defining cell type susceptibility to neurodegeneration. This project’s goal was to create a combined neural network classification and segmentation quantification approach to generate anatomically‐accurate pathology maps of the human hippocampus and investigate the spatial distribution of distinct types of pathology.

Method

Whole slide images of 4G8 and Gallyas stained coronal tissue sections from the USC ADRC Neuropathology Core were imaged using a slide scanning microscope (29 patients with low, intermediate, or high AD stages). 4G8 sections were manually annotated and segmented using the Allen Human Brain Atlas into thirteen layered hippocampal subregions and delineated using Quantitative Imaging Toolkit (QIT). The machine learning toolkit Ilastik was utilized to quantify and classify 4G8 amyloid staining (dense vs diffuse plaques) and Gallyas (neurofibrillary tangles) within each hippocampal subregion and overlaid to segmentation maps.

Result

In the hippocampus, we found diffuse amyloid pathology increased more rapidly across progressive AD stages compared to dense amyloid plaques or tau neurofibrillary tangles. The ratio of diffuse/dense labeling was highest in the presubiculum and parasubiculum in high AD patients. Patient APOE4 genotype also significantly increased the fraction of diffuse amyloid labeling in high AD patients.

Conclusion

Integrating machine learning quantification and classification approaches with anatomical segmentation maps allows for rapid characterization of AD pathology across hippocampal subregions and provides new insights into AD progression across the brain.