Transcript levels in plasma contribute substantial predictive value as potential Alzheimer's disease biomarkers in African Americans

Reddy, Joseph S.; Jin, Jiangli; Lincoln, Sarah; Ho, Charlotte C.G.; Crook, Julia E.; Wang, Xue; Malphrus, Kimberly G.; Nguyen, Thuy; Tamvaka, Nikoleta; Greig‐Custo, Maria T.; Lucas, John A.; Graff‐Radford, Neill R.; Ertekin-Taner, Nilüfer; Carrasquillo, Minerva M.

doi:10.1016/j.ebiom.2022.103929

Cited by 4 publications

(4 citation statements)

References 43 publications

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“…In contrast, the AMP-AD Target Discovery and Preclinical Validation Project generated [21–24] and broadly shared [25] multi-omics data on >2,500 brain samples, primarily from NHW donors. These multi-omics data revealed brain molecular alterations in specific biological pathways, including but not limited to innate immunity, synaptic biology, myelination, vascular biology, and mitochondrial energetics [28–30,32–34,37,39,45,54,86–89], thereby supporting complex, heterogeneous molecular etiologies, resulting in >600 therapeutic candidates with a step closer to precision medicine in ADRD.…”

Section: Discussionmentioning

confidence: 99%

“…We must emphasize that multi-omics studies alone are unlikely to be sufficient to discover all causes of ADRD or explain the disparities in risk observed for AA and LA participants [4,6,90]. Rather, this requires a full understanding of the role of the exposome, including sex, race, ethnicity, lifetime health measures, co-morbidities, and additional structural and social determinants of health (SSDoH) [54,[91][92][93][94][95][96]. Only by capturing the exposome and evaluating its complex interactions with multi-omics measures and disease-related outcomes can we have a holistic lens into the etiopathogenesis of ADRD.…”

Section: Discussionmentioning

confidence: 99%

“…Despite these advances, such multi-omics studies of AD and related disorders (ADRD) have lacked sampling from AA and LA populations with few exceptions [54,55]. To bridge the data and knowledge gap in multi-omics studies of underrepresented populations in AD research, AMP-AD investigators launched a diversity initiative to expand molecular profiling of brain tissue to multi-ethnic donors.…”

Section: Introductionmentioning

confidence: 99%

“…These target nominations and the associated data, including a set of curated genomic analyses and information on their druggability, have been made available via the AMP-AD open-source platform Agora (https://agora.ampadportal.org/). Despite these advances, such multi-omics studies of AD and related disorders (ADRD) have lacked sampling from AA and LA populations with few exceptions [54,55]. To bridge the 6 data and knowledge gap in multi-omics studies of underrepresented populations in AD research, AMP-AD investigators launched a diversity initiative to expand molecular profiling of brain tissue to multi-ethnic donors.…”

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confidence: 99%

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Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer’s Disease and Older Controls in Multi-Ethnic Populations

Reddy,

Heath,

Linden

et al. 2024

Preprint

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INTRODUCTION: Multi-omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked African Americans (AA) and Latin Americans (LA), who are disproportionately affected by AD. METHODS: To bridge this gap, Accelerating Medicines Partnership in AD (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors. RESULTS: We generated multi-omics data and curated and harmonized phenotypic data from AA (n=306), LA (n=326), or AA and LA (n=4) brain donors plus Non-Hispanic White (n=252) and other (n=20) ethnic groups, to establish a foundational dataset enriched for AA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSION: Inclusion of traditionally underrepresented groups in multi-omics studies is essential to discover the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer’s Disease and Older Controls in Multi-Ethnic Populations

Reddy,

Heath,

Linden

et al. 2024

Preprint

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The Caribbean-Hispanic Alzheimer's disease brain transcriptome reveals ancestry-specific disease mechanisms

Felsky

Santa-María

Coşacak

et al. 2023

Neurobiology of Disease

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c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer’s disease

Jemimah,

Abuhantash,

AlShehhi

2024

Preprint

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Alzheimers disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently available diagnostic biomarkers preclude early diagnosis, necessitating the development of alternative biomarkers and methods, such as blood-based diagnostics. We propose c-Triadem (constrained triple-input Alzheimers disease model), a novel deep neural network to identify potential blood-based biomarkers for AD and predict mild cognitive impairment (MCI) and AD with high accuracy. The model utilizes genotyping data, gene expression data, and clinical information to predict the disease status of participants, i.e., cognitively normal (CN), MCI, or AD. The nodes of the neural network represent genes and their related pathways, and the edges represent known relationships among the genes and pathways. We trained the model with blood genotyping data, microarray, and clinical features from the Alzheimers Neuroimaging Disease Initiative (ADNI). We demonstrate that our models performance is superior to previous models with an AUC of 97% and accuracy of 89%. We then identified the most influential genes and clinical features for prediction using SHapley Additive exPlanations (SHAP). Our SHAP analysis shows that CASP9, LCK, and SDC3 SNPs and PINK1, ATG5, and ubiquitin (UBB, UBC) expression have a higher impact on model performance. Our model has facilitated the identification of potential blood-based genetic markers of DNA damage response and mitophagy in affected regions of the brain. The model can be used for detection and biomarker identification in other related dementias.

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Transcript levels in plasma contribute substantial predictive value as potential Alzheimer's disease biomarkers in African Americans

Cited by 4 publications

References 43 publications

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer’s Disease and Older Controls in Multi-Ethnic Populations

Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer’s Disease and Older Controls in Multi-Ethnic Populations

The Caribbean-Hispanic Alzheimer's disease brain transcriptome reveals ancestry-specific disease mechanisms

c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer’s disease

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