[IC‐P‐050]: TOWARD DISCOVERY OF MULTI‐OMICS BIOTYPES OF ALZHEIMER's DISEASE: A FOCUSED REVIEW AND PROPOSED ROAD MAP
Notice bibliographique
Résumé
A major aim of the Canadian Consortium on Neurodegeneration in Aging (CCNA) is to characterize the pathophysiological basis of Alzheimer's disease (AD) and precisely differentiate it from other age-related neurodegenerative diseases. Accordingly, CCNA enrols a large cohort (n=1600) representative of the spectrum of neurodegeneration in aging. Participants will undergo extensive phenotyping for unbiased biomarker discovery, including neuroimaging (connectomics), genomics, and metabolomics. The high-dimensional nature and distinctive methods of these ‘omics’ modalities makes it challenging to extract clinically relevant information, and further integrate this information across modalities. Recent trends in omics data analysis have been to either reduce or select from the pertinent complex data. Examples include (1) neuroimaging advances in the identification of biotypes (i.e. groups of individuals who share similar brain characteristics) and (2) metabolomics advances in multi-pathway panels of diagnostic biomarkers. This focused review by the CCNA Biomarker Team discusses literature on the emerging field of multi-omics and proposes a “roadmap” to discovering multi-omics AD biotypes within the CCNA sample. We review recent AD literature for biotyping, pathway panels, and multiplicative risk indexes from connectomics, genomics, and metabolomics approaches. All searches were conducted in PUBMED and restricted to the last five years. We propose a model combining across biotyping methods and integrating with key biomarker candidates to advance the discrimination of AD from related dementia. Eight neuroimaging studies have reported biotypes that were found to be associated with differences in cognitive symptoms, cerebral amyloid deposition, glucose metabolism, biofluid-based biomarker profiles, and/or AD risk. Comparable biotypes appearing in recent genomics and metabolomics literature are identifiable through multi-modal interactions, polygenic risk indexes, and metabolomics diagnostic panels. We reviewed recent techniques to discover multi-omics biomarkers of AD. Although omics biomarkers are demonstrated separately by modality, we develop a “roadmap” for representing their combined effects and relevance to translation (Figure 1). In addition, the roadmap leads to (1) enriching multi-omics biomarkers with modifying factors (age, sex, vascular health), (2) validating their diagnostic power within the heterogeneous clinical sample of the CCNA, and (3) using multi-omics biotypes to predict progression of clinical symptoms in preclinical individuals during longitudinal follow-up. Proposed roadmap to discovering multi-omics AD biomarkers. Despite diagnostic labels, Alzheimer's disease and related dementias are inherently heterogeneous entities both in clinical presentation and underlying pathophysiology. We reviewed recent techniques to discover multi-omics biomarkers of AD. Biotypes derived from the integration of multi-omics data using semi-supervised machine learning techniques will better identify individuals on an AD spectrum trajectory. Abbreviations: Alzheimer's Disease (AD), FrontoTemporal Dementia spectrum (FTD), Lewy Body Disease (LBD), Vascular Cognitive Impairment (VCI), Mixed etiology dementia (Mixed), Healthy Controls (HC), Subjective Cognitive Impairment (SCI). Mild Cognitive Impairment (MCI).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,007 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».