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Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity
Iturria-Medina Y, Adewale Q, Khan AF, Ducharme S, Rosa-Neto P, O'Donnell K, Petyuk VA, Gauthier S et al.. Science advances, 2022-11-18
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data ( N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.
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APA 7th
Iturria-Medina, Y., Adewale, Q., Khan, A. F., Ducharme, S., Rosa-Neto, P., O'Donnell, K., et al. (2022). Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity. Science advances. https://doi.org/10.1126/sciadv.abo6764
Vancouver
Iturria-Medina Y, Adewale Q, Khan AF, Ducharme S, Rosa-Neto P, O'Donnell K, et al. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity. Science advances. 2022. doi:10.1126/sciadv.abo6764. PMID: 36399579.
BibTeX
@article{iturriamedina2022-36399579,
author = {Iturria-Medina, Y. and Adewale, Q. and Khan, A. F. and Ducharme, S. and Rosa-Neto, P. and O'Donnell, K. and Petyuk, V. A. and Gauthier, S. and others},
title = {{Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity}},
journal = {Science advances},
year = {2022},
doi = {10.1126/sciadv.abo6764},
pmid = {36399579},
url = {https://doi.org/10.1126/sciadv.abo6764}
}
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Key findings
- In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data ( N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy.
- We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD.
- These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood.
- In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms.
- These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.
Why it matters
Using brain and blood samples from 1,863 people, this study identified three molecular subtypes of Alzheimer's that differ in DNA methylation, RNA, proteins and metabolites. The authors say this taxonomy may point to distinct targets for prevention or treatment, but no treatment was tested.
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Study details
- Participants1,863
- Evidence strengthLow
How this study scored 60/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Science advances) |
| Journal's citation record | 3 / 4 | Science Advances: journal h-index 378 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,863 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 100 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 16.7 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 4 / 6 | Cited 9.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 3 / 4 | Cited 87 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author David Alan Bennett: h-index 197 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Yale University: institution h-index 1,927 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 10 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36399579) |
| Free to read | 2 / 2 | Free to read |
| Total | 60 / 100 | — |
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