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Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
Young AL, Marinescu RV, Oxtoby NP, Bocchetta M, Yong K, Firth NC, Cash DM, Thomas DL et al.. Nature communications, 2018-10-15
Topic: Risk, prevention and care in population studies
The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique-Subtype and Stage Inference (SuStaIn)-able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer's disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10 -4 ) or temporal stage (p = 3.96 × 10 -5 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.
Key findings
- Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration.
Why it matters
This machine-learning technique uncovered subgroups with distinct trajectories of regional neurodegeneration in genetic frontotemporal dementia and three subtypes in Alzheimer's disease, and improved prediction of conversion between diagnostic categories. It is a data analysis method for stratifying patients and does not itself test any treatment, cure, or prevention.
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Study details
- Study designcross-sectional
- Evidence strengthLow
How this study scored 67/100
Published in a leading specialty journal, and it is a snapshot of people at one point in time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Nature communications) |
| Journal's citation record | 4 / 4 | Nature Communications: journal h-index 747 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| 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 | 2 / 5 | 2018 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 6 / 7 | Cited 598 times (OpenAlex, 2026-10-10) |
| Citations per year | 5 / 5 | 59.8 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 5 / 6 | Cited 21.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 4 / 6 | Cited by 34 other studies in this library |
| Still being cited | 4 / 4 | Cited 310 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Ronald G. Thomas: h-index 87 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | University of Washington: institution h-index 3,540 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 57 institution(s) in 9 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30323170) |
| Free to read | 2 / 2 | Free to read |
| Total | 67 / 100 | — |
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Read the original
- DOI: 10.1038/s41467-018-05892-0
- PubMed: 30323170
- Original source →
- Free full text: Nature Communications →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Young, A. L., Marinescu, R. V., Oxtoby, N. P., Bocchetta, M., Yong, K., Firth, N. C., et al. (2018). Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nature communications. https://doi.org/10.1038/s41467-018-05892-0
Vancouver
Young AL, Marinescu RV, Oxtoby NP, Bocchetta M, Yong K, Firth NC, et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nature communications. 2018. doi:10.1038/s41467-018-05892-0. PMID: 30323170.
BibTeX
@article{young2018-30323170,
author = {Young, A. L. and Marinescu, R. V. and Oxtoby, N. P. and Bocchetta, M. and Yong, K. and Firth, N. C. and Cash, D. M. and Thomas, D. L. and others},
title = {{Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference}},
journal = {Nature communications},
year = {2018},
doi = {10.1038/s41467-018-05892-0},
pmid = {30323170},
url = {https://doi.org/10.1038/s41467-018-05892-0}
}
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