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Deep multimodal clustering identifies biological subtypes of normal aging with divergent cognitive decline risk
Diaz, M. M.; Dayan, E.. medRxiv (preprint), 2026-08-19
Topic: Risk, prevention and care in population studies
Cognitively normal older adults are often regarded as a homogeneous population in preventive and disease-modifying clinical trials for dementia. However, longer-term cognitive aging outcomes vary substantially in this population, and this variability remains poorly understood. Here, we leveraged rich multimodal, multi-domain biomarker profiles from a large prospective cohort (N=1,136), and Deep Embedded Clustering, to cluster cognitively normal older adults into biologically distinct subgroups. Input data included cortical thickness derived from MRI, plasma Alzheimers disease (AD) biomarkers, plasma inflammatory biomarkers, and vascular measures. The deep clustering algorithm identified three biologically distinct subgroups within the sample, stratified along a gradient of neurobiological burden (low, intermediate, and high). Cortical thinning and inflammatory burden were the primary drivers of clustering assignments. The High-Burden subgroup showed significantly worse memory and executive function, elevated cardiometabolic comorbidity, and markedly higher rates of conversion to mild cognitive impairment or dementia within two years. The results were validated in an independent external sample. The study reveals marked variability among individuals who are otherwise all defined as cognitively normal, and provides a data-driven stratification framework for enriching disease-modifying and preventive trials by identifying cognitively normal individuals at high risk for future cognitive decline.
Key findings
- The High-Burden subgroup showed significantly worse memory and executive function, elevated cardiometabolic comorbidity, and markedly higher rates of conversion to mild cognitive impairment or dementia within two years.
- The results were validated in an independent external sample.
Why it matters
Among cognitively normal older adults, a high-burden subgroup identified by clustering had worse memory and executive function and much higher rates of conversion to mild cognitive impairment or dementia within two years. The authors present this as a data-driven way to stratify people for prevention trials.
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Study details
- Study designprospective cohort
- Participants1,136
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 34 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for effect reported and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 32 of the 94 points we could judge it on, which is 34 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 7 / 12 | A prospective cohort study, followed forward from enrollment |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,136 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 | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 0 / 4 | Last-listed author the Frontotemporal Lobar Degeneration Neuroimaging Initiative: h-index 4 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of North Carolina at Chapel Hill: institution h-index 1,674 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 32 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 32 of the 94 points we could judge it on is 34 out of 100. |
| Total | 34 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
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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. Check the citation against the original before you use it.
APA 7th
Diaz, M. M., & Dayan, E. (2026). Deep multimodal clustering identifies biological subtypes of normal aging with divergent cognitive decline risk. medRxiv (preprint). https://doi.org/10.64898/2026.08.18.26360683
Vancouver
Diaz MM, Dayan E. Deep multimodal clustering identifies biological subtypes of normal aging with divergent cognitive decline risk. medRxiv (preprint). 2026. doi:10.64898/2026.08.18.26360683.
BibTeX
@article{diaz2026-a064c939,
author = {Diaz, M. M. and Dayan, E.},
title = {{Deep multimodal clustering identifies biological subtypes of normal aging with divergent cognitive decline risk}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.08.18.26360683},
url = {https://doi.org/10.64898/2026.08.18.26360683}
}
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