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A Precision Aging Approach to Cognitive Aging: Clustering Multiple Domains of Risk
Ryan, L.; Ortiz, O. S.; Pettigrew, C. A.; Soldan, A.; LaFleur, B.; Levin, B.; Lah, J. J.; Hay et al.. medRxiv (preprint), 2026-08-21
Topic: Risk, prevention and care in population studies
The term Precision Aging describes an approach that focuses on multi-domain profiles of risks impacting individual trajectories of age-related cognitive functioning. The goal of the present study was to identify profiles of risk within a sample of 555 adults, ages 50 to 79, without diagnosis of dementia. Using cluster analyses, we considered 38 risk factors associated with five categories of risk known to negatively impact cognitive aging - cardiovascular insufficiency, glucose dysregulation, inflammation, immune dysfunction, and neuropathology. Results yielded five profiles, including a group with low risk in all five risk categories, and four groups with prominent risks in specific domains. Importantly, all four high risk groups performed more poorly relative to the low risk group on multiple memory measures from a well-established neuropsychological test, the Auditory Verbal Learning Test. The results highlight the importance of considering multiple domains of risk within the same cohort to predict age-related cognitive functioning.
Key findings
- Using cluster analyses, we considered 38 risk factors associated with five categories of risk known to negatively impact cognitive aging - cardiovascular insufficiency, glucose dysregulation, inflammation, immune dysfunction, and neuropathology.
- Results yielded five profiles, including a group with low risk in all five risk categories, and four groups with prominent risks in specific domains.
- The results highlight the importance of considering multiple domains of risk within the same cohort to predict age-related cognitive functioning.
Why it matters
Among adults without a dementia diagnosis, those with prominent risk in specific domains performed worse on memory tests than a low-risk group. This is a clustering study of risk profiles and does not test any treatment or prevention.
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Study details
- Participants555
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 31 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, 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 29 of the 94 points we could judge it on, which is 31 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 | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| 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 | 2 / 4 | 555 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 | 3 / 4 | Last-listed author Matthew J. Huentelman: h-index 75 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Johns Hopkins University: institution h-index 2,306 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 29 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 29 of the 94 points we could judge it on is 31 out of 100. |
| Total | 31 / 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
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
Ryan, L., Ortiz, O. S., Pettigrew, C. A., Soldan, A., LaFleur, B., Levin, B., et al. (2026). A Precision Aging Approach to Cognitive Aging: Clustering Multiple Domains of Risk. medRxiv (preprint). https://doi.org/10.64898/2026.08.18.26360626
Vancouver
Ryan L, Ortiz OS, Pettigrew CA, Soldan A, LaFleur B, Levin B, et al. A Precision Aging Approach to Cognitive Aging: Clustering Multiple Domains of Risk. medRxiv (preprint). 2026. doi:10.64898/2026.08.18.26360626.
BibTeX
@article{ryan2026-b847704e,
author = {Ryan, L. and Ortiz, O. S. and Pettigrew, C. A. and Soldan, A. and LaFleur, B. and Levin, B. and Lah, J. J. and Hay and others},
title = {{A Precision Aging Approach to Cognitive Aging: Clustering Multiple Domains of Risk}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.08.18.26360626},
url = {https://doi.org/10.64898/2026.08.18.26360626}
}
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