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Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach
Raghav Tandon, Yajun Mei, James J. Lah, Cassie S. Mitchell. International Journal of Molecular Sciences, 2025-06-09
Topic: Risk, prevention and care in population studies
Alzheimer’s disease (AD) presents significant challenges in clinical practice due to its heterogeneous manifestation and variable progression rates. This work develops a comprehensive anatomical staging framework to predict progression from mild cognitive impairment (MCI) to AD. Using the ADNI database, the scalable Subtype and Stage Inference (s-SuStaIn) model was applied to 118 neuroanatomical features from cognitively normal (n = 504) and AD (n = 346) participants. The framework was validated on 808 MCI participants through associations with clinical progression, CSF and FDG-PET biomarkers, and neuropsychiatric measures, while adjusting for common confounders (age, gender, education, and APOE ε4 alleles). The framework demonstrated superior prognostic accuracy compared to traditional risk assessment (C-index = 0.73 vs. 0.62). Four distinct disease subtypes showed differential progression rates, biomarker profiles (FDG-PET and CSF Aβ42), and cognitive trajectories: Subtype 1, subcortical-first pattern; Subtype 2, executive–cortical pattern; Subtype 3, disconnection pattern; and Subtype 4, frontal–executive pattern. Stage-dependent changes revealed systematic deterioration across diverse cognitive domains, particularly in learning acquisition, visuospatial processing, and functional abilities. This data-driven approach captures clinically meaningful disease heterogeneity and improves prognostication in MCI, potentially enabling more personalized therapeutic strategies and clinical trial design.
Key findings
- Alzheimer’s disease (AD) presents significant challenges in clinical practice due to its heterogeneous manifestation and variable progression rates.
- The framework demonstrated superior prognostic accuracy compared to traditional risk assessment (C-index = 0.73 vs. 0.62).
- Stage-dependent changes revealed systematic deterioration across diverse cognitive domains, particularly in learning acquisition, visuospatial processing, and functional abilities.
Why it matters
This study developed an anatomical staging framework using brain imaging data that predicted progression from MCI to Alzheimer's disease with better accuracy than traditional risk assessment, identifying four distinct disease subtypes with different progression rates. The work is about predicting progression and capturing disease heterogeneity, not about treating or curing the disease.
Study details
- Participants504
- Evidence strengthLow
How this study scored 41/100
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 (International Journal of Molecular Sciences) |
| Journal's citation record | 3 / 4 | International Journal of Molecular Sciences: journal h-index 426 (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 | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 504 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 | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Cassie S. Mitchell: h-index 23 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | New York University: institution h-index 1,612 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40564976) |
| Free to read | 2 / 2 | Free to read |
| Total | 41 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3390/ijms26125514
- PubMed: 40564976
- Original source →
- Free full text: International Journal of Molecular Sciences →
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. Check the citation against the original before you use it.
APA 7th
Tandon, R., Mei, Y., Lah, J. J., & Mitchell, C. S. (2025). Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms26125514
Vancouver
Tandon R, Mei Y, Lah JJ, Mitchell CS. Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach. International Journal of Molecular Sciences. 2025. doi:10.3390/ijms26125514. PMID: 40564976.
BibTeX
@article{tandon2025-40564976,
author = {Tandon, Raghav and Mei, Yajun and Lah, James J. and Mitchell, Cassie S.},
title = {{Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach}},
journal = {International Journal of Molecular Sciences},
year = {2025},
doi = {10.3390/ijms26125514},
pmid = {40564976},
url = {https://doi.org/10.3390/ijms26125514}
}
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