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Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study
Hall A, Pekkala T, Polvikoski T, van Gils M, Kivipelto M, Lötjönen J, Mattila J, Kero M et al.. Alzheimer's research & therapy, 2019-01-22
Topic: Risk, prevention and care in population studies
Background We developed multifactorial models for predicting incident dementia and brain pathology in the oldest old using the Vantaa 85+ cohort. Methods We included participants without dementia at baseline and at least 2 years of follow-up (N = 245) for dementia prediction or with autopsy data (N = 163) for pathology. A supervised machine learning method was used for model development, considering sociodemographic, cognitive, clinical, vascular, and lifestyle factors, as well as APOE genotype. Neuropathological assessments included β-amyloid, neurofibrillary tangles and neuritic plaques, cerebral amyloid angiopathy (CAA), macro- and microscopic infarcts, α-synuclein pathology, hippocampal sclerosis, and TDP-43. Results Prediction model performance was evaluated using AUC for 10 × 10-fold cross-validation. Overall AUCs were 0.73 for dementia, 0.64-0.68 for Alzheimer's disease (AD)- or amyloid-related pathologies, 0.72 for macroinfarcts, and 0.61 for microinfarcts. Predictors for dementia were different from those in previous reports of younger populations; for example, age, sex, and vascular and lifestyle factors were not predictive. Predictors for dementia versus pathology were also different, because cognition and education predicted dementia but not AD- or amyloid-related pathologies. APOE genotype was most consistently present across all models. APOE alleles had a different impact: ε4 did not predict dementia, but it did predict all AD- or amyloid-related pathologies; ε2 predicted dementia, but it was protective against amyloid and neuropathological AD; and ε3ε3 was protective against dementia, neurofibrillary tangles, and CAA. Very few other factors were predictive of pathology. Conclusions Differences between predictors for dementia in younger old versus oldest old populations, as well as for dementia versus pathology, should be considered more carefully in future studies.
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APA 7th
Hall, A., Pekkala, T., Polvikoski, T., van Gils, M., Kivipelto, M., Lötjönen, J., et al. (2019). Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-018-0450-3
Vancouver
Hall A, Pekkala T, Polvikoski T, van Gils M, Kivipelto M, Lötjönen J, et al. Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study. Alzheimer's research & therapy. 2019. doi:10.1186/s13195-018-0450-3. PMID: 30670070.
BibTeX
@article{hall2019-30670070,
author = {Hall, A. and Pekkala, T. and Polvikoski, T. and van Gils, M. and Kivipelto, M. and Lötjönen, J. and Mattila, J. and Kero, M. and others},
title = {{Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study}},
journal = {Alzheimer's research \& therapy},
year = {2019},
doi = {10.1186/s13195-018-0450-3},
pmid = {30670070},
url = {https://doi.org/10.1186/s13195-018-0450-3}
}
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.
Key findings
- Results Prediction model performance was evaluated using AUC for 10 × 10-fold cross-validation.
- Overall AUCs were 0.73 for dementia, 0.64-0.68 for Alzheimer's disease (AD)- or amyloid-related pathologies, 0.72 for macroinfarcts, and 0.61 for microinfarcts.
- Predictors for dementia versus pathology were also different, because cognition and education predicted dementia but not AD- or amyloid-related pathologies.
- Conclusions Differences between predictors for dementia in younger old versus oldest old populations, as well as for dementia versus pathology, should be considered more carefully in future studies.
Why it matters
In the oldest old, machine learning models predicted dementia and some brain pathologies with only modest accuracy, and predictors differed from those in younger populations. It is a prediction study and reports no treatment or prevention.
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Study details
- Participants245
- Evidence strengthModerate
How this study scored 51/100
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 (Alzheimer's research & therapy) |
| Journal's citation record | 1 / 4 | Alzheimer s Research & Therapy: journal h-index 146 (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 | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 245 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 | 4 / 7 | Cited 55 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.1 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 5.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 2 / 4 | Cited 15 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Alina Solomon: h-index 61 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Karolinska Institutet: institution h-index 1,477 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 7 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30670070) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
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Read the original
- DOI: 10.1186/s13195-018-0450-3
- PubMed: 30670070
- Original source →
- Free full text: Alzheimer s Research & Therapy →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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