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Development of a midlife-specific CogDrisk algorithm (CogDrisk-ML) to enable validated implementation of dementia risk assessment from midlife to late life
Md Hamidul Huque, Heidi Welberry, Ranmalee Eramudugolla, Nicola T. Lautenschlager, Kaarin Jane Anstey. Age and Ageing, 2025-07-01
Topic: Risk, prevention and care in population studies
CogDrisk-ML outperformed CAIDE in the UK Biobank and Whitehall II cohorts; however, it provided similar C statistics in the ARIC dataset. C statistics (95% confidence interval) for CogDrisk-ML were 0.71 (0.69, 0.74) for the ARIC, 0.75 (0.73, 0.77) for the UK Biobank and 0.70 (0.62, 0.79) for the Whitehall II study. The novel CogDrisk-ML for assessing dementia risk in midlife offers improved predictive accuracy. Combined with the CogDrisk tool for late life, it provides a comprehensive framework for dementia prevention throughout the life course.
Key findings
- CogDrisk-ML outperformed CAIDE in the UK Biobank and Whitehall II cohorts; however, it provided similar C statistics in the ARIC dataset.
- C statistics (95% confidence interval) for CogDrisk-ML were 0.71 (0.69, 0.74) for the ARIC, 0.75 (0.73, 0.77) for the UK Biobank and 0.70 (0.62, 0.79) for the Whitehall II study.
- The novel CogDrisk-ML for assessing dementia risk in midlife offers improved predictive accuracy.
- Combined with the CogDrisk tool for late life, it provides a comprehensive framework for dementia prevention throughout the life course.
Why it matters
The CogDrisk-ML algorithm for assessing dementia risk in midlife outperformed CAIDE in two cohorts but provided similar results in a third. Combined with the late-life CogDrisk tool, it offers a framework for dementia risk assessment across the life course, though it is a risk prediction tool rather than a treatment.
Study details
- Study designmeta-analysis
- Evidence strengthHigh
How this study scored 59/100
Published in a peer-reviewed journal, and it is a meta-analysis pooling many studies. Scored zero for sample size, effect reported and studied in people because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Age and Ageing) |
| Journal's citation record | 2 / 4 | Age and Ageing: journal h-index 231 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A meta-analysis pooling many studies |
| Strength of evidence | 5 / 5 | Rated “high” 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 | 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 | A review of other studies — randomisation is not credited here |
| Times cited | 2 / 7 | Cited 7 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 4.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 1 / 4 | Cited 7 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Kaarin Jane Anstey: h-index 103 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | The University of Melbourne: institution h-index 1,342 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40685637) |
| Free to read | 2 / 2 | Free to read |
| Total | 59 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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APA 7th
Huque, M. H., Welberry, H., Eramudugolla, R., Lautenschlager, N. T., & Anstey, K. J. (2025). Development of a midlife-specific CogDrisk algorithm (CogDrisk-ML) to enable validated implementation of dementia risk assessment from midlife to late life. Age and Ageing. https://doi.org/10.1093/ageing/afaf201
Vancouver
Huque MH, Welberry H, Eramudugolla R, Lautenschlager NT, Anstey KJ. Development of a midlife-specific CogDrisk algorithm (CogDrisk-ML) to enable validated implementation of dementia risk assessment from midlife to late life. Age and Ageing. 2025. doi:10.1093/ageing/afaf201. PMID: 40685637.
BibTeX
@article{huque2025-40685637,
author = {Huque, Md Hamidul and Welberry, Heidi and Eramudugolla, Ranmalee and Lautenschlager, Nicola T. and Anstey, Kaarin Jane},
title = {{Development of a midlife-specific CogDrisk algorithm (CogDrisk-ML) to enable validated implementation of dementia risk assessment from midlife to late life}},
journal = {Age and Ageing},
year = {2025},
doi = {10.1093/ageing/afaf201},
pmid = {40685637},
url = {https://doi.org/10.1093/ageing/afaf201}
}
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