Imaging markers of cerebral small vessel disease are associated with Alzheimer’s disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective This study aims to assess the correlation between cerebral small vessel disease (CSVD) imaging markers and the risk of Alzheimer’s disease (AD) through a systematic review and meta-analysis. Methods In July 2024, two researchers independently conducted a thorough literature search across databases such as PubMed, Embase, Web of Science, and the Cochrane Library. The selected studies investigated the correlations between white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), lacunar infarction (LI), and enlarged perivascular spaces (EPVS) and the risk of AD. The Newcastle-Ottawa Scale (NOS) was employed to assess the risk of bias in the included cohort and case–control studies. Results A total of 6,487 articles were identified, with 15 articles meeting the inclusion criteria. Pooled analyses showed that WMHs (HR: 1.38, 95% CI: 1.10–1.74, N = 7,661), CMBs (HR: 1.60, 95% CI: 1.07–2.40, N = 6,567), and EPVS (HR: 1.84, 95% CI: 1.24–2.72, N = 3,045) were associated with an increased risk of AD, with EPVS showing the strongest correlation. LI did not show a statistically significant association with an increased risk of AD (HR: 1.41, 95% CI: 0.98–2.01, N = 4,014). Conclusion WMHs, CMBs, and EPVS are associated with an elevated risk of AD, whereas LI is considered a potential risk factor. However, additional studies are required to determine the role of CSVD markers in AD progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".