Impact of cerebral small vessel disease on cognitive outcomes in early age at onset MCI and dementia: Findings from the DIASPORA study
Bibliographic record
Abstract
BACKGROUND: This study assessed the impact of cerebral small vessel disease (CSVD) on cognition in individuals with early-onset (EO; <65 years) and late-onset (LO; ≥65 years) cognitive complaints. METHODS: Participants underwent prospective evaluations including cognitive testing, hyperphosphorylated tau-217 (p-tau217) and neurofilament-light-chain (NfL), and magnetic resonance imaging (MRI). Each CSVD marker was modeled for interaction with group age on results on cognitive outcomes: Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), Neuropsychiatric Inventory Questionnaire (NPI-Q), and Clinical Dementia Rating (CDR) scale plus National Alzheimer's Coordinating Center-Frontotemporal Lobar Degeneration module (NACC-FTLD). RESULTS: Altogether, 168 patients (91 EO) were included. white matter hyperintensity (WMH) volume was associated with worse CDR+NACC-FTLD in EO (β = 17.8, p = 0.013), remaining significant after adjusting for p-tau217 and NfL, but not gray matter atrophy. Lacunes were associated with worse CDR plus NACC-FTLD in EO (β = 4.3, p = 0.011), with age-dependent associations with MoCA, MMSE, CDR + NACC-FTLD, and NPI-Q (p < 0.01). DISCUSSION: CSVD markers, although less prevalent in EO, had greater clinical impact. These findings highlight an increased vulnerability to vascular pathology in EO patients and the importance of early detection. HIGHLIGHTS: Cerebral small vessel disease (CSVD) markers were more impactful in early-onset than late-onset dementia. White matter hyperintensity (WMH) volume predicted functional decline in early onset, independent of neurodegeneration. Lacunes showed age-dependent effects on multiple cognitive outcomes. Findings support early detection of CSVD in younger individuals with dementia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".