Biomarkers of synaptic and neuronal dysfunction in relation to mild behavioral impairment: a study of dementia‐free older adults
Bibliographic record
Abstract
Abstract Background Mild behavioral impairment (MBI) represents a high‐risk state for incident cognitive decline and dementia, capturing later‐life emergent and persistent neuropsychiatric symptoms in five domains: 1) decreased motivation; 2) affective dysregulation 3) impulse dyscontrol; 4) social inappropriateness; and 5) psychosis. We examined cross‐sectional associations between MBI and cerebrospinal fluid levels of markers of synaptic/neuronal dysfunction including 1) alpha‐synuclein (α‐syn); 2) growth‐associated protein 43 (GAP‐43); 3) neurogranin (NGRN); 4) synaptosomal‐associated protein 25 (SNAP‐25); 5) visinin‐like protein 1 (VILIP‐1); and 6) chitinase‐3‐like protein 1 (YKL‐40). Longitudinally, we determined associations between these biomarkers and incident MBI or dementia. Method Alzheimer’s Disease Neuroimaging Initiative participants with normal cognition and mild cognitive impairment were included (n = 853). MBI scores were derived from the Neuropsychiatric Inventory using a published algorithm. Participants were classified as MBI+ if MBI symptoms were present at ≥2 consecutive visits. Cross‐sectional associations between MBI total score and each log‐transformed biomarker were modelled using multivariable‐adjusted linear regression. Biomarkers with cross‐sectional associations meeting a pre‐defined p‐value threshold (p<0.1) were then incorporated as exposures into adjusted longitudinal Cox proportional hazard regression models (n = 111) of 1) MBI and 2) dementia as the outcomes, the latter of which also included interactions between MBI status and biomarker levels. Result Demographics are summarized in Table 1. Cross‐sectionally, only MBI associations with VILIP‐1 (beta = 3.3%, 95%CI:+0.2% to +6.5%, p = 0.04) and SNAP‐25 (beta = 2.0%, 95%CI:‐0.4% to 6.3%, p = 0.09) met the pre‐defined p‐value for inclusion in Cox models. Longitudinally, higher VILIP‐1 levels were associated with a 3.02‐fold greater rate of incident MBI (95%CI:1.43‐6.40, p = 0.004) and a 3.83‐fold greater rate of incident dementia (95%CI:1.22‐12.03, p = 0.021). The relationship between VILIP‐1 and dementia did not depend on participants having MBI. MBI status predicted incident dementia with an HR = 4.75 (95%CI:2.36‐9.55, p<0.001) and HR = 4.06 (95%CI:2.01‐8.21, p<0.001) after adjusting for VILIP‐1. Conclusion Elevated VILIP‐1 levels are associated with greater incidence of MBI and greater incidence of dementia, suggesting that synaptic dysfunction traditionally related to cognitive decline and dementia may also contribute to behavioral changes. Treatments aimed to mitigate MBI symptoms and dementia may explore VILIP‐1—a neuronal calcium‐sensor protein—as a therapeutic target, though more research is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".