Brain Derived Neurotrophic Factor Interacts with White Matter Hyperintensities to Influence Processing Speed and Hippocampal Volume in Older Adults
Bibliographic record
Abstract
BACKGROUND: Brain-derived neurotrophic factor (BDNF) is a neurotrophin that plays an important role in regulating synaptic activity and plasticity. OBJECTIVE: Given that type-2 diabetes (T2DM) increases the risk of cognitive decline, and studies have suggested lower BDNF levels may be a risk factor of diabetic neurovascular complications, we sought to investigate total white matter hyperintensities (WMH) as a moderator of the effect of BDNF on hippocampal volume and cognition. METHODS: Older adults without dementia from the Alzheimer's Disease Neuroimaging Initiative (N = 454 including 49 with T2DM and 405 without diabetes) underwent neuropsychological evaluation, magnetic resonance imaging to quantify hippocampal and WMH volumes, and blood draw to assess BDNF. RESULTS: Adjusting for age, sex, and APOE ɛ4 carrier status, there was a significant interaction between total WMH and BDNF on bilateral hippocampal volume in the non-T2DM group (t = 2.63, p = 0.009). Examination of main effect models with a dichotomous high/low BNDF group revealed a significant main effect for low BDNF (t = -4.98, p < 0.001), such that as WMH increased, bilateral hippocampal volume decreased. There was also a significant interaction between total WMH and BDNF on processing speed in the non-T2DM group (t = 2.91, p = 0.004). There was a significant main effect for low BDNF (t = -3.55, p < 0.001) such that as WMH increased, processing speed decreased. The interactions were not significant in the T2DM group. CONCLUSION: These results further elucidate the protective role that BDNF plays on cognition, as well as the cognitive effects of WMH.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".