Associations Between High-Density Lipoprotein Cholesterol Efflux and Brain Grey Matter Volume
Bibliographic record
Abstract
Objective: High-density lipoprotein cholesterol efflux function may prevent brain amyloid beta deposition and neurodegeneration. However, the relevance of this finding has not been established in the diverse middle-aged population. Methods: We examined 1826 adults (47% Black adults) who participated in the Dallas Heart Study to determine associations between high-density lipoprotein (HDL) measures and brain structure and function. White matter hyperintensities (WMH) and whole-brain grey matter volume (GMV) were measured using brain MRI, and the Montreal Cognitive Assessment (MoCA) was used to measure neurocognitive function. HDL cholesterol efflux capacity (HDL-CEC) was assessed using fluorescence-labeled cholesterol efflux from J774 macrophages, and HDL particle size measures were assessed using nuclear magnetic resonance (NMR) spectroscopy (LipoScience). Multivariable linear regressions were performed to elucidate associations between HDL-CEC and brain and cognitive phenotypes after adjustment for traditional risk factors such as age, smoking status, time spent in daily physical activity, and education level. Results: Higher HDL-CEC and small HDL particle (HDL-P) concentration were positively associated with higher GMV normalized to total cranial volume (TCV) (GMV/TCV) after adjustment for relevant risk factors (β = 0.078 [95% CI: 0.029, 0.126], p = 0.002, and β = 0.063 [95% CI: 0.014, 0.111], p = 0.012, respectively). Conversely, there were no associations between HDL measures and WMH or MoCA (all p > 0.05). Associations of HDL-CEC and small HDL-P with GMV/TCV were not modified by ApoE-ε4 status or race/ethnicity. Interpretation: Higher HDL cholesterol efflux and higher plasma concentration of small HDL-P were associated with higher GMV/TCV. Additional studies are needed to explore the potential neuroprotective functions of HDL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".