Early-onset total cholesterol abnormality is associated with gray matter atrophy and decreased microstructural integrity
Bibliographic record
Abstract
The occurrence of abnormal total cholesterol (TC) can gradually and persistently induce brain damage. Early-onset TC abnormality indicates the accumulation of risks. However, evidence about the impact of early-life TC abnormality on brain morphology remains unclear. This study aimed to investigate the effect of early-onset TC abnormality on brain macrostructure and microstructure. Based on the 16-year follow-up clinical data of participants in the Kailuan study, the involved participants were categorized into three groups: normal TC, early-onset TC abnormality (before 45 years old), and late-onset TC abnormality (after 45 years old). Multimodal neuroimaging data were obtained to evaluate the brain volume and white matter integrity at the voxel-wise level. A generalized linear model was employed to evaluate the association between TC abnormalities and neuroimaging features. Among the 590 involved participants, early-onset TC abnormality was associated with smaller gray matter volume (β = -0.480; 95% CI = -0.914, -0.047), predominantly in the frontal lobe, anterior cingulate gyrus, paracingulate gyrus, specifically in the hippocampus (β = -0.008; 95% CI = -0.013, -0.003). A negative association between early-onset TC abnormality and abnormal fractional anisotropy and mean diffusivity in the fornix. Relative volume reduction of the frontal lobe and arcuate fasciculus injury were observed at the voxel level in late-onset TC abnormality participants. The Montreal Cognitive Assessment score was significantly associated with an increased gray matter volume and an increased white matter fractional anisotropy in participants with early-onset TC abnormality. Moreover, the Montreal Cognitive Assessment score was associated with microstructural integrity in the late-onset group. Early-onset TC abnormality was associated with multiple-region gray matter atrophy and decreased microstructural integrity. Our findings highlight the importance of early-life TC control to decrease the injury of brain morphology and maintain brain health.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".