Relationships of the Surface Charge of Low-Density Lipoprotein (LDL) with the Serum LDL-Cholesterol and Atherosclerosis Levels in a Japanese Population: The DOSANCO Health Study
Bibliographic record
Abstract
AIM: This study investigated the associations of the surface charge of low-density lipoprotein (LDL) with the serum LDL-cholesterol and atherosclerosis levels in a community-based Japanese population. METHODS: The study had a cross-sectional design and included 409 community residents aged 35-79 years who did not take medications for dyslipidemia. The potential electric charge of LDL and the zeta potential, which indicate the surface charge of LDL, were measured by laser Doppler microelectrophoresis. The correlations of the zeta potential of LDL (-mV) with the serum LDL-cholesterol levels (mg/dL), cardio-ankle vascular index (CAVI), and serum high-sensitivity C-reactive protein (hsCRP) levels (log-transformed values, mg/L) were examined using Pearson's correlation coefficient (r). Linear regression models were constructed to examine these associations after adjusting for potential confounding factors. RESULTS: A total of 201 subjects with correctly stored samples were included in the primary analysis for zeta potential measurement. An inverse correlation was observed between the LDL zeta potential and the serum LDL-cholesterol levels (r=-0.20; p=0.004). This inverse association was observed after adjusting for sex, age, dietary cholesterol intake, smoking status, alcohol intake, body mass index, and the serum levels of the major classes of free fatty acids (standardized β=-6.94; p=0.005). However, the zeta potential of LDL showed almost no association with CAVI or the serum hsCRP levels. Similar patterns were observed in the 208 subjects with compromised samples as well as all the original 409 subjects. CONCLUSION: A higher electronegative surface charge of LDL was associated with lower serum LDL-cholesterol levels in the general Japanese population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".