Association between high-density lipoprotein cholesterol and 7-autoantibodies: a study on physical examination data from 2018 to 2023
Bibliographic record
Abstract
Background Limited research has explored the effect of high-density lipoprotein cholesterol (HDL-C) on lung cancer’s seven autoantibodies (7-AABs). This study investigated the association between serum HDL-C and 7-AABs among 5,574 Chinese adults aged ≥ 18 years from January 2018 to December 2023. Methods This cross-sectional study utilized physical examination data from the Department of Health Management at Henan Provincial People’s Hospital. The associations between HDL-C and autoantibodies, such as tumor protein 53(P53), SRY-box containing gene 2 (SOX2), and ATP-dependent RNA helicase 4-5 (GBU4-5), were modeled using a restricted cubic spline logistic regression model. Results After the adjustment for factors, such as age and body mass index, the binary logistic regression model showed distinct correlations between serum HDL-C levels and autoantibodies, including P53, SOX2, and 7-AABs. Restricted cubic spline logistic regression analysis indicated that the increased level of serum HDL-C was associated with a decreased risk of positive P53 (all participants: HDL-C: 1.227–1.366 mmol/L, PHDL-C=0.028), SOX2 (all participants: HDL-C ≥ 1.227 mmol/L, PHDL-C =0.021; all women: HDL-C ≥ 1.224 mmol/L, PHDL-C=0.037), GBU4-5 (all women: HDL-C ≥ 1.269 mmol/L, PHDL-C=0.039), and 7-AABs (all women: HDL-C ≥ 1.224 mmol/L, PHDL-C=0.015). In women, HDL-C levels between 1.163 and 1.224 mmol/L correlated with an increased risk of positive 7-AABs test results. Conclusions Elevated HDL-C levels exhibited an independent association with a reduced risk of positivity for 7-AABs of lung cancer, especially in the female physical examination population. These findings suggest that high HDL-C levels may play a role in hindering lung cancer development with gender differences. However, further confirmation is still needed in the future.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".