Apolipoprotein B/A1 Ratio as a Diagnostic Alternative to Triglycerides and HDL-Cholesterol for the Prediction of Metabolic Syndrome among Hypertensives in Kazakhstan
Bibliographic record
Abstract
Apolipoproteins (Apo) are known atherogenic factors that play important roles in many mechanisms related to coronary heart disease. The ApoB/ApoA1 ratio is a promising diagnostic tool for metabolic syndrome (MS) in different populations, though its use is not established in Kazakhstan. This study aimed to assess the relationship between MS and the ApoB/ApoA1 ratio among hypertensive patients and to evaluate its diagnostic use for identifying MS as an alternative to triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C). A cross-sectional study was conducted in 800 eligible men and women with primary hypertension from April 2015 to December 2016. Data were collected on socio-demographics, lifestyle parameters, family history of cardiovascular disease, and hypertension. Dietary Quality Score (DQS), anthropometric data, and blood pressure were recorded; ApoA1 and ApoB levels were measured in blood samples. We found a significant positive association between MS and the ApoB/ApoA1 ratio by multiple logistic regression, as shown by a linear trend of increase of the odds ratio (OR) for MS across the quartiles of ApoB/ApoA1 (p < 0.0001). ROC analysis revealed diagnostic significance of the ApoB/ApoA1 ratio for MS, and comparative ROC analysis demonstrated equal diagnostic value of ApoB/ApoA1 ratio and TG levels (AUC = 0.71 (95% CI 0.69; 0.74) and 0.72 (95% CI 0.69, 0.76), respectively), which was significantly higher than those of HDL, ApoA1, ApoB (AUC = 0.27 (95% CI 0.23; 0.31), AUC = 0.37 (95% CI 0.33; 0.41), AUC = 0.67, (95% CI 0.63; 0.71), respectively). The diagnostic value of the ApoB/ApoA1 ratio in Kazakhs with MS appeared to equal that of TG and was significantly higher than that of HDL-C. Adjusting for gender, smoking, and DQS significantly strengthened the association between MS and the ApoB/ApoA1 ratio in the Kazakh 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 | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".