The positive association between the atherogenic index of plasma and the risk of new-onset hypertension: a nationwide cohort study in China
Bibliographic record
Abstract
Background The atherogenic index of plasma (AIP) is a novel metabolic biomarker of atherosclerosis. Nevertheless, the association between the AIP and new-onset hypertension has not been elucidated in the Chinese population.Methods Prospective data were obtained from 3150 participants aged ≥ 18 years in the China Health and Nutrition Survey from 2009 to 2015. The AIP is a logarithmically transformed ratio of triglycerides to high-density lipoprotein cholesterol in molar concentration. Cox regression analysis was used to determine the association of AIP index with new-onset hypertension.Results After the six-year follow-up, 1054 (33.4%) participants developed new-onset hypertension. The participants were divided into AIP quartile groups (Q1-Q4). Compared with those in Q1, subjects in Q3–4 had nearly 1.35 times the risk of new-onset hypertension after full adjustment [Q3: hazard ratio (HR): 1.35, 95% confidence interval (CI): 1.13–1.62; Q4: HR: 1.35, 95% CI: 1.13–1.64]. The risks of new-onset hypertension were nearly 1.30 times higher in subjects in Q2–4 than in subjects in Q1 (p < .01) after the full adjustment when we excluded subjects with diabetes and/or chronic kidney diseases. There was a significant difference [HR (CI): 1.27 (1.04–1.54) vs. 0.90 (0.69–1.18)] when subjects were divided into two groups according to body mass index (BMI) level (<24 vs. ≥24 kg/m2).Conclusions The present study suggested that individuals with a higher AIP index are associated with new-onset hypertension, independent of kidney function and glucose levels. The association was stronger in subjects with normal BMI, which may provide early screening of metabolomics in hypertension prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".