Plasma Lipidomic Profile Signature of Hypertension in Mexican American Families
Bibliographic record
Abstract
Both as a component of metabolic syndrome and as an independent entity, hypertension poses a continued challenge with regard to its diagnosis, pathogenesis, and treatment. Previous studies have documented connections between hypertension and indicators of lipid metabolism. Novel technologies, such as plasma lipidomic profiling, promise a better understanding of disorders in which there is a derangement of the lipid metabolism. However, association of plasma lipidomic profiles with hypertension in a high-risk population, such as Mexican Americans, has not been evaluated before. Using the rich data and sample resource from the ongoing San Antonio Family Heart Study, we conducted plasma lipidomic profiling by combining high-performance liquid chromatography with tandem mass spectroscopy to characterize 319 lipid species in 1192 individuals from 42 large and extended Mexican American families. Robust statistical analyses using polygenic regression models, liability threshold models, and bivariate trait analyses implemented in the SOLAR software were conducted after accounting for obesity, insulin resistance, and relative abundance of various lipoprotein fractions. Diacylglycerols, in general, and the DG 16:0/22:5 and DG 16:0/22:6 lipid species, in particular, were significantly associated with systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP), as well as liability of incident hypertension measured during 7140.17 person-years of follow-up. Four lipid species, including the DG 16:0/22:5 and DG 16:0/22:6 species, showed significant genetic correlations with the liability of hypertension in bivariate trait analyses. Our results demonstrate the value of plasma lipidomic profiling in the context of hypertension and identify disturbance of diacylglycerol metabolism as an independent biomarker of hypertension.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".