Plasma Metabolite N-Formylmethionine Is Associated With Higher Blood Pressure in the Multiethnic HELIUS Cohort and Triggers Vascular Dysfunction
Bibliographic record
Abstract
BACKGROUND: Since the existing literature on plasma metabolites and blood pressure (BP) is predominantly focused on populations of European ancestry, we aimed to study associations between metabolite profiles and BP in a multiethnic cohort. METHODS: From the HELIUS study (Healthy Life in an Urban Setting), 369 individuals of Dutch, Ghanaian, African Surinamese, and South-Asian Surinamese ethnicity were included for a cross-sectional analysis. Office BP was recorded, and plasma metabolites were measured using untargeted liquid chromatography tandem mass spectometry. Associations between metabolite profiles and BP were assessed with XGBoost machine learning prediction models, followed by linear regression models. The associations with the highest-ranked metabolite were validated in 2 cohorts, and its effects were investigated in human aortic endothelial and vascular smooth muscle cells. RESULTS: The plasma metabolite N-formylmethionine (fMet) showed the most consistent associations with BP across age, sex, and ethnicity. Notably, fMet was associated with higher systolic (+4.14 mm Hg, 95% CI, 2.11-6.17 per SD increase) and diastolic BP (+2.61 mm Hg, 1.41-3.82), and these associations were validated in 2 independent cohorts. Mechanistically, we found that fMet likely contributes to elevated BP by suppressing endothelial nitric oxide synthase expression, and inducing oxidative stress and mitochondrial dysfunction in endothelial cells. In addition, fMet elicited an early inflammatory response, disrupted the endothelial barrier and upregulated myosin light chain expression in vascular smooth muscle. CONCLUSIONS: We identified the metabolite fMet as a novel independent factor associated with BP in an ethnically diverse population. It likely contributes to higher BP by triggering endothelial cell dysfunction, while augmenting contractile proteins in vascular smooth muscle cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".