Natural killer cells, gamma delta T cells and classical monocytes are associated with systolic blood pressure in the multi-ethnic study of atherosclerosis (MESA)
Bibliographic record
Abstract
Abstract Background Hypertension is a major source of cardiovascular morbidity and mortality. Recent evidence from mouse models, genetic, and cross-sectional human studies suggest increased proportions of selected immune cell subsets may be associated with levels of systolic blood pressure (SBP). Methods We assayed immune cells from cryopreserved samples collected at the baseline examination (2000–2002) from 1195 participants from the multi-ethnic study of atherosclerosis (MESA). We used linear mixed models, with adjustment for age, sex, race/ethnicity, smoking, exercise, body mass index, education, diabetes, and cytomegalovirus titers, to estimate the associations between 30 immune cell subsets (4 of which were a priori hypotheses) and repeated measures of SBP (baseline and up to four follow-up measures) over 10 years. The analysis provides estimates of the association with blood pressure level. Results The mean age of the MESA participants at baseline was 64 ± 10 years and 53% were male. A one standard deviation (1-SD) increment in the proportion of γδ T cells was associated with 2.40 mmHg [95% confidence interval (CI) 1.34–3.42] higher average systolic blood pressure; and for natural killer cells, a 1-SD increment was associated with 1.88 mmHg (95% CI 0.82–2.94) higher average level of systolic blood pressure. A 1-SD increment in classical monocytes (CD14++CD16−) was associated with 2.01 mmHG (95% CI 0.79–3.24) lower average systolic blood pressure. There were no associations of CD4+T helper cell subsets with average systolic blood pressure. Conclusion These findings suggest that the innate immune system plays a role in levels of SBP whereas there were no associations with adaptive immune 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".