Genetic predisposition to high blood pressure and out-of-office hypertension: insights from a population sample in Liechtenstein
Bibliographic record
Abstract
Abstract Genetic predisposition is a risk factor for office hypertension. We tested whether genetic background could identify individuals with ambulatory daytime hypertension in a sample of white Europeans from Liechtenstein. We evaluated two measures of predisposition to hypertension: family history and polygenic risk scores (PRS). Our analytic sample contained 1444 participants aged 25 to 41. 12% of participants had office hypertension, while 37% had out-of-office hypertension. The correlation between blood pressure PRS and family history of hypertension was low (R2 = 4.96x10-3), but both were strongly associated with ambulatory blood pressure (2.2 mmHg per 1 SD increase [95% CI: 1.6, 2.7] & 2.4 mmHg increase with positive family history [95% CI: 1.3, 3.4], respectively). PRS provides incremental improvement in predicting ambulatory systolic blood pressure beyond a validated blood pressure prediction algorithm (ΔAIC = -33), whereas family history does not (ΔAIC = 1). The difference in performance between a baseline algorithm for identifying ambulatory systolic daytime hypertension (positive likelihood ratio of 6.87 [95% CI: 5.56, 8.49]; negative likelihood ratio of 0.45 [95% CI: 0.39, 0.51]) and the same model with PRS integrated (positive likelihood ratio of 7.69 [95% CI: 6.18, 9.57]; negative likelihood ratio of 0.43 [95% CI: 0.37, 0.49]) was modest. In conclusion, in a European sample from Liechtenstein, PRS and family history represent distinct constructs associated with ambulatory blood pressure. Unlike family history, polygenic risk scores provide incremental information identifying individuals with ambulatory hypertension. However, these gains are modest and warrant further development to improve performance at the point-of-care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".