Accounting for antihypertensive medication in Mendelian randomization studies of blood pressure: methodological considerations in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Mendelian randomization (MR) studies investigating determinants of blood pressure (BP) do not account for antihypertensive medication consistently, which may explain discrepancies across studies. We performed an MR study of the association between body mass index (BMI) and systolic BP (SBP) using five methods to account for antihypertensive medication and evaluated their impact on the estimation of the causal effect and on the assessment of the invalidity of the instruments used in MR. METHODS: Baseline and follow-up data on 20 430 participants from the Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort (2011-2018) were used. The five methods to account for antihypertensive medication in the MR study were: no correction, adjustment for antihypertensive medication as a covariate in models, exclusion of treated individuals, addition of a constant value of 15 mmHg to measured values of SBP in treated individuals, and using hypertension as a binary outcome. RESULTS: The magnitude of the estimated MR causal effect for SBP (mmHg) varied across the methods of accounting for antihypertensive medication effects ranging from 0.68 (effect per 1 kg/m 2 increase in BMI) in scenario adjusting MR models for medication covariate to 1.35 in that adding 15 mmHg to measured SBP in treated individuals. Conversely, the assessment of the validity of the instruments did not differ across methods of accounting for antihypertensive medication. CONCLUSIONS: Methods to account for antihypertensive medication in MR studies may affect the estimation of the causal effects and must be selected with caution.
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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 | 0.419 | 0.645 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".