Relationship Between Nutrition and Blood Pressure: A Cross-Sectional Analysis from the NutriNet-Sante Study, a French Web-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Hypertension is the most prevalent chronic disease worldwide. Lifestyle behaviors for its prevention and control are recommended within worldwide guidelines. Nevertheless, their combined relationship with blood pressure (BP) level, particularly in the general population, would need more investigations. Our aim in this study was to evaluate the relative impact of lifestyle and nutritional factors on BP level. METHODS: Cross-sectional analyses were performed using data from 8,670 volunteers from the NutriNet-Santé Study, an ongoing French web-based cohort study. Dietary intakes were assessed using three 24-hour records. Information on lifestyle factors was collected using questionnaires and 3 BP measurements following a standardized protocol. Age-adjusted associations and then multivariate associations between systolic BP (SBP) and lifestyle behaviors were estimated using multiple linear regressions. RESULTS: SBP was higher in participants with elevated body mass indices (BMIs). Salt intake was positively associated with SBP in men but not in women. The negative relationship between consumption of fruits and vegetables and SBP was significant in both sexes. Alcohol intake was positively associated with SBP in both sexes; physical activity was not. The 5 parameters representing the well-accepted modifiable factors for hypertension reduction plus age and education level, accounted for 19.7% of the SBP variance in women and 12.8% in men. Considering their squared partial correlation coefficient, age and BMI were the most important parameters relating to SBP level. Salt intake was not associated with SBP in either sex after multiple adjustments. CONCLUSIONS: BMI was the main contributory modifiable factor of BP level after multiple adjustments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".