Evaluating the Effects of Stress, Depression, and Anxiety on Hypertension
Bibliographic record
Abstract
INTRODUCTION: Hypertension is a persistent global health issue, with psychological factors like stress, anxiety, and depression increasingly studied as potential contributors. However, their independent associations with hypertension remain unclear. OBJECTIVE: To examine the relationship between stress (using self-reported irritability as a proxy), anxiety, and depression with both the prevalence and severity of hypertension in a nationally representative adult population. METHODS: A cross-sectional analysis was conducted using publicly available data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative survey conducted by the Centers for Disease Control and Prevention (CDC). The study included 18,891 adults aged 18 and older. Hypertension was defined by self-reported medical diagnosis. Psychological variables included depression and anxiety scores, with stress proxied by reported irritability. Covariates included age, gender, race/ethnicity, physical activity, and BMI. Due to missing data, multiple imputation by chained equations (MICE) was applied. Multivariable logistic regression was used to assess associations. RESULTS: After adjustment, stress was significantly linked to higher odds of hypertension (β = 0.30, p < 0.03), while anxiety and depression were not. Older age, higher body mass index (BMI), and physical inactivity were also significant risk factors. Non-Hispanic Black and White participants had greater odds of hypertension compared to Mexican Americans. CONCLUSION: Stress was independently associated with hypertension, emphasizing the relevance of emotional strain in cardiovascular risk. Anxiety and depression showed no significant association. Findings support the inclusion of stress assessment in hypertension screening, though results are limited by the study's cross-sectional design and reliance on imputed data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".