Abstract P269: Combined Associations of Objective Sleep Efficiency and Overweight With the Prevalence of Hypertension in Japanese Adults
Bibliographic record
Abstract
Introduction: Poor sleep efficiency is a risk for prevalent hypertension, and also overweight is one of the major risk factors for hypertension. Generally, overweight participants have poor sleep efficiency, and thus, overweight may modify the association between poor sleep efficiency and hypertension. However, there are no previous reports to examine the impact of overweight on the association between poor sleep efficiency and hypertension. Hypothesis: Poor sleep efficiency is associated with increased with prevalent hypertension, particularly in individuals with non-overweight. Methods: We conducted a cross-sectional study of 779 participants aged 20 years or older who lived in Miyagi prefecture, Japan. All the participants were recruited from June 2017 to March 2018. Sleep efficiency was measured by HSL-101 sleep sensor, and then we classified all the participants into four groups according to their sleep efficiency (good; ≥90%/poor; <90%) and the presence or absence of overweight which was defined as BMI of 23 kg/m 2 or higher based on the Western Pacific Region of WHO criteria for Japanese. Hypertension was defined as morning home blood pressure ≥135/85 mmHg or receiving treatment for hypertension. Multivariable logistic regression models were used to obtain odds ratios (ORs) and 95% confidence intervals (CIs) to assess the combined associations of poor sleep efficiency and overweight with prevalent hypertension. Models were adjusted for sex, age, alcohol drinking status, smoking status, average daily steps, urinary sodium/potassium ratio, and sleep duration. Results: Of the 779 participants (68.3% women, mean age 61.0 years), 252 (32.3%) had poor sleep efficiency, 331 (42.5%) had overweight, and 303 (38.9%) had hypertension. The prevalence of poor sleep efficiency was higher in men (41.7% in men vs. 28.0% in women), and the individuals with poor sleep efficiency had a higher proportion of overweight (52.8 % in participants with poor sleep efficiency vs. 37.6 % in those with good sleep efficiency) and shorter sleep duration. In a multivariable analysis, compared with individuals with good sleep efficiency and non-overweight for hypertension, the adjusted ORs (95% CIs) of those with poor sleep efficiency and non-overweight, good sleep efficiency and overweight, and poor sleep efficiency and overweight for hypertension were 1.79 (1.08 to 2.98), 2.99 (1.99 to 4.49), and 4.15 (2.56 to 6.71), respectively. Conclusions: Poor sleep efficiency was associated with increased prevalence of hypertension even in individuals with non-overweight, and additionally the risk of poor sleep efficiency for prevalent hypertension in individuals with overweight was relatively higher than that in individuals with non-overweight.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".