Abstract P269: Combined Associations of Objective Sleep Efficiency and Overweight With the Prevalence of Hypertension in Japanese Adults
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».