The Effect of Sleep on Metabolism, Musculoskeletal Disease, and Mortality in the General US Population: Analysis of Results From the National Health and Nutrition Examination Survey
Notice bibliographique
Résumé
BACKGROUND: Sleep is an important physiological behavior in humans that is associated with the occurrence and development of various diseases. However, the association of sleep duration with health-related outcomes, including obesity-related factors, musculoskeletal diseases, and mortality because of different causes, has not been systematically reported. OBJECTIVE: This study aims to systematically investigate the effect of sleep duration on health-related outcomes. METHODS: Overall, 54,664 participants with sleep information from 8 survey cycles of the National Health and Nutrition Examination Survey (2005-2020) were included in the analysis. Health-related outcomes comprised obesity-related outcomes (ie, BMI, obesity, waist circumference, and abdominal obesity), metabolism-related outcomes (ie, uric acid, hyperuricemia, and bone mineral density [BMD]), musculoskeletal diseases (ie, osteoarthritis [OA] and rheumatoid arthritis [RA]), and mortality because of different causes. The baseline information of participants including age, sex, race, educational level, marital status, total energy intake, physical activity, alcohol consumption, smoking, hypertension, and diabetes was also collected as covariates. Information about the metabolism index, disease status, and covariates was acquired from the laboratory, examination, and questionnaire data. Survival information, including survival status, duration, and cause of death, was obtained from the National Death Index records. Quantile regression models and Cox regression models were used for association analysis between sleep duration and health-related outcomes. In addition, the threshold effect analysis, along with smooth curve fitting method, was applied for the nonlinear association analysis. RESULTS: Participants were divided into 4 groups with different sleep durations. The 4 groups showed significant differences in terms of baseline data (P<.001). The quantile regression analysis indicated that participants with increased sleep duration showed decreased BMI (β=-.176, 95% CI -.220 to -.133; P<.001), obesity (odds ratio [OR] 0.964, 95% CI 0.950-0.977; P<.001), waist circumference (β=-.219, 95% CI -.320 to -.117; P<.001), abdominal obesity (OR 0.975, 95% CI 0.960-0.990; P<.001), OA (OR 0.965, 95% CI 0.942-0.990; P=.005), and RA (OR 0.940, 95% CI 0.912-0.968; P<.001). Participants with increased sleep duration also showed increased BMD (β=.002, 95% CI .001-.003; P=.005), as compared with participants who slept <5.5 hours. A significant saturation effect of sleep duration on obesity, abdominal obesity, and hyperuricemia was detected through smooth curve fitting and threshold effect analysis (sleep duration>inflection point). In addition, a significant threshold effect of sleep duration on BMD (P<.001); OA (P<.001); RA (P<.001); and all-cause (P<.001), cardiovascular disease-cause (P<.001), cancer-cause (P=.005), and diabetes-cause mortality (P<.001) was found. The inflection point was between 6.5 hours and 9 hours. CONCLUSIONS: The double-edged sword effect of sleep duration on obesity-related outcomes, embolism-related diseases, musculoskeletal diseases, and mortality because of different causes was detected in this study. These findings provided epidemiological evidence that proper sleep duration may be an important factor in the prevention of multisystem diseases.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».