AN EXPLORATORY ANALYSIS OF DEPRESSION COMORBIDITY AND SLEEP DISTURBANCE IN CHINA KADOORIE BIOBANK
Notice bibliographique
Résumé
Abstract Background High levels of stress often lead to insomnia and sleep problems. Sleep loss can disrupt homeostasis which triggers body’ s stress response system implicated in the development of adverse effects on the neuroendocrine, metabolic, gastrointestinal, and immune systems (The Lancet, 2022). The high level of depression comorbidity with other mental and medical illnesses may result from substantial overlap in symptoms (e.g. sleep disorder as a bridging symptom) across various disorders and/or common etiological factors (e.g. family history) (Freeman et al., 2020; Garber and Weersing, 2010; Singh et al., 2022), with debilitating effects on functioning and treatment outcomes. Aims & Objectives To evaluate the epidemiological evidence linking stressful life events and sleep problems and their effects on depression comorbidities with major medical conditions such as diabetes, heart diseases, in addition to other psychiatric illness. Method Using a large, community-based study of 512,715 Chinese adults conducted in 10 regions (five urban and five rural) across China, we hypothesized that the improvement of sleep disturbance from baseline to follow-up in the re-survey reduced risk for depression co-morbidities. Results Of 512,715 participants, 43,440 (8.5%) experienced at least 1 major stressful life events with higher prevalence of sleep disturbance (27.1%, n = 11,783; insomnia or use sleep medication) compared to participants without these stressful events (15.8%, n = 74,317, N = 469,275). Among the 15,538 participants with depressive symptoms at baseline, the presence of sleep disturbance was associated with increased likelihood of depression comorbidities with other mental illness (anxiety, psychiatric disorders, neurasthenia, panic attack, phobia, or pain) as well as major chronic conditions (e.g. rheumatic heart disease, rheumatoid arthritis, CHD, diabetes, hypertension). Among the 1176 families that had children diagnosed with mental disorders, 241 (20.5%) also had first-degree relatives diagnosed with mental disorders. Risk of mental illness in offspring was associated with mental status of parents (Odds Ratio [OR] = 3.49), depression comorbidity with other mental illness or medical diseases (OR= 1.51), sleep disorder (OR = 1.33). Resolution of baseline sleep problems at 2-year follow-up was associated with decreased risk of depression as well as depression comorbidity with other mental disorders and medical diseases. Discussion & Conclusion The present study based on data from a large, community-based study in China indicate a significant link between stressful life events and sleep disturbance and the presence of sleep problems increased risk of depression comorbidity with other mental disorders and medical diseases. References 1.The Lancet. Waking up to the importance of sleep. Lancet. 2022 Sep 24;400(10357):973. doi: 10.1016/S0140-6736(22)01774-3. Epub 2022 Sep 14. PMID: 36115369. 2.Freeman D, Sheaves B, Waite F, et al. Sleep disturbance and psychiatric disorders. Lancet Psychiatry. 2020; 7: 628–637 3.Garber J and Weersing VR. Comorbidity of anxiety and depression in youth: implications for treatment and prevention. Clin Psychol. 2010; 17: 293-306. 4.Singh MK, Siu C, Tocco M, Pikalov A, Loebel A. Sleep Disturbance, Irritability, and Response to Lurasidone Treatment in Children and Adolescents with Bipolar Depression. Curr Neuropharmacol. 2023;21(6):1393-1404.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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) | 0,000 | 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 tête enseignante, 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 ».