AN EXPLORATORY ANALYSIS OF DEPRESSION COMORBIDITY AND SLEEP DISTURBANCE IN CHINA KADOORIE BIOBANK
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".