Prodromal symptoms of Chinese patients with bipolar disorder

Guo, Tong; Yang, Yuan; Zhao, Qian; Zhang, Lei; Ng, Chee H.; Cheung, Teris; Li, Yang; Zhu, Xuequan; Li, Xiaohong; Xiang, Yu‐Tao

doi:10.1016/j.jad.2021.07.079

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Insomnia, reduction of enjoyment, and trouble concentrating can be prodromal signs of a depressive episode. 10 Furthermore, individuals with BD are disproportionally affected by overweight and obesity, compared to the general public associated with metabolic and cardiovascular comorbidities and an earlier mortality. 11,12 Another affected and clinically underexplored domain is the cognitive impairment associated with BD.…”

Section: Introductionmentioning

confidence: 99%

“…For manic episodes, they can include being overly cheerful, experiencing racing thoughts, a reduced need for sleep, and being more talkative. Insomnia, reduction of enjoyment, and trouble concentrating can be prodromal signs of a depressive episode 10 . Furthermore, individuals with BD are disproportionally affected by overweight and obesity, compared to the general public associated with metabolic and cardiovascular comorbidities and an earlier mortality 11,12 …”

Section: Introductionmentioning

confidence: 99%

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Machine‐based learning of multidimensional data in bipolar disorder – pilot results

Birner,

Mairinger,

Elst

et al. 2024

Bipolar Disorders

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IntroductionOwing to the heterogenic picture of bipolar disorder, it takes approximately 8.8 years to reach a correct diagnosis. Early recognition and early intervention might not only increase quality of life, but also increase life expectancy as a whole in individuals with bipolar disorder. Therefore, we hypothesize that implementing machine learning techniques can be used to support the diagnostic process of bipolar disorder and minimize misdiagnosis rates.Materials and MethodsTo test this hypothesis, a de‐identified data set of only demographic information and the results of cognitive tests of 196 patients with bipolar disorder and 145 healthy controls was used to train and compare five different machine learning algorithms.ResultsThe best performing algorithm was logistic regression, with a macro‐average F1‐score of 0.69 [95% CI 0.66–0.73]. After further optimization, a model with an improved macro‐average F1‐score of 0.75, a micro‐average F1‐score of 0.77, and an AUROC of 0.84 was built. Furthermore, the individual amount of contribution per variable on the classification was assessed, which revealed that body mass index, results of the Stroop test, and the d2‐R test alone allow for a classification of bipolar disorder with equal performance.ConclusionUsing these data for clinical application results in an acceptable performance, but has not yet reached a state where it can sufficiently augment a diagnosis made by an experienced clinician. Therefore, further research should focus on identifying variables with the highest amount of contribution to a model's classification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine‐based learning of multidimensional data in bipolar disorder – pilot results

Birner,

Mairinger,

Elst

et al. 2024

Bipolar Disorders

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“…The evolving nature of BD research symptom standardizations and diagnostic criteria underscores the pressing need for refined symptomatic investigations. 3 While there’s growing attention to prodromal and mixed symptoms and tailored symptom management in BD research, 3 , 4 more research is needed on BD as a comprehensive multivariate disease system, with a lack of network analyses focusing on residual and subsyndromal symptoms in BD.…”

Section: Introductionmentioning

confidence: 99%

Manic Residual Symptoms Also Deserve Attention: A Symptom Network Analysis of Residual Symptoms in Bipolar Disorder

Zhao,

Zhang,

Zheng

et al. 2024

NDT

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Background Lots of patients with bipolar disorder (BD) continue to have residual symptoms after treatment in their remission, BD exhibits intricate characteristics and transformation patterns in its residual symptoms, residual symptoms of different polarities and degrees can mix with and transform to each other. There is a need for further investigation of BD as a comprehensive multivariate disease system. The current research lacks network analyses focusing on BD’s residual and subsyndromal symptoms. Methods 242 patients were included with bipolar disorder in remission. We compared demographic data and differences in symptoms between populations with and without residual symptoms using t -tests and chi-square tests, with FDR applied for multiple comparison correction. Logistic regression was used to identify influencing factors for residual symptoms. Symptom networks were compared by network analysis to analyze the relationships between different types of residual symptoms. Results Depressive residual symptoms (N=111) were more common than manic residual symptoms (n=29) in the patients included. The comparison between two groups with and without residual symptoms shows no difference in demographic data and medical history information. The main influencing factors related to residual symptoms were time from diagnosis to first treatment (OR=0.88), the first(OR=1.51) and second (OR=17.1)factors of the Mood Disorder Questionnaire (MDQ), the Quick Inventory of Depressive Symptomatology Self-Report (QIDS)(OR=5.28), the psychological(OR=0.68) and environment (OR=1.53) subscale of the World Health Organization Quality of Life Short Form (WHOQOL-BREF). There was a significant difference in network structure between the groups with and without residual symptoms (network invariance difference=0.4, p =0.025). At the same time, there was no significant difference between the groups with and without depressive residual symptoms. However, the symptom network in patients with depressive residual symptoms is more loosely structured than in those without, with symptoms exhibiting weaker interconnections. When there is no depressive or manic residual symptom, it can still form a symptom network and cause an impact on social function. Conclusion This study underscores the complexity of bipolar disorder’s residual symptoms. Although it primarily manifests as loosely structured depressive residual symptoms, manic residual symptoms should not be ignored. Future research should explore network-based interventions targeting specific symptom clusters or connections to improve residual symptom management and patient outcomes.

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“…Sleep disorders are a serious problem, and chronic sleep deprivation can negatively affect a worker’s mood, health and ability to work. Long-term sleep disorders may lead to memory loss, slow reaction speed, lethargy and other effects, and may even lead to depression and suicidal tendencies, seriously affecting the quality of workers’ working lives ( 3 , 4 ).…”

Section: Introductionmentioning

confidence: 99%

The interaction of occupational stress, mental health, and cytokine levels on sleep in Xinjiang oil workers: A cross-sectional study

Xue²,

Yi³

et al. 2022

Front. Psychiatry

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BackgroundSleep occupies one third of a person’s life, and good sleep quality is an important factor to ensure good health.PurposeThis study investigated and analyzed the occupational stress, mental health and sleep quality of oil workers, analyzed the effects of occupational stress and mental health on sleep, and explored the effects of the interaction between occupational stress, mental health and cytokines on sleep.Materials and methodsIn this study, stratified cluster random sampling was used to conduct a cross-sectional survey on the occupational stress, mental health and sleep quality of 1,141 oil workers in the Occupational Health Examination Department of Karamay Central Hospital, from June 2019 to January 2020, and 30% of the participants were randomly selected for measurement of their cytokine levels: interleukin 2 (IL-2), interleukin 6 (IL-6), interleukin 8 (IL-8), and tumor necrosis factor α (TNF-α). The objectives were to analyze the effects of occupational stress and mental health on sleep quality, and to explore the effects of occupational stress, mental disorders and cytokine interactions on sleep.ResultsThere were 646 individuals (56.6%) who suffered from sleep disorders; the incidence of sleep disorders differed according to sex, age, professional title, working years, type of work and shift (P < 0.05). The scores for occupational stress, mental health, and sleep quality were positively correlated (P < 0.05). Multivariate logistic regression analysis showed that age (30–45 years) (OR = 1.753, 95% CI: 1.067–2.881), junior college and above (OR = 1.473, 95% CI: 1.025–2.118), borehole operation (OR = 2.689, 95% CI: 1.508–4.792), extraction of oil (OR = 2.405, 95% CI: 1.229–4.705), drilling (OR = 1.791, 95% CI: 1.017–3.155), shift work (OR = 2.144, 95% CI: 1.615–2.846), occupational stress (OR = 1.007, 95% CI: 1.002–1.013), and mental disorders (OR = 1.020, 95% CI: 1.009–1.032) are risk factors for sleep disorders. In the high occupational stress group and the mental disorders group as the control group, the results showed that the interaction between IL-2 and moderate occupational stress (OR = 0.778, 95% CI: 0.778–0.942), IL-2 and non-mental disorders (OR = 0.398, 95% CI: 0.398–0.468) were protective factors for the occurrence of sleep disorders. The results of structural equation modeling analysis showed that occupational stress and mental health had positive predictive effects on IL-2 level and sleep quality [normalized path coefficients (β) were 0.10\0.06\0.05\0.71, respectively]. Occupational stress had a direct positive predictive effect on mental disorder (β = 0.25), and sleep disorder had a direct negative predictive effect on IL-2 concentration (β = −0.21).ConclusionOil workers have serious sleep problems, so effective measures should be taken to reduce occupational stress and relieve mental health problems, and cytokine levels can be used as a predictor of sleep disorders.

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Prodromal symptoms of Chinese patients with bipolar disorder

Cited by 5 publications

References 38 publications

Machine‐based learning of multidimensional data in bipolar disorder – pilot results

Machine‐based learning of multidimensional data in bipolar disorder – pilot results

Manic Residual Symptoms Also Deserve Attention: A Symptom Network Analysis of Residual Symptoms in Bipolar Disorder

The interaction of occupational stress, mental health, and cytokine levels on sleep in Xinjiang oil workers: A cross-sectional study

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