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BMC Psychiatry Jun 2023Bipolar disorder (BD) is characterized by intensive mood fluctuations. While hormones imbalance plays important role in the mood swings, it is unknown whether peripheral...
BACKGROUND
Bipolar disorder (BD) is characterized by intensive mood fluctuations. While hormones imbalance plays important role in the mood swings, it is unknown whether peripheral hormones profiles could differentiate the manic and depressive mood episodes in BD. In this study, we investigated the changes of various hormones and inflammatory markers across distinct mood episodes of BD in a large clinical study to provide mood episode-specific peripheral biomarkers for BD.
METHODS
A total of 8332 BD patients (n = 2679 depressive episode; n = 5653 manic episode) were included. All patients were in acute state of mood episodes and need hospitalization. A panel of blood tests were performed for levels of sex hormones (serum levels of testosterone, estradiol, and progesterone), stress hormones (adrenocorticotropic hormone and cortisol), and an inflammation marker (C-reactive protein, CRP). A receiver operating characteristic (ROC) curve was used to analyze the discriminatory potential of the biomarkers for mood episodes.
RESULTS
In overall comparison between mood episodes, the BD patients expressed higher levels of testosterone, estradiol, progesterone, and CRP (P < 0.001) and lower adrenocorticotropic hormone (ACTH) level (P < 0.001) during manic episode. The episode-specific changes of testosterone, ACTH, and CRP levels remained between the two groups (P < 0.001) after correction for the confounding factors including age, sex, BMI, occupation, marital status, tobacco use, alcohol consumption, psychotic symptoms, and age at onset. Furthermore, we found a sex- and age-specific impact of combined biomarkers in mood episodes in male BD patients aged ≥ 45 years (AUC = 0.70, 95% CI, 0.634-0.747), not in females.
CONCLUSIONS
While both hormone and inflammatory change is independently associated with mood episodes, we found that the combination of sex hormones, stress hormones and CRP could be more effective to differentiate the manic and depressive episode. The biological signatures of mood episodes in BD patients may be sex- and age-specific. Our findings not only provide mood episode-related biological markers, but also better support for targeted intervention in BD treatments.
Topics: Female; Humans; Male; Bipolar Disorder; Mania; Progesterone; Hydrocortisone; Biomarkers; Adrenocorticotropic Hormone; Testosterone; Estradiol
PubMed: 37340368
DOI: 10.1186/s12888-023-04846-1 -
Frontiers in Oncology 2023Endocrine therapy-related symptoms are associated with early discontinuation and quality of life among breast cancer survivors. Although previous studies have examined...
BACKGROUND
Endocrine therapy-related symptoms are associated with early discontinuation and quality of life among breast cancer survivors. Although previous studies have examined these symptoms and clinical covariates, little is known about the interactions among different symptoms and correlates. This study aimed to explore the complex relationship of endocrine therapy-related symptoms and to identify the core symptoms among breast cancer patients.
METHODS
This is a secondary data analysis conducted based on a multicenter cross-sectional study of 613 breast cancer patients in China. All participants completed the 19-item Chinese version of the Functional Assessment of Cancer Therapy-Endocrine Subscale (FACT-ES). Multivariate linear regression analysis was performed to identify significant factors. A contemporaneous network with 15 frequently occurring symptoms was constructed after controlling for age, payment, use of aromatase inhibitors, and history of surgery. Network comparison tests were used to assess differences in network structure across demographic and treatment characteristics.
RESULTS
All 613 participants were female, with an average age of 49 years (SD = 9.4). The average duration of endocrine therapy was 3.6 years (SD = 2.3) and the average symptom score was 18.99 (SD = 11.43). Irritability (n = 512, 83.52%) and mood swings (n = 498, 81.24%) were the most prevalent symptoms. Lost interest in sex (mean = 1.95, SD = 1.39) and joint pain (mean = 1.57, SD = 1.18) were the most severe symptoms. The edges in the clusters of emotional symptoms ("irritability-mood swings"), vasomotor symptoms ("hot flashes-cold sweats-night sweats"), vaginal symptoms ("vaginal discharge-vaginal itching"), sexual symptoms ("pain or discomfort with intercourse-lost interest in sex-vaginal dryness"), and neurological symptoms ("headaches-dizziness") were the thickest in the network. There were no significant differences in network structure (P = 0.088), and global strength (P = 0.330) across treatment types (selective estrogen receptor modulators vs. aromatase inhibitors). Based on an evaluation of the centrality indices, irritability and mood swings appeared to be structurally important nodes after adjusting for the clinical covariates and after performing subgroup comparisons.
CONCLUSION
Endocrine therapy-related symptoms are frequently reported issues among breast cancer patients. Our findings demonstrated that developing targeted interventions focused on emotional symptoms may relieve the overall symptom burden for breast cancer patients during endocrine therapy.
PubMed: 37064124
DOI: 10.3389/fonc.2023.1081786 -
Clinical Psychopharmacology and... May 2022Bipolar disorder is a mental illness that causes extreme mood swings and has a chronic course. However, the mechanism by which mood episodes with completely opposite... (Review)
Review
Bipolar disorder is a mental illness that causes extreme mood swings and has a chronic course. However, the mechanism by which mood episodes with completely opposite characteristics appear repeatedly, or a mixture of symptoms appears, in patients with bipolar disorder remains unknown. Therefore, mood stabilizers are indicated only for single mood episodes, such as manic episodes and depressive episodes, and no true mood-stabilizing drugs effective for treating both manic and depressive episodes currently exist. Therefore, in this review, therapeutic targets that facilitate the development of mood stabilizers were examined by reviewing the current understanding of the neuromolecular etiology of bipolar disorder.
PubMed: 35466094
DOI: 10.9758/cpn.2022.20.2.228 -
Maturitas Mar 2024Menopause is a natural physiological phase during which women experience dramatic hormonal fluctuations. These lead to many symptoms, such as depression and anxiety,... (Review)
Review
Menopause is a natural physiological phase during which women experience dramatic hormonal fluctuations. These lead to many symptoms, such as depression and anxiety, which, in turn, can negatively affect quality of life. Proper nutrition has an influential role in alleviating depression as well as anxiety. It is well known that gut microbiota dysbiosis contributes to the development of mood disorder. There is mounting evidence that modulating the gut-brain axis may aid in improving mood swings. In this context, this narrative review summarizes recent findings on how aging changes the composition of the gut microbiota and on the association between gut microbiota and mood disorders. In addition, it evaluates the effectiveness of psychobiotics and fermented foods in treating mood swings in middle-aged and older women. A search was done using PubMed, Scopus, and Google Scholar, and thirteen recent articles are included in this review. It is evident that psychobiotic supplementation and fermented foods can improve mood swings via several routes. However, these conclusions are based on only a few studies in middle-aged and older women. Therefore, long-term, well-designed randomized controlled trials are required to fully evaluate whether psychobiotics and fermented foods can be used to treat mood swings in this population.
Topics: Humans; Female; Middle Aged; Aged; Quality of Life; Affect; Mood Disorders; Gastrointestinal Microbiome; Fermented Foods; Probiotics
PubMed: 38157685
DOI: 10.1016/j.maturitas.2023.107903 -
BJPsych Open Apr 2024Many studies have found an association between mood-disorder-related traits and endometriosis and adenomyosis. However, the cause-effect relationship remains unclear.
BACKGROUND
Many studies have found an association between mood-disorder-related traits and endometriosis and adenomyosis. However, the cause-effect relationship remains unclear.
AIMS
We conducted Mendelian randomisation analyses to evaluate any causal relationship between mood disorders and endometriosis as well as different sites of endometriosis.
METHOD
Summary-level statistics for mood-disorder-related traits and endometriosis (8288 cases, 68 969 controls) in European populations were derived from large-scale data-sets of genome-wide association studies. A two-sample Mendelian randomisation was performed using the inverse-variance weighted and weight median methods. Further sensitivity analyses, including heterogeneity, pleiotropy and leave-one-out analyses, were conducted to test the consistency of the results.
RESULTS
Genetically determined mood swings (odds ratio = 2.557, 95% CI: 1.192-5.483, = 0.016) and major depression (odds ratio = 1.233, 95% CI: 1.019-1.493, = 0.031) were causally associated with an increased risk of endometriosis. Mood swings (odds ratio = 4.238, 95% CI: 1.194-15.048, = 0.025) and major depression (odds ratio = 1.512, 95% CI: 1.052-2.173, = 0.025) were also causally associated with the risk of adenomyosis. Sensitivity analyses confirmed the reliability of the results.
CONCLUSIONS
Our results suggest that mood-disorder-related traits increase the risk of endometriosis and adenomyosis. This study provides new insights into the potential pathogenesis of endometriosis and adenomyosis, and highlights the importance of preventing endometriosis and adenomyosis in patients with mood-disorder-related traits.
PubMed: 38622955
DOI: 10.1192/bjo.2024.46 -
Trends in Cognitive Sciences Apr 2024Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel... (Review)
Review
Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.
Topics: Humans; Adolescent; Mood Disorders; Affect; Reinforcement, Psychology; Cognition
PubMed: 38503636
DOI: 10.1016/j.tics.2024.02.006 -
JMIR MHealth and UHealth Mar 2021Major depressive disorder (MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable...
Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study.
BACKGROUND
Major depressive disorder (MDD) is a common mental illness characterized by persistent sadness and a loss of interest in activities. Using smartphones and wearable devices to monitor the mental condition of patients with MDD has been examined in several studies. However, few studies have used passively collected data to monitor mood changes over time.
OBJECTIVE
The aim of this study is to examine the feasibility of monitoring mood status and stability of patients with MDD using machine learning models trained by passively collected data, including phone use data, sleep data, and step count data.
METHODS
We constructed 950 data samples representing time spans during three consecutive Patient Health Questionnaire-9 assessments. Each data sample was labeled as Steady or Mood Swing, with subgroups Steady-remission, Steady-depressed, Mood Swing-drastic, and Mood Swing-moderate based on patients' Patient Health Questionnaire-9 scores from three visits. A total of 252 features were extracted, and 4 feature selection models were applied; 6 different combinations of types of data were experimented with using 6 different machine learning models.
RESULTS
A total of 334 participants with MDD were enrolled in this study. The highest average accuracy of classification between Steady and Mood Swing was 76.67% (SD 8.47%) and that of recall was 90.44% (SD 6.93%), with features from all types of data being used. Among the 6 combinations of types of data we experimented with, the overall best combination was using call logs, sleep data, step count data, and heart rate data. The accuracies of predicting between Steady-remission and Mood Swing-drastic, Steady-remission and Mood Swing-moderate, and Steady-depressed and Mood Swing-drastic were over 80%, and the accuracy of predicting between Steady-depressed and Mood Swing-moderate and the overall Steady to Mood Swing classification accuracy were over 75%. Comparing all 6 aforementioned combinations, we found that the overall prediction accuracies between Steady-remission and Mood Swing (drastic and moderate) are better than those between Steady-depressed and Mood Swing (drastic and moderate).
CONCLUSIONS
Our proposed method could be used to monitor mood changes in patients with MDD with promising accuracy by using passively collected data, which can be used as a reference by doctors for adjusting treatment plans or for warning patients and their guardians of a relapse.
TRIAL REGISTRATION
Chinese Clinical Trial Registry ChiCTR1900021461; http://www.chictr.org.cn/showprojen.aspx?proj=36173.
Topics: Affect; Depressive Disorder, Major; Humans; Machine Learning; Prospective Studies; Smartphone
PubMed: 33683207
DOI: 10.2196/24365 -
Psychopathology 1987Dysphoric conditions are increasingly postulated as representing independent mood disorders. However, despite much effort at clarification, their psychopathological... (Review)
Review
Dysphoric conditions are increasingly postulated as representing independent mood disorders. However, despite much effort at clarification, their psychopathological definitions remain unclear and variable. This paper reviews some examples of these divergent definitions, most of which are based on quality of mood, as well as responsiveness to external stimuli. The paper then introduces a strategy in possible solution of the above-mentioned definition problems. Setting out from restriction of the term dysphoria to conditions of a morose, tense and irritated mood, as suggested by Snaith and Taylor, we support the opinion that dysphoria should be accepted as a third possibility of mood swing, as a psychopathological disturbance which can be well distinguished from stable and unstable mixed states.
Topics: Depressive Disorder; Humans; Mood Disorders; Psychopathology
PubMed: 3321124
DOI: 10.1159/000284485 -
BMJ Case Reports Jan 2024Vertebral artery dissections are a rare pathology that carries a high risk of stroke in a younger population. They may be caused by minor mechanisms and the index of...
Vertebral artery dissections are a rare pathology that carries a high risk of stroke in a younger population. They may be caused by minor mechanisms and the index of suspicion should be high. Treatment with anticoagulation or antiplatelets should follow if no surgical management is indicated.We describe a case of a female in her 30s who fell backward off a swing and rolled over her head and complained of continued posterior neck pain. The patient was found to have a vertebral artery dissection on MRI. The patient was then anticoagulated with high-dose apixaban and low-dose aspirin.The emergency medicine provider should be aware of possible low-impact mechanisms that can cause vertebral artery dissection and should have a high index of suspicion. If surgical management is not indicated, anticoagulation should be initiated.
Topics: Female; Humans; Affect; Anticoagulants; Aspirin; Awareness; Vertebral Artery Dissection; Adult
PubMed: 38195187
DOI: 10.1136/bcr-2023-255923 -
Medical Hypotheses Feb 2021Bipolar disorder (BD) is a unique disorder where the same patient exhibits depression and mania, states with polar opposite mood symptoms. Lithium is an alkali metal...
Bipolar disorder (BD) is a unique disorder where the same patient exhibits depression and mania, states with polar opposite mood symptoms. Lithium is an alkali metal that is widely used for the treatment of BD. However, it is largely unknown why lithium can stabilize mood. Lithium is known to inhibit glycogen synthase kinase-3β (GSK3 β). Interestingly, both in the glutamatergic system and GABAergic system, active GSK3 β decreases neuronal excitability, whereas inhibition of GSK3 β increases neuronal excitability, suggesting that activation of GSK3 β leads to depressive mood, and inhibition of GSK3 β leads to manic mood. The activity of GSK3β is regulated by many kinases and a phosphatase, and they are further controlled by several neurotransmitters and signaling molecules. Thus, these complicated control systems might make the swing of GSK3β activity, the swing of GSK3β activity makes the swing of neuronal excitability and finally resulting in the intrinsic swing of mood, usually observed in healthy human. BD is considered that the amplitude of the mood swing is enhanced by many factors. Lithium can dose-dependently decrease the amplitude of the swing of GSK3β activity. In addition, lithium also inhibits K channel activation, leading to the elongation of refractory period, resulting in the inhibition of neuronal excitability. Therefore, in depressive mood, lithium can increase neuronal activity via the inhibition of neuronal GSK3beta activity, and in manic mood, lithium can inhibit neuronal excitability via inhibiting K channel activation, therefore the amplitude of the mood swing is decreased, i.e. alleviating the depressive state and the manic state, resulting in the normalization of the mood swing.
Topics: Affect; Bipolar Disorder; Glycogen Synthase Kinase 3; Glycogen Synthase Kinase 3 beta; Humans; Lithium; Neurons
PubMed: 33444905
DOI: 10.1016/j.mehy.2021.110484