Introduction: Lithium carbonate is a valuable and effective medication for treating and preventing mood disorders, especially bipolar disorder. Its narrow therapeutic window necessitates regular blood lithium monitoring. Existing literature does not conclusively support the reliability of salivary or erythrocyte lithium measurements. This study aimed to evaluate the predictive relationships between lithium concentrations in saliva, plasma, and erythrocytes at multiple time points during the day in patients receiving lithium carbonate treatment. The goal was to assess the temporal dynamics and relative utility of these biological matrices for therapeutic monitoring, while also considering the potential clinical advantages of noninvasive sampling methods.
Methods: Subjects were recruited from patients undergoing treatment at the Clinical Department of Psychiatry, Sestre Milosrdnice University Hospital Center, Zagreb, Croatia, during the period between August 2024 and December 2024. We measured lithium levels in saliva, erythrocytes, and plasma of n = 77 participants with bipolar disorder or other psychiatric conditions at six time points during the day. A spectrophotometric method was used to determine lithium concentration.
Results: Regression analyses demonstrated that erythrocyte lithium concentration was the most consistent and robust predictor of plasma levels across most time points. Salivary lithium showed weaker and more variable performance, but was significantly associated with plasma levels at selected time points in univariate models. The strongest overall model fit in the multiple linear regression analysis was observed at 3:00 PM (r2 = 0.665, p < 0.01), primarily driven by erythrocyte lithium.
Conclusion: These findings support the use of erythrocyte lithium as a reliable and robust biomarker for estimating plasma levels, particularly in complex clinical scenarios where intracellular lithium may better reflect treatment response or toxicity. While salivary lithium demonstrated weaker and more time-dependent predictive accuracy, it still holds conditional clinical value as a noninvasive alternative - particularly in outpatient, emergency, or resource-limited settings where individualized or more accessible monitoring may be beneficial.
Background: Somatic symptoms, such as chronic pain, fatigue, and gastrointestinal disturbances, are commonly reported in individuals with a history of childhood maltreatment (CM), which includes various forms of abuse and neglect experienced before age 18. Although CM is strongly associated with somatic symptoms, the specific relationships between CM subtypes and these symptoms, as well as the mechanisms connecting them, remain insufficiently understood. This review examines the complex interaction between CM and somatic symptoms, which often coexist with mental disorders and significantly impact quality of life and healthcare systems.
Summary: Somatic symptoms, frequently a mix of "explained" and "unexplained" conditions, are associated with personal distress and pose diagnostic challenges. CM has been linked to these symptoms through neurobiological mechanisms, such as HPA axis dysregulation and allostatic load, while theoretical models emphasize the roles of hyperawareness, cultural factors, and vulnerability in symptom development. However, existing research often fails to account for specific CM subtypes, the full range of somatic symptoms, and cultural and situational factors, leading to inconsistencies in findings.
Key messages: Bridging gaps in literature requires adopting the World Health Organization's CM subtype definitions and ICD-11 codes (MA00-MH2Y) to encompass a broader spectrum of somatic symptoms. Employing rigorous methodologies, such as systematic reviews and meta-analyses, is essential for advancing understanding. These approaches can enhance diagnostic accuracy, support tailored interventions, and promote a biopsychosocial framework for CM research, ultimately improving patient outcomes and alleviating societal burdens.
Introduction: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms.
Methods: The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD. Multivariable linear regression was used to explore the association between lymphocyte subsets and the severity of GAD. Receiver operating characteristic (ROC) analysis was used to determine the predictive value of these immunological parameters for GAD. Machine learning technology was applied to classify the collected data from patients in the GAD and healthy control groups.
Results: Of the 340 patients enrolled, 171 were GAD patients, and 169 were non-GAD patients as healthy control. The levels of neutrophil, monocytes, and systemic immune-inflammation index (SII) were significantly elevated in GAD patients (p < 0.01), and the count and proportion of immune cells, including CD3+CD4+ T cells, CD3+CD8+ T cells, CD19+ B cells, and CD3-CD16+CD56+ NK cells (p < 0.001), have undergone large changes. The classification analysis conducted by machine learning using a weighted ensemble-L2 algorithm demonstrated an accuracy of 95.00 ± 2.04% in assessing the predictive value of these lymphocyte subsets in GAD. In addition, the feature importance analysis score is 0.255, which was much more predictive of GAD severity than for other lymphocyte subsets.
Conclusion: In the presented work, we show the level of lymphocyte subsets altered in GAD. Lymphocyte subsets, specifically CD3+CD4+ T %, can serve as neuroinflammatory biomarkers for GAD diagnostics. Furthermore, the application of machine learning offers a highly efficient approach for investigating neuroinflammatory biomarkers and predicting GAD. Our research has provided novel insights into the involvement of cellular immunity in GAD and highlighted the potential predictive value and therapeutic targets of lymphocyte subsets in this disorder.
Introduction: The etiology of generalized anxiety disorder (GAD) has not been fully understood, and oxidative stress may potentially contribute to its pathogenesis. However, there is no published evidence concerning the possible influence of oxidative stress on antidepressant treatment outcomes. This study investigated the ability of oxidative stress markers to predict treatment outcomes in GAD patients treated with selective serotonin reuptake inhibitors (SSRIs).
Methods: One hundred-one GAD patients and 100 healthy controls (HCs) were included in this study. The 101 GAD patients were selected for treatment with escitalopram (n = 52) or sertraline (n = 49) for 8 weeks. Hamilton Anxiety Rating Scale (HAM-A) assessments were conducted before and after treatment. The serum levels of eight oxidative stress makers, malondialdehyde (MDA), lipid hydroperoxides (LPO), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT), cortisol, high-density lipoprotein (HDL), and nitric oxide (NO) were measured using enzyme-linked immunosorbent assays (ELISA) before and after SSRI treatment in GAD patients and at the time of HC enrollment.
Results: The serum levels of MDA, cortisol, and LPO were higher in GAD patients than in HCs (all p < 0.001), while SOD, GSH-Px, and CAT were lower than in HCs (all p < 0.001). The baseline MDA, LPO, NO, and cortisol levels were positively correlated with anxiety severity, while GSH-Px was negatively correlated. After 8 weeks of SSRI treatment, the GSH-Px levels increased, and MDA and LPO decreased (all p < 0.05). Alterations in MDA levels covaried with changes in anxiety measures (all p < 0.05). The ability of the receiver-operating characteristic (ROC) area of the baseline MDA levels to predict the SSRI endpoint treatment response was 0.804 (p < 0.05).
Conclusion: The pathogenesis of GAD might involve oxidative stress. Moreover, serum MDA levels might predict treatment response to SSRIs. However, more research is warranted to confirm these findings.
Introduction: Bipolar disorder has been associated with significant structural brain changes, potentially driven by central nervous system (CNS) inflammation. This study aimed to investigate the relationship between inflammation biomarkers in cerebrospinal fluid (CSF) and longitudinal structural brain changes.
Methods: We included 29 individuals with bipolar disorder and 34 healthy controls, analyzing three selected inflammation-related biomarkers - interleukin-6 (IL-6), interleukin-8 (IL-8), and chitinase-3-like protein 1 (YKL-40) - in both blood serum and CSF. Structural brain changes were assessed through magnetic resonance imaging at two timepoints, focusing on cortical thickness of the middle temporal cortex and inferior frontal gyrus, as well as ventricular volume.
Results: In healthy controls, baseline CSF levels of YKL-40 predicted ventricular enlargement in both hemispheres. Among individuals with bipolar disorder, higher baseline levels of IL-8 were associated with a decline in cortical thickness in the right and left middle temporal cortex, as well as the right inferior frontal gyrus. No significant associations were observed with serum biomarkers.
Conclusions: These findings suggest that CSF IL-8 may contribute to cortical decline in bipolar disorder. The lack of association between serum biomarkers and brain changes highlights the specificity of CNS inflammation in these processes. Additionally, the observed link between CSF YKL-40 and ventricular enlargement in healthy controls may indicate a role of CNS inflammation processes in normal brain aging.
Introduction: Bipolar disorder has been associated with significant structural brain changes, potentially driven by central nervous system (CNS) inflammation. This study aimed to investigate the relationship between inflammation biomarkers in cerebrospinal fluid (CSF) and longitudinal structural brain changes.
Methods: We included 29 individuals with bipolar disorder and 34 healthy controls, analyzing three selected inflammation-related biomarkers - interleukin-6 (IL-6), interleukin-8 (IL-8), and chitinase-3-like protein 1 (YKL-40) - in both blood serum and CSF. Structural brain changes were assessed through magnetic resonance imaging at two timepoints, focusing on cortical thickness of the middle temporal cortex and inferior frontal gyrus, as well as ventricular volume.
Results: In healthy controls, baseline CSF levels of YKL-40 predicted ventricular enlargement in both hemispheres. Among individuals with bipolar disorder, higher baseline levels of IL-8 were associated with a decline in cortical thickness in the right and left middle temporal cortex, as well as the right inferior frontal gyrus. No significant associations were observed with serum biomarkers.
Conclusions: These findings suggest that CSF IL-8 m
Introduction: Recent research suggests various app-based-programs to promote mental health, resilience, and stress management. Insights gained from studies with healthy participants could potentially offer training strategies that could also prove beneficial for people with mental disorders. The effectiveness of an app-based resilience training was evaluated.
Methods: In the present study, 68 mentally healthy participants were included. They all received both the intervention as 2-month resilience training via an app and the control condition (waiting group) as part of a crossover design. In addition, the participants were interviewed before, and after each condition with the Stress and Coping Inventory (SCI), the Brief Symptom Inventory (BSI), and the Resilience Scale (RS13), measuring psychological stress and symptoms.
Results: The results of the analyses of co-variance indicate that the app-training does not significantly improve resilience in healthy people (p = 0.278). However, it significantly enhances stress regulation in the intervention group and the control group (p = 0.030), independent of the initial stress level. Furthermore, a significant positive correlation was found between effective stress regulation and improved mental health (measured by the BSI).
Conclusion: Emphasizing mindfulness and reflection through resilience training and the enhanced perception of mental health, can improve stress regulation, thereby underscoring its crucial role. To maximize the benefits of resilience training, it is imperative to further develop training apps, enhancing their attractiveness and suitability for long-term use, and extend its use. Future work should focus on refining these interventions to ensure sustained engagement and effectiveness.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：