Interleukin-6-white matter network differences explained the susceptibility to depression after stressful life events

Long-term risk of late-life depression in widowed elderly: a five-year follow-up study

BACKGROUND

Late-life depression (LLD) poses a significant health risk among the elderly, with widowhood as a prominent contributing factor. However, the mechanisms that render some widowed individuals susceptible to depression while others remain resilient remain poorly understood.

METHODS

In this five-year longitudinal study, we followed 203 cognitively healthy, widowed elderly individuals (mean age: 65.2 years, 100 women). The median follow-up time was 4.8 years. Brain structural networks were constructed via diffusion tensor imaging and analyzed using graph theory metrics. Logistic regression and Cox proportional hazards models were employed to assess the predictive role of structural network attributes in depression onset. Moderation models further examined the influence of psychosocial factors on depression risk.

RESULTS

During our follow-up, 22 participants developed LLD (mean age: 65.6 years, 12 women). Altered brain structural network properties, alongside key psychosocial factors, were observed in those at risk of developing depression prior to symptom emergence. Logistic and Cox regression models revealed that decreased rich-club connections, reduced nodal efficiency in the left hippocampus (HIP.L), and lower network modularity significantly predicted depression onset. Additionally, these network alterations correlated with greater depression severity at follow-up. Moderation analyses indicated that weekly exercise frequency and time spent with children notably mitigated the effects of network disruptions on depression severity.

CONCLUSIONS

Among cognitively healthy widowed elders, diminished rich-club connections, modularity, and HIP.L nodal efficiency are strong predictors of future depression risk. Furthermore, low physical activity and limited family interaction may amplify susceptibility within this high-risk group, suggesting that targeted early interventions could reduce depression risk in this vulnerable population.

CLINICAL TRIAL NUMBER

Not applicable.

Reduced brain modularity may underlie accelerated disease progression in first-episode, drug-naïve depression

BACKGROUND

Depression presents considerable heterogeneity in its clinical course, yet reliable biomarkers for predicting individual trajectories remain elusive. Brain modularity, a fundamental topological property of structural networks, reflects the balance between functional segregation and integration. This study investigates the prognostic significance of brain modularity in depression progression and its association with white matter alterations.

METHODS

In this longitudinal study, 142 first-episode, medication-naïve patients with depression underwent diffusion MRI-based structural network analysis. Based on baseline modularity values, participants were stratified into high- and low-modularity groups. Key white matter network metrics-including rich-club connections, global efficiency, and nodal efficiency-were assessed. Depression severity was measured using the Hamilton Depression Rating Scale (HDRS). Logistic regression and receiver operating characteristic (ROC) analyses were employed to evaluate the prognostic utility of brain modularity in predicting symptom progression.

RESULTS

At baseline, patients with lower modularity exhibited disrupted network organization. Longitudinally, these individuals showed a steeper decline in rich-club connections, global efficiency, and left hippocampal nodal efficiency, alongside significantly greater HDRS worsening. Baseline modularity was inversely correlated with the rate of depression progression, with logistic regression confirming its predictive value. ROC analysis demonstrated robust classification performance.

CONCLUSIONS

Reduced brain modularity predisposes individuals to accelerated white matter network alterations and worsening depressive symptoms. These findings highlight brain modularity as a potential biomarker for identifying individuals at heightened risk of depression progression, offering a novel target for early intervention.

Local connections enhancement as a neuroprotective strategy against depression recurrence: Insights from structural brain network analysis

BACKGROUND

Depression recurrence significantly impacts patients' well-being and presents a major clinical challenge. Identifying the risk of recurrence during remission could enable early intervention and prevent disease progression.

METHODS

This study included 115 patients in remission from their first depressive episode and 47 healthy controls (HCs). Participants underwent diffusion tensor imaging (DTI), neuropsychological assessments, and follow-up evaluations every three months over a two-year period. Structural brain networks were constructed using deterministic fiber tracking and graph theory analysis.

RESULTS

Non-recurrence patients exhibited significantly higher baseline local connections compared to the recurrence group (t = 8.148; P < 0.001), which emerged as a robust negative predictor of recurrence (AUC = 0.853 [95 % CI: 0.774-0.912]; OR = 0.594 [95 % CI: 0.489-0.722]; P < 0.001). Rich-club connections were inversely correlated with depression severity (r = -0.510; P < 0.001) and duration (r = -0.221; P = 0.018). Additionally, increases in local connections during remission correlated positively with subsequent rich-club connections (r = 0.540; P < 0.05).

CONCLUSION

Elevated local connections during remission after the first depressive episode significantly reduce the risk of recurrence. This suggests a compensatory neuroprotective mechanism, where enhanced local connections stabilize rich-club connections, thereby maintaining the integrity of the whole-brain network. These findings highlight local connections as a critical factor in preventing depression recurrence and as a potential target for early clinical intervention.

Exploring the active components and potential mechanisms of Zhimu-Huangbai herb-pair in the treatment of depression

BACKGROUND

The Zhimu-Huangbai herb-pair (ZB) is commonly used to treat depression. Previous research has verified that ZB is effective as an antidepressant. Nevertheless, its active components and potential mechanism still require further elucidation.

PURPOSE

This study aims to analyze the compounds of ZB penetrating into the brain using UPLC-MS and investigate the potential mechanisms of ZB in the treatment of depression through in vivo and in vitro experiments.

METHODS

The compounds of ZB that penetrate into the brain were identified using the UPLC-MS method. Network pharmacology analysis was employed to predict the therapeutic targets and mechanisms of the compounds of ZB in the brain for the treatment of depression. Subsequently, the molecular docking method was used to analyze the binding between active compounds and target proteins. Rat depression models induced by CUMS were used to investigate the impact of ZB on depression. Finally, the mechanism of ZB treatment for depression was investigated using the LPS-induced BV2 cell inflammation model.

RESULTS

A total of 17 compounds were identified in ZB that crossed the blood-brain barrier (BBB). The network pharmacological analysis showed that the anti-depressant mechanism of ZB is closely related to inflammatory cytokines, including TNF and IL-6. Furthermore, KEGG and PPI analyses demonstrated that ZB regulates the microglia M1/M2 phenotypic polarization by modulating inflammation-related pathways. ZB was found to improve depression-like behavior in vivo. The molecular docking indicated that the compounds in ZB that penetrate into the brain have a strong binding ability to RELA and PPAR-γ. ZB inhibited the expression of p-p65 and increased the expression of PPAR-γ in the mPFC. By rebalancing the ratio of pro-inflammatory/anti-inflammatory cytokines, ZB was able to reduce neuroinflammation in the mPFC and hippocampus regions. The immunofluorescence results showed that ZB-containing serum reduced M1 polarization induced by LPS in BV2 cells.

CONCLUSION

This study reveals that ZB effectively alleviates depression by regulating the M1/M2 phenotypic polarization of microglial cells. The mechanism may be that the active compounds of ZB reduce M1 phenotypic polarization by inhibiting P65 and increase M2 phenotypic polarization by promoting PPARγ.

Inflammatory proteins related to depression in multiple sclerosis: A systematic review and meta-analysis

Background

Up to 50% of individuals with multiple sclerosis (MS) experience depression. Depression has been accompanied by increases in inflammatory proteins. This meta-analysis summarized the data on inflammatory protein concentrations and level of depression in individuals with MS.

Methods

We performed a meta-analysis of studies measuring inflammatory protein concentrations and level of depression in individuals with MS with a database search of the English literature (to October 2024) and a manual search of references. Quality of evidence was also assessed.

Results

Fifteen studies involving measurements of inflammatory proteins and level of depression in 1102 individuals with MS were included in the meta-analysis: five for interleukin (IL)-10 (LPS and PHA), four for tumour necrosis factor (TNF)-α, four for interferon (IFN)-γ, and four for IL-6 (LPS and PHA). A meta-analysis showed that higher concentrations of TNF-α, IFN-γ, IL-6 and IL-10 were significantly associated with higher levels of depression in individuals with MS (r = 0.35, 95% CI [0.6,0.03], p = .015. Meta-analyses undertaken for individual inflammatory proteins of IFN-γ and IL-10 found positive associations between these proteins and level of depression although these did not reach statistical significance. Most studies were rated 'poor quality'.

Conclusion

This meta-analysis reports significant associations between higher concentrations of TNF-α, IFN-γ, IL-6 and IL-10 and level of depresson in individuals with MS. Future longitudinal studies with improved reporting of data are needed to replicate these results and confirm the mechanisms through which these inflammatory proteins are present. Meta-analytic findings lend support to depression being associated with the activation of the inflammatory system in individuals with MS.

Abnormalities in rich-club connections are associated with an exacerbation of genetic susceptibility to schizophrenia

BACKGROUND

Schizophrenia (SZ) is a highly heritable and heterogeneous disorder that is often associated with widespread structural brain abnormalities. However, the causes of interindividual differences in genetic susceptibility remain largely unknown. This study attempted to address this important issue by utilizing a prospective study in which unaffected first-degree relatives of SZ (FH+) were recruited.

METHODS

A total of 198 participants (143 FH + and 55 healthy control participants) were recruited and completed diffusion tensor imaging scans, graph theory analysis and semiannual standardized clinical evaluations within the first three years.

RESULTS

FH + participants who developed SZ (SZ/FH+) had similar but pronounced structural network changes at baseline compared to FH + participants who did not (HC/FH+). Additionally, among network properties, rich-club connections showed a good correlation with the severity of SZ, which was the most significant and stable effect. Logistic regression analyses showed that rich-club connections at baseline had high predictive accuracy for the subsequent occurrence of SZ.

CONCLUSIONS

Among healthy people with a familial history of SZ, those who exhibit decreased rich-club connections are susceptible to developing this disease. Our findings may aid in the development of timely interventions to prevent SZ and possibly assist researchers and clinicians in evaluating the efficacy of interventions.

The links between neuroinflammation, brain structure and depressive disorder: A cross-sectional study protocol

INTRODUCTION

It is known that symptoms of major depressive disorder (MDD) are associated with neurodegeneration, that lipopolysaccharide (LPS) can induce symptoms of MDD, and that blood LPS levels are elevated in neurodegeneration. However, it is not known whether blood LPS and cytokine levels correlate with MDD, cognition and brain structure, and this is tested in this study.

METHODS AND ANALYSIS

This cross-sectional study includes individuals with MDD (n = 100) and a control group of individuals with no one-year history of a mental disorder (n = 50). A comprehensive evaluation is performed, including the collection of basic sociodemographic information, data on smoking status, body mass index, course of MDD, past treatment, comorbid diseases, and current use of medications. Diagnosis of MDD is performed according to the WHO's [2019] International Classification of Diseases and related health problems by psychiatrist and severity of MDD is evaluated using the Montgomery-Åsberg Depression Scale. The Cambridge Neuropsychological Test Automated Battery is used to evaluate cognitive functioning. Venous blood samples are taken to measure genetic and inflammatory markers, and multiparametric brain magnetic resonance imaging is performed to evaluate for blood-brain barrier permeability, structural and neurometabolic brain changes. Descriptive and inferential statistics, including linear and logistic regression, will be used to analyse relationships between blood plasma LPS and inflammatory cytokine concentrations in MDD patients and controls. The proposed sample sizes are suitable for identifying significant differences between the groups, according to a power analysis.

ADMINISTRATIVE INFORMATION

Trial registration: Clinicaltrials.gov NCT06203015.

Social determinants of inflammatory markers linking depression and type 2 diabetes among women: A scoping review

OBJECTIVE

Inflammation is implicated in the pathophysiology of depression and type 2 diabetes (T2D) and is linked to social determinants of health (SDoH) associated with socioeconomic disadvantage. The objective of this review is to identify and map the range of SDoHs associated with inflammation in depression, T2D, or their co-occurrence among women.

METHODS

PubMed, CINAHL, PsychINFO, and Web of Science were searched March-July 2023 to identify studies where 1) an SDoH was a predictor or independent variable, 2) depression or T2D was a clinical focus, 3) inflammatory markers were collected, and 4) analysis was specific to women. We used the National Institute on Minority Health and Health Disparities research framework to guide searching SDoHs, organize findings, and identify gaps.

RESULTS

Of the 1135 studies retrieved, 46 met criteria. Within the reviewed studies, the most used inflammatory measures were C-reactive protein, interleukin-6, and tumor necrosis factor-α, and the most studied SDoHs were early life stress and socioeconomic status. Individual and interpersonal-level variables comprised the bulk of SDoHs in the included studies, while few to no studies examined built environment (n = 6) or health system level (n = 0) factors. Disadvantageous SDoHs were associated with higher levels of inflammation across the included studies.

CONCLUSION

The scope and intersection of depression and T2D represent a syndemic that contributes to and results from socioeconomic inequities and disproportionately affects women. Simultaneous inclusion of social and inflammatory measures, particularly understudied SDoHs, is needed to clarify potent targets aimed at advancing health and equity.

Selected Biomarkers of Depression: What Are the Effects of Cytokines and Inflammation?

Depression is one of the leading mental illnesses worldwide and lowers the quality of life of many. According to WHO, about 5% of the worldwide population suffers from depression. Newer studies report a staggering global prevalence of 27.6%, and it is rising. Professionally, depression belonging to affective disorders is a psychiatric illness, and the category of major depressive disorder (MDD) comprises various diagnoses related to persistent and disruptive mood disorders. Due to this fact, it is imperative to find a way to assess depression quantitatively using a specific biomarker or a panel of biomarkers that would be able to reflect the patients' state and the effects of therapy. Cytokines, hormones, oxidative stress markers, and neuropeptides are studied in association with depression. The latest research into inflammatory cytokines shows that their relationship with the etiology of depression is causative. There are stronger cytokine reactions to pathogens and stressors in depression. If combined with other predisposing factors, responses lead to prolonged inflammatory processes, prolonged dysregulation of various axes, stress, pain, mood changes, anxiety, and depression. This review focuses on the most recent data on cytokines as markers of depression concerning their roles in its pathogenesis, their possible use in diagnosis and management, their different levels in bodily fluids, and their similarities in animal studies. However, cytokines are not isolated from the pathophysiologic mechanisms of depression or other psychiatric disorders. Their effects are only a part of the whole pathway.

Quantitative magnetic resonance spectroscopy of depression: The value of short-term metabolite changes in predicting treatment response

BACKGROUND

Although various prediction models of the antidepressant response have been established, the results have not been effectively applied to heterogeneous depression populations, which has seriously limited their clinical value. This study tried to build a more specific and stable model to predict treatment response in depression based on short-term changes in hippocampal metabolites.

MATERIALS AND METHODS

Seventy-four major depressive disorder (MDD) patients and 20 healthy controls in the test set were prospectively collected and retrospectively analyzed. Subjects underwent magnetic resonance spectroscopy (MRS) once a week during 6 weeks of treatment. Hippocampal regions of interest (ROIs) were extracted by using a voxel iteration scheme combined with standard brain templates. The short-term differences in hippocampal metabolites between and within groups were screened. Then, the association between hippocampal metabolite changes and clinical response was analyzed, and a prediction model based on logistic regression was constructed. In addition, a validation set (n = 60) was collected from another medical center to validate the predictive abilities.

RESULTS

After 2-3 weeks of antidepressant treatment, the differences in indicators (tCho wee0-2, tCho wee0-3 and NAA week0-3) were successfully screened. Then, the predictive abilities of these three indicators were revealed in the logistic regression model, and the optimal prediction effect was found in d(tCho) week0-3-d(NAA) week0-3 (AUC = 0.841, 95%CI = 0.736-0.946). In addition, their predictive abilities were further confirmed with the validation set.

LIMITATIONS

The small sample size and the need for multiple follow-ups limited the statistical ability to detect other findings.

CONCLUSION

The predictive model in this study presented accurate prediction and strong verification effects, which may provide early guidance for adjusting the treatment regimens of depression and serve as a checkpoint at which the eventual treatment outcome can be predicted.