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Background: Stressful life events (SLEs) are well-established proximal predictors of the onset of depression. However, the fundamental causes of interindividual differences in depression outcomes are poorly understood. This study addressed this depression susceptibility mechanism using a well-powered sample of adults living in China.Methods: Healthy participants with SLEs (n = 185; mean = 47.51 years, 49.73% female), drawn from a longitudinal study on the development of depression, underwent diffusion tensor imaging, interleukin-6 (IL-6) level measurement, and trimonthly standardized clinical and scale evaluations within a two-year period.Results: Receiver operating characteristic analyses indicated that reduced feeder connection and HIP.R nodal efficiency improved the predictive accuracy of post-SLEs depression (ORfeeder = 0.623, AUC = 0.869, P < 0.001; ORHIP = 0.459, AUC = 0.855, P < 0.001). The successfully established path analysis model confirmed the significant partial effect of SLEs-IL-6-white matter (WM) network differences-depression (onset and severity) (x2/8 = 1.453, goodness-of-fit [GFI] = 0.935, standard root-mean-square error of approximation [SRMR] = 0.024). Females, individuals with lower exercise frequency (EF) or annual household income (AHI) were more likely to have higher IL-6 level after SLEs (βint-female⁎SLEs = -0.420, P < 0.001; βint-exercise⁎SLEs = -0.412, P < 0.001; βint-income⁎SLEs = -0.302, P = 0.005).Limitations: The sample size was restricted due to the limited incidence rate and prospective follow-up design.Conclusions: Our results suggested that among healthy adults after SLEs, those who exhibited abnormal IL-6-WM differences were susceptible to developing depression. Females, lower AHI or EF might account for an increased risk of developing these abnormal IL-6-WM differences. [ABSTRACT FROM AUTHOR]

Background: Although several pharmacological treatment options for depression are currently available, a large proportion of patients still do not achieve a complete remission or respond adequately to the initial antidepressant prescribed for reasons that remain relatively unknown. This study explored the application of serum biomarkers to the predict the efficacy of escitalopram for treating depression, to guide clinical drug selection.Method: In this study, 306 patients suffering from depression were treated with escitalopram (10 mg) for 6 weeks. After 6 weeks of treatment, the patients were divided into an escitalopram-sensitive group (ES, n = 172) and an escitalopram-insensitive group (EIS, n = 134) according their HAMD-24 scores after 6 weeks of treatment. Serum samples from all participants were collected on the first day, and 10 different serum biomarkers were analysed. Data from 100 patients in the ES group and 100 patients in the EIS group were then used to build a logistic regression model, and a receiver operating characteristic (ROC) curve was drawn. To validate the accuracy of our model, another 72 patients in the ES group and 34 patients in the EIS group were studied.Results: Of the 10 selected serum biomarkers, 4 were screened to build the regression model. BDNF, FGF-2, TNF-α and 5-HT. The regression equation was Z = 1/[1 + e-(-5.065+0.145 (BDNF)+0.029 (FGF-2)-0.368 (TNF-α)+0.813 (5-HT))], and the 4 biomarkers-combined detection achieved an AUC (area under the ROC curve) of 0.929 and a predictive accuracy of 88.70%.Limitation: Decision support tools based on our combined biomarker prediction models hold comparatively great promises; however, they need to be validated on a much larger scales than current studies provide.Conclusion: The logistic regression model and ROC curves based of the serum biomarkers used in this study provide a more reliable means to predict the efficacy of escitalopram in patients with depression, and provide clinical evidence for drug selection. [ABSTRACT FROM AUTHOR]