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Alteration in stress response seems to affect the development of psychiatric disorders. In this study, we aimed to investigate whether baseline peripheral biomarkers could predict the reduction of stress response among patients with major depressive disorder (MDD) and panic disorder (PD). Patients with MDD (n = 41) and PD (n = 52) and healthy controls (HC, n = 59) were selected and regularly followed up with five visits for 12 weeks. The severity of stress at every visit was assessed using the Stress Response Inventory (SRI), and peripheral biomarkers were measured by blood tests at baseline and 2, 4, 8, and 12 weeks. Interleukin (IL)-6, IL-10, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, C-reactive protein (CRP), adiponectin, and leptin levels were analyzed using enzyme-linked immunosorbent assays. Reduction of stress response was defined as the difference in SRI score between baseline and 12 weeks divided by the baseline score. SRI scores were significantly (p < 0.0001) higher in patients with MDD and PD than in HC at every visit after adjusting for variables. In multivariable linear regression, adiponectin levels at baseline were significantly associated with reduction of stress response in patients with PD. When adiponectin increased 1 mg/l, stress response decreased 0.781 points (β = −0.781, S.E. = 0.220, p = 0.001). Among the subscales of SRI, somatization had a moderate negative correlation with adiponectin levels (r = −0.469). There was no significant association between baseline peripheral biomarkers and reduction of stress response in patients with MDD. Our study showed an inverse association between baseline adiponectin levels and stress response changes in patients with PD, but not in patients with MDD. Thus, differentiated approaches for assessing and treating stress responses of patients with PD and MDD might be helpful. Larger and longitudinal studies are necessary to establish the role and mechanism of action of adiponectin in regulating stress responses in PD. [ABSTRACT FROM AUTHOR]

Previous studies have investigated the role of inflammatory markers in suicidality of patients with major depressive disorder (MDD) or panic disorder (PD). However, few studies have investigated associations between serum inflammatory cytokine levels and suicidality. We hypothesized that MDD and PD status might be significantly associated with serum inflammatory cytokines and that we could predict levels of improvement in suicide ideation intensity using serum inflammatory biomarkers in patients with MDD and PD. For this study, 41 patients with MDD, 52 patients with PD, and 59 healthy control (HC) subjects were enrolled. Psychological measurements and serum inflammatory markers such as interleukin (IL) −6, −10, interferon (IFN)-γ, tumor necrosis factor (TNF)-α, and C reactive protein (CRP) were examined. A total of five visits were completed during 12 weeks. After controlling for confounding factors, log-transformed IL-6 (ln_IL-6) at baseline (MDD: 0.297 ± 0.626; PD: 0.342 ± 0.723; HC: −0.121 ± 0.858; p = 0.007, >0.0017, 0.05/30) and mean ln_IL-6 (MDD: 0.395 ± 0.550, PD: 0.249 ± 0.544, HC: −0.139 ± 0.622, p = 0.002, >0.0017, 0.05/30) levels were trends towards significantly higher in patients with MDD and PD than in HC. In MDD patients, a higher level of basal ln_TNF-α was a significant predictor of ΔSSI (changes in SSI scores between baseline and week 12) even after controlling for changes of depression symptoms and baseline SSI scores (standardized β = 0.541, p = 0.002 < 0.0028, 0.05/18). In conclusion, we could predict ΔSSI using baseline inflammatory biomarkers for patients with MDD. [ABSTRACT FROM AUTHOR]

Background: A link between brain-derived neurotrophic factor (BDNF) expression and the mood regulatory effect of leptin has been suggested in the pathophysiology of major depressive disorder (MDD). We investigated treatment response and pre-treatment leptin and BDNF in patients with MDD and with panic disorder (PD).Methods: We recruited 41 patients with MDD, 52 patients with PD, and 59 matched healthy controls. All subjects completed five visits (at baseline, 2, 4, 8, and 12 weeks), and both MDD and PD patients were treated with standard pharmacotherapy for 12 weeks. Plasma BDNF (pBDNF) and blood leptin levels were obtained along with a 17-item Hamilton Depression Scale rating (HDRS-17) score at every visit.Results: The ratio of pre-treatment pBDNF to leptin was significantly lower in patients with MDD and PD compared to healthy controls (p = 0.024), but was not associated with severity of depressive or anxiety symptoms. Pre-treatment pBDNF:leptin ratio was significantly higher in treatment responders than in non-responders (p = 0.012) in MDD but not in PD. This difference was larger in MDD patients with appetite loss (p = 0.034). In multivariate analysis, pre-treatment pBDNF:leptin ratio was significantly associated with treatment responsiveness (Adjusted Odds Ration [AOR] = 2.50, 95% CI 1.02-6.14) in MDD.Limitation: small sample size; limited information on detailed pharmacological effects.Conclusions: A relatively higher ratio of pre-treatment pBDNF to leptin was associated with greater treatment response in MDD but not in PD. Further research should focus on exploration of a link between BDNF and leptin underlying neuronal plasticity in depression. [ABSTRACT FROM AUTHOR]

Background: The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires.Objective: Autonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated.Methods: The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification.Results: The entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2.Conclusion: Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition. [ABSTRACT FROM AUTHOR]

Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states. [ABSTRACT FROM AUTHOR]

Background: Heart Rate Variability (HRV) parameters have been used to evaluate the autonomic nervous system. We hypothesized that patients with major depressive disorder (MDD) and panic disorder (PD) showed different HRV profiles compared to healthy controls. We also hypothesized that we could predict the responder groups in the MDD and PD patients, using differences in HRV indices between the stress and rest phases.Methods: 28 MDD patients and 29 PD patients were followed for 12 weeks, and we also followed 39 healthy control subjects. We measured HRV parameters at the rest, stress, and recovery phases.Results: Patients with MDD and PD demonstrated lower pNN50 than controls during the stress (F = 7.49, p = 0.001), and recovery phases (F = 9.43, p = 0.0001). Patients with MDD and PD also showed higher LF/HF ratio than controls during the stress phase (F = 6.15, p = 0.002). Responders in the PD group presented a lower level of LF/HF ratio during the stress phase compared to non-responders (F = 10.14, p = 0.002), while responders in the MDD group showed a lower level of heart rate during all three phases, compared to non-responders. Additionally, we could predict treatment response in patients with MDD using ΔLF/HF ratio (OR: 1.33, 95% CI = 1.07-1.65, p = 0.011) and ΔpNN50 (OR: 1.49, 95% CI 1.09-1.77, p = 0.014).Conclusion: The changes of HRV parameters of pNN50 and LF/HF ratio between the stress and recovery phase may be clinical markers of predictors of treatment responsiveness in MDD and PD patients. [ABSTRACT FROM AUTHOR]

• Classification of panic disorder vs. major depressive disorder and controls using a 2-channel EEG system (FP1, FP2). • Panic disorder and major depressive disorder subjects show reduced cortical complexity. • Presents a rapid and practical paradigm for use in primary care settings. The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder. [ABSTRACT FROM AUTHOR]