Background: Inflammation and immune status are correlated with the severity of major depressive disorder (MDD).The purpose of this study was to establish an optimization model of peripheral blood parameters to predict the severity of MDD. Methods: MDD severity in the training and validation cohorts (n = 99 and 97) was classified using the Hamilton Depression Scale, Thirty-eight healthy individuals as controls. Significant severity-associated factors were identified using a multivariate logistic model and combined to develop a joint index through binary logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to identify the optimal model and evaluate the discriminative performance of the index. Results: In the training cohort, lower CD4+/CD8+ T cell ratio, albumin level, and a higher monocyte percentage (M%) were significant as operating sociated with severe disease (P < 0.05 for all). The index was developed using these factors and calculated as CD4+/CD8+ T cell ratio, albumin level, and M%, with a sensitivity and specificity of 90 and 70%, respectively. The AUC values for the index in the training and validation cohorts were 0.85 and 0.75, respectively, indicating good discriminative performance. Conclusion: We identified disease severity-associated joint index that could be easily evaluated: CD4+/CD8+ T cell ratio, albumin level, and M%.

Depression is a common comorbid psychiatric symptom in patients with Alzheimer’s disease (AD), and the prevalence of depression is higher among people with AD compared with healthy older adults. Comorbid depression in AD may increase the risk of cognitive decline, impair patients’ function, and reduce their quality of life. However, the mechanisms of depression in AD remain unclear. Here, our aim was to identify neurometabolic characteristics in the brain that are associated with depression in patients with mild AD. Thirty-seven patients were evaluated using the Neuropsychiatric Inventory (NPI) and Hamilton Depression Rating Scale (HAMD-17), and divided into two groups: 17 AD patients with depression (D-AD) and 20 non-depressed AD patients (nD-AD). Using proton magnetic resonance spectroscopy, we characterized neurometabolites in the anterior cingulate gyrus (ACG) of D-AD and nD-AD patients. Compared with nD-AD patients, D-AD patients showed lower N-acetylaspartate/creatine (NAA/Cr) and higher myo-inositol/creatine (mI/Cr) in the left ACG. NPI score correlated with NAA/Cr and mI/Cr in the left ACG, while HAMD correlated with NAA/Cr. Our findings show neurometabolic alterations in D-AD patients. Thus, D-AD pathogenesis may be attributed to abnormal activity of neurons and glial cells in the left ACG.

Anxious major depressive disorder is a common subtype of major depressive disorder; however, its unique neural mechanism is not well-understood currently. Using multimodal MRI data, this study examined common and specific alterations of amygdala subregions between patients with and without anxiety. No alterations were observed in the gray matter volume or intra-region functional integration in either patient group. Compared with the controls, both patient groups showed decreased functional connectivity between the left superficial amygdala and the left putamen, and between the right superficial amygdala and the bilateral anterior cingulate cortex and medial orbitofrontal cortex, while only patients with anxiety exhibited decreased activity in the bilateral laterobasal and superficial amygdala. Moreover, the decreased activity correlated negatively with the Hamilton depression scale scores in the patients with anxiety. These findings provided insights into the pathophysiologic processes of anxious major depressive disorder and may help to develop new and effective treatment programs.

Objectives: The aim was to develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aiding community healthcare workers in the early identification of individuals at high risk of mild cognitive impairment (MCI). Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively. Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). The calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained a similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801). Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.

Importance: Major depressive disorder (MDD) and bipolar disorder (BD) are often misdiagnosed during depressive episodes, therefore, exploring biomarkers for differential diagnosis is important. Objective: To identify circadian biomarker signatures in patients’ peripheral blood that differentiate MDD from BD during depressive states. Design, setting, and participants: This case-control study recruited patients with MDD and BD at depressive state diagnosed by the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) and Health control (HC) subjects from January 2021 to May 2023. We collected serum samples to detect levels of Period (PER)1/PER2/phosphorylated cAMP reaction element binding protein (pCREB). Main outcomes and measures: Participants’ clinical data were evaluated by HAMD-17 and MDQ scales. Blood samples ( n = 100) were collected and extracted for serum followed by detecting serum PER1, PER2 and pCREB levels by ELISA. Results: There were 100 participants in the cohort, including 40 in the MDD group, 30 in the BD group and 30 in the health control (HC) group. 71% were female in the cohort. The mean (SD) serum PER1 level in the HC group was 12.05 (2.96) ng/mL, in the MDD group was 8.41 (2.96) ng/mL, significantly decreased vs the HCs, and in the BPD group that was 16.05 (3.60) ng/mL, significantly increased vs the HCs (adjusted P < 0.0001). The serum pCREB level in the HC group was 205.2 (49.57) ng/mL, in the MDD group was 192.6 (38.52) ng/mL, and that in the BPD group was 290.0 (72.10) ng/mL, which was significantly increased vs the HCs and vs the MDD group. For the differential diagnosis between MDD and BPD, PER1 & pCREB (AUC, 0.9858) show similar high diagnostic efficiency as combined biomarkers of PER1, PER2 & pCREB (AUC, 0.9906). Conclusions and relevance: This study reveals the importance of serum PER1, combined use of serum PER1 and pCREB as well as that of serum pCREB, PER1 and PER2 in differentiating MDD from BPD. Key Points: Question: Can major depressive disorder (MDD) be distinguished from bipolar disorder (BD) during their depressive episodes by circadian biomarkers in patient’s peripheral blood? Findings: In this case-control study of 100 participants, serum PER1 levels were significantly decreased in the MDD group but were significantly increased in the BPD group, serum PER2 levels were significantly elevated in both MDD and BPD groups, and serum pCREB levels were significantly increased in the BPD group, compared with the HC group. Besides, In the MDD group, serum PER1 levels were significantly positively associated with mood disorder questionnaire (MDQ) scores; and serum pCREB levels were significantly negatively associated with MDQ scores. In the BPD group, serum pCREB levels were significantly negatively associated with HAMD-17 scores. The combined use of serum pCREB and PER1 and the three biomarkers (pCREB, PER1 and PER2) had similar diagnostic values in differentiating MDD from BPD. Meaning: The serum PER1 has great value for differentiating MDD from BPD, and combined use of serum biomarkers of pCREB/PER1 or pCREB/PER1/PER2 shows higher efficacy in differentiating MDD from BPD.