Depressive disorder is a psychiatric disorder primarily characterized by low emotions. Clinical observational studies indicate relationships between depressive disorder and certain physical diseases or characteristics, though the causality of these relationships remains unclear. Mendelian Randomization has gradually become an important tool to explore potential causal relationships between diseases, leveraging the advantages of genetic data analysis. It has been used to examine the relationship between depressive disorder and other diseases. This review summarizes the findings of causal relationships between depressive disorder and diseases affecting the cardiovascular, neurological, digestive, endocrine, and female reproductive systems, as well as some physical characteristics, including physical activity level, insomnia, circadian rhythm preferences, obesity, and carbohydrate intake. The review also introduces the principle of Mendelian Randomization and discusses its future applications.

Objective:To investigate the alterations in the topological properties of gray matter and white matter dynamic and static functional brain networks in patients with major depressive disorder (MDD) and bipolar depression (BDD) using graph theory analysis, and to evaluate the potential of their combination as biomarkers for differential diagnosis between unipolar and bipolar depression.Methods:From March 2021 to April 2024, inpatients were recruited from the Department of Psychosomatic Medicine, Zhongda Hospital, Southeast University, including 132 patients with MDD, 84 patients with BDD, and 91 healthy controls (HCs). Resting-state structural and functional MRI data were collected, and dynamic and static functional brain networks of gray matter and white matter were constructed. Graph theory analysis was applied to calculate global and nodal network properties, differences in topological attributes among the three groups were compared by One-way analysis of covariance, and Turkey′s post hoc test was used for further pairwise comparison. The network topology attribute indicators with statistically significant inter-group differences were selected using the Least Absolute Shrinkage and Selection Operator regression (LASSO) for feature classification. The diagnostic performance of combined gray and white matter network features for distinguishing MDD from BDD was assessed using receiver operating characteristic (ROC) curves and a random forest model.Results:In the analysis of the static gray matter functional network, both MDD and BDD patients showed abnormal local topological properties. Compared with HCs, the MDD group exhibited abnormal betweenness centrality (BC) in the left inferior frontal gyrus, left precuneus, left ventromedial occipital cortex, right ventromedial occipital cortex, and right anterior thalamus ( t=-3.95-3.62, all P<0.05). The degree centrality (DC) of the left and right anterior thalamus was also abnormal in the MDD group ( t=3.78,4.14, both P<0.001), as was the nodal efficiency (Ne) of the left precuneus and bilateral anterior thalamus ( t=2.37, 3.61, 3.82, all P<0.05). Compared with HCs, the BDD group showed abnormalities in DC and Ne of the left precuneus ( t=-2.76, P=0.014; t=-3.01, P=0.007). In the analysis of the dynamic white matter functional network, both MDD and BDD patients demonstrated abnormal temporal variability of local topological properties. Compared with HCs, the MDD and BDD groups showed reduced BC temporal variability in the left superior corona radiata ( t=-2.39, P=0.047; t=-4.28, P<0.001), and there were significant differences in DC temporal variability in the right posterior limb of the internal capsule and lentiform nucleus ( t=2.65, P=0.021; t=3.49, P=0.001) in MDD group compared with HCs and BBD. The differential diagnosis model combining gray and white matter dynamic and static network topological features achieved an area under the ROC curve of 0.80. Conclusion:Both MDD and BDD exhibit altered topological properties in static gray matter functional networks and dynamic white matter functional networks. The combination of these features may aid in the differential diagnosis of MDD and BDD.

Depressive disorder is a psychiatric disorder primarily characterized by low emotions. Clinical observational studies indicate relationships between depressive disorder and certain physical diseases or characteristics, though the causality of these relationships remains unclear. Mendelian Randomization has gradually become an important tool to explore potential causal relationships between diseases, leveraging the advantages of genetic data analysis. It has been used to examine the relationship between depressive disorder and other diseases. This review summarizes the findings of causal relationships between depressive disorder and diseases affecting the cardiovascular, neurological, digestive, endocrine, and female reproductive systems, as well as some physical characteristics, including physical activity level, insomnia, circadian rhythm preferences, obesity, and carbohydrate intake. The review also introduces the principle of Mendelian Randomization and discusses its future applications.

Objective:To investigate the alterations in the topological properties of gray matter and white matter dynamic and static functional brain networks in patients with major depressive disorder (MDD) and bipolar depression (BDD) using graph theory analysis, and to evaluate the potential of their combination as biomarkers for differential diagnosis between unipolar and bipolar depression.Methods:From March 2021 to April 2024, inpatients were recruited from the Department of Psychosomatic Medicine, Zhongda Hospital, Southeast University, including 132 patients with MDD, 84 patients with BDD, and 91 healthy controls (HCs). Resting-state structural and functional MRI data were collected, and dynamic and static functional brain networks of gray matter and white matter were constructed. Graph theory analysis was applied to calculate global and nodal network properties, differences in topological attributes among the three groups were compared by One-way analysis of covariance, and Turkey′s post hoc test was used for further pairwise comparison. The network topology attribute indicators with statistically significant inter-group differences were selected using the Least Absolute Shrinkage and Selection Operator regression (LASSO) for feature classification. The diagnostic performance of combined gray and white matter network features for distinguishing MDD from BDD was assessed using receiver operating characteristic (ROC) curves and a random forest model.Results:In the analysis of the static gray matter functional network, both MDD and BDD patients showed abnormal local topological properties. Compared with HCs, the MDD group exhibited abnormal betweenness centrality (BC) in the left inferior frontal gyrus, left precuneus, left ventromedial occipital cortex, right ventromedial occipital cortex, and right anterior thalamus ( t=-3.95-3.62, all P<0.05). The degree centrality (DC) of the left and right anterior thalamus was also abnormal in the MDD group ( t=3.78,4.14, both P<0.001), as was the nodal efficiency (Ne) of the left precuneus and bilateral anterior thalamus ( t=2.37, 3.61, 3.82, all P<0.05). Compared with HCs, the BDD group showed abnormalities in DC and Ne of the left precuneus ( t=-2.76, P=0.014; t=-3.01, P=0.007). In the analysis of the dynamic white matter functional network, both MDD and BDD patients demonstrated abnormal temporal variability of local topological properties. Compared with HCs, the MDD and BDD groups showed reduced BC temporal variability in the left superior corona radiata ( t=-2.39, P=0.047; t=-4.28, P<0.001), and there were significant differences in DC temporal variability in the right posterior limb of the internal capsule and lentiform nucleus ( t=2.65, P=0.021; t=3.49, P=0.001) in MDD group compared with HCs and BBD. The differential diagnosis model combining gray and white matter dynamic and static network topological features achieved an area under the ROC curve of 0.80. Conclusion:Both MDD and BDD exhibit altered topological properties in static gray matter functional networks and dynamic white matter functional networks. The combination of these features may aid in the differential diagnosis of MDD and BDD.

Objective:To explore the differences of the circadian rhythm gene polymorphisms between patients with major depressive disorder and those with bipolar disorder, providing a genetic basis for differential diagnosis.Methods:70 patients who were still diagnosed with major depressive disorder after 5 years and 68 patients who were still diagnosed with bipolar disorder from Zhongda Hospital affiliated to Southeast University from 2012 to 2018 were included in this study. Single nucleotide polymorphisms (SNPs) of circadian rhythm gene were selected for genetic testing. And the differences in genotype frequency, allele frequency, and haplotypes of each SNP between major depressive disorder and bipolar disorder were analyzed using UNPHASED 3.1.7.Results:The analysis of genotype frequency revealed statistically significant differences in genotype frequency of PER1rs2253820, PER1rs2735611, PER3rs12566042, PER3rs17031614, and PER3rs79372391 between the two groups ( OR(95% CI)=2.386(1.173-4.854), 2.357(1.166-4.764), 0.351(0.176-0.703), 0.389(0.196-0.773), 0.389(0.196-0.773) respectively; all P<0.05). Haplotype analysis showed that the T-C-C-T-G haplotype, in CLOCK loci (rs12505266, rs2272073, rs3817444, rs11133389 and rs12505265) was significantly different between major depressive disorder group and bipolar disorder group ( OR(95% CI)=0.108(0.010-1.185), P=0.027). Conclusion:There are significant differences in circadian rhythm gene polymorphisms between patients with major depressive disorder and bipolar disorder. Carrying the PER1rs2253820TT and PER1rs2735611GG genotypes is a risk factors for bipolar disorder.

Serotonin syndrome (SS) is an infrequent drug-induced syndrome resulting from excessive 5-HT levels in the central and peripheral nervous system due to drug administration and interactions, commonly observed following the utilization of 5-HTergic drugs or concurrent use of monoamine oxidase inhibitors (MAOI) with 5-HTergic agents. There is a case report on the management of two patients with SS who received combination therapy of paroxetine and bupropion. Given its rapid progression and potential fatality in severe instances, healthcare professionals are reminded to promptly recognize early symptoms and mitigate serious consequences when co-administering such medications.

Serotonin syndrome (SS) is an infrequent drug-induced syndrome resulting from excessive 5-HT levels in the central and peripheral nervous system due to drug administration and interactions, commonly observed following the utilization of 5-HTergic drugs or concurrent use of monoamine oxidase inhibitors (MAOI) with 5-HTergic agents. There is a case report on the management of two patients with SS who received combination therapy of paroxetine and bupropion. Given its rapid progression and potential fatality in severe instances, healthcare professionals are reminded to promptly recognize early symptoms and mitigate serious consequences when co-administering such medications.

Objective:To explore the differences of the circadian rhythm gene polymorphisms between patients with major depressive disorder and those with bipolar disorder, providing a genetic basis for differential diagnosis.Methods:70 patients who were still diagnosed with major depressive disorder after 5 years and 68 patients who were still diagnosed with bipolar disorder from Zhongda Hospital affiliated to Southeast University from 2012 to 2018 were included in this study. Single nucleotide polymorphisms (SNPs) of circadian rhythm gene were selected for genetic testing. And the differences in genotype frequency, allele frequency, and haplotypes of each SNP between major depressive disorder and bipolar disorder were analyzed using UNPHASED 3.1.7.Results:The analysis of genotype frequency revealed statistically significant differences in genotype frequency of PER1rs2253820, PER1rs2735611, PER3rs12566042, PER3rs17031614, and PER3rs79372391 between the two groups ( OR(95% CI)=2.386(1.173-4.854), 2.357(1.166-4.764), 0.351(0.176-0.703), 0.389(0.196-0.773), 0.389(0.196-0.773) respectively; all P<0.05). Haplotype analysis showed that the T-C-C-T-G haplotype, in CLOCK loci (rs12505266, rs2272073, rs3817444, rs11133389 and rs12505265) was significantly different between major depressive disorder group and bipolar disorder group ( OR(95% CI)=0.108(0.010-1.185), P=0.027). Conclusion:There are significant differences in circadian rhythm gene polymorphisms between patients with major depressive disorder and bipolar disorder. Carrying the PER1rs2253820TT and PER1rs2735611GG genotypes is a risk factors for bipolar disorder.

Allostatic load is the cost of adjustments of the internal physiological milieu to accommodate external stress. A series of physiological and psychological changes will occur when the stress exceeds an individual′s coping skills, which may further lead to physical damage. Stress plays a major role in the etiology and development of major depressive disorder. The introduction of the concept of allostatic load provides new insight for the prevention, diagnosis and treatment of major depressive disorder. This article discussed the evaluation method of allostatic load and its impact on the diagnosis and treatment of depressive disorder.

Illness anxiety disorder (IAD) is a psychiatric disorder defined by excessive worry about having or developing a serious undiagnosed medical condition. IAD can affect the social functions of patients and increase burdens imposed on the family and society. The concept of IAD was first proposed in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in 2013. However, the rates of screening and identification of IAD are at a relatively low level in clinical practice, and the effectiveness of pharmacological and psychological intervention on IAD are still in the process of exploration and validation. The recognition of IAD should be enhanced and the efficacy of intervention need to be further improved. This article reviews the progress of clinical studies on IAD to improve clinicians′ understanding of the disease, and help them adopt appropriate treatments as early as possible to alleviate patient suffering and improve disease prognosis.