Mapping the global, regional and national burden of bipolar disorder from 1990 to 2019: trend analysis on the Global Burden of Disease Study 2019.

BACKGROUND: Data on trends in the epidemiological burden of bipolar disorder are scarce. AIMS: To provide an overview of trends in bipolar disorder burden from 1990 to 2019. METHOD: Revisiting the Global Burden of Disease Study 2019, we analysed the number of cases, calculated the age-standardised rate (per 100 000 population) and estimated annual percentage change (EAPC) of incidence, prevalence and years lived with disability (YLDs) for bipolar disorder from 1990 to 2019. The independent effects of age, period and cohort were estimated by the age-period-cohort modelling. RESULTS: Globally, the bipolar disorder-related prevalent cases, incident cases and number of YLDs all increased from 1990 to 2019. Regionally, the World Health Organization Region of the Americas accounted for the highest estimated YLD number and rate, with the highest age-standardised prevalence rate in 1990 and 2019 and highest EAPC of prevalence. By sociodemographic index (SDI) quintiles, all five SDI regions saw an increase in estimated incident cases. Nationally, New Zealand reported the highest age-standardised rate of incidence, prevalence and YLDs in 1990 and 2019. The most prominent age effect on incidence rate was in those aged 15-19 years. Decreased effects of period on incidence, prevalence and YLD rates was observed overall and in females, not in males. The incidence, prevalence and YLD rates showed an unfavourable trend in the younger cohorts born after 1990, with males reporting a higher cohort risk than females. CONCLUSIONS: From 1990 to 2019, the overall trend of bipolar disorder burden presents regional and national variations and differs by age, sex, period and cohort.

Validation of the THINC-It Tool for Assessment of Cognitive Impairment in Patients with Bipolar Depression.

Background: Cognitive impairment is one of the core features of bipolar depression. A unified, reliable, and valid assessment tool is key to screening and assessing cognitive impairment. The THINC-Integrated Tool (THINC-it) is a simple and quick battery for screening cognitive impairment in patients with major depressive disorder. However, the use of the tool has not been validated in patients with bipolar depression. Methods: The cognitive functions of 120 patients with bipolar depression and 100 healthy controls were evaluated using the THINC-it tool including Spotter, Symbol Check, Codebreaker, Trials, and the only one subjective test (PDQ-5-D) and five corresponding standard tests. A psychometric analysis of the THINC-it tool was performed. Results: The overall Cronbach's alpha coefficient of the THINC-it tool was 0.815. The intra-group correlation coefficient (ICC) of retest reliability ranged from 0.571 to 0.854 (P<0.001), while the correlation r of parallel validity ranged from 0.291 to 0.921 (P<0.001). There were significant differences in the two groups Z-scores of THINC-it total score, Spotter, Codebreaker, Trails, and PDQ-5-D (P<0.05). Construct validity was analyzed using exploratory factor analysis (EFA). The Kaiser-Meyer-Olkin (KMO) value was 0.749. Using Bartlett's Sphericity test, the χ 2 (10) value was 198.257 (P<0.001). The factor loading coefficients of Spotter, Symbol Check, Codebreaker, and Trails on the common factor 1 were -0.724, 0.748, 0.824, and -0.717, respectively, and the factor loading coefficient of PDQ-5-D on the common factor 2 was 0.957. Results revealed that the correlation coefficient of the two common factors was 0.125. Conclusion: The THINC-it tool has good reliability and validity in assessing patients with bipolar depression.

Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder.

BACKGROUND: Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD). METHODS: This study enrolled 2470 hospitalized patients with BD (n = 1333) or MDD (n = 1137) at reproductive age from 2009 to 2018 in China. We extracted 8 parameters, uric acid (UA), direct bilirubin (DBIL), indirect bilirubin (IDBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), high-density lipoprotein (HDL) and prealbumin of male, patients and 12 parameters, UA, DBIL, IBIL, LDH, FT3, TSH, glutamic-pyruvic transaminase (GPT), white blood cell (WBC), alkaline phosphatase (ALP), fasting blood glucose (FBG), triglyceride and low-density lipoprotein (LDL) of female patients. Backward stepwise multivariate regression analysis and the Chi-Square Automatic Interaction Detection (CHAID) segmentation analysis via SPSS Decision Tree were implemented to define the discrimination of BD and MDD. RESULTS: DBIL was extracted as the first splitting variable, with LDH and IBIL as the second, TSH and prealbumin as the third in the model of male patients (p-value < .05). For the model of female patients, DBIL was also extracted as the first splitting variable, with UA, LDH, and IBIL as the second, triglyceride and FT3 as the third (p-value < .05). The predictive accuracies of the Decision Tree and multiple logistic regression models were similar (74.9% vs 76.9% in males; 74.4% vs 79.5% in females). CONCLUSIONS: This study suggests the value of the Decision Tree models, which employ biochemical parameters as diagnostic predictors for BD and MDD. The CHAID Decision Tree identified that patients with concomitantly increased LDH, IBIL, and decreased DBIL could be in the group that showed the highest risk of being diagnosed as BD.

Comorbidity of depressive and anxiety disorders: challenges in diagnosis and assessment.

SUMMARY: Comorbid anxiety is common in patients with depressive disorders. It complicates the clinical presentation of depressive disorders and can contribute to treatment resistance. Clinicians can assess the degree of overlap between depressive and anxiety symptoms either by measuring the severity of anxiety symptoms in individuals who meet diagnostic criteria for depression or by determining whether or not an individual with depression simultaneously meets criteria for an anxiety disorder. However, multiple factors in the Chinese clinical setting make it difficult to accurately assess patients with comorbid conditions. The resultant under-diagnosis of comorbid depression and anxiety - the most common type of comorbid psychiatric condition in China - seriously diminishes the effectiveness of treatments for common mental disorders in the country. We argue that the widespread use of valid and reliable dimensional assessment tools in Chinese clinical settings will help improve the diagnosis and treatment of the many individuals who have concurrent depressive and anxiety symptoms.

The role of BDNF, NTRK2 gene and their interaction in development of treatment-resistant depression: data from multicenter, prospective, longitudinal clinic practice.

BACKGROUND: Although genetic variants may play a key role in development of treatment-resistant depression (TRD), relevant research is scarce. METHODS: To examine whether the polymorphisms of BDNF (rs6265) and NTRK2 (rs1387923, rs2769605 and rs1565445) genes confer risk for TRD in major depressive disorder (MDD), a total of 948 MDD patients were recruited in a 12-week, multicenter, prospective longitudinal study. RESULTS: Our study showed a significant allelic association between rs1565445 and TRD with an excess of the T allele in the TRD group, compared to non-TRD group (OR = 1.43, 95%CI: 1.16-1.76, p = 0.0008); while patients with genotype C/C and T/C in rs1565445 were less likely to develop TRD than those carrying T/T (OR = 0.52, 95%CI: 0.33-0.82; OR = 0.72, 95%CI: 0.54-0.97, respectively; p = 0.005). Haplotype T-T (rs1565445 and rs1387923) had 1.41-fold increased risk of TRD (p = 0.0014). Furthermore, significant four-locus (rs1387923-rs1565445-rs2769605-rs6265) gene-gene interactions were detected by the Multifactor-dimensionality reduction (MDR) method. DISCUSSION: These results suggest that the interactions of BDNF (rs6265) with NTRK2 (rs1387923, rs2769605 and rs1565445) gene polymorphisms likely play an essential role in the development of TRD in Han Chinese MDD patients.