Predictors of metabolic syndrome in first manic episode

Utilizing systematic Mendelian randomization to identify potential therapeutic targets for mania

Background

Mania has caused incalculable economic losses for patients, their families, and even society, but there is currently no effective treatment plan for this disease without side effects.

Methods

Using bioinformatics and Mendelian randomization methods, potential drug target genes and key substances associated with mania were explored at the mRNA level. We used the chip expression profile from the GEO database to screen differential genes and used the eQTL and mania GWAS data from the IEU database for two-sample Mendelian randomization (MR) to determine core genes by colocalization. Next, we utilized bioinformatics analysis to identify key substances involved in the mechanism of action and determined related gene targets as drug targets.

Results

After differential expression analysis and MR, a causal relationship between the expression of 46 genes and mania was found. Colocalization analysis yielded six core genes. Five key substances were identified via enrichment analysis, immune-related analysis, and single-gene GSVA analysis of the core genes. MR revealed phenylalanine to be the only key substance that has a unidirectional causal relationship with mania. In the end, SBNO2, PBX2, RAMP3, and QPCT, which are significantly associated with the phenylalanine metabolism pathway, were identified as drug target genes.

Conclusion

SBNO2, PBX2, RAMP3, and QPCT could serve as potential target genes for mania treatment and deserve further basic and clinical research. Medicinal target genes regulate the phenylalanine metabolism pathway to achieve the treatment of mania. Phenylalanine is an important intermediate substance in the treatment of mania that is regulated by drug target genes.

Racial/Ethnic Differences in Associations Between Traumatic Childhood Experiences and Both Metabolic Syndrome Prevalence and Type 2 Diabetes Risk Among a Cohort of U.S. Women

OBJECTIVE

Childhood adversity has been associated with metabolic syndrome (MetS) and type 2 diabetes risk in adulthood. However, studies have yet to investigate traumatic childhood experiences (TCEs) beyond abuse and neglect (e.g., natural disaster) while considering potential racial/ethnic differences.

RESEARCH DESIGN AND METHODS

To investigate race/ethnicity as a potential modifier of the association between TCEs, MetS, and type 2 diabetes, we used prospectively collected data from 42,173 eligible non-Hispanic White (NHW; 88%), Black/African American (BAA; 7%), and Hispanic/Latina (4%) Sister Study participants (aged 35-74 years) enrolled from 2003 to 2009. A modified Brief Betrayal Trauma Survey captured TCEs. At least three prevalent metabolic abnormalities defined MetS, and self-report of a new diagnosis during the study period defined type 2 diabetes. We used adjusted Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% CIs for type 2 diabetes over a mean ± SD follow-up of 11.1 ± 2.7 years, overall and by race/ethnicity. We also tested for modification and mediation by MetS.

RESULTS

Incident cases of type 2 diabetes were reported (n = 2,479 among NHW, 461 among BAA, and 281 among Latina participants). Reporting any TCEs (50% among NHW, 53% among BAA, and 51% among Latina participants) was associated with a 13% higher risk of type 2 diabetes (HR 1.13; 95% CI 1.04-1.22). Associations were strongest among Latina participants (HR 1.64 [95% CI 1.21-2.22] vs. 1.09 for BAA and NHW). MetS was not a modifier but mediated (indirect effect, HR 1.01 [95% CI 1.00-1.01]; P = 0.02) the overall association.

CONCLUSIONS

TCE and type 2 diabetes associations varied by race/ethnicity and were partially explained by MetS.

Bipolar Spectrum Disorder May Be Associated With Family History of Diseases

Background

This study aims at investigating into the presence of family history of diabetes, ischemic heart disease, thyroid disease, cancer, cerebrovascular disease, and epilepsy in bipolar patients.

Methods

Totally 1,148 patients admitted to our outpatient unit between January 2018 and January 2020, who were diagnosed with bipolar disorder according to Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V), from whom informed consent was obtained, were cross-sectionally and consecutively evaluated. Each patient was questioned regarding a family history of diabetes, ischemic heart disease, thyroid disease, cancer (gastrointestinal, breast and prostate cancer, leukemia, and lymphoma), cerebrovascular disease and epilepsy in first- and second-degree relatives.

Results

Diabetes, ischemic heart disease, cancer, cerebrovascular disease and epilepsy were more common in the family histories than in bipolar patients. A strong correlation was found between family history positive for epilepsy and bipolar disorder with psychotic symptoms. Also, a correlation was found between family history for diabetes and seasonal course and family history positive for thyroid disease and comorbid anxiety disorder.

Conclusions

This study is the first to investigate into the frequency of physical diseases in the family histories of bipolar patients. Current therapies target the association between common leading pathways and symptoms whereas it is the association between stress and neural circuits that underlie the pathophysiology that should be targeted.

Epigenetics of Metabolic Syndrome as a Mood Disorder

Mood disorders comprise major depressive disorder (MDD), bipolar disorder (BD) and the milder forms of these two disorders. Reccurring MDD is also known as unipolar disorder. The distinction between unipolar and bipolar disorders was first suggested in 1957 by Leonard and was made official after support by several studies in 1980. Indeed, in 150 AD, Aretaeus of Cappadocia wrote “It seems to me that melancholia is the beginning and a part of mania”. Additionally, Kraepelin, who proposed the first medical disease model in psychiatry a century ago, considered recurrent unipolar depression cases under the category of bipolar disorder and conceptualized spectrum disorders. Because today’s classification systems conduct cross-sectional diagnosis, they do not consider family history, long-term characteristics and multidimensional approaches on symptoms. This method prioritizes reliability over validity and it rules out psychiatric disorders in etiology. Actually, a spectrum model which covers physical diseases is conceivable. The concept of epigenetics considers mood disorders, Alzheimer’s disease, attention deficit and hyperactivity disorder, Carney syndrome, multiple endocrine neoplasia type I and II, breast and prostate cancers, carsinoid tumors, cerebrovascular and cardiovascular diseases and metabolic syndrome together. This review addressed the relationship between metabolic syndrome and mood disorders in this context along with genetic, clinical and environmental factors such as climate, geographic factors, migration and changeable lifestyles. Genetic and clinical variables are affective temperament, childhood trauma and use of antidepressants and antipsychotics.