Quantification of diet quality utilizing the rapid eating assessment for participants-shortened version in bipolar disorder: Implications for prospective depression and cardiometabolic studies.

OBJECTIVES: Recognizing bipolar disorder as a multi-system metabolic condition driven, in part, by binge eating behavior and atypical depressive symptoms, this study aimed to quantify diet quality and evaluate clinical correlates in a bipolar disorder cohort. METHODS: Participants from the Mayo Clinic Bipolar Disorder Biobank (n = 734) completed the Rapid Eating Assessment for Participants - Shortened version (REAP-S) to determine diet quality. The average REAP-S score for a U.S. omnivorous diet is 32 (range 13 to 39) with higher scores indicating healthier diet. Demographic variables were collected in a standardized clinical questionnaire. Depressive symptoms were assessed by the Bipolar Inventory of Symptoms Scale. Cardiometabolic variables were retrieved from the electronic health record. Associations between continuous variables and REAP-S scores (total, 'healthy foods' and 'avoidance of unhealthy foods') were assessed using linear regression. RESULTS: Overall, our sample had a mean REAP-S score of 27.6 (4.9), suggestive of a lower diet quality than the average general population in the US. There was a significant inverse relationship between mean REAP-S lower scores with increased BMI, waist circumference, disordered eating and depression. All these associations were significantly stronger in female participants. LIMITATIONS: EHR cross-sectional data. CONCLUSIONS: Our data suggest unhealthy diet quality in bipolar disorder is associated with depression, obesity and cardiometabolic abnormalities. Additional work is encouraged to prospectively track mood and diet quality to further understand the bidirectional relationship and clarify if dietary interventions can positively impact mood. Further delineating potential sex differences in diet quality and depression may provide greater appreciation of modifiable risk factors for future cardiometabolic burden.

Assessment of Proteomic Measures Across Serious Psychiatric Illness.

The diagnoses of serious psychiatric illnesses, such as schizophrenia, schizoaffective disorder, and bipolar disorder, rely on the subjective recall and interpretation of often overlapping symptoms, and are not based on the objective pathophysiology of the illnesses. The subjectivity of symptom reporting and interpretation contributes to the delay of accurate diagnoses and limits effective treatment of these illnesses. Proteomics, the study of the types and quantities of proteins an organism produces, may offer an objective biological approach to psychiatric diagnosis. For this pilot study, we used the Myriad RBM Discovery Map 250+ platform to quantify 205 serum proteins in subjects with schizophrenia (n=26), schizoaffective disorder (n=20), bipolar disorder (n=16), and healthy controls with no psychiatric illness (n=23). Fifty-seven analytes that differed significantly between groups were used for multivariate modeling with linear discriminant analysis (LDA). Diagnoses generated from these models were compared to SCID-generated clinical diagnoses to determine whether the proteomic markers: 1) distinguished the three disorders from controls, and 2) distinguished between the three disorders. We found that a series of binary classification models including 8-12 analytes produced separation between all subjects and controls, and between each diagnostic group and controls. There was a high degree of accuracy in the separations, with training areas-under-the-curve (AUC) of 0.94-1.0, and cross-validation AUC of 0.94-0.95. Models with 7-14 analytes produced separation between the diagnostic groups, though less robustly, with training AUC of 0.72-1.0 and validation AUC of 0.69-0.89. While based on a small sample size, not adjusted for medication state, these preliminary results support the potential of proteomics as a diagnostic aid in psychiatry. The separation of schizophrenia, schizoaffective disorder, and bipolar disorder suggests that further work in this area is warranted.

Clinical features of bipolar spectrum with binge eating behaviour.

OBJECTIVE: To determine whether bipolar spectrum disorder with binge eating behavior (BE) is an important clinical sub-phenotype. METHODS: Prevalence rates and correlates of different levels of BE were assessed in 1114 bipolar spectrum patients participating in a genetic biobank. BE and eating disorders (EDs) were assessed with the Eating Disorder Diagnostic Scale (EDDS). Psychiatric illness burden was evaluated with measures of suicidality, psychosis, mood instability, anxiety disorder comorbidity, and substance abuse comorbidity. Medical illness burden was evaluated with body mass index (BMI) and the Cumulative Index Rating Scale (CIRS). RESULTS: Thirty percent of patients had any BE and 27% had BE plus an ED diagnosis. Compared with bipolar spectrum patients without BE, bipolar spectrum patients with BE were younger and more likely to be female; had significantly higher levels of eating psychopathology, suicidality, mood instability, and anxiety disorder comorbidity; had a significantly higher mean BMI and a significantly higher rate of obesity; and had a significantly higher medical illness burden. Bipolar spectrum patients with BE but no ED diagnosis were more similar to bipolar spectrum patients without BE than to those with an ED. Nonetheless, the positive predictive value and specificity of BE predicting an ED was 0.90 and 0.96, respectively. LIMITATIONS: As only two patients had co-occurring anorexia nervosa, these results may not generalize to bipolar spectrum patients with restricting EDs. CONCLUSION: Bipolar spectrum disorder with broadly-defined BE may not be as clinically relevant a sub-phenotype as bipolar spectrum disorder with an ED but may be an adequate proxy for the latter when phenotyping large samples of individuals.