Fasting insulinemia as biomarker of illness relapse in patients with severe mental illness?

Severe Mental Illness (SMI) is often associated with metabolic alteration and/or metabolic syndrome, which may determine an increased mortality due to a further increased cardiovascular risk. The relationship with metabolic syndrome is often bidirectional, resulting in a pathoplastic effect of these dysmetabolisms. Among the several hormones involved, insulin appears to play a key role, albeit not entirely clear. The aim of our real-world cross-sectional observational study is to investigate a set of metabolic biomarkers of illness relapse/recurrence/onset in a cohort of 310 adult SMI inpatients consecutively admitted to the Psychiatry Clinic of the Azienda Ospedaliero Universitaria of Marche, in Ancona (Italy), between February 2021 and February 2024. According to the stepwise multivariate regression model, a higher number of acute episodes per year was positively predicted by the age of illness onset, the lifetime number of suicidal attempts and fasting insulinemia and negatively by the participant's age. A second stepwise multivariate regression model using only the metabolic characteristics as independent variables, found that a higher number of acute episodes per year was predicted positively by the fasting insulinemia and red blood cells and negatively by the abdominal circumference. Overall, our findings could provide practical implications for the treatment and management of SMI patients, emphasizing the importance of monitoring and managing metabolic factors, particularly insulinemia, metabolic syndrome and insulin resistance. Finally, insulinemia could potentially act as metabolic biomarker of illness relapse, though more larger and longitudinal studies should be carried out to confirm these results.

Depressive and Anxious Temperaments as Predictors of Late Onset Bipolar Disorder? Preliminary Results of a "Real World" Exploratory Study.

INTRODUCTION: Bipolar disorder (BD) onset typically occurs between 15 and 30 years, being diagnosed under the age of 50 in 90% of cases, named "non-late onset BD" (non-LOBD). However, clinical observation of late-onset BD (LOBD) raised some concern regarding a differential psychopathological pattern, outcomes and treatment, including a specific affective temperament vulnerability. Therefore, an exploratory study in the "real world" was carried out by investigating psychopathological and temperamental features of a psychogeriatric cohort of LOBD and non-LOBD subjects. METHODS: A total of 180 patients affected with BD-I, BD-II, and Cyclothymic Disorder were screened in a Mood Disorder Outpatient Service, during the timeframe January 2019-August 2021. Out of 78 enrolled outpatients, 66 (33 non-LOBD, 33 LOBD) were recruited, by the retrospective collection of sociodemographic, cognitive, psychopathological and clinical assessment, including the short-version of the Temperament Evaluation of Memphis, Pisa, and San Diego (TEMPS-M). RESULTS: LOBD is significantly associated with higher rates of BD-II diagnosis (χ2 = 27.692, p < 0.001), depressive episodes (p = 0.025), mixed states (p = 0.009), predominant depressive and anxious affective temperaments (p < 0.001). Non-LOBD is significantly associated with higher endocrinological (χ2 = 6.988, p = 0.008) and metabolic comorbidity (χ2 = 5.987, p = 0.014), a diagnosis of BD-I, manic episodes, and predominant hyperthymic affective temperaments (p = 0.001). GDS (p < 0.001) and MSRS (p = 0.005) scores were significantly higher in LOBD. CONCLUSION: Further longitudinal studies with larger sample sizes and a control group are needed to determine whether LOBD may represent a distinct psychopathological entity from non-LOBD and evaluate differences (if any) in terms of prognosis and treatment between non-LOBD and LOBD.

Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?

Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the 'moment-by-moment quantification of the individual-level human phenotype in its own environment', represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians' capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.