Parsing metabolic heterogeneity in mood disorders: A hypothesis-driven cluster analysis of glucose and insulin abnormalities.

OBJECTIVES

Metabolically based distinctions for disturbances in glucose and insulin may provide meaningful insights both clinically and mechanistically.

METHODS

Data were derived from 352 subjects of previously completed clinical studies with a mood disorder (MD) (bipolar disorder: n = 179, major depressive disorder: n = 173) and 218 healthy controls from the Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy. We conducted a factor analysis to replicate a priori dissociable factors informed by glucose and insulin levels and indices of insulin resistance and beta-cell function: elevated insulin and insulin resistance ("insulin-IR"), and increased fasting glucose and reduced insulin secretion ("glucotoxicity"). Cluster analyses were conducted, separately in men and women, to evaluate the clinical relevance of subtyping individuals with MDs using insulin-IR and glucotoxicity (GT) factor scores.

RESULTS

Factors insulin-IR and GT explained 92.64% and 92.09% of the variance in men and women respectively. Three clusters were replicated in men and women separately: metabolically healthy (MH), high GT, and insulin-resistant (IR). After adjusting for age, gender, study cohort, MD diagnosis, and antipsychotics use, body mass index (BMI) and mean arterial pressure were higher in IR- vs GT- or MH-clustered individuals; GT-clustered individuals had more metabolic syndrome components and higher C-reactive protein. Glucotoxic-clustered subjects reported greater impairments in cognitive function and global functioning when compared to MH- or IR-clustered subjects.

CONCLUSIONS

Using simple, cost-effective, and accessible measures, we identified stable, gender-convergent, subgroups of individuals that significantly diverged on measures of cognitive dysfunction, self-reported anhedonia, functional disability, BMI, and blood pressure.