A recent hypothesis postulates the existence of an ‘immune-metabolic depression’ (IMD) dimension characterized by metabolic dysregulations. Combining data on metabolomics and depressive symptoms, we aimed to identify depressions associated with an increased risk of adverse metabolic alterations.

Clustering data were from 1094 individuals with major depressive disorder in the last 6 months and measures of 149 metabolites from a 1H-NMR platform and 30 depressive symptoms (IDS-SR30). Canonical correlation analyses (CCA) were used to identify main independent metabolite-symptom axes of variance. Then, for the replication, we examined the association of the identified dimensions with metabolites from the same platform and cardiometabolic diseases in an independent population-based cohort (n = 6572).

CCA identified an overall depression dimension and a dimension resembling IMD, in which symptoms such as sleeping too much, increased appetite, and low energy level had higher relative loading. In the independent sample, the overall depression dimension was associated with lower cardiometabolic risk, such as (i.e. per s.d.) HOMA-1B −0.06 (95% CI −0.09 – −0.04), and visceral adipose tissue −0.10 cm2 (95% CI −0.14 – −0.07). In contrast, the IMD dimension was associated with well-known cardiometabolic diseases such as higher visceral adipose tissue 0.08 cm2 (95% CI 0.04–0.12), HOMA-1B 0.06 (95% CI 0.04–0.09), and lower HDL-cholesterol levels −0.03 mmol/L (95% CI −0.05 – −0.01).

Combining metabolomics and clinical symptoms we identified a replicable depression dimension associated with adverse metabolic alterations, in line with the IMD hypothesis. Patients with IMD may be at higher cardiometabolic risk and may benefit from specific treatment targeting underlying metabolic dysregulations.

Estimating the risk of a complicated course of Clostridium difficile infection (CDI) might help doctors guide treatment. We aimed to validate 3 published prediction models: Hensgens (2014), Na (2015), and Welfare (2011).

The validation cohort comprised 148 patients diagnosed with CDI between May 2013 and March 2014. During this period, 70 endemic cases of CDI occurred as well as 78 cases of CDI related to an outbreak of C. difficile ribotype 027. Model calibration and discrimination were assessed for the 3 prediction rules.

A complicated course (ie, death, colectomy, or ICU admission due to CDI) was observed in 31 patients (21%), and 23 patients (16%) died within 30 days of CDI diagnosis. The performance of all 3 prediction models was poor when applied to the total validation cohort with an estimated area under the curve (AUC) of 0.68 for the Hensgens model, 0.54 for the Na model, and 0.61 for the Welfare model. For those patients diagnosed with CDI due to non-outbreak strains, the prediction model developed by Hensgens performed the best, with an AUC of 0.78.

All 3 prediction models performed poorly when using our total cohort, which included CDI cases from an outbreak as well as endemic cases. The prediction model of Hensgens performed relatively well for patients diagnosed with CDI due to non-outbreak strains, and this model may be useful in endemic settings.

Infect Control Hosp Epidemiol 2017;38:897–905