The hypothalamic-pituitary-adrenal (HPA) axis and the inflammatory response system have been suggested as pathophysiological mechanisms implicated in the etiology of major depressive disorder (MDD). Although meta-analyses do confirm associations between depression and these biological systems, effect sizes vary greatly among individual studies. A potentially important factor explaining variability is heterogeneity of MDD. Aim of this study was to evaluate the association between depressive subtypes (based on latent class analysis) and biological measures. Data from 776 persons from the Netherlands Study of Depression and Anxiety, including 111 chronic depressed persons with melancholic depression, 122 with atypical depression and 543 controls were analyzed. Inflammatory markers (C-reactive protein, interleukin-6, tumor necrosis factor-α), metabolic syndrome components, body mass index (BMI), saliva cortisol awakening curves (area under the curve with respect to the ground (AUCg) and with respect to the increase (AUCi)), and diurnal cortisol slope were compared among groups. Persons with melancholic depression had a higher AUCg and higher diurnal slope compared with persons with atypical depression and with controls. Persons with atypical depression had significantly higher levels of inflammatory markers, BMI, waist circumference and triglycerides, and lower high-density lipid cholesterol than persons with melancholic depression and controls. This study confirms that chronic forms of the two major subtypes of depression are associated with different biological correlates with inflammatory and metabolic dysregulation in atypical depression and HPA-axis hyperactivity in melancholic depression. The data provide further evidence that chronic forms of depressive subtypes differ not only in their symptom presentation, but also in their biological correlates. These findings have important implications for future research on pathophysiological pathways of depression and treatment.

Growing evidence suggests that immune dysregulation may be involved in depressive disorders, but the exact nature of this association is still unknown and may be restricted to specific subgroups. This study examines the association between depressive disorders, depression characteristics and antidepressant medication with inflammation in a large cohort of controls and depressed persons, taking possible sex differences and important confounding factors into account. Persons (18–65 years) with a current (N=1132) or remitted (N=789) depressive disorder according to DSM-IV criteria and healthy controls (N=494) were selected from the Netherlands Study of Depression and Anxiety. Assessments included clinical characteristics (severity, duration and age of onset), use of antidepressant medication and inflammatory markers (C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α)). After adjustment for sociodemographics, currently depressed men, but not women, had higher levels of CRP (1.33 versus 0.92 mg l−1, P<0.001, Cohen's d=0.32) and IL-6 (0.88 versus 0.72 pg ml−1, P=0.01, Cohen's d=0.23) than non-depressed peers. Associations reduced after considering lifestyle and disease indicators — especially body mass index — but remained significant for CRP. After full adjustment, highest inflammation levels were found in depressed men with an older age of depression onset (CRP, TNF-α). Furthermore, inflammation was increased in men using serotonin–norepinephrine reuptake inhibitors (CRP, IL-6) and in men and women using tri- or tetracyclic antidepressants (CRP), but decreased among men using selective serotonin reuptake inhibitors (IL-6). In conclusion, elevated inflammation was confirmed in depressed men, especially those with a late-onset depression. Specific antidepressants may differ in their effects on inflammation.

BackgroundThe association between depression after myocardial infarction and increased risk of mortality and cardiac morbidity may be due to cardiac disease severity.AimsTo combine original data from studies on the association between post-infarction depression and prognosis into one database, and to investigate to what extent such depression predicts prognosis independently of disease severity.MethodAn individual patient data meta-analysis of studies was conducted using multilevel, multivariable Cox regression analyses.ResultsSixteen studies participated, creating a database of 10 175 post-infarction cases. Hazard ratios for post-infarction depression were 1.32 (95% CI 1.26–1.38, P<0.001) for all-cause mortality and 1.19 (95% CI 1.14–1.24, P<0.001) for cardiovascular events. Hazard ratios adjusted for disease severity were attenuated by 28% and 25% respectively.ConclusionsThe association between depression following myocardial infarction and prognosis is attenuated after adjustment for cardiac disease severity. Still, depression remains independently associated with prognosis, with a 22% increased risk of all-cause mortality and a 13% increased risk of cardiovascular events per standard deviation in depression z-score.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.