Abstract
Inflammation and metabolic dysregulation are age-related physiological changes and are associated with depressive disorder. We tried to identify subgroups of depressed older patients based on their metabolic-inflammatory profile and examined the course of depression for these subgroups. This clinical cohort study was conducted in a sample of 364 depressed older (⩾60 years) patients according to DSM-IV criteria. Severity of depressive symptoms was monitored every 6 months and a formal diagnostic interview repeated at 2-year follow-up. Latent class analyses based on baseline metabolic and inflammatory biomarkers were performed. Adjusted for confounders, we compared remission of depression at 2-year follow-up between the metabolic-inflammatory subgroups with logistic regression and the course of depression severity over 2-years by linear mixed models. We identified a 'healthy' subgroup (n = 181, 49.7%) and five subgroups characterized by different profiles of metabolic-inflammatory dysregulation. Compared to the healthy subgroup, patients in the subgroup with mild 'metabolic and inflammatory dysregulation' (n = 137, 37.6%) had higher depressive symptom scores, a lower rate of improvement in the first year, and were less likely to be remitted after 2-years [OR 0.49 (95% CI 0.26-0.91)]. The four smaller subgroups characterized by a more specific immune-inflammatory dysregulation profile did not differ from the two main subgroups regarding the course of depression. Nearly half of the patients with late-life depressions suffer from metabolic-inflammatory dysregulation, which is also associated with more severe depression and a worse prognosis. Future studies should examine whether these depressed older patients benefit from a metabolic-inflammatory targeted treatment.
Highlights
Depression is a common and disabling disorder in later life (Kok & Reynolds, 2017), and has a poorer prognosis compared with depression in younger individuals (Jeuring et al, 2018; Schaakxs et al, 2018)
The best fitting latent class analyses (LCA) model according to the Akaike information criterion (AIC) and Bayesian information criterion (BIC) was the six-class model, which had good interpretability
Class A (n = 181, 49.7%) consisted of a relatively healthy subgroup of depressed patients with low scores across metabolic syndrome (MetS) and inflammation markers and is referred to as ‘Healthy’
Summary
Depression is a common and disabling disorder in later life (Kok & Reynolds, 2017), and has a poorer prognosis compared with depression in younger individuals (Jeuring et al, 2018; Schaakxs et al, 2018) Among other factors, this worse prognosis of late-life depression (LLD) might be explained by aging-related physiological changes such as the occurrence of metabolic syndrome (MetS) and inflammation (Dowlati et al, 2010; Howren, Lamkin, & Suls, 2009; Koponen, Jokelainen, Keinänen-Kiukaanniemi, Kumpusalo, & Vanhala, 2008; Köhler et al, 2017; Marijnissen et al, 2013; Repousi, Masana, Sanchez-Niubo, Haro, & Tyrovolas, 2018). We tried to identify subgroups of depressed older patients based on their metabolic-inflammatory profile and examined the course of depression for these subgroups. Future studies should examine whether these depressed older patients benefit from a metabolic-inflammatory targeted treatment
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