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Major depressive disorder (MDD) is defined by an array of symptoms that make it challenging to understand the condition at a population level. Subtyping offers a way to unpick this phenotypic diversity for improved disorder characterisation. We aimed to identify depression subtypes longitudinally using the Inventory of Depressive Symptomatology: Self-Report (IDS-SR). A secondary analysis of a two-year cohort study called Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD), which collected data every three months from patients with a history of recurrent MDD in the United Kingdom, the Netherlands, and Spain (N = 619). We used latent class and latent transition analysis to identify subtypes at baseline, determined their consistency at 6- and 12-month follow-ups, and examined transitions over time. We identified a 4-class solution: (1) severe with appetite decrease, (2) severe with appetite increase, (3) moderate severity and (4) low severity. These same classes were identified at 6- and 12-month follow-ups, and participants tended to remain in the same class over time. We found no statistically significant differences between the two severe subtypes regarding baseline clinical and sociodemographic characteristics. Our findings emphasize severity differences over symptom types, suggesting that current subtyping methods provide insights akin to existing severity measures. When examining transitions, participants were most likely to remain in their respective classes over 1-year, indicating chronicity rather than oscillations in depression severity. Future work recommendations are made. [ABSTRACT FROM AUTHOR]

Background: Elevated night resting heart rate (HR) has been associated with increased depression severity, yet the underlying mechanisms remain elusive. This study aimed to investigate the mediating role of sleep disturbance and the influence of anxiety on the relationship between night resting HR and depression severity., Methods: This is a secondary data analysis of data collected in the Remote Assessment of Disease and Relapse (RADAR) Major Depressive Disorder (MDD) longitudinal mobile health study, encompassing 461 participants (1774 observations) across three national centers (Netherlands, Spain, and the UK). Depression severity, anxiety, and sleep disturbance were assessed every three months. Night resting HR parameters in the 2 weeks preceding assessments were measured using a wrist-worn Fitbit device. Linear mixed models and causal mediation analysis were employed to examine the impact of sleep disturbance and anxiety on night resting HR on depression severity. Covariates included age, sex, BMI, smoking, alcohol consumption, antidepressant use, and comorbidities with other medical conditions., Results: Higher night resting HR was linked to subsequent depressive severity, through the mediation of sleep disturbance. Anxiety contributed to an exacerbated level of sleep disturbance, subsequently intensifying depression severity. Anxiety exhibited no direct effect on night resting HR., Conclusions: Our findings underscore the mediating role of sleep disturbance in the effect of night resting HR on depression severity, and anxiety on depression severity. This insight has potential implications for early identification of indicators signalling worsening depression symptoms, enabling clinicians to initiate timely and responsive treatment measures., Competing Interests: Declaration of competing interest Vaibhav A Narayan is employee of Janssen Research and Development LLC. Peter Annas is employed by the pharmaceutical company H. Lundbeck A/S. Josep Maria Haro has received economic compensation for participating in advisory boards or giving educational lectures from Eli Lilly & Co, Sanofi, Lundbeck, and Otsuka. No other authors have competing interests to declare., (Copyright © 2025. Published by Elsevier B.V.)