Inflammatory and metabolic disturbances are associated with more severe trajectories of late-life depression

Late-life depression is a highly prevalent mental health condition with devastating consequences even from its earliest stages. Alterations in physiological functions, such as inflammatory and metabolic, have been described in patients with depression. However, little is known on the association bet...

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Detalles Bibliográficos
Autores: Torre Luque, Alejandro de la, Ayuso Mateos, José Luis, Sánchez Carro, Yolanda, Fuente, Javier de la, López García, María Pilar
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/731440
Acceso en línea:https://hdl.handle.net/10486/731440
https://dx.doi.org/10.1016/j.psyneuen.2019.104443
Access Level:acceso abierto
Palabra clave:Depression
Inflammation
Metabolic disease risk
Longitudinal trajectories
Healthy ageing
Medicina
Descripción
Sumario:Late-life depression is a highly prevalent mental health condition with devastating consequences even from its earliest stages. Alterations in physiological functions, such as inflammatory and metabolic, have been described in patients with depression. However, little is known on the association between depression symptom course and metabolic and inflammation dysregulation. This study aimed to depict the course of depression symptoms while ageing, taking into consideration inter-individual heterogeneity. Moreover, it intended to study the associations between inflammatory and metabolic risk profiles and symptom trajectories. To do so, data from 13,203 adults aged 50–90 years (52.75% women; mean age at baseline = 65.07, SD = 10.00) were used. Blood sample and blood pressure measures were taken from 1536 participants (56.58% women; mean age at baseline = 61.73 years, sd = 7.64). Depression symptoms were assessed every two years across a 10-year follow-up. Trajectories were identified by means of latent class mixed modelling. Inflammation and metabolic risk profile scores were obtained from plasma and diagnostic-based indicators in the follow-up, using a robust latent-factor approach. Multigroup modelling was used to study the associations between the profiles and symptom trajectories. As a result, three heterogeneous trajectories of symptoms were identified (low-symptom, moderate-symptom and high-symptom trajectory). Participants depicting a high-symptom trajectory showed the greatest inflammation profile score and high metabolic risk. Moderate-symptom trajectory was also related to high inflammation and metabolic risk. To sum up, at-risk trajectories of symptoms were associated with high inflammation and risk of metabolic diseases. This study provides valuable evidence to advance personalised medicine and mental health precision, considering person-specific profiles and physiological concomitants