Inflammatory and metabolic disturbances are associated with more severe trajectories of late-life depression
Inflammatory and metabolic disturbances are associated with more severe trajectories of late-life depression
复制标题
DOI：
10.1016/j.psyneuen.2019.104443
复制
发表时间：
2019-12-01
影响因子：
3.7
通讯作者：
Lopez-Garcia, Pilar
中科院分区：
文献类型：
--
作者：
de la Torre-Luque, Alejandro;Luis Ayuso-Mateos, Jose;Lopez-Garcia, Pilar
晚年抑郁症是一种非常普遍的心理健康状况，即使在早期阶段也会造成毁灭性的后果。生理功能的改变，如炎症和代谢，已被描述为抑郁症患者。然而，对抑郁症状病程与代谢和炎症失调之间的关系知之甚少。本研究旨在描述抑郁症状的过程，随着年龄的增长，考虑到个体间的异质性。此外，它旨在研究炎症和代谢风险概况和症状轨迹之间的关系。为此，使用了13203名50-90岁成年人的数据（52.75%为女性，基线平均年龄为65.07岁，SD = 10.00）。从1536名参与者（56.58%为女性，基线时平均年龄= 61.73岁，sd = 7.64）中采集血样和血压测量。在10年的随访中，每两年评估一次抑郁症状。通过潜类混合模型识别轨迹。在随访中，使用强大的潜在因素方法，从血浆和基于诊断的指标获得炎症和代谢风险概况评分。使用多组模型来研究概况和症状轨迹之间的关联。结果，确定了三种不同的症状轨迹（低症状、中度症状和高症状轨迹）。描述高症状轨迹的参与者表现出最高的炎症概况评分和高代谢风险。中度症状轨迹也与高炎症和代谢风险相关。综上所述，症状的危险轨迹与高炎症和代谢性疾病的风险相关。本研究为推进个体化医疗和心理健康精准性提供了有价值的证据，同时考虑了个人特征和生理伴随因素。
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.