Mechanisms and consequences of depression-related multimorbidity over the life course: coordinated analysis of population and primary care data

多病（MM）被定义为两种或两种以上精神和/或身体慢性疾病的共存，对临床医生、参与者及其家属来说是一个复杂的挑战。目前的预防工作在一定程度上失败了，因为它们一次只关注一种疾病，而且太迟了。因此，迫切需要确定生命早期有MM风险的个人群体，并制定干预措施，以预防MM及其不良健康后果。许多调查表明，临床抑郁症不仅是MM群中最常见的疾病，而且对患者的生活质量危害最大。与大多数在晚年发生（并开始聚集）的疾病相比，抑郁症往往在成年生活中开始得更早。由于大多数关于MM的现有研究都集中在老年人（60岁以上）身上，这意味着一个重要的组成部分被相对忽视了。成年早期抑郁发作与后来的MM集群之间究竟有什么关系？对一群人进行多年跟踪研究的生命历程方法是解决这个问题的最佳方法。在拟议的研究中，我们将分析世界上最大的三个出生队列（均位于英国），其中包含约40,000名参与者，他们在出生后的几十年里定期接受研究。我们将确定那些在青年和中年（20至64岁）有抑郁记录的参与者，并检查他们早期出现的心理困难如何与后来的身体和精神疾病相互作用。特别是，我们将探索这些关系在不同的时间和不同的子群体，由性别和社会经济背景定义。此外，我们计划通过从常规全科实践中收集的数据来评估队列研究结果的临床价值。CPRD数据库现在包含数百万患者的记录，我们预计将检查超过20万名抑郁症患者。这些大的数字应该足以以合理的精度确定抑郁症相关MM的重要机制和后果。Dregan博士（PI）制定了一项从初级保健数据库获取电子健康记录的协议，该协议将为拟议研究的这一部分提供信息。因此，我们有信心，我们将获得大量的个人，以确定重要的风险因素（包括生理、行为习惯、治疗和社会支持），抑郁症相关的多病集群跨越青年和中年。我们应该能够确定哪些与抑郁症相关的MM集群在青年或中年有最有害的影响，并测试这些关系如何受到性别、社会阶层和抑郁症的特定属性（如睡眠障碍）的影响。我们将采用先进的统计方法，尽可能得出最可靠的结果。有了庞大的队列和初级保健数据库，这些重要的问题将为未来的临床试验提供信息并改善公共卫生，可以以比随机试验低得多的成本得到解决。我们还将使用请求的资源，使其他研究人员能够使用队列数据来解决重要的研究问题。

Multimorbidity (MM), defined as the co-existence of two or more mental and/or physical chronic conditions, represents a complex challenge for clinicians, participants, and their families. Current preventive efforts have partly failed because they have focused on one disease at a time and too late in life. For this reason, there is a critical need to identify groups of individuals at risk of MM earlier in life, and to develop interventions to prevent MM and its adverse health outcomes. A number of surveys have shown that not only clinical depression the most common illness in any MM cluster, it also is the most important for harming a patient's quality of life. Compared with most diseases that occur (and begin to cluster) in later years, depression tends to begin much earlier in adult life. As most existing research on MM has focused on older adults (over 60 years), this means an important component has been relatively ignored. What exactly is the relationship between the onset of depression in early adult years and later MM clusters? A life course approach that follows groups of people over many years is the best way of addressing this question.In the proposed study we will analyse three of the world largest birth cohorts (all based in the UK) that contain around 40,000 participants who have been regularly studied in the decades since their birth. We will identify those participants with a record of depression in young and mid-adult years (20 to 64 years of age) and examine how their early onset psychological difficulties interacted with later illnesses, both physical and mental. In particular, we will explore these relationships across time and within different subgroups, defined by gender and socioeconomic background.In addition, we plan to evaluate the clinical value of the cohort findings with data collected from routine general practice. The CPRD database now contains millions of patients' records and we anticipate examining in excess of 200,000 patients with depression. These large numbers should be sufficient to identify important mechanisms and consequences of depression related MM with reasonable precision. Dr Dregan (PI) has developed a protocol for accessing electronic health records from the primary care database, which will inform this part of the proposed study. Thus, we are confident that we will access to a large number of individuals in order to identify the important risk factors (including physiological, behavioural habits, treatment, and social support) for depression-related multimorbidity clusters across young and mid-adult years. We should be able to determine which depression-related MM clusters have the most harmful impacts during young or mid-adult years and test how these relationships are affected by gender, social class, and specific attributes of depression (such as sleep disturbance).We will use advanced statistical methods to produce the most reliable results possible. With the large cohort and primary care databases, these important questions that will inform future clinical trials and improve public health, can be addressed at a considerably lower cost than a randomised trial. We will also use the requested resources to enable other researchers to use cohort data to address important research questions.