Bio-Social Pathways to Poor Mental Health in The UK Population: Using Blood Samples to Index Neurobiological Factors

心理健康状况不佳是由遗传和环境因素在整个生命过程中复杂的相互作用造成的，并可能由压力性生活事件（SLEs）引发。对这些相互作用的全面了解有助于识别风险和保护因素。我们保持弹性的能力可能会受到基因-环境相互作用（GxE）的影响，这种相互作用发生在儿童和青少年的大脑发育和成熟过程中。此外，在血液中测量的一些表观遗传和蛋白质组生物标志物可能通过它们测量神经生物学因素的能力，为我们在特定时刻的弹性提供线索。本论文将整合遗传学、表观遗传学和蛋白质组学生物标志物，沿着SLE，以了解这些生物标志物如何在精神健康和恢复力差的途径上相互作用。 本论文将产生三篇研究论文。前两篇论文将通过与精神健康症状的关联来评估遗传、表观遗传和蛋白质组学生物标志物的表面有效性，第三篇论文将把重要的生物标志物整合到一个更全面的纵向模型中，该模型将SLE以及保护和风险因素结合起来。论文1：一系列GxE将与心理健康结果相关。对于“E”，将使用三种早期生活暴露：社会经济地位（母亲教育和父亲职业）和家庭破裂（完整的家庭、父母迁出或父母死亡）。对于“G”，将生成代表结构、功能和分子脑测量的28 x多基因指数：6x与抑郁症状、双相情感障碍和/或神经质遗传相关的皮质下脑区域体积，19 x与精神分裂症、双相情感障碍、重度抑郁障碍和/或交叉障碍性状遗传相关的功能连接性状，和3x基于人脑组织中糖皮质激素受体（NR 3C 1，一种关键的应激反应蛋白）的基因表达研究。这些GxEs代表了发生在儿童和青少年时期的神经发育过程。主要结果将是基于超过GHQ-12或SF-12-MCS既定阈值的二元指标。论文二：DNA甲基化（DNAm）数据和蛋白质组学数据将用于导出一系列生物标志物：a）与心理社会应激相关的五种充分研究的基因的DNAm生物标志物：两种应激反应蛋白NR 3C 1和FKBP 5、5-羟色胺转运蛋白（SLC 6A 4）、催产素受体（OXTR）和脑源性神经营养因子（BDNF），和B）两种蛋白质组学生物标志物：创伤后应激障碍（PTSD）风险评分和海马体积评分。将测试这些生物标志物与上述心理健康结果的前瞻性关联，以及与第一篇论文中任何显著的GxE相互作用因素的关联。这些分析可能表明，DNAm和蛋白质组学生物标志物作为神经发育因素和心理健康结果之间的介质，并可能表明其效用作为应激暴露的措施，或作为风险生物标志物。论文三：路径建模将纳入精神健康结果与前两篇论文中确定的早期生活暴露和生物标志物（遗传，表观遗传和蛋白质组学），SLE的纵向测量以及风险和保护因素，其中可能包括收入，社会支持，慢性压力源（工作质量，邻里因素），运动，教育和亲社会行为。通过对这些途径进行建模，本文旨在帮助我们了解心理健康状况不佳的弹性和脆弱性，包括遗传因素，以及在政策层面和个人层面可以修改的因素。它还旨在强调一系列神经生物学因素在精神健康状况不佳中的重要性，并证明其在外周血中的可测量性。

Poor mental health results from a complex interplay between genetic and environmental factors across the life course, and can be triggered by stressful life events (SLEs). A comprehensive understanding of these interactions could help identify risk and protective factors. Our ability to remain resilient may be influenced by the gene-environment interaction (GxE) which takes place during brain development and maturation in childhood and adolescence. Furthermore, some epigenetic and proteomic biomarkers measured in the blood may hold clues as to how resilient we are at a given moment, by their ability to measure neurobiological factors. This thesis will integrate genetic, epigenetic and proteomic biomarkers, along with SLEs, to understand how these interact on the pathway to poor mental health and resilience. The thesis will generate three research papers. The first two papers will assess the face validity of genetic, epigenetic and proteomic biomarkers by their association with mental health symptoms, and the third will integrate significant biomarkers into a more comprehensive longitudinal model which incorporates SLEs and protective and risk factors. Paper 1: A range of GxE will be modelled in relation to mental health outcomes. For "E", three early life exposures will be used: socioeconomic position (maternal education and father's occupation) and family disruption (intact family, parent moved out, or parent died). For "G", 28x polygenic indices which proxy structural, functional and molecular brain measurements will be generated: 6x subcortical brain regional volumes which are genetically correlated with depressive symptoms, bipolar disorder and/or neuroticism, 19x functional connectivity traits which are genetically correlated with schizophrenia, bipolar disorder, major depressive disorder and/or a cross-disorder trait, and 3x based on gene expression studies of the glucocorticoid receptor (NR3C1, a key stress response protein) in human brain tissue. These GxEs represent neurodevelopmental processes which take place during childhood and adolescence. The primary outcomes will be binary measures based on exceeding established thresholds for GHQ-12 or SF12-MCS. Paper 2: DNA methylation (DNAm) data and proteomic data will be used to derive a range of biomarkers: a) DNAm biomarkers of five well-studied genes related to psychosocial stress: two stress response proteins NR3C1 and FKBP5, serotonin transporter (SLC6A4), oxytocin receptor (OXTR), and brain-derived neurotrophic factor (BDNF), and b) two proteomic biomarkers: a post-traumatic stress disorder (PTSD) risk score, and a hippocampal volume score. These biomarkers will be tested for prospective associations with the aforementioned mental health outcomes, and for associations with any significant GxE interacting factors from the first paper. These analyses could suggest that the DNAm and proteomic biomarkers act as mediators between neurodevelopmental factors and mental health outcomes, and may indicate their utility as measures of stress exposure, or as risk biomarkers. Paper 3: Path modelling will incorporate mental health outcomes with significant early life exposures and biomarkers (genetic, epigenetic and proteomic) identified in the first two papers, longitudinal measures of SLEs, and risk and protective factors, which may include income, social support, chronic stressors (work quality, neighbourhood-level factors), exercise, education and prosocial behaviour. By modelling these pathways, this paper aims to aid our understanding of resilience and vulnerability to poor mental health, including genetic factors, and factors which may be modifiable at the policy level and at the individual level. It also aims to underscore the importance of a range of neurobiological factors in poor mental health, and to demonstrate their measurability in peripheral blood.