Exploring the potential role of allostatic load biomarkers in risk assessment of patients presenting with depressive symptoms

Background: Depression is a major health problem worldwide and the majority of patients presenting with depressive symptoms are managed in primary care. Current approaches for assessing depressive symptoms in primary care are not accurate in predicting future clinical outcomes, which may potentially lead to over or under treatment. The Allostatic Load (AL) theory suggests that by measuring multi-system biomarker levels as a proxy of measuring multi-system physiological dysregulation, it is possible to identify individuals at risk of having adverse health outcomes at a prodromal stage. Allostatic Index (AI) score, calculated by applying statistical formulations to different multi-system biomarkers, have been associated with depressive symptoms. Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers will form a predictive algorithm in defining clinically meaningful outcomes in a population of patients presenting with depressive symptoms. The key objectives were: 1. To explore the relationship between various allostatic load biomarkers and prevalence of depressive symptoms in patients, especially in patients diagnosed with three common cardiometabolic diseases (Coronary Heart Disease (CHD), Diabetes and Stroke). 2 To explore whether allostatic load biomarkers predict clinical outcomes in patients with depressive symptoms, especially in patients with three common cardiometabolic diseases (CHD, Diabetes and Stroke). 3 To develop a predictive tool to identify individuals with depressive symptoms at highest risk of adverse clinical outcomes. Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existing cardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’ was a research data source containing health related information from 666 participants recruited from the general population. The clinical outcomes for 3 both datasets were studied using electronic data linkage to hospital and mortality health records, undertaken by Information Services Division, Scotland. Cross-sectional associations between allostatic load biomarkers calculated at baseline, with clinical severity of depression assessed by a symptom score, were assessed using logistic and linear regression models in both datasets. Cox’s proportional hazards survival analysis models were used to assess the relationship of allostatic load biomarkers at baseline and the risk of adverse physical health outcomes at follow-up, in patients with depressive symptoms. The possibility of interaction between depressive symptoms and allostatic load biomarkers in risk prediction of adverse clinical outcomes was studied using the analysis of variance (ANOVA) test. Finally, the value of constructing a risk scoring scale using patient demographics and allostatic load biomarkers for predicting adverse outcomes in depressed patients was investigated using clinical risk prediction modelling and Area Under Curve (AUC) statistics. Key Results: Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkers were statistically significant in predicting six different clinical outcomes in participants with depressive symptoms. Outcomes related to both mental health (depressive symptoms) and physical health were statistically associated with pre-treatment levels of peripheral biomarkers; however only two studies investigated outcomes related to physical health. Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainly cardiometabolic) were found to have a non-linear association with increased probability of co-morbid depressive symptoms (as assessed by Hospital Anxiety and Depression Score HADS-D≥8). A composite AI score constructed using five biomarkers did not lead to any improvement in the observed strength of the association. In Psobid, BMI was found to have a significant cross-sectional association with the probability of depressive symptoms (assessed by General Health Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive 4 protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have a significant cross-sectional relationship with the continuous measure of GHQ-28. A composite AI score constructed using 12 biomarkers did not show a significant association with depressive symptoms among Psobid participants. Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-cause death, all-cause hospital admissions and composite major adverse cardiovascular outcome-MACE (cardiovascular death or admission due to MI/stroke/HF). Presence of depressive symptoms and composite AI score calculated using mainly peripheral cardiometabolic biomarkers was found to have a significant association with all three clinical outcomes over the following four years in DepChron patients. There was no evidence of an interaction between AI score and presence of depressive symptoms in risk prediction of any of the three clinical outcomes. There was a statistically significant interaction noted between SBP and depressive symptoms in risk prediction of major adverse cardiovascular outcome, and also between HbA1c and depressive symptoms in risk prediction of all-cause mortality for patients with diabetes. In Psobid, depressive symptoms (assessed by GHQ-28≥5) did not have a statistically significant association with any of the four outcomes under study at seven years: all cause death, all cause hospital admission, MACE and incidence of new cancer. A composite AI score at baseline had a significant association with the risk of MACE at seven years, after adjusting for confounders. A continuous measure of IL-6 observed at baseline had a significant association with the risk of three clinical outcomes- all-cause mortality, all-cause hospital admissions and major adverse cardiovascular event. Raised total cholesterol at baseline was associated with lower risk of all-cause death at seven years while raised waist hip ratio- WHR at baseline was associated with higher risk of MACE at seven years among Psobid participants. There was no significant interaction between depressive symptoms and peripheral biomarkers (individual or combined) in risk prediction of any of the four clinical outcomes under consideration. Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eight baseline demographic and clinical variables to predict the risk of MACE over four years. The AUC value for the risk scoring system was modest at 56.7% (95% CI 55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to the low event rate observed for the clinical outcomes. Conclusion: Individual peripheral biomarkers were found to have a cross-sectional association with depressive symptoms both in patients with cardiometabolic disease and middle-aged participants recruited from the general population. AI score calculated with different statistical formulations was of no greater benefit in predicting concurrent depressive symptoms or clinical outcomes at follow-up, over and above its individual constituent biomarkers, in either patient cohort. SBP had a significant interaction with depressive symptoms in predicting cardiovascular events in patients with cardiometabolic disease; HbA1c had a significant interaction with depressive symptoms in predicting all-cause mortality in patients with diabetes. Peripheral biomarkers may have a role in predicting clinical outcomes in patients with depressive symptoms, especially for those with existing cardiometabolic disease, and this merits further investigation.

The role of oxidative stress as an underlying mechanism in life-history trade-offs

A key aspect underpinning life-history theory is the existence of trade-offs. Trade-offs occur because resources are limited, meaning that individuals cannot invest in all traits simultaneously, leading to costs for traits such as growth and reproduction. Such costs may be the reason for the sub-maximal growth rates that are often observed in nature, though the fitness consequences of these costs would depend on the effects on lifetime reproductive success. Recently, much attention has been given to the physiological mechanism that might underlie these life-history trade-offs, with oxidative stress (OS) playing a key role. OS is characterised by a build-up of oxidative damage to tissues (e.g. protein, lipids and DNA) from attack by reactive species (RS). RS, the majority of which are by-products of metabolism, are usually neutralised by antioxidants, however OS occurs when there is an imbalance between the two. There are two main theories linking OS with growth and reproduction. The first is that traits like growth and reproduction, being metabolically demanding, lead to an increase in RS production. The second involves the diversion of resources away from self-maintenance processes (e.g. the redox system) when individuals are faced with enhanced growth or reproductive expenditure. Previous research investigating trade-offs involving growth or reproduction and self-maintenance has been equivocal. One reason for this could be that associations among redox biomarkers can vary greatly so that the biomarker selected for analysis can influence the conclusion reached about an individual’s oxidative status. Therefore the first aim of my thesis was to explore the strength and pattern of integration of five biomarkers of OS (three antioxidants, one damage and one general oxidation measure) in wild blue tit (Cyanistes caeruleus) adults and nestlings (Chapter 2). In doing so, I established that all five biomarkers should be included in future analyses, thus using this collection of biomarkers I explored my next aims; whether enhanced growth (Chapters 3 and 4) or reproductive effort (Chapter 5) can lead to increased OS levels, if these traits are traded off against self-maintenance. I accomplished these aims using both a meta-analytic and experimental approach, the latter involving manipulation of brood size in wild blue tits in order to experimentally alter growth rate of nestlings and provisioning rate (a proxy for reproductive expenditure) of adults. I also investigated the potential for redox integration to be used as an index of body condition (Chapter 2), allowing predictions about future fitness consequences of changes to oxidative state to be made. A growth – self-maintenance trade off was supported by my meta-analytic results (Chapter 4) which found OS to be a constraint on growth. However, when faced with experimentally enhanced growth, animals were typically not able to adjust this trade-off so that oxidative damage resulted. This might support the idea that energetically expensive growth causes resources to be diverted away from the redox system; however, antioxidants did not show an overall reduction in response to growth in the meta-analysis suggesting that oxidative costs of growth may result from increased RS production due to the greater metabolism needed for enhanced growth. My experimental data (Chapter 3) showed a similar pattern, with raised protein damage levels (protein carbonyls; PCs) in the fastest growing blue tit chicks in a brood, compared with their slower growing sibs. These within-brood differences in OS levels likely resulted from within-brood hierarchies and might have masked any between-brood differences, which were not observed here. Despite evidence for a growth – self-maintenance trade off, my experimental results on blue tits found no support for the hypothesis that self-maintenance is also traded off against reproduction, another energetically demanding trait. There was no link between experimentally altered reproductive expenditure and OS, nor was there a direct correlation between reproductive effort and OS (Chapter 5). However, there are various factors that likely influence whether oxidative costs are observed, including environmental conditions and whether such costs are transient. This emphasises the need for longitudinal studies following the same individuals over multiple years and across a wide range of habitats that differ in quality. This would allow investigation into how key life events interact; it might be that raised OS levels from rapid early growth have the potential to constrain reproduction or that high parental OS levels constrain offspring growth. Any oxidative costs resulting from these life-history trade-offs have the potential to impact on future fitness. Redox integration of certain biomarkers might prove to be a useful tool in making predictions about fitness, as I found in Chapter 2, as well as establishing how the redox system responds, as a whole, to changes to growth and reproduction. Finally, if the tissues measured can tolerate a given level of OS, then the level of oxidative damage might be irrelevant and not impact on future fitness at all.