Politics and protestant Lordship in North East Scotland during the reign of James VI: The life of George Keith, fourth Earl Marischal, 1554-1623

George Keith, fourth Earl Marischal is a case study of long-term, quietly successful and stable lordship through the reign of James VI. Marischal’s life provides a wholly underrepresented perspective on this era, where the study of rebellious and notorious characters has dominated. He is also a counter-example to the notion of a general crisis among the European nobility, at least in the Scottish context, as well as to the notion of a ‘conservative’ or ‘Catholic’ north east. In 1580 George inherited the richest earldom in Scotland, with a geographical extent stretching along the east coast from Caithness to East Lothian. His family came to be this wealthy as a long term consequence of the Battle of Flodden (1513) where a branch of the family, the Inverugie Keiths had been killed. The heiress of this branch was married to the third earl and this had concentrated a large number of lands, and consequently wealth, in the hands of the earls. This had, however, also significantly decreased the number of members and hence power of the Keith kindred. The third earl’s conversion to Protestantism in 1544 and later his adherence to the King’s Party during the Marian Civil War forced the Keiths into direct confrontation with their neighbours in the north east, the Gordons (led by the Earls of Huntly), a Catholic family and supporters of the Queen’s Party. Although this feud was settled for a time at the end of the war, the political turmoil caused by a succession of short-lived factional regimes in the early part of the personal reign of James VI (c.1578-1585) led the new (fourth) Earl Marischal into direct confrontation with the new (sixth) Earl of Huntly. Marischal was outclassed, outmanoeuvred and outgunned at both court and in the locality in this feud, suffering considerably. However, Huntly’s over-ambition in wider court politics meant that Marischal was able to join various coalitions against his rival, until Huntly was exiled in 1595. Marischal also came into conflict briefly with Chancellor John Maitland of Thirlestane as a consequence of Marischal’s diplomatic mission to Denmark in 1589-1590, but was again outmatched politically and briefly imprisoned. Both of these feuds reveal Marischal to be relatively cautious and reactionary, and both reveal the limitations of his power. Elsewhere, the study of Marischal’s activities in the centre of Scottish politics reveal him to be unambitious. He was ready to serve King James, the two men having a healthy working relationship, but Marischal showed no ambition as a courtier, to woo the king’s favour or patronage, instead delegating interaction with the monarch to his kinsmen. Likewise, in government, Marischal rarely attended any of the committees he was entitled to attend, such as the Privy Council, although he did keep a keen eye on the land market and the business conducted under the Great Seal. Although personally devout and a committed Protestant, the study of Marischal’s interaction with the national Kirk and the parishes of which he was patron reveal that he was at times a negligent patron and exercised his right of ministerial presentation as lordly, not godly patronage. The notion of a ‘conservative North East’ is, however, rejected. Where Marischal was politically weak at court and weak in terms of force in the locality, we see him pursuing sideways approaches to dealing with this. Thus he was keen to build up his general influence in the north and in particular with the burgh of Aberdeen (one result of this being the creation of Marischal College in 1593), pursued disputes through increasing use of legal methods rather than bloodfeud (thus exploiting his wealth and compensating for his relative lack of force) and developed a sophisticated system of maritime infrastructure, ultimately expressed through the creating of the burghs of Peterhead and Stonehaven. Although his close family caused him a number of problems over his lifetime, he was able to pass on a stable and enlarged lordship to his son in 1623.

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.