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.

Climate variability of the last 1000 years in the NW Pacific: high resolution, multi-biomarker records from Lake Toyoni

The East Asian Monsoon (EAM) is an active component of the global climate system and has a profound social and economic impact in East Asia and its surrounding countries. Its impact on regional hydrological processes may influence society through industrial water supplies, food productivity and energy use. In order to predict future rates of climate change, reliable and accurate reconstructions of regional temperature and rainfall are required from all over the world to test climate models and better predict future climate variability. Hokkaido is a region which has limited palaeo-climate data and is sensitive to climate change. Instrumental data show that the climate in Hokkaido is influenced by the East Asian Monsoon (EAM), however, instrumental data is limited to the past ~150 years. Therefore down-core climate reconstructions, prior to instrumental records, are required to provide a better understanding of the long-term behaviour of the climate drivers (e.g. the EAM, Westerlies, and teleconnections) in this region. The present study develops multi-proxy reconstructions to determine past climatic and hydrologic variability in Japan over the past 1000 years and aid in understanding the effects of the EAM and the Westerlies independently and interactively. A 250-cm long sediment core from Lake Toyoni, Hokkaido was retrieved to investigate terrestrial and aquatic input, lake temperature and hydrological changes over the past 1000-years within Lake Toyoni and its catchment using X-Ray Fluorescence (XRF) data, alkenone palaeothermometry, the molecular and hydrogen isotopic composition of higher plant waxes (δD(HPW)). Here, we conducted the first survey for alkenone biomarkers in eight lakes in the Hokkaido, Japan. We detected the occurrence of alkenones within the sediments of Lake Toyoni. We present the first lacustrine alkenone record from Japan, including genetic analysis of the alkenone producer. C37 alkenone concentrations in surface sediments are 18µg C37 g−1 of dry sediment and the dominant alkenone is C37:4. 18S rDNA analysis revealed the presence of a single alkenone producer in Lake Toyoni and thus a single calibration is used for reconstructing lake temperature based on alkenone unsaturation patterns. Temperature reconstructions over the past 1000 years suggest that lake water temperatures varies between 8 and 19°C which is in line with water temperature changes observed in the modern Lake Toyoni. The alkenone-based temperature reconstruction provides evidence for the variability of the EAM over the past 1000 years. The δD(HPW) suggest that the large fluctuations (∼40‰) represent changes in temperature and source precipitation in this region, which is ultimately controlled by the EAM system and therefore a proxy for the EAM system. In order to complement the biomarker reconstructions, the XRF data strengthen the lake temperature and hydrological reconstructions by providing information on past productivity, which is controlled by the East Asian Summer monsoon (EASM) and wind input into Lake Toyoni, which is controlled by the East Asian Winter Monsoon (EAWM) and the Westerlies. By combining the data generated from XRF, alkenone palaeothermometry and the δD(HPW) reconstructions, we provide valuable information on the EAM and the Westerlies, including; the timing of intensification and weakening, the teleconnections influencing them and the relationship between them. During the Medieval Warm Period (MWP), we find that the EASM dominated and the EAWM was suppressed, whereas, during the Little Ice Age (LIA), the influence of the EAWM dominated with time periods of increased EASM and Westerlies intensification. The El Niño Southern Oscillation (ENSO) significantly influenced the EAM; a strong EASM occurred during El Niño conditions and a strong EAWM occurred during La Niña. The North Atlantic Oscillation, on the other hand, was a key driver of the Westerlies intensification; strengthening of the Westerlies during a positive NAO phase and weakening of the Westerlies during a negative NAO phase. A key finding from this study is that our data support an anti-phase relationship between the EASM and the EAWM (e.g. the intensification of the EASM and weakening of the EAWM and vice versa) and that the EAWM and the Westerlies vary independently from each other, rather than coincide as previously suggested in other studies.