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

RA Public aspects of medicine;RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry

Jani, Bhautesh Dinesh ; Scottish Executive Health Department (SEHHD-CSO) ; Mair, Frances

University:University of Glasgow

Department:Institute of Health and Wellbeing

关键词: Allostatic load, cardiometabolic disease, depression screenin, cardiovascular outcomes.;   

Others  :  http://theses.gla.ac.uk/7658/1/2016janiphd.pdf

来源: University of Glasgow

【 摘 要 】

Background:Depression is a major health problem worldwide and the majority of patientspresenting with depressive symptoms are managed in primary care. Currentapproaches for assessing depressive symptoms in primary care are not accuratein predicting future clinical outcomes, which may potentially lead to over orunder treatment. The Allostatic Load (AL) theory suggests that by measuringmulti-system biomarker levels as a proxy of measuring multi-system physiologicaldysregulation, it is possible to identify individuals at risk of having adversehealth outcomes at a prodromal stage. Allostatic Index (AI) score, calculated byapplying statistical formulations to different multi-system biomarkers, havebeen associated with depressive symptoms.Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers willform a predictive algorithm in defining clinically meaningful outcomes in apopulation of patients presenting with depressive symptoms.The key objectives were:1. To explore the relationship between various allostatic load biomarkers andprevalence of depressive symptoms in patients, especially in patients diagnosedwith three common cardiometabolic diseases (Coronary Heart Disease (CHD),Diabetes and Stroke).2 To explore whether allostatic load biomarkers predict clinical outcomes inpatients with depressive symptoms, especially in patients with three commoncardiometabolic diseases (CHD, Diabetes and Stroke).3 To develop a predictive tool to identify individuals with depressive symptomsat highest risk of adverse clinical outcomes.Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existingcardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’was a research data source containing health related information from 666participants recruited from the general population. The clinical outcomes for3both datasets were studied using electronic data linkage to hospital andmortality health records, undertaken by Information Services Division, Scotland.Cross-sectional associations between allostatic load biomarkers calculated atbaseline, with clinical severity of depression assessed by a symptom score, wereassessed using logistic and linear regression models in both datasets. Cox’sproportional hazards survival analysis models were used to assess therelationship of allostatic load biomarkers at baseline and the risk of adversephysical health outcomes at follow-up, in patients with depressive symptoms.The possibility of interaction between depressive symptoms and allostatic loadbiomarkers in risk prediction of adverse clinical outcomes was studied using theanalysis of variance (ANOVA) test. Finally, the value of constructing a riskscoring scale using patient demographics and allostatic load biomarkers forpredicting adverse outcomes in depressed patients was investigated usingclinical risk prediction modelling and Area Under Curve (AUC) statistics.Key Results:Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkerswere statistically significant in predicting six different clinical outcomes inparticipants with depressive symptoms. Outcomes related to both mental health(depressive symptoms) and physical health were statistically associated withpre-treatment levels of peripheral biomarkers; however only two studiesinvestigated outcomes related to physical health.Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainlycardiometabolic) were found to have a non-linear association with increasedprobability of co-morbid depressive symptoms (as assessed by Hospital Anxietyand Depression Score HADS-D≥8). A composite AI score constructed using fivebiomarkers did not lead to any improvement in the observed strength of theassociation. In Psobid, BMI was found to have a significant cross-sectionalassociation with the probability of depressive symptoms (assessed by GeneralHealth Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive4protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have asignificant cross-sectional relationship with the continuous measure of GHQ-28.A composite AI score constructed using 12 biomarkers did not show a significantassociation with depressive symptoms among Psobid participants.Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-causedeath, all-cause hospital admissions and composite major adverse cardiovascularoutcome-MACE (cardiovascular death or admission due to MI/stroke/HF).Presence of depressive symptoms and composite AI score calculated using mainlyperipheral cardiometabolic biomarkers was found to have a significantassociation with all three clinical outcomes over the following four years inDepChron patients. There was no evidence of an interaction between AI scoreand presence of depressive symptoms in risk prediction of any of the threeclinical outcomes. There was a statistically significant interaction notedbetween SBP and depressive symptoms in risk prediction of major adversecardiovascular outcome, and also between HbA1c and depressive symptoms inrisk prediction of all-cause mortality for patients with diabetes. In Psobid,depressive symptoms (assessed by GHQ-28≥5) did not have a statisticallysignificant 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 ofMACE at seven years, after adjusting for confounders. A continuous measure ofIL-6 observed at baseline had a significant association with the risk of threeclinical outcomes- all-cause mortality, all-cause hospital admissions and majoradverse cardiovascular event. Raised total cholesterol at baseline was associatedwith 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 amongPsobid participants. There was no significant interaction between depressivesymptoms and peripheral biomarkers (individual or combined) in risk predictionof any of the four clinical outcomes under consideration.Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eightbaseline demographic and clinical variables to predict the risk of MACE over fouryears. The AUC value for the risk scoring system was modest at 56.7% (95% CI55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to thelow event rate observed for the clinical outcomes.Conclusion: Individual peripheral biomarkers were found to have a cross-sectional associationwith depressive symptoms both in patients with cardiometabolic disease andmiddle-aged participants recruited from the general population. AI scorecalculated with different statistical formulations was of no greater benefit inpredicting 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 predictingcardiovascular events in patients with cardiometabolic disease; HbA1c had asignificant interaction with depressive symptoms in predicting all-causemortality in patients with diabetes. Peripheral biomarkers may have a role inpredicting clinical outcomes in patients with depressive symptoms, especially forthose with existing cardiometabolic disease, and this merits furtherinvestigation.