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

Jani, Bhautesh Dinesh (2016) Exploring the potential role of allostatic load biomarkers in risk assessment of patients presenting with depressive symptoms. PhD thesis, University of Glasgow.

Abstract

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.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Allostatic load, cardiometabolic disease, depression screenin, cardiovascular outcomes.
• Subjects: R Medicine > RA Public aspects of medicine; R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
• Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > General Practice and Primary Care
• Funder's Name: Scottish Executive Health Department (SEHHD-CSO)
• Supervisor's Name: Mair, Prof. Frances, Cavanagh, Prof. Jonathan, Barry, Dr. Sarah and Sattar, Prof. Naveed
• Date of Award: 2016
• Depositing User: Dr Bhautesh Jani
• Unique ID: glathesis:2016-7658
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 19 Oct 2016 12:15
• Last Modified: 14 Nov 2016 11:22
• URI: https://theses.gla.ac.uk/id/eprint/7658