DNA methylation signatures of impaired respiratory function aids prediction of major depressive disorder in chronic obstructive pulmonary disease

Mairead Bermingham, Kathryn Evans, David Porteous, Ian Deary, Caroline Hayward, Rosie Walker, Riccardo Marioni, Stewart Morris, Konrad Rawlik, Yanni Zeng, Archibald Campbell, Paul Redmond, Heather Sibley, Mark James Adams

Research output: Contribution to conferencePosterpeer-review

Abstract / Description of output

Major depressive disorder (MDD) is a common comorbidity in chronic obstructive pulmonary disease (COPD) but is often undiagnosed. Current diagnostic tools use somatic symptoms, which overlap with symptoms of COPD. The identification of biomarkers is therefore of great importance to aid the diagnosis of MDD in COPD. DNA methylation profiling has allowed for the development of molecular predictors for the early diagnosis of many diseases. We hypothesised that differential methylation could underlie comorbid MDD in COPD. Here, we evaluated the predictive value of differentially methylated sites associated with respiratory function and COPD in the classification of comorbid MDD in COPD. DNA methylation was profiled using the 850K Illumina EPIC array. We performed epigenome-wide association studies of respiratory function (3,364 participants) and COPD (73 cases; 2,738 controls) in peripheral blood samples from the Generation Scotland: Scottish Family Health Study cohort (GS: SFHS). In independent COPD case data (56 MDD cases; 56 controls), significantly differentially methylated sites (p<3.6×10−8) associated with respiratory function and COPD were evaluated for their added power to predict MDD in COPD to a model including the variables, age, sex and smoking history using receiver operating characteristic analysis. We identified 16 respiratory function and/or COPD associated differentially methylated sites. The final model included 13 the differentially methylated sites in addition to ever smoker and pack years of smoking with an area under the curve (AUC) of 0.62 (95% CI 0.439, 0.801), an increase compared to an AUC of 0.59 (95% CI 0.403, 0.767) for a model with age, sex and smoking history alone. This model may be of value in predicting comorbid MDD.
Original languageEnglish
Publication statusPublished - 12 Mar 2019
EventAnnual Molecular Epidemiology Group UK (MEG) Meeting : Prediction & Causal Pathways Towards Disease – What Can Molecular Markers Tell Us? - Engine Shed, Bristol, United Kingdom
Duration: 12 Mar 201912 Mar 2019
https://www.meg-uk.org/bristol2019

Conference

ConferenceAnnual Molecular Epidemiology Group UK (MEG) Meeting
Country/TerritoryUnited Kingdom
CityBristol
Period12/03/1912/03/19
Internet address

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