Biomarkers for cardiovascular risk prediction in people with type 2 diabetes
Price, Anna Helen
Introduction: Type 2 diabetes continues to be one of the most common non-communicable diseases worldwide and complications due to type 2 diabetes, such as cardiovascular disease (CVD) can cause severe disability and even death. Despite advances in the development and validation of cardiovascular risk scores, those used in clinical practice perform inadequately for people with type 2 diabetes. Research has suggested that particular non-traditional biomarkers and novel omics data may provide additional value to risk scores over-and-above traditional predictors. Aims: To determine whether a small panel of non-traditional biomarkers improve prediction models based on a current cardiovascular risk score (QRISK2), either individually or in combination, in people with type 2 diabetes. Furthermore, to investigate a set of 228 metabolites and their associations with CVD, independent of well-established cardiovascular risk factors, in order to identify potential new predictors of CVD for future research. Methods: Analyses used the Edinburgh Type 2 Diabetes Study (ET2DS), a prospective cohort of 1066 men and women with type 2 diabetes aged 60-75 years at baseline. Participants were followed for eight years, during which time 205 had a cardiovascular event. Additionally, for omics analyses, four cohorts from the UCL-LSHTM-Edinburgh-Bristol (UCLEB) consortium were combined with the ET2DS. Across all studies, 1005 (44.73%) participants had CVD at baseline or experienced a cardiovascular event during follow-up. Results: In the ET2DS, higher levels of high sensitivity cardiac troponin (hs-cTnT) and N-terminal pro-brain natriuretic peptide (NT-proBNP) and lower levels of ankle brachial pressure index (ABI) were associated with incident cardiovascular events, independent of QRISK2 and pre-existing cardiovascular disease (odds ratios per one SD increase in biomarker 1.35 (95% CI: 1.13, 1.61), 1.23 (1.02, 1.49) and 0.86 (0.73, 1.00) respectively). The addition of each biomarker to a model including just QRISK2 variables improved the c-statistic, with the biggest increase for hs-cTnT (from 0.722 (0.681, 0.763) to 0.732 (0.690, 0.774)). When multiple biomarkers were considered in combination, the greatest c-statistic was found for a model which included ABI, hs-cTnT and gamma-glutamyl transpeptidase (0.740 (0.699, 0.781)). In the combined cohorts from the UCLEB consortium, a small number of high-density lipoprotein (HDL) particles were found to be significantly associated with CVD: concentration of medium HDL particles, total lipids in medium HDL, phospholipids in medium HDL and phospholipids in small HDL. These associations persisted after adjustment for a range of traditional cardiovascular risk factors including age, sex, blood pressure, smoking and HDL to total cholesterol ratio. Conclusions: In older people with type 2 diabetes, a range of non-traditional biomarkers increased predictive ability for cardiovascular events over-and-above the commonly used QRISK2 score, and a combination of biomarkers may provide the best improvement. Furthermore, a small number of novel omics biomarkers were identified which may further improve risk scores or provide better prediction than traditional lipid measurements such as HDL cholesterol.