Heart to Predict: Advancements in early recognition of cardiovascular disease risk in people with type 2 diabetes

Type 2 diabetes (T2D) is a chronic metabolic disorder that currently affects approximately one in ten (10%) adults worldwide, and the prevalence is projected to further increase. Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in people with T2D, who have a two- to threefold increased risk compared to the general population. Over the last few decades cardiovascular risk management (CVRM) has focused on traditional and predominantly atherosclerotic cardiovascular risk factors. Its effectiveness has led to reduction in CVD subtypes with a mainly atherosclerotic origin such as coronary heart disease (CHD) and ischemic cerebrovascular accidents (CVA). Still, there remains a high or even rising prevalence and incidence of primarily the other CVD types, such as atrial fibrillation (AF), heart failure (HF), specifically with preserved ejection fraction (HFpEF), and sudden cardiac arrest (SCA). Adjuvant to periodical screening for the traditional cardiovascular risk factors, guidelines recommend the use of risk prediction models that calculate risk scores to better stratify incident CVD risk and help guide treatment decisions. However, CVD risk prediction models only preform moderately at correctly classifying people with T2D at increased risk. So in reality, it is hard to predict who will and will not get cardiovascular complications. We need to look beyond the traditional risk factors, prediction models and study designs to better understand CVD risk and differentiate between risk for CVD subtypes. Interestingly, a few guidelines mention or allude that a (periodic) resting ECG can be incrementally informative. Moreover, routine healthcare records are an interesting new source of data that might be useful for the recognition of CVD risk, particularly for the study of unexpected and hitherto less well understood CVD types such as SCA. The main aims of this thesis were to: • evaluate the use of ECG abnormalities in the assessment of incident CVD risk in people with T2D without a CVD history. • gain insight into novel risk predictors of SCA in people with T2D. • assess the usefulness of routine care data for the study of SCA and CVD at large in people with T2D. PART ONE describes aspects concerning the use of ECG abnormalities for assessing incident CVD risk in people with T2D. PART TWO describes aspects of gaining insight into novel risk predictors of SCA and the usefulness of routine primary care data for the study of SCA and CVD at large in people with T2D. In conclusion, ECG abnormalities should be part of CVD risk assessment in people with T2D, because they are relatively common including in those without CVD and independently convey a two to fourfold increased risk of cardiac events, predominantly for cardiac events that have rising incidence or high prevalence and are difficult to detect. ECG abnormalities should also be considered when using, developing or updating incident CVD risk prediction models for people with T2D. Moreover, ECG abnormalities can assist in the development of precision prediction of cardiometabolic disease for people with T2D. The findings in routine primary care data support the importance of current cardiovascular risk management in T2D for the reduction of SCA risk and accentuate that GPs should be conscious of the hazards of too strict glycaemic control and the prescription of commonly used QTc-prolonging antipsychotics, prokinetics and antibiotics. Additionally, future SCA studies should stratify on the clinically distinct groups of people with and without a CVD history. Routine primary care data is useful and holds potential for SCA and overall cardiometabolic disease research in people with T2D. Routine primary care data could well become an integral part of the computing infrastructure of the future’s evidence based precision medicine for cardiometabolic disease.