Diabetes and cardiovascular disease: Risk factors and individualized prevention

Abstract

Cardiovascular disease (CVD) and diabetes, both type 1 and type 2, are closely related. Due to an aging population and lifestyle changes, the prevalence of CVD and type 2 diabetes (T2D) is increasing. Pharmacological treatments aimed at reducing the risk of these conditions are associated with costs and risks of side effects. Moreover, an increase in prescribed medications may result in decreased adherence among patients. It is therefore important to identify patients at increased risk and those who would benefit most from treatment.

Part 1 of this thesis focuses on the risk of CVD and the factors influencing this risk in individuals with type 1 diabetes (T1D). The aim is to tailor cardiovascular risk management to the individual patient with T1D by evaluating heterogeneity and cardiovascular risk within this patient group. Factors like age, cholesterol levels, and blood pressure are known to increase the risk of CVD in individuals with T1D. A less well-known risk factor is insulin resistance. This condition, often linked with T2D, is increasingly seen in T1D patients, primarily due to rising obesity levels. We showed that a higher level of insulin resistance is related to an increased risk of CVD in individuals with T1D. Identifying individuals with T1D who are more insulin-resistant is important, as these individuals often require more insulin. We also describe the development and validation of the LIFE-T1D model, designed to predict an individual’s lifetime risk of CVD. This model, based on data from over 43,000 individuals across Sweden, Denmark, and the UK, helps clinicians estimate the benefits of specific treatments for individual patients, thus enabling more efficient medication use and promoting shared decision-making.

Part 2 focuses on the long-term complications arising from adipose tissue dysfunction in those at increased risk of CVD. The CVD2DM model was introduced, which predicts the 10-year and lifetime risk of T2D in individuals with a history of CVD. This model could motivate patients towards healthier lifestyles and assist in the early detection of T2D among those previously affected by CVD. Additionally, we investigated the relationship between adipose tissue dysfunction and the risk of cancer, finding that in individuals without T2D, both increased adipose tissue and its dysfunction are linked to a higher cancer risk. The findings of this study underline the importance of obesity prevention.

In conclusion, CVD and diabetes are closely related and constitute a significant global health problem. Estimating the risk of these conditions, for instance using the models described in this thesis, ensures that patients are better informed. Additionally, it enables us to tailor treatments more closely to individual patients, promoting shared decision-making. With the availability of larger datasets following patients over longer periods, combined with advancements in methodological approaches, the accuracy and clinical applicability of such models are expected to improve further in the future.