Worldwide phenotypes of multiple cardiometabolic risk factors

Non-communicable diseases (NCDs), including chronic respiratory disease, cancer,
cardiovascular diseases (CVDs) and Diabetes Mellitus Type II (hereafter referred as
diabetes), currently constitute one of the largest causes of death, disease burden and
healthcare cost worldwide. CVDs and diabetes are responsible alone for 19 million deaths
worldwide each year (Tzoulaki et al., 2016). Asian and Western high-income countries
(HICs) have reliable historical data on mortality and medical causes of death, reporting that
age-standardised death rates from CVDs have consistently declined for decades (Ezzati et al.,
2015). In contrast, CVD and diabetes burden in low- and middle-income countries (LMICs)
has been increasing, now accounting for three quarters of global CVD deaths and one third of
LMIC deaths (Di Cesare et al., 2013). Considering population growth and ageing, and
epidemiological transition, CVD and diabetes deaths are expected to continue rising in
LMICs as well as increasing the absolute burden in HICs. Furthermore, although CVD rates
in HIC have declined, they remain a leading cause of death, with diabetes mortality steadily
increasing (Kontis et al., 2014). Consequently, a United Nations High-Level Meeting on
Prevention and Control of NCDs established as an imperative need to reduce the burden of
NCDs across countries of all incomes, by reducing exposure to preventable risk factors
(WHO, 2016).

This PhD thesis aims at generating and applying the data and analytical tools to guide the
choice and integration of population-based and personal interventions for reducing
worldwide multiple cardiometabolic risk factors for CVD. The objectives of my thesis are:

Objective 1. To systematically update and expand the NCD Risk Factor Collaboration
(NCD-RisC) database of worldwide population-based data to allow combined analysis of
multiple cardiometabolic risk factors including weight, height, blood pressure and lipids, and
diabetes.

Objective 2. To develop a statistical model for estimating the joint distribution of
multiple cardiometabolic risk factors.

Objective 3. To apply the model of Objective 2 to estimate the joint distribution of
multiple cardiometabolic risk factors for every country in the world from 1990 to 2018.

Objective 4. To identify distinguishable phenotypes of exposure to multiple risk
factor, and estimate the frequency with which they occur across geographical regions and
over time.