Worldwide phenotypes and transitions in obesity-related multimorbidity

Gaining too much weight without getting taller damages different organs and functions of human body and causes many diseases. This means that people with obesity tend to have multiple diseases at the same time. This is called multimorbidity and has negative impacts on a person's overall health and wellbeing. Obesity has gone up for years in most countries, but how much it has increased differs between different regions of the world. In some parts of the world, obesity exists side by side with being undernourished. It is also believed that some people are more likely to gain weight in the form of fat that deposits in the abdomen compared to other parts of the body. Also, by how much those affected by obesity also experience other harmful social and lifestyle factors, like smoking, is different across the world. For these reasons, we expect that multimorbidity in people with obesity is a complex phenomenon that is different in different regions of the world. But there is very limited information on multimorbidity in people affected by obesity in terms of what combination of diseases they have and how common each combination is. This is especially the case in lower income countries.

This research study will use a large collection of data that have measured weight and height in tens of millions of people from different countries. The same data also have information about different diseases that people have or have had in the past, behaviours like smoking and alcohol use, and measures like blood pressure and cholesterol. The data do not have any information that can identify individual persons but can tell us what is happening in whole countries. The data have been brought together by hundreds of researchers, doctors and public health experts from around the world.

Our research team will use advanced methods to identify people who have similar set of diseases and measure how common each group (or cluster) of people is. For example, some people with obesity may have a combination of diabetes and kidney disease, and have had a stroke in the past. Another group may have liver disease, diabetes and back pain, and so on. We will also study how much differences in multimorbidity in people who have similar weight for their height is due factors like their smoking, alcohol use, blood pressure or cholesterol. Through this information, we can better use our budgets for providing optimal health care that will benefit people in each country, and to understand how obesity affects multiple diseases so that we can improve disease prevention. This work involves collaboration between researchers in the UK and in other regions of the world so that by the time it is completed, we will each be able to do studies like this in our own region and compare results to learn about the best ways to prevent and treat disease, and improve people's health.

Obesity and multimorbidity are key challenges to population health in the UK and around the world. Obesity has increased in most countries but there is significant variation in the prevalence of obesity; distribution among class I, II and II obesity; body fat distribution; and whether obesity occurs alongside undernutrition and other risk factors such as tobacco use and high blood pressure and cholesterol. The overall aim of our interdisciplinary programme is to generate the evidence that will help select and prioritize interventions for prevention and treatment of the health burden of obesity throughout the world. To achieve this aim, in four integrated work packages, we will characterise and quantify key clusters of multimorbidity in those who are obese in comparison with those who are under-, normal- or over-weight in different world regions; estimate changes in the diseases contained in obesity-related multimorbidity clusters and in the prevalence of the clusters since 1990; quantify the role of individual demographic, behavioural, physiological and health care characteristics in variations in obesity-related multimorbidity; and establish an interdisciplinary UK-LMIC network for research on obesity and multimorbidity, and translation into clinical practice and policy. Our data will be from the largest global collection of population-based studies, collated through the NCD Risk Factor Collaboration. We will use statistical machine learning methods for probabilistic clustering of participants based on multiple morbidities, and for understanding the role of individual traits in variations in cluster allocation. Our programme will present a comprehensive view of the phenotypes and transitions in obesity-related multimorbidity in diverse populations, significantly advance global research on obesity from a focus on measurement to characterisation of multifaceted impacts on population health, and guide clinical and public health decisions.