Adolescent and adulthood BMI And Cancer risk using obese-year metrics: the ABACus 2 project
In ABACus 2, we will focus on establishing how the association between body fatness and cancer changes throughout the life-course, thereby identifying whether targeted intervention through easy modifications at relevant and critical time points in the life course can minimise the body fatness related cancer risk. Using methodology comparable to tobacco smoking pack-years, we will determine individual obese-year metrics that takes into account both the intensity and the duration of body fatness and relate this to cancer risk. Landmark analysis and other longitudinal methodologies will be used to understand the critical age periods across the life-course. In epidemiology literature, primary focus in most studies is on body fatness exposure in adulthood. Limited research is available on body fatness exposure in childhood possibly due to the lack of availability of repeated BMI measures due to the need for repeated consent. With an increasing prevalence of body fatness in children alongside study observations on the contributions to the body fatness related cancer risk (3,4), it is fundamental to research body fatness exposure in childhood. To understand exposure to body fatness earlier on in the life-course, we will use novel methods to backward extrapolate body fatness exposure using child growth curve algorithms. More specific quantification of the exposure to body fatness and understanding of critical age periods, where the obesity associated cancer risk is stronger, will better inform of underlying mechanisms associated with the obesity-cancer link. The ABACus 2 project will help optimise targeted prevention strategies, establish changes in public health policy and enable translation to clinical practice.
Using the ABACus2 consortium will undertake the following four workstreams (WS):
WS 1: Determine individual obese-year metrics, for instance, duration of obesity, level of obesity, obese-years and cumulative obese-years using repeated BMI measurements and develop models including an IPD meta-analysis to determine a causal effect to the cancer risk.
WS 2: To help develop targeted prevention strategies, using the models used in WS 1, we will explore the impact of effect modifiers such as gender, smoking and Hormone Replacement Therapy (HRT) use;
WS 3: Assess for critical age periods – for example, early adulthood or perimenopausal, using landmark analysis and other novel approaches.
Nadin Hawwash, MB-PhD student, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
Dr Glen Martin. Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
Dr Matthew Sperrin. Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
Professor Andrew Renehan. Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
Dr Melina Arnold. International Agency for Research on Cancer.
Dr Isabelle Soerjomataram. International Agency for Research on Cancer.
Dr Michael Cook. Division of Cancer, Epidemiology and Genetics, National Cancer Institute, USA
Dr Linda Liao. Division of Cancer Epidemiology and Genetics, National Cancer Institute