Dr N Heslehurst
Prof J Rankin
Prof P Burton
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
There is a global obesity crisis. Prevalence has tripled over 40 years and WHO estimates that 13% of adults globally were obese in 2016, with highest prevalence among women. Globally, an estimated 41 million 0-5 year olds are overweight or obese and 340 million 5-19 year olds. Obesity prevalence is increasing in low- and middle-income countries. Almost half of overweight and obesity in 0-5 year olds is in Asia, and there was a 50% increase in Africa between 2000 and 2016 highlighting the global inequalities. Continual increasing prevalence demonstrates the limited effect of interventions to date in halting or reversing the obesity trend.
Halting childhood obesity is essential for the health of the population due to the increased risks in childhood and subsequent adulthood such as reduced lung function, hypertension, cardiovascular disease, diabetes, cancers and mental health. The cost of treating obesity and related comorbidities is estimated to be 76% higher than for patients with a recommended BMI further demonstrating the need for preventative action.
Childhood obesity prevention interventions have produced disappointing results. For many years obesity prevention interventions have targeted environmental settings, such as schools; however, we observe increasing obesity prevalence in preschool children. In recognition of the importance of early life exposures on lifelong health, there is a focus on the first 1000 days of life (conception to 2 years). Evidence on developmental origins of health and disease suggests that this is not early enough and preconception intervention is critical to end child obesity. However, there is a lack of evidence on which early life course stage will produce most beneficial prevention effects (e.g. preconception, pregnancy, post-birth) and which determinant(s) should be prioritised.
This data science PhD will use existing international data sources to examine early life exposures and the development of childhood obesity. The PhD will use machine learning to identify translational opportunities for targeted preventative interventions to address the limitations of the failing interventions to date that have had little impact on childhood obesity.
A recent systematic review investigated the association between one early life exposure (maternal pre-pregnancy BMI) and offspring obesity (PROSPERO:CRD42016035599). This identified 91 studies reporting data for 48 international cohorts. These cohorts include data on early life exposures to childhood weight status (0-18 years) and provide an existing large, international IPD data source. The datasets will be interrogated for early life course stages (preconception, pregnancy, post-birth) and multiple potential exposure variables (behavioural, socio-economic, demographic, clinical factors) in the development of childhood obesity. Using machine learning will enable the development of predictive algorithms from this complex picture to enhance our understanding of potential pathways between early life exposures and the development of childhood obesity. Participants included in existing cohorts may not have consented for their IPD to be shared. Data anonymisation is a legal mechanism that permits data sharing as retrospective consent is not practical and incurs systematic consent bias. DataSHIELD, originally developed for IPD meta-analysis for a pan-European “healthy obesity” study, is an established approach to enable secure meta-analysis without physically sharing data.
This studentship is part of the MRC Discovery Medicine North (DiMeN) partnership and is funded for 3.5 years. Including the following financial support:
Tax-free maintenance grant at the national UK Research Council rate
Full payment of tuition fees at the standard UK/EU rate
Research training support grant (RTSG)
Travel allowance for attendance at UK and international meetings
Opportunity to apply for Flexible Funds for further training and development
Please carefully read eligibility requirements and how to apply on our website, then use the link on this page to submit an application: http://www.dimen.org.uk/how-to-apply/application-overview
Stephenson J, Heslehurst N, Hall J, Schoenaker DAJM, Hutchinson J, Cade JE, Poston L, Barrett G, Crozier SR, Barker M, Kumaran K, Yajnik C, Baird J, Mishra GD. Preconception health 1 Before the beginning: nutrition and lifestyle in the preconception period and its importance for future health. The Lancet (in press)
Heslehurst N, Vieira R, Hayes L, Crowe L, Jones D, Robalino S, Slack E, Rankin J. Maternal body mass index and post-term birth: a systematic review and meta-analysis. Obesity Reviews 2017, 18:293-308
Wilson RC, Butters OW, Avraam D, Baker J, Tedds JA, Turner A, Murtagh M, Burton PR. DataSHIELD – New Directions and Dimensions. Data Science Journal 2017, 16, 21.