Weight, weight change and obesity have all been shown in research to be associated with an increased risk of particular diseases although scientific findings to date are conflicting. In this project, we will make use of data collected from hospitals and primary care during routine clinical care to investigate the trends of weight change, along with their relationship with a range of cardiovascular diseases (CVDs).
At first, short-, mid- and long-term weight change patterns, at different population groups over time will be calculate and potential differences will be investigated. We will also estimate the effect of different weight change patterns on cardiovascular disease and examine whether these relationships are modified at different groups of patients. A relative advantage of this study is that clinical trials cannot enroll large number of participants and follow them up for a sufficient period of time to evaluate the impact of weight loss or increase on less frequent cardiovascular diseases.
Finally, we will examine to what extend weight change can improve the predictive ability of risk scores for cardiovascular diseases
Weight change trajectories over time and the subsequent effects on cardiovascular diseases (CVDs) have not been investigated in large scale populations with contemporary levels of obesity. This study aims to fill this gap through evaluation of linked Electronic Health Records [Clinical Practice Research Datalink (CPRD), Hospital Episode Statistics (HES) and Office for National Statistics (ONS). Firstly, we will calculate the short-, mid- and long-term patterns of weight fluctuation at different population groups. Moreover, we will use marginal structural Cox models, based on the inverse probability of weighting to tackle time-dependent confounding and estimate the impact of different weight change patterns (combined with other CVD factors) with the initial onset and progression of CVD overall, as well as of different and pathologically-diverse CVDs, for which randomized controlled trials are not viable. More specifically, using the inverse probability weights, we create a pseudo-population where treatment and control patients have the same probability of being assigned to treatment, at each time point, conditional on observed data. Finally, risk prediction scores for CVDs, like for Framingham risk score and QRISK will be evaluated and subsequently updated, by including weight change into them, to check to what extend their predictive ability is improved.
myocardial infarction
- heart failure
- ischaemic/ haemorrhage/ unclassified stroke
- stable/unstable angina
- atrial fibrillation
- Subarachnoid haemorrhage
- unheralded coronary death
- sudden cardiac death
- peripheral artery disease
- coronary revascularization
- Abdominal aortic aneurysm
- coronary revascularization
- composite CVD outcome - containing all CVDs
- hypertrophic cardiomyopathy
- composite CVD outcome - containing all CVDs
- Overall mortality
- CVD mortality - Composite CVD outcome
Michail Katsoulis - Chief Investigator - Farr Institute of Health Informatics Research
Michail Katsoulis - Corresponding Applicant - Farr Institute of Health Informatics Research
Aasiyah Rashan - Collaborator - University College London ( UCL )
Amitava Banerjee - Collaborator - University College London ( UCL )
Asma Alfayez - Collaborator - University College London ( UCL )
Burcu Ozaltin - Collaborator - University College London ( UCL )
Harry Hemingway - Collaborator - University College London ( UCL )
Manuel Gomes - Collaborator - University College London ( UCL )
Miguel Hernan - Collaborator - Harvard University
Spiros Denaxas - Collaborator - University College London ( UCL )
Tianyi Liu - Collaborator - University College London ( UCL )
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation