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Studies examining risk factors for vascular disease can improve our understanding of the causes of disease and facilitate the development of future therapies. Lipoprotein(a) [Lp(a)] levels are mainly genetically determined and recent genetic studies have demonstrated strong support for Lp(a) as a causal risk factor for coronary heart disease. Lp(a) levels are altered by cardiovascular therapies currently under development but the impact of this and the mechanism by which Lp(a) affects disease risk remains unclear. Kidney impairment is an established risk factor for vascular disease and there is also evidence to suggest that kidney function is associated with Lp(a) levels. Furthermore, elevations in Lp(a) levels in patients with poor kidney function appear to be dependent on genetic factors but previous studies have shown conflicting results. Therefore there remains uncertainty about the relationship between Lp(a), kidney function and vascular risk.

This project will enable you to explore the complex epidemiological inter-relationships as well as genetic determinants of Lp(a) levels in relation to kidney disease phenotypes and biomarkers (such as cystatin C and estimated glomerular filtration rate), and their relevance for risk of vascular disease. The project offers the unique opportunity to work with well characterised phenotype and genotype data from the HPS, SHARP and PROCARDIS studies, with many of the studies having multiple biomarker measurements of kidney function and large numbers of vascular events. In addition, you will work with data from UK Biobank, a prospective study involving 500,000 individuals, which provides extensive data on individual characteristics, renal biomarkers, genetic variants and disease outcomes.

Relevant references

  • UK Biobank‎
  • Kronenberg F et al.  Lipoprotein(a) serum concentrations and apolipoprotein(a) phenotypes in mild and moderate renal failure. J Am Soc Nephrol 2000; 11: 105–15.
  • Hopewell JC et al. Impact of Lp(a) levels and apolipoprotein(a) isoform size on risk of coronary heart disease. Journal of Internal Medicine. 2014; 276(3):260-8.  
  • Kronenberg F, Utermann G. Lipoprotein(a): resurrected by genetics. Journal of Internal Medicine. 2013; 273(1):6-30.

Research Experience, Research Methods and Training

You will gain experience of statistical and genetic epidemiology and handling large datasets, learn about renal disease biomarkers and progression, and develop research planning and design skills. You will learn from an experienced multi-disciplinary team of statisticians, clinicians, statistical geneticists and programmers.

Field Work, Secondments, Industry Placements and Training

Additional training in statistical programming and cardiovascular epidemiology will be provided. The successful applicant will have opportunities to attend and present work at relevant meetings.

Prospective Candidate

The project involves statistical analysis of big data, including electronic records and genetic data, and requires previous statistical programming training/experience (e.g. R, SAS) and an interest in developing these skills. Examples of desirable prior qualifications are an MMath or MSc in Medical/Applied Statistics. An understanding of genetic epidemiology would also be advantageous.