Adiposity associated mechanisms for specific diseases in Mexican adults

BACKGROUND

Obesity affects millions of adults worldwide and the rate of mortality attributed to obesity has been increasing in many countries. Obesity and premature mortality are common in Mexico, but mechanistic studies through which excess fat or various adipose markers increase the risk from different diseases in Hispanic populations are scarce. 

The Mexico City Prospective Study (MCPS) is a prospective cohort study of 150,000 participants with socio-demographic and lifestyle characteristics, medication and disease history, biological characteristics (including genetics and NMR-metabolomics), 20 years of follow-up for cause-specific mortality, and accruing morbidity data. This project aims to systematically assess metabolomic mediators (~160 individual biomarkers) that may explain causal associations of various adiposity markers with specific causes of death in MCPS. 

The specific aims of the project may include: 

1. Mendelian randomisation approaches to assess causality of associations between different adipose traits and specific non-fatal or fatal diseases.
2. Mediation analyses to investigate how different genetically determined levels of adiposity and circulating biomarkers are linked with risk and mechanisms for specific diseases and causes of mortality.
3. Hyper-dimensional genetic colocalization of common traits, decomposition and tissue-enrichment assessments.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will gain experience in research methodology, genetic epidemiology systematic reviews, statistical programming, data analysis and scientific writing.

The student will be supported to present findings at symposiums and conferences and to publish peer-reviewed papers emerging from their DPhil.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The project will be based within the MCPS group at the Big Data Institute, a world-class community for population health research. In-house training in statistical and epidemiological methods, programming, and scientific writing will be provided. 

PROSPECTIVE  STUDENT

The ideal candidate will have a good first degree and MSc in statistics, epidemiology, genetics, biomedical sciences or a related subject, and proficiency with programing analyses in R, Python, STATA or SAS.