Glycaemia and cause-specific mortality among Mexican adults- causal relevance and mechanistic insights

Diabetes is highly prevalent in Mexico and typically associated with much higher mortality risks (e.g. from renal, infectious and cardiovascular diseases) than in many other populations. Notably high levels of glycaemia among individuals with diabetes in Mexico likely contribute to these observed extreme mortality risks. However, the causal relevance of glycaemia for some diseases (e.g. infections) is uncertain. Moreover, less extreme risks are seen in other populations with similarly suboptimal diabetes management, possibly reflecting population differences in the relevance of glycaemia, or the influence of other factors.  

The role of glycaemia in influencing disease risks in individuals without diabetes is similarly unclear. Studies have shown continuous positive associations throughout the full—including normoglycaemic—range with risk of some (e.g. cardiovascular) diseases. However, findings are mixed and relationships with non-vascular diseases are less clear. Moreover, there is limited large-scale prospective evidence from Latin American populations, and none examining the causal relevance of glycaemia.  

This project seeks to address these evidence gaps using data from 150,000 Mexican adults aged ≥35 years in the Mexico City Prospective Study (MCPS). At recruitment in 1998-2004 from two areas of Mexico City, participants completed questionnaires, physical measurements were taken, and blood samples were collected. Genome-wide genotype data, plasma NMR metabolomic profiling data and HbA1c levels are available for all participants. Follow-up is on-going for cause-specific mortality. 

Using these data, the project aims to characterise the associations of measured HbA1c levels with risk of all-cause and cause-specific mortality, among individuals both with and without diabetes. Mendelian randomisation approaches will subsequently be used to understand the likely causality of observed associations. Further analyses, employing data on plasma metabolomics and other major risk factors (e.g. blood pressure) will seek to understand underlying mechanisms.