Approved Research

Use of protein biomarkers to estimate the Healthy Lifestyle Index and morbidity and mortality risk (Submitted on behalf of Health Outlook Corp.)

Lay summary

Both genetic and lifestyle factors affect an individual's risk of developing life-threatening illnesses such as cardiovascular disease and cancer. To study the relationship between lifestyle and disease, scientists have developed a score called the Healthy Lifestyle Index (HLI), based on diet quality, alcohol consumption, smoking, physical activity, and body mass index or waist circumference. Consistent with the importance of lifestyle being a determinant of disease risk, a high HLI score has been associated with reduced risk of various cancers and cardiovascular disease. However, a significant limitation of the HLI which has prevented it from being used outside the research setting is the time taken to collect the requisite information. Assessing factors such as diet quality and physical activity requires significant time and effort, making this impractical outside of a research setting. A test based on age, sex and blood biomarkers that mirrored the HLI score would be potentially transformative.

There is growing evidence that signatures based on blood levels of proteins (proteomics) reflect underlying disease and mortality risk. Here we posit that it will be possible to develop a practical test that includes multiple blood proteins that correlates with the HLI score. Indeed, we already know of protein biomarkers in blood that reflect components of the HLI. For example, increased waist size is associated with elevated levels of C-reactive protein, a marker of inflammation. Currently, a company called Olink is carrying out an analysis of approximately 3,000 proteins in blood from about 57,000 UK Biobank participants. Once these data become available, we propose to undertake analyses to test our hypothesis. Specifically, we will build and verify a statistical model that includes basic demographic variables (age, sex) as well as blood protein markers to predict the HLI score. We will then use the scores derived from the model to examine their association with risk of cardiovascular disease, selected cancers, and death. Further, we will compare the performance of this model for predicting mortality risk with that of a more comprehensive model of mortality risk that includes classical mortality risk factors (e.g., age, sex, body mass index, cigarette smoking history, etc.) and blood biomarkers.

Potentially, the project described here might lead to the development of a pragmatic HLI test that could be used to personalize healthcare recommendations or to monitor the efficacy of interventions designed to improve the health of a work force or population.