Strong effects of genetic and lifestyle factors on biomarker variation and use of personalized cutoffs.

Stefan Enroth,Sofia Bosdotter Enroth,Åsa Johansson,Ulf Gyllensten

doi:10.1038/ncomms5684

Stefan Enroth, Sofia Bosdotter Enroth + Show 2 more

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https://doi.org/10.1038/ncomms5684

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Abstract

Ideal biomarkers used for disease diagnosis should display deviating levels in affected individuals only and be robust to factors unrelated to the disease. Here we show the impact of genetic, clinical and lifestyle factors on circulating levels of 92 protein biomarkers for cancer and inflammation, using a population-based cohort of 1,005 individuals. For 75% of the biomarkers, the levels are significantly heritable and genome-wide association studies identifies 16 novel loci and replicate 2 previously known loci with strong effects on one or several of the biomarkers with P-values down to 4.4 × 10−58. Integrative analysis attributes as much as 56.3% of the observed variance to non-disease factors. We propose that information on the biomarker-specific profile of major genetic, clinical and lifestyle factors should be used to establish personalized clinical cutoffs, and that this would increase the sensitivity of using biomarkers for prediction of clinical end points.

Highlights

Ideal biomarkers used for disease diagnosis should display deviating levels in affected individuals only and be robust to factors unrelated to the disease
The biomarkers we analyse here constitute a research panel directed against multiple cancers and contain proteins implicated in autoimmune diseases such as rheumatoid arthritis (RA) and Graves’ disease
We first determine the effect of a wide range of clinical variables and lifestyle factors, including age, sex, blood pressure, blood group or body mass index (BMI), medication and smoking, on biomarker levels

Summary

Introduction

Ideal biomarkers used for disease diagnosis should display deviating levels in affected individuals only and be robust to factors unrelated to the disease. We aim to understand the factors that influence normal variation in plasma levels of established and potential biomarkers for cancer, autoimmune diseases and inflammation with the specific goal to facilitate the establishment of individualized clinical cutoffs. To this end, we use the highly sensitive and specific proximity extension assay (PEA)[9] to estimate the abundance of 92 established or potential biomarkers in plasma from 1,005 individuals from a longitudinal cross-sectional population-based study in Sweden. By integration of genetic, clinical and lifestyle data, we identify the set of biomarker-specific factors that can be used to determine appropriate individual clinical cutoffs, and thereby enable a more efficient use of each biomarker in personalized cancer management

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