Long-term within-person variation of routinely measured biomarkers are associated with mortality and cardiovascular health

Biomarker measurements can assist with disease diagnosis and the assessment of disease risks, with the most recent measurements usually used by disease-risk models. However, a growing number of studies suggest that in addition to a biomarkers value, its inherent variability, estimated from several measurements over many days or years in an individual, can convey independent prognostic information about disease risks. Variance estimates require an individuals biomarker data to have been measured a sufficient number of times, ideally across a long time period, and are usually only available in a hospital setting or clinical trial. Furthermore, a single biomarker measurement will involve a combination of measurement-error, natural short-term variation over a daily time-period, variation over time periods of weeks and months, and slower age-dependent changes over several years. This paper develops a statistical method that accounts for these latter concerns, and applies it to Clinical Practice Research Datalink (CPRD) data collected by UK General Practitioners. It studies the associations between cardiovascular health outcomes and the within-person variances of eight routinely measured biomarkers. This involved Sequential Monte Carlo modeling to convert an individuals biomarker measurements (collected over months or years), into estimates for the biomarkers mean, linear age-dependent slope, within-person variance, and a variance due to variation on a daily time period or measurement errors. The result is a proof-of-principle that UK primary care Electronic Health Records (from CPRD) can be effectively used for this purpose. After adjusting for mean biomarker values, clear associations were found between mortality or cardiovascular disease risks and within-person variances for 6 of 8 biomarkers.