Toward new biomarkers of cardiometabolic diseases

Abstract

Cardiometabolic diseases can be present long before becoming clinically apparent. Accurate predictors of disease are of particular importance since the delay or prevention of morbidity is possible via pharmacological and behavioral interventions. Metabolomics is increasingly applied to biomarker discovery. Understanding how metabolites relate with established cardiometabolic risk factors is critical in evaluating their potential value as clinical biomarkers. Large epidemiological cohort studies can assess whether metabolite biomarkers improve upon existing disease markers. Furthermore, experimental work in model systems and integration with other functional genomic approaches will facilitate the discovery of causal links between select biomarkers and disease pathogenesis.

Figure 1

Increment in discrimination from adding hypothetical biomarkers, according to the degree of marker-marker correlation (r). The simulated hazards ratio for the outcome is 1.35 per SD increment in the biomarker. The y axis shows the C statistic from a model containing traditional risk factors plus a variable number of simulated biomarkers (x axis), each with a fixed association with the outcome.

Figure 2

Intercorrrelation of circulating metabolites in 200 normal individuals from the Framingham Heart Study.