Bias in Associations of Emerging Biomarkers With Cardiovascular Disease

Importance: Numerous cardiovascular biomarkers are proposed as potential predictors of cardiovascular risk. Objective: To evaluate whether there is evidence for biases favoring statistically significant results and inflating associations in this literature. Design and Setting: PubMed search for meta-analyses of cardiovascular biomarkers that are not part of the Framingham Risk Score. Main Outcome Measures: We estimated summary effects and between-study heterogeneity (considered "very large" for I-2 > 75%). We evaluated whether large studies had significantly more conservative results than smaller studies (small-study effects) and whether there were too many studies with statistically significant results compared with what would be expected on the basis of the findings of the largest study in each meta-analysis. Results: Of 56 eligible meta-analyses, 49 had statistically significant results. Very large heterogeneity and small-study effects were seen in 9 and 13 meta-analyses, respectively. In 29 meta-analyses (52%), there was a significant excess of studies with statistically significant results. Only 13 of the statistically significant meta-analyses had more than 1000 cases and no hints of large heterogeneity, small-study effects, or excess significance. These included the associations of glomerular filtration rate and albumin to creatinine ratio in general and high-risk populations with cardiovascular disease mortality and of non-high-density lipoprotein cholesterol, serum albumin, Chlamydia pneumoniae IgG, glycosylated hemoglobin, nonfasting insulin, apolipoprotein B/AI ratio, erythrocyte sedimentation rate, and lipoprotein-associated phospholipase mass or activity with coronary heart disease. Conclusions and Relevance: Selective reporting biases may be common in the evidence on emerging cardiovascular biomarkers. Most of the proposed associations of these biomarkers may be inflated.