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Bias in Associations of Emerging Biomarkers With Cardiovascular Disease
Study Questions:
Is there evidence of bias in reporting associations between biomarkers and cardiovascular disease (CVD)?
Methods:
Meta-analyses of cardiac biomarkers, independent of Framingham score, were analyzed. Authors evaluated whether large studies had significantly more conservative results than smaller studies, 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 1,000 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 CV mortality, and of non–high-density lipoprotein cholesterol, serum albumin, Chlamydia pneumoniae immunoglobulin G, glycated hemoglobin, insulin, apolipoprotein B/AI ratio, erythrocyte sedimentation rate, and lipoprotein-associated phospholipase mass or activity with coronary heart disease.
Conclusions:
The authors concluded that selective reporting biases may be common in the evidence on emerging CV biomarkers, with most of the proposed associations inflated.
Perspective:
Bias in the scientific literature toward publication of positive results is well appreciated. This ‘excess significance’ bias has been previously analyzed in relation to Alzheimer’s disease, brain volume abnormalities, gene linkage analyses, antiarrhythmic efficacy, and other conditions. The current study strongly suggests that positive publication bias is also rampant in the field of cardiac biomarkers. Large-scale studies analyzing multiple biomarkers simultaneously may be one approach to reduce this bias and put the magnitude of associations in proper context.
Keywords: Immunoglobulin G, Hemoglobin A, Coronary Artery Disease, Insulin, Bias, Lipoproteins, Creatinine, Cholesterol, Serum Albumin, Biomarkers, Troponin I, Cardiology, Cardiovascular Diseases
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