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Obesity is a major risk factor for myocardial infarction (MI). However, how to measure whole-risk with simple baseline characteristics? Anthropometrically, association for metrics does not equate causation on incident MI. Besides, association may present effects of bias rather than the true putative risk may be responsible for all or much of the epidemiological causality, and a different body composition between groups with similar baseline confounding variables may provide false-positives in outcomes. Thus, in evaluating whole-risk by anthropometry all metrics are not enterely valid at all times, and the lack of balance between measurements will be particularly prone to the generation of false-positive results. The purpose of this article is to critically review key findings for association biases from different studies. From the INTERHEART, waist-to-hip ratio (WHR) has been deemed as an excellent MI risk predictor, and other results have conferred to WHR a greater excess risk in women than in men. Nevertheless, a novel insight have revealed that WHR-associated risk would appear biased if metrics to compare had no balance and equivalence relation. Baseline characteristics of thousands of MI cases are well known, but anthropometry, mathematics and epidemiology have taught us something, and comment on it below. To date, no method was used to address biases for balancing the distribution of measurements between groups to be compared. Thus, WHR and waist circumference as being mathematical fraction and unit of whole-length, repectivelly, presented association biases when true unhealthy body composition was not well compared by group and by sex. It occurred for unbalancing both measurements and unhealthy body composition when comparing strength of association for metrics. Only waist-to-height ratio as being measure directly associated to a volume of risk yields no biases and should be the metric used to compare the body composition of risk, either by age or by sex.