A, A critical thinking. Why when using anthropometrics in predicting myocardial infarction risk medical research and cardiology were always in error?: Arguments evidencing biases : A critical thinking. Why when using anthropometrics in predicting myocardial infarction risk medical research was always confused?: We evidence association biases - Angel, Angel Martin Castellanos (British Journal of Healthcare and Medical Research, 2022)
Cardiovascular diseases (CVDS) mainly heart disease and stroke are the leading causes of death globaly. Obesity is a major risk factor for myocardial infarction (MI). However, how to measure whole-risk with simple baseline anthropometric characteristics? Anthropometrically, association for metrics does not equate causation on incident MI/CVD. Besides, a different body composition between groups with similar baseline confounding variables may provide false-positives in outcomes. Thus, in predicting whole-risk all metrics are not enterely valid, and the lack of balance between the simple body measurements will be particularly prone to the generation of false-positive results. Baseline characteristics of thousands of MI cases are well known, but anthropometry and mathematics have taught us novel something. Thus, our findings reveal that anthropometrically-associated risk would appear biased if metrics to compare had no balance and equivalence relation for the whole-risk. WHR and waist circumference, present association biases when whole-risk is not conditioned on the covariate that receives true-risk. It occurs for unbalancing body measurements when healthy and cases were compared worlwide. It is clear, in any risk cutoff for WHR <1 and WHtR >0.5 is always fullfilled: HC >WC >height/2, and therefore occurring protective overestimation of hip circumference respect to waist circumference and height as well as risk overestimation for waist concerning height. Only waist-to-height ratio as being directly associated to a realtive volume of risk yields no biases and should be the metric correctelly used to predict the anthropometrically-measured whole-risk in both sexes. Our arguments are mathematically demonstrable.