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) and CVD. However, how to measure CVD risk with simple baseline anthropometric characteristics? Besides, association of anthropometrics and CVD may present effects of bias, and in evaluating risk, the lack of balance between simple measurements will be particularly prone to the generation of false-positive results. The purpose of this paper is to provide the key concepts for demonstrating association biases for metrics taken from multiple large-scale studies worldwide. Epidemiologically, waist-to-hip ratio (WHR) is a confounding variable with respect to waist circumference (WC) and waist-to-height ratio (WHtR). This is due to different imbalances between hip circumference (HC)-WC and HC-height, respectively, occurring in a protective overestimation for HC concerning WC and height. Similarly, WC may be a confounding variable with respect to WHtR due to an imbalance in WC-height: This occurs if, and only if, the mean WC > height/2 (WHtR risk cut-off >0.5). This, therefore, overestimates risk in tallest people and lead to underestimations in the shortest people. Anthropometrically, only WHtR is the only measure that is directly associated to a relative risk volume and yields no biases, and it should therefore be the metric used to compare the anthropometrically-measured causal risk.

Despite the impact of the COVID‑19 pandemic, myocardial infarction remains the leading cause of cardiovascular deaths in Europe. Body mass index (BMI)-defined obesity is a major risk factor for myocardial infarction. However, in the association of anthropometrics and myocardial infarction, the lack of balance between the simple body measurements when comparing healthy and unhealthy cases has demonstrated that affects the outcome. Thus, regardless of association strength of anthropometrics, other criteria to judge the biological causality must be investigated. We aim to assess different studies worldwide to understand the key concepts to demonstrate association biases for anthropometrics when predicting myocardial infarction risk. In this approach, natural mathematical inequalities between simple measurements in healthy subjects were investigated. Weight, height, height/2, waist circumference and hip circumference mathematically represent absolute values that do not express mathematically equality for the true risk. That way, the mathematical concept of fraction or ratio in anthropometrics such as BMI, waist-to-hip ratio (WHR) or waist-to-height ratio (WHtR) plays an important role. Thus, some anthropometrics may be seen as confounding variables when measuring high-risk body composition. Weight is a confounding factor without indicating a high-risk body composition, meaning that BMI is not fully predictive. WHR is a confounding variable concerning waist and WHtR due to imbalances between the mean hip–waist and hip–height, respectively, which indicates a protective overestimation for hip concerning waist and height. Waist measure may be a confounding variable concerning WHtR due to an imbalance in the mean waist–height. This occurs if, and only if, WHtR risk cut-off is >0.5 and if height is ignored as volume factor, therefore creating an overestimation of risk for waist circumference in the tallest people and underestimation in the shortest. Mathematically/anthropometrically, only WHtR-associated risk above BMI, waist and WHR holds true while considering it as a relative risk volume linked to a causal pathway of higher cardiometabolic risk. In conclusion, WHtR is the only metric that is directly associated to a risk volume and having more biological plausibility. It should be used to assess the anthropometrically-measured myocardial infarction risk, once the imbalances between measurements and association biases are recognised.

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Abdominal obesity and myocardial infarction risk-We demonstrate the anthropometric and mathematical reasons that justify the association bias of the waist-to-hip ratio Obesidad abdominal y riesgo de infarto de miocardio: demostramos las razones antropométricas y matemáticas que justifican el sesgo de asociación del índice cintura-cadera

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.