Anthropometrics in Predicting Cardiovascular Disease Risk: Our Research Work Mathematically Demonstrates that Cardiovascular Sciences Were Always Confused for a Long Time

Abstract

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.

Waist-to-Height Ratio is an Optimal Metric in Predicting Myocardial Infarction Risk. Other Anthropometrics Widely used by Cardiology to Promote Cardiovascular Health Present Association Biases

“…Therefore, the observed associations may be attributable to differences other than the risk being investigated, and causality cannot be assumed. Similarly, differences in BC between groups with similar baseline confounding variables may result in bias if the true risk assignment does not account for covariates that predict the true risk 8,9 . Hence, a major limitation when using observational data to estimate causal effects is confounding factors.…”

“…Therefore, any anthropometric epidemiologically may be associated with CVD and MI; however, this may demonstrate over-or under-estimations if confounding factors are present. As a result, the risk assignment for anthropometrics -such as BMI, WHR, and the waist circumference (WC) -may be systematically biased for causal inferences if they do not capture the true risk or if the values for the simple body measurements do not present a balanced distribution between healthy and unhealthy cases 8,9 . Hence, the notion of mathematical equivalence for the different simple measures should be respected between groups being compared.…”

“…Hence, the notion of mathematical equivalence for the different simple measures should be respected between groups being compared. If not, the lack of a balanced distribution between the simple measurements will be particularly prone to the generation of biases in outcomes 8,9 . In this approach, a nuclear observation relates to the anthropometric knowledge of the mathematical relationships and differences between the simple body measurements in anthropometrically healthy subjects and normal weight (<25 kg/m²) range 10 (Table 1).…”

“…Similarly, variables, where body weight is distributed by unit of height: [Height (cm)-100] or [Height (m²)] as in BMI formula, were added. Mathematically, the absolute value of each simple measurement (weight, FFM, FM, height, WC and hip circumference [HC]) depends on the bodily components involved in each dimension, but never estimates an identical high-risk BC 8,9 . Evidence supports that WC is a metrics linked to visceral adipose tissue and, together with the waist-toheight ratio (WHtR), can better predict CVD, MI, and cardiometabolic risks [2][3][4][5]8,9,[11][12][13][14][15][16][17][18][19][20][21][22][23][24] .…”

“…Mathematically, the absolute value of each simple measurement (weight, FFM, FM, height, WC and hip circumference [HC]) depends on the bodily components involved in each dimension, but never estimates an identical high-risk BC 8,9 . Evidence supports that WC is a metrics linked to visceral adipose tissue and, together with the waist-toheight ratio (WHtR), can better predict CVD, MI, and cardiometabolic risks [2][3][4][5]8,9,[11][12][13][14][15][16][17][18][19][20][21][22][23][24] . However, from the INTERHEART and other studies about MI, WHR appeared to show the best predictive value compared with BMI, WC, and WHtR, as appropriate [2][3][4][5]8,9 .…”