Anthropometrics and Myocardial Infarction Risk: A Misleading Evidence Was Accepted by Cardiovascular Sciences When Errors of Bias Were Overlooked Worldwide. When Should We End Discussion about the Optimal Metric?

Abstract

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.

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

“…Thus, either for HC-WC or HCheight/2, the imbalance was significantly slanted toward one of the groups with a mean |x|>0, and it created a protective overestimation of HC with respect to WC and height 8,9,20,26 (see Table 2). This mathematically demonstrated that any WHRassociated risk beyond that of WC and WHtR was a spurious-risk providing a false causal inference, and was anthropometrically impossible 8,9,20,26,33 20 . In this situation, and since in healthy people WC usually is lower than height/2 (see Table 1), the imbalance with a mean |x|>0 was significantly slanted towards the cases status, creating a risk overestimation for WC in the tallest people and an underestimation in the shortest.…”

“…However, this situation epidemiologically never happens. By contrast, in any WHtR risk cut-off >0.5, area and volume measurements will never mathematically express the same whole-risk nor the same whole-body fat percentage 8,9,27,28,33 . Thus, when assigning true risk, only WHtR from a risk cutoff above 0.5 better captures a high-risk BC 8,9,17,28,33 (see Figure 1).…”

“…By contrast, in any WHtR risk cut-off >0.5, area and volume measurements will never mathematically express the same whole-risk nor the same whole-body fat percentage 8,9,27,28,33 . Thus, when assigning true risk, only WHtR from a risk cutoff above 0.5 better captures a high-risk BC 8,9,17,28,33 (see Figure 1). Regarding BMI, most studies have showed a BMI risk cut-off representing the category of overweight (>24.9 to <30 kg/m²) 2-5,8-10,19-26,28,30-33 (see Table 2).…”

“…Update on epidemiologic risk cut-off points for the associations of anthropometrics and cardiovascular disease and myocardial infarction worldwide. Mathematical relationships between the different simple measurements and ratios or ratio of ratios (2)(3)(4)(5)(8)(9)(10)(16)(17)(18)(19)(20)(21)(23)(24)(25)(26)28,(30)(31)(32)(33). BMI indicates body mass index; FFM, fat free mass in kg or % as appropriate; FM, fat mass in kg or % as appropriate; FMFFMR, fat mass-to-fat free mass ratio; HC, hip circumference; Ht, height; HCHt/2D, absolute difference between hip and half of height; HHt/2R, hip-to-height/2 ratio; W, weight in kg; WC, waist circumference; WCHt/2D, absolute difference between waist and half of height; WHD, absolute difference between hip and waist; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; WHt/2R, waist-to-height/2 ratio; "X", absolute difference between the corresponding simple measurements.…”

“…From a mathematical and geometric perspective, a two-dimensional area (cm²) calculated using a WC is not the same as a three-dimensional volume (cm³) measured using a WC as area of the base and WHtR cm as an unit of height 8,9,17,20,33 (Figure 1). Subsequently, only when WC presents a balanced distribution with respect to height (2WC=height: WC=height/2: WHtR risk cut-off =0.5), then WC and WHtR refer to the same whole as a mathematical object, where 2WC/height and WC/(height/2) are equivalent fractions with de same value (both ratios =1: improper fractions).…”