Why Predicting Health Risks from Either Body Mass Index or Waist-to-Hip Ratio Presents Causal Association Biases Worldwide: A Mathematical Demonstration

Association of Body Mass Index and Abdominal Obesity with Myocardial Infarction: We Reveal Confounding Factors that Historically Distorted Causal Inferences

“…Epidemiological anthropometric data has intrinsic limitations regarding the assessment of causality that are not completely mitigated even following the application of statistical methods designed for nonexperimental data. For instance, in several studies 2,3,5,[7][8][9] , association biases when handling anthropometric data have been demonstrated 4,[10][11][12][13][14] . Novel research has proven that some obesity metrics may present causal association biases between groups when comparing the risk associations of different body compositions (BCs) 4,6,[10][11][12][13][14] .…”

“…For instance, in several studies 2,3,5,[7][8][9] , association biases when handling anthropometric data have been demonstrated 4,[10][11][12][13][14] . Novel research has proven that some obesity metrics may present causal association biases between groups when comparing the risk associations of different body compositions (BCs) 4,6,[10][11][12][13][14] . In cardiovascular prevention, an accurate assessment of BC and body fat distribution is important before assuming any causal risk assigned to each metric [10][11][12][13][14][15] .…”

“…Novel research has proven that some obesity metrics may present causal association biases between groups when comparing the risk associations of different body compositions (BCs) 4,6,[10][11][12][13][14] . In cardiovascular prevention, an accurate assessment of BC and body fat distribution is important before assuming any causal risk assigned to each metric [10][11][12][13][14][15] . Doing so ensures that the true anthropometric risk is derived from a high-risk BC rather than any obesity metric statistically indicating an association.…”

“…Doing so ensures that the true anthropometric risk is derived from a high-risk BC rather than any obesity metric statistically indicating an association. Therefore, a high BMI (>24.9 kg/m²), WHR in women and men (>0.85 and >0.90, respectively), or WC in women and men (>80-88 cm and >94-102 cm, respectively) does not indicate an unhealthy BC, particularly if you overlook other key variables that also are associated with high-risk BCs [10][11][12][13][14] . Therefore, although high BMI, WHR and WC values have been associated with an increased risk of MI and mortality, they might not be optimal indicators of a causal inference due to different origins and densities of the main body components contributing to them 4,6,[10][11][12][13][14]16 .…”

“…Therefore, a high BMI (>24.9 kg/m²), WHR in women and men (>0.85 and >0.90, respectively), or WC in women and men (>80-88 cm and >94-102 cm, respectively) does not indicate an unhealthy BC, particularly if you overlook other key variables that also are associated with high-risk BCs [10][11][12][13][14] . Therefore, although high BMI, WHR and WC values have been associated with an increased risk of MI and mortality, they might not be optimal indicators of a causal inference due to different origins and densities of the main body components contributing to them 4,6,[10][11][12][13][14]16 . These components include fat mass (FM) and fat-free mass (FFM) constituting total body weight, and abdominal fat and musculoskeletal structures of hip circumference (HC) determining for WHR.…”