Papers by Angel MARTIN CASTELLANOS
Medical Research Archives, 2024
Cardiovascular diseases, mainly myocardial infarction and stroke, are the leading cause of death ... more Cardiovascular diseases, mainly myocardial infarction and stroke, are the leading cause of death globally. Therefore, epidemiological research seems necessary to prevent cardiovascular events and mortality. However, real-world data from obesity metrics has intrinsic limitations for the assessment of causality. Despite of historical studies showing that the body mass index (BMI), the waist-to-hip ratio (WHR) and the waist circumference (WC) have been associated with increased risk of myocardial infarction, they might not be accurate from a causal inference. Our aim was to summarize historical and novel findings about obesity metrics and myocardial infarction to evidence causal association biases. Method: an epidemiological review study was conducted while being original research when adding new anthropometrics in study design. Mathematical inequalities between the simple body measurements in anthropometrically healthy adults were described. Mean values and cutoffs for classic and several newer anthropometric variables were established. Classic metrics, ratios between the means of the simple measurements, a modulus |x| as a result of subtracting some measurement means from others (e.g., mean fat free mass minus fat mass) and somatotype ratings were collated. Mathematically, a nonzero difference for each modulus |x| in any population study would indicate an unbalanced distribution of the measurements between groups being compared, and therefore, the risk exposure levels differing. Thus, when between-groups the high-risk body compositions and somatotype ratings differ, any metric-associated risk is biased from a causal inference. After investigating large epidemiological studies, the historical omission of key anthropometric variables is stated, and as being uncontrolled confounding factors distorted causal inferences. Therefore, a protective overestimate of fat free mass and hip circumference over fat mass and WC, respectively, always occurred. Similarly, when the waist-to-height ratio values of >0.5 are associated; a protective underestimate of height over WC occurs. Any metricassociated risk is biased if prediction is made from WC or technologically measured body compositions without accounting for relative risk volume measures. In conclusion, summarizing the historical and novel findings regarding risk prediction, BMI, WHR and WC alone show evidence of causal association biases because of high-risk body compositions and risk exposure levels always differ between the groups being compared.
Acta Scientific Medical Sciences, 2023
Elevated body mass index (BMI) and waist-to-hip ratio (WHR) are associated with increased health ... more Elevated body mass index (BMI) and waist-to-hip ratio (WHR) are associated with increased health risks. However, both of these obesity metrics may present causal association biases when assessing different individuals with identical risk values for each anthropometric. Thus, an accurate interpretation of the body composition as well as body fat excess or musculoskeletal mass deficit is important before inferring any causal risk. Hence, although higher BMI and WHR may be associated with health outcomes, they might not be appropriate for causal inference due to different origins in the bodily components contributing to them (i.e., fat mass [FM] and fat-free mass [FFM] within BMI, and waist and hip circumferences within WHR). Biologically, each body measurement and ratio between two measurements present a different relationship with the risk. Thus, two conflicting factors as being the numerator and denominator of an abstract fraction (e.g., FM vs. FFM and waist vs. hip) may generate over-or underestimates of the overall risk if the mentioned factors are differentially distributed between groups being compared. That way, if the absolute differences between mean FM and FFM, or between mean waist circumference and hip, are not balanced when comparing healthy with unhealthy cases, false outcomes may be generated. This approach considers the absolute difference between two means (e.g., mean FFM minus FM) as a new variable or modulus |x|. Thus, any difference in means of nonzero (i.e., mean |x|>0) means that you are comparing for diferent "x" values between groups, and therefore, assessing for a different body composition. After investigating, in most population studies, an unbalanced distribution for the corresponding mean differences of the |x| values may be demonstrated, irrespective of any anthropometrically or technologically-measured body composition. Thus, causal association biases occurred worldwide when using BMI-or WHR-cutoffs without taking into account the modulus |x| as potential confounding factor, and therefore, accepting a protective overestimate of FFM and hip with respect to FM and waist, respectively. It may be demonstrated mathematically and in the Cartesian space that any mean FM-to-FFM ratio <1 and WHR <1 may never represent the overall risk. We recommend that the historical paradigm in predicting health risks from BMI and WHR should be shifted.
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Papers by Angel MARTIN CASTELLANOS