Anthropometrically and conceptually, association does not equate causation. However, regarding anthropometric indicators (i.e., metrics of general and abdominal obesity), most of studies worldwide focus in determining the association degree with cardiovascular diseases or even all-cause and cardiovascular mortality. In this approach, mathematical inequalities between the simple body measurements (e.g., weight, height, waist circumference, hip circumference, muscle perimeters, bone diameters etc.,) were always overlooked when comparing the healthy and unhealthy groups.
For the first time, we have revealed confounding factors that historically distorted causal inferences. Similarly, we mathematically have demonstrated bias errors when a same value of any anthropometric (e.g., BMI, WHR or WC) may indicate different high-risk body composition between groups being compared. Effectively, high BMI is not equivalent to general obesity and high-risk body composition. Moreover, abdominal obesity measured by WC or WHR is not the same as an abdominal volume of risk from WHtR. It is clear, each anthropometric express a different body composition of risk and risk exposure level, and therefore, they may never be compared to assess the same health risk.
In our opinion, talking about anthropometrics and health risks, the historical paradigm should be shifted. By using anthropometrics (e.g., BMI, WHR, WC) without balancing for the simple measurements (i.e, fat mas vs. fat free mass, waist circumference vs. hip and waist circumference vs. height) between the healthy and unhealthy cases has been a historical anthropometric error that always distorted causality.
1. Angel Martin Castellanos. “Why Predicting Health Risks from Either Body Mass Index or Waist-to-Hip Ratio Presents Causal Association Biases Worldwide: A Mathematical Demonstration”. Acta Scientific Medical Sciences 7.7 (2023): 112-120. DOI: 10.31080/ASMS.2023.07.1605
2. Castellanos AM., 2024. Association of Body Mass Index and Abdominal Obesity with Myocardial Infarction: We Reveal Confounding Factors that Historically Distorted Causal Inferences, Medical Research Archives, [online] 12(3). https://doi.org/10.18103/mra.v12i3.5102
DOI https://doi.org/10.18103/mra.v12i3.5102
Metabolic disorders occur due to our bad habits of eating process. Like food, water, and liquids, etc. Due to mutations in all our food in saliva, the imbalance of minerals in our body parts and some parts of our body and endocardial gland malfunction. If we do not change our habits, then we will feel difficulties, and these habits, abnormalities, and things are going to our inborn children. And problems are increasing day by day. We are seeing that our inborn children suffer from metabolic disorders from birth, like diabetes, etc. These habits, abnormalities, and genes we cannot correct with any medicine, substitutes, or research like stem cells, gene therapy, etc. "Habits Is a Second Nature “I request all people. Please read my book and follow the eating rules. Then we can achieve victory over metabolic disorders. I welcome all of you at these links:- https://www.lulu.com/shop/innovator-pramod-stephen/your-health-is-in-your-mouth/paperback/product-zgvvk6.html?page=1&pageSize=4 https://www.flipkart.com/aapka-swasth-aapke-muh-me/p/itm22edae101e74a?pid=9789393385543
The question is how measure body composition to assess metabolic disorders. Anthropometry is an easy method. However, when associating anthropometric mesures with health risks, historical errors are committed by cardiovascular sciences. We have demonstrated such bias errors. Unfortunately, our findings published in open access are ignored by cardiology and important scientific societies. From our observations, BMI, WHR and even WC alone are misleading indicators to evaluate the true cardiometabolic risk derived from bad habits of eating process.
As an example, Impact of BMI on all-cause mortality has been investigated in many studies with millions of participants worldwide. In this approach, the nadir of mortality always appeared to be in normal or overweight range (>18.5-
Association can imply causality, but the scientist must demonstrate that not only is the, say, dyslipidemia associated with the disease but that correction of the dyslipidemia corrects the disease. BTW, I do agree that randomized controlled clinical trials are selection biased and hence are not applicable to the general population, as I noted a number of years ago in a letter to the editor of the Lancet.
Thanks for your answer. Effectively, in medical research, it is important to estimate the causal risk of an exposure factor on subject-important outcomes because the association of anthropometrics does not always equate to causation regarding cardiovascular events or mortality.
The causal risk can be estimated in observational studies, which can often present numerous inherent shortcomings. However, the major limitation in
using observational data to estimate causal effects is the confounding factors. For the first time, we have published and revealed confounding factors when predicting health risks from anthropometrric risk indicators. Unfortunately, our findings are overlooked, and besides, being ignored in many studies and in important scientific societies.
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