Science topic
Abdominal Obesity - Science topic
A condition of having excess fat in the abdomen. Abdominal obesity is typically defined as waist circumferences of 40 inches or more in men and 35 inches or more in women. Abdominal obesity raises the risk of developing disorders, such as diabetes, hypertension and METABOLIC SYNDROME X.
Questions related to Abdominal Obesity
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
I need to know how these compounds can affect obesity, especially in humans, and how they work
greetings to you all
Hi
I am working on Systems Biology study of a disease, Metabolic Syndrome. I need omics data including Genomics, Metablomics, Microbiomics and Proteiomics to study the molecular level interaction of the disease. I need following help:
1. Can anyone help any data repository?
2. As the disease is a cluster of some risk factors like Hypertension, obesity, hyperglycemia, insulin resistance, dislipidemia. Should I search dataset for each risk factor separately?
3. Dataset should be in raw form or should I go for different data models?
Thanks
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