Association of Body Mass Index and Abdominal Obesity with Myocardial Infarction: We Reveal Confounding Factors that Historically Distorted Causal Inferences
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Abstract
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 cut-offs 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 non-zero 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 metric-associated 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.
Keywords:
Myocardial infarction, body mass index, general obesity, abdominal obesity, kinanthropometry, bias, somatotype
Article Details
How to Cite
CASTELLANOS, Angel Martin.
Association of Body Mass Index and Abdominal Obesity with Myocardial Infarction: We Reveal Confounding Factors that Historically Distorted Causal Inferences.
Medical Research Archives, [S.l.], v. 12, n. 3, mar. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5102>. Date accessed: 22 dec. 2024.
doi: https://doi.org/10.18103/mra.v12i3.5102.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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3. Egeland GM, Igland J, Vollset SE, et al. High population attributable fractions of myocardial infarction associated with waist-hip ratio. Obesity. 2016; 24 (5):1162-9.
4. Martín-Castellanos A, Cabañas-Armesilla MD, Barca-Durán FJ, et al. Obesity and risk of Myocardial Infarction in a Sample of European Males. Waist To-Hip-Ratio Presents Information Bias of the Real Risk of Abdominal Obesity. Nutr Hosp. 2017; 34: 88-95. doi.org/10.20960/nh.982. https://pubmed.ncbi.nlm.nih.gov/28244777
5. Peters SAE, Bots SH, Woodward M. Sex Differences in the Association Between Measures of General and Central Adiposity and the Risk of Myocardial Infarction: Results From the UK Biobank. J Am Heart Assoc. 2018; 7(5). pii: e008507. Doi: 10.1161/JAHA.117.008507.
6. Martin-Castellanos A, Martin-Castellanos P, Cabañas MD, et al. Adiposity-associated anthropometric indicators and myocardial infarction risk: Keys for waist to-height-ratio as metric in cardiometabolic health. AJFNH. 2018;3(5):100-107
7. Knowles R, Carter J, Jebb SA, et al. Associations of Skeletal Muscle Mass and Fat Mass With Incident Cardiovascular Disease and All-Cause Mortality: A Prospective Cohort Study of UK Biobank Participants. J Am Heart Assoc. 2021;10 (9):e019337. doi: 10.1161/JAHA.120.019337.
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9. Magnussen C, Ojeda FM, Leong DP, et al. Global Effect of Modifiable Risk Factors on Cardiovascular Disease and Mortality. N Engl J Med. 2023; 389(14):1273-1285. doi: 10.1056/NJMoa2206916.
10. Martin Castellanos A. Anthropometrics in Predicting Cardiovascular Disease Risk: Our Research Work Mathematically Demonstrates that Cardiovascular Sciences Were Always Confused for a Long Time [Internet]. Novel Pathogenesis and Treatments for Cardiovascular Disease. IntechOpen; 2023. Available from: http://dx.doi.org/10.5772/intechopen.105098
11. Martin-Castellanos A, MartinCastellanos P, Martin E, et al. Abdominal obesity and myocardial infarction risk: We demonstrate the anthropometric and mathematical reasons that justify the association bias of waist-to-hip ratio. Nutr Hosp. 2021; 38 (3): 502-510. doi.org/10.20960/nh.03416. https://pubmed.ncbi.nlm.nih.gov/33757289/
12. Castellanos AM. Anthropometric measures in predicting myocardial infarction risk. Do we know what we are measuring? Bias in research occurred worldwide when the true unhealthy body composition was not well compared. Medical Research Archives. 2021;9 (6):2-19. Available at: https://esmed.org/MRA/mra/article/view/2447. doi: https://doi.org/10.18103/mra.v9i6.2447
13. Castellanos AM. 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? Medical Research Archives. 2022;10 (7):2-16. Available at: https://doi.org/10.18103/mra.v10i12.3463.
doi: https://doi.org/10.18103/mra.v10i12.3463
14. Martin Castellanos A. 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.
15. Powell-Wiley, Powell-Wiley TM, Poirier P, et al. Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation. 2021; 25; 143 (21):e984- e1010. doi: 10.1161/CIR.0000000000000973.
16. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage ─ A crosssectional study in American adult individuals. Scientific Reports. 2018; 8 (1):10980.doi:10.1038/s41598-018-29362-1.
17. World Health Organization (WHO). Obesity and overweight-WHO/World Health Organization. Available in: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
18. Neovius M, Teixeira-Pinto A, Rasmussen F. Shift in the composition of obesity in young adult men in Sweden over a third of a century. Int J Obes (Lond). 2008; 32(5):832-6. doi: 10.1038/sj.ijo.0803784.
19. Mohammadi H, Ohm J, Discacciati A, et al. Abdominal obesity and the risk of recurrent atherosclerotic cardiovascular disease after myocardial infarction. Eur J Prev Cardiol. 2020; 27 (18):1944-52. Doi: 10.1177/2047487319898019.
20. Angeras O, Albertsson P, Karason K, et al. Evidence for obesity paradox in patients with acute coronary syndromes: a report from the Swedish Coronary Angiography and Angioplasty Registry. Eur Heart J. 2013; 34: 34 (5): 245–53. Doi: 10.1093/eurheartj/ehs217.
21. Castellanos AM. 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. Medical Research Archives. 2023;10 (12):2-16. Available at: https://esmed.org/MRA/mra/article/view/3463. doi: https://doi.org/10.18103/mra.v10i12.3463.
22. Martín-Castellanos A, Cabañas MD, Martín-Castellanos P, et al. The body composition and risk prediction in myocardial infarction men. Revealing biological and statistical error bias for both general obesity and waist-to-hip ra-tio. Card Res Med 2018; 2: 13-20.
23. Williams SR1, Jones E, Bell W, et al. Body habitus and coronary heart disease in men. A review with reference to methods of body habitus assessment. Eur Heart J. 1997; 18: 376-393.
24. Williams SR, Goodfellow J, Davies B, et al. Somatotype and angiographically determined atherosclerotic coronary artery disease in men. Am J Hum Biol. 2000; 12: 128-138.
25. Carte JEL. The Heath-Carter Anthropometric Somatotype Instruction Manual. Department of Exercise and Nutritional Sciencies. San Diego State University: San Diego CA: USA. 2002.
26. Berglund L, Risérus U, Hambreaus K. Repeated measures of body mass index and waist circumference in the assessment of mortality risk in patients with myocardial infarction. Ups J Med Sci. 2019; 124(1):78-82. doi:10.1080/03009734.2018.14946 44.
27. Aune D, Sen A, Prasad M, et al. BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3·74 million deaths among 30·3 million participants. BMJ. 2016; 353: i2156.
28. Bhaskaran K, Dos-Santos-Silva I, Leon DA, et al. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. Lancet Diabetes Endocrinol. 2018; 6 (12):944-953. doi: 10.1016/S2213-8587(18)30288-2.
29. Liu J, Tse LA, Liu Z, et al. PURE (Prospective Urban Rural Epidemiology) study in China. Predictive Values of Anthropometric Measurements for Cardiometabolic Risk Factors and Cardiovascular Diseases Among 44 048 Chinese. J Am Heart Assoc. 2019. 8 (16): e010870. doi: 10.1161/JAHA. 118.010870.
30. Mehran L, Amouzegar A, Fanaei SM, et al. Anthropometric measures and risk of all-cause and cardiovascular mortality: An 18 years follow-up. Obes Res Clin Pract. 2022; 16 (1):63-71. doi: 10.1016/j.orcp.2021.12.004.
31. Dikaiou P, Björck L, Adiels M, et al. Obesity, overweight and risk for cardiovascular disease and mortality in young women. Eur J Prev Cardiol. 2021; 28 (12):1351-1359. doi: 10.1177/2047487320908983.
32. Rost, S., Freuer, D., Peters, A. et al. New indexes of body fat distribution and sex-specific risk of total and cause-specific mortality: a prospective cohort study. BMC Public Health. 2018;18 (1): 427. https://doi.org/10.1186/s12889-018-5350-8
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