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?

Main Article Content

Angel Martin Castellanos

Abstract

Despite the impact of the COVID‑19 pandemic, myocardial infarction remains the leading cause of cardiovascular deaths in Europe. Body mass index (BMI)-defined obesity is a major risk factor for myocardial infarction. However, in the association of anthropometrics and myocardial infarction, the lack of balance between the simple body measurements when comparing healthy and unhealthy cases has demonstrated that affects the outcome. Thus, regardless of association strength of anthropometrics, other criteria to judge the biological causality must be investigated.


We aim to assess different studies worldwide to understand the key concepts to demonstrate association biases for anthropometrics when predicting myocardial infarction risk. In this approach, natural mathematical inequalities between simple measurements in healthy subjects were investigated. Weight, height, height/2, waist circumference and hip circumference mathematically represent absolute values that do not express mathematically equality for the true risk. That way, the mathematical concept of fraction or ratio in anthropometrics such as BMI, waist-to-hip ratio (WHR) or waist-to-height ratio (WHtR) plays an important role. Thus, some anthropometrics may be seen as confounding variables when measuring high-risk body composition. Weight is a confounding factor without indicating a high-risk body composition, meaning that BMI is not fully predictive. WHR is a confounding variable concerning waist and WHtR due to imbalances between the mean hip–waist and hip–height, respectively, which indicates a protective overestimation for hip concerning waist and height. Waist measure may be a confounding variable concerning WHtR due to an imbalance in the mean waist–height. This occurs if, and only if, WHtR risk cut-off is >0.5 and if height is ignored as volume factor, therefore creating an overestimation of risk for waist circumference in the tallest people and underestimation in the shortest. Mathematically/anthropometrically, only WHtR-associated risk above BMI, waist and WHR holds true while considering it as a relative risk volume linked to a causal pathway of higher cardiometabolic risk.


In conclusion, WHtR is the only metric that is directly associated to a risk volume and having more biological plausibility. It should be used to assess the anthropometrically-measured myocardial infarction risk, once the imbalances between measurements and association biases are recognised.

Article Details

How to Cite
CASTELLANOS, Angel Martin. 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, [S.l.], v. 10, n. 7, july 2022. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/2917>. Date accessed: 08 aug. 2022. doi: https://doi.org/10.18103/mra.v10i7.2917.
Section
Research Articles

References

1. OECD/European Union (2020). Mortality following acute myocardial infarction (AMI), in Health at a Glance: Europe 2020: State of Health in the EU Cycle, OECD Publishing, Paris. https://doi.org/10.1787/0cf23378-en.

2. Lloyd-Jones DM, Hong J, Labarthe D, et al. Defining and Setting National Goals for Cardiovascular Health Promotion and Disease Reduction: The American Heart Association´s strategic impact Goal through 2020 and beyond. Circulation. 2010; 121: 586-613. Doi: 10.1161/CIRCULATIONAHA.109.192703
3. 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. (Accessed Nov 2021).
4. 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
5. Zhang Z. Propensity score method: a non-parametric technique to reduce model dependence. Ann Trans Med. 2017; 5 (1): 7.doi: 10.21037/atm.2016.08.57
6. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research. 2011; 46: 399-424. doi: 10.1080/00273171.2011.568786
7. Grimes DA, Schulz KF. Epidemiology series. Bias and causal associations in observational research. The lancet. 2002; 359: 248-52. doi: 10.1016/S0140-6736(02)07451-2
8. Yusuf S, Hawken S, Ounpuu S, et al. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet. 2005; 366; 1640-9.
9. Gruson E, Montaye M, Kee F, et al. Anthropometric assessment of abdominal obesity and coronary heart disease risk in men: the PRIME study. Heart. 2010; 96 (2):136-40. doi: 10.1136/hr.2009.171447
10. Zhu J, Su X, Li G, et al. The incidence of acute myocardial infarction in relation to overweight and obesity: a meta-analysis. Arch Med Sci. 2014; 10 (5):855-62. doi:10.5114/aoms.2014.46206
11. Gavriilidou NN, Pihlsgard M, Elmstahl S. Anthropometric reference data for elderly Swedes and its disease related pattern. Eur J Clin Nutr. 2015; 69 (9):1066-75.
12. Chen Y, Jiang J, Shi J, et al. Association of Visceral Fat Index and percentage Body Fat and Anthropometric Measures with Myocardial Infarction and Stroke. J Hypertens. 2016; 5: 235. doi: 10.4172/2167-1095.1000235
13. 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.
14. Lee HW, Hong TJ, Hong JY, et al. Korea Working Group on Myocardial Infarction Investigators. Waist-hip ratio and 1-year clinical outcome in patients with non-ST-elevation myocardial infarctions. Coron Artery Dis. 2016; 27 (5):357-64. Doi: 10.1097/MCA.0000000000000369
15. Nilson G, Hedberg P, Leppert J, et al. Basic Anthropometric Measures in Acute Myocardial Infarction Patients and Individually Sex-and Age-Matched Controls from the General Population. J Obes. 2018; 2018: 3839482. doi: 10.1155/2018/3839482
16. Lassale C, Tzoulaki I, Moons KGM, et al. Separate and combined associations of obesity and metabolic health with coronary heart disease: a pan-European case-cohort analysis. Eur Heart J. 2018; 39 (5): 397-406. Doi.10.1093/eurheartj/ehx448
17. Cao Q, Yu S, Xiong W, et al. Waist-hip ratio as a predictor of myocardial infarction risk. A systematic review and meta-analysis. Medicine. 2018; 27-30 (e11639). doi.org/10.1097/MD.0000000000011639
18. Choi D, Choi S, Son JS, et al. Impact of discrepancies in General and Abdominal Obesity on Major Adverse Cardiac Events. J Am Heart Assoc. 2019; 8 (18): e013471. doi:10.1161/JAHA.119.013471
19. 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
20. 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
21. Dhar S, Das PK, Bhattacharjee B, et al. Predictive Value of Waist Height Ratio, Waist Hip Ratio and Body Mass Index in Assessing Angiographic Severity of Coronary Artery Disease in Myocardial Infarction Patients. Mymensingh Med J. 2020; 29 (4): 906-13
22. National Cholesterol Education Program (NCEP). Executive Summary of the Third Report of the National Cholesterol Education Program. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation 2002; 106: 3143- 421.
23. Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120 (16):1640-5.
24. World Health Organization. Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. World Health Organization 2011. http://www.who.int/iris/handle/10665/44583. (Accessed Feb 2022).

25. Ladeiras-Lopes R, Sampaio F, Bettencourt N, et al. The Ratio Between Visceral and Subcutaneous Abdominal Fat Assessed by Computed Tomography Is an Independent Predictor of Mortality and Cardiac Events. Rev Esp Cardiol (Engl Ed). 2017; 70 (5): 331-7.
26. Brown JC, Harhay MO, Harhay MN. Anthropometrically-predicted visceral adipose tissue and mortality among men and women in the third national health and nutrition examination survey (NHANES III). Am J Hum Biol. 2017; 29:e22898. doi:10.1002/ajhb.22898
27. Piché ME, Poirier P, Lemieux I, et al. Overview of epidemiology and contribution of obesity and body fat distribution to cardiovascular disease: an update. Prog Cardiovasc Dis. 2018; 61:103– 113. doi: 10.1016/j.pcad.2018.06.004
28. Sahakyan KR, Somers VK, Rodriguez-Escudero JP, et al. Normal-weight central obesity: implications for total and cardiovascular mortality. Ann Intern Med. 2015; 163:827–835. doi: 10.7326/M14-2525
29. 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:10.1080/03009734.2018.1494644
30. Nicklas BJ, Penninx BH, Cesari M, et al. Association of Visceral Adipose Tissue with Incident Myocardial Infarction in Older Men and Women The Health, Aging and Body Composition Study. Am J Epidemiol. 2004; 160:741-9. doi: 10.1093/aje/kwh281
31. 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 (1): 88-95. doi.org/10.20960/nh.982
32. 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): 100-7. http://www.aascit.org/journal/ajfnh.

33. Schneider HJ, Friedrich N, Klotsche J, et al. The predictive value of different measures of obesity for incident cardiovascular events and mortality. J Clin Endocrinol Metab. 2010; 95 (4):1777-85. doi: 10.1210/jc.2009-1584
34. Song X, Jousilahti P, Stehouwer CD, et al. Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur J Clin Nutr. 2013; 67 (12): 1298-302. doi: 10.1038/ejcn.2013.203
35. 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. doi: 10.1186/s12889-018-5350-8
36. 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
37. 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
38. Martin-Castellanos A, Martin-Castellanos 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: 10.20960/nh.03416
39. 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. MRA, 2021. 9 (6): 1-19. https://doi.org/10.18103/mra.v9i6.2447.

40. Bastick, T. (1993) ‘Teaching the Understanding of Mathematics: Using Affective Contexts That Represent Abstract Mathematical Concepts’, in B.Atweh, C. Kanes, M. Carss, G. Booker (Eds.). Contexts in Mathematics Education: Conference Proceedings, Brisbane: MERGA, pp 93-99.
41. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage ─ A cross sectional study in American adult individuals. Scientific Reports. 2018; 8 (1):10980.doi:10.1038/s41598-018-29362-1
42. Tchernof A, Despres JP. Pathophysiology of human visceral obesity: an update. Physiol Rev. 2013; 93:359–404.
43. Gruzdeva O, Borodkina D, Uchasova E, et al. Localization of fat depots and cardiovascular risk. Lipids Health Dis. 2018; 17 (1): 218. https://doi.org/10.1186/s12944-018-0856-8
44. Wu Y, Zhang A, Hamilton DJ et al. Epicardial Fat in the Maintenance of Cardiovascular Health. Methodist DeBakey Cardiovascular Journal. 2017; 13 (1): 20-24. DOI: http//doi.org/10.14797/mdcj-13-1-20
45. Williams SR, 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: 376-93.
46. Martin-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 ratio. Card Res Med. 2018; 2: 13-20.
47. Romero-Corral A, Montori VM, Somers VK, Korinek J, Thomas RJ, Allison TG, et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet. 2006; 368: 666-78.
48. De Ulíbarri Pérez JI. Proyecto CONUT®. La desnutrición clínica en 2014; patogenia, detección precoz y consecuencias; desnutrición y trofopatía. Esp Nutr Hosp 2014; 29(4):785-96.
49. 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
50. Medina-Inojosa JR, Batsis JA, Supervia M, et al. Relation of Waist-Hip Ratio to Long-Term Cardiovascular Events in Patients With Coronary Artery Disease. Am J Cardiol. 2018; 121 (8): 903-9. doi: 10.1016/j.amjcard.2017.12.038
51. Nalini M, Sharafkhah M, Poustchi H, et al. Comparing Anthropometric Indicators of Visceral and General Adiposity as Determinants of Overall and Cardiovascular Mortality. F. Arch Iran Med. 2019; 22 (6):301-9.
52. Bowman K, Atkins JL, Delgado J, et al. Central adiposity and the overweight risk paradox in aging: follow-up of 130,473 UK Biobank participants. Am J Clin Nutr. 2017; 106 (1): 130-135. doi: 10.3945/ajcm.116.147157