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Prevalence of adiposity-based chronic disease and its association with anthropometric and clinical indices: a cross-sectional study
Published online by Cambridge University Press: 22 September 2022
Abstract
The present study aimed to determine the prevalence of adiposity-based chronic disease (ABCD) and its association with anthropometric indices in the Mexican population. A cross-sectional study was conducted in 514 adults seen at a clinical research unit. The American Association of Clinical Endocrinology/AACE/ACE criteria were used to diagnose ABCD by first identifying subjects with BMI ≥ 25 kg/m2 and those with BMI of 23–24·9 kg/m2 and waist circumference ≥ 80 cm in women or ≥ 90 cm in men. The presence of metabolic and clinical complications associated with adiposity, such as factors related to metabolic syndrome, prediabetes, type 2 diabetes, dyslipidaemia and arterial hypertension, were subsequently evaluated. Anthropometric indices related to cardiometabolic risk factors were then determined. The results showed the prevalence of ABCD was 87·4 % in total, 91·5 % in men and 86 % in women. The prevalence of ABCD stage 0 was 2·4 %, stage 1 was 33·7 % and stage 2 was 51·3 %. The prevalence of obesity according to BMI was 57·6 %. The waist/hip circumference index (prevalence ratio (PR) = 7·57; 95 % CI 1·52, 37·5) and the conicity index (PR = 3·46; 95 % CI 1·34, 8·93) were better predictors of ABCD, while appendicular skeletal mass % and skeletal muscle mass % decreased the risk of developing ABCD (PR = 0·93; 95 % CI 0·90, 0·96; and PR = 0·95; 95 % CI 0·93, 0·98). In conclusion, the prevalence of ABCD in our study was 87·4 %. This prevalence increased with age. It is important to emphasise that one out of two subjects had severe obesity-related complications (ABCD stage 2).
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- © The Author(s), 2022. Published by Cambridge University Press on behalf of The Nutrition Society
Footnotes
†
These authors contributed equally to this work.
References
Hales, CM, Carroll, MD, Fryar, CD, et al. (2020) Prevalence of obesity and severe obesity among adults: united States, 2017–2018. NCHS Data Brief 360, 1–8.Google Scholar
INEGI (2018) National Institute of Public Health. National Health and Nutrition Survey. Mexico: INEGI.Google Scholar
Vazquez-Duran, M, Jimenez-Corona, ME, Moreno-Altamirano, L, et al. (2020) Social determinants for overweight and obesity in a highly marginalized population from Comitan, Chiapas, Mexico. Salud Publica Mex 62, 477–486.CrossRefGoogle Scholar
Thomas, DM, Bredlau, C, Bosy-Westphal, A, et al. (2013) Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity 21, 2264–2271.CrossRefGoogle ScholarPubMed
Batsis, JA, Mackenzie, TA, Bartels, SJ, et al. (2016) Diagnostic accuracy of body mass index to identify obesity in older adults: NHANES 1999–2004. Int J Obes 40, 761–767.CrossRefGoogle ScholarPubMed
Fruhbeck, G, Busetto, L, Dicker, D, et al. (2019) The ABCD of obesity: an EASO position statement on a diagnostic term with clinical and scientific implications. Obes Facts 12, 131–136.CrossRefGoogle ScholarPubMed
Garvey, WT, Garber, AJ, Mechanick, JI, et al. (2014) American association of clinical endocrinologists and American college of endocrinology consensus conference on obesity: building an evidence base for comprehensive action. Endocr Pract 20, 956–976.CrossRefGoogle Scholar
Nieto-Martinez, R, Gonzalez-Rivas, JP & Mechanick, JI (2021) Cardiometabolic risk: new chronic care models. JPEN J Parenter Enteral Nutr 45, 85–92.CrossRefGoogle ScholarPubMed
Orozco-Ruiz, X, Pichardo-Ontiveros, E, Tovar, AR, et al. (2018) Development and validation of new predictive equation for resting energy expenditure in adults with overweight and obesity. Clin Nutr 37, 2198–2205.CrossRefGoogle ScholarPubMed
Gonzalez-Salazar, LE, Pichardo-Ontiveros, E, Palacios-Gonzalez, B, et al. (2021) Effect of the intake of dietary protein on insulin resistance in subjects with obesity: a randomized controlled clinical trial. Eur J Nutr 60, 2435–2447.CrossRefGoogle ScholarPubMed
Sullivan, PA, Still, CD, Jamieson, ST, et al. (2019) Evaluation of multi-frequency bioelectrical impedance analysis for the assessment of body composition in individuals with obesity. Obes Sci Pract 5, 141–147.CrossRefGoogle ScholarPubMed
Purcell, SA, Mackenzie, M, Barbosa-Silva, TG, et al. (2020) Prevalence of sarcopenic obesity using different definitions and the relationship with strength and physical performance in the Canadian longitudinal study of aging. Front Physiol 11, 583825.CrossRefGoogle ScholarPubMed
Lohman, TG & Roche, AF (1988) Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics Books.Google Scholar
Valdez, R (1991) A simple model-based index of abdominal adiposity. J Clin Epidemiol 44, 955–956.CrossRefGoogle ScholarPubMed
Bergman, RN, Stefanovski, D, Buchanan, TA, et al. (2011) A better index of body adiposity. Obesity 19, 1083–1089.CrossRefGoogle ScholarPubMed
Krakauer, NY & Krakauer, JC (2012) A new body shape index predicts mortality hazard independently of body mass index. PLoS One 7, e39504.CrossRefGoogle ScholarPubMed
Matthews, DR, Hosker, JP, Rudenski, AS, et al. (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412–419.CrossRefGoogle ScholarPubMed
Medina, C, Barquera, S & Janssen, I (2013) Validity and reliability of the International physical activity questionnaire among adults in Mexico. Rev Panam Salud Publica 34, 21–28.Google ScholarPubMed
Mehta, S & Ostrum, RF (1998) A calcaneal fracture with extrusion of the posterior facet. Foot Ankle Int 19, 248–251.CrossRefGoogle ScholarPubMed
James, PT (2004) Obesity: the worldwide epidemic. Clin Dermatol 22, 276–280.CrossRefGoogle ScholarPubMed
Grundy, SM, Cleeman, JI, Daniels, SR, et al. (2005) Diagnosis and management of the metabolic syndrome: an American heart association/National heart, lung, and blood institute scientific statement. Circulation 112, 2735–2752.CrossRefGoogle ScholarPubMed
American Diabetes Association Professional Practice Committee (2022) 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2022. Diabetes Care 45, S17–S38.CrossRefGoogle Scholar
Unger, T, Borghi, C, Charchar, F, et al. (2020) 2020 International society of hypertension global hypertension practice guidelines. Hypertension 75, 1334–1357.CrossRefGoogle ScholarPubMed
Graham, I, Atar, D, Borch-Johnsen, K, et al. (2007) European guidelines on cardiovascular disease prevention in clinical practice: executive summary. Atherosclerosis 194, 1–45.CrossRefGoogle ScholarPubMed
Guo, F, Moellering, DR & Garvey, WT (2014) The progression of cardiometabolic disease: validation of a new cardiometabolic disease staging system applicable to obesity. Obesity 22, 110–118.CrossRefGoogle ScholarPubMed
Mechanick, JI, Hurley, DL & Garvey, WT (2017) Adiposity-based chronic disease as a new diagnostic term: the American Association of Clinical Endocrinologists and American College of Endocrinology Position Statement. Endocr Pract 23,372–378
CrossRefGoogle Scholar
Gonzalez-Rivas, JP, Mechanick, JI, Hernandez, JP, et al. (2021) Prevalence of adiposity-based chronic disease in middle-aged adults from Czech Republic: the Kardiovize study. Obes Sci Pract 7, 535–544.CrossRefGoogle ScholarPubMed
Khandelwal, S (2020) Obesity in midlife: lifestyle and dietary strategies. Climacteric 23, 140–147.CrossRefGoogle ScholarPubMed
Guo, F & Garvey, WT (2016) Trends in cardiovascular health metrics in obese adults: national health and nutrition examination survey (NHANES), 1988–2014. J Am Heart Assoc 5, e003619.CrossRefGoogle ScholarPubMed
Mechanick, JI, Farkouh, ME, Newman, JD, et al. (2020) Cardiometabolic-based chronic disease, adiposity and dysglycemia drivers: JACC state-of-the-art review. J Am Coll Cardiol 75, 525–538.CrossRefGoogle ScholarPubMed
Lean, ME, Han, TS & Morrison, CE (1995) Waist circumference as a measure for indicating need for weight management. BMJ 311, 158–161.CrossRefGoogle ScholarPubMed
Nazare, JA, Smith, J, Borel, AL, et al. (2015) Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile (from the INSPIRE ME IAA study). Am J Cardiol 115, 307–315.CrossRefGoogle ScholarPubMed
Kim, G & Kim, JH (2020) Impact of skeletal muscle mass on metabolic health. Endocrinol Metab 35, 1–6.CrossRefGoogle ScholarPubMed
Jeremic, N, Chaturvedi, P & Tyagi, SC (2017) Browning of white fat: novel insight into factors, mechanisms, and therapeutics. J Cell Physiol 232, 61–68.CrossRefGoogle ScholarPubMed
Kistner, TM, Pedersen, BK & Lieberman, DE (2022) Interleukin 6 as an energy allocator in muscle tissue. Nat Metab 4, 170–179.CrossRefGoogle ScholarPubMed
Nishikawa, H, Fukunishi, S, Asai, A, et al. (2021) Pathophysiology and mechanisms of primary sarcopenia. Int J Mol Med 48, 156.CrossRefGoogle ScholarPubMed
Nieto-Martínez, R, González-Rivas, J, Ugel, E, et al. (2018) Application of the AACE/ACE advanced framework for the diagnosis of obesity and cardiometabolic disease staging in a general population from 3 regions of Venezuela: the Vemsols Study Results. Endocr Pract 24, 6–13
CrossRefGoogle Scholar
Fox, CS, Massaro, JM, Hoffmann, U, et al. (2007) Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation 116, 39–48.CrossRefGoogle ScholarPubMed
González-Salazar et al. supplementary material
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González-Salazar et al. supplementary material
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