Valores óptimos de punto de corte de circunferencia abdominal para predecir alteraciones cardiometabólicas en una muestra representativa nacional de Venezuela. El estudio EVESCAM
[Optimal waist circumference cutoff values to predict cardiometabolic alterations in a Venezuela national representative sample. The EVESCAM study]
Status PubMed-not-MEDLINE Jazyk španělština Země Mexiko Médium electronic
Typ dokumentu časopisecké články
PubMed
33362194
PubMed Central
PMC8351653
DOI
10.24875/acm.20000165
Knihovny.cz E-zdroje
- Klíčová slova
- Abdominal obesity, Adiposity, Metabolic syndrome, Venezuela, Waist circumference,
- Publikační typ
- časopisecké články MeSH
OBJECTIVE:: Waist circumference (WC) value reflects abdominal adiposity, but the amount abdominal fat that is associated to cardiometabolic risk factors varies among ethnicities. Determination of metabolic abnormalities has not undergone a WC adaptation process in Venezuela. The aim of the study was (1) to determine the optimal WC cutoff value associated with ≥2 cardiometabolic alterations and (2) incorporating this new WC cutoff, to determine the prevalence of abdominal obesity and cardiometabolic risk factors related in Venezuela. METHODS:: The study was national population-based, cross-sectional, and randomized sample, from 2014 to 2017. To assess performance of WC for identifying cardiometabolic alterations, receiver operating characteristics curves, area under the curve (AUC), sensitivity, specificity, and positive likelihood ratios were calculated. RESULTS:: Three thousand three hundred eighty-seven adults were evaluated with mean age of 41.2 ± 15.8 years. Using the best tradeoff between sensitivity and specificity, WC cutoffs of 90 cm in men (sensitivity = 72.4% and specificity = 66.1%) and 86 cm in women (sensitivity = 76.2% and specificity = 61.4%) were optimal for aggregation of ≥2 cardiometabolic alterations. AUC was 0.75 in men and 0.73 in women using these new cutoffs. Prevalence of abdominal obesity and metabolic syndrome was 59.6% (95 CI; 57.5-61.7) and 47.6% (95 CI; 45.2-50.0), respectively. Cardiometabolic risk factors were associated with being men, higher age, adiposity, and living in northern or western regions. CONCLUSION:: The optimal WC values associated with cardiometabolic alterations were 90 cm in men and 86 cm in women. More than half of the Venezuelan population had abdominal obesity incorporating this new WC cutoff.
BACKGROUND: Waist circumference (WC) value reflects abdominal adiposity, but the amount abdominal fat that is associated to cardiometabolic risk factors varies among ethnicities. Determination of metabolic abnormalities has not undergone a WC adaptation process in Venezuela. AIMS: The aim of the study was (1) to determine the optimal WC cutoff value associated with ≥2 cardiometabolic alterations and (2) incorporating this new WC cutoff, to determine the prevalence of abdominal obesity and cardiometabolic risk factors related in Venezuela. METHODS: The study was national population-based, cross-sectional, and randomized sample, from 2014 to 2017. To assess performance of WC for identifying cardiometabolic alterations, receiver operating characteristics curves, area under the curve (AUC), sensitivity, specificity, and positive likelihood ratios were calculated. RESULTS: Three thousand three hundred eighty-seven adults were evaluated with mean age of 41.2 ± 15.8 years. Using the best tradeoff between sensitivity and specificity, WC cutoffs of 90 cm in men (sensitivity = 72.4% and specificity = 66.1%) and 86 cm in women (sensitivity = 76.2% and specificity = 61.4%) were optimal for aggregation of ≥2 cardiometabolic alterations. AUC was 0.75 in men and 0.73 in women using these new cutoffs. Prevalence of abdominal obesity and metabolic syndrome was 59.6% (95 CI; 57.5-61.7) and 47.6% (95 CI; 45.2-50.0), respectively. Cardiometabolic risk factors were associated with being men, higher age, adiposity, and living in northern or western regions. CONCLUSION: The optimal WC values associated with cardiometabolic alterations were 90 cm in men and 86 cm in women. More than half of the Venezuelan population had abdominal obesity incorporating this new WC cutoff.
Zobrazit více v PubMed
[Last accessed on Sep 2019];Institute for Health Metrics and Evaluation, Venezuela Profile. 2019 Available from: http://www.healthdata.org/venezuela .
WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63. PubMed
Mechanick JI, Hurley DL, Garvey WT. Adiposity-based chronic disease as a new diagnostic term:the American association of clinical endocrinologists and American college of endocrinology position statement. Endocr Pract. 2017;23:372–8. PubMed
Mechanick JI, Garber AJ, Grunberger G, Handelsman Y, Garvey T. Dysglycemia-based chronic disease:an American association of clinical endocrinologists position statement. Endocr Pract. 2018;24:995–1011. PubMed
Batsis JA, Nieto-Martinez RE, Lopez-Jimenez F. Metabolic syndrome:from global epidemiology to individualized medicine. Clin Pharmacol Ther. 2007;82:509–24. PubMed
Galassi A, Reynolds K, He J. Metabolic syndrome and risk of cardiovascular disease:a meta-analysis. Am J Med. 2006;119:812–9. PubMed
Dragsbæk K, Neergaard JS, Laursen JM, Hansen HB, Christiansen C, Beck-Nielsen H, et al. Metabolic syndrome and subsequent risk of Type 2 diabetes and cardiovascular disease in elderly women:challenging the current definition. Medicine. 2016;95:e4806. PubMed PMC
Esposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome and risk of cancer:a systematic review and meta-analysis. Diabetes Care. 2012;35:2402–11. PubMed PMC
Alberti KG, Zimmet P, Shaw J. Metabolic syndrome-a new world-wide definition. A consensus statement from the international diabetes federation. Diabet Med. 2006;23:469–80. PubMed
Cardinal TR, Vigo A, Duncan BB, Matos SM, da Fonseca MJ, Barreto SM, et al. Optimal cut-off points for waist circumference in the definition of metabolic syndrome in Brazilian adults:baseline analyses of the longitudinal study of adult health (ELSA-Brasil) Diabetol Metab Syndr. 2018;10:49. PubMed PMC
Torres-Valdez M, Ortiz-Benavides R, Sigüenza-Cruz W, Ortiz-Benavides A, Añez R, Salazar J, et al. Punto de corte de circunferencia abdominal para el agrupamiento de factores de riesgo metabólico:una propuesta para la población adulta de Cuenca, Ecuador. Rev Argent Endocrinol Metab. 2016;53:59–66.
Bermudez V, Rojas J, Salazar J, Añez R, Chavez-Castillo M, Gonzalez R, et al. Optimal waist circumference cut-off point for multiple risk factor aggregation:results from the Maracaibo city metabolic syndrome prevalence study. Epidemiol Res Int. 2014;2014:718571.
Villalobos E, Mata K, Guerrero Y, Añez R, Rojas J, Bermúdez V. Determinación del Punto de Corte Óptimo Para la Circunferencia Abdominal Mediante su Agregación con Múltiples Factores de Riesgo:una Propuesta Para la Población Adulta de San Cristóbal, Estado Táchira. 2016
Aschner P, Buendía R, Brajkovich I, Gonzalez A, Figueredo R, Juarez XE, et al. Determination of the cutoff point for waist circumference that establishes the presence of abdominal obesity in Latin American men and women. Diabetes Res Clin Pract. 2011;93:243–7. PubMed
Selvaraj S, Martinez EE, Aguilar FG, Kim KY, Peng J, Sha J, et al. Association of central adiposity with adverse cardiac mechanics:findings from the hypertension genetic epidemiology network study. Circ Cardiovasc Imaging. 2016;9(6) 10.1161/CIRCIMAGING.115.004396 e004396. PubMed PMC
Nieto-Martínez R, Marulanda MI, Ugel E, Duran M, González-Rivas J, Patiño M, et al. Venezuelan study of cardio-metabolic health (EVESCAM):general description and sampling. Med Interna. 2015;31:102–11.
Nieto-Martínez R, Marulanda MI, González-Rivas JP, Ugel E, Durán M, Barengo N, et al. Cardio-metabolic health Venezuelan study (EVESCAM):design and implementation. Invest Clin. 2017;58:56–61. PubMed
Nieto-Martínez R, González JP, Lima-Martínez M, Stepenka V, Rísquez A, Mechanick JI. Diabetes care in Venezuela. Ann Glob Health. 2015;81:776–91. PubMed
von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The strengthening the reporting of observational studies in epidemiology (STROBE) statement:guidelines for reporting observational studies. PLoS Med. 2007;4:e296. PubMed PMC
Vera-Cala LM, Orostegui M, Valencia-Angel LI, Lopez N, Bautista LE. Accuracy of the Omron HEM-705 CP for blood pressure measurement in large epidemiologic studies. Arq Bras Cardiol. 2011;96:393–8. PubMed
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, 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;120:1640–5. PubMed
Méndez-Castellano H, Méndez MC. Estratificación social y humana. Método de graffar modificado. Arch Venez Pueric Pediatr. 1986;49:93–104.
Raposo L, Severo M, Santos AC. Adiposity cut-off points for cardiovascular disease and diabetes risk in the Portuguese population:the PORMETS study. PLoS One. 2018;13:e0191641. PubMed PMC
Hu H, Kurotani K, Sasaki N, Murakami T, Shimizu C, Shimizu M, et al. Optimal waist circumference cut-off points and ability of different metabolic syndrome criteria for predicting diabetes in Japanese men and women:Japan epidemiology collaboration on occupational health study. BMC Public Health. 2016;16:220. PubMed PMC
Ekoru K, Murphy GA, Young EH, Delisle H, Jerome CS, Assah F, et al. Deriving an optimal threshold of waist circumference for detecting cardiometabolic risk in sub-Saharan Africa. Int J Obes (Lond) 2017;42:487–94. PubMed PMC
Brajkovich I, González-Rivas J, Ugel E, Rísquez A, Nieto-Martínez R. Prevalence of metabolic syndrome in three regions in Venezuela:the VEMSOLS study. Int J Cardiovasc Sci. 2018;31:603–9.
Wong-McClure RA, Gregg EW, Barcelo A, Lee K, Abarca-Gomez L, Sanabria-Lopez L, et al. Prevalence of metabolic syndrome in Central America:a cross-sectional population-based study. Rev Panam Salud Publica. 2015;38:202–8. PubMed
Aguilar M, Bhuket T, Torres S, Liu B, Wong RJ. Prevalence of the metabolic syndrome in the United States, 2003-2012. JAMA. 2015;313:1973–4. PubMed
National Cholesterol Education Program (NCEP) Expert Panel on Detection. Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation. 2002;106:3143–421. PubMed
Lopez-Jaramillo P, Lahera V, Lopez-Lopez J. Epidemic of cardiometabolic diseases:a Latin American point of view. Ther Adv Cardiovasc Dis. 2011;5:119–31. PubMed
Marquez-Sandoval F, Macedo-Ojeda G, Viramontes-Horner D, Ballart JD, Salvado JS, Vizmanos B. The prevalence of metabolic syndrome in Latin America:a systematic review. Public Health Nutr. 2011;14:1702–13. PubMed
Bermudez V, Rojas J, Martinez MS, Apruzzese V, Chavez-Castillo M, Gonzalez R, et al. Epidemiologic behavior and estimation of an optimal cut-off point for homeostasis model assessment-2 insulin resistance:a report from a Venezuelan population. Int Sch Res Notices. 2014;2014:616271. PubMed PMC
Lima-Martinez MM, Blandenier C, Iacobellis G. Epicardial adipose tissue:more than a simple fat deposit? Endocrinol Nutr. 2013;60:320–8. PubMed
González-Rivas J, Molina T. Síndrome metabólico. Med Interna. 2011;27:156–63.
Lewington S, Clarke R, Qizilbash N, Peto R, Collins R. Age-specific relevance of usual blood pressure to vascular mortality:a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360:1903–13. PubMed
McEwen BS, Wingfield JC. The concept of allostasis in biology and biomedicine. Horm Behav. 2003;43:2–15. PubMed
Yau YH, Potenza MN. Stress and eating behaviors. Minerva Endocrinol. 2013;38:255–67. PubMed PMC
Kelly SJ, Ismail M. Stress and Type 2 diabetes:a review of how stress contributes to the development of Type 2 diabetes. Annu Rev Public Health. 2015;36:441–62. PubMed
Yau YH, Potenza MN. Stress and eating behaviors. Minerva Endocrinol. 2013;38:255–67. PubMed PMC
Tsigos C, Kyrou I, Kassi E, Chrousos GP. Stress, endocrine physiology and pathophysiology. In: de Groot LJ, Chrousos G, Dungan K, Feingold KR, Grossman A, Hershman JM, et al., editors. Endotext. South Dartmouth, MA: MDText.Com, Inc; 2000.