Marcus Vinicius Nascimento-Ferreira1, Tara Rendo-Urteaga2, Regina Célia Vilanova-Campelo3, Heráclito Barbosa Carvalho4, Germano da Paz Oliveira5, Mauricio Batista Paes Landim6, Francisco Leonardo Torres-Leal7. 1. Youth/Child Cardiovascular Risk and Environmental (YCARE) Research Group, School of Medicine, University of São Paulo, São Paulo, Brazil; Growth, Exercise, Nutrition and Development (GENUD) Research Group, Faculty of Health Sciences, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Universidad de Zaragoza, Zaragoza, Spain; Metabolic Diseases, Exercise and Nutrition (DOMEN) Research Group, Federal University of Piauí, Teresina, Brazil. Electronic address: marcus1986@usp.br. 2. Youth/Child Cardiovascular Risk and Environmental (YCARE) Research Group, School of Medicine, University of São Paulo, São Paulo, Brazil; Growth, Exercise, Nutrition and Development (GENUD) Research Group, Faculty of Health Sciences, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Universidad de Zaragoza, Zaragoza, Spain. 3. Youth/Child Cardiovascular Risk and Environmental (YCARE) Research Group, School of Medicine, University of São Paulo, São Paulo, Brazil; Health Sciences Center, Federal University of Piauí, Teresina, Brazil; Metabolic Diseases, Exercise and Nutrition (DOMEN) Research Group, Federal University of Piauí, Teresina, Brazil. 4. Youth/Child Cardiovascular Risk and Environmental (YCARE) Research Group, School of Medicine, University of São Paulo, São Paulo, Brazil. 5. University Hospital of Federal University of Piauí, Teresina, Brazil. 6. University Hospital of Federal University of Piauí, Teresina, Brazil; Health Sciences Center, Federal University of Piauí, Teresina, Brazil. 7. Metabolic Diseases, Exercise and Nutrition (DOMEN) Research Group, Federal University of Piauí, Teresina, Brazil.
Abstract
BACKGROUND & AIMS: Lipid accumulation product (LAP) is emergent predictor of central lipid accumulation linked to diabetes risk and cardiovascular disease. In this study, our aims were (i) to assess the accuracy of the LAP for predicting metabolic syndrome (MS) in subjects without diagnosis of cardiovascular disease (CVD) and type 2 diabetes mellitus compared with other classical anthropometric parameters and (ii) to estimate the optimal LAP cut-off point to predict MS in this population. SUBJECTS/ METHODS: We conducted a cross-sectional study with representative undiagnosed subjects aged 20-79 years (n = 201; 37.8% men), selected by simple random sampling. In this study, subjects without previous diagnosis of CVD and type 2 diabetes mellitus, and those who did not make use of continuous medication were included. The independent variables were body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR) and LAP. MS was defined by American Heart Association and the National Heart, Lung, and Blood Institute (AHA/NHLBI); International Diabetes Federation (IDF); and a harmonized criteria between AHA/NHLBI and IDF. RESULTS: The prevalence of MS was 78.9% and 69.6% for men and women, respectively. LAP showed better area under the curve (AUC) for MS in three different criteria than those indexes based on body mass index, waist circumference, waist-to-height ratio and waist-to-hip ratio, even after adjusting for age and sex. In the harmonized criteria, the cut-off point of 34.2 cm.mmol/L for LAP showed the highest accuracy for MS (sensitivity 0.90, specificity 0.61, positive likelihood ratio of 2.31 and negative likelihood ratio of 0.17). CONCLUSIONS: LAP is a simple and accurate predictor tool for MS in undiagnosed adults. Moreover, it has significantly higher predictability than other screening tools commonly used to find subjects at risk of CVD and type 2 diabetes mellitus development, with the best performance at the 34.2 cm.mmol/L cut-off point.
BACKGROUND & AIMS:Lipid accumulation product (LAP) is emergent predictor of central lipid accumulation linked to diabetes risk and cardiovascular disease. In this study, our aims were (i) to assess the accuracy of the LAP for predicting metabolic syndrome (MS) in subjects without diagnosis of cardiovascular disease (CVD) and type 2 diabetes mellitus compared with other classical anthropometric parameters and (ii) to estimate the optimal LAP cut-off point to predict MS in this population. SUBJECTS/ METHODS: We conducted a cross-sectional study with representative undiagnosed subjects aged 20-79 years (n = 201; 37.8% men), selected by simple random sampling. In this study, subjects without previous diagnosis of CVD and type 2 diabetes mellitus, and those who did not make use of continuous medication were included. The independent variables were body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR) and LAP. MS was defined by American Heart Association and the National Heart, Lung, and Blood Institute (AHA/NHLBI); International Diabetes Federation (IDF); and a harmonized criteria between AHA/NHLBI and IDF. RESULTS: The prevalence of MS was 78.9% and 69.6% for men and women, respectively. LAP showed better area under the curve (AUC) for MS in three different criteria than those indexes based on body mass index, waist circumference, waist-to-height ratio and waist-to-hip ratio, even after adjusting for age and sex. In the harmonized criteria, the cut-off point of 34.2 cm.mmol/L for LAP showed the highest accuracy for MS (sensitivity 0.90, specificity 0.61, positive likelihood ratio of 2.31 and negative likelihood ratio of 0.17). CONCLUSIONS: LAP is a simple and accurate predictor tool for MS in undiagnosed adults. Moreover, it has significantly higher predictability than other screening tools commonly used to find subjects at risk of CVD and type 2 diabetes mellitus development, with the best performance at the 34.2 cm.mmol/L cut-off point.
Authors: Marzena Ratajczak; Damian Skrypnik; Piotr Krutki; Joanna Karolkiewicz Journal: Int J Environ Res Public Health Date: 2020-11-24 Impact factor: 3.390
Authors: Sung Hoon Jeong; Bich Na Jang; Seung Hoon Kim; Sung-In Jang; Eun-Cheol Park Journal: Int J Environ Res Public Health Date: 2021-04-14 Impact factor: 3.390