Judit Muñoz-Hernando1, Veronica Luque2,3, Natalia Ferré1, Albert Feliu1,4, Ricardo Closa-Monasterolo1, Desirée Gutiérrez-Marin5, Josep Basora6, Ana Pedraza7, Olga Salvado7, Susana Vidal-Piedra8, Joaquín Escribano9,10. 1. Paediatrics, Nutrition and Development Research Unit, Universitat Rovira i Virgili, IISPV, 43201, Reus, Spain. 2. Paediatrics, Nutrition and Development Research Unit, Universitat Rovira i Virgili, IISPV, 43201, Reus, Spain. veronica.luque@urv.cat. 3. Serra Hunter Fellow, Universitat Rovira i Virgili, 43201, Reus, Spain. veronica.luque@urv.cat. 4. Hospital Universitari Sant Joan de Reus, 43204, Reus, Spain. 5. Parc Sanitari Sant Joan de Déu, 08830, Sant Boi de Llobregat, Barcelona, Spain. 6. Unitat de Suport a la Recerca Tarragona-Reus, IDIAPJGol, 43204, Reus, Spain. 7. CAP Llibertat, Institut Català de la Salut, 43203, Reus, Spain. 8. CAP Miami Platja, Institut Català de la Salut, 43892, Mont-roig del Camp, Tarragona, Spain. 9. Paediatrics, Nutrition and Development Research Unit, Universitat Rovira i Virgili, IISPV, 43201, Reus, Spain. joaquin.escribano@urv.cat. 10. Hospital Universitari Sant Joan de Reus, 43204, Reus, Spain. joaquin.escribano@urv.cat.
Abstract
BACKGROUND: Waist-to-height ratio (WHtR) predicts abdominal fat and cardiometabolic risk. In children with obesity, the most adequate cut-off to predict cardiometabolic risk as well as its ability to predict risk changes over time has not been tested. Our aim was to define an appropriate WHtR cut-off to predict cardiometabolic risk in children with obesity, and to analyze its ability to predict changes in cardiometabolic risk over time. METHODS: This is an observational prospective study secondary to the OBEMAT2.0 trial. We included data from 218 participants (8-15 years) who attended baseline and final visits (12 months later). The main outcome measure was a cardiometabolic risk score derived from blood pressure, lipoproteins, and HOMA index of insulin resistance. RESULTS: The optimal cut-off to predict the cardiometabolic risk score was WHtR ≥0.55 with an area under the curve of 0.675 (95% CI: 0.589-0.760) at baseline and 0.682 (95% CI: 0.585-0.779) at the final visit. Multivariate models for repeated measures showed that changes in cardiometabolic risk were significantly associated with changes in WHtR. CONCLUSION: This study confirms the clinical utility of WHtR to predict changes in cardiometabolic risk over time in children with obesity. The most accurate cut-off to predict cardiometabolic risk in children with obesity was WHtR ≥0.55. IMPACT: In children, there is no consensus on a unique WHtR cut-off to predict cardiometabolic risk. The present work provides sufficient evidence to support the use of the 0.55 boundary. We have a large sample of children with obesity, with whom we compared the previously proposed boundaries according to cardiometabolic risk, and we found the optimal WHtR cut-off to predict it. We also analyzed if a reduction in the WHtR was associated with an improvement in their cardiometabolic profile.
BACKGROUND: Waist-to-height ratio (WHtR) predicts abdominal fat and cardiometabolic risk. In children with obesity, the most adequate cut-off to predict cardiometabolic risk as well as its ability to predict risk changes over time has not been tested. Our aim was to define an appropriate WHtR cut-off to predict cardiometabolic risk in children with obesity, and to analyze its ability to predict changes in cardiometabolic risk over time. METHODS: This is an observational prospective study secondary to the OBEMAT2.0 trial. We included data from 218 participants (8-15 years) who attended baseline and final visits (12 months later). The main outcome measure was a cardiometabolic risk score derived from blood pressure, lipoproteins, and HOMA index of insulin resistance. RESULTS: The optimal cut-off to predict the cardiometabolic risk score was WHtR ≥0.55 with an area under the curve of 0.675 (95% CI: 0.589-0.760) at baseline and 0.682 (95% CI: 0.585-0.779) at the final visit. Multivariate models for repeated measures showed that changes in cardiometabolic risk were significantly associated with changes in WHtR. CONCLUSION: This study confirms the clinical utility of WHtR to predict changes in cardiometabolic risk over time in children with obesity. The most accurate cut-off to predict cardiometabolic risk in children with obesity was WHtR ≥0.55. IMPACT: In children, there is no consensus on a unique WHtR cut-off to predict cardiometabolic risk. The present work provides sufficient evidence to support the use of the 0.55 boundary. We have a large sample of children with obesity, with whom we compared the previously proposed boundaries according to cardiometabolic risk, and we found the optimal WHtR cut-off to predict it. We also analyzed if a reduction in the WHtR was associated with an improvement in their cardiometabolic profile.
Authors: Alfonso Soto González; Diego Bellido; María Manuela Buño; Sonia Pértega; Daniel De Luis; Miguel Martínez-Olmos; Ovidio Vidal Journal: Nutrition Date: 2007-01 Impact factor: 4.008
Authors: Markus Juonala; Costan G Magnussen; Gerald S Berenson; Alison Venn; Trudy L Burns; Matthew A Sabin; Sathanur R Srinivasan; Stephen R Daniels; Patricia H Davis; Wei Chen; Cong Sun; Michael Cheung; Jorma S A Viikari; Terence Dwyer; Olli T Raitakari Journal: N Engl J Med Date: 2011-11-17 Impact factor: 91.245
Authors: D López-González; A Miranda-Lora; M Klünder-Klünder; G Queipo-García; M Bustos-Esquivel; M Paez-Villa; E Villanueva-Ortega; I Chávez-Requena; E Laresgoiti-Servitje; N Garibay-Nieto Journal: Endocr Pract Date: 2016-06-13 Impact factor: 3.443
Authors: Marie Ng; Tom Fleming; Margaret Robinson; Blake Thomson; Nicholas Graetz; Christopher Margono; Erin C Mullany; Stan Biryukov; Cristiana Abbafati; Semaw Ferede Abera; Jerry P Abraham; Niveen M E Abu-Rmeileh; Tom Achoki; Fadia S AlBuhairan; Zewdie A Alemu; Rafael Alfonso; Mohammed K Ali; Raghib Ali; Nelson Alvis Guzman; Walid Ammar; Palwasha Anwari; Amitava Banerjee; Simon Barquera; Sanjay Basu; Derrick A Bennett; Zulfiqar Bhutta; Jed Blore; Norberto Cabral; Ismael Campos Nonato; Jung-Chen Chang; Rajiv Chowdhury; Karen J Courville; Michael H Criqui; David K Cundiff; Kaustubh C Dabhadkar; Lalit Dandona; Adrian Davis; Anand Dayama; Samath D Dharmaratne; Eric L Ding; Adnan M Durrani; Alireza Esteghamati; Farshad Farzadfar; Derek F J Fay; Valery L Feigin; Abraham Flaxman; Mohammad H Forouzanfar; Atsushi Goto; Mark A Green; Rajeev Gupta; Nima Hafezi-Nejad; Graeme J Hankey; Heather C Harewood; Rasmus Havmoeller; Simon Hay; Lucia Hernandez; Abdullatif Husseini; Bulat T Idrisov; Nayu Ikeda; Farhad Islami; Eiman Jahangir; Simerjot K Jassal; Sun Ha Jee; Mona Jeffreys; Jost B Jonas; Edmond K Kabagambe; Shams Eldin Ali Hassan Khalifa; Andre Pascal Kengne; Yousef Saleh Khader; Young-Ho Khang; Daniel Kim; Ruth W Kimokoti; Jonas M Kinge; Yoshihiro Kokubo; Soewarta Kosen; Gene Kwan; Taavi Lai; Mall Leinsalu; Yichong Li; Xiaofeng Liang; Shiwei Liu; Giancarlo Logroscino; Paulo A Lotufo; Yuan Lu; Jixiang Ma; Nana Kwaku Mainoo; George A Mensah; Tony R Merriman; Ali H Mokdad; Joanna Moschandreas; Mohsen Naghavi; Aliya Naheed; Devina Nand; K M Venkat Narayan; Erica Leigh Nelson; Marian L Neuhouser; Muhammad Imran Nisar; Takayoshi Ohkubo; Samuel O Oti; Andrea Pedroza; Dorairaj Prabhakaran; Nobhojit Roy; Uchechukwu Sampson; Hyeyoung Seo; Sadaf G Sepanlou; Kenji Shibuya; Rahman Shiri; Ivy Shiue; Gitanjali M Singh; Jasvinder A Singh; Vegard Skirbekk; Nicolas J C Stapelberg; Lela Sturua; Bryan L Sykes; Martin Tobias; Bach X Tran; Leonardo Trasande; Hideaki Toyoshima; Steven van de Vijver; Tommi J Vasankari; J Lennert Veerman; Gustavo Velasquez-Melendez; Vasiliy Victorovich Vlassov; Stein Emil Vollset; Theo Vos; Claire Wang; XiaoRong Wang; Elisabete Weiderpass; Andrea Werdecker; Jonathan L Wright; Y Claire Yang; Hiroshi Yatsuya; Jihyun Yoon; Seok-Jun Yoon; Yong Zhao; Maigeng Zhou; Shankuan Zhu; Alan D Lopez; Christopher J L Murray; Emmanuela Gakidou Journal: Lancet Date: 2014-05-29 Impact factor: 79.321