OBJECTIVES: To determine the point prevalences of metabolic syndrome (MetS) and its components among healthy weight, overweight, and obese inner-city public high school students, to compare the prevalences of MetS when using 2 different definitions (one with the impaired fasting glucose [IFG] level and the other with a homeostasis model assessment of insulin resistance [HOMA-IR] of 3.99 or higher to define the glucose regulation component), and to compare the degree to which HOMA-IR and fasting glucose level are associated with the other MetS components. DESIGN: Cross-sectional analysis. SETTING: Two New York City public high schools, from April 2008 through August 2011. PARTICIPANTS: Convenience sample of 1185 high school youth, comprising predominantly Hispanic and African American students from low-income households, participating in The Banishing Obesity and Diabetes in Youth Project, a medical screening and education program. MAIN OUTCOME MEASURES: Prevalences of the following individual MetS components: IFG threshold, HOMA-IR, hypertension, central adiposity, hypertriglyceridemia, and low high-density lipoprotein cholesterol. Rates of MetSIFG and MetSHOMA-IR were also assessed. RESULTS: MetSIFG and MetSHOMA-IR point prevalences were both 0.3% in the healthy weight group; they were 2.6% and 5.9%, respectively, in the overweight group and were 22.9% and 35.1%, respectively, in the obese group (P < .05 for both). An IFG threshold of 100 mg/dL or higher was found in 1.0% of participants, whereas a HOMA-IR of 3.99 or higher was found in 19.5% of participants. CONCLUSIONS: An elevated HOMA-IR is much more sensitive than an IFG threshold in identifying adolescents with metabolic dysregulation. Using a HOMA-IR threshold of 3.99 identifies more youth with MetS than using an IFG threshold of 100 mg/dL. In addition to increasing the sensitivity of MetS detection, HOMA-IR has a much higher association with the other MetS components than the IFG threshold and may better reflect a unified underlying pathologic process useful to identify youth at risk for disease.
OBJECTIVES: To determine the point prevalences of metabolic syndrome (MetS) and its components among healthy weight, overweight, and obese inner-city public high school students, to compare the prevalences of MetS when using 2 different definitions (one with the impaired fasting glucose [IFG] level and the other with a homeostasis model assessment of insulin resistance [HOMA-IR] of 3.99 or higher to define the glucose regulation component), and to compare the degree to which HOMA-IR and fasting glucose level are associated with the other MetS components. DESIGN: Cross-sectional analysis. SETTING: Two New York City public high schools, from April 2008 through August 2011. PARTICIPANTS: Convenience sample of 1185 high school youth, comprising predominantly Hispanic and African American students from low-income households, participating in The Banishing Obesity and Diabetes in Youth Project, a medical screening and education program. MAIN OUTCOME MEASURES: Prevalences of the following individual MetS components: IFG threshold, HOMA-IR, hypertension, central adiposity, hypertriglyceridemia, and low high-density lipoprotein cholesterol. Rates of MetSIFG and MetSHOMA-IR were also assessed. RESULTS: MetSIFG and MetSHOMA-IR point prevalences were both 0.3% in the healthy weight group; they were 2.6% and 5.9%, respectively, in the overweight group and were 22.9% and 35.1%, respectively, in the obese group (P < .05 for both). An IFG threshold of 100 mg/dL or higher was found in 1.0% of participants, whereas a HOMA-IR of 3.99 or higher was found in 19.5% of participants. CONCLUSIONS: An elevated HOMA-IR is much more sensitive than an IFG threshold in identifying adolescents with metabolic dysregulation. Using a HOMA-IR threshold of 3.99 identifies more youth with MetS than using an IFG threshold of 100 mg/dL. In addition to increasing the sensitivity of MetS detection, HOMA-IR has a much higher association with the other MetS components than the IFG threshold and may better reflect a unified underlying pathologic process useful to identify youth at risk for disease.
Authors: Julia Steinberger; Stephen R Daniels; Robert H Eckel; Laura Hayman; Robert H Lustig; Brian McCrindle; Michele L Mietus-Snyder Journal: Circulation Date: 2009-01-12 Impact factor: 29.690
Authors: Gregory E Miller; Edith Chen; Casey C Armstrong; Ann L Carroll; Sekine Ozturk; Kelsey J Rydland; Gene H Brody; Todd B Parrish; Robin Nusslock Journal: Proc Natl Acad Sci U S A Date: 2018-11-05 Impact factor: 11.205
Authors: Najat Yahia; Carrie A Brown; Ericka Snyder; Stephanie Cumper; Andrea Langolf; Chelsey Trayer; Chelsea Green Journal: J Community Health Date: 2017-08
Authors: N Leite; M C Tadiotto; P R P Corazza; F J de Menezes Junior; M E C Carli; G E Milano-Gai; W A Lopes; A R Gaya; C Brand; J Mota; R B Radominski Journal: J Endocrinol Invest Date: 2021-11-15 Impact factor: 4.256
Authors: Leah Mechanic; Armando Mendez; Lori Merrill; John Rogers; Marnie Layton; Deborah Todd; Arti Varanasi; Barbara O'Brien; William A Meyer Iii; Ming Zhang; Rosemary L Schleicher; Jack Moye Journal: Clin Chem Lab Med Date: 2013-12 Impact factor: 3.694
Authors: Gabriela Saravia; Fernando Civeira; Yamilee Hurtado-Roca; Eva Andres; Montserrat Leon; Miguel Pocovi; Jose Ordovas; Eliseo Guallar; Antonio Fernandez-Ortiz; Jose Antonio Casasnovas; Martin Laclaustra Journal: PLoS One Date: 2015-08-04 Impact factor: 3.240