BACKGROUND: There are well-established predisposing factors for the development of metabolic syndrome (MetS) in childhood or adolescence, but no specific risk profile has been identified as yet. The Prediction of Metabolic Syndrome in Adolescence (PREMA) study was conducted (1) to construct a classification score that could detect children at high risk for MetS in adolescence and (2) to test its predictive accuracy. METHODS AND RESULTS: In the derivation cohort (1270 children), data from natal and parental profile and from initial laboratory assessment at 6 to 8 years of age were used to detect independent predictors of MetS at 13 to 15 years of age according to the International Diabetes Federation definition. In the validation cohort (1091 adolescents), the discriminatory capacity of the derived prediction score was tested on an independent adolescent population. MetS was diagnosed in 105 adolescents in the derivation phase (8%), whereas birth weight <10th percentile (odds ratio, 6.02; 95% confidence interval, 2.53-10.12, P<0.001), birth head circumference <10th percentile (odds ratio, 4.15; 95% confidence interval, 2.04-7.14, P<0.001), and parental overweight or obesity (in at least 1 parent; odds ratio, 3.22; 95% confidence interval, 1.30-5.29, P<0.01) were independently associated with diagnosis of MetS in adolescence. Among adolescents in the validation cohort (86 [8%] with MetS), the presence of all these 3 predictors predicted MetS with a sensitivity of 91% and a specificity of 98%. CONCLUSIONS: The coexistence of low birth weight, small head circumference, and parental history of overweight or obesity may be useful for detection of children at risk of developing MetS in adolescence.
BACKGROUND: There are well-established predisposing factors for the development of metabolic syndrome (MetS) in childhood or adolescence, but no specific risk profile has been identified as yet. The Prediction of Metabolic Syndrome in Adolescence (PREMA) study was conducted (1) to construct a classification score that could detect children at high risk for MetS in adolescence and (2) to test its predictive accuracy. METHODS AND RESULTS: In the derivation cohort (1270 children), data from natal and parental profile and from initial laboratory assessment at 6 to 8 years of age were used to detect independent predictors of MetS at 13 to 15 years of age according to the International Diabetes Federation definition. In the validation cohort (1091 adolescents), the discriminatory capacity of the derived prediction score was tested on an independent adolescent population. MetS was diagnosed in 105 adolescents in the derivation phase (8%), whereas birth weight <10th percentile (odds ratio, 6.02; 95% confidence interval, 2.53-10.12, P<0.001), birth head circumference <10th percentile (odds ratio, 4.15; 95% confidence interval, 2.04-7.14, P<0.001), and parental overweight or obesity (in at least 1 parent; odds ratio, 3.22; 95% confidence interval, 1.30-5.29, P<0.01) were independently associated with diagnosis of MetS in adolescence. Among adolescents in the validation cohort (86 [8%] with MetS), the presence of all these 3 predictors predicted MetS with a sensitivity of 91% and a specificity of 98%. CONCLUSIONS: The coexistence of low birth weight, small head circumference, and parental history of overweight or obesity may be useful for detection of children at risk of developing MetS in adolescence.
Authors: Alan S Go; Dariush Mozaffarian; Véronique L Roger; Emelia J Benjamin; Jarett D Berry; Michael J Blaha; Shifan Dai; Earl S Ford; Caroline S Fox; Sheila Franco; Heather J Fullerton; Cathleen Gillespie; Susan M Hailpern; John A Heit; Virginia J Howard; Mark D Huffman; Suzanne E Judd; Brett M Kissela; Steven J Kittner; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; Rachel H Mackey; David J Magid; Gregory M Marcus; Ariane Marelli; David B Matchar; Darren K McGuire; Emile R Mohler; Claudia S Moy; Michael E Mussolino; Robert W Neumar; Graham Nichol; Dilip K Pandey; Nina P Paynter; Matthew J Reeves; Paul D Sorlie; Joel Stein; Amytis Towfighi; Tanya N Turan; Salim S Virani; Nathan D Wong; Daniel Woo; Melanie B Turner Journal: Circulation Date: 2013-12-18 Impact factor: 29.690
Authors: Emelia J Benjamin; Michael J Blaha; Stephanie E Chiuve; Mary Cushman; Sandeep R Das; Rajat Deo; Sarah D de Ferranti; James Floyd; Myriam Fornage; Cathleen Gillespie; Carmen R Isasi; Monik C Jiménez; Lori Chaffin Jordan; Suzanne E Judd; Daniel Lackland; Judith H Lichtman; Lynda Lisabeth; Simin Liu; Chris T Longenecker; Rachel H Mackey; Kunihiro Matsushita; Dariush Mozaffarian; Michael E Mussolino; Khurram Nasir; Robert W Neumar; Latha Palaniappan; Dilip K Pandey; Ravi R Thiagarajan; Mathew J Reeves; Matthew Ritchey; Carlos J Rodriguez; Gregory A Roth; Wayne D Rosamond; Comilla Sasson; Amytis Towfighi; Connie W Tsao; Melanie B Turner; Salim S Virani; Jenifer H Voeks; Joshua Z Willey; John T Wilkins; Jason Hy Wu; Heather M Alger; Sally S Wong; Paul Muntner Journal: Circulation Date: 2017-01-25 Impact factor: 29.690
Authors: Valentina Chiavaroli; Maria Loredana Marcovecchio; Tommaso de Giorgis; Laura Diesse; Francesco Chiarelli; Angelika Mohn Journal: PLoS One Date: 2014-08-12 Impact factor: 3.240
Authors: Manman Chen; Yanhui Li; Li Chen; Di Gao; Zhaogeng Yang; Ying Ma; Tao Ma; Bin Dong; Yanhui Dong; Jun Ma; Jie Hu Journal: Front Pediatr Date: 2021-05-20 Impact factor: 3.418
Authors: Marco Matteo Ciccone; Pietro Scicchitano; Christian Salerno; Michele Gesualdo; Fara Fornarelli; Annapaola Zito; Lucia Filippucci; Roberta Riccardi; Francesca Cortese; Francesca Pini; Lucia Angrisani; Antonio Di Mauro; Federico Schettini; Nicola Laforgia Journal: Biomed Res Int Date: 2013-07-25 Impact factor: 3.411