BACKGROUND: Rising obesity has been observed in all age groups. Anthropometric cut-points have been used to predict metabolic risk in children, although they are not based on known outcomes. AIM: We examined the trends, associations and predictions of metabolic health from anthropometry in prepubertal children. METHOD: Three hundred and seven healthy children were examined annually between 5 and 8 yr. MEASURES: height, weight, body mass index (BMI), sum of skinfold thickness at five sites (SSF) and waist circumference (WC). OUTCOME MEASURES: homeostasis model assessment of insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). RESULTS: Two hundred and thirty-one [131 boys (B) and 100 girls (G)] children had complete data sets at all four time points. (i) All measures of adiposity rose from 5 to 8 yr (BMI - B: +3.4%, G: +5.7%; WC - B: +10.4%, G: +11.8%; SSF - B: +23.3%, G: +30.7%, all p < 0.001). HOMA-IR unexpectedly fell (B: -16.6%, p = 0.01; G: -32.5%, p < 0.001). This fall was significant between 5 and 6 yr in both genders (5-6 yr - B: -17.8%, p < 0.001; G: -20.0%, p = 0.002) and between 6 and 7 yr in girls only (6-7 yr - B: -10.8%, p = 0.12; G: -19.2%, p = 0.001). HDL-C rose (B: +17.8%, G: +17.1%, both p < 0.001) and TG fell (B: -4.8%, p = 0.16; G: -11.6%, p = 0.006). (ii) Correlations between insulin resistance (IR) and anthropometry were poor at 5 yr but strengthened by 8 yr (BMI - B: r = 0.20/0.38, G: r = 0.28/0.49; WC - B: r = 0.25/0.40, G: r = 0.32/0.58; SSF - B: r = 0.11/0.36, G: r = 0.18/0.53). (iii) In girls, but not boys, adiposity at 5 yr predicted IR better at 8 yr (BMI - r(2 )= 0.17; WC - r(2 )= 0.28; SSF - r(2 )= 0.17, all p < 0.001) than it did at 5 yr (BMI - r(2 )= 0.08, p < 0.01; WC - r(2 )= 0.10, p < 0.01; SSF - r(2 )= 0.03, p = 0.07). CONCLUSIONS: Cross-sectional association cannot indicate direction of trend or predict the future. Predicting metabolic health from anthropometric measures in prepubertal children requires longitudinal data, tracking variables from childhood into adulthood. Until the data set reaches adulthood, it is probably not safe to make recommendations on which children to 'target' or whether early intervention would be of benefit.
BACKGROUND: Rising obesity has been observed in all age groups. Anthropometric cut-points have been used to predict metabolic risk in children, although they are not based on known outcomes. AIM: We examined the trends, associations and predictions of metabolic health from anthropometry in prepubertal children. METHOD: Three hundred and seven healthy children were examined annually between 5 and 8 yr. MEASURES: height, weight, body mass index (BMI), sum of skinfold thickness at five sites (SSF) and waist circumference (WC). OUTCOME MEASURES: homeostasis model assessment of insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). RESULTS: Two hundred and thirty-one [131 boys (B) and 100 girls (G)] children had complete data sets at all four time points. (i) All measures of adiposity rose from 5 to 8 yr (BMI - B: +3.4%, G: +5.7%; WC - B: +10.4%, G: +11.8%; SSF - B: +23.3%, G: +30.7%, all p < 0.001). HOMA-IR unexpectedly fell (B: -16.6%, p = 0.01; G: -32.5%, p < 0.001). This fall was significant between 5 and 6 yr in both genders (5-6 yr - B: -17.8%, p < 0.001; G: -20.0%, p = 0.002) and between 6 and 7 yr in girls only (6-7 yr - B: -10.8%, p = 0.12; G: -19.2%, p = 0.001). HDL-C rose (B: +17.8%, G: +17.1%, both p < 0.001) and TG fell (B: -4.8%, p = 0.16; G: -11.6%, p = 0.006). (ii) Correlations between insulin resistance (IR) and anthropometry were poor at 5 yr but strengthened by 8 yr (BMI - B: r = 0.20/0.38, G: r = 0.28/0.49; WC - B: r = 0.25/0.40, G: r = 0.32/0.58; SSF - B: r = 0.11/0.36, G: r = 0.18/0.53). (iii) In girls, but not boys, adiposity at 5 yr predicted IR better at 8 yr (BMI - r(2 )= 0.17; WC - r(2 )= 0.28; SSF - r(2 )= 0.17, all p < 0.001) than it did at 5 yr (BMI - r(2 )= 0.08, p < 0.01; WC - r(2 )= 0.10, p < 0.01; SSF - r(2 )= 0.03, p = 0.07). CONCLUSIONS: Cross-sectional association cannot indicate direction of trend or predict the future. Predicting metabolic health from anthropometric measures in prepubertal children requires longitudinal data, tracking variables from childhood into adulthood. Until the data set reaches adulthood, it is probably not safe to make recommendations on which children to 'target' or whether early intervention would be of benefit.
Authors: Hye Jin Lee; Youn-Hee Lim; Yun-Chul Hong; Choong Ho Shin; Young Ah Lee Journal: Front Endocrinol (Lausanne) Date: 2022-05-23 Impact factor: 6.055
Authors: Young-Gyu Cho; Jae-Heon Kang; Yang-Im Hur; Jihyun Song; Kang-Sook Lee Journal: Int J Environ Res Public Health Date: 2011-12-12 Impact factor: 3.390
Authors: Richard D Telford; Ross B Cunningham; Rohan M Telford; Jennifer Kerrigan; Peter E Hickman; Julia M Potter; Walter P Abhayaratna Journal: PLoS One Date: 2012-10-12 Impact factor: 3.240