Abstract:Anthropometric indices have been used as indicators for predicting hypertension (HTN) in children and adolescents but it is not clear which anthropometric measures are a better index for identifying elevated blood pressure (EBP) risk factors in pediatric population. Body mass index (BMI), waist circumference (WC), weight‐height ratio (WHR), a body shape index (ABSI) and blood pressure were measured in 14 008 children and adolescents aged 7‐18 years in a national school‐aged survey CASPIN V. Hypertension (HTN) … Show more
“…Forty-one cross-sectional studies were included in the present study, including 138,561 children and adolescents (50% girls) aged between 3 and 19 years. The studies were conducted in the following countries: 11 studies were conducted in countries from the European region (Denmark, Estonia, Portugal, Spain, Scotland, Wales, Finland, Sweden, UK, Greece, Italy, Hungary, Belgium, France, Germany, and Austria) [20][21][22][23][24][25][26][27][28][29][30] ; 10 studies were conducted in countries from the Latin American region (Chile, Brazil, Mexico, and Venezuela) [31][32][33][34][35][36][37][38][39][40] ; 11 studies were conducted in East and Southeast Asian countries (Malaysia, South Korea, China, Taiwan, and Vietnam) [41][42][43][44][45][46][47][48][49][50][51] ; 3 studies were conducted in the USA [52][53][54] ; 1 study was conducted in Algeria 55 ; 1 study was conducted in India 56 ; 1 study was conducted in Iran 57 ; and 3 studies were conducted in South Africa. [58][59][60] Sample sizes ranged from 152 35 to 32,036 49 participants.…”
Section: Study Characteristicsmentioning
confidence: 99%
“…[58][59][60] Sample sizes ranged from 152 35 to 32,036 49 participants. WHtR cutoffs ranged between 0.41 20 and 0.59 35 as follows: 10 studies reported cutoffs of 0.41 to 0.45 20,22,[24][25][26]32,42,48,58,60 ; 23 studies reported cutoffs of 0.46 to 0.50 [23][24][25][26][27][28][29][30][32][33][34]36,[43][44][45][46][47]51,[53][54][55]57,59 ; and 12 studies reported cutoffs ≥0.51. 21,30,31,33,35,[37][38][39]48,52,54,...…”
The present systematic review with meta-analysis sought to estimate the accuracy of different waist-to-height ratio (WHtR) cutoff ranges as risk indicators for cardiometabolic health in different populations of children and adolescents. Systematic searches were undertaken to identify studies in apparently healthy participants aged 3-18 years that conducted receiver operating characteristic curve analysis and reported area under the receiver operating characteristic curves for WHtR with any cardiometabolic biomarker. Forty-one cross-sectional studies were included in the meta-analysis, including 138,561 young individuals (50% girls). Higher area under summary receiver operating characteristic (AUSROC) values were observed in cutoffs between 0.46 and 0.50 (AUSROC = 0.83, 95%CI: 0.80-0.86) and ≥0.51 (AUSROC = 0.87, 95%CI: 0.84-0.90) (p < 0.001 in comparison with cutoffs 0.41 to 0.45), with similar results in both sexes. The AUSROC value increased in the East and Southeast Asian regions using a WHtR cutoff of ≥0.46 (AUSROC = 0.90, 95%CI: 0.87 to 0.92). A cutoff of ≥0.54 was optimal for the Latin American region (AUSROC = 0.96, 95%CI: 0.94-0.97). Our meta-analysis identified optimal cutoff values of WHtR for use in children and adolescents from different regions. Despite the widely accepted WHtR cutoff of 0.50, the present study indicated that a single cutoff value of WHtR may be inappropriate.
“…Forty-one cross-sectional studies were included in the present study, including 138,561 children and adolescents (50% girls) aged between 3 and 19 years. The studies were conducted in the following countries: 11 studies were conducted in countries from the European region (Denmark, Estonia, Portugal, Spain, Scotland, Wales, Finland, Sweden, UK, Greece, Italy, Hungary, Belgium, France, Germany, and Austria) [20][21][22][23][24][25][26][27][28][29][30] ; 10 studies were conducted in countries from the Latin American region (Chile, Brazil, Mexico, and Venezuela) [31][32][33][34][35][36][37][38][39][40] ; 11 studies were conducted in East and Southeast Asian countries (Malaysia, South Korea, China, Taiwan, and Vietnam) [41][42][43][44][45][46][47][48][49][50][51] ; 3 studies were conducted in the USA [52][53][54] ; 1 study was conducted in Algeria 55 ; 1 study was conducted in India 56 ; 1 study was conducted in Iran 57 ; and 3 studies were conducted in South Africa. [58][59][60] Sample sizes ranged from 152 35 to 32,036 49 participants.…”
Section: Study Characteristicsmentioning
confidence: 99%
“…[58][59][60] Sample sizes ranged from 152 35 to 32,036 49 participants. WHtR cutoffs ranged between 0.41 20 and 0.59 35 as follows: 10 studies reported cutoffs of 0.41 to 0.45 20,22,[24][25][26]32,42,48,58,60 ; 23 studies reported cutoffs of 0.46 to 0.50 [23][24][25][26][27][28][29][30][32][33][34]36,[43][44][45][46][47]51,[53][54][55]57,59 ; and 12 studies reported cutoffs ≥0.51. 21,30,31,33,35,[37][38][39]48,52,54,...…”
The present systematic review with meta-analysis sought to estimate the accuracy of different waist-to-height ratio (WHtR) cutoff ranges as risk indicators for cardiometabolic health in different populations of children and adolescents. Systematic searches were undertaken to identify studies in apparently healthy participants aged 3-18 years that conducted receiver operating characteristic curve analysis and reported area under the receiver operating characteristic curves for WHtR with any cardiometabolic biomarker. Forty-one cross-sectional studies were included in the meta-analysis, including 138,561 young individuals (50% girls). Higher area under summary receiver operating characteristic (AUSROC) values were observed in cutoffs between 0.46 and 0.50 (AUSROC = 0.83, 95%CI: 0.80-0.86) and ≥0.51 (AUSROC = 0.87, 95%CI: 0.84-0.90) (p < 0.001 in comparison with cutoffs 0.41 to 0.45), with similar results in both sexes. The AUSROC value increased in the East and Southeast Asian regions using a WHtR cutoff of ≥0.46 (AUSROC = 0.90, 95%CI: 0.87 to 0.92). A cutoff of ≥0.54 was optimal for the Latin American region (AUSROC = 0.96, 95%CI: 0.94-0.97). Our meta-analysis identified optimal cutoff values of WHtR for use in children and adolescents from different regions. Despite the widely accepted WHtR cutoff of 0.50, the present study indicated that a single cutoff value of WHtR may be inappropriate.
“…Many anthropometric indices are used to define obesity-associated hypertension. These include the most commonly used body mass index (BMI), waist circumference (WC), weight-to-hip ratio (WHR), and weight-to-height ratio (WHtR) [ 13 ]. Nonetheless, research shows that the predictive powers of anthropometric measures for hypertension are countries and ethnicities dependent [ 14 ].…”
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65,
F
-score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes’ Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes’ Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension.
“…1 . Initially, 9642 articles were identified from the PubMed, Web of Science, and Cochrane databases, 26 studies were excluded after browsed the full-text, and 21 original articles were included for the meta-analysis in the end [ 6 , 22 – 41 ]. Fig.…”
Background
Childhood obesity is more likely to increase the chance of many adult health problems. Numerous studies have shown obese children to be more prone to elevated blood pressure (BP) and hypertension. It is important to identify an obesity anthropometric index with good discriminatory power for them in pediatric population.
Methods
MEDLINE/PubMed, Web of Science, and Cochrane databases were retrieved comprehensively for eligible studies on childhood obesity and hypertension/elevated BP through June 2021. The systematic review and meta-analysis of studies used receiver operating characteristics (ROC) curves for evaluating the discriminatory power of body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) in distinguishing children with elevated BP and hypertension.
Results
21 cross-sectional studies involving 177,943 children and 3–19 years of age were included in our study. Meta-analysis showed that the pooled area under the reporting receiver-operating characteristic curves (AUC) and 95% confidence intervals (CIs) for BMI, WC, and WHtR to detect hypertension of boys were 0.68 (0.64, 0.72), 0.69 (0.64, 0.74), 0.67 (0.63, 0.71), for elevated BP, the pooled AUCs and 95% CIs were 0.67 (0.61, 0.73), 0.65 (0.58, 0.73), 0.65 (0.61, 0.71). The pooled AUCs and 95% CIs for BMI, WC and WHtR of predicting hypertension were 0.70 (0.66, 0.75), 0.69 (0.64, 0.75), 0.67 (0.63, 0.72) in girls, the pooled AUCs and 95% CIs of predicting elevated BP were 0.63 (0.61, 0.65), 0.62 (0.60, 0.65), 0.62 (0.60, 0.64) respectively. There was no anthropometric index was statistically superior in identifying hypertension and elevated BP, however, the accuracy of BMI predicting hypertension was significantly higher than elevated BP in girls (P < 0.05). The subgroup analysis for the comparison of BMI, WC and WHtR was performed, no significant difference in predicting hypertension and elevated BP in pediatric population.
Conclusions
This systematic review showed that no anthropometric index was superior in identifying hypertension and elevated BP in pediatric population. While compared with predicting elevated BP, all the indicators showed superiority in predicting hypertension in children, the difference was especially obvious in girls. A better anthropometric index should be explored to predict children’s early blood pressure abnormalities.
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