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ObjectivesPredicting the potential risk factors of high blood pressure (HBP) among children and adolescents is still a knowledge gap. Our study aimed to establish and validate a nomogram-based model for identifying youths at risk of developing HBP.MethodsHBP was defined as systolic blood pressure or diastolic blood pressure above the 95th percentile, using age, gender, and height-specific cut-off points. Penalized regression with Lasso was used to identify the strongest predictors of HBP. Internal validation was conducted by a 5-fold cross-validation and bootstrapping approach. The predictive variables and the advanced nomogram plot were identified by conducting univariate and multivariate logistic regression analyses. A nomogram was constructed by a training group comprised of 239,546 (69.9%) participants and subsequently validated by an external group with 103,190 (30.1%) participants.ResultsOf 342,736 children and adolescents, 55,480 (16.2%) youths were identified with HBP with mean age 11.51 ± 1.45 years and 183,487 were boys (53.5%). Nine significant relevant predictors were identified including: age, gender, weight status, birth weight, breastfeeding, gestational hypertension, family history of obesity and hypertension, and physical activity. Acceptable discrimination [area under the receiver operating characteristic curve (AUC): 0.742 (development group), 0.740 (validation group)] and good calibration (Hosmer and Lemeshow statistics, P > 0.05) were observed in our models. An available web-based nomogram was built online on https://hbpnomogram.shinyapps.io/Dyn_Nomo_HBP/.ConclusionsThis model composed of age, gender, early life factors, family history of the disease, and lifestyle factors may predict the risk of HBP among youths, which has developed a promising nomogram that may aid in more accurately identifying HBP among youths in primary care.

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With little knowledge on the joint effects of metal exposure on dyslipidemia, we aimed to investigate the relationship between exposure to metal and dyslipidemia among U.S adults based on the National Health and Nutrition Examination Survey(NHANES). Based on the ve NHANES waves(2011-2020), we selected ve metals in blood as exposure, namely, Cadmium(Cd), Lead(Pb), Total Mercury(Hg), Manganese(Mn) and Selenium(Se), which were detected by inductively coupled plasma mass spectrometry. Survey-multivariable logistic regression, Generalized Weighted Quantile Sum(WQS) and Bayesian kernel machine regression(BKMR) were performed to determine whether dyslipidemia was associated with single metals or mixed metals. Our study included 12,526 participants aged from 20 to 80, representing 577.1 million non-institutionalized U.S. adults. We found a positive association between several metals including Pb [Adjusted odds ratio(