DataSheet_1_Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity.docx

ObjectiveThis study aims to outline the clinical characteristics of pediatric NAFLD, as well as establish and validate a prediction model for the disease.Materials and methodsThe retrospective study enrolled 3216 children with obesity from January 2003 to May 2021. They were divided into obese without NAFLD, nonalcoholic fatty liver (NAFL), and nonalcoholic steatohepatitis (NASH) groups. Clinical data were retrieved, and gender and chronologic characteristics were compared between groups. Data from the training set (3036) were assessed using univariate analyses and stepwise multivariate logistic regression, by which a nomogram was developed to estimate the probability of NAFLD. Another 180 cases received additional liver hydrogen proton magnetic resonance spectroscopy (1H-MRS) as a validation set.ResultsThe prevalence of NAFLD was higher in males than in females and has increased over the last 19 years. In total, 1915 cases were NAFLD, and the peak onset age was 10-12 years old. Hyperuricemia ranked first in childhood NAFLD comorbidities, followed by dyslipidemia, hypertension, metabolic syndrome (MetS), and dysglycemia. The AUROC of the eight-parameter nomogram, including waist-to-height ratio (WHtR), hip circumference (HC), triglyceride glucose-waist circumference (TyG-WC), alanine aminotransferase (ALT), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1(ApoA1), insulin sensitivity index [ISI (composite)], and gender, for predicting NAFLD was 0.913 (sensitivity 80.70%, specificity 90.10%). Calibration curves demonstrated a great calibration ability of the model.Conclusion and relevanceNAFLD is the most common complication in children with obesity. The nomogram based on anthropometric and laboratory indicators performed well in predicting NAFLD. This can be used as a quick screening tool to assess pediatric NAFLD in children with obesity.</p

Image_1_Clinical spectrum transition and prediction model of nonalcoholic fatty liver disease in children with obesity.pdf

ObjectiveThis study aims to outline the clinical characteristics of pediatric NAFLD, as well as establish and validate a prediction model for the disease.Materials and methodsThe retrospective study enrolled 3216 children with obesity from January 2003 to May 2021. They were divided into obese without NAFLD, nonalcoholic fatty liver (NAFL), and nonalcoholic steatohepatitis (NASH) groups. Clinical data were retrieved, and gender and chronologic characteristics were compared between groups. Data from the training set (3036) were assessed using univariate analyses and stepwise multivariate logistic regression, by which a nomogram was developed to estimate the probability of NAFLD. Another 180 cases received additional liver hydrogen proton magnetic resonance spectroscopy (1H-MRS) as a validation set.ResultsThe prevalence of NAFLD was higher in males than in females and has increased over the last 19 years. In total, 1915 cases were NAFLD, and the peak onset age was 10-12 years old. Hyperuricemia ranked first in childhood NAFLD comorbidities, followed by dyslipidemia, hypertension, metabolic syndrome (MetS), and dysglycemia. The AUROC of the eight-parameter nomogram, including waist-to-height ratio (WHtR), hip circumference (HC), triglyceride glucose-waist circumference (TyG-WC), alanine aminotransferase (ALT), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1(ApoA1), insulin sensitivity index [ISI (composite)], and gender, for predicting NAFLD was 0.913 (sensitivity 80.70%, specificity 90.10%). Calibration curves demonstrated a great calibration ability of the model.Conclusion and relevanceNAFLD is the most common complication in children with obesity. The nomogram based on anthropometric and laboratory indicators performed well in predicting NAFLD. This can be used as a quick screening tool to assess pediatric NAFLD in children with obesity.</p

Additional file 2: Table S1. of A randomised, open-labelstudy of insulin glargine or neutral protamine Hagedorn insulin in Chinese paediatric patients with type 1 diabetes mellitus

Patient demographics and baseline disease characteristics. (DOCX 15 kb

Additional file 1: of A randomised, open-labelstudy of insulin glargine or neutral protamine Hagedorn insulin in Chinese paediatric patients with type 1 diabetes mellitus

Supplementary Methods and Results. (DOC 25 kb

The Effects of Genetic Variation in <i>FTO</i> rs9939609 on Obesity and Dietary Preferences in Chinese Han Children and Adolescents

<div><p>The association of the rs9939609 single nucleotide polymorphism in <i>FTO</i> gene with obesity has been extensively investigated in studies of populations of European, African, and Asian ancestry. However, inconsistent results have been reported in Asian populations, and the relationship of <i>FTO</i> variation and dietary behaviors has only rarely been examined in Chinese children and adolescents. The aim of this study was to assess the association of rs9939609 with obesity and dietary preferences in childhood in a Chinese population. Epidemiological data including dietary preferences were collected in interviews using survey questionnaires, and rs9939609 genotype was determined by real-time PCR. The associations of rs9939609 genotypes with obesity and dietary preferences were analyzed by multivariate logistic regression using both additive and dominant models. The results showed that subjects with a TA or AA genotype had an increased risk of obesity compared with the TT participants; the odds ratios (ORs) were 1.47 (95% CI: 1.25–1.71, <i>P</i> = 1.73×10⁻⁶), and 3.32 (95% CI: 2.01–5.47, <i>P</i> = 2.68×10⁻⁶), respectively. After adjusting for age and gender, body mass index, waist circumference, hip circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose, triglycerides, and low-density lipoprotein cholesterol were higher, and high-density lipoprotein cholesterol was lower in TA and AA participants than in those with the TT genotype. After additionally controlling for body mass index, the association remained significant only for systolic blood pressure (<i>P</i> = 0.005). Compared with TT participants, those with the AA genotype were more likely to prefer a meat-based diet (OR = 2.81, 95% CI: 1.52–5.21). The combined OR for obesity in participants with TA/AA genotypes and preference for a meat-based diet was 4.04 (95% CI: 2.8–5.81) compared with the TT participants who preferred a plant-based diet. These findings indicate the genetic variation of rs9939609 is associated with obesity and dietary preferences in Chinese children and adolescents.</p></div

Association of the <i>FTO</i> rs9939609 genotype and obesity.

<p>Abbreviations: OR, odds ratio; CI, confidence interval.</p><p><i>P-</i>value was calculated with Logistic regression using additive model^§, and dominant model* adjusted for age and gender.</p

Joint effects between the <i>FTO</i> rs9939609 genotypes, obesity, and dietary preferences.

a<p>: Meat-based diet preference: +, meat-based diet; −, plant-based diet or balanced diet;</p>b<p>: Salty flavor preference: +, like; −, dislike/no strong preference;</p>c<p>Sweet flavor preference: +, like; −, dislike / no strong preference.</p>#<p>Adjusted for age and gender.</p><p>*Logistic regression.</p

Characteristics of the obese and control participants.

&<p>Excluding missing genotype data in 76 participants (52 obese and 24 controls).</p><p>*<i>P</i>-values for differences in distribution of characteristics between the obese and control groups.</p><p>Fasting blood glucose, total triglyceride, total cholesterol, LDLC, HDLC are expressed as median (lower quartile–upper quartile); other variables are expressed as mean ±SD.</p>a<p>Independent <i>t</i>-test;</p>b<p>χ² test;</p>c<p>Kruskal–Wallis test.</p><p>Abbreviations: BMI, body mass index; LDLC, low-density lipoprotein cholesterol; HDLC, high-density lipoprotein cholesterol.</p

Association of the <i>FTO</i> rs9939609 genotypes with clinical and metabolic measurements.

<p>*Data are expressed as mean±SD;</p>#<p>Data are expressed as medians (lower quartile–upper quartile) and are square root transformed before regression analysis.</p><p><i>P-</i>value was calculated with Linear regression using additive model, ^a: adjusted for age and gender; ^b: adjusted for age, gender and BMI.</p><p>Abbreviations: BMI, body mass index; LDLC, low-density lipoprotein cholesterol; HDLC, high-density lipoprotein cholesterol.</p

Participant selection and disposition.

<p>Participant selection and disposition.</p