Datasheet2_Analysis of risk factors and construction of a prediction model for short stature in children.docx

BackgroundShort stature in children is an important global health issue. This study aimed to analyze the risk factors associated with short stature and to construct a clinical prediction model and risk classification system for short stature.MethodsThis cross-sectional study included 12,504 children aged 6–14 years of age from 13 primary and secondary schools in Pingshan District, Shenzhen. A physical examination was performed to measure the height and weight of the children. Questionnaires were used to obtain information about children and their parents, including sex, age, family environment, social environment, maternal conditions during pregnancy, birth and feeding, and lifestyle. The age confounding variable was adjusted through a 1 : 1 propensity score matching (PSM) analysis and 1,076 children were selected for risk factor analysis.ResultsThe prevalence of short stature in children aged 6–14 years was 4.3% in the Pingshan District, Shenzhen. The multivariate logistic regression model showed that the influencing factors for short stature were father's height, mother's height, annual family income, father's level of education and parents’ concern for their children's height in the future (P ConclusionThis study analyzed the risk factors for short stature in children and constructed a nomogram prediction model and a risk classification system based on these risk factors, as well as providing short stature screening and assessment individually.</p

Datasheet1_Analysis of risk factors and construction of a prediction model for short stature in children.pdf

BackgroundShort stature in children is an important global health issue. This study aimed to analyze the risk factors associated with short stature and to construct a clinical prediction model and risk classification system for short stature.MethodsThis cross-sectional study included 12,504 children aged 6–14 years of age from 13 primary and secondary schools in Pingshan District, Shenzhen. A physical examination was performed to measure the height and weight of the children. Questionnaires were used to obtain information about children and their parents, including sex, age, family environment, social environment, maternal conditions during pregnancy, birth and feeding, and lifestyle. The age confounding variable was adjusted through a 1 : 1 propensity score matching (PSM) analysis and 1,076 children were selected for risk factor analysis.ResultsThe prevalence of short stature in children aged 6–14 years was 4.3% in the Pingshan District, Shenzhen. The multivariate logistic regression model showed that the influencing factors for short stature were father's height, mother's height, annual family income, father's level of education and parents’ concern for their children's height in the future (P ConclusionThis study analyzed the risk factors for short stature in children and constructed a nomogram prediction model and a risk classification system based on these risk factors, as well as providing short stature screening and assessment individually.</p

Forest plot of randomized controlled trials comparing the effect of probiotics on HOMA-IR with placebo/comparator.

<p>Weighted mean differences (95% CIs) for HOMA-IR are shown. Pooled estimates (<i>diamonds</i>) calculated by the random effects method. IV, inverse variance.</p

Forest plot of randomized controlled trials comparing the effect of probiotics on fasting blood glucose with placebo/comparator.

<p>Weighted mean differences (95% CIs) for fasting blood glucose are shown. Pooled estimates (<i>diamonds</i>) calculated by the random effects method. IV, inverse variance.</p

Forest plot of randomized controlled trials comparing the effect of probiotics on fasting plasma insulin with placebo/comparator.

<p>Weighted mean differences (95% CIs) for fasting plasma insulin are shown. Pooled estimates (<i>diamonds</i>) calculated by the random effects method. IV, inverse variance.</p

Publication bias funnel plots.

<p>Publication bias funnel plots for fasting glucose (A), fasting insulin (B) and HOMA-IR (C). The solid line represents the pooled effect estimate expressed as the weighted mean difference for each analysis. The dashed lines represent pseudo-95% confidence limits. P-values displayed in the top right corner of each funnel plot are derived from quantitative assessment of publication bias by Egger’s test.</p

Flowchart for the selection of eligible studies.

<p>Flowchart for the selection of eligible studies.</p

Cumulative Meta-analysis of the probiotics for fasting blood glucose.

<p>Error bars indicate the 95% CI of the cumulative meta-analysis estimates as randomized patients accumulate through time. WMD, weight mean difference.</p

Characteristics of included studies.

<p>C: capsule; Y: yogurt; D: drink; CFU, colony-forming unit; DB, double blind; HC, hypercholesterolemia; Met.S, metabolic syndrome; NASH, non-alcoholic steatohepatitis; OB, obesity; P, parallel; PC, placebo control; SB, single blind; T2DM, type 2 diabetes mellitus.</p><p>Characteristics of included studies.</p

Multiple logistic model for cardiometabolic risk factors for diabetic chronic complications.

<p>Multiple logistic model for cardiometabolic risk factors for diabetic chronic complications.</p