BackgroundDyslipidemia in pregnancy are associated with gestational diabetes mellitus (GDM), preeclampsia, preterm birth and other adverse outcomes, which has been extensively studied in western countries. However, similar studies have rarely been conducted in Asian countries. Our study was aimed at investigating the associations between maternal dyslipidemia and adverse pregnancy outcomes among Chinese population.MethodsData were derived from 934 pairs of non-diabetic mothers and neonates between 2010 and 2011. Serum blood samples were assayed for fasting total cholesterol (TC), triglycerides (TG), high-density lipoprotein-cholesterol (HDL-C), and low-density lipoprotein-cholesterol (LDL-C) concentrations during the first, second and third trimesters. The present study explored the associations between maternal lipid profile and pregnancy complications and perinatal outcomes. The pregnancy complications included GDM, preeclampsia and intrahepatic cholestasis of pregnancy (ICP); the perinatal outcomes included preterm birth, small/large for gestational age (SGA/LGA) infants and macrosomia. Odds ratios (ORs) and 95 % confidence intervals (95 % CIs) were calculated and adjusted via stepwise logistic regression analysis. Optimal cut-off points were determined by ROC curve analysis.ResultsAfter adjustments for confounders, every unit elevation in third-trimester TG concentration was associated with increased risk for GDM (OR = 1.37, 95 % CI: 1.18-1.58), preeclampsia (OR = 1.50, 95 % CI: 1.16-1.93), ICP (OR = 1.28, 95 % CI: 1.09-1.51), LGA (OR = 1.13, 95 % CI: 1.02-1.26), macrosomia (OR = 1.19, 95 % CI: 1.02-1.39) and decreased risk for SGA (OR = 0.63, 95 % CI: 0.40-0.99); every unit increase in HDL-C concentration was associated with decreased risk for GDM and macrosomia, especially during the second trimester (GDM: OR = 0.10, 95 % CI: 0.03-0.31; macrosomia: OR = 0.25, 95 % CI: 0.09-0.73). The optimal cut-off points for third-trimester TG predicting GDM, preeclampsia, ICP, LGA and SGA were separately ≥3.871, 3.528, 3.177, 3.534 and ≤2.530 mmol/L. The optimal cut-off points for third-trimester HDL-C identifying GDM, macrosomia and SGA were respectively ≤1.712, 1.817 and ≥2.238 mmol/L.ConclusionsAmong Chinese population, maternal high TG in late pregnancy was independently associated with increased risk of GDM, preeclampsia, ICP, LGA, macrosomia and decreased risk of SGA. Relative low maternal HDL-C during pregnancy was significantly associated with increased risk of GDM and macrosomia; whereas relative high HDL-C was a protective factor for both of them.

Phalangeal microgeodes may represent bone absorption and destruction in response to exaggerated peripheral circulatory impairment following chilblain, and mainly occur in bone growth spurts.

BackgroundSmall- and large-for-gestational-age (SGA, LGA) newborns are associated with metabolic syndrome in their later life. Cord blood C-peptide, insulin, glycosylated hemoglobin (HbA1c), and lipids levels may be altered in SGA and LGA newborns; however, the results are conflicting. Therefore, this study aimed to determine the effect of cord blood markers on SGA and LGA newborns.Material/MethodsThis was a prospective cohort study and included 2873 term newborns of non-diabetic women. Among these newborns, 83 (2.9%) were SGA, 2236 (77.8%) were appropriate-for-gestational-age (AGA), and 554 (19.3%) were LGA newborns. Cord blood C-peptide, insulin, HbA1c, triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were measured. The chi-square, Kruskal-Wallis, and Mann-Whitney tests were used to analyze categorical variables and continuous variables, respectively. Multinomial logistic regression analysis was used to determine the independent effect of these variables on SGA and LGA newborns.ResultsCord serum TG level was significantly higher in the SGA group than in AGA and LGA groups (p<0.05). The LGA group had significantly higher cord serum insulin level than AGA and SGA groups (p<0.05). After adjustment for confounding variables, including maternal age, parity, pre-pregnancy body mass index (BMI), education, annual household income, pregnancy-induced hypertension (PIH), mode of delivery, and newborn sex, high TG and insulin levels remained significantly associated with SGA and LGA newborns, respectively (p<0.05).ConclusionsHigh cord serum TG and insulin levels are independently associated with SGA and LGA newborns, respectively.

Background. The objective of this study was to investigate the independent and combined effects of maternal prepregnancy body mass index (BMI) and gestational weight gain (GWG) on offspring growth at 0–3 years old. Methods. A total of 826 pairs of nondiabetic mothers and their offspring were recruited in this study. Maternal information was abstracted from medical records and questionnaires. Offspring growth trajectories of weights and BMIs were depicted based on anthropometric measurements. Results. Offspring of mothers who were prepregnancy overweight/obese or obtained excessive GWGs continuously had greater weight and BMI Z-scores throughout the first 3 years of life. Children of prepregnancy overweight/obese mothers with excessive GWGs had a phenotype of higher weight and BMI Z-scores than those prepregnancy overweight/obese ones with nonexcessive GWGs from birth to 18 months. Maternal excessive GWGs increased offspring's risk of overweight/obesity at 12 months (AOR = 1.43, 95% CI: 1.03–2.00) and 24 months (AOR = 1.51, 95% CI: 1.02–2.25). Combination of excessive prepregnancy BMIs and GWGs was significantly associated with offspring's overweight/obesity at 30 months (AOR = 2.98, 95% CI: 1.36–6.53). Conclusions. Maternal prepregnancy overweight/obesity and excessive GWG are both significantly associated with rapid offspring growth from birth to 3 years old. Excessive GWGs strengthen the effects of high maternal prepregnancy BMIs on excessive offspring growth during their early life.

BackgroundEarly detection of children with autism spectrum disorder (ASD) and comorbid intellectual disability (ID) can help in individualized intervention. Appropriate assessment and diagnostic tools are lacking in primary care. This study aims to explore the applicability of machine learning (ML) methods in diagnosing ASD comorbid ID compared with traditional regression models.MethodFrom January 2017 to December 2021, 241 children with ASD, with an average age of 6.41 ± 1.96, diagnosed in the Developmental Behavior Department of the Children’s Hospital Affiliated with the Medical College of Zhejiang University were included in the analysis. This study trained the traditional diagnostic models of Logistic regression (LR), Support Vector Machine (SVM), and two ensemble learning algorithms [Random Forest (RF) and XGBoost]. Socio-demographic and behavioral observation data were used to distinguish whether autistic children had combined ID. The hyperparameters adjustment uses grid search and 10-fold validation. The Boruta method is used to select variables. The model’s performance was evaluated using discrimination, calibration, and decision curve analysis (DCA).ResultAmong 241 autistic children, 98 (40.66%) were ASD comorbid ID. The four diagnostic models can better distinguish whether autistic children are complicated with ID, and the accuracy of SVM is the highest (0.836); SVM and XGBoost have better accuracy (0.800, 0.838); LR has the best sensitivity (0.939), followed by SVM (0.952). Regarding specificity, SVM, RF, and XGBoost performed significantly higher than LR (0.355). The AUC of ML (SVM, 0.835 [95% CI: 0.747–0.944]; RF, 0.829 [95% CI: 0.738–0.920]; XGBoost, 0.845 [95% CI: 0.734–0.937]) is not different from traditional LR (0.858 [95% CI: 0.770–0.944]). Only SVM observed a good calibration degree. Regarding DCA, LR, and SVM have higher benefits in a wider threshold range.ConclusionCompared to the traditional regression model, ML model based on socio-demographic and behavioral observation data, especially SVM, has a better ability to distinguish whether autistic children are combined with ID.

ObjectiveThis prospective cohort study was aimed at investigating the associations between cord blood metabolic factors and early-childhood growth, further elucidating the relationships between cord blood metabolites and overweight and obesity in early life.MethodsA total of 2,267 pairs of mothers and offspring were recruited in our study. Cord blood plasma was assayed for triglycerides (TGs), total cholesterol (TC), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), C-peptide, insulin, and glycosylated hemoglobin type A1C (HbA1c) levels. Data of anthropometric measurements were collected from offspring at birth, 6 months, 12 months, and 18 months. Multiple linear regression models were used to evaluate the correlations between cord blood metabolic factors and weight Z-scores, body mass index (BMI) Z-scores, and weight gains at the early stage of life. Forward stepwise logistic regression analyses were applied to explore the associations between cord blood metabolic factors and early-childhood overweight and obesity. Receiver operating characteristic (ROC) curve analyses were applied to determine the optimal cutoff points for cord blood metabolic factors in predicting early-childhood overweight and obesity.ResultsAfter adjustments for covariates, cord blood TG concentrations and TG/TC ratios were negatively associated with weight Z-scores from birth to 18 months. Cord blood C-peptide and HbA1c levels were inversely associated with weight Z-scores at 6 months and 18 months. Cord blood TG concentrations and TG/TC ratios were negatively correlated with BMI Z-scores up to 18 months. Cord blood C-peptide levels and HbA1c levels were inversely correlated with BMI Z-scores at 18 months. Cord blood TG, TG/TC ratios, C-peptide, and HbA1c had negative correlations with weight gains from birth to 6 months, but the correlations attenuated as time went on. Increase in cord blood TG and HbA1c levels and TG/TC ratios were significantly associated with decreased risks of overweight and obesity at 6 months, 12 months, and 18 months.ConclusionsCord blood metabolic factors were significantly associated with early-childhood growth patterns.

ObjectiveThe present study was aimed at investigating the intelligence profiles and adaptive behaviors of children with high-functioning autism spectrum disorder (HFASD) and developmental speech and language disorders (DSLDs). We compared the similarities and differences of cognitive capabilities and adaptive functions and explored their correlations in the HFASD and DSLDs groups.Methods128 patients with HFASD, 111 patients with DSLDs and 114 typically developing (TD) children were enrolled into our study. Wechsler Intelligence Scale for Children-IV (WISC-IV) and Adaptive Behavior Assessment System-II (ABAS-II) were respectively applied to evaluate intelligence profiles and adaptive behaviors. Intelligence quotient (IQ) scores and adaptive functioning scores among the HFASD, DSLDs and TD groups were compared through one-way ANOVA. Pearson correlation coefficient was applied to examine the relationships between WISC indices and ABAS domains.ResultsOutcomes showed significantly poorer intelligence profiles and adaptive behaviors in HFASD and DSLDs groups. Both children with HFASD and DSLDs demonstrated impairments in verbal comprehension and executive functions. Processing speed and working memory were the predominant defects of children with HFASD and DSLDs in the field of executive functions, respectively. Whereas perceptual reasoning was a relative strength for them. Children with DSLDs had balanced scores of all the domains in ABAS-II; nevertheless, HFASD individuals demonstrated striking impairments in Social domain. Correlation analysis showed IQs of children with HFASD were positively correlated with all the domains and General Adaptive Composite (GAC) of ABAS-II. Additionally, IQs were positively correlated with Conceptual domain and GAC for children with DSLDs. Compared with DSLDs group, intelligence displayed stronger correlations with adaptive behaviors in HFASD group.ConclusionOur study expanded insights regarding intelligence profiles and adaptive behaviors of children with HFASD and DSLDs. Moreover, this study made breakthroughs in discovering positive correlations between IQs and adaptive functions in the two neurodevelopmental disorders.