Associations between cord blood metabolic factors and early-childhood growth and overweight and obesity.

Objective: This 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. Methods: A 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. Results: After 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. Conclusions: Cord blood metabolic factors were significantly associated with early-childhood growth patterns.

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability.

Background: Early 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. Method: From 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). Result: Among 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. Conclusion: Compared 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.

Intelligence profiles and adaptive behaviors of high-functioning autism spectrum disorder and developmental speech and language disorders.

Objective: The 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. Methods: 128 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. Results: Outcomes 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. Conclusion: Our 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.

Phalangeal microgeodic syndrome in childhood.

BACKGROUND: Phalangeal microgeodic syndrome is an uncommon benign self-limiting condition that often occurs during cold weather. The etiology and the pathogenesis of the disease remain unclear. OBJECTIVE: To report a series of children with phalangeal microgeodic syndrome. MATERIALS AND METHODS: Twenty children with phalangeal microgeodic syndrome were retrospectively identified at our hospital after 2007. The clinical data, radiologic manifestation and pathologic appearance were analyzed. RESULTS: The average age was 10.3 years (range: 6.5-14.6 years). Twelve patients were boys. Twenty-five phalanges were affected radiographically (23 middle phalanges [92%] and 2 proximal phalanges [8%]). On radiographs, there were multiple small phalangeal lacunae in all cases. Metaphyseal rarefaction was seen in 15 phalanges, and metaphyseal transverse lucent bands were found in 7 phalanges. Epiphyseal rarefaction was seen in three phalanges. On magnetic resonance imaging (MRI), diffuse signal abnormalities of affected phalanges were observed in all cases. Multiple other phalanges and metacarpals also showed marrow edema in three cases. CONCLUSION: Phalangeal microgeodes may represent bone absorption and destruction in response to exaggerated peripheral circulatory impairment following chilblain, and mainly occur in bone growth spurts.

Independent and Combined Effects of Maternal Prepregnancy Body Mass Index and Gestational Weight Gain on Offspring Growth at 0-3 Years of Age.

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.

Associations between maternal lipid profile and pregnancy complications and perinatal outcomes: a population-based study from China.

BACKGROUND: Dyslipidemia 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. METHODS: Data 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. RESULTS: After 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. CONCLUSIONS: Among 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.

[Progress on association between low-density lipoprotein receptor and metabolic syndrome].

Low-density lipoprotein receptor (LDLR) and metabolic syndrome (MS) are closely correlated. Changes in LDLR expression, feedback regulation and degradation, impacts of LDLR deficiency on blood lipid levels, roles of LDLR in islet ß cell dysfunction and cholesterol homeostasis dysregulation, expression of LDLR gene nuclear transcription factors and polymorphism of LDLR gene segments are all involved in the development of specific components of MS. In recent years, a variety of targets and intervention mechanisms in relation to LDLR and MS have been extensively studied. Knowledge about association between LDLR and MS may contribute to the development of strategies for prevention and treatment of MS. This article reviews the update on the association between LDLR and MS.

Cord blood C-peptide, insulin, HbA1c, and lipids levels in small- and large-for-gestational-age newborns.

BACKGROUND: Small- 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 AND METHODS: This 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. RESULTS: Cord 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). CONCLUSIONS: High cord serum TG and insulin levels are independently associated with SGA and LGA newborns, respectively.