Background: This study aimed to explore the correlation between body mass index (BMI) and dyslipidemia and the optimal cut-off point for BMI to distinguish the risk of dyslipidemia in lactating women. Methods: A total of 2295 lactating women subjects were included in this study, all within 2 years postpartum. All samples were from “China Children and Lactating Mothers Nutritional Health Surveillance (2016–2017)”. BMI, blood lipids, demographic information, lifestyle habits, and other serum indicators were obtained in this survey. Generalized linear model, logistic regression, restricted cubic spline (RCS) and ROC curve analysis were used to evaluate the relationship among BMI, blood lipids, and dyslipidemia. Results: BMI in lactating women was positively correlated with total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), but negatively correlated with high-density lipoproteincholesterol (HDL-C) (p < 0.05). Higher BMI in lactating women was associated with higher ORs of dyslipidemia (hypercholesterolemia, hypertriglyceridemia, high-LDL-cholesterolemia, low HDL-cholesterolemia) (p < 0.05). These associations were stable across age groups, breastfeeding child age (months), parity, physical activity level, fasting plasma glucose (FPG), and hemoglobin. These factors did not interact with this relationship (p > 0.05). The optimal cut-off point for BMI was 24.85 kg/m² determined by using ROC analysis, which can distinguish the risk of dyslipidemia. Conclusions: BMI was positively correlated with risk of dyslipidemia. Maintaining an ideal weight may prevent dyslipidemia in lactating women, and BMI is recommended to be controlled below 24.85 kg/m².

We evaluated the iodine nutritional status and related factors among school-age children based on the 2016 National Nutrition and Health Surveillance of Children and Lactating Women; 3808 children from Hebei, Guangxi, and Zhejiang province were included in the study. Urinary iodine concentration (UIC), thyroid-stimulating hormone (TSH), body mass index (BMI), vitamin A (VA), and vitamin D (VD) were measured. The abnormal rate of UIC and TSH were assessed. Relationships between UIC/TSH and the possible factors were analyzed. The overall median UIC was 185.14 µg/L, and the median UIC of children aged 8–10 was 164.60 µg/L. Prevalence of iodine deficiency and excess was 13.84% and 14.36%, respectively, and 12.87% of children showed TSH excess. UIC, as well as the abnormal rates of iodine deficiency (ID) and TSH, were significantly different among the three provinces. The median UICs and excess rates increased with age, reaching 211.45 µg/L and 21.35% at age of 14~, while TSH showed the opposite trend. Overweight children tended to have lower UIC and higher TSH. Higher UIC and TSH were found in VA sufficient group (p < 0.01). Further, the VD deficient group had a higher TSH compared to the sufficient group (p < 0.01). Moreover, UI and TSH distribution was obviously different among different vitamin A/D status (p < 0.05). Although the median UIC of school-age children was optimal, there were pockets of inadequate and excessive UI in the three provinces. Compared to the national IDD monitoring results in 2014, the iodine nutritional status of children was greatly improved. Considerations of region, age, BMI, VA, or VD are needed in the future iodine evaluation and surveillance.

Background: To investigate relationships between five single nucleotide polymorphisms (SNP) in four maternal genes involved in one carbon metabolism and adverse pregnancy outcomes, including preterm birth (PTB), low birth weight (LBW), and small-for-gestational-age (SGA). Methods: This was a prospective mother and child cohort study in Wuqiang, China. Pregnant women (n = 939) were recruited from Jun 2016 to Oct 2018. Pregnancy outcomes (PTB, LBW, and SGA) were extracted from medical records and other information including age at childbearing, maternal education level, gravidity, parity, pre-pregnancy weight and height was collected by using a structured questionnaire. The maternal serum folate concentration was measured by using Abbott Architect i2000SR chemiluminescence analyzer in the first prenatal care visit. DNA genotyping of methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C, methionine synthase reductase (MTRR) A66G, methionine synthase (MTR) A2756G, and thymidylate synthetase (TYMS) rs3819102 was processed by Sequenom MassARRAY iPLEX Platform. Univariate and multivariate logistics regression analysis were used to test the relationships between 5 SNPs and PTB, LBW, SGA. Results: Totally, 849 dyads of women and infants were included in the analysis. The prevalence of PTD, LBW, and SGA were 3.76%, 1.58%, and 5.31% respectively. The homozygote frequencies of MTHFR C677T, MTHFR A1298C, MTRR A66G, MTR A2756G, and TYMS rs3819102 were 44.2%, 1.4%, 6.7%, 1.3%, and 3.2%, and the alt allele frequencies were 66.1%, 10.8%, 24.9%, 10.5%, and 20.5% respectively. The average serum folate concentration was 11.95 ng/mL and the folate deficiency rate was 0.47%. There were no significant associations between MTHFR C677T, MTHFR A1298C, MTRR A66G, MTR A2756G, TYMS rs3819102 alleles and PTD, LBW, SGA (p > 0.05). Conclusions: In the population with adequate folate status and low prevalence of adverse pregnancy outcomes, MTHFR C677T, MTHFR A1298C, MTRR A66G, MTR A2756G, TYMS rs3819102 alleles may not be related to PTD, LBW, and SGA.

Background High-sensitivity C-reactive protein (hs-CRP) is a protein that indicates inflammation and the risk of cardiovascular diseases. The intake of dietary amino acids can influence immune and inflammatory reactions. However, studies on the relationship between dietary amino acids and hs-CRP, especially in children, remain scarce. Methods This cross-sectional study analyzed data from the Nutrition and China Children and Lactating Women Nutrition and Health Survey (2016–2019), focusing on 3514 children (724 with elevated hs-CRP ≥ 3 mg/L and 2790 with normal levels). Dietary information was gathered via a food frequency questionnaire, and hs-CRP levels were obtained from blood samples. Boruta algorithm and propensity scores were used to select and match dietary factors and sample sizes. Machine learning (ML) algorithms and logistic regression models assessed the link between amino acid intake and elevated hs-CRP risk, adjusting for age, sex, BMI, and lifestyle factors. Results The odds ratios (ORs) for elevated hs-CRP were significant for several amino acids, including Ile, Leu, Lys, Ser, Cys, Tyr, His, Pro, SAA, and AAA, with values ranging from 1.10 to 2.07. The LightGBM algorithm was the most effective in predicting elevated hs-CRP risk, achieving an AUC of 0.927. Tyrosine, methionine, cysteine, and proline were identified as important features by SHAP analysis and logistic regression. The intake of Ser, Cys, Tyr, and Pro showed a linear increase in the risk of elevated hs-CRP, especially in individuals with low protein intake and normal weight (p < 0.1). Conclusions Intake of amino acids like Ser, Cys, Tyr, and Pro significantly impacts hs-CRP levels in children, indicating that regulating these could help prevent inflammation-related diseases. This study supports future dietary and health management strategies. This is first large-scale ML study linking amino acids to pediatric inflammation in China. The main limitations are the cross-section design and the use of self-reported dietary data.

Background: Plasma branched-chain amino acid (BCAA) concentrations are positively associated with low-density lipoprotein cholesterol (LDL-C) levels. However, the relationship between dietary branched-chain amino acids and hyper-LDL-cholesterolemia is unclear in children and adolescents. Methods: This study explored the correlation between BCAAs and hyper-LDL-cholesterolemia risk through propensity score matching and conditional logistic regression. Machine learning based on LightGBM indicated the important role of BCAAs in the prediction of hyper-LDL-cholesterolemia. To examine the dose–response relationship, Restricted Cubic Splines (RCS) and receiver operating characteristic curves (ROC) were employed. The causal link between BCAA and cardiovascular disease (CVD) was explored via mediation Mendelian randomization. Results: For every 1 g/day increment in the intake of isoleucine, leucine, and valine, there was a corresponding 30%, 11%, and 16% rise in the risk of hyper-LDL-cholesterolemia, respectively. The optimal cut-off values stood at 5.53, 6.40, and 4.18 g/day, respectively. Utilizing the inverse variance weighted method for estimation revealed that the total effect of BCAA on CVD was OR = 1.06 (95% CI: 1.02~1.11), with p = 0.005. The indirect effect, mediated by LDL-C, was OR = 1.02 (95% CI: 1.00~1.02), with p = 0.026. The direct effect was noted at OR = 1.05 (95% CI: 1.01~1.09), with p = 0.017. Conclusions: Dietary BCAAs are positively correlated with hyper-LDL-cholesterolemia in children and adolescents. LDL-C serve as a mediator of CVD caused by BCAAs.

Background: Dietary cystine may influence lipid metabolism, but epidemiological evidence in children is limited. This study aimed to investigate the association between dietary cystine intake and dyslipidemia and its subtypes in Chinese children. Methods: Data were derived from the China National Nutrition and Health Surveillance of Children and Lactating Mothers (CNNHSCLM). After propensity score matching (1:1, caliper = 0.2), 3676 children aged 6–17 years (1838 with dyslipidemia, 1838 controls) were included. The Quantile g-computation (qgcomp) model assessed the joint effect of 20 amino acids. Multivariate logistic regression, subgroup analysis, restricted cubic splines (RCS), and five machine learning models (including XGBoost with Shapley Additive Explanation (SHAP) analysis) were applied to evaluate the association between cystine intake and dyslipidemia. Results: The qgcomp model showed that cystine had a negative weighting contribution to reducing the risk of hypo-HDL cholesterolemia. Multivariate logistic regression revealed that cystine intake was significantly negatively correlated with hypo-HDL cholesterolemia (OR = 0.67, 95%CI: 0.53–0.86, p = 0.002) and total dyslipidemia (OR = 0.84, 95%CI: 0.74–0.96, p = 0.010), but not with other subtypes. Subgroup analyses indicated interactions with BMI and sex. RCS showed a non-linear dose–response relationship for hypo-HDL cholesterolemia and a linear negative relationship for total dyslipidemia. The XGBoost model achieved the best predictive performance (AUC = 0.902), and SHAP analysis identified cystine as the most important feature inversely associated with dyslipidemia. Decision curve analysis confirmed its clinical net benefit. Conclusions: Dietary cystine intake is negatively associated with the risk of hypo-HDL cholesterolemia and total dyslipidemia in children, and cystine is an important negative correlate of dyslipidemia. These findings provide new scientific evidence for dietary prevention of dyslipidemia in children.