Gut microbiota and obesity are deeply interconnected. However, the causality in the relationship between these factors remains unclear. Therefore, this study aimed to elucidate the genetic relationship between gut microbiota and childhood obesity. Genetic summary statistics for the gut microbiota were obtained from the MiBioGen consortium. Genome-wide association studies (GWAS) summary data for childhood obesity were obtained from North American, Australian, and European collaborative genome-wide meta-analyses. Mendelian randomization (MR) analyses were performed using the inverse variance weighting method. 16 children with obesity and 16 without obesity were included for clinical observation, and their weight, body mass index, blood lipid levels, and gut microbiology were assessed. Paired t-test was the primary method of data analysis, and statistical significance was set at P < 0.05. MR identified 16 causal relationships between the gut microbiome and childhood obesity. In the case-control study, we found that five gut microorganisms differed between children with and without obesity, whereas three gut microorganisms changed after weight loss in children with obesity. Our study provides new insights into the genetic mechanisms underlying gut microbiota and childhood obesity. ChiCTR2300072179. Change of intestinal flora and plasma metabolome in obese children and their weight loss intervention: a randomized controlled tria URL OF REGISTRY: https://www.chictr.org.cn/showproj.html. 2023-06-06. 2023-06-07.

Epilepsy is a severe neurological condition affecting 50–65 million individuals worldwide that can lead to brain damage. Nevertheless, the etiology of epilepsy remains poorly understood. Meta-analyses of genome-wide association studies involving 15,212 epilepsy cases and 29,677 controls of the ILAE Consortium cohort were used to conduct transcriptome-wide association studies (TWAS) and protein-wide association studies (PWAS). Furthermore, a protein-protein interaction (PPI) network was generated using the STRING database, and significant epilepsy-susceptible genes were verified using chip data. Chemical-related gene set enrichment analysis (CGSEA) was performed to determine novel drug targets for epilepsy. TWAS analysis identified 21,170 genes, of which 58 were significant (TWAS_fdr < 0.05) in ten brain regions, and 16 differentially expressed genes were verified based on mRNA expression profiles. The PWAS identified 2249 genes, of which 2 were significant (PWAS_fdr < 0.05). Through chemical-gene set enrichment analysis, 287 environmental chemicals associated with epilepsy were identified. We identified five significant genes (WIPF1, IQSEC1, JAM2, ICAM3, and ZNF143) that had causal relationships with epilepsy. CGSEA identified 159 chemicals that were significantly correlated with epilepsy (P_cgsea < 0.05), such as pentobarbital, ketone bodies, and polychlorinated biphenyl. In summary, we performed TWAS, PWAS (for genetic factors), and CGSEA (for environmental factors) analyses and identified several epilepsy-associated genes and chemicals. The results of this study will contribute to our understanding of genetic and environmental factors for epilepsy and may predict novel drug targets.

Celiac disease (CeD) is one of the most common intestinal inflammatory diseases, and its incidence and prevalence have increased over time. CeD affects multiple organs and systems in the body, and environmental factors play a key role in its complex pathogenesis. Although gluten exposure is known to be the causative agent, many unknown environmental factors can trigger or exacerbate CeD. In this study, we investigated the influence of genetic and environmental factors on CeD. Data from a CeD genome-wide association study that included 12,041 CeD cases and 12,228 controls were used to conduct a transcriptome-wide association study (TWAS) using FUSION software. Gene expression reference data were obtained for the small intestine, whole blood, peripheral blood, and lymphocytes. We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the significant genes identified by the TWAS and conducted a protein–protein interaction network analysis based on the STRING database to detect the function of TWAS-identified genes for CeD. We also performed a chemical-related gene set enrichment analysis (CGSEA) using the TWAS-identified genes to test the relationships between chemicals and CeD. The TWAS identified 8,692 genes, including 101 significant genes (p_adjusted < 0.05). The CGSEA identified 2,559 chemicals, including 178 chemicals that were significantly correlated with CeD. This study performed a TWAS (for genetic factors) and CGSEA (for environmental factors) and identified several CeD-associated genes and chemicals. The findings expand our understanding of the genetic and environmental factors related to immune-mediated diseases.

Objective: This study aimed to identify novel proteomic and lipidomic biomarkers of insulin resistance (IR) in young children with obesity and to assess the ability of hub lipids and proteins in the diagnosis of IR. Methods: The discovery cohort consisted of 50 prepubertal children, including 30 children with obesity and 20 lean. The validation cohort included 25 children with obesity and IR (obese‐IR) and 25 children with obesity without IR (obese‐NIR). Fasting plasma was collected from all participants for Olink proteomics and untargeted lipidomics. Pearson correlation analysis was used to identify proteins and lipids associated with IR, and area under the receiver operating characteristic (AUROC) was applied to compare the ability of the identified proteins and lipids with traditional indices in the diagnosis of IR. Results: In the discovery cohort, a total of 15 lipids and 10 proteins had significant correlation with IR. In the validation cohort, protein fatty acid binding protein 4 (FABP4) and gene serpin family E member 1 (PAI) were overexpressed in obese‐IR children compared to obese‐NIR children, while insulin like growth factor binding protein 1 (IGFBP‐1) and paraoxonase 3 (PON3) were lower in the IR group than in the obese‐NIR group; five lipids including sphingosine (d16:0), coenzyme (Q8), ceramides phosphate (d42:2), phosphatidylethanolamine (37:2e), and phosphatidylcholine (18:1e_16:0), showed significant ( p < 0.05) change in obese‐IR children compared to obese‐NIR children. In addition, the AUC‐ROC was 0.89 for IGFBP‐1, 0.81 for PON3, and 0.65 for PAI. The ability of IGFBP‐1, PON3, and PAI to diagnose IR was better than that of adiponectin and leptin. The AUROC of phosphatidylcholine (18:1e_16:0) and coenzyme (Q8) were 0.80 and 0.73, respectively, which was significantly higher than the AUROC of triglycerides(TGs), total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐C), and low‐density lipoprotein cholesterol (LDL‐C). Conclusion: Proteomic and lipidomic analysis can allow for the identification of potential new candidate biomarkers for IR. The ability of novel biomarkers to diagnose IR was better than traditional indicators. Trial Registration: Chinese Clinical Trial Registry: ChiCTR2300072179

Introduction: Milk fat globule epidermal growth factor 8 (MFGE8) represents a recently characterized secreted integrin ligand involved in modulating systemic metabolic processes through impacts on nutrient absorption, gastrointestinal motility, and lipid homeostasis. Current literature supports its relevance in obesity pathogenesis. However, the exploration of MFGE8’s function specifically within pediatric obesity contexts remains insufficient. This investigation aimed to delineate relationships between circulating MFGE8 concentrations and diverse obesity-associated parameters in children. Methods: Our study included 104 participants (32 females, 72 males), consisting of 64 children diagnosed with obesity and 40 normal-weight controls. We measured serum MFGE8 levels employing enzyme-linked immunosorbent assay methodology. Multiple clinical and biochemical measures were evaluated, encompassing body mass index (BMI), waist and hip circumferences, blood pressure, hepatic enzyme patterns, cholesterol parameters, and fasting insulin (FINS). Results: Obese participants exhibited substantially increased serum MFGE8 concentrations relative to non-obese controls (p < 0.05). MFGE8 levels correlated positively with numerous anthropometric measures: systolic blood pressure, heart rate (BPM), bone age, height, weight, BMI, waist circumference, hip circumference, waist-to-hip ratio, and waist-to-height ratio. Additionally, MFGE8 demonstrated significant positive relationships with multiple metabolic and inflammatory indicators: alanine aminotransferase, triglyceride, FINS, homeostatic model assessment of insulin resistance (HOMA-IR), basal metabolic rate, body fat percentage, uric acid, glutamyl transpeptidase, tumor necrosis factor-α, adiponectin, and leptin. Inverse correlations were identified with albumin and direct bilirubin. Multivariable linear regression modeling identified BMI, HOMA-IR, and male gender as independent positive predictors of MFGE8 concentrations. Conclusion: Our results demonstrate that elevated MFGE8 levels are linked to pediatric obesity, implying its potential contribution to the development of this condition and associated metabolic complications.