Bariatric surgery is an efficient treatment for weight loss in obese patients and for resolving obesity comorbidities. However, the mechanisms behind these outcomes are unclear. Recent studies have indicated significant alterations in the transcriptome after surgery, specifically in the differential expression of microRNAs. In order to summarize the recent findings, we conducted a systematic summary of studies comparing microRNA expression levels before and after surgery. We identified 17 animal model and human studies from four databases (Ovid, Scopus, Web of Science, and PubMed) to be enrolled in this meta-analysis. From these studies, we identified 14 miRNAs which had the same direction of modulation of their expression after surgery in at least two studies (downregulated: hsa-miR-93-5p, hsa-miR-106b-5p, hsa-let-7b-5p, hsa-let-7i-5p, hsa-miR-16-5p, hsa-miR-19b-3p, hsa-miR-92a-3p, hsa-miR-222-3p, hsa-miR-142-3p, hsa-miR-140-5p, hsa-miR-155-5p, rno-miR-320-3p; upregulated: hsa-miR-7-5p, hsa-miR-320c). Pathway analysis for these miRNAs was done using database resources (DIANA-TarBase and KEGG pathway database) and their predicted target genes were discussed in relation with obesity and its comorbidities. Discrepancies in study design, such as miRNA source, bariatric surgery type, time of observation after surgery, and miRNA profiling methods, were also discussed.

Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.

Type 2 diabetes (T2D) is a deficiency in how the body regulates glucose. Uncontrolled T2D will result in chronic high blood sugar levels, eventually resulting in T2D complications. These complications, such as kidney, eye, and nerve damage, are even harder to treat. Identifying individuals at high risk of developing T2D and its complications is essential for early prevention and treatment. Numerous studies have been done to identify biomarkers for T2D diagnosis and prognosis. This review focuses on recent T2D biomarker studies based on circulating nucleic acids using different omics technologies: genomics, transcriptomics, and epigenomics. Omics studies have profiled biomarker candidates from blood, urine, and other non-invasive samples. Despite methodological differences, several candidate biomarkers were reported for the risk and diagnosis of T2D, the prognosis of T2D complications, and pharmacodynamics of T2D treatments. Future studies should be done to validate the findings in larger samples and blood-based biomarkers in non-invasive samples to support the realization of precision medicine for T2D.

Objective: This study aimed to evaluate microRNAs (miRNAs) as predictive biomarkers for type 2 diabetes (T2D) remission 12 months after sleeve gastrectomy (SG).Methods: A total of 179 serum miRNAs were profiled, and 26 clinical variables were collected from 46 patients. Two patients were later excluded because of hemolysis, and six patients with unclear remission status were set aside to evaluate the prediction models. The remaining 38 patients were included for model building. Variable selection was done using different approaches, including Least Absolute Shrinkage and Selection Operator (LASSO). Prediction models were then developed using LASSO and assessed in the validation set.Results: A total of 26 out of 38 patients achieved T2D remission 12 months after SG. The prediction model with only clinical variables misclassified two patients, which were correctly classified using miRNAs. Two miRNA‐only models achieved an accuracy of one but performed poorly for the validation set. The best miRNA model was a mixed model (accuracy: 0.974) containing four miRNAs (hsa‐miR‐32‐5p, hsa‐miR‐382‐5p, hsa‐miR‐1‐3p, and hsa‐miR‐21‐5p) and four clinical variables (T2D medication, sex, age, and fasting blood glucose). These miRNAs are involved in pathways related to obesity and insulin resistance.Conclusions: This study suggests that four serum miRNAs might be predictive biomarkers for T2D remission 12 months after SG, but further validation studies are needed.