Circulating MicroRNAs predict glycemic improvement and response to a behavioral intervention

Longitudinal Associations Between MicroRNAs and Weight in the Diabetes Prevention Program

OBJECTIVE

Circulating microRNAs show cross-sectional associations with overweight and obesity. Few studies provided data to differentiate between a snapshot perspective on these associations versus how microRNAs characterize prodromal risk from disease pathology and complications. This study assessed longitudinal relationships between circulating microRNAs and weight at multiple time-points in the Diabetes Prevention Program trial.

RESEARCH DESIGN AND METHODS

A subset of participants (n=150) from the Diabetes Prevention Program were included. MicroRNAs were measured from banked plasma using a Fireplex Assay. We used generalized linear mixed models to evaluate relationships between microRNAs and changes in weight at baseline, year-1, and year-2. Logistic regression was used to evaluate whether microRNAs at baseline were associated with weight change after 2 years.

RESULTS

In fully adjusted models that included relevant covariates, seven miRs (i.e., miR-126, miR-15a, miR-192, miR-23a, and miR-27a) were statistically associated with weight over 2 years. MiR-197 and miR-320a remained significant after adjustment for multiple comparisons. Baseline levels of let-7f, miR-17, and miR-320c were significantly associated with 3% weight loss after 2 years in fully adjusted models.

DISCUSSION

This study provided evidence for longitudinal relationships between circulating microRNAs and weight. Because microRNAs characterize the combined effects of genetic determinants and responses to behavioral determinants, they may provide insights about the etiology of overweight and obesity in the context or risk for common, complex diseases. Additional studies are needed to validate the potential genes and biological pathways that might be targeted by these microRNA biomarkers and have mechanistic implications for weight loss and disease prevention.

Plasma miRNAs and Treatment Failure in Participants in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) Study

OBJECTIVE

To identify plasma miRNAs related to treatment failure in youth with type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

We examined whether a panel of miRNAs could predict treatment failure in training/test data sets among participants in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study (N = 209). We also examined whether individual miRNAs were associated with treatment failure.

RESULTS

Participants were age 14.5 years, and 62% were female. A panel of miRNAs did not predict treatment failure. However, for each doubling, miR-4306 was associated with a 12% decrease (P = 0.040) and miR-483-3p was marginally associated with a 12% increase (P = 0.080) in failure independently of sex, race/ethnicity, BMI, Tanner stage, HbA1c, maternal diabetes, oral disposition index, and treatment arm. The addition of both miRNAs improved model fit (log likelihood without vs. with miRNAs -360.3 vs. -363.5; P = 0.040).

CONCLUSIONS

miR-483-3p and miR-4306 may be associated with treatment failure in youth with T2D.

Epigenetic Biomarkers of Metabolic Responses to Lifestyle Interventions

Studies have examined the possible utility of epigenetic phenomena (DNA methylation changes, covalent histone modifications, and miRNA expression patterns) in predicting individual responses to different lifestyle programs. Nonetheless, most available evidence is focused on identifying epigenetic marks eventually associated with body composition and adiposity outcomes, whereas their roles in metabolic endings remain less explored. This document comprehensively reviewed the evidence regarding the use of epigenetic signatures as putative biomarkers of metabolic outcomes (glycemic, lipid, blood pressure, and inflammatory/oxidative stress features) in response to different lifestyle interventions in humans. Although more investigation is still necessary in order to translate this knowledge in clinical practice, these scientific insights are contributing to the design of advanced strategies for the precise management of cardiometabolic risk, gaining understanding on metabolic heterogeneity, allowing for the prediction of metabolic outcomes, and facilitating the design of epigenome-based nutritional strategies for a more customized approach for metabolic alterations treatment under the scope of precision nutrition.

Glycemia-Induced miRNA Changes: A Review

Diabetes is a rapidly increasing global health concern that significantly strains the health system due to its downstream complications. Dysregulation in glycemia represents one of the fundamental obstacles to achieving glycemic control in diabetic patients. Frequent hyperglycemia and/or hypoglycemia events contribute to pathologies that disrupt cellular and metabolic processes, which may contribute to the development of macrovascular and microvascular complications, worsening the disease burden and mortality. miRNAs are small single-stranded non-coding RNAs that regulate cellular protein expression and have been linked to various diseases, including diabetes mellitus. miRNAs have proven useful in the diagnosis, treatment, and prognosis of diabetes and its complications. There is a vast body of literature examining the role of miRNA biomarkers in diabetes, aiming for earlier diagnoses and improved treatment for diabetic patients. This article reviews the most recent literature discussing the role of specific miRNAs in glycemic control, platelet activity, and macrovascular and microvascular complications. Our review examines the different miRNAs involved in the pathological processes leading to the development of type 2 diabetes mellitus, such as endothelial dysfunction, pancreatic beta-cell dysfunction, and insulin resistance. Furthermore, we discuss the potential applications of miRNAs as next-generation biomarkers in diabetes with the aim of preventing, treating, and reversing diabetes.

MicroRNAs Associated with Incident Diabetes in the Diabetes Prevention Program

OBJECTIVE

MicroRNAs (miRs) are short (i.e., 18-26 nucleotide) regulatory elements of messenger RNA translation to amino acids. The purpose of this study was to assess whether miRs are predictive of incident T2D in the Diabetes Prevention Program (DPP) trial.

RESEARCH DESIGN AND METHODS

This was a secondary analysis (n = 1,000) of a subset of the DPP cohort that leveraged banked biospecimens to measure miRs. We used random survival forest and Lasso to identify the optimal miR predictors and cox proportional hazards to model time to T2D overall and within intervention arms.

RESULTS

We identified five miRs (miR-144, miR-186, miR-203a, miR-205, miR-206) that constituted the optimal predictors of incident T2D after adjustment for covariates (hazards ratio 2.81 (95% confidence interval (CI) 2.05, 3.87); p < 0.001). Predictive risk scores following cross-validation showed the HR for the highest quartile risk group compared to the lowest quartile risk group was 5.91 (95% CI (2.02, 17.3); p < 0.001). There was significant interaction between the intensive lifestyle (HR 3.60, 95% CI (2.50, 5.18); p < 0.001) and the metformin (HR 2.72; 95% CI (1.47, 5.00); p = 0.001) groups compared to placebo. Of the five miRs identified, one targets a gene with prior known associations with risk for T2D.

DISCUSSION

We identified five miRs that are optimal predictors of incident T2D in the DPP cohort. Future directions include validation of this finding in an independent sample in order to determine whether this risk score may have potential clinical utility for risk stratification of individuals with prediabetes, and functional analysis of the potential genes and pathways targeted by the miRs that were included in the risk score.

Competing endogenous RNA networks related to prognosis in chronic lymphocytic leukemia: comprehensive analyses and construction of a novel risk score model

BACKGROUND

Chronic lymphocytic leukemia (CLL) is a heterogeneous B-cell malignancy that lacks specific biomarkers and drug targets. Competing endogenous RNAs (ceRNAs) play vital roles in oncogenesis and tumor progression by sponging microRNAs (miRNAs). Nevertheless, the regulatory mechanisms of survival-related ceRNA networks in CLL remain to be uncovered.

METHODS

We included 865 de novo CLL patients to investigate RNA expression profiles and Illumina sequencing was performed on four CLL patients, two CLL cell lines and six healthy donors in our center. According to univariate Cox regression, LASSO regression as well as multivariate Cox regression analyses, we established a novel risk score model in CLL patients. Immune signatures were compared between the low- and high-risk groups with CIBERSORT and ESTIMATE program. Afterwards, we analyzed the relationship between differentially expressed miRNAs (DEmiRNAs) and IGHV mutational status, p53 mutation status and del17p. Based on the survival analyses and differentially expressed RNAs with targeting relationships, the lncRNA/circRNA-miRNA-mRNA ceRNA networks were constructed. In addition, the circRNA circ_0002078/miR-185-3p/TCF7L1 axis was verified and their interrelations were delineated by dual-luciferase reporter gene assay.

RESULTS

Totally, 57 differentially expressed mRNAs (DEmRNAs) and 335 DEmiRNAs were identified between CLL patient specimens and normal B cells. A novel risk score model consisting of HTN3, IL3RA and NCK1 was established and validated. The concordance indexes of the model were 0.825, 0.719 and 0.773 in the training, test and total sets, respectively. The high-risk group was related to del(13q14) as well as shorter overall survival (OS). Moreover, we identified DEmiRNAs that related to cytogenetic abnormality of CLL patients, which revealed that miR-324-3p was associated with IGHV mutation, p53 mutation and del17p. The survival-related lncRNA/circRNA-miRNA-mRNA ceRNA networks were constructed to further facilitate the development of potential predictive biomarkers. Besides, the expression of circ_0002078 and TCF7L1 were significantly elevated and miR-185-3p was obviously decreased in CLL patients. Circ_0002078 regulated TCF7L1 expression by competing with TCF7L1 for miR-185-3p.

CONCLUSIONS

The comprehensive analyses of RNA expression profiles provide pioneering insights into the molecular mechanisms of CLL. The novel risk score model and survival-related ceRNA networks promote the development of prognostic biomarkers and potential therapeutic vulnerabilities for CLL.

Co‑expressed microRNAs, target genes and pathways related to metabolism, inflammation and endocrine function in individuals at risk for type 2 diabetes

MicroRNAs (miRNAs) may be considered important regulators of risk for type 2 diabetes (T2D). The aim of the present study was to identify novel sets of miRNAs associated with T2D risk, as well as their gene and pathway targets. Circulating miRNAs (n=59) were measured in plasma from participants in a previously completed clinical trial (n=82). An agnostic statistical approach was applied to identify novel sets of miRNAs with optimal co-expression patterns. In silico analyses were used to identify the messenger RNA and biological pathway targets of the miRNAs within each factor. A total of three factors of miRNAs were identified, containing 18, seven and two miRNAs each. Eight biological pathways were revealed to contain genes targeted by the miRNAs in all three factors, 38 pathways contained genes targeted by the miRNAs in two factors, and 55, 18 and two pathways were targeted by the miRNAs in a single factor, respectively (all q<0.05). The pathways containing genes targeted by miRNAs in the largest factor shared a common theme of biological processes related to metabolism and inflammation. By contrast, the pathways containing genes targeted by miRNAs in the second largest factor were related to endocrine function and hormone activity. The present study focused on the pathways uniquely targeted by each factor of miRNAs in order to identify unique mechanisms that may be associated with a subset of individuals. Further exploration of the genes and pathways related to these biological themes may provide insights about the subtypes of T2D and lead to the identification of novel therapeutic targets.

Systematic review of transcriptome and microRNAome associations with gestational diabetes mellitus

PURPOSE

Gestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM.

DESIGN

The systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

METHODS

PubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM.

RESULTS

Sixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person.

CONCLUSIONS

The mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.