MicroRNAs Associated with Metformin Treatment in the Diabetes Prevention Program.

The Diabetes Prevention Program (DPP) randomized controlled trial demonstrated that metformin treatment reduced progression to type 2 diabetes (T2D) by 31% compared to placebo in adults with prediabetes. Circulating micro-ribonucleic acids (miRs) are promising biomarkers of T2D risk, but little is known about their associations with metformin regimens for T2D risk reduction. We compared the change in 24 circulating miRs from baseline to 2 years in a subset from DPP metformin intervention (n = 50) and placebo (n = 50) groups using Wilcoxon signed rank tests. Spearman correlations were used to evaluate associations between miR change and baseline clinical characteristics. Multiple linear regression was used to adjust for covariates. The sample was 73% female, 17% Black, 13% Hispanic, and 50 ± 11 years. Participants were obese, normotensive, prediabetic, and dyslipidemic. Change in 12 miR levels from baseline to 2 years was significantly different in the metformin group compared with placebo after adjusting for multiple comparisons: six (let-7c-5p, miR-151a-3p, miR-17-5p, miR-20b-5p, miR-29b-3p, and miR-93-5p) were significantly upregulated and six (miR-130b-3p, miR-22-3p, miR-222-3p, miR-320a-3p, miR-320c, miR-92a-3p) were significantly downregulated in the metformin group. These miRs help to explain how metformin is linked to T2D risk reduction, which may lead to novel biomarkers, therapeutics, and precision health strategies.

Behavioral Analytics for Myopic Agents.

Many multi-agent systems have a single coordinator providing incentives to a large number of agents. Two challenges faced by the coordinator are a finite budget from which to allocate incentives, and an initial lack of knowledge about the utility function of the agents. Here, we present a behavioral analytics approach for solving the coordinator's problem when the agents make decisions by maximizing utility functions that depend on prior system states, inputs, and other parameters that are initially unknown. Our behavioral analytics framework involves three steps: first, we develop a model that describes the decision-making process of an agent; second, we use data to estimate the model parameters for each agent and predict their future decisions; and third, we use these predictions to optimize a set of incentives that will be provided to each agent. The framework and approaches we propose in this paper can then adapt incentives as new information is collected. Furthermore, we prove that the incentives computed by this approach are asymptotically optimal with respect to a loss function that describes the coordinator's objective. We optimize incentives with a decomposition scheme, where each sub-problem solves the coordinator's problem for a single agent, and the master problem is a pure integer program. We conclude with a simulation study to evaluate the effectiveness of our approach for designing a personalized weight loss program. The results show that our approach maintains efficacy of the program while reducing its costs by up to 60%, while adaptive heuristics provide substantially less savings.

Risk of early birth by body mass index in a propensity score-matched sample: A retrospective cohort study.

OBJECTIVE: Evaluate the risk of preterm (<37 weeks) or early term birth (37 or 38 weeks) by body mass index (BMI) in a propensity score-matched sample. DESIGN: Retrospective cohort analysis. SETTING: California, USA. POPULATION: Singleton live births from 2011-2017. METHODS: Propensity scores were calculated for BMI groups using maternal factors. A referent sample of women with a BMI between 18.5 and <25.0 kg/m2 was selected using exact propensity score matching. Risk ratios for preterm and early term birth were calculated. MAIN OUTCOME MEASURES: Early birth. RESULTS: Women with a BMI <18.5 kg/m2 were at elevated risk of birth of 28-31 weeks (relative risk [RR] 1.2, 95% CI 1.1-1.4), 32-36 weeks (RR 1.3, 95% CI 1.2-1.3), and 37 or 38 weeks (RR 1.1, 95% CI 1.1-1.1). Women with BMI ≥25.0 kg/m2 were at 1.2-1.4-times higher risk of a birth <28 weeks and were at reduced risk of a birth between 32 and 36 weeks (RR 0.8-0.9) and birth during the 37th or 38th week (RR 0.9). CONCLUSION: Women with a BMI <18.5 kg/m2 were at elevated risk of a preterm or early term birth. Women with BMI ≥25.0 kg/m2 were at elevated risk of a birth <28 weeks. Propensity score-matched women with BMI ≥30.0 kg/m2 were at decreased risk of a spontaneous preterm birth with intact membranes between 32 and 36 weeks, supporting the complexity of BMI as a risk factor for preterm birth. TWEETABLE ABSTRACT: Propensity score-matched women with BMI ≥30 kg/m2 were at decreased risk of a late spontaneous preterm birth.

Direct-to-consumer genomic testing: Are nurses prepared?

Genomic testing is increasingly common in the consumer marketplace. The role of nurses in educating and counseling patients requires them to be prepared to respond to questions about the results of direct-to-consumer genomic testing. This article describes one individual's reflections upon undergoing this testing, the challenges of interpreting the results, and nursing considerations for integrating these results into clinical practice.

Pairing pedagogical and genomic advances to prepare advanced practice nurses for the era of precision health.

BACKGROUND: Broadly accessible curriculum that equips Advanced Practice Nurses (APNs) with knowledge and skills to apply genomics in practice in the era of precision health is needed. Increased accessibility of genomics courses and updated curriculum will prepare APNs to be leaders in the precision health initiative. METHODS: Courses on genomics were redesigned using contemporary pedagogical approaches to online teaching. Content was based on the Essential Genetic and Genomic Competencies for Nurses with Graduate Degrees. RESULTS: The number of students enrolled (n = 10) was comparable to previous years with greater breadth of representation across nursing practice specialty areas (53% vs. 20%). Prior to the first course, students reported agreement with meeting 8% (3/38) of the competencies. By completion of the 3rd course, students reported 100% (38/38) agreement with meeting the competencies. CONCLUSIONS: Content on genomics sufficient to obtain self-perceived attainment of genomics competencies can be successfully delivered using contemporary pedagogical teaching approaches.

Behavioral Modeling in Weight Loss Interventions.

Designing systems with human agents is difficult because it often requires models that characterize agents' responses to changes in the system's states and inputs. An example of this scenario occurs when designing treatments for obesity. While weight loss interventions through increasing physical activity and modifying diet have found success in reducing individuals' weight, such programs are difficult to maintain over long periods of time due to lack of patient adherence. A promising approach to increase adherence is through the personalization of treatments to each patient. In this paper, we make a contribution towards treatment personalization by developing a framework for predictive modeling using utility functions that depend upon both time-varying system states and motivational states evolving according to some modeled process corresponding to qualitative social science models of behavior change. Computing the predictive model requires solving a bilevel program, which we reformulate as a mixed-integer linear program (MILP). This reformulation provides the first (to our knowledge) formulation for Bayesian inference that uses empirical histograms as prior distributions. We study the predictive ability of our framework using a data set from a weight loss intervention, and our predictive model is validated by comparison to standard machine learning approaches. We conclude by describing how our predictive model could be used for optimization, unlike standard machine learning approaches which cannot.

Minority stress and leukocyte gene expression in sexual minority men living with treated HIV infection.

Sexual minority (i.e., non-heterosexual) individuals experience poorer mental and physical health, accounted for in part by the additional burden of sexual minority stress occurring from being situated in a culture favoring heteronormativity. Informed by previous research, the purpose of this study was to identify the relationship between sexual minority stress and leukocyte gene expression related to inflammation, cancer, immune function, and cardiovascular function. Sexual minority men living with HIV who were on anti-retroviral medication, had viral load < 200 copies/mL, and had biologically confirmed, recent methamphetamine use completed minority stress measures and submitted blood samples for RNA sequencing on leukocytes. Differential gene expression and pathway analyses were conducted comparing those with clinically elevated minority stress (n = 18) and those who did not meet the clinical cutoff (n = 20), covarying reactive urine toxicology results for very recent stimulant use. In total, 90 differentially expressed genes and 138 gene set pathways evidencing 2-directional perturbation were observed at false discovery rate (FDR) < 0.10. Of these, 41 of the differentially expressed genes and 35 of the 2-directionally perturbed pathways were identified as functionally related to hypothesized mechanisms of inflammation, cancer, immune function, and cardiovascular function. The neuroactive-ligand receptor pathway (implicated in cancer development) was identified using signaling pathway impact analysis. Our results suggest several potential biological pathways for future work investigating the relationship between sexual minority stress and health.

Recent stimulant use and leukocyte gene expression in methamphetamine users with treated HIV infection.

Stimulant use may accelerate HIV disease progression through biological and behavioral pathways. However, scant research with treated HIV-positive persons has examined stimulant-associated alterations in pathophysiologic processes relevant to HIV pathogenesis. In a sample of 55 HIV-positive, methamphetamine-using sexual minority men with a viral load less than 200 copies/mL, we conducted RNA sequencing to examine patterns of leukocyte gene expression in participants who had a urine sample that was reactive for stimulants (n = 27) as compared to those who tested non-reactive (n = 28). Results indicated differential expression of 32 genes and perturbation of 168 pathways in recent stimulant users. We observed statistically significant differential expression of single genes previously associated with HIV latency, cell cycle regulation, and immune activation in recent stimulant users (false discovery rate p < 0.10). Pathway analyses indicated enrichment for genes associated with inflammation, innate immune activation, neuroendocrine hormone regulation, and neurotransmitter synthesis. Recent stimulant users displayed concurrent elevations in plasma levels of tumor necrosis factor - alpha (TNF-α) but not interleukin 6 (IL-6). Further research is needed to examine the bio-behavioral mechanisms whereby stimulant use may contribute to HIV persistence and disease progression.

Differential expression of genes and differentially perturbed pathways associated with very high evening fatigue in oncology patients receiving chemotherapy.

PURPOSE: Fatigue is the most common symptom associated with cancer and its treatment. Investigation of molecular mechanisms associated with fatigue in oncology patients may identify new therapeutic targets. The objectives of this study were to evaluate the relationships between gene expression and perturbations in biological pathways and evening fatigue severity in oncology patients who received chemotherapy (CTX). METHODS: The Lee Fatigue Scale (LFS) and latent class analysis were used to identify evening fatigue phenotypes. We measured 47,214 ribonucleic acid transcripts from whole blood collected prior to a cycle of CTX. Perturbations in biological pathways associated with differential gene expression were identified from public data sets (i.e., Kyoto Encyclopedia Gene and Genomes, BioCarta). RESULTS: Patients were classified into Moderate (n = 65, mean LFS score 3.1) or Very High (n = 195, mean LFS score 6.4) evening fatigue groups. Compared to patients with Moderate fatigue, patients with Very High fatigue exhibited differential expression of 29 genes. A number of the perturbed pathways identified validated prior mechanistic hypotheses for fatigue, including alterations in immune function, inflammation, neurotransmission, energy metabolism, and circadian rhythms. Based on our findings, energy metabolism was further divided into alterations in carbohydrate metabolism and skeletal muscle energy. Alterations in renal function-related pathways were identified as a potential new mechanism. CONCLUSIONS: This study identified differential gene expression and perturbed biological pathways that provide new insights into the multiple and likely inter-related mechanisms associated with evening fatigue in oncology patients.

MicroRNAs associated with exercise and diet: a systematic review.

MicroRNAs are posttranscriptional regulators of gene expression. MicroRNAs reflect individual biologic adaptation to exposures in the environment. As such, measurement of circulating microRNAs presents an opportunity to evaluate biologic changes associated with behavioral interventions (i.e., exercise, diet) for weight loss. The aim of this study was to perform a systematic review of the literature to summarize what is known about circulating microRNAs associated with exercise, diet, and weight loss. We performed a systematic review of three scientific databases. We included studies reporting on circulating microRNAs associated with exercise, diet, and weight loss in humans. Of 1,219 studies identified in our comprehensive database search, 14 were selected for inclusion. Twelve reported on microRNAs associated with exercise, and two reported on microRNAs associated with diet and weight loss. The majority of studies used a quasiexperimental, cross-sectional design. There were numerous differences in the type and intensity of exercise and dietary interventions, the biologic source of microRNAs, and the methodological approaches used quantitate microRNAs. Data from several studies support an association between circulating microRNAs and exercise. The evidence for an association between circulating microRNAs and diet is weaker because of a small number of studies. Additional research is needed to validate previous observations using methodologically rigorous approaches to microRNA quantitation to determine the specific circulating microRNA signatures associated with behavioral approaches to weight loss. Future directions include longitudinal studies to determine if circulating microRNAs are predictive of response to behavioral interventions.

Use of plasma triglyceride/high-density lipoprotein cholesterol ratio to identify increased cardio-metabolic risk in young, healthy South Asians.

BACKGROUND & OBJECTIVES: Prevalence of insulin resistance and associated dyslipidaemia [high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C) concentrations] are increased in South Asian individuals; likely contributing to their increased risk of type-2 diabetes and cardiovascular disease. The plasma concentration ratio of TG/HDL-C has been proposed as a simple way to identify apparently healthy individuals at high cardio-metabolic risk. This study was carried out to compare the cardio-metabolic risk profiles of high-risk South Asian individuals identified by an elevated TG/HDL-C ratio versus those with a diagnosis of the metabolic syndrome. METHODS: Body mass index, waist circumference, blood pressure, and fasting plasma glucose, insulin, TG, and HDL-C concentrations were determined in apparently healthy men (n=498) and women (n=526). The cardio-metabolic risk profile of "high risk" individuals identified by TG/HDL-C ratios in men (≥ 3.5) and women (≥2.5) was compared to those identified by a diagnosis of the metabolic syndrome. RESULTS: More concentrations of all cardio-metabolic risk factors were significantly higher in "high risk" groups, identified by either the TG/HDL-C ratio or a diagnosis of the metabolic syndrome. TG, HDL-C, and insulin concentrations were not significantly different in "high risk" groups identified by either criterion, whereas plasma glucose and blood pressure were higher in those with the metabolic syndrome. INTERPRETATION & CONCLUSIONS: Apparently healthy South Asian individuals at high cardio-metabolic risk can be identified using either the TG/HDL-C ratio or the metabolic syndrome criteria. The TG/HDL-C ratio may be used as a simple marker to identify such individuals.

Hyperglycemia is associated with corrected QT prolongation and mortality in acutely ill patients.

BACKGROUND: The QT interval on an electrocardiogram represents ventricular repolarization time. Increased length of this interval, known as corrected QT (QTc) prolongation, can be a precursor to torsade de pointes, a potentially life-threatening ventricular dysrhythmia. An association exists between blood glucose and QTc interval in ambulatory populations. Because both hyperglycemia and QTc prolongation are common in critically ill patients, we sought to examine the relationship between blood glucose, QTc interval prolongation, and all-cause mortality in critically ill patients. METHODS: We studied adult patients admitted to cardiac monitoring units. Blood glucose and other clinical variables were abstracted from the medical record. Corrected QT measurements were automatically derived from continuous bedside cardiac monitoring systems. RESULTS: Twenty-five percent (233/940) of the patients had QTc prolongation, and 53% had elevated blood glucose (>140 mg/dL) during hospitalization. Adjusted odds for QTc prolongation were 2.1 (95% confidence interval, 1.5-3.1) for moderately elevated blood glucose (140-180 mg/dL) and 3.7 (95% confidence interval, 2.5-5.4) for severely elevated blood glucose (>180 mg/dL). Mortality rate was highest (16%) in patients experiencing both severely elevated blood glucose (>180 mg/dL) and QTc interval prolongation. CONCLUSIONS: Hyperglycemia is linked with QTc prolongation, and both are associated with increased odds of mortality in critically ill patients. Further studies are needed to extrapolate the relationship between glucose and ventricular repolarization, as well as appropriate glucose control parameters and QTc interval monitoring in critical care units.

MicroRNAs Associated with Metformin Treatment in the Diabetes Prevention Program.

The Diabetes Prevention Program (DPP) randomized controlled trial demonstrated that metformin treatment reduced progression to type 2 diabetes (T2D) by 31% compared to placebo in adults with prediabetes. Circulating micro-ribonucleic acids (miRs) are promising biomarkers of T2D risk, but little is known about their associations with metformin regimens for T2D risk reduction. We compared the change in 24 circulating miRs from baseline to 2 years in a subset from DPP metformin intervention (n = 50) and placebo (n = 50) groups using Wilcoxon signed rank tests. Spearman's correlations were used to evaluate associations between miR change and baseline clinical characteristics. Multiple linear regression was used to adjust for covariates. The sample was 73% female, 17% Black, 13% Hispanic, and 50 ± 11 years. Participants were obese, normotensive, prediabetic, and dyslipidemic. Change in 12 miR levels from baseline to 2 years was significantly different in the metformin group compared with placebo after adjusting for multiple comparisons: six (let-7c-5p, miR-151a-3p, miR-17-5p, miR-20b-5p, miR-29b-3p, and miR-93-5p) were significantly upregulated and six (miR-130b-3p, miR-22-3p, miR-222-3p, miR-320a-3p, miR-320c, miR-92a-3p) were significantly downregulated in the metformin group. These miRs help to explain how metformin is linked to T2D risk reduction, which may lead to novel biomarkers, therapeutics, and precision-health strategies.

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.

MicroRNA associated with dyslipidemia and coronary disease in humans.

MicroRNAs are structural components of an epigenetic mechanism of posttranscriptional regulation of messenger RNA translation. Recently, there has been significant interest in the application of microRNA as a blood-based biomarker of underlying physiological conditions. Dyslipidemia is a complex, heterogeneous condition conferring substantially increased risk for cardiovascular disease. The purpose of this review is to describe the current body of knowledge on the role of microRNA regulation of lipoprotein metabolism in humans and to discuss relevant methodological and study design considerations. We highlight the potential roles for microRNA in gene-environment interactions.

A culturally specific health coaching program targeting cardiovascular disease risk in South Asians: rationale, design, and baseline data.

Health coaching is an effective strategy for improving cardiovascular disease risk factors. Coaching interventions have primarily been studied in Caucasians, and the effectiveness in other ethnic groups is not known. Further, adaptation of coaching to include culturally specific components has not been studied. Our aim is to describe a culturally specific coaching program targeted at reducing cardiovascular disease risk in South Asians. Participants initially underwent comprehensive cardiovascular disease risk screening, then received individualized risk assessment and behavioral recommendations. A health coach then contacted participants regularly for one year to provide encouragement with behavior change, troubleshoot challenges, and assess adherence. In the first five years of the program, 3,180 people underwent risk assessment, 3,132 were candidates for coaching, 2,726 indicated a desire to participate in coaching, 1,359 received coaching, and 1,051 completed coaching for at least one year. Culturally specific health coaching is an appealing and feasible intervention for reducing cardiovascular disease risk in South Asians, with very low attrition. Coaching strategies for risk reduction are proven to be effective, but further longitudinal research is needed to determine whether the impact of incorporating cultural specificity improves the effectiveness. This program utilizes non-medically trained personnel as coaches and is relatively inexpensive, with potential for great cost savings in prevention of cardiovascular disease.

Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes.

Background: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases.Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics.Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov-Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types.Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible.

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis.

BACKGROUND: Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy. OBJECTIVE: The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D. METHODS: The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss. RESULTS: Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]). CONCLUSIONS: Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939.

MicroRNAs Associated With Incident Diabetes in the Diabetes Prevention Program.

CONTEXT: MicroRNAs (miRs) are short (ie, 18-26 nucleotide) regulatory elements of messenger RNA translation to amino acids. OBJECTIVE: The purpose of this study was to assess whether miRs are predictive of incident type 2 diabetes (T2D) in the Diabetes Prevention Program (DPP) trial. METHODS: This was a secondary analysis (n = 1000) of a subset of the DPP cohort that leveraged banked biospecimens to measure miRs. We used random survival forest and Lasso methods to identify the optimal miR predictors and the Cox proportional hazards to model time to T2D overall and within intervention arms. RESULTS: We identified 5 miRs (miR-144, miR-186, miR-203a, miR-205, miR-206) that constituted the optimal predictors of incident T2D after adjustment for covariates (hazard ratio [HR] 2.81, 95% CI 2.05, 3.87; P < .001). Predictive risk scores following cross-validation showed the HR for the highest quartile risk group compared with the lowest quartile risk group was 5.91 (95% CI 2.02, 17.3; P < .001). There was significant interaction between the intensive lifestyle (HR 3.60, 95% CI 2.50, 5.18; P < .001) and the metformin (HR 2.72; 95% CI 1.47, 5.00; P = .001) groups compared with placebo. Of the 5 miRs identified, 1 targets a gene with prior known associations with risk for T2D. CONCLUSION: We identified 5 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.

Review of databases for experimentally validated human microRNA-mRNA interactions.

MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR-messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR-mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/.