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.

Multi-Tiered Assessment of Gene Expression Provides Evidence for Mechanisms That Underlie Risk for Type 2 Diabetes.

Introduction: Integrated transcriptome and microRNA differential gene expression (DEG) analyses may help to explain type 2 diabetes (T2D) pathogenesis in at-risk populations. The purpose of this study was to characterize DEG in banked biospecimens from underactive adult participants who responded to a randomized clinical trial measuring the effects of lifestyle interventions on T2D risk factors. DEGs were further examined within the context of annotated biological pathways. Methods: Participants (n = 52) in a previously completed clinical trial that assessed a 12-week behavioural intervention for T2D risk reduction were included. Participants who showed >6mg/dL decrease in fasting blood glucose were identified as responders. Gene expression was measured by RNASeq, and overrepresentation analysis within KEGG pathways and weighted gene correlation network analysis (WGCNA) were performed. Results: No genes remained significantly differentially expressed after correction for multiple comparisons. One module derived by WGCNA related to body mass index was identified, which contained genes located in KEGG pathways related to known mechanisms underlying risk for T2D as well as pathways related to neurodegeneration and protein misfolding. A network analysis showed indirect connections between genes in this module and islet amyloid polypeptide (IAPP), which has previously been hypothesized as a mechanism for T2D. Discussion: We validated prior studies that showed pathways related to metabolism, inflammation/immunity, and endocrine/hormone function are related to risk for T2D. We identified evidence for new potential mechanisms that include protein misfolding. Additional studies are needed to determine whether these are potential therapeutic targets to decrease risk for T2D.

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.

MicroRNA biomarkers target genes and pathways associated with type 2 diabetes.

AIMS/HYPOTHESIS: Our prior analysis of the Diabetes Prevention Program study identified a subset of five miRNAs that predict incident type 2 diabetes. The purpose of this study was to identify mRNAs and biological pathways targeted by these five miRNAs to elucidate potential mechanisms of risk and responses to the tested interventions. METHODS: Using experimentally validated data from miRTarBase version 8.0 and R (2021), we identified mRNAs with strong evidence to be regulated by individual or combinations of the five predictor miRNAs. Overrepresentation of the mRNA targets was assessed in pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation database. RESULTS: The five miRNAs targeted 167 pathways and 122 mRNAs. Nine of the pathways have known associations with type 2 diabetes: Insulin signaling, Insulin resistance, Diabetic cardiomyopathy, Type 2 diabetes, AGE-RAGE signaling in diabetic complications, HIF-1 signaling, TGF-beta signaling, PI3K/Akt signaling, and Adipocytokine signaling pathways. Vascular endothelial growth factor A (VEGFA) has prior genetic associations with risk for type 2 diabetes and was the most commonly targeted mRNA for this set of miRNAs. CONCLUSIONS/INTERPRETATION: These findings show that miRNA predictors of incident type 2 diabetes target mRNAs and pathways known to underlie risk for type 2 diabetes. Future studies should evaluate miRNAs as potential therapeutic targets for preventing and treating type 2 diabetes.

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/.

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.

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.

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.

Healthcare Mistreatment, State-Level Policy Protections, and Healthcare Avoidance Among Gender Minority People.

Introduction: This study examined whether past experiences of mistreatment in healthcare were associated with greater healthcare avoidance due to anticipated mistreatment among gender minority (GM) people. We evaluated whether state-level healthcare policy protections moderated this relationship. Methods: Data from the 2018 Annual Questionnaire of The PRIDE Study, a national longitudinal study on sexual and gender minority people's health, were used in these analyses. Logistic regression modeling tested relationships between lifetime healthcare mistreatment due to gender identity or expression and past-year healthcare avoidance due to anticipated mistreatment among GM participants. Interactions between lifetime healthcare mistreatment and state-level healthcare policy protections and their relationship with past-year healthcare avoidance were tested. Results: Participants reporting any lifetime healthcare mistreatment had greater odds of past-year healthcare avoidance due to anticipated mistreatment among gender expansive people (n = 1290, OR = 4.71 [CI]: 3.57-6.20), transfeminine people (n = 263, OR = 10.32 [CI]: 4.72-22.59), and transmasculine people (n = 471, OR = 3.90 [CI]: 2.50-6.13). Presence of state-level healthcare policy protections did not moderate this relationship in any study groups. Conclusions: For GM people, reporting lifetime healthcare mistreatment was associated with healthcare avoidance due to anticipated mistreatment. State-level healthcare policy protections were not a moderating factor in this relationship. Efforts to evaluate the implementation and enforcement of state-level policies are needed. Continued efforts to understand instances of and to diminish healthcare mistreatment of GM people are recommended. Supplementary Information: The online version contains supplementary material available at 10.1007/s13178-022-00748-1.

The Role of Racial and Ethnic Factors in MicroRNA Expression and Risk for Type 2 Diabetes.

Prior studies focused on circulating microRNAs and the risk for complex diseases have shown inconsistent findings. The majority of studies focused on European and East Asian racial or ethnic groups, however, ancestry was not typically reported. We evaluated the risk for type 2 diabetes as an exemplar to show that race and ethnic group may contribute to inconsistent validation of previous findings of associations with microRNAs.

Co­expressed 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 co­expression 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.

Serine, N-acetylaspartate differentiate adolescents with juvenile idiopathic arthritis compared with healthy controls: a metabolomics cross-sectional study.

BACKGROUND: In comparison with the general population, adolescents with juvenile idiopathic arthritis (JIA) are at higher risk for morbidity and mortality. However, limited evidence is available about this condition's underlying metabolic profile in adolescents with JIA relative to healthy controls. In this untargeted, cross-sectional metabolomics study, we explore the plasma metabolites in this population. METHODS: A sample of 20 adolescents with JIA and 20 controls aged 13-17 years were recruited to complete surveys, provide medical histories and biospecimens, and undergo assessments. Fasting morning plasma samples were processed with liquid chromatography-mass spectrometry. Data were centered, scaled, and analyzed using generalized linear models accounting for age, sex, and medications (p-values adjusted for multiple comparisons using the Holm method). Spearman's correlations were used to evaluate relationships among metabolites, time since diagnosis, and disease severity. RESULTS: Of 72 metabolites identified in the samples, 55 were common to both groups. After adjustments, 6 metabolites remained significantly different between groups. Alpha-glucose, alpha-ketoglutarate, serine, and N-acetylaspartate were significantly lower in the JIA group than in controls; glycine and cystine were higher. Seven additional metabolites were detected only in the JIA group; 10 additional metabolites were detected only in the control group. Metabolites were unrelated to disease severity or time since diagnosis. CONCLUSIONS: The metabolic signature of adolescents with JIA relative to controls reflects a disruption in oxidative stress; neurological health; and amino acid, caffeine, and energy metabolism pathways. Serine and N-acetylaspartate were promising potential biomarkers, and their metabolic pathways are linked to both JIA and cardiovascular disease risk. The pathways may be a source of new diagnostic, treatment, or prevention options. This study's findings contribute new knowledge for systems biology and precision health approaches to JIA research. Further research is warranted to confirm these findings in a larger sample.

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.

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.

Circulating microRNAs are associated with variability in fasting blood glucose over 12-months and target pathways related to type 2 diabetes: A pilot study.

INTRODUCTION: MicroRNAs (miRs) may be important regulators of risk for type 2 diabetes (T2D). Circulating miRs may provide information about which individuals are at risk for T2D. The purpose of this study was to assess longitudinal associations between circulating miR expression and variability in fasting blood glucose (FBG) and to identify miR-targeted genes and biological pathways. METHODS: Variability in FBG was estimated using standard deviation from participants (n = 20) in a previously completed yoga trial. Expression of 402 miRs was measured using hydrogel particle lithography. MirTarBase was used to identify mRNAs, and miRPathDB was used to identify pathways targeted by differentially expressed miRs. RESULTS: Six circulating miRs (miR-192, miR-197, miR-206, miR-424, miR-486, and miR-93) were associated with variability in FBG and targeted 143 genes and 23 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Six mRNAs (AKT1, CCND1, ESR1, FASN, SMAD7, and VEGFA) were targeted by at least two miRs and four of those were located in miR-targeted KEGG pathways. CONCLUSIONS: Circulating miRs are associated with variability in FBG in individuals at risk for T2D. Further studies are needed to determine whether miRs may be prodromal biomarkers that can identify which individuals are at greatest risk to progress to T2D and which biological pathways underlie this risk.

The association of COVID-19 infection in pregnancy with preterm birth: A retrospective cohort study in California.

INTRODUCTION: Our understanding of the association between coronavirus disease 19 (COVID-19) and preterm or early term birth among racially and ethnically diverse populations and people with chronic medical conditions is limited. METHODS: We determined the association between COVID-19 and preterm (PTB) birth among live births documented by California Vital Statistics birth certificates between July 2020 and January 2021 (n=240,147). We used best obstetric estimate of gestational age to classify births as very preterm (VPTB, <32 weeks), PTB (< 37 weeks), early term (37 and 38 weeks), and term (39-44 weeks), as each confer independent risks to infant health and development. Separately, we calculated the joint effects of COVID-19 diagnosis, hypertension, diabetes, and obesity on PTB and VPTB. FINDINGS: COVID-19 diagnoses on birth certificates increased for all racial/ethnic groups between July 2020 and January 2021 and were highest for American Indian/Alaska Native (12.9%), Native Hawaiian/Pacific Islander (11.4%), and Latinx (10.3%) birthing people. COVID-19 diagnosis was associated with an increased risk of VPTB (aRR 1.6, 95% CI [1.4, 1.9]), PTB (aRR 1.4, 95% CI [1.3, 1.4]), and early term birth (aRR 1.1, 95% CI [1.1, 1.2]). There was no effect modification of the overall association by race/ethnicity or insurance status. COVID-19 diagnosis was associated with elevated risk of PTB in people with hypertension, diabetes, and/or obesity. INTERPRETATION: In a large population-based study, COVID-19 diagnosis increased the risk of VPTB, PTB, and early term birth, particularly among people with medical comorbidities. Considering increased circulation of COVID-19 variants, preventative measures, including vaccination, should be prioritized for birthing persons. FUNDING: UCSF-Kaiser Department of Research Building Interdisciplinary Research Careers in Women's Health Program (BIRCWH) National Institute of Child Health and Human Development (NICHD) and the Office of Research on Women's Health (ORWH) [K12 HD052163] and the California Preterm Birth Initiative, funded by Marc and Lynn Benioff.

Circulating MicroRNAs predict glycemic improvement and response to a behavioral intervention.

BACKGROUND: MicroRNAs may be important regulators of risk for type 2 diabetes. The purpose of this longitudinal observational study was to assess whether circulating microRNAs predicted improvements in fasting blood glucose, a major risk factor for type 2 diabetes, over 12 months. METHODS: The study included participants (n = 82) from a previously completed trial that tested the effect of restorative yoga on individuals with prediabetes. Circulating microRNAs were measured using a flow cytometry miRNA assay. Linear models were used to determine the optimal sets of microRNA predictors overall and by intervention group. RESULTS: Subsets of microRNAs were significant predictors of final fasting blood glucose after 12-months (R2 = 0.754, p < 0.001) and changes in fasting blood glucose over 12-months (R2 = 0.731, p < 0.001). Three microRNAs (let-7c, miR-363, miR-374b) were significant for the control group only, however there was no significant interaction by intervention group. CONCLUSIONS: Circulating microRNAs are significant predictors of fasting blood glucose in individuals with prediabetes. Among the identified microRNAs, several have previously been associated with risk for type 2 diabetes. This is one of the first studies to use a longitudinal design to assess whether microRNAs predict changes in fasting blood glucose over time. Further exploration of the function of the microRNAs included in these models may provide new insights about the complex etiology of type 2 diabetes and responses to behavioral risk reduction interventions. TRIAL REGISTRATION: This study was a secondary analysis of a previously completed clinical trial that is registered at clinicaltrials.gov (NCT01024816) on December 3, 2009.

Socioeconomic status, diabetes, and gestation length in Native American and White women.

OBJECTIVE: "Diminishing returns" of socioeconomic status (SES) suggests that higher SES may not confer equivalent health benefits for ethnic minority individuals as compared to White individuals. Little research has tested whether diminishing returns also affects Native Americans. The objective of this study was to determine whether higher SES is associated with lower diabetes risk and longer gestational length in both Native American and White women, and whether SES predicts gestational length indirectly via diabetes risk. METHOD: A sample of 674,014 Native American and White women was drawn from a population-based California cohort of singleton births (2007-2012). Education, public health insurance status, gestational length, and diabetes diagnosis were extracted from a state-maintained birth cohort database. Covariates were age, health behaviors, pregnancy variables, residence rurality, and prepregnancy body mass index. RESULTS: In logistic regression models, the race by SES interaction (both education and insurance status) was associated with diabetes risk. Compared to high-SES White women, high- and low-SES Native American women had highest and equivalent diabetes risk. In path analyses, the race by SES interaction indirectly predicted gestational length through diabetes, ps < .001. For White women, an indirect effect of diabetes was detected, ps < .001, such that higher SES was associated with reduced risk for diabetes and thus longer gestational length. For Native American women, no indirect effect was detected, ps > .067. CONCLUSIONS: Among Native American women, higher SES did not confer protection against diabetes or shorter gestational length. These findings are consistent with the diminishing returns of SES phenomenon. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Circulating microRNAs associated with prediabetes and geographic location in Latinos.

BACKGROUND: Globally, type 2 diabetes is highly prevalent in individuals of Latino ancestry. The reasons underlying this high prevalence are not well understood, but both genetic and lifestyle factors are contributors. Circulating microRNAs are readily detectable in blood and are promising biomarkers to characterize biological responses (i.e., changes in gene expression) to lifestyle factors. Prior studies identified relationships between circulating microRNAs and risk for type 2 diabetes, but Latinos have largely been under-represented in these study samples. AIMS/HYPOTHESIS: The aim of this study was to assess for differences in expression levels of three candidate microRNAs (miR-126, miR-146, miR-15) between individuals who had prediabetes compared to normal glycemic status and between individuals who self-identified with Latino ancestry in the United States (US) and native Mexicans living in or near Leon, Mexico. METHODS: This was a cross-sectional study that included 45 Mexicans and 21 Latino participants from the US. Prediabetes was defined as fasting glucose 100-125 mg/dL or 2-h post-glucose challenge between 140 and 199 mg/dL. Expression levels of microRNAs from plasma were measured by qPCR. Linear and logistic regression models were used to assess relationships between individual microRNAs and glycemic status or geographic site. RESULTS: None of the three microRNAs was associated with risk for type 2 diabetes. MiR-146a and miR-15 were significantly lower in the study sample from Mexico compared to the US. There was a significant interaction between miR-146a and BMI associated with fasting blood glucose. CONCLUSIONS/INTERPRETATION: This study did not replicate in Latinos prior observations from other racial groups of associations between miR-126, miR-146a, and miR-15 and risk for type 2 diabetes. Future studies should consider other microRNAs related to different biological pathways as possible biomarkers for type 2 diabetes in Latinos.

Pregnancy after bariatric surgery in women with rheumatic diseases and association with adverse birth outcomes.

BACKGROUND: Autoimmune rheumatic diseases (ARDs) and bariatric surgery are each risk factors for adverse birth outcomes. To date, no study has investigated their combined impact on birth outcomes. OBJECTIVES: The objective of this study was to evaluate the impact of bariatric surgery on pregnancy outcomes in women with an ARD. As a secondary comparison, we assessed the risk of bariatric surgery on the same outcomes in women without an ARD. SETTING: Records maintained by the California Office of Statewide Health Planning and Development. METHODS: This cohort study included infants born between 20-44 weeks of gestation in California between 2011-2018. Risks of adverse pregnancy outcomes were evaluated for women with a history of bariatric surgery as compared to women without a history of bariatric surgery, stratified by ARD, using log-linear regression with a Poisson distribution. RESULTS: The study included 3,574,165 infants, of whom 10,823 (0.3%) were born to women who had an ARD and 13,529 (0.38%) to women with a history of bariatric surgery. There were 155 infants born to women (0.0043%) with both an ARD and a history of bariatric surgery. In women with an ARD and without bariatric surgery, the prevalence of preterm births was 18%, compared to 17.4% in women with both ARD and bariatric surgery; in women without ARD but with prior bariatric surgery, the prevalence of preterm births was 13.7%, compared to 8.2% in women without bariatric surgery. Except for neonatal intensive care unit (NICU) admissions, women with an ARD and history of bariatric surgery were not at a statistically increased risk of having other adverse pregnancy outcomes as compared to women with an ARD and no history of bariatric surgery. CONCLUSION: Our study shows that women with ARD already have a high occurrence of several adverse birth outcomes, and this was not further increased by a history of bariatric surgery. The infants born to women with a history of ARD and bariatric surgery were admitted to the NICU significantly more than the infants born to women with an ARD and no history of bariatric surgery.