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Gadgil, M.

Publications and source records attributed to Gadgil, M..

4 recordsLinked to original sources

Co-Expressed MicroRNAs Identify Potential Mechanisms Underlying Risk for Multimorbid Depression and Type 2 Diabetes in Midlife Women

BackgroundWomen are disproportionately affected by multimorbid depression and type 2 diabetes (T2D), with prevalence peaking during midlife (40-64 years), a biologically dynamic timeframe due to changes associated with reproductive aging. Yet, phenotypic and mechanistic factors contributing to midlife womens disproportionate risk for co-occurrence remain poorly defined. We previously identified co-expressed microRNAs (miRs) in midlife women with prediabetes that increased odds of assignment to a high psychometabolic risk phenotype. Here, we extend these findings by characterizing putative mRNA targets of these co-expressed miRs and pathways overrepresented among mRNAs, providing insights into potential mechanisms underlying psychometabolic risk in midlife women. MethodsThis study included baseline data from midlife women (ages 40-64 years) with prediabetes who participated in the Diabetes Prevention Program (DPP) (n = 603). In silico analyses were performed using miRTarBase to identify mRNAs regulated by 3 or more of the miRs that most prominently loaded a principal component previously identified to increase odds of assignment to a high psychometabolic risk phenotype defined in this sample. Pathway enrichment analysis was conducted to assess for overrepresentation of KEGG pathways among predicted mRNA targets. To enhance interpretability, pathways were thematically clustered based on their evidenced role in human physiology. ResultsWe identified a total of 13 mRNAs targeted by co-expressed miRs associated with increased odds of assignment to a high psychometabolic risk phenotype in midlife women with prediabetes. Pathway enrichment analysis revealed a total of 71 KEGG pathways with overrepresentation of identified mRNA targets. Four overarching biological themes emerged, reflecting involvement of metabolic, inflammatory, endocrine, and stress/biological weathering-related processes. ConclusionsExperimentally validated mRNA targets related biological pathways were identified, providing multisystem insights into potential mechanisms underlying risk for multimorbid depression and T2D in midlife women. Findings offer mechanistic targets for experimental validation and future precision health research focused on this high-risk population. Overall, this work positions the utility of miRs as context-sensitive biomarkers in the characterization of risk for complex, multimorbid conditions in women during biologically dynamic timeframes.

genomics↗

Prediabetes Phenotype Clusters in the Diabetes Prevention Program Study

ObjectiveThe purpose of this study was to apply clustering methods to identify and characterize prediabetes phenotypes and their relationships with treatment arm and type 2 diabetes (T2D) outcomes in the Diabetes Prevention Program (DPP), and to compare the utility of additional clustering measures in phenotype characterization and T2D risk stratification. Research Design and MethodsThis was a secondary analysis of data from a subset of participants (n=994) from the previously completed Diabetes Prevention Program trial. Unsupervised k-means clustering analysis was applied to derive the optimal number of clusters of participants based on common clinical risk factors alone or common risk factors plus more comprehensive measures of glucose tolerance and body composition. ResultsFive clusters were derived from common clinical characteristics and the addition of comprehensive measures of glucose tolerance and body composition. Within each modeling approach, participants show significantly different levels of risk factors. The clinical only model showed higher accuracy for time to T2D, however the more comprehensive models further differentiated a metabolically health overweight phenotype. For both models, the greatest differentiation in determining time to T2D was in the metformin arm of the trial. ConclusionsData driven clustering of patients with prediabetes allows for identification of prediabetes phenotypes at greater risk for disease progression and responses to risk reduction interventions. Further investigation into phenotypic differences in treatment response could enable better personalization of prediabetes and T2D prevention and treatment choices.

genomics↗

Longitudinal Associations Between MicroRNAs and Weight in the Diabetes Prevention Program

OBJECTIVECirculating 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 METHODSA 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. RESULTSIn 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. DISCUSSIONThis 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.

genomics↗

Mathematical model of the multi-aminoacid multi-transporter system predicts uptake flux in CHO cells

Chinese hamster ovary (CHO) cells express several amino acid (AA) transporters including uniporters and exchangers. Each transporter transports multiple AAs, making prediction of the effect of changed medium composition or transporter levels on individual AA transport rate challenging. A general kinetic model and a simplified analytical expression for the uptake rate is presented. A CHO cell-specific AA transport model, to our knowledge the first such network model for any cell type, is constructed. The model is validated by its prediction of reported uptake flux and amino acid inter-dependencies from experiments that were not used in model construction or parameter estimation. The model defines theoretical conditions for synergistic/repressive effect on the uptake rates of other AAs upon external addition of one AA. This model will help formulate testable hypotheses of the effect of process changes on AA initial uptake, and serve as the AA transport component of kinetic models for cellular metabolism.

bioengineering↗