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Aim: To describe the design and methods of an intervention that engaged women with previous gestational diabetes mellitus in a tailored approach for diabetes prevention. Methods: Women participated in biometric tests for BMI and hemoglobin A1c, psychosocial questionnaires and an informed decision-making process to select a lifestyle change program for Type 2 diabetes prevention based on their needs and priorities. Measure time points were at baseline, 6 months and 12 months. Results: The authors recruited 116 women. The outcomes of this study will evaluate the effect of this strategy on participant engagement in lifestyle change programs for Type 2 diabetes prevention. Conclusion: This paper describes a variety of lifestyle change programs and an informed decision-making process for tailoring diabetes prevention programs for a high-risk population.

This study describes exposure to repetitive head impacts (RHI) by player position and activity during a collegiate football season, and investigates the relationship between RHI and acute (i.e., daily and weekly) and short-term (i.e., pre- to post-season) changes in balance, reaction time, symptoms, and cognition. We recorded RHI exposure in twenty Division I collegiate American football players during a single season using the Riddell InSite system. Participants sustained 4,586 impacts (4.20% high impact, i.e., >63 g; 95.79% low impact, i.e., 20–63 g). Greatest exposure to RHI was observed in running backs and defensive ends during games, and tight ends and defensive ends during practices. Running plays and team drills placed players at greatest risk for exposure during practice. Cumulative RHI exposure across the season was associated with short-term declines in reaction time (p = 0.045), but not balance or cognition. Acute decline in balance was associated with the number of impacts sustained in the past week (p 0.05). Acute increase in total symptom score was also associated with the number of impacts sustained in the past week (p 0.05). Reaction time did not decline based on impact exposure in the past 24 hours or week. This study identifies activities and positions that may put players at risk for RHI exposure, and demonstrates that RHI sustained during the course of typical American football play by non-concussed individuals may result in small changes in balance, reaction time, and symptoms, but not cognition.

Having a child with type 1 diabetes (T1D) impacts the entire family system. Parental distress and burden have been well studied, but other family members, including siblings, have received little attention. Based on research about family life and sibling experiences in other chronic condition populations (e.g., autism, cancer), we expected parents of youth with T1D would report that siblings participated in T1D management and that T1D had a psychological impact on siblings. As part of a larger qualitative study, parents of youth with T1D age 5-17 (M = 10.8 ± 3.6 years) participated in semistructured interviews about T1D-specific health-related quality of life. For this study, we conducted secondary analyses on transcripts from 20 parents (95% mothers) from households with at least 1 sibling of the child with T1D. Three themes emerged: (a) siblings share the workload and help with T1D management, (b) T1D takes an emotional toll on siblings, and (c) parents feel guilty about prioritizing T1D over siblings' needs and desires. Parents recognized siblings have impactful roles in T1D management and family functioning. Future research into these themes can guide clinical and research efforts to develop sibling-inclusive resources and interventions for families with T1D. Enhancing family-focused interventions to recognize and support the needs of siblings may ultimately improve family T1D-related quality of life. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Background: The prevalence of youth diagnosed with prediabetes is increasing, yet there is a lack of guidelines on how to manage this condition clinically. Objectives: The aim was to determine the short-term outcomes of patients referred with prediabetes and to determine predictors of worsening dysglycemia in youth. Study Design: This is a retrospective chart review of patients referred to our Youth Diabetes Prevention Clinic (YDPC) with laboratory tests indicating an increased risk for type 2 diabetes (T2D). We defined glycemic categories by HbA1c with normoglycemia as HbA1c Results: Among 562 patients seen, 336 had both screening and YDPC HbA1c values. Race (p < 0.001) and screening glycemic category (p < 0.001) were significantly associated with dysglycemia at the YDPC visit, while sex (p = 0.50), BMI z-score change (p = 0.27), and days from referral (p = 0.83) were not. As compared to those who reverted to normoglycemia, patients with prediabetes at YDPC were 7 times more likely to have a higher screening HbA1c (both P1 and P2). The majority of patients referred with prediabetes had lower HbA1c at the YDPC (75.4–82.6%). Conclusion: Patients with screening HbA1c

The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. Recent work to improve nearest-neighbor feature selection algorithms has focused on new neighborhood estimation methods and distance metrics. However, little attention has been given to the distributional properties of pairwise distances as a function of the metric or data type. Thus, we derive general analytical expressions for the mean and variance of pairwise distances for Lq metrics for normal and uniform random data with p attributes and m instances. The distribution moment formulas and detailed derivations provide a resource for understanding the distance properties for metrics and data types commonly used with nearest-neighbor methods, and the derivations provide the starting point for the following novel results. We use extreme value theory to derive the mean and variance for metrics that are normalized by the range of each attribute (difference of max and min). We derive analytical formulas for a new metric for genetic variants, which are categorical variables that occur in genome-wide association studies (GWAS). The genetic distance distributions account for minor allele frequency and the transition/transversion ratio. We introduce a new metric for resting-state functional MRI data (rs-fMRI) and derive its distance distribution properties. This metric is applicable to correlation-based predictors derived from time-series data. The analytical means and variances are in strong agreement with simulation results. We also use simulations to explore the sensitivity of the expected means and variances in the presence of correlation and interactions in the data. These analytical results and new metrics can be used to inform the optimization of nearest neighbor methods for a broad range of studies, including gene expression, GWAS, and fMRI data.

Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of RNA-Seq data, and detect significant gene-expression modules associated with depression severity. We performed an RNA-Seq gene co-expression network analysis of mRNA data obtained from the peripheral blood mononuclear cells of unmedicated MDD (n = 78) and healthy control (n = 79) subjects. Across the combined MDD and HC groups, we assigned genes into modules using hierarchical clustering with a dynamic tree cut method and projected the expression data onto a lower-dimensional module space by computing the single-sample gene set enrichment score of each module. We tested the single-sample scores of each module for association with levels of depression severity measured by the Montgomery-Åsberg Depression Scale (MADRS). Independent of MDD status, we identified 23 gene modules from the co-expression network. Two modules were significantly associated with the MADRS score after multiple comparison adjustment (adjusted p = 0.009, 0.028 at 0.05 FDR threshold), and one of these modules replicated in a previous RNA-Seq study of MDD (p = 0.03). The two MADRS-associated modules contain genes previously implicated in mood disorders and show enrichment of apoptosis and B cell receptor signaling. The genes in these modules show a correlation between network centrality and univariate association with depression, suggesting that intramodular hub genes are more likely to be related to MDD compared to other genes in a module.

Background There is a lack of neuroscience-based biomarkers for the diagnosis, treatment and monitoring of individuals with substance use disorders (SUD). The resource allocation index (RAI), a measure of the interrelationship between salience, executive control and default-mode brain networks (SN, ECN, and DMN), has been proposed as one such biomarker. However, the RAI has yet to be extensively tested in SUD samples. Methods The present analysis compared RAI scores between individuals with stimulant and/or opioid use disorders (SUD; n = 139, abstinent 4–365 days) and healthy controls (HC; n = 56) who had completed resting-state functional magnetic resonance imaging (fMRI) scans within the context of the Tulsa 1000 cohort. First, we used independent component analysis (ICA) to identify the SN, ECN, and DMN and extract their time series data. Second, we used multiple permutations of automatically identified networks to compute RAI as reported in the fMRI literature. Results First, the RAI as a metric depended substantially on the approach that was used to define the network components. Second, regardless of the selection of networks, after controlling for multiple testing there was no difference in RAI scores between SUD and HC. Third, the RAI was not associated with any substance use-related self-report measures. Conclusion Taken together, these findings do not provide evidence that RAI can be used as an fMRI-derived biomarker for the severity or diagnosis of individuals with SUD.

Pediatric obesity is a public health concern with lifestyle intervention as the first-line treatment. Forever-Fit Summer Camp (FFSC) is a 6-week summer day program offering physical activity, nutrition education, and well-balanced meals to youth at low cost. The aim of the study was to assess the efficacy of this program that does not emphasize weight loss rather emphasizes healthy behaviors on body mass index, cardiovascular and physical fitness. Methods: The inclusion criteria were adolescents between 8 and 12 years and body mass index (BMI) ≥85th percentile. The data were collected at baseline and week 6 (wk-6) and was analyzed for 2013-2018 using paired-sample t tests. Results: The participants’ (N = 179) average age was 10.6 ± 1.6 years with a majority of females (71%) and black race/ethnicity (70%). At wk-6, BMI and waist circumference decreased by 0.8 ± 0.7 kg/m2 and 1.0 ± 1.3 in, respectively. Resting heart rate, diastolic and systolic blood pressure decreased by 8.5 ± 11.0 bpm, 6.3 ± 8.8 mmHg, and 6.4 ± 10.1 mmHg, respectively. The number of pushups, curl-ups, and chair squats were higher by 5.8 ± 7.5, 6.7 ± 9.1, and 7.7 ± 8.5, respectively. Conclusion: The FFSC is efficacious for improving BMI, cardiovascular, and physical fitness in the short term. The effect of similar episodic efforts that implement healthy lifestyle modifications throughout the school year should be investigated.

Recent developments in genetic technologies allow deep analysis of the sequence diversity of immune repertoires, but little work has been reported on the architecture of immune repertoires in mucosal tissues. Antibodies are the key to prevention of infections at the mucosal surface, but it is currently unclear whether the B cell repertoire at mucosal surfaces reflects the dominant antibodies found in the systemic compartment or whether mucosal tissues harbor unique repertoires. We examined the expressed antibody variable gene repertoires from 10 different human tissues using RNA samples derived from a large number of individuals. The results revealed that mucosal tissues such as stomach, intestine and lung possess unique antibody gene repertoires that differed substantially from those found in lymphoid tissues or peripheral blood. Mutation frequency analysis of mucosal tissue repertoires revealed that they were highly mutated, with little evidence for the presence of naïve B cells, in contrast to blood. Mucosal tissue repertoires possessed longer heavy chain complementarity determining region 3 loops than lymphoid tissue repertoires. We also noted a large increase in frequency of both insertions and deletions in the small intestine antibody repertoire. These data suggest that mucosal immune repertoires are distinct in many ways from the systemic compartment.

Solid particle erosion is an inevitable problem in oil and gas production and transportation systems, and it can cause severe damage in pipe fittings in which the flow direction changes suddenly. Erosion occurs in a variety of fittings, and one common fitting which is one of the most vulnerable to erosion is the elbow. Repair and/or replacement of pipe fittings in the appropriate period can avoid catastrophic disasters such as hydrocarbon release events. Therefore, accurate prediction of erosion is a subject of interest for the oil and gas industry. Both experimental and modeling approaches have been conducted in the past under different flow conditions and materials. The goal of this study is to use relevant experimental parameters as predictor variables and utilize a machine learning algorithm to predict erosion rate and validate the prediction under different conditions with test data. It is assumed that important parameters in erosion magnitude are material characteristics, pipe diameter, particles properties and size and carrier fluid properties. 201 experimental data points with a broad range of conditions were selected for the analysis. Predicted erosion rates through different machine learning techniques were compared, and Random Forest regression model was selected as an effective and alternative approach for erosion prediction. This method was examined on data points and compared with experimental results. Good agreement was observed in this method with experiments which provides the framework for a new way to predict solid particle erosion which is simpler yet accurate in comparison with available prediction models.

Summary Machine learning feature selection methods are needed to detect complex interaction-network effects in complicated modeling scenarios in high-dimensional data, such as GWAS, gene expression, eQTL and structural/functional neuroimage studies for case–control or continuous outcomes. In addition, many machine learning methods have limited ability to address the issues of controlling false discoveries and adjusting for covariates. To address these challenges, we develop a new feature selection technique called Nearest-neighbor Projected-Distance Regression (NPDR) that calculates the importance of each predictor using generalized linear model regression of distances between nearest-neighbor pairs projected onto the predictor dimension. NPDR captures the underlying interaction structure of data using nearest-neighbors in high dimensions, handles both dichotomous and continuous outcomes and predictor data types, statistically corrects for covariates, and permits statistical inference and penalized regression. We use realistic simulations with interactions and other effects to show that NPDR has better precision-recall than standard Relief-based feature selection and random forest importance, with the additional benefit of covariate adjustment and multiple testing correction. Using RNA-Seq data from a study of major depressive disorder (MDD), we show that NPDR with covariate adjustment removes spurious associations due to confounding. We apply NPDR to eQTL data to identify potentially interacting variants that regulate transcripts associated with MDD and demonstrate NPDR’s utility for GWAS and continuous outcomes. Availability and implementation Available at: https://insilico.github.io/npdr/. Supplementary information Supplementary data are available at Bioinformatics online.

The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. The effects of the distribution and metric, as well as the presence of correlation and interactions, are reflected in the expected moments of the distribution of pairwise distances. We derive general analytical expressions for the mean and variance of pairwise distances for Lq metrics for normal and uniform random data with p attributes and m instances. We use extreme value theory to derive results for metrics that are normalized by the range of each attribute (max – min). In addition to these expressions for continuous data, we derive similar analytical formulas for a new metric for genetic variants (categorical data) in genome-wide association studies (GWAS). The genetic distance distributions account for minor allele frequency and transition/transversion ratio. We introduce a new metric for resting-state functional MRI data (rs-fMRI) and derive its distance properties. This metric is applicable to correlation-based predictors derived from time series data. Derivations assume independent data, but empirically we also consider the effect of correlation. These analytical results and new metrics can be used to inform the optimization of nearest neighbor methods for a broad range of studies including gene expression, GWAS, and fMRI data. The summary of distribution moments and detailed derivations provide a resource for understanding the distance properties for various metrics and data types.

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug&ndash, gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug&ndash, gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug&ndash, gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast&ndash, human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

The prevalence of T2D is increasing in youth partly because of the lack of effective prevention strategies. Lifestyle intervention is established for adult T2D prevention and family-based lifestyle intervention is the most efficacious non-surgical treatment of obesity in youth. We developed a 16-wk family-based program modeled after the U.S. Diabetes Prevention Program, using Expert Committee Recommendations Regarding the Prevention, Assessment and Treatment of Child and Adolescent Overweight and Obesity. Sessions included physical activity, separate adult/youth nutrition education and goal setting, and a shared meal. Youth inclusion criteria were age 7-18 y, BMI > 85th %ile, a caregiver to participate, and ≥ 2 risk factors: family history of T2D, maternal history of gestational diabetes, minority race/ethnicity. Paired t-tests were used to compare BMI and HbA1c at baseline (BL) and 4 months (M4) and at BL and 12 months (M12, data set currently incomplete). Between Feb 2017 and Nov 2018, 79 eligible youth enrolled (Table) the majority of whom had government-issued health insurance. Youth HbA1c was maintained at M4 (p = 1.0) and M12 (p = 0.45). Similarly, BMI SDS were stable at M4 (p = 0.67) and M12 (p = 0.12) while BMI scores increased at M4 (p = 0.007), but not at M12 (p = 0.23). As excessive weight-gain is correlated with T2D development, programs that maintain BMI could have significant impact on preventing or delaying T2D in youth. Disclosure H.K. El Mikati: None. J. Pike: None. K. Haberlin-Pittz: None. L. Yazel-Smith: None. B.M. McKinney: None. T.S. Hannon: Advisory Panel; Self; Eli Lilly and Company. Funding Indiana University School of Medicine; Indiana University

Vaccination is an effective prevention of influenza infection. However, certain individuals develop a lower antibody response after vaccination, which may lead to susceptibility to subsequent infection. An important challenge in human health is to find baseline gene signatures to help identify individuals who are at higher risk for infection despite influenza vaccination. We developed a multi-level machine learning strategy to build a predictive model of vaccine response using pre&minus, vaccination antibody titers and network interactions between pre&minus, vaccination gene expression levels. The first-level baseline&minus, antibody model explains a significant amount of variation in post-vaccination response, especially for subjects with large pre&minus, existing antibody titers. In the second level, we clustered individuals based on pre&minus, vaccination antibody titers to focus gene&minus, based modeling on individuals with lower baseline HAI where additional response variation may be predicted by baseline gene expression levels. In the third level, we used a gene&minus, association interaction network (GAIN) feature selection algorithm to find the best pairs of genes that interact to influence antibody response within each baseline titer cluster. We used ratios of the top interacting genes as predictors to stabilize machine learning model generalizability. We trained and tested the multi-level approach on data with young and older individuals immunized against influenza vaccine in multiple cohorts. Our results indicate that the GAIN feature selection approach improves model generalizability and identifies genes enriched for immunologically relevant pathways, including B Cell Receptor signaling and antigen processing. Using a multi-level approach, starting with a baseline HAI model and stratifying on baseline HAI, allows for more targeted gene&minus, based modeling. We provide an interactive tool that may be extended to other vaccine studies.

INTRODUCTION: Emerging infectious diseases are a major threat to public health, and while vaccines have proven to be one of the most effective preventive measures for infectious diseases, we still do not have safe and effective vaccines against many human pathogens, and emerging diseases continually pose new threats. The purpose of this review is to discuss how the creation of vaccines for these new threats has been hindered by limitations in the current approach to vaccine development. Recent advances in high-throughput technologies have enabled scientists to apply systems biology approaches to collect and integrate increasingly large datasets that capture comprehensive biological changes induced by vaccines, and then decipher the complex immune response to those vaccines. AREAS COVERED: This review covers advances in these technologies and recent publications that describe systems biology approaches to understanding vaccine immune responses and to understanding the rational design of new vaccine candidates. EXPERT OPINION: Systems biology approaches to vaccine development provide novel information regarding both the immune response and the underlying mechanisms and can inform vaccine development.

Background and Hypothesis: The objective of this study was to evaluate anthropometric and physical fitness measures from participants in the Forever-Fit Summer Camp (FFSC) between the years 2013 and 2017, comparing baseline and post-participation values. We hypothesized that participation in the FFSC would decrease the BMI Z-score of the campers during the 6-week program, as well as improve physical fitness measures. Further, we hypothesized that there would be an overall decrease in BMI and weight seen in campers who participated for multiple years. Experimental Design: In this 5-year cross-sectional evaluation, a total of 64 participants were included. During FFSC, biometrics, strength, endurance and flexibility were compared at baseline (Week 1) and 6 weeks after. This data from participants was compared using paired T-tests and compared the BMI Z-Score of campers over the years using repeated measures ANOVA. Results: Compared to baseline for each year, there was an overall decrease in BMI Z-Score and weight after the 6-week intervention. We observed an increase in strength and endurance, as seen with the 1-mile run and various strength test. Interestingly, we saw a decrease in flexibility in the campers as they progressed through the camp. Further, we observed an overall decrease in BMI Z-Score from 2013 to 2017. Conclusion and Potential Impact: The FFSC 6-week summer program had significantly decreased BMI and improved fitness measures. The activities performed in the FFSC could be implemented in other programs for youth to combat chronic conditions, such as Type 2 Diabetes.

Motivation An important challenge in gene expression analysis is to improve hub gene selection to enrich for biological relevance or improve classification accuracy for a given phenotype. In order to incorporate phenotypic context into co-expression, we recently developed an epistasis-expression network centrality method that blends the importance of gene–gene interactions (epistasis) and main effects of genes. Further blending of prior knowledge from functional interactions has the potential to enrich for relevant genes and stabilize classification. Results We develop two new expression-epistasis centrality methods that incorporate interaction prior knowledge. The first extends our SNPrank (EpistasisRank) method by incorporating a gene-wise prior knowledge vector. This prior knowledge vector informs the centrality algorithm of the inclination of a gene to be involved in interactions by incorporating functional interaction information from the Integrative Multi-species Prediction database. The second method extends Katz centrality to expression-epistasis networks (EpistasisKatz), extends the Katz bias to be a gene-wise vector of main effects and extends the Katz attenuation constant prefactor to be a prior-knowledge vector for interactions. Using independent microarray studies of major depressive disorder, we find that including prior knowledge in network centrality feature selection stabilizes the training classification and reduces over-fitting. Availability and implementation Methods and examples provided at https://github.com/insilico/Rinbix and https://github.com/insilico/PriorKnowledgeEpistasisRank. Supplementary information Supplementary data are available at Bioinformatics online.

Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of RNA-Seq data, and detect significant gene-expression modules associated with depression severity. We performed an RNA-Seq gene co-expression network analysis of mRNA data obtained from the peripheral blood mononuclear cells of unmedicated MDD (n = 78) and healthy control (n = 79) subjects. Across the combined MDD and HC groups, we assigned genes into modules using hierarchical clustering with a dynamic tree cut method and projected the expression data onto a lower-dimensional module space by computing the single-sample gene set enrichment score of each module. We tested the single-sample scores of each module for association with levels of depression severity measured by the Montgomery-Åsberg Depression Scale (MADRS). Independent of MDD status, we identified 23 gene modules from the co-expression network. Two modules were significantly associated with the MADRS score after multiple comparison adjustment (adjusted p = 0.009, 0.028 at 0.05 FDR threshold), and one of these modules replicated in a previous RNA-Seq study of MDD (p = 0.03). The two MADRS-associated modules contain genes previously implicated in mood disorders and show enrichment of apoptosis and B cell receptor signaling. The genes in these modules show a correlation between network centrality and univariate association with depression, suggesting that intramodular hub genes are more likely to be related to MDD compared to other genes in a module.