Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection.

Clostridioides difficile (C. diff.) infection (CDI) is a leading cause of hospital acquired diarrhea in North America and Europe and a major cause of morbidity and mortality. Known risk factors do not fully explain CDI susceptibility, and genetic susceptibility is suggested by the fact that some patients with colons that are colonized with C. diff. do not develop any infection while others develop severe or recurrent infections. To identify common genetic variants associated with CDI, we performed a genome-wide association analysis in 19,861 participants (1349 cases; 18,512 controls) from the Electronic Medical Records and Genomics (eMERGE) Network. Using logistic regression, we found strong evidence for genetic variation in the DRB locus of the MHC (HLA) II region that predisposes individuals to CDI (P > 1.0 × 10-14; OR 1.56). Altered transcriptional regulation in the HLA region may play a role in conferring susceptibility to this opportunistic enteric pathogen.

Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19.

MOTIVATION: Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. RESULTS: We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19. AVAILABILITY AND IMPLEMENTATION: GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unmanaged Pharmacogenomic and Drug Interaction Risk Associations with Hospital Length of Stay among Medicare Advantage Members with COVID-19: A Retrospective Cohort Study.

Unmanaged pharmacogenomic and drug interaction risk can lengthen hospitalization and may have influenced the severe health outcomes seen in some COVID-19 patients. To determine if unmanaged pharmacogenomic and drug interaction risks were associated with longer lengths of stay (LOS) among patients hospitalized with COVID-19, we retrospectively reviewed medical and pharmacy claims from 6025 Medicare Advantage members hospitalized with COVID-19. Patients with a moderate or high pharmacogenetic interaction probability (PIP), which indicates the likelihood that testing would identify one or more clinically actionable gene-drug or gene-drug-drug interactions, were hospitalized for 9% (CI: 4-15%; p < 0.001) and 16% longer (CI: 8-24%; p < 0.001), respectively, compared to those with low PIP. Risk adjustment factor (RAF) score, a commonly used measure of disease burden, was not associated with LOS. High PIP was significantly associated with 12-22% longer LOS compared to low PIP in patients with hypertension, hyperlipidemia, diabetes, or chronic obstructive pulmonary disease (COPD). A greater drug-drug interaction risk was associated with 10% longer LOS among patients with two or three chronic conditions. Thus, unmanaged pharmacogenomic risk was associated with longer LOS in these patients and managing this risk has the potential to reduce LOS in severely ill patients, especially those with chronic conditions.

Combinatorial Analysis of Phenotypic and Clinical Risk Factors Associated With Hospitalized COVID-19 Patients.

Characterization of the risk factors associated with variability in the clinical outcomes of COVID-19 is important. Our previous study using genomic data identified a potential role of calcium and lipid homeostasis in severe COVID-19. This study aimed to identify similar combinations of features (disease signatures) associated with severe disease in a separate patient population with purely clinical and phenotypic data. The PrecisionLife combinatorial analytics platform was used to analyze features derived from de-identified health records in the UnitedHealth Group COVID-19 Data Suite. The platform identified and analyzed 836 disease signatures in two cohorts associated with an increased risk of COVID-19 hospitalization. Cohort 1 was formed of cases hospitalized with COVID-19 and a set of controls who developed mild symptoms. Cohort 2 included Cohort 1 individuals for whom additional laboratory test data was available. We found several disease signatures where lower levels of lipids were found co-occurring with lower levels of serum calcium and leukocytes. Many of the low lipid signatures were independent of statin use and 50% of cases with hypocalcemia signatures were reported with vitamin D deficiency. These signatures may be attributed to similar mechanisms linking calcium and lipid signaling where changes in cellular lipid levels during inflammation and infection affect calcium signaling in host cells. This study and our previous genomics analysis demonstrate that combinatorial analysis can identify disease signatures associated with the risk of developing severe COVID-19 separately from genomic or clinical data in different populations. Both studies suggest associations between calcium and lipid signaling in severe COVID-19.

Determining Cost-Optimal Next-Generation Sequencing Panels for Rare Disease and Pharmacogenomics Testing.

BACKGROUND: Multi-gene panel sequencing using next-generation sequencing (NGS) methods is a key tool for genomic medicine. However, with an estimated 140 000 genomic tests available, current system inefficiencies result in high genetic-testing costs. Reduced testing costs are needed to expand the availability of genomic medicine. One solution to improve efficiency and lower costs is to calculate the most cost-effective set of panels for a typical pattern of test requests. METHODS: We compiled rare diseases, associated genes, point prevalence, and test-order frequencies from a representative laboratory. We then modeled the costs of the relevant steps in the NGS process in detail. Using a simulated annealing-based optimization procedure, we determined panel sets that were more cost-optimal than whole exome sequencing (WES) or clinical exome sequencing (CES). Finally, we repeated this methodology to cost-optimize pharmacogenomics (PGx) testing. RESULTS: For rare disease testing, we show that an optimal choice of 4-6 panels, uniquely covering genes that comprise 95% of the total prevalence of monogenic diseases, saves $257-304 per sample compared with WES, and $66-135 per sample compared with CES. For PGx, we show that the optimal multipanel solution saves $6-7 (27%-40%) over a single panel covering all relevant gene-drug associations. CONCLUSIONS: Laboratories can reduce costs using the proposed method to obtain and run a cost-optimal set of panels for specific test requests. In addition, payers can use this method to inform reimbursement policy.

Unfolding of hidden white blood cell count phenotypes for gene discovery using latent class mixed modeling.

Resting-state white blood cell (WBC) count is a marker of inflammation and immune system health. There is evidence that WBC count is not fixed over time and there is heterogeneity in WBC trajectory that is associated with morbidity and mortality. Latent class mixed modeling (LCMM) is a method that can identify unobserved heterogeneity in longitudinal data and attempts to classify individuals into groups based on a linear model of repeated measurements. We applied LCMM to repeated WBC count measures derived from electronic medical records of participants of the National Human Genetics Research Institute (NHRGI) electronic MEdical Record and GEnomics (eMERGE) network study, revealing two WBC count trajectory phenotypes. Advancing these phenotypes to GWAS, we found genetic associations between trajectory class membership and regions on chromosome 1p34.3 and chromosome 11q13.4. The chromosome 1 region contains CSF3R, which encodes the granulocyte colony-stimulating factor receptor. This protein is a major factor in neutrophil stimulation and proliferation. The association on chromosome 11 contain genes RNF169 and XRRA1; both involved in the regulation of double-strand break DNA repair.

The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype.

The Electronic Medical Records and Genomics (eMERGE) network is a network of medical centers with electronic medical records linked to existing biorepository samples for genomic discovery and genomic medicine research. The network sought to unify the genetic results from 78 Illumina and Affymetrix genotype array batches from 12 contributing medical centers for joint association analysis of 83,717 human participants. In this report, we describe the imputation of eMERGE results and methods to create the unified imputed merged set of genome-wide variant genotype data. We imputed the data using the Michigan Imputation Server, which provides a missing single-nucleotide variant genotype imputation service using the minimac3 imputation algorithm with the Haplotype Reference Consortium genotype reference set. We describe the quality control and filtering steps used in the generation of this data set and suggest generalizable quality thresholds for imputation and phenotype association studies. To test the merged imputed genotype set, we replicated a previously reported chromosome 6 HLA-B herpes zoster (shingles) association and discovered a novel zoster-associated loci in an epigenetic binding site near the terminus of chromosome 3 (3p29).

Risk prediction for complex diseases: application to Parkinson disease.

PURPOSE: The aim of this study was to evaluate the risk of Parkinson disease using clinical and demographic data alone and when combined with information from genes associated with Parkinson disease. METHODS: A total of 1,967 participants in the dbGAP NeuroGenetics Research Consortium data set were included. Single-nucleotide polymorphisms associated with Parkinson disease at a genome-wide significance level in previous genome-wide association studies were included in risk prediction. Risk allele scores were calculated as the weighted count of the minor alleles. Five models were constructed. Discriminatory capability was evaluated using the area under the curve. RESULTS: Both family history and genetic risk scores increased risk for Parkinson disease. Although the fullest model, which included both family history and genetic risk information, resulted in the highest area under the curve, there were no significant differences between models using family history alone and those using genetic information alone. CONCLUSION: Adding genome-wide association study-derived genotypes, family history information, or both to standard demographic risk factors for Parkinson disease resulted in an improvement in discriminatory capacity. In the full model, the contributions of genotype data and family history information to discriminatory capacity were similar, and both were statistically significant. This suggests that there is limited overlap between genetic risk factors identified through genome-wide association study and unmeasured susceptibility variants captured by family history. Our results are similar to those of studies of other complex diseases and indicate that genetic risk prediction for Parkinson disease requires identification of additional genetic risk factors and/or better methods for risk prediction in order to achieve a degree of risk prediction that is clinically useful.Genet Med 2013:15(5):361-367.

Awareness and uptake of direct-to-consumer genetic testing among cancer cases, their relatives, and controls: the Northwest Cancer Genetics Network.

AIMS: To determine if awareness of, interest in, and use of direct-to-consumer (DTC) genetic testing is greater in a sample of high-risk individuals (cancer cases and their relatives), compared to controls. METHODS: Participants were recruited from the Northwest Cancer Genetics Network. A follow-up survey was mailed to participants to assess DTC genetic testing awareness, interest, and use. RESULTS: One thousand two hundred sixty-seven participants responded to the survey. Forty-nine percent of respondents were aware of DTC genetic testing. Of those aware, 19% indicated interest in obtaining and <1% reported having used DTC genetic testing. Additional information supplied by respondents who reported use of DTC genetic tests indicated that 55% of these respondents likely engaged in clinical genetic testing, rather than DTC genetic testing. CONCLUSION: Awareness of DTC genetic testing was greater in our sample of high-risk individuals than in controls and population-based studies. Although interest in and use of these tests among cases in our sample were equivalent to other population-based studies, interest in testing was higher among relatives and people who self-referred for a registry focused on cancer than among cases and controls. Additionally, our results suggest that there may be some confusion about what constitutes DTC genetic testing.

Capacity of diabetes education programs to provide both diabetes self-management education and to implement diabetes prevention services.

OBJECTIVE: The purpose of this study was to assess the capacity of diabetes self-management education (DSME) programs in urban and rural counties to provide services to patients with diagnosed diabetes, lifestyle services to persons at high risk for developing diabetes, and to assess the potential barriers to providing diabetes prevention services. METHODS: In 2009, the Montana Department of Public Health and Human Services conducted an Internet-based survey of all DSME programs in Montana. RESULTS: Thirty of the 39 (77%) DSME programs completed the survey. Seventy-seven percent of the urban programs and 50% of the rural programs reported a capacity to provide DSME to additional patients with diagnosed diabetes. More than 70% of the urban and the rural programs currently provide lifestyle services to patients with abnormal glucose tolerance but without diabetes. Eighty-four percent of the urban programs and 60% of the rural programs reported a capacity to provide lifestyle services to additional persons at high risk for diabetes. Eighty-five percent of the urban programs and 58% of the rural programs have already implemented or intend to implement a lifestyle intervention service consistent with the Diabetes Prevention Program. Overall, the most frequently reported barriers to implementing a diabetes prevention services were lack of reimbursement (80%) and the lack of staff to provide the service (60%). CONCLUSION: Urban and rural DSME programs in Montana have the capacity to implement both DSME for patients with diagnosed diabetes and diabetes prevention lifestyle services to additional people at high risk for diabetes. Reimbursement for diabetes prevention services is critical to ensure program development and implementation.

Factors associated with achieving a weight loss goal among participants in an adapted Diabetes Prevention Program.

AIMS: To assess the factors associated with achieving the 7% weight loss goal among participants enrolled in an adapted Diabetes Prevention Program (DPP). METHODS: Adults at high-risk (N=989) for CVD and diabetes were enrolled in the lifestyle intervention. Multiple logistic regression analyses were used to identify factors associated with achieving the weight loss goal. RESULTS: Overall 37% of participants achieved the weight loss goal. Participants who were older, male, had a lower baseline BMI, self-monitored their fat and caloric intake more frequently, and who achieved higher levels of physical activity were more likely to achieve the weight loss goal compared to participants without these characteristics. In multivariate analyses more frequent self-monitoring of fat and caloric intake and higher levels of weekly physical activity were the only factors independently associated with participant achievement of the weight loss goal. CONCLUSIONS: In a real-world translation of the DPP lifestyle intervention participants who achieved the weight loss goal were more likely to have monitored their dietary intake frequently and increased their physical activity markedly both in a dose-response relationship. Our findings highlight the importance of supporting participants in lifestyle interventions to initiate and maintain dietary self-monitoring and increased levels of physical activity.

Factors associated with the maintenance or achievement of the weight loss goal at follow-up among participants completing an adapted diabetes prevention program.

AIMS: This study evaluated factors associated with achievement or maintenance of a 7% weight loss goal post intervention among adults at high-risk for cardiovascular disease (CVD) and diabetes who participated in an adapted Diabetes Prevention Program (DPP) intervention. MATERIALS AND METHODS: High-risk adults completed the intervention in 2008 or 2009 (N=466). In 2010, we conducted a follow-up survey of participants to assess characteristics, behaviors and barriers associated with the maintenance or achievement of the weight loss goal. RESULTS: Thirty-nine percent of respondents (73/188) maintained or achieved the goal post intervention. Participants who achieved the goal were more likely to have attended more intervention sessions, and to have lost more weight during the intervention compared to participants who did not achieve the goal. Participants who achieved or maintained the goal post intervention were more likely to engage in behaviors related to weight loss maintenance. DISCUSSION: Our findings suggest maintenance or achievement of a weight loss goal post intervention among participants in an adapted lifestyle intervention is consistent with the original DPP. Our findings also highlight the relationship between maintaining or achieving a weight loss goal post intervention and behaviors that can be reinforced and barriers that can be mitigated.

Implementing a state-based cardiovascular disease and diabetes prevention program.

OBJECTIVE: To evaluate weight loss and cardiometabolic risk reduction achieved through an adapted Diabetes Prevention Program intervention among adults at high risk for cardiovascular disease (CVD) and diabetes. RESEARCH DESIGN AND METHODS: Eight health care facilities implemented a group-based lifestyle intervention beginning in 2008. Participants attended 16 weekly core sessions followed by 6 monthly after core sessions. RESULTS: A total of 1,003 participants were enrolled, 816 (81%) completed the core and 578 (58%) completed the after core. Of participants completing the core and after core, 45 and 49% achieved the 7% weight loss goal, respectively. There were significant improvements in blood pressure, fasting glucose, and LDL cholesterol among participants completing the intervention. CONCLUSIONS: Our findings indicate it is feasible for state-coordinated CVD and diabetes prevention programs to achieve significant weight loss and improve cardiometabolic risk.

Effectiveness of a lifestyle intervention program among persons at high risk for cardiovascular disease and diabetes in a rural community.

PURPOSE: To evaluate the feasibility of translating the Diabetes Prevention Program (DPP) lifestyle intervention into practice in a rural community. METHODS: In 2008, the Montana Diabetes Control Program worked collaboratively with Holy Rosary Healthcare to implement an adapted group-based DPP lifestyle intervention. Adults at high risk for diabetes and cardiovascular disease were recruited and enrolled (N = 101). Participants set targets to reduce fat intake and increase physical activity (> or = 150 mins/week) in order to achieve a 7% weight loss goal. FINDINGS: Eighty-three percent (n = 84) of participants completed the 16-session core program and 65 (64%) participated in 1 or more after-core sessions. Of those completing the core program, the mean participation was 14.4 +/- 1.6 and 3.9 +/- 1.6 sessions during the core and after core, respectively. Sixty-five percent of participants met the 150-min-per-week physical activity goal during the core program. Sixty-two percent achieved the 7% weight loss goal and 78% achieved at least a 5% weight loss during the core program. The average weight loss per participant was 7.5 kg (range, 0 to 19.7 kg), which was 7.5% of initial body weight. At the last recorded weight in the after core, 52% of participants had met the 7% weight loss goal and 66% had achieved at least a 5% weight loss. CONCLUSION: Our findings suggest that it is feasible to implement a group-based DPP in a rural community and achieve weight loss and physical goals that are comparable to those achieved in the DPP.

Adapted diabetes prevention program lifestyle intervention can be effectively delivered through telehealth.

PURPOSE: The purpose of this study was to assess the feasibility of delivering an adapted group-based version of the Diabetes Prevention Program's (DPP) lifestyle intervention through telehealth video conferencing. METHODS: In 2009, the Montana Department of Public Health and Human Services in collaboration with Holy Rosary Heathcare implemented the DPP lifestyle intervention, which was provided to an on-site group in 1 community and simultaneously through telehealth to a second group in a remote frontier community. Participants obtained medical clearance from their primary care physician and were eligible if they were overweight and had 1 or more of the following risk factors: prediabetes, impaired glucose tolerance/impaired fasting glucose (IGT/IFG), a history of gestational diabetes (GDM) or the delivery of an infant >9 pounds, hypertension, or dyslipidemia. RESULTS: A total of 13 and 16 eligible adults enrolled in the on-site and the telehealth program, and 13 (100%) and 14 (88%) participants completed the 16-week program, respectively. Both the on-site and telehealth groups achieved high levels of weekly physical activity and there were no significant differences between groups. Over 45% of on-site and telehealth participants achieved the 7% weight loss goal with the average weight loss per participant greater than 6.4 kg in both groups. CONCLUSIONS: Our findings suggest that it is feasible to deliver an adapted group-based DPP lifestyle intervention through telehealth resulting in weight loss outcomes similar to the original DPP.

Translating the diabetes prevention program into practice in the general community: findings from the Montana Cardiovascular Disease and Diabetes Prevention Program.

PURPOSE: The purpose of this study was to evaluate the feasibility of translating the Diabetes Prevention Program (DPP) lifestyle intervention into practice in the general community. METHODS: In 2008, the Montana Diabetes Control Program, working collaboratively with 4 health care facilities, implemented an adapted group-based DPP lifestyle intervention. Adults at high risk for diabetes and cardiovascular disease were recruited and enrolled (n = 355). Eighty-three percent (n = 295) of participants completed the 16-session program. Participants set targets to reduce fat intake and increase physical activity (>or=150 minutes per week) to achieve a weight loss goal of 7%. RESULTS: Seventy percent of participants achieved the physical activity goal of >or=150 minutes per week. There was a significant decrease among participants' weight from baseline (mean +/- SD, 99.3 +/- 19.7 kg) to week 16 (92.6 +/- 18.8 kg; mean difference, 6.7 +/- 4.0 kg, P < .001). Forty-five percent of the participants achieved the 7% weight loss goal, and 67% achieved at least 5% weight loss. Participants who were 60 years of age and older, had a diagnosis of hypertension, met their physical activity goal of >or=150 minutes per week, and those more frequently monitoring their fat intake were more likely to meet the 7% weight loss goal compared with participants without these characteristics. CONCLUSION: The findings suggest that it is feasible to recruit and retain high-risk participants and achieve weight loss and physical goals in a group setting that are comparable with those achieved in the DPP.