Poly-omic risk scores predict inflammatory bowel disease diagnosis.

Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's R2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.

Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review.

BACKGROUND: A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. METHODS: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. RESULTS: Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. CONCLUSIONS: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.

This study reviews the available evidence on which patient features (such as age, sex, and blood test results) are associated with different outcomes for two recently introduced type 2 diabetes medications: SGLT2-inhibitors and GLP1-receptor agonists. Understanding what individual characteristics are associated with different response patterns may help clinical providers and people living with diabetes make more informed decisions about which type 2 diabetes treatments will work best for an individual. We focus on three outcomes: blood glucose levels (raised blood glucose is the primary symptom of diabetes and a primary aim of diabetes treatment is to lower this), heart disease, and kidney disease. We identified some potential factors that reduce effects on blood glucose levels, including poorer kidney function for SGLT2-inhibitors and lower production of the glucose-lowering hormone insulin for GLP1-receptor agonists. We did not identify clear factors that alter heart and kidney disease outcomes for either medication. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification.

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss.

Obesity has been linked to the gut microbiome, epigenome, and diet, yet these factors have not been studied together during obesity treatment. Our objective was to evaluate associations among gut microbiota (MB), DNA methylation (DNAme), and diet prior to and during a behavioral weight loss intervention. Adults (n = 47, age 40.9 ± 9.7 years, body mass index (BMI) 33.5 ± 4.5 kg/m2, 77% female) with data collected at baseline (BL) and 3 months (3 m) were included. Fecal MB was assessed via 16S sequencing and whole blood DNAme via the Infinium EPIC array. Food group and nutrient intakes and Healthy Eating Index (HEI) scores were calculated from 7-day diet records. Linear models were used to test for the effect of taxa relative abundance on DNAme and diet cross-sectionally at each time point, adjusting for confounders and a false discovery rate of 5%. Mean weight loss was 6.2 ± 3.9% at 3 m. At BL, one MB taxon, Ruminiclostridium, was associated with DNAme of the genes COL20A1 (r = 0.651, p = 0.029), COL18A1 (r = 0.578, p = 0.044), and NT5E (r = 0.365, p = 0.043). At 3 m, there were 14 unique MB:DNAme associations, such as Akkermansia with DNAme of GUSB (r = -0.585, p = 0.003), CRYL1 (r = -0.419, p = 0.007), C9 (r = -0.439, p = 0.019), and GMDS (r = -0.559, p = 0.046). Among taxa associated with DNAme, no significant relationships were seen with dietary intakes of relevant nutrients, food groups, or HEI scores. Our findings indicate that microbes linked to mucin degradation, short-chain fatty acid production, and body weight are associated with DNAme of phenotypically relevant genes. These relationships offer an initial understanding of the possible routes by which alterations in gut MB may influence metabolism during weight loss.

Precision medicine in type 2 diabetes: A systematic review of treatment effect heterogeneity for GLP1-receptor agonists and SGLT2-inhibitors.

Background: A precision medicine approach in type 2 diabetes requires identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. Methods: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. Results: After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. The majority of papers had methodological limitations precluding robust assessment of treatment effect heterogeneity. For glycaemic outcomes, most cohorts were observational, with multiple analyses identifying lower renal function as a predictor of lesser glycaemic response with SGLT2-inhibitors and markers of reduced insulin secretion as predictors of lesser response with GLP1-receptor agonists. For cardiovascular and renal outcomes, the majority of included studies were post-hoc analyses of randomized control trials (including meta-analysis studies) which identified limited clinically relevant treatment effect heterogeneity. Conclusions: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care. Plain language summary: This review identifies research that helps understand which clinical and biological factors that are associated with different outcomes for specific type 2 diabetes treatments. This information could help clinical providers and patients make better informed personalized decisions about type 2 diabetes treatments. We focused on two common type 2 diabetes treatments: SGLT2-inhibitors and GLP1-receptor agonists, and three outcomes: blood glucose control, heart disease, and kidney disease. We identified some potential factors that are likely to lessen blood glucose control including lower kidney function for SGLT2-inhibitors and lower insulin secretion for GLP1-receptor agonists. We did not identify clear factors that alter heart and renal disease outcomes for either treatment. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

Human microbiome transfer in the built environment differs based on occupants, objects, and buildings.

Compared to microbiomes on other skin sites, the bacterial microbiome of the human hand has been found to have greater variability across time. To increase understanding regarding the longitudinal transfer of the hand microbiome to objects in the built environment, and vice versa, 22 participants provided skin microbiome samples from their dominant hands, as well as from frequently and infrequently touched objects in their office environments. Additional longitudinal samples from home environments were obtained from a subset of 11 participants. We observed stability of the microbiomes of both the hand and built environments within the office and home settings; however, differences in the microbial communities were detected across the two built environments. Occupants' frequency of touching an object correlated to that object having a higher relative abundance of human microbes, yet the percent of shared microbes was variable by participants. Finally, objects that were horizontal surfaces in the built environment had higher microbial diversity as compared to objects and the occupants' hands. This study adds to the existing knowledge of microbiomes of the built environment, enables more detailed studies of indoor microbial transfer, and contributes to future models and building interventions to reduce negative outcomes and improve health and well-being.

The Gut Microbiota during a Behavioral Weight Loss Intervention.

Altered gut microbiota has been linked to obesity and may influence weight loss. We are conducting an ongoing weight loss trial, comparing daily caloric restriction (DCR) to intermittent fasting (IMF) in adults who are overweight or obese. We report here an ancillary study of the gut microbiota and selected obesity-related parameters at the baseline and after the first three months of interventions. During this time, participants experienced significant improvements in clinical health measures, along with altered composition and diversity of fecal microbiota. We observed significant associations between the gut microbiota features and clinical measures, including weight and waist circumference, as well as changes in these clinical measures over time. Analysis by intervention group found between-group differences in the relative abundance of Akkermansia in response to the interventions. Our results provide insight into the impact of baseline gut microbiota on weight loss responsiveness as well as the early effects of DCR and IMF on gut microbiota.

Genetic Interactions with Intrauterine Diabetes Exposure in Relation to Obesity: The EPOCH and Project Viva Studies.

To examine whether BMI-associated genetic risk variants modify the association of intrauterine diabetes exposure with childhood BMI z-scores, we assessed the interaction between 95 BMI-associated genetic variants and in utero exposure to maternal diabetes among 459 children in the Exploring Perinatal Outcomes among Children historical prospective cohort study (n = 86 exposed; 373 unexposed) in relation to age- and sex-standardized childhood BMI z-scores (mean age = 10.3 years, standard deviation = 1.5 years). For the genetic variants showing a nominally significant interaction, we assessed the relationship in an additional 621 children in Project Viva, which is an independent longitudinal cohort study, and used meta-analysis to combine the results for the two studies. Seven of the ninety-five genetic variants tested exhibited a nominally significant interaction with in utero exposure to maternal diabetes in relation to the offspring BMI z-score in EPOCH. Five of the seven variants exhibited a consistent direction of interaction effect across both EPOCH and Project Viva. While none achieved statistical significance in the meta-analysis after accounting for multiple testing, three variants exhibited a nominally significant interaction with in utero exposure to maternal diabetes in relation to offspring BMI z-score: rs10733682 near LMX1B (interaction ß = 0.39; standard error (SE) = 0.17), rs17001654 near SCARB2 (ß = 0.53; SE = 0.22), and rs16951275 near MAP2K5 (ß = 0.37; SE = 0.17). BMI-associated genetic variants may enhance the association between exposure to in utero diabetes and higher childhood BMI, but larger studies of in utero exposures are necessary to confirm the observed nominally significant relationships.

Genetic Risk Score for Type 2 Diabetes and Traits Related to Glucose-Insulin Homeostasis in Youth: The Exploring Perinatal Outcomes Among Children (EPOCH) Study.

OBJECTIVE: The metabolic phenotype of youth-onset type 2 diabetes (T2D) differs from that of adult-onset T2D, but little is known about genetic contributions. We aimed to evaluate the association between a T2D genetic risk score (GRS) and traits related to glucose-insulin homeostasis among healthy youth. RESEARCH DESIGN AND METHODS: We used data from 356 youth (mean age 16.7 years; 50% female) in the Exploring Perinatal Outcomes Among Children (EPOCH) cohort to calculate a standardized weighted GRS based on 271 single nucleotide polymorphisms associated with T2D in adults. We used linear regression to assess associations of the GRS with log-transformed fasting glucose, 2-h glucose, HOMA of insulin resistance (HOMA-IR), oral disposition index, and insulinogenic index adjusted for age, sex, BMI z score, in utero exposure to maternal diabetes, and genetic principal components. We also evaluated effect modification by BMI z score, in utero exposure to maternal diabetes, and ethnicity. RESULTS: Higher weighted GRS was associated with lower oral disposition index (ß = -0.11; 95% CI -0.19, -0.02) and insulinogenic index (ß = -0.08; 95% CI -0.17, -0.001), but not with fasting glucose (ß = 0.01; 95% CI -0.01, 0.02), 2-h glucose (ß = 0.03; 95% CI -0.0004, 0.06), or HOMA-IR (ß = 0.02; 95% CI -0.04, 0.07). BMI z score and in utero exposure to maternal diabetes increased the effect of the GRS on glucose levels. CONCLUSIONS: Our results suggest that T2D genetic risk factors established in adults are relevant to glucose-insulin homeostasis in youth and that maintaining a healthy weight may be particularly important for youth with high genetic risk of T2D.

Associations of Nutrient Intake Changes During Childhood with Adolescent Hepatic Fat: The Exploring Perinatal Outcomes Among CHildren Study.

OBJECTIVE: To examine associations of dietary changes from childhood to adolescence with adolescent hepatic fat and whether the PNPLA3 rs738409 risk allele, a strong genetic risk factor for hepatic fat, modifies associations. STUDY DESIGN: Data were from 358 participants in the Exploring Perinatal Outcomes among CHildren (EPOCH) study, a longitudinal cohort in Colorado. Diet was assessed by food frequency questionnaire in childhood (approximately 10 years of age) and adolescence (approximately 16 years of age) and converted to nutrient densities. Hepatic fat was assessed in adolescence by magnetic resonance imaging. Linear regression was used to test associations of dietary changes from childhood to adolescence with adolescent hepatic fat. RESULTS: Increases in fiber, vegetable protein, and polyunsaturated fat intake from childhood to adolescence were associated with lower adolescent hepatic fat, and increases in animal protein were associated with higher hepatic fat (ß per 5-unit increase on log-hepatic fat: -0.12 [95% CI, -0.21 to -0.02] for ▵fiber; -0.26 [95% CI, -0.45 to -0.07] for ▵vegetable protein; -0.18 [95% CI, -0.35 to -0.02] for ▵polyunsaturated fat; 0.13 [95% CI, 0.04-0.22] for ▵animal protein). There was evidence of effect modification by PNPLA3 variant, whereby inverse associations of ▵fiber and ▵vegetable protein and positive associations of ▵saturated fat with adolescent hepatic fat were stronger in risk allele carriers. Most conclusions were similar after adjusting for obesity in adolescence, but associations of ▵saturated fat with hepatic fat were attenuated toward the null. CONCLUSIONS: Our results suggest that nutrient intake changes between childhood and adolescence, particularly decreases in fiber and vegetable protein and increases in saturated fat intake, interact with the PNPLA3 variant to predict higher hepatic fat in adolescence, and may be targets for reducing hepatic fat in high-risk youth.

Soluble CD14 Levels in the Jackson Heart Study: Associations With Cardiovascular Disease Risk and Genetic Variants.

[Figure: see text].

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention.

OBJECTIVE: Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention. METHODS: Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed. RESULTS: A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways. CONCLUSIONS: These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.

Characterization of the gut microbiota among Veterans with unique military-related exposures and high prevalence of chronic health conditions: A United States-Veteran Microbiome Project (US-VMP) study.

The gut microbiome is impacted by environmental exposures and has been implicated in many physical and mental health conditions, including anxiety disorders, affective disorders, and trauma- and stressor-related disorders such as posttraumatic stress disorder (PTSD). United States (US) military Veterans are a unique population in that their military-related exposures can have consequences for both physical and mental health, but the gut microbiome of this population has been understudied. In this publication, we describe exposures, health conditions, and medication use of Veterans in the US Veteran Microbiome Project (US-VMP) and examine the associations between these characteristics and the gut microbiota. This cohort included 331 US Veterans seeking healthcare with the Veterans Health Administration who were 83% male with an average (±SD) age of 47.6 â€‹± â€‹13.4 years. The cohort displayed a high prevalence of PTSD (49.8%) and history of traumatic brain injuries (76.1%), and high current use of prescription medications (74.9%) to treat various acute and chronic conditions. We observed significant associations between the gut microbiota composition and gastroenteritis, peripheral vascular disease (PVD), bipolar disorders, symptoms of severe depression based on the Beck Depression Inventory, stimulant and opioid use disorders, beta-blockers, serotonin and norepinephrine reuptake inhibitor antidepressants, diabetes medications, and proton pump inhibitors. Many of the Veteran characteristics examined were associated with altered relative abundances of specific taxa. We found that PVD and cardiovascular disease were associated with lower microbiota diversity in the gut (i.e., α-diversity), while supplemental vitamin use was associated with higher α-diversity. Our study contributes novel insights as to whether the unique exposures of Veterans in this cohort correlate with gut microbiota characteristics and, in line with previous findings with other population-level studies of the microbiome, confirms associations between numerous health conditions and medications with the gut microbiome.

Feasibility of collection and analysis of microbiome data in a longitudinal randomized trial of community gardening.

Aim: We explored the feasibility of collecting and analyzing human microbiome data in a longitudinal randomized controlled trial of community gardening. Methods & materials: Participants were randomly assigned to gardening (N = 8) or control (N = 8). Participants provided stool, mouth, hand and forehead microbiome samples at six timepoints. Analyses combined mixed models with Qiita output. Results: Participant satisfaction was high, with 75% of participants completing evaluations. While no microbial effects were statistically significant due to small sample size, the analysis pipeline utility was tested. Conclusion: Longitudinal collection and analysis of microbiome data in a community gardening randomized controlled trial is feasible. The analysis pipeline will be useful in larger studies for assessment of the pathway between microbiota, gardening and health outcomes.

Variation in contrast-associated acute kidney injury prophylaxis for percutaneous coronary intervention: insights from the Veterans Affairs Clinical Assessment, Reporting, and Tracking (CART) program.

BACKGROUND: Contrast-Associated Acute Kidney Injury (CA-AKI) is a serious complication associated with percutaneous coronary intervention (PCI). Patients with chronic kidney disease (CKD) have an elevated risk for developing this complication. Although CA-AKI prophylactic measures are available, the supporting literature is variable and inconsistent for periprocedural hydration and N-acetylcysteine (NAC), but is stronger for contrast minimization. METHODS: We assessed the prevalence and variability of CA-AKI prophylaxis among CKD patients undergoing PCI between October 2007 and September 2015 in any cardiac catheterization laboratory in the VA Healthcare System. Prophylaxis included periprocedural hydration with normal saline or sodium bicarbonate, NAC, and contrast minimization (contrast volume to glomerular filtration rate ratio ≤ 3). Multivariable hierarchical logistic regression models quantified site-specific prophylaxis variability. As secondary analyses, we also assessed CA-AKI prophylaxis measures in all PCI patients regardless of kidney function, periprocedural hydration in patients with comorbid CHF, and temporal trends in CA-AKI prophylaxis. RESULTS: From 2007 to 2015, 15,729 patients with CKD underwent PCI. 6928 (44.0%) received periprocedural hydration (practice-level median rate 45.3%, interquartile range (IQR) 35.5-56.7), 5107 (32.5%) received NAC (practice-level median rate 28.3%, IQR 22.8-36.9), and 4656 (36.0%) received contrast minimization (practice-level median rate 34.5, IQR 22.6-53.9). After adjustment for patient characteristics, there was significant site variability with a median odds ratio (MOR) of 1.80 (CI 1.56-2.08) for periprocedural hydration, 1.95 (CI 1.66-2.29) for periprocedural hydration or NAC, and 2.68 (CI 2.23-3.15) for contrast minimization. These trends were similar among all patients (with and without CKD) undergoing PCI. Among patients with comorbid CHF (n = 5893), 2629 (44.6%) received periprocedural hydration, and overall had less variability in hydration (MOR of 1.56 (CI 1.38-1.76)) compared to patients without comorbid CHF (1.89 (CI 1.65-2.18)). Temporal trend analysis showed a significant and clinically relevant decrease in NAC use (64.1% of cases in 2008 (N = 1059), 6.2% of cases in 2015 (N = 128, p = < 0.0001)) and no significant change in contrast-minimization (p = 0.3907). CONCLUSIONS: Among patients with CKD undergoing PCI, there was low utilization and significant site-level variability for periprocedural hydration and NAC independent of patient-specific risk. This low utilization and high variability, however, was also present for contrast minimization, a well-established measure. These findings suggest that a standardized approach to CA-AKI prophylaxis, along with continued development of the evidence base, is needed.

Development and Validation of a Semi-Automated Surveillance Algorithm for Cardiac Device Infections: Insights from the VA CART program.

Procedure-related cardiac electronic implantable device (CIED) infections have high morbidity and mortality, highlighting the urgent need for infection prevention efforts to include electrophysiology procedures. We developed and validated a semi-automated algorithm based on structured electronic health records data to reliably identify CIED infections. A sample of CIED procedures entered into the Veterans' Health Administration Clinical Assessment Reporting and Tracking program from FY 2008-2015 was reviewed for the presence of CIED infection. This sample was then randomly divided into training (2/3) validation sets (1/3). The training set was used to develop a detection algorithm containing structured variables mapped from the clinical pathways of CIED infection. Performance of this algorithm was evaluated using the validation set. 2,107 unique CIED procedures from a cohort of 5,753 underwent manual review; 97 CIED infections (4.6%) were identified. Variables strongly associated with true infections included presence of a microbiology order, billing codes for surgical site infections and post-procedural antibiotic prescriptions. The combined algorithm to detect infection demonstrated high c-statistic (0.95; 95% confidence interval: 0.92-0.98), sensitivity (87.9%) and specificity (90.3%) in the validation data. Structured variables derived from clinical pathways can guide development of a semi-automated detection tool to surveil for CIED infection.

Genetic Risk for Hepatic Fat among an Ethnically Diverse Cohort of Youth: The Exploring Perinatal Outcomes among Children Study.

OBJECTIVE: To assess the importance of genetic and nongenetic risk factors contributing to hepatic fat accumulation in a multiethnic population of youth. STUDY DESIGN: We investigated the relationship between genetic factors and hepatic fat fraction (HFF) in 347 children aged 12.5-19.5 years. We examined 5 single nucleotide polymorphisms previously associated with HFF and a weighted genetic risk score (GRS) and examined how these associations varied with ethnicity (Hispanic vs non-Hispanic white) and body mass index (BMI) category. We also compared how much variation in HFF was explained by genetic factors vs cardiometabolic factors (BMI z-score and the Homeostasis Model of Insulin Resistance) or diet. RESULTS: PNPLA3 rs738409 and the GRS were each associated with HFF among Hispanic (ß = 0.39; 95% CI, 0.16-0.62; P = .001; and ß = 0.20; 95% CI, 0.05-0.34; P = .007, respectively) but not non-Hispanic white (ß = 0.04; 95% CI, -0.18 to 0.26; P = .696; and ß = 0.03; 95% CI, -0.09 to 0.14; P = .651, respectively) youth. Cardiometabolic risk factors explained more of the variation in HFF than genetic risk factors among non-lean Hispanic individuals (27.2% for cardiometabolic markers vs 6.4% for rs738409 and 4.3% for the GRS), and genetic risk factors were more important among lean individuals (2.7% for cardiometabolic markers vs 12.6% for rs738409 and 4.4% for the GRS). CONCLUSIONS: Poor cardiometabolic health may be more important than genetic factors when predicting HFF in overweight and obese young populations. Genetic risk is an important contributor to pediatric HFF among lean Hispanics, but further studies are necessary to elucidate the strength of the association between genetic risk and HFF in non-Hispanic white youth.

Novel methodology to measure pre-procedure antimicrobial prophylaxis: integrating text searches with structured data from the Veterans Health Administration's electronic medical record.

BACKGROUND: Antimicrobial prophylaxis is an evidence-proven strategy for reducing procedure-related infections; however, measuring this key quality metric typically requires manual review, due to the way antimicrobial prophylaxis is documented in the electronic medical record (EMR). Our objective was to electronically measure compliance with antimicrobial prophylaxis using both structured and unstructured data from the Veterans Health Administration (VA) EMR. We developed this methodology for cardiac device implantation procedures. METHODS: With clinician input and review of clinical guidelines, we developed a list of antimicrobial names recommended for the prevention of cardiac device infection. We trained the algorithm using existing fiscal year (FY) 2008-15 data from the VA Clinical Assessment Reporting and Tracking-Electrophysiology (CART-EP), which contains manually determined information about antimicrobial prophylaxis. We merged CART-EP data with EMR data and programmed statistical software to flag an antimicrobial orders or drug fills from structured data fields in the EMR and hits on text string searches of antimicrobial names documented in clinician's notes. We iteratively tested combinations of these data elements to optimize an algorithm to accurately classify antimicrobial use. The final algorithm was validated in a national cohort of VA cardiac device procedures from FY2016-2017. Discordant cases underwent expert manual review to identify reasons for algorithm misclassification. RESULTS: The CART-EP dataset included 2102 procedures at 38 VA facilities with manually identified antimicrobial prophylaxis in 2056 cases (97.8%). The final algorithm combining structured EMR fields and text note search results correctly classified 2048 of the CART-EP cases (97.4%). In the validation sample, the algorithm measured compliance with antimicrobial prophylaxis in 16,606 of 18,903 cardiac device procedures (87.8%). Misclassification was due to EMR documentation issues, such as antimicrobial prophylaxis documented only in hand-written clinician notes in a format that cannot be electronically searched. CONCLUSIONS: We developed a methodology with high accuracy to measure guideline concordant use of antimicrobial prophylaxis before cardiac device procedures using data fields present in modern EMRs. This method can replace manual review in quality measurement in the VA and other healthcare systems with EMRs; further, this method could be adapted to measure compliance in other procedural areas where antimicrobial prophylaxis is recommended.

Gut microbiota phenotypes of obesity.

Obesity is a disease with a complex etiology and variable prevalence across different populations. While several studies have reported gut microbiota composition differences associated with obesity in humans, there has been a lack of consistency in the nature of the reported changes; it has been difficult to determine whether methodological differences between studies, underlying differences in the populations studied, or other factors are responsible for this discordance. Here we use 16 S rRNA data from previously published studies to explore how the gut microbiota-obesity relationship varies across heterogeneous Western populations, focusing mainly on the relationship between (1) alpha diversity and (2) Prevotella relative abundance with BMI. We provide evidence that the relationship between lower alpha diversity and higher BMI may be most consistent in non-Hispanic white (NHW) populations and/or those with high socioeconomic status, while the relationship between higher Prevotella relative abundance and BMI may be stronger among black and Hispanic populations. We further examine how diet may impact these relationships. This work suggests that gut microbiota phenotypes of obesity may differ with race/ethnicity or its correlates, such as dietary components or socioeconomic status. However, microbiome cohorts are often too small to study complex interaction effects and non-white individuals are greatly underrepresented, creating substantial challenges to understanding population-level patterns in the microbiome-obesity relationship. Further study of how population heterogeneity influences the relationship between the gut microbiota and obesity is warranted.

Real-world effectiveness of infection prevention interventions for reducing procedure-related cardiac device infections: Insights from the veterans affairs clinical assessment reporting and tracking program.

OBJECTIVE: To measure the association between receipt of specific infection prevention interventions and procedure-related cardiac implantable electronic device (CIED) infections. DESIGN: Retrospective cohort with manually reviewed infection status. SETTING: Setting: National, multicenter Veterans Health Administration (VA) cohort. PARTICIPANTS: Sampling of procedures entered into the VA Clinical Assessment Reporting and Tracking-Electrophysiology (CART-EP) database from fiscal years 2008 through 2015. METHODS: A sample of procedures entered into the CART-EP database underwent manual review for occurrence of CIED infection and other clinical/procedural variables. The primary outcome was 6-month incidence of CIED infection. Measures of association were calculated using multivariable generalized estimating equations logistic regression. RESULTS: We identified 101 procedure-related CIED infections among 2,098 procedures (4.8% of reviewed sample). Factors associated with increased odds of infections included (1) wound complications (adjusted odds ratio [aOR], 8.74; 95% confidence interval [CI], 3.16-24.20), (2) revisions including generator changes (aOR, 2.4; 95% CI, 1.59-3.63), (3) an elevated international normalized ratio (INR) >1.5 (aOR, 1.56; 95% CI, 1.12-2.18), and (4) methicillin-resistant Staphylococcus colonization (aOR, 9.56; 95% CI, 1.55-27.77). Clinically effective prevention interventions included preprocedural skin cleaning with chlorhexidine versus other topical agents (aOR, 0.41; 95% CI, 0.22-0.76) and receipt of ß-lactam antimicrobial prophylaxis versus vancomycin (aOR, 0.60; 95% CI, 0.37-0.96). The use of mesh pockets and continuation of antimicrobial prophylaxis after skin closure were not associated with reduced infection risk. CONCLUSIONS: These findings regarding the real-world clinical effectiveness of different prevention strategies can be applied to the development of evidence-based protocols and infection prevention guidelines specific to the electrophysiology laboratory.