Search

Background: Understanding the effect of lifestyle and genetic risk on the lifetime risk of coronary heart disease (CHD) is important to improving public health initiatives. Our objective was to quantify remaining lifetime risk and years free of CHD according to polygenic risk and the American Heart Association’s Life’s Simple 7 (LS7) guidelines in a population-based cohort study. Methods: Our analysis included data from participants of the ARIC (Atherosclerosis Risk in Communities) study: 8372 White and 2314 Black participants; 45 years of age and older; and free of CHD at baseline examination. A polygenic risk score (PRS) comprised more than 6 million genetic variants was categorized into low (80th percentile). An overall LS7 score was calculated at baseline and categorized into “poor,” “intermediate,” and “ideal” cardiovascular health. Lifetime risk and CHD-free years were computed according to polygenic risk and LS7 categories. Results: The overall remaining lifetime risk was 27%, ranging from 16.6% in individuals with an ideal LS7 score to 43.1% for individuals with a poor LS7 score. The association of PRS with lifetime risk differed according to ancestry. In White participants, remaining lifetime risk ranged from 19.8% to 39.3% according to increasing PRS categories. Individuals with a high PRS and poor LS7 had a remaining lifetime risk of 67.1% and 15.9 fewer CHD-free years than did those with intermediate polygenic risk and LS7 scores. In the high-PRS group, ideal LS7 was associated with 20.2 more CHD-free years compared with poor LS7. In Black participants, remaining lifetime risk ranged from 19.1% to 28.6% according to increasing PRS category. Similar lifetime risk estimates were observed for individuals of poor LS7 regardless of PRS category. In the high-PRS group, an ideal LS7 score was associated with only 4.5 more CHD-free years compared with a poor LS7 score. Conclusions: Ideal adherence to LS7 recommendations was associated with lower lifetime risk of CHD for all individuals, especially in those with high genetic susceptibility. In Black participants, adherence to LS7 guidelines contributed to lifetime risk of CHD more so than current PRSs. Improved PRSs are needed to properly evaluate genetic susceptibility for CHD in diverse populations.

BackgroundHeterogeneity in the long-term metabolic response to dietary macronutrient composition can be partially explained by genetic factors. However, few studies have demonstrated reproducible gene-diet interactions (GDIs), likely due in part to measurement error in dietary intake estimation as well as insufficient capture of rare genetic variation. Discovery analyses in ancestry-diverse cohorts that include rare genetic variants from whole-genome sequencing (WGS) could help identify genetic variants modifying the effects of dietary macronutrient composition on glycemic phenotypes.ObjectiveWe aimed to identify macronutrient GDIs across the genetic frequency spectrum associated with continuous glycemic traits in genetically and culturally diverse cohorts.MethodsWe analyzed N=33,187 diabetes-free participants from 10 cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program with WGS, self-reported diet, and glycemic traits (fasting glucose [FG], insulin [FI], and hemoglobin A1c [HbA1c]). We fit multivariable-adjusted linear mixed models for the main effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and for its interactions with genetic variants genome-wide. Tests were performed for both common variants and gene-based rare variant sets in each cohort followed by a combined cohort meta-analysis.ResultsIn main effect models, participants consuming more calories from carbohydrate at the expense of fat had modestly lower glycemic trait values (β per 250 kcal substitution for FG: −0.030 mmol/L, p=2.7×10−6; lnFI: −0.008 log(pmol/L), p=0.17; HbA1c: −0.013 %, p=0.025). In GDI analyses, a common African ancestry-enriched variant (rs79762542; 78 kb upstream of the FRAS1 gene) reached study-wide significance (p = 1.14×10−8) indicating a higher HbA1c with greater proportion of calories from carbohydrate vs. fat among minor allele carriers only. This interaction was replicated in the UK Biobank cohort. Simulations revealed that there is (1) a substantial impact of measurement error on statistical power for GDI discovery at these sample sizes, especially for rare genetic variants, and (2) over 150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error.ConclusionsOur analysis identified a potential genetic interaction modifying the dietary macronutrient-HbA1c association while highlighting the importance of precise exposure measurement and significantly increased sample size to identify additional similar effects.

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

Background Quantifying nurses’ perceptions of workload burden when managing critically ill patients is essential for designing interventions to ease nurses’ workday. Objectives To explore pediatric intensive care unit (PICU) nurses’ perceptions of their workload when caring for critically ill patients and managing protocolized therapies. Methods This study was embedded in a multicenter randomized clinical trial where participants were assigned to receive either lower-target or higher-target glucose control. Nurses from 35 participating PICUs completed a baseline survey containing questions about their perceptions of PICU workload in general. They completed an intervention survey after caring for a study patient. Two workload measurement instruments, the Subjective Workload Assessment Technique (SWAT) and the National Aeronautics and Space Administration–Task Load Index (NASA-TLX), were embedded in these surveys. Results Baseline surveys were completed by 1476 PICU nurses, predominantly female with a bachelor’s degree and a median (interquartile range) of 6 (3-11) years of nursing experience and 4 (2-9) years of PICU experience. Most nurses (65%) rated time burden as the most important component of their workload, followed by cognitive (22%) or psychological stress (13%) burden. Work performance was selected most often as contributing to workload, followed by cognitive demand, time pressure, effort, and physical demand. Intervention surveys were completed by 73% of enrolled participants (505 of 693). Nurses managing the lower glucose target group reported higher levels of workload burden as measured by the SWAT (P = .002) and NASA-TLX (P < .001). Conclusions This study describes the workload burden perceived by PICU nurses when managing critically ill patients in general and when managing protocolized therapies.

Purpose The purpose of this study was to describe medical device-related pressure injuries (MDRPIs) in hospitalized pediatric patients. Design A prospective, descriptive study. Sample/subjects and setting The sample comprised 625 patients cared for in 8 US pediatric hospitals. Participants were aged preterm to 21 years, on bed rest for at least 24 hours, and had a medical device in place. Methods Two nursing teams, blinded to the other's assessments, worked in tandem to assess pressure injury risk, type of medical devices in use, and preventive interventions for each medical device. They also identified the presence, location, and stage of MDRPI. Subjects were observed up to 8 times over 4 weeks, or until discharge, whichever occurred first. Results Of 625 enrolled patients, 42 (7%) developed 1 or more MDRPIs. Two-thirds of patients with MDRPIs were younger than 8 years. Patients experiencing MDRPIs had higher acuity scores on hospital admission, were more frequently cognitively and/or functionally impaired, or were extreme in body mass index. Respiratory devices caused the most injuries (6.19/1000 device-days), followed by immobilizers (2.40/1000 device-days), gastric tubes (2.24/1000 device-days), and external monitoring devices (1.77/1000 device-days). Of the 6336 devices in place, 36% did not have an MDRPI preventive intervention in place. Clinical variables contributing to MDRPI development included intensive care unit care (odds ratio [OR] 8.9, 95% confidence interval [CI] 1.9-43.6), use of neuromuscular blockade (OR 3.7, 95% CI 1.7-7.8), and inotropic/vasopressor medications (OR 2.7, 95% CI 1.7-4.3). Multivariable analysis indicated that Braden QD scores alone predicted MDRPI development. Conclusion Medical devices are common in hospitalized infants and children and these medical devices place patients at risk for MDRPI.

Objectives The impact of early enteral nutrition on clinical outcomes in critically ill children has not been adequately described. We hypothesized that early enteral nutrition is associated with improved clinical outcomes in critically ill children. Design Secondary analysis of the Heart and Lung Failure-Pediatric Insulin Titration randomized controlled trial. Setting Thirty-five PICUs. Patients Critically ill children with hyperglycemia requiring inotropic support and/or invasive mechanical ventilation who were enrolled for at least 48 hours with complete nutrition data. Interventions Subjects received nutrition via guidelines that emphasized enteral nutrition and were classified into early enteral nutrition (enteral nutrition within 48 hr of study randomization) and no early enteral nutrition (enteral nutrition after 48 hr of study randomization, or no enteral nutrition at any time). Measurements and main results Of 608 eligible subjects, 331 (54%) received early enteral nutrition. Both early enteral nutrition and no early enteral nutrition groups had similar daily caloric intake over the first 8 study days (median, 36 vs 36 kcal/kg/d; p = 0.93). After controlling for age, body mass index z scores, primary reason for ICU admission, severity of illness, and mean Vasopressor-Inotrope Score at the time of randomization, and adjusting for site, early enteral nutrition was associated with lower 90-day hospital mortality (8% vs 17%; p = 0.007), more ICU-free days (median, 20 vs 17 d; p = 0.02), more hospital-free days (median, 8 vs 0 d; p = 0.003), more ventilator-free days (median, 21 vs 19 d; p = 0.003), and less organ dysfunction (median maximum Pediatric Logistic Organ Dysfunction, 11 vs 12; p Conclusions In critically ill children with hyperglycemia requiring inotropic support and/or mechanical ventilation, early enteral nutrition was independently associated with better clinical outcomes.

OBJECTIVE Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed. RESEARCH DESIGN AND METHODS Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD). RESULTS Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway. CONCLUSIONS Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Aims: Both lifestyle factors and genetic background contribute to the development of type 2 diabetes. Estimation of the lifetime risk of diabetes based on genetic information has not been presented, and the extent to which a normal body weight can offset a high lifetime genetic risk is unknown. Methods: We used data from 15,671 diabetes-free participants of European ancestry aged 45 years and older from the prospective population-based ARIC study and Rotterdam Study (RS). We quantified the remaining lifetime risk of diabetes stratified by genetic risk and quantified the effect of normal weight in terms of relative and lifetime risks in low, intermediate and high genetic risk. Results: At age 45 years, the lifetime risk of type 2 diabetes in ARIC in the low, intermediate and high genetic risk category was 33.2%, 41.3% and 47.2%, and in RS 22.8%, 30.6% and 35.5% respectively. The absolute lifetime risk for individuals with normal weight compared to individuals with obesity was 24% lower in ARIC and 8.6% lower in RS in the low genetic risk group, 36.3% lower in ARIC and 31.3% lower in RS in the intermediate genetic risk group, and 25.0% lower in ARIC and 29.4% lower in RS in the high genetic risk group. Conclusions: Genetic variants for type 2 diabetes have value in estimating the lifetime risk of type 2 diabetes. Normal weight mitigates partly the deleterious effect of high genetic risk.

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome and exome arrays, resulting in over 100 associated variants. We extended this work with a high-coverage whole genome sequencing (WGS) analysis from fifteen cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. More than 23,000 non-diabetic individuals from five self-reported race/ethnicities (African, Asian, European, Hispanic and Samoan) were included for each trait. We analyzed 60M variants in race/ethnicity-specific and pooled single variant and rare variant aggregate tests. Twenty-two variants across sixteen gene regions were found significantly associated with FG or FI, eight of which were rare (Minor Allele Frequency, MAFG6PC2locus we identified a distinct FG signal at rare variant rs2232326 (MAF=0.01) after conditioning on known common variants. Functional annotations show rs2232326 to be disruptive and likely damaging while being weakly transcribed in islets. A pair of FG-associated variants were identified near theSLC30A8locus. These variants, one of which was rare (MAF=0.001) and Asian race/ethnicity-specific, were shown to be in islet-specific active enhancer regions. Other associated regions include rare variants nearROBO1andPTPRT, and common variants nearMTNR1B, GCK, GCKR, FOXA2, APOB, TCF7L2, andADCY5. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions down to a minor allele count of 20, creating a foundation for future sequencing-based investigation of glycemic traits.

Type 2 diabetes is increasing in all ancestry groups1. Part of its genetic basis may reside among the rare (minor allele frequency 2. We analyzed high-coverage (mean depth 38.2x) whole genome sequencing from 9,639 individuals with T2D and 34,994 controls in the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program2 to show that rare, non-coding variants that are poorly captured by genotyping arrays or imputation panels contribute h2=53% (P=4.2×10−5) to the genetic component of risk in the largest (European) ancestry subset. We coupled sequence variation with islet epigenomic signatures3 to annotate and group rare variants with respect to gene expression4, chromatin state5 and three-dimensional chromatin architecture6, and show that pancreatic islet regulatory elements contribute to T2D genetic risk (h2=8%, P=2.4×10−3). We used islet annotation to create a non-coding framework for rare variant aggregation testing. This approach identified five loci containing rare alleles in islet regulatory elements that suggest novel biological mechanisms readily linked to hypotheses about variant-to-function. Large scale whole genome sequence analysis reveals the substantial contribution of rare, non-coding variation to the genetic architecture of T2D and highlights the value of tissue-specific regulatory annotation for variant-to-function discovery.

Objectives To investigate adaptive skills, behavior, and quality health-related quality of life in children from 32 centers enrolling in the Heart And Lung Failure-Pediatric INsulin Titration randomized controlled trial. Study design This prospective longitudinal cohort study compared the effect of 2 tight glycemic control ranges (lower target, 80-100 mg/dL vs higher target, 150-180 mg/dL) 1-year neurobehavioral and health-related quality of life outcomes. Subjects had confirmed hyperglycemia and cardiac and/or respiratory failure. Patients aged 2-16 years old enrolled between April 2012 and September 2016 were studied at 1 year after intensive care discharge. The primary outcome, adaptive skills, was assessed using the Vineland Adaptive Behavior Scale. Behavior and health-related quality of life outcomes were assessed as secondary outcomes using the Pediatric Quality of Life and Child Behavior Checklist at baseline and 1-year follow-up. Group differences were evaluated using regression models adjusting for age category, baseline overall performance, and risk of mortality. Results Of 369 eligible children, 358 survived after hospital discharge and 214 (60%) completed follow-up. One-year Vineland Adaptive Behavior Scale-II composite scores were not different (mean ± SD, 79.9 ± 25.5 vs 79.4 ± 26.9, lower vs higher target; P = .20). Improvement in Pediatric Quality of Life total health from baseline was greater in the higher target group (adjusted mean difference, 8.2; 95% CI, 1.1-15.3; P = .02). Conclusions One-year adaptive behavior in critically ill children with lower vs higher target glycemic control did not differ. The higher target group demonstrated improvement from baseline in overall health. This study affirms the lack of benefit of lower glucose targeting. Trial registration ClinicalTrials.gov : NCT01565941 .

Objectives Test whether hyperglycemic critically ill children with cardiovascular and/or respiratory failure experience more ICU-free days when assigned to tight glycemic control with a normoglycemic versus hyperglycemic blood glucose target range. Design Multi-center randomized clinical trial. Setting Pediatric ICUs at 35 academic hospitals. Patients Children aged 2 weeks to 17 years receiving inotropic support and/or acute mechanical ventilation, excluding cardiac surgical patients. Interventions Patients receive intravenous insulin titrated to either 80–110 mg/dL (4.4–6.1 mmol/L) or 150–180 mg/dL (8.3–10.0 mmol/L). The intervention begins upon confirmed hyperglycemia and ends when the patient meets study-defined ICU discharge criteria or after 28 days. Continuous glucose monitoring, a minimum glucose infusion, and an explicit insulin infusion algorithm are deployed to achieve the BG targets while minimizing hypoglycemia risk. Measurements and main results The primary outcome is ICU-free days (equivalent to 28-day hospital mortality-adjusted ICU length of stay). Secondary outcomes include 90-day hospital mortality, organ dysfunction scores, ventilator-free days, nosocomial infection rate, neurodevelopmental outcomes, and nursing workload. To detect an increase of 1.25 ICU-free days (corresponding to a 20% relative reduction in 28-day hospital mortality and a one-day reduction in ICU length of stay), 1414 patients are needed for 80% power using a two-sided 0.05 level test. Conclusions This trial tests whether hyperglycemic critically ill children randomized to 80–110 mg/dL benefit more than those randomized to 150–180 mg/dL. This study implements validated bedside support tools including continuous glucose monitoring and a computerized algorithm to enhance patient safety and ensure reproducible bedside decision-making in achieving glycemic control.

Objective To describe the development and initial testing of the Braden QD Scale to predict both immobility-related and medical device–related pressure injury risk in pediatric patients. Study design This was a multicenter, prospective cohort study enrolling hospitalized patients, preterm to 21 years of age, on bedrest for at least 24 hours with a medical device in place. Receiver operating characteristic curves using scores from the first observation day were used to characterize Braden QD Scale performance, including areas under the curve (AUC) with 95% CIs. Results Eight centers enrolled 625 patients. A total of 86 hospital-acquired pressure injures were observed in 49 (8%) patients: 22 immobility-related pressure injuries in 14 (2%) patients and 64 medical device–related pressure injuries in 42 (7%) patients. The Braden QD Scale performed well in predicting immobility-related and medical device–related pressure injuries in the overall sample, with an AUC of 0.78 (95% CI 0.73-0.84). At a cutoff score of 13, the AUC was 0.72 (95% CI 0.67-0.78), providing a sensitivity of 0.86 (95% CI 0.76-0.92), specificity of 0.59 (95% CI 0.55-0.63), positive predictive value of 0.15 (95% CI 0.11-0.19), negative predictive value of 0.98 (95% CI 0.97-0.99), and a positive likelihood ratio of 2.09 (95% CI 0.95-4.58). Conclusions The Braden QD Scale reliably predicts both immobility-related and device-related pressure injuries in the pediatric acute care environment and will be helpful in monitoring care and in guiding resource use in the prevention of hospital-acquired pressure injuries.