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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.

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.

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.