Lipidome Changes Associated with a Diet-Induced Reduction in Hepatic Fat among Adolescent Boys with Metabolic Dysfunction-Associated Steatotic Liver Disease.

Little is known about lipid changes that occur in the setting of metabolic-dysfunction-associated steatotic liver disease (MASLD) regression. We previously reported improvements in hepatic steatosis, de novo lipogenesis (DNL), and metabolomic profiles associated with oxidative stress, inflammation, and selected lipid metabolism in 40 adolescent boys (11-16 y) with hepatic steatosis ≥5% (98% meeting the definition of MASLD). Participants were randomized to a low-free-sugar diet (LFSD) (n = 20) or usual diet (n = 20) for 8 weeks. Here, we employed untargeted/targeted lipidomics to examine lipid adaptations associated with the LFSD and improvement of hepatic steatosis. Our LC-MS/MS analysis revealed decreased triglycerides (TGs), diacylglycerols (DGs), cholesteryl esters (ChE), lysophosphatidylcholine (LPC), and phosphatidylcholine (PC) species with the diet intervention (p < 0.05). Network analysis demonstrated significantly lower levels of palmitate-enriched TG species post-intervention, mirroring the previously shown reduction in DNL in response to the LFSD. Targeted oxylipins analysis revealed a decrease in the abundance of 8-isoprostane and 14,15-DiHET and an increase in 8,9-DiHET (p < 0.05). Overall, we observed reductions in TGs, DGs, ChE, PC, and LPC species among participants in the LFSD group. These same lipids have been associated with MASLD progression; therefore, our findings may indicate normalization of key biological processes, including lipid metabolism, insulin resistance, and lipotoxicity. Additionally, our targeted oxylipins assay revealed novel changes in eicosanoids, suggesting improvements in oxidative stress. Future studies are needed to elucidate the mechanisms of these findings and prospects of these lipids as biomarkers of MASLD regression.

Management of complex pediatric and adolescent liver trauma: adult vs pediatric level 1 trauma centers.

PURPOSE: Management of high-grade pediatric and adolescent liver trauma can be complex. Studies suggest that variation exists at adult (ATC) vs pediatric trauma centers (PTC); however, there is limited granular comparative data. We sought to describe and compare the management and outcomes of complex pediatric and adolescent liver trauma between a level 1 ATC and two PTCs in a large metropolitan city. METHODS: A retrospective review of pediatric and adolescent (age < 21 years) patients with American Association for the Surgery of Trauma (AAST) Grade 4 and 5 liver injuries managed at an ATC and PTCs between 2016 and 2022 was performed. Demographic, clinical, and outcome data were obtained at the ATC and PTCs. Primary outcomes included rates of operative management and use of interventional radiology (IR). Secondary outcomes included packed red blood cell (pRBC) utilization, intensive care unit (ICU) length of stay (LOS), and hospital LOS. RESULTS: One hundred forty-four patients were identified, seventy-five at the ATC and sixty-nine at the PTC. The cohort was predominantly black (65.5%) males (63.5%). Six injuries (8.7%) at the PTC and forty-five (60%) injuries at the ATC were penetrating trauma. Comparing only blunt trauma, ATC patients had higher Injury Severity Score (median 37 vs 26) and ages (20 years vs 9 years). ATC patients were more likely to undergo operative management (26.7% vs 11.0%, p = 0.016) and utilized IR more (51.9% vs 4.8%, p < 0.001) compared to the PTC. The patients managed at the ATC required higher rates of pRBC transfusions though not statistically significant (p = 0.06). There were no differences in mortality, ICU, or hospital LOS. CONCLUSION: Our retrospective review of high-grade pediatric and adolescent liver trauma demonstrated higher rates of IR and operating room use at the ATC compared to the PTC in the setting of higher Injury Severity Score and age. While the PTC successfully managed > 95% of Grade 4/5 liver injuries non-operatively, prospective data are needed to determine the optimal algorithm for management in the older adolescent population. LEVEL OF EVIDENCE: Level IV.

Glucagon-like Peptide-1 Receptor Agonists-A Potential New Medication for Pediatric Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD).

Metabolic-dysfunction-associated steatotic liver disease (MASLD) is the most common liver disease in children in the US and, if untreated, may progress to end-stage liver disease. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have shown improvement in MASLD markers in adults with type 2 diabetes mellitus (T2DM). Currently, there is a lack of medications available for the treatment of pediatric MASLD. We aimed to provide preliminary data on the effects of GLP-1 RAs on markers of MASLD in a retrospective study, in an effort to bridge this gap in the pharmacotherapies available. Nine patients from a T2DM clinic who met the following inclusion criteria were included in this study: patients diagnosed with pre-diabetes or T2DM, prescribed a GLP-1 RA in the prior 12 months, and having alanine aminotransferase (ALT) elevated to twice the upper limit of the normal range, indicating evidence of MASLD. The average change between baseline and the first measurement after starting a GLP-1 RA was calculated for ALT, hemoglobin A1c, and BMI. ALT decreased by an average of 98 points. A1c decreased by an average of 2.2 points. BMI decreased by an average of 2.4 points. There was greater reduction in ALT and A1c compared to BMI, suggesting that improvement in MASLD may be independent of weight loss. This is a preliminary study that shows potential, and prospective studies are needed to evaluate the effects of GLP-1 RAs in the management of pediatric MASLD.

Validation of a screening panel for pediatric metabolic dysfunction-associated steatotic liver disease using metabolomics.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD, is the most common liver disease in children. Liver biopsy remains the gold standard for diagnosis, although more efficient screening methods are needed. We previously developed a novel NAFLD screening panel in youth using machine learning applied to high-resolution metabolomics and clinical phenotype data. Our objective was to validate this panel in a separate cohort, which consisted of a combined cross-sectional sample of 161 children with stored frozen samples (75% male, 12.8±2.6 years of age, body mass index 31.0±7.0 kg/m2, 81% with MASLD, 58% Hispanic race/ethnicity). METHODS: Clinical data were collected from all children, and high-resolution metabolomics was performed using their fasting serum samples. MASLD was assessed by MRI-proton density fat fraction or liver biopsy and cardiometabolic factors. Our previously developed panel included waist circumference, triglycerides, whole-body insulin sensitivity index, 3 amino acids, 2 phospholipids, dihydrothymine, and 2 unknowns. To improve feasibility, a simplified version without the unknowns was utilized in the present study. Since the panel was modified, the data were split into training (67%) and test (33%) sets to assess the validity of the panel. RESULTS: Our present highest-performing modified model, with 4 clinical variables and 8 metabolomics features, achieved an AUROC of 0.92, 95% sensitivity, and 80% specificity for detecting MASLD in the test set. CONCLUSIONS: Therefore, this panel has promising potential for use as a screening tool for MASLD in youth.

Engineering Improved CAR T Cell Products with A Multi-Cytokine Particle Platform for Hematologic and Solid Tumors.

Despite the remarkable clinical efficacy of chimeric antigen receptor (CAR) T cells in hematological malignancies, only a subset of patients achieves a durable complete response (dCR). DCR has been correlated with CAR T cell products enriched with T cells memory phenotypes. Therefore, reagents that consistently promote memory phenotypes during the manufacturing of CAR T cells have the potential to significantly improve clinical outcomes. A novel modular multi-cytokine particle (MCP) platform is developed that combines the signals necessary for activation, costimulation, and cytokine support into a single "all-in-one" stimulation reagent for CAR T cell manufacturing. This platform allows for the assembly and screening of compositionally diverse MCP libraries to identify formulations tailored to promote specific phenotypes with a high degree of flexibility. The approach is leveraged to identify unique MCP formulations that manufacture CAR T cell products from diffuse large B cell patients   with increased proportions of memory-like phenotypes MCP-manufactured CAR T cells demonstrate superior anti-tumor efficacy in mouse models of lymphoma and ovarian cancer through enhanced persistence. These findings serve as a proof-of-principle of the powerful utility of the MCP platform to identify "all-in-one" stimulation reagents that can improve the effectiveness of cell therapy products through optimal manufacturing.

Trends in sedation-analgesia practices in pediatric liver transplant patients admitted postoperatively to the pediatric intensive care unit: An analysis of data from the pediatric health information system (PHIS) database.

BACKGROUND: Children admitted to the pediatric intensive care unit (PICU), after liver transplantation, frequently require analgesia and sedation in the immediate postoperative period. Our objective was to assess trends and variations in sedation and analgesia used in this cohort. METHODS: Multicenter retrospective cohort study using the Pediatric Health Information System from 2012 to 2022. RESULTS: During the study period, 3963 patients with liver transplantation were admitted to the PICU from 32 US children's hospitals with a median age of 2 years [IQR: 0.00, 10.00]. 54 percent of patients received mechanical ventilation (MV). Compared with patients without MV, those with MV were more likely to receive morphine (57% vs 49%, p < .001), fentanyl (57% vs 44%), midazolam (45% vs 31%), lorazepam (39% vs. 24%), dexmedetomidine (38% vs 30%), and ketamine (25% vs 12%), all p < .001. Vasopressor usage was also higher in MV patients (22% vs. 35%, p < .001). During the study period, there was an increasing trend in the utilization of dexmedetomidine and ketamine, but the use of benzodiazepine decreased (p < .001). CONCLUSION: About 50% of patients who undergo liver transplant are placed on MV in the PICU postoperatively and receive a greater amount of benzodiazepines in comparison with those without MV. The overall utilization of dexmedetomidine and ketamine was more frequent, whereas the administration of benzodiazepines was less during the study period. Pediatric intensivists have a distinctive opportunity to collaborate with the liver transplant team to develop comprehensive guidelines for sedation and analgesia, aimed at enhancing the quality of care provided to these patients.

Contingent Mother's Voice Intervention Targeting Feeding in Hospitalized Infants with Critical Congenital Heart Defects.

Infants with critical congenital heart defects (CCHD) are at high risk for feeding challenges and neurodevelopmental delays; however, few interventions promoting the neurodevelopmental progression of feeding have been studied with this population. Contingent mother's voice has been successfully used as positive reinforcement for non-nutritive suck (NNS) in studies with preterm infants, leading to improved weight gain and more rapid cessation of tube feedings; however, this type of intervention has not been studied in infants with CCHD. This study aimed to determine whether an NNS-training protocol using the mother's voice as positive reinforcement and validated in preterm infants could improve oral feeding outcomes in hospitalized infants with CCHD undergoing cardiac surgical procedures. Infants were randomized to receive the contingent mother's voice intervention before or after cardiac surgery, with a control comparison group receiving passive exposure to the mother's voice after surgery. There were no significant differences in discharge weight, PO intake, length of stay, time to full feeds, or feeding status at 1-month post-discharge between infants who received contingent mother's voice compared to those who did not. There were significant differences in PO intake and time to full feeds following surgery based on infants' pre-enrollment PO status and severity of illness. At 1-month post-discharge, parents of infants in the intervention group expressed a higher rate of positive feelings and fewer concerns regarding their infant's feeding compared to parents of infants in the control group. While the current protocol of 5 sessions was not associated with improved feeding outcomes in infants with CCHD, it empowered parents to contribute to their infant's care and demonstrated the feasibility of using the mother's voice as positive reinforcement for infants with CCHD. Further study of timing, intensity, and duration of interventions leveraging the mother's voice in this population is needed. ClinicalTrials.gov Identifier: NCT03035552.

Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization.

BACKGROUND: Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in developing microbial interventions for treatment of disease and optimization of crop yields which requires identification of microbiome features that impact the outcome in the population of interest. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. In the presence of such confounding, variable selection and estimation procedures may have unsatisfactory performance in identifying microbial features with an effect on the outcome. RESULTS: In this manuscript, we aim to estimate population-level effects of individual microbiome features while controlling for confounding by a categorical variable. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder followed by a standardization approach to estimation of population-level effects of individual features. Comprehensive simulation studies demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We conducted an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. In this study, the proposed approach identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. CONCLUSIONS: Standardization enables more accurate identification of individual microbiome features with an effect on the outcome of interest compared to other variable selection and estimation procedures when there is confounding by a categorical variable.

Comparing the estimates of effect obtained from statistical causal inference methods: An example using bovine respiratory disease in feedlot cattle.

The causal effect of an exposure on an outcome of interest in an observational study cannot be estimated directly if the confounding variables are not controlled. Many approaches are available for estimating the causal effect of an exposure. In this manuscript, we demonstrate the advantages associated with using inverse probability weighting (IPW) and doubly robust estimation of the odds ratio in terms of reduced bias. IPW approach can be used to adjust for confounding variables and provide unbiased estimates of the exposure's causal effect. For cluster-structured data, as is common in animal populations, inverse conditional probability weighting (ICPW) approach can provide a robust estimation of the causal effect. Doubly robust estimation can provide a robust method even when the specification of the model form is uncertain. In this paper, the usage of IPW, ICPW, and doubly robust approaches are illustrated with a subset of data with complete covariates from the Australian-based National Bovine Respiratory Disease Initiative as well as simulated data. We evaluate the causal effect of prior bovine viral diarrhea exposure on bovine respiratory disease in feedlot cattle. The results show that the IPW, ICPW and doubly robust approaches would provide a more accurate estimation of the exposure effect than the traditional outcome regression model, and doubly robust approaches are the most preferable overall.

Using structural-nested models to estimate the effect of cluster-level adherence on individual-level outcomes with a three-armed cluster-randomized trial.

Much attention has been paid to estimating the causal effect of adherence to a randomized protocol using instrumental variables to adjust for unmeasured confounding. Researchers tend to use the instrumental variable within one of the three main frameworks: regression with an endogenous variable, principal stratification, or structural-nested modeling. We found in our literature review that even in simple settings, causal interpretations of analyses with endogenous regressors can be ambiguous or rely on a strong assumption that can be difficult to interpret. Principal stratification and structural-nested modeling are alternative frameworks that render unambiguous causal interpretations based on assumptions that are, arguably, easier to interpret. Our interest stems from a wish to estimate the effect of cluster-level adherence on individual-level binary outcomes with a three-armed cluster-randomized trial and polytomous adherence. Principal stratification approaches to this problem are quite challenging because of the sheer number of principal strata involved. Therefore, we developed a structural-nested modeling approach and, in the process, extended the methodology to accommodate cluster-randomized trials with unequal probability of selecting individuals. Furthermore, we developed a method to implement the approach with relatively simple programming. The approach works quite well, but when the structural-nested model does not fit the data, there is no solution to the estimating equation. We investigate the performance of the approach using simulated data, and we also use the approach to estimate the effect on pupil absence of school-level adherence to a randomized water, sanitation, and hygiene intervention in western Kenya.

Conditional pseudolikelihood methods for clustered ordinal, multinomial, or count outcomes with complex survey data.

In order to adjust individual-level covariate effects for confounding due to unmeasured neighborhood characteristics, we have recently developed conditional pseudolikelihood methods to estimate the parameters of a proportional odds model for clustered ordinal outcomes with complex survey data. The methods require sampling design joint probabilities for each within-neighborhood pair. In the present article, we develop a similar methodology for a baseline category logit model for clustered multinomial outcomes and for a loglinear model for clustered count outcomes. All of the estimators and asymptotic sampling distributions we present can be conveniently computed using standard logistic regression software for complex survey data, such as sas proc surveylogistic. We demonstrate validity of the methods theoretically and also empirically by using simulations. We apply the new method for clustered multinomial outcomes to data from the 2008 Florida Behavioral Risk Factor Surveillance System survey in order to investigate disparities in frequency of dental cleaning both unadjusted and adjusted for confounding by neighborhood.

Adjusting for confounding by neighborhood using complex survey data.

Recently, we examined methods of adjusting for confounding by neighborhood of an individual exposure effect on a binary outcome, using complex survey data; the methods were found to fail when the neighborhood sample sizes are small and the selection bias is strongly informative. More recently, other authors have adapted an older method from the genetics literature for application to complex survey data; their adaptation achieves a consistent estimator under a broad range of circumstances. The method is based on weighted pseudolikelihoods, in which the contribution from each neighborhood involves all pairs of cases and controls in the neighborhood. The pairs are treated as if they were independent, a pairwise pseudo-conditional likelihood is thus derived, and then the corresponding score equation is weighted with inverse-probabilities of sampling each case-control pair. We have greatly simplified the implementation by translating the pairwise pseudo-conditional likelihood into an equivalent ordinary weighted log-likelihood formulation. We show how to program the method using standard software for ordinary logistic regression with complex survey data (e.g. SAS PROC SURVEYLOGISTIC). We also show that the methodology applies to a broader set of sampling scenarios than the ones considered by the previous authors. We demonstrate the validity of our simplified implementation by applying it to a simulation for which previous methods failed; the new method performs beautifully. We also apply the new method to an analysis of 2009 National Health Interview Survey (NHIS) public-use data, to estimate the effect of education on health insurance coverage, adjusting for confounding by neighborhood.

Efforts to adjust for confounding by neighborhood using complex survey data.

In social epidemiology, one often considers neighborhood or contextual effects on health outcomes, in addition to effects of individual exposures. This paper is concerned with the estimation of an individual exposure effect in the presence of confounding by neighborhood effects, motivated by an analysis of National Health Interview Survey (NHIS) data. In the analysis, we operationalize neighborhood as the secondary sampling unit of the survey, which consists of small groups of neighboring census blocks. Thus the neighborhoods are sampled with unequal probabilities, as are individuals within neighborhoods. We develop and compare several approaches for the analysis of the effect of dichotomized individual-level education on the receipt of adequate mammography screening. In the analysis, neighborhood effects are likely to confound the individual effects, due to such factors as differential availability of health services and differential neighborhood culture. The approaches can be grouped into three broad classes: ordinary logistic regression for survey data, with either no effect or a fixed effect for each cluster; conditional logistic regression extended for survey data; and generalized linear mixed model (GLMM) regression for survey data. Standard use of GLMMs with small clusters fails to adjust for confounding by cluster (e.g. neighborhood); this motivated us to develop an adaptation. We use theory, simulation, and analyses of the NHIS data to compare and contrast all of these methods. One conclusion is that all of the methods perform poorly when the sampling bias is strong; more research and new methods are clearly needed.