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Low protein (LP) diets are associated with a decreased risk of diabetes in humans, and a low protein diet promotes leanness and glycemic control in both rodents and humans. While the effects of a LP diet on glycemic control are mediated by reduced dietary levels of the branched- chain amino acids (BCAAs), we have observed that reducing dietary levels of the other six essential amino acids leads to changes in body composition. Here, we find that dietary histidine plays a key role in the response to a LP diet in male C57BL/6J mice. Specifically reducing dietary levels of histidine by 67% reduces weight gain of young, lean male mice, reducing both adipose and lean mass gain, without altering glucose metabolism. Specifically reducing dietary histidine rapidly reverses diet-induced obesity and hepatic steatosis in diet-induced obese male mice, increasing insulin sensitivity; this normalization of metabolic health was associated not with caloric restriction or increased activity, but with increased energy expenditure. We find that the effects of histidine restriction surprisingly does not require the energy balance hormone Fgf21. Histidine restriction started in mid-life promoted leanness and glucose tolerance in aged males but not females, but did not affect frailty or lifespan in either sex. Finally, we demonstrate that variation in dietary histidine levels helps to explain body mass index differences in humans. Overall, our findings demonstrate that dietary histidine is a key regulator of weight and body composition in male mice and in humans, and suggest that reducing dietary levels of histidine may be a highly translatable option for the treatment of obesity.Key PointsProtein restriction (PR) promotes metabolic health in rodents and humans and extends rodent lifespan.Restriction of specific individual essential amino acids can recapitulate the benefits of PR.Reduced histidine promotes leanness and increased energy expenditure in mice.Reduced histidine does not extend the lifespan of mice when begun in mid-life.Dietary levels of histidine are positively associated with BMI in humans.

Background: Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. Methods: Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. Findings: No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. Interpretation: Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation. Funding: NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC)

Objectives Early antibiotic administration is a central component of sepsis guidelines, and delays may increase mortality. However, prior studies have examined the delay to first antibiotic administration as a single time period even though it contains two distinct processes: antibiotic ordering and antibiotic delivery, which can each be targeted for improvement through different interventions. The objective of this study was to characterize and compare patients who experienced order or delivery delays, investigate the association of each delay type with mortality, and identify novel patient subphenotypes with elevated risk of harm from delays. Design Retrospective analysis of multicenter inpatient data. Setting Two tertiary care medical centers (2008-2018, 2006-2017) and four community-based hospitals (2008-2017). Patients All patients admitted through the emergency department who met clinical criteria for infection. Interventions None. Measurements and main results Patient demographics, vitals, laboratory values, medication order and administration times, and in-hospital survival data were obtained from the electronic health record. Order and delivery delays were calculated for each admission. Adjusted logistic regression models were used to examine the relationship between each delay and in-hospital mortality. Causal forests, a machine learning method, was used to identify a high-risk subgroup. A total of 60,817 admissions were included, and delays occurred in 58% of patients. Each additional hour of order delay (odds ratio, 1.04; 95% CI, 1.03-1.05) and delivery delay (odds ratio, 1.05; 95% CI, 1.02-1.08) was associated with increased mortality. A patient subgroup identified by causal forests with higher comorbidity burden, greater organ dysfunction, and abnormal initial lactate measurements had a higher risk of death associated with delays (odds ratio, 1.07; 95% CI, 1.06-1.09 vs odds ratio, 1.02; 95% CI, 1.01-1.03). Conclusions Delays in antibiotic ordering and drug delivery are both associated with a similar increase in mortality. A distinct subgroup of high-risk patients exist who could be targeted for more timely therapy.

Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear.Our objective was to utilize a large, nationally representative dataset of critically ill adults with COVID-19 to determine which factors explain mortality variability.In this multicenter cohort study, we examined adults hospitalized in intensive care units with COVID-19 at 70 United States hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effects logistic regression was used to identify factors associated with interhospital variation. The median odds ratio (OR) was calculated to compare outcomes in higher- vs. lower-mortality hospitals. A gradient boosted machine algorithm was developed for individual-level mortality models.A total of 4,019 patients were included, 1537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0-82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (OR decline from 2.06 [95% CI, 1.73-2.37] to 1.22 [95% CI, 1.00-1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%).There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

INTRODUCTION: Despite the well-established benefits of physical activity (PA), a large portion of U.S. adults are not meeting recommended health-based guidelines. Although PA occurs in several domains, population-based studies tend to focus on leisure-time PA, with few studies examining occupational activity (OA) level as a separate determinant of overall PA. METHODS: Data were obtained from the 2014–2016 Survey of Health of Wisconsin (SHOW). Currently employed SHOW participants (n = 822) were categorized into OA level categories. Bivariate analyses and multinomial logistic regression analyses were used to identify predictors and to test associations between OA and odds of meeting total PA guidelines using both self-reported and accelerometer-based data. RESULTS: Individuals with high OA level jobs tended to be males (p

Low-protein diets promote metabolic health in rodents and humans, and the benefits of low-protein diets are recapitulated by specifically reducing dietary levels of the three branched-chain amino acids (BCAAs), leucine, isoleucine, and valine. Here, we demonstrate that each BCAA has distinct metabolic effects. A low isoleucine diet reprograms liver and adipose metabolism, increasing hepatic insulin sensitivity and ketogenesis and increasing energy expenditure, activating the FGF21-UCP1 axis. Reducing valine induces similar but more modest metabolic effects, whereas these effects are absent with low leucine. Reducing isoleucine or valine rapidly restores metabolic health to diet-induced obese mice. Finally, we demonstrate that variation in dietary isoleucine levels helps explain body mass index differences in humans. Our results reveal isoleucine as a key regulator of metabolic health and the adverse metabolic response to dietary BCAAs and suggest reducing dietary isoleucine as a new approach to treating and preventing obesity and diabetes.

Interdisciplinary approaches are needed to measure the additive or multiplicative impacts of chemical and non-chemical stressors on child development outcomes. The lack of interdisciplinary approaches to environmental health and child development has led to a gap in the development of effective intervention strategies. It is hypothesized that a broader systems approach can support more effective interventions over time. To achieve these goals, detailed study protocols are needed. Researchers in child development typically focus on psychosocial stressors. Less attention is paid to chemical and non-chemical stressors and how the interaction of these stressors may impact child development. This feasibility study aims to bridge the gap between child development and environmental epidemiology research by trialing novel methods of gathering ultrafine particle data with a wearable air sensor, while simultaneously gathering language and noise data with the Language Environment Analysis (LENA) system. Additionally, psychosocial data (e.g., parenting quality, caregiver depression, and household chaos) was gathered from parent reports. Child participants (age 3&ndash, 4 years) completed cognitive tasks to assess self-regulation and receptive language skills, and provided a biospecimen analyzed for inflammatory biomarkers. Data collection was completed at two time points, roughly corresponding to fall and spring. Twenty-six participants were recruited for baseline data, and 11 participants completed a follow-up session. Preliminary results indicate that it is feasible to gather personal Particulate Matter (PM2.5), language, and noise data, cognitive assessments, and biospecimens from our sample of 3-4-year-old children. While there are obstacles to overcome when working with this age group, future studies can benefit from adapting lessons learned regarding recruitment strategies, study design, and protocol implementation.

INTRODUCTION:Mothers undergoing cesarean delivery may experience later skin-to-skin contact, greater difficulty breastfeeding, and decreased satisfaction than mothers delivering vaginally. Mimicking a vaginal delivery, the family-centered cesarean section (FCS) allows a mother to watch the birth thr