Yu and Richardson et al. find that restriction of dietary isoleucine or valine promotes metabolic health in mice and that restriction of dietary isoleucine is required for the metabolic benefits of a low-protein diet. Furthermore, higher dietary isoleucine levels are associated with increased BMI in humans.

Rationale: Variation in hospital mortality has been described for coronavirus disease (COVID-19), but the factors that explain these differences remain unclear. Objective: Our objective was to use a large, nationally representative data set of critically ill adults with COVID-19 to determine which factors explain mortality variability. Methods: In this multicenter cohort study, we examined adults hospitalized in ICUs with COVID-19 at 70 U.S. hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effect logistic regression was used to identify factors associated with interhospital variation. The median odds ratio was calculated to compare outcomes in higher- versus lower-mortality hospitals. A gradient-boosted machine algorithm was developed for individual-level mortality models. Measurements and Main Results: A total of 4,019 patients were included, 1,537 (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 (odds ratio decline from 2.06 [95% confidence interval (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 as follows: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%). Conclusions: 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.

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.

The increasing prevalence of obesity is a serious threat to global health. 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. The effects of a LP diet on glycemic control are mediated by reduced dietary levels of the branched-chain amino acids (BCAAs). However, 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. Specifically reducing dietary levels of histidine by 67% reduces weight gain of young, lean C57BL/6J 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 mice, increasing insulin sensitivity; this normalization of metabolic health was associated not with caloric restriction or increased activity, but with increased energy expenditure that surprisingly did not require Fgf21. Histidine restriction started in mid-life promoted leanness and glucose tolerance in aged males, 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 both mice and humans, and suggest that reducing dietary levels of histidine may be a highly translatable option for the treatment of obesity.

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.