Utility of epitope-specific IgE, IgG4, and IgG1 antibodies for the diagnosis of wheat allergy.

BACKGROUND: The bead-based epitope assay (BBEA) has been used to identify epitope-specific (es) antibodies and successfully utilized to diagnose clinical allergy to milk, egg and peanut. OBJECTIVE: This study aimed to identify es-IgE, es-IgG4 and es-IgG1 of wheat proteins and determine the optimal peptides to differentiate wheat-allergic from wheat-tolerant using the BBEA. METHODS: Children and adolescents who underwent an oral food challenge to confirm their wheat allergy status were enrolled. Seventy-nine peptides from alpha/beta-gliadin, gamma-gliadin (γ-gliadin), omega-5-gliadin (ω-5-gliadin), high and low molecular weight glutenin were commercially synthesized and coupled to LumAvidin beads. Machine learning (ML) methods were used to identify diagnostic epitopes and performance was evaluated using DeLong's test. RESULTS: The analysis includes 122 children (83 wheat-allergic and 39 wheat-tolerant, 57.4% male). ML coupled with simulations identified wheat es-IgE, but not es-IgG4 or es-IgG1 to be most informative for diagnosing wheat allergy. Higher es-IgE binding intensity correlated with the severity of allergy phenotypes, with wheat anaphylaxis exhibiting the highest es-IgE binding intensity. In contrast, wheat-dependent exercise-induced anaphylaxis (WDEIA) showed lower es-IgG1 binding than all other groups. A set of 4 informative epitopes from ω-5-gliadin, and γ-gliadin were the best predictors of wheat allergy with an AUC of 0.908 (sensitivity=83.4%, specificity=88.4%), higher than the performance exhibited by wheat-specific IgE (AUC=0.646, p < 0.001). The predictive ability of our model was confirmed in an external cohort of 71 patients (29 allergic, 42 non-allergic), with an AUC of 0.908 (sensitivity=75.9%, specificity=90.5%). CONCLUSION: The wheat BBEA demonstrated greater diagnostic accuracy compared to existing specific IgE tests for wheat allergy.

The TBI Model Systems Neighborhood Socioeconomic Disadvantage Index (TBIMS-NSDI): Development and Comparison to Individual Socioeconomic Characteristics.

OBJECTIVE: To create a census-based composite neighborhood socioeconomic deprivation index (NSDI) from geocoded residential addresses and to quantify how NSDI aligns with individual-level socioeconomic factors among people with traumatic brain injury (TBI). SETTING: Community. PARTICIPANTS: People enrolled in the TBI Model Systems National Database (TBIMS NDB). DESIGN: Secondary analysis of a longitudinal cohort study. MAIN MEASURES: The TBIMS-NSDI was calculated at the census tract level for the United States population based on a principal components analysis of eight census tract-level variables from the American Community Survey. Individual socioeconomic characteristics from the TBIMS NDB were personal household income, education (years), and unemployment status. Neighborhood:Individual NSDI residuals represent the difference between predicted neighborhood disadvantage based on individual socioeconomic characteristics versus observed neighborhood disadvantage based on the TBIMS-NSDI. RESULTS: A single principal component was found to encompass the eight socioeconomic neighborhood-level variables. It was normally distributed across follow-up years 2, 5, and 10 post-injury in the TBIMS NDB. In all years, the TBIMS-NDSI was significantly associated with individual-level measures of household income and education but not unemployment status. Males, persons of Black and Hispanic background, Medicaid recipients, persons with TBI caused by violence, and those living in urban areas, as well as in the Northeast or Southern regions of the United States, were more likely to have greater neighborhood disadvantage than predicted based on their individual socioeconomic characteristics. CONCLUSIONS: The TBIMS-NSDI provides a neighborhood-level indicator of socioeconomic disadvantage, an important social determinant of outcomes from TBI. The Neighborhood:Individual NSDI residual adds another dimension to the TBIMS-NSDI by summarizing how a person's socioeconomic status aligns with their neighborhood socioeconomics. Future studies should evaluate how both measures affect TBI recovery and life quality. Research studying neighborhood socioeconomic disadvantage may improve our understanding of how systemic adversity influences outcomes after TBI.

Long-Term Temporal Trends of Real-World Healthcare Costs Associated with Nivolumab Plus Ipilimumab and Pembrolizumab Plus Axitinib as First-Line Treatment for Advanced or Metastatic Renal Cell Carcinoma.

INTRODUCTION: Nivolumab plus ipilimumab (NIVO + IPI) and pembrolizumab plus axitinib (PEM + AXI) are first-line (1L) treatments for advanced or metastatic renal cell carcinoma (aRCC), although the long-term trends in their associated real-world healthcare costs are not well defined. We compared the real-world healthcare costs of patients with aRCC who received 1L NIVO + IPI or PEM + AXI over 24 months. METHODS: Adults with RCC and secondary malignancy who initiated 1L NIVO + IPI or PEM + AXI were identified in the Merative MarketScan Commercial and Medicare Supplemental Databases (01/01/2004 to 09/30/2021). All-cause and RCC-related healthcare costs (unadjusted and adjusted) were assessed per patient per month (PPPM) at 6-month intervals post-treatment initiation (index date) up to 24 months, and differences between the NIVO + IPI and PEM + AXI cohorts were compared. RESULTS: Of 325 patients with aRCC, 219 received NIVO + IPI and 106 received PEM + AXI as the 1L treatment. According to patients' follow-up length, the analyses for months 7-12 included 210 patients in the NIVO + IPI cohort and 103 in the PEM + AXI cohort; months 13-18 included 119 and 48 patients, respectively; and months 19-24 included 81 and 25 patients. PPPM unadjusted all-cause total costs were $46,348 for NIVO + IPI and $38,097 for PEM + AXI in months 1-6; $26,840 versus $27,983, respectively, in months 7-12; $22,899 versus $25,137 in months 13-18; and $22,279 versus $27,947 in months 19-24. PPPM unadjusted RCC-related costs were $44,059 for NIVO + IPI and $36,456 for PEM + AXI in months 1-6; $25,144 versus $26,692, respectively, in months 7-12; $21,645 versus $23,709 in months 13-18; and $20,486 versus $25,515 in months 19-24. PPPM costs declined more rapidly for patients receiving NIVO + IPI compared to those receiving PEM + AXI, resulting in significantly lower all-cause costs associated with NIVO + IPI during months 19-24 (difference - $10,914 [95% confidence interval - $21,436, - $1091]) and RCC-related costs during months 7-12 (- $4747 [(- $8929, - $512]) and 19-24 (- $10,261 [- $20,842, - $421]) after adjustment. Cost savings for NIVO + IPI versus PEM + AXI were driven by differences in drug costs which, after adjustment, were significantly lower in months 7-12 (difference - $5555 [all-cause], - $5689 [RCC-related]); 13-18 (- $7217 and - $6870, respectively); and 19-24 (- $16,682 and - $16,125). CONCLUSION: Although the real-world PPPM healthcare costs of 1L NIVO + IPI were higher compared with PEM + AXI in the first 6 months of treatment, the costs associated with NIVO + IPI rapidly declined thereafter, resulting in significantly lower costs vs. PEM + AXI from months 7 to 24.

Fine-scale spatial and social patterns of SARS-CoV-2 transmission from identical pathogen sequences.

Pathogen genomics can provide insights into disease transmission patterns, but new methods are needed to handle modern large-scale pathogen genome datasets. Genetically proximal viruses indicate epidemiological linkage and are informative about transmission events. Here, we leverage pairs of identical sequences using 114,298 SARS-CoV-2 genomes collected via sentinel surveillance from March 2021 to December 2022 in Washington State, USA, with linked age and residence information to characterize fine-scale transmission. The location of pairs of identical sequences is highly consistent with expectations from mobility and social contact data. Outliers in the relationship between genetic and mobility data can be explained by SARS-CoV-2 transmission between postal codes with male prisons, consistent with transmission between prison facilities. Transmission patterns between age groups vary across spatial scales. Finally, we use the timing of sequence collection to understand the age groups driving transmission. This work improves our ability to characterize transmission from large pathogen genome datasets.

Relationships between body composition, anthropometrics, and standard lipid panels in a normative population.

Introduction: More than one third of adults in the United States (US) meet the clinical criteria for a diagnosis of metabolic syndrome, but often diagnosis is challenging due to healthcare access, costs and discomfort with the process and invasiveness associated with a standard medical examination. Less invasive and more accessible approaches to collecting biometric data may have utility in identifying individuals at risk of diagnoses, such as metabolic syndrome or dyslipidemia diagnoses. Body composition is one such source of biometric data that can be non-invasively acquired in a home or community setting that may provide insight into an individual's propensity for a metabolic syndrome diagnosis. Here we investigate possible associations between body composition, anthropometrics and lipid panels in a normative population. Methods: Healthy participants visited the Lab100 clinic location at a hospital setting in New York City and engaged in a wellness visit led by a nurse practitioner. Blood was analyzed at point-of-care using the Abbott Piccolo Xpress portable diagnostic analyzer (Abbott Laboratories, IL, USA) and produced direct measures of total cholesterol (TC), high density lipoprotein (HDL-C), low density lipoprotein (LDL-C), very-low density lipoprotein (VLDL-C), and triglycerides (TG). Body composition and anthropometric data were collected using two separate pieces of equipment during the same visit (Fit3D and InBody570). Regression analysis was performed to evaluate associations between all variables, after adjusting for age, sex, race, AUDIT-C total score (alcohol use), and current smoking status. Results: Data from 199 participants were included in the analysis. After adjusting for variables, percentage body fat (%BF) and visceral fat levels were significantly associated with every laboratory lipid value, while waist-to-hip ratio also showed some significant associations. The strongest associations were detected between %BF and VLDL-C cholesterol levels (t = 4.53, p = 0.0001) and Triglyceride levels (t = 4.51, p = 0.0001). Discussion: This initial, exploratory analysis shows early feasibility in using body composition and anthropometric data, that can easily be acquired in community settings, to identify people with dyslipidemia in a normative population.