Preliminary Findings: Relationship Between IgG-Based Food Elimination and Whole-Body Inflammation

High levels of whole-body inflammation are associated with increased risk of poor health outcomes and chronic disease. Inflammatory symptoms (e.g., digestive, psychological, and whole-body irritation) are commonly addressed via food elimination diets, yet individual differences may exist for persons with unique immunoglobulin-G (IgG) mediated food sensitivities. Few studies have examined IgG food sensitivities using an understood biomarker of inflammation, high-sensitivity C-Reactive Protein (hsCRP). Identification of IgG mediated food sensitivities may be a feasible means for targeted-food elimination seeking to address inflammatory symptoms. PURPOSE: To assess measurable changes in primary outcomes, hsCRP and inflammatory symptomology, within subjects following an IgG targeted-food elimination diet compared to standard diet. METHODS: From 2021-2023, 20 subjects (male: n=20, Age=26 ± 7.6, Wt (kg)=88.43 ± 20.74, LBM (kg)=67.44 ± 10.15) underwent both a 4-week standard diet and 4-week IgG-targeted elimination diet, in a cross-over design ordered by random assignment. Body composition (InBody 570, BIA), inflammation (hsCRP blood draw), and symptomology (Inflammatory Symptom Screening Questionnaire) were assessed at baseline. Participants completed hsCRP and symptom screeners at the following appointments: start and end of baseline (days 1, 8), after week one and week four of first (days 15, 36) and second diet assignment (days 43, 64). Food logging was done throughout the duration of the study. Correlations and ANOVAs were run to assess relationships between demographics and hsCRP and symptom screener scores, as well as any interaction between diet condition, time point, or diet order. Data are reported as mean ± standard error. RESULTS: No meaningful correlations were found between InBody assessments and primary outcomes. No differences were found in hsCRP measurements between any of the time points in the standard and elimination diet conditions (p=.810). On the contrary, differences in inflammatory symptom scores were dependent on diet condition (p\u3c.001). During their standard diet, participants reported increased symptom frequency at week one (20.40) and week four (20.33). Greater differences in inflammatory symptomology were found the longer participants eliminated food; after one week of elimination (15.20) compared to one week of standard diet (-5.20, p=.001) and four weeks of standard diet (-5.13, p=.003). Differences were magnified by the fourth week of elimination (10.67) compared to week one (-9.73, p\u3c.001) and week four of standard diet (-9.667, p\u3c.001). CONCLUSION: This study suggests targeted IgG-based food elimination diets significantly reduce inflammatory symptoms despite finding no detectable changes in whole body inflammation via hsCRP. The results presented here influenced a subsequent study examining the effect of plant versus animal protein on athletic performance in individuals with and without whey IgG sensitivities

Comparison of Actual versus Recommended Intake of Collegiate Athletes Across Gender and Season at a Small Division I University

Collegiate athletes often struggle to consume foods that provide adequate energy and nutrition to fuel their demanding physical performance and recovery needs. The increased energy expenditure during in- versus off-season training may further hinder an athlete’s ability to meet recommended intake guidelines. PURPOSE: to assess Division I student-athlete nutrition intake (calories, protein, carbohydrates, and fat) throughout training seasons of the 2019-2021 school years. METHODS: From 2019-2021, 29 athletes (male: n=9, Wt=76.9 ± 2.2kg, LBM=66.0 ± 6.7kg; female: n=20, Wt=68.6 ± 3.7, LBM=51.4 ± 1.7kg) completed 3-day intake (ASA24) logs monthly. Body composition (InBody 570, BIA) and sport nutrition knowledge (NSKQ) were assessed at baseline. Paired samples t-tests were used to assess differences between actual intake and recommended values (based on kg/BW and sport) while repeated measures ANOVAs were used to assess gender by season interactions; data are reported as mean ± standard error. RESULTS: Regardless of training season athletes failed to meet recommended intake guidelines for carbohydrates (-319.5 ± 27.5g; P\u3c0.001) and calories (-552.7 ± 144.2kcals; P=0.001). Fat was the only macronutrient consistently overconsumed throughout the year (+19.3 ± 4.7g; P\u3c0.001) while protein intake was not significantly different from recommendations (-1.7 ± 6.7g, P=0.804). Males and females did not differ in their ability to meet recommendations. When comparing training seasons, the carbohydrate underconsumption was greater in-season (-379 ± 26.2g) than off-season (-245 ± 26.4g; P=0.001) while the fat overconsumption was greater in-season (+34.7 ± 5.9g) than off-season (+17.1 ± 6.2g; P=0.002). When comparing gender by season interactions, males significantly overconsumed fat off-season (+12.8 ± 10.3g; P=0.031) while females significantly under consumed protein in-season (-9.3 ± 8.2g; P=0.044). Knowledge scores (51.7 ± 2.3%) were not correlated with matching intake, regardless of season and across macronutrients, however this sample’s narrow distribution of scores may have prevented significant findings. CONCLUSION: This study suggests that collegiate athletes at a small D1 university can meet protein and fat recommendations yet struggle to meet calorie and carbohydrate guidelines. Gender differences may also exist, and sport nutrition knowledge was not found to influence an athlete’s nutrient intake

Comparative Study of Active Flow Control Strategies for Lift Enhancement of a Simplified High-Lift Configuration

Numerical simulations have been performed for a simplified high-lift (SHL) version of the Common Research Model (CRM) configuration, where the Fowler flaps of the conventional high-lift (CRM-HL) configuration are replaced by a set of simple hinged flaps. These hinged flaps are equipped with integrated modular active flow control (AFC) cartridges on the suction surface, and the resulting geometry is known as the CRM-SHL-AFC configuration. The main objective is to make use of AFC devices on the CRM-SHL-AFC configuration to recover the aerodynamic performance (lift) of the CRM-HL configuration. In the current paper, a Lattice Boltzmann method-based computational fluid dynamics (CFD) code, known as PowerFLOWQ is used to simulate the entire flow field associated with the CRM-SHL-AFC configuration equipped with several different types of AFC devices. The transonic version of the PowerFLOWQ code that has been validated for high speed flows is used to accurately simulate the flow field generated by the high-momentum actuators required to mitigate reversed flow regions on the suction surfaces of the main wing and the flap. The numerical solutions predict the expected trends in aerodynamic forces as the actuation levels are increased. More efficient AFC systems and actuator arrangements emerged based on the parametric studies performed prior to a Fall 2018 wind tunnel test. Preliminary comparisons of the numerical solutions for lift and surface pressures are presented here with the experimental data, demonstrating the usefulness of CFD for predicting the flow field and lift characteristics of AFC-enabled high-lift configurations

Assessing Body Measurements, Nutritional Behaviors, And Sleep Behaviors Following Implementation Of MHealth In Appalachian State University College Students

Chronic diseases such as obesity are now more common in college students. College students struggle to develop strategies to maintain healthy weight and lifestyle behaviors when adapting to a college lifestyle and routine. MHealth programs are accessible to college students' schedules and routines due to the high usage of mobile devices. My Quest in the High Country collaborated with Appalachian State University (ASU) Student Health Services and the Blue Cross Institute for Health and Human Services Interprofessional Clinic to create a 24-week mHealth intervention to improve weight status, health behaviors, sleep status and biometrics in ASU students. Recruitment of ASU students occurred from November 2020-January 2021 through flyers, social media, and email. During pre-assessment, eligibility was confirmed; afterward informed consent, biometrics, and a pre-assessment survey were collected. Participants received a scale, Fitbit, and caloric intake goals. From weeks 1-12, participants received text messages (n=1/day), eNewsletters (n=1/wk), and physical activity feedback based on individual physical activity and step counts. At midpoint, Fitbits were returned, biometrics were taken, and a midpoint survey was completed. From weeks 13-24, text messages and eNewsletters continued. At post-assessment, biometrics and post-assessment surveys were collected. Statistical analyses included Wilcoxon Signed Rank, McNemar, paired t-test, and descriptives (frequencies and percentages). Significance was set at p<.05