1. Results (cont’d) Results. Abstract Methods Methods (cont’d) Purpose Conclusions Author: Aderemi, Eunice T., Faculty sponsors: J.R. Wilson, Ph.D, Mark Ricard, Ph.D, : Neuromuscular Research Laboratory, The University of Texas at Arlington, Arlington, TX; THE RELATIONSHIP OF VARIOUS MEASURES OF BODY COMPOSITION TO BLOOD SUGAR LEVELS Background : Body composition is a highly indicative factor for many primary and secondary cardiovascular (CV) diseases. Waist to hip ratio (WHR) is a variable that determines android or gynoid fat distributions among men and women. Skinfold measures and Bioelectrical Impedance Analyzers (BIA) also allow an investigator to determine the distribution of body fat (BF), with it's percentage in the body, and Body Mass Index (BMI). These all give valid measurements that are guidelines for the obesity scale. and have indications of where a subject may be located in terms of risk factors for diseases (dz) such as diabetes, a secondary characteristic of CV dz. For instance, populations with a larger WHR (.8-1.0) are at greater risk for heart dz and obstructive pulmonary dz. The purpose of this study was to determine whether there is a relationship between varied body composition and blood sugar levels. Methods : All subjects participating in this study were current students at the University of Texas at Arlington. The study began with 8 subjects, two of which were excluded due to non attendance of the second testing day. The mean age for the remaining 6 subjects (4 males, 2 females) was 20.83 ± 1.72 years with the height of 173.9 ± 13.3 cm, and weight of 79.83 ± 17.1 kg. Members of this study came in for two days of testing. Day 1 included body composition measures of Height/weight, WH ratio, skinfold measures, and Hydrostatic measures in respective order. Also on that day, two separate measures of blood sugar were taken: Fasting Glucose (FG) and the Hemoglobin A1C (HgA1C). Subjects then had a BIA measure and pre/post measure of glucose levels on a second day of testing that were separated by a cardiorespiratory exercise session. Several correlations were run on the data along with two t test for the second day of data that showed no statistical significance p = 0.505 Results: BMI measurements from the BIA (26.1 ± 2.7 kg/m2) and FG (89.8 ± 8.68 mg/mL) were highly correlated r =.74 overall and an r =.78 for men. BMI and the difference of the pre/post glucose testing (2.83 ± 9.66 mg/mL including negative values) yielded an r =.76. FG levels and WHR (.80 ±.05) were also found to be highly correlated r =.85. The highest correlation was found between the difference of the pre and post glucose testing and skinfold measurement with an r value of.988. The t test between Pre-glucose and Post-glucose testing as well as BF and the change in glucose were both found not to be significant (p =.505, p =.693, two tailed, equal variance). Conclusion : Results from the correlations between FG versus the subjects BMI and WHR indicate that, as there is an increase with the two, resting glucose levels in the blood will generally start higher. The very high correlation between pre and post glucose testing and skinfold measures may suggest that fat distributed under the skin may lead to a decrease in the ability to transfer glucose into the muscles during exercise. No definitive conclusion can be made from this study concerning internal fat and glucose levels. The purpose of this study was to determine whether there was a relationship between varied body composition and blood sugar levels in young adults. All subjects participating in this study were current students at the University of Texas at Arlington. The study began with 8 subjects, two of which were excluded due to incomplete data. The mean age for the remaining 6 subjects (4 males, 2 females) was 20.83 ± 1.72 years with the height of 173.9 ± 13.3 cm, and weight of 79.83 ± 17.1 kg. There were 3 African Americans, 1 Caucasian, and 1 Indian. Members of this study came in for two days of testing. Day 1 included body composition measures of Height/weight, WH R, a 3 site skinfold measure, and Hydrostatic measures in respective order. Also on that day, two separate measures of blood sugar were taken: Fasting Glucose (FG) (TRUE result’s home diagnostics, 2009) and the Hemoglobin A1C (HgA1C) (Bayer A1CNow+, 2008). Participants were required not to eat anything after 12 midnight on the eve of testing for fasting blood levels. On the second day of testing BMI and BF% were measured with a BIA (Omron HBF 306 Body Logic Pro Body Fat Analyzer). Glucose levels were measured before and after a cardiorespiratory exercise session (P90X Cardio, 2008). BMI measurements (26.1 ± 2.7 kg/m2) and FG (89.8 ± 8.68 mg/mL) were highly correlated (r =.74) for both men and women, with an r =.78 for men. BMI and the difference between the pre and post glucose testing (2.83 ± 9.66 mg/mL) yielded an r =.76. The subjects overall FG levels compared with WHR (.80 ±.05) was also found to be strongly correlated r =.85. The highest correlation was found between the difference of the pre and post glucose testing and skinfold measurement with an r value of.98. The t test analyzing Pre-glucose and Post-glucose measures was found not to be significant (p =.505). BF percentages and the change in glucose were also not significant (p = 0.69). Results from the correlations between FG versus the subjects BMI indicate that as there is an increase, resting glucose levels in the blood will generally start higher. The same is true for WHR and FG. In this group of subjects the “average” person was at low risk according to WHR measurements. However, as the ratio approached 1.0, subjects were found to have a higher fasting glucose level. The very high correlation between the difference of pre and post glucose testing and BF% from skinfold measures, may suggest that fat distributed under the skin may lead to a decrease in the ability to transfer glucose into musculoskeletal tissue during exercise. No definitive conclusion can be made from this study concerning BF, or internal fat, and glucose levels. Although there were some variables in this study that brought out key correlations, limitations in this study hindered the accuracy of some results. In the future, investigators should limit their subjects to one ethnicity or have an equal amount. Choosing physical characteristics may lead to more reliable data. Because this study only contained two females, all correlations for their category yielded an r = 1.0.