Accuracy of predictive equations for resting metabolic rate in Korean athletic and non-athletic adolescents

BACKGROUND/OBJECTIVES: Athletes generally desire changes in body composition in order to enhance their athletic performance. Often, athletes will practice chronic energy restrictions to attain body composition changes, altering their energy needs. Prediction of resting metabolic rates (RMR) is important in helping to determine an athlete's energy expenditure. This study compared measured RMR of athletic and non-athletic adolescents with predicted RMR from commonly used prediction equations to identify the most accurate equation applicable for adolescent athletes. SUBJECTS/METHODS: A total of 50 athletes (mean age of 16.6 +/- 1.0 years, 30 males and 20 females) and 50 non-athletes (mean age of 16.5 +/- 0.5 years, 30 males and 20 females) were enrolled in the study. The RMR of subjects was measured using indirect calorimetry. The accuracy of 11 RMR prediction equations was evaluated for bias, Pearson's correlation coefficient, and Bland-Altman analysis. RESULTS: Until more accurate prediction equations are developed, our findings recommend using the formulas by Cunningham (-29.8 kcal/day, limits of agreement -318.7 and +259.1 kcal/day) and Park (-0.842 kcal/day, limits of agreement -198.9 and +196.9 kcal/day) for prediction of RMR when studying male adolescent athletes. Among the new prediction formulas reviewed, the formula included in the fat-free mass as a variable [RMR = 730.4 + 15 x fat-free mass] is paramount when examining athletes. CONCLUSIONS: The RMR prediction equation developed in this study is better in assessing the resting metabolic rate of Korean athletic adolescents.

Comparison of Activity Factor, Predicted Resting Metabolic Rate, and Intakes of Energy and Nutrients Between Athletic and Non-Athletic High School Students

This study compared activity factor, predicted resting metabolic rate (RMR), and nutrient intakes between athletic and non-athletic high school students in Gangwon-do. Fifty soccer players (30 males and 20 females; mean ages 16.7+/-1.0 years and 16.4+/-1.1 years, respectively) and 50 non-athletic (30 males and 20 females; mean ages 17.5+/-0.4 years and 16.4+/-1.1 years respectively) high school students were included. Anthropometric measurements included: weight and height, triceps skinfold, mid-arm circumference, and body fat. Prediction equations consisted of those from the Harris-Benedict, FAO/WHO/UNU, IMNA, Cunningham, Mifflin et al., and Owen et al. A one-day activity diary was collected by interview, and the 24-hour recall method was used to analyze nutrient intakes of subjects. The activity factors of the male and female athletic groups (2.23 and 2.16, respectively) were significantly higher than those (1.52 and 1.46, respectively) of the non-athletic group. There was only a significant difference in RMR by use of the Cunningham's equation between two groups. For the males, almost all nutrient intakes of the athletic group (except carbohydrate, iron, vitamin B1, B6, and niacin) of athletic group were significantly higher than those of the non-athletic group. The female athletic group showed significantly higher nutrient intakes with the exception of most vitamins. These results suggest that assessments of energy balance between energy intake and energy expenditure by employing RMR and activity factors would be useful to prevent and treat obesity in high school athletes. In addition, the Cunningham's equation would be appropriate for predicting their energy needs.

Comparison of Activity Factor, Predicted Resting Metabolic Rate, and Intakes of Energy and Nutrients Between Athletic and Non-Athletic High School Students

This study compared activity factor, predicted resting metabolic rate (RMR), and nutrient intakes between athletic and non-athletic high school students in Gangwon-do. Fifty soccer players (30 males and 20 females; mean ages 16.7+/-1.0 years and 16.4+/-1.1 years, respectively) and 50 non-athletic (30 males and 20 females; mean ages 17.5+/-0.4 years and 16.4+/-1.1 years respectively) high school students were included. Anthropometric measurements included: weight and height, triceps skinfold, mid-arm circumference, and body fat. Prediction equations consisted of those from the Harris-Benedict, FAO/WHO/UNU, IMNA, Cunningham, Mifflin et al., and Owen et al. A one-day activity diary was collected by interview, and the 24-hour recall method was used to analyze nutrient intakes of subjects. The activity factors of the male and female athletic groups (2.23 and 2.16, respectively) were significantly higher than those (1.52 and 1.46, respectively) of the non-athletic group. There was only a significant difference in RMR by use of the Cunningham's equation between two groups. For the males, almost all nutrient intakes of the athletic group (except carbohydrate, iron, vitamin B1, B6, and niacin) of athletic group were significantly higher than those of the non-athletic group. The female athletic group showed significantly higher nutrient intakes with the exception of most vitamins. These results suggest that assessments of energy balance between energy intake and energy expenditure by employing RMR and activity factors would be useful to prevent and treat obesity in high school athletes. In addition, the Cunningham's equation would be appropriate for predicting their energy needs.

Leukocytosis Prevalence according to Smoking among Men

BACKGROUND: Cigarette smoke exposure accelerates the release of leukocytes from the bone marrow, which is postulated to be mediated by inflammatory cytokines and haematopoietic growth factors released from the lung. METHODS: The study population consisted of 2,270 healthy men above 20 years of age who had visited a health promotion center in a university hospital from January 2005 to December 2005. Baseline health check, self- reported cigarette smoking habit and blood sample were evaluated. The mean count of leukocytes and the leukocytosis prevalence according to smoking amounts were investigated. RESULTS: As the daily smoking amount was increased and as pack years smoked was increased, the mean total WBC count values and the prevalence of leukocytosis were higher (P=0.000). The odds ratio (OR) for leukocytosis according to daily smoking amount compared to no smokers was 1.98 (95% confidence interval (CI) 1.01~3.88) in those who smoked 1/2~1 pack per day and 4.17 (95% CI 1.83~9.48) in those who smoked more than 1 pack per day. The OR for leukocytosis according to smoking pack years compared to no smokers was 2.58 (95% CI 1.22~5.42) in those who smoked 20~30 pack years, 3.31 (95% CI 1.38~7.93) in those who smoked 30~40 pack years and 5.17 (95% CI 2.00~13.32) in those who smoked more than 40 pack years. CONCLUSION: The mean count of leukocytes and the prevalence of leukocytosis were higher in current smokers than no smokers and ex-smokers.