|Year : 2017 | Volume
| Issue : 2 | Page : 134-140
Urine creatinine and anthropometric indices of sportsmen and women
Matilda Steiner-Asiedu1, Juliet Vickar1, Frederick Vuvor1, Isaac Agbemafle2, Yusif M Tahiru3
1 Department of Nutrition and Food Science, University of , Legon, Accra, Ghana
2 Department of Nutrition and Dietetics, School of Public Health, University of Health and Allied Sciences, Ho, Ghana
3 University of Sport Council, University of , Legon, Accra, Ghana
|Date of Web Publication||23-Nov-2017|
Department of Nutrition and Food Science, School of Biological Sciences, College of Basic and Applied Sciences, University of Ghana, P. O. Box LG 134, Legon, Accra
Source of Support: None, Conflict of Interest: None
Background: Interpretation of the different aerobic and anaerobic metabolism, training, and anthropometric indices of sportspersons from different sports discipline can aid in improving sports performance. This study sought to evaluate the association between urine creatinine concentration (UCR) and percentage body fat (%BF), body mass index (BMI), and duration of training (DOT) among University of Ghana sportspersons. Methods: A cross-sectional study design was used. Information on background characteristics, body weight, height, %BF, and sporting activity profiles were gathered on a convenient sample of 101 University of Ghana sportsmen and women across all levels of study. BMI and %BF were determined using the Omron Fat Loss Monitor™. Creatinine concentration in 24-h urine samples was analyzed using Jaffe's reaction, and absorbance read spectrophotometrically at 530 nm. Results: Mean age was 22 ± 3 years. Thirty-one percent (31%) of the participants played football, whereas 69% took part in other sports (handball, volleyball, rugby, and baseball). Average daily DOT of 113.6 ± 44 min (males) and 95.1 ± 54 min (females) was reported by the participants. Mean BMI and %BF were 23.1 ± 3.7 Kg/m2 and 23.2 ± 9.0%, respectively. The mean BMI was within normal range as per the WHO standards, whereas %BF exceeded required limits for each sports discipline. UCR was higher for males (1.2 ± 0.5 g/d) as compared to females (0.8 ± 0.5 g/d). There was a weak positive correlation between UCR and BMI (r = 0.123; P = 0.22) and between UCR and DOT (r = 0.074; P = 0.46). %BF and UCR were inversely related (r = −0.114, P = 0.26). There was no association between UCR and type of sports (P = 0.24). There was a significant association between the type of sports and BMI (P = 0.002). Conclusion: There was a weak positive correlation between creatinine and BMI as well as training duration. Type of sports played is a significant predictor of sportspersons' BMI.
Keywords: Body mass index, percentage body fat, physical activity, sports performance, urine creatinine
|How to cite this article:|
Steiner-Asiedu M, Vickar J, Vuvor F, Agbemafle I, Tahiru YM. Urine creatinine and anthropometric indices of sportsmen and women. Biomed Biotechnol Res J 2017;1:134-40
|How to cite this URL:|
Steiner-Asiedu M, Vickar J, Vuvor F, Agbemafle I, Tahiru YM. Urine creatinine and anthropometric indices of sportsmen and women. Biomed Biotechnol Res J [serial online] 2017 [cited 2019 Aug 19];1:134-40. Available from: http://www.bmbtrj.org/text.asp?2017/1/2/134/219108
| Introduction|| |
Creatinine is a derivative of creatine found in muscle, blood, and urine. Urine creatinine is a waste product formed by the spontaneous essentially irreversible dehydration of body creatine and creatine phosphate from muscle metabolism. A total of 94%–98% of creatine is accumulated within skeletal muscle. The rate of creatinine formation is fairly constant with approximately about 2% of body creatine converted to creatinine every 24 h. The 24-h urinary creatinine excretion method is an important alternative in assessing total body skeletal muscle mass. Creatinine excretion may vary within the 24-h period, but total 24-h urine creatinine concentration (UCR) is reasonably constant from day to day. Variations can occur if a sedentary participant has an unusually active day. A dramatic increase in muscle use will result in an increase in creatine phosphate breakdown and an increase in creatinine excretion.
The extent of creatinine excretion depends on kidney function, age, sex, body mass, race, dietary proteins, dietary supplements, diuretics, antibiotics, salt, water, and physical activity levels., For example, cooked meat and heat-treated milk contain a considerable amount of creatinine, which are excreted in urine after ingestion. Furthermore, a change of 1 Kg intake of animal protein could account for a change of 9% in daily urinary creatinine output. The amount of creatinine the body produces each day depends on the person's muscle mass: a young, muscular man produces more creatinine than a petite older woman. Thus, failure to consider variations in creatinine production due to differences in muscle mass may lead to misinterpretation of urine creatinine levels. Even though elevated urine creatinine commonly represents renal pathology, a low urine creatinine in certain muscle-wasting conditions such as malnutrition, and amputation does not exclude an underlying renal dysfunction. In obesity, excess mass in fat does not contribute to increased creatinine generation.
The reference values (0.4–0.8 g/day) of urine creatinine commonly used for sportspersons are those defined for normal sedentary people. Usually, sportspersons are thought to be physically normal and healthy by definition, but the high training workload and psychophysical stress from competitions may modify their homeostasis inducing creatinine concentrations to change. Therefore, definitions of the behavior of creatinine and its reference ranges in sportspersons are important to prevent misinterpretation of laboratory data in sportspersons. Furthermore, sportspersons from different sports disciplines are characterized by different aerobic and anaerobic metabolism, competition season, training, and anthropometric values. Serum creatinine concentrations of sportspersons were higher than those measured in age-matched sedentary participants as total muscle mass is an important determinant of creatine pool size and of creatinine production. Nonetheless, a correlation between creatinine and body mass index (BMI) has been reported in middle-aged people in a general population.
Creatinine is a fairly stable variable in sportspersons, but its concentration may differ from those of sedentary people and sometimes among sports and also at different stages of the competitive seasons in the same sportspersons. Interpretation of creatinine concentration in sportsmen and women should take into account the specific sports of the athlete. In a study by Banfi and Del Fabbro, some aerobic sports, –for example, cycling, the BMI values were fairly homogenous, whereas in others, for example, rugby, the values were heterogenous. In the latter sports, there were sportspersons with quite different anthropometric characteristics, often linked to their role. In rugby, for example, the BMI of forwards was generally higher than that of backs; also in soccer, goalkeepers tend to have a higher BMI than other players. The correlation between creatinine concentrations and BMI is not necessarily only connected with muscle mass. Lean body mass is not crucial for defining creatinine concentration as represented in general populations.
Today, it is widely accepted by experts that top performance in sports is achieved if an athlete possesses a set of tactics, technical, nutritional, physiological, and physical factors. Therefore, sportspersons in any particular sport must possess such typical characteristics which are of advantage to their performance; for example, different sports require different BMI, body weight, and body fat. Thus, body composition significantly contributes to an individual's level of fitness for performance. According to Bangsbo, more than 90% of the energy spent during sports is supplied by aerobic metabolism. During metabolism, creatine is formed. Creatine is a molecule of major importance for energy production in muscles and also serves as a precursor for the production of creatinine. Hence, urine creatinine levels may point to certain muscular disorders such as muscle sprains, and this may have implications for sports performance. According to Buford et al., preparation of an athlete may be harmed by the competition calendar, appearance of sprains, total body mass reduction, and dehydration; hence, the need to monitor creatinine levels in relation to sports performance is paramount.
Improving sports performance is of great concern worldwide including Ghana. However, most of the activities are focused on increasing physical fitness of sportspersons other than body composition. Knowledge of body composition and urine creatinine of sportsmen and women is essential for management of strains, body mass reduction, and dehydration as well as designing training regimes for sportspersons to improve sports performance. It is against this background that this study was planned to determine the association between urine creatinine levels and body composition among University of Ghana sportsmen and women.
| Methods|| |
This study design was cross-sectional. This study was conducted in the University of Ghana, Legon campus. University of Ghana is a tertiary institution for both males and females. The institution offers Bachelors and Postgraduate degrees in the sciences, social sciences, and fine arts. The institution provides a heterogeneous environment for people from the different socioeconomic background, where students are able to unleash their potentials through sports and other social activities. The university has five traditional halls, namely, Akuafo hall, Commonwealth hall, Legon hall, Mensah Sarbah hall, and Volta hall which were used for the study.
A sample size of 101 sportspersons was obtained assuming a z-statistic of 1.96, 10% margin of error and a confidence interval of 95%. Sportspersons resident in the traditional halls in University of Ghana who were willing to be part of the study were recruited as study participants. Purposive sampling technique was used and this was based on hall-to-hall visits to fully describe participation to an eligible sportsperson. To be included in the study, the sportsperson must be a student and must have signed a consent form and be willing to donate a 24-h urine. This study was approved by the Noguchi Memorial Institution for Medical Research Review Board and permission also sought from the Director of the University of Ghana Sports Directorate.
A pretested, semi-structured questionnaire was used to collect information on background characteristics, type of sports, frequency, and duration of exercise. The self-administered questionnaire was given to consenting participants to answer questions on background characteristics including age, sex, level of education, and residential status. Information on physical activity level, the type of sports they engage in, the frequency and duration of exercise, the time of training and a self-rating of their level of performance in competitions was obtained through the self-administered questionnaire. Anthropometric data of weight and height were measured using a Seca scale and a stadiometer, respectively, and the values obtained were used to calculate BMI. Percentage body fat (%BF) was determined using the Omron Fat Loss Monitor™. The body fat tissue amount, height, sex, and age information were used to calculate the %BF based on the principle of bioelectric impedance analysis.
UCR was determined from a 24-h urine sample collected from each sportsperson. The samples were collected on 1 ml of dilute acetic acid to preserve the urine samples from dissociation. The samples were then frozen and were thawed before analysis. Creatinine determination was based on the Jaffe reaction as described by Frankel and Bennett. The principle is based on the fact that the creatinine and the picrate ion react in an alkaline medium to form sodium picrate (a red-orange solution). A blank and creatinine standard solutions were also prepared. A milliliter of each urine sample was pipetted into 100 ml volumetric flask as the test, and 1 ml of 1N sodium hydroxide and 2 ml of saturated picric acid added. Creatinine concentrations were read spectrophotometrically at a wavelength of 530 nm. The concentrations were then determined using the formula below: Urine creatinine (mg per 100 ml) = [(Test-Blank)/(standard– Blank)] ×100 ml of sample. The value obtained was then extrapolated to the total urine volume output of the individual in the 24 h and recorded as amount in g/d of creatinine concentration in the urine.
The data were analyzed using SPSS version 20 (SPSS Inc, South Wacker Drive, Chicago, IL, USA). Means and standard deviations were computed for continuous variables such as weight, height, BMI, %BF, and urinary creatinine levels. For categorical variables such as the self-rating of the level of performance, proportions, and frequencies were computed. Pearson's correlation coefficient was used to find the correlation between the urine creatinine levels, body composition, and the duration of physical training. The P < 0.05 was considered statistically significant.
| Results|| |
One hundred and one sportspersons completed the study. Male participants formed the largest percentage (57.4%), and the mean age was (21.8 ± 2.9) years. About 90% of the sportspersons were resident in the traditional halls while the remaining were nonresident students as shown in [Table 1]. Majority of the sportsmen were soccer players [Table 2], the duration of training (DOT) was 105.7 ± 49.6 min in a day and each participant trained at least once in a week. The sportsmen and women had reasons for training and the data show that most of them trained to improve their health and a small percentage (21.1%) trained for an impending competition. Time for training was in the evenings, and this was skewed toward female sportspersons (55.8%) which showed a statistically significant difference between the sexes (P = 0.011) for training times. Self-rating of performance by participants based on their output during competitions indicated that most of their performances were very good.
The mean body weight of participants was 66.3 ± 13.4 Kg. There were significant differences between male and female sportspersons' %BF (P < 0.001) and UCR (P = 0.004) but not BMI (P = 0.104). %BF of the females was significantly higher than that of the male sportspersons as shown in [Table 3]. There was a trend for the males' UCR which were higher than the females' and the difference between the two means was significant (males' UCR = 1.2 ± 0.8 g/d; females' UCR = 0.8 ± 0.54 g/d; P = 0.004). There was a positive but weak correlation between UCR and height of participants (P = 0.014); [Table 4]. There was an inverse but no statistically significant relationship between %BF and UCR of all participants. There was no significant difference between UCR and anthropometric indices as well as DOT for either sexes or all participants as shown in [Table 4]. UCR was highest among volleyball players [Table 5]. No significant difference was seen among the different sport disciplines and UCR. BMI was in the normal range for most of the sportspersons in the various disciplines and this was statistically significant (P = 0.003); [Table 5]. The most frequently reported DOT among the different sport disciplines was within 60–120 min. There was a significant difference between the sport of type and DOT (P = 0.003). High levels of %BF were seen among handball players, and this differed significantly from one sports to the other as shown in [Table 5].
|Table 3: Anthropometric indices and urine creatinine concentrations by sex|
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|Table 4: Association between 24-h urine creatinine and selected study variables|
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| Discussion|| |
Physical exercise changes an organism's homeostasis. There are reports on the effect of the kind of physical activity, duration, and the position of the performed task on the volume and composition of body fluids., In this study, there was no association between the average training duration of 105.7 ± 49.6 min/week and the mean UCR. It could be inferred that this average DOT was moderate and could not alter creatinine excretion levels. Usually, creatinine is not influenced by training or extreme training levels and competition,, although some increases (20%) have been reported in endurance and ultra-endurance performances. Creatinine is, therefore, a fairly stable variable in sportsmen and women, but its concentration may differ from those of sedentary people and among sports, sex, and at different stages of the competitive season in the same sportsperson. Most of the study participants trained in the evenings which according to Guder and Heidland, a 24-h profile of creatinine does not change in the same participants in the urine collected before and after exercise. There is only a slight variability in creatinine excretion depending on time of the day. Averagely, the males' intensity and DOT were higher than the females', yet there was no significant correlation with UCR. This presupposes that the high levels of urine creatinine in the male participants were not affected significantly by the DOT. In the general population, a positive correlation between the DOT and UCRs was noticed although it was not statistically significant. According to Banfi and Del Fabbro, creatinine is not influenced by training or extreme training and competition, although some increase has been reported in endurance and ultra-endurance performances.
Ranking the correlations between sexes, that of the males' was weaker, although higher BMI and UCRs was associated with the males throughout the study and this is in keeping with a study by Vikse et al., to determine gender differences in creatinine excretions in humans. BMI of males was strongly associated with 24-h urine creatinine excreted. The correlation between the creatinine concentrations and BMI is not necessarily connected to the muscle mass alone since BMI takes into account the total weight of an individual which includes body fat. Furthermore, lean mass is not crucial for defining creatinine concentrations as reported in broad populations. A study by Banfi and Del Fabbro proved a strong correlation between BMI and UCRs when participants were categorized by the type of sports discipline they are involved. Moreover, an evaluation during an ultramarathon cycling race showed an increase in creatinine when body weight decreased. In this study, participants' anthropometric indices differed significantly by the type of sports, but this difference was not significant between type of sport and UCR. It is possible that the small sample size obtained for some sports may have masked the effect of the different types of sports on UCR. However, a study conducted by Gerth et al. is in agreement with this present study by confirming that different sportsmen and women possess quite different anthropometric characteristics which are often linked to their role in sports and this may affect their BMI which may have implications for UCR.
Interpretation of creatinine concentration was also done by putting into consideration the specific sport of the study participants. Handball had most players (70.6%) recording high creatinine levels, followed by volleyball and soccer players. This could be associated with intra-individual and inter-individual variability of creatinine in sportspersons (0.33) which is lower than 0.60 considered for the general population as stated by Fraser. In athletics and handball, the BMI values were fairly homogenous, whereas in soccer, volleyball, and the others, BMI values were heterogeneous. The heterogeneity of BMI among participants who played soccer and the other sports including rugby are often linked to their role in the sports, for example, in soccer, goalkeepers tend to have higher BMI than the other players. This was also proven by a study with Bamfi and Del Fabbro, where rugby and soccer players had wide ranges of BMI as compared to cyclists and Alpine skiers. Higher %BF was distributed across the participants of the different sport disciplines. This proves the existence of energy imbalance between their intake and expenditure in terms of training as sportspersons.
The most interesting finding derived from the comparison between %BF and UCR revealed that as %BF increased, UCR decreased. This implies that muscle mass decrease with increasing %BF. The result emphasizes the role of adiposity but supported the importance of muscle mass. Furthermore, it suggests that above a certain threshold of body fatness, physical fitness might be greatly affected.
There was a positive correlation between BMI and urine creatinine but this association was weak and not statistically significant. Earlier in 2006, Banfi and Del Fabbro reported similar findings and concluded that their observation was because of the limited number of study subjects.
Majority of the sportspersons play soccer and the mean BMI was 23.1 ± 3.7 Kg/m2. Soccer as a sport is characterized by normal BMI values of about 23.0 Kg/m2 in adults. Furthermore, a study by Nikolaidis, comparing groups with different BMI reveals that those with lower or normal BMI performed better in competitions and physical test. This was in agreement with the present study as all participants fell within normal range of BMI, based on the WHO standards. The participation in sport per se does not guarantee that an individual cannot be overweight. This study revealed that some sportspersons who played rugby, short putt, handball, soccer, and volley with the exception of athletes recorded high overweight frequencies. Apparently, different sports require different body composition and shape. A very muscular hand ball player may be classified as overweight but with low body fat. This makes the application of BMI in sports questionable because it is associated with fat mass as well as fat-free mass. Measuring fat-free mass (muscle) will be an ideal option in evaluating BMI in sportspersons.
The percentage body fat (%BF) values in this study varied according to sex. As expected, the females recorded the highest %BF, a confirmation of an earlier study by Mougois, which indicated that females tend to have more adipose tissue than males. According to Turcotte, 6-13% of body fat in males is optimal for good performance in sports, whereas for females, the optimal range is 14%–20%. However, the mean values obtained for both sexes exceeded the range speculated by Turcotte. Moreover, a comparison within different sport disciplines revealed that 40%–75% of participants in all the sport disciplines had more than 20% of body fat. This could be as a result of the participants' level and DOT which was not intense enough so as to cause decrease in the body fat levels. These high levels of body fat among study participants could also be as a result of imbalance between energy intake and energy expenditure as well as genetic and environmental factors. This has implications on their level of performance although most of them rated their performance to be very good.
Furthermore, the equipment used in determining the body fat and BMI of the participants uses bioelectric impedance analysis method, and this makes it possible to estimate relative body fat percentage with an error of 3% to 4%, and to estimate fat-free mass within 2.5–3.5 Kg. Thus, if the actual body fat percentage of a participant was 15%, then predicted values could range from 12% to 18% (assuming a 3% error). If the actual fat-free mass is 50 Kg, then predicted values could range from 47.5 to 52.5 Kg, assuming an error of 2.5 Kg. However, the errors associated with the body fat estimate were much larger, making it inappropriate to set a specific body-fat percentage goal for an individual athlete in this study. This clearly shows that variations in human body fat of sportsmen and women are as a result of a complex multifactorial entanglement of lifestyle, the type of sports engaged in, and the environmental and genetic differences.
| Conclusion|| |
There was a weak positive correlation between creatinine and BMI as well as training duration across different sports disciplines. Type of sports played is a significant predictor of a sportspersons' BMI, %BF, and training duration. A structured 24-h urine collection, direct observation of training regimes, and dietary monitoring are essential to understanding the relationship between urine creatinine, anthropometric indices, and dietary intake to inform sportspersons for better performance.
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| References|| |
Baig A. Biochemical Composition of Normal Urine. Faculty of Medicine and Health Sciences, Universiti Sultan Zainal Abidin Nature Precedings; 2011. doi:10.1038/npre.2011.6595.1.
Seeley RR, Stephens TD, Tate P. Essentials of Anatomy & Physiology. St. Louis: Mosby-Year Book, Inc.; 1996.
Fox J. How does civil society thicken? The political construction of social capital in rural Mexico. World Dev 1996;24:1089-103.
Bazari H. Approach to the patient with renal disease. Cecil Medicine. 23rd
ed. Philadelphia, PA: Saunders Elsevier; 2007. p. 569-80.
Remer T, Manz F. Role of nutritional status in the regulation of adrenarche. J Clin Endocrinol Metab 1999;84:3936-44.
Heymsfield SB, Gallagher D, Poehlman ET, Wolper C, Nonas K, Nelson D, et al.
Menopausal changes in body composition and energy expenditure. Exp Gerontol 1994;29:377-89.
Stevens LA, Levey AS. Measurement of kidney function. Med Clin North Am 2005;89:457-73.
Banfi G, Del Fabbro M, Lippi G. Relation between serum creatinine and body mass index in elite athletes of different sport disciplines. Br J Sports Med 2006;40:675-8.
Lehmann M, Dickhuth HH, Gendrisch G, Lazar W, Thum M, Kaminski R, et al.
Training-overtraining. A prospective, experimental study with experienced middle- and long-distance runners. Int J Sports Med 1991;12:444-52.
Gerth J, Ott U, Fünfstück R, Bartsch R, Keil E, Schubert K, et al.
The effects of prolonged physical exercise on renal function, electrolyte balance and muscle cell breakdown. Clin Nephrol 2002;57:425-31.
Petibois C, Cazorla G, Cassaigne A, Déléris G. Application of FT-IR spectrometry to determine the global metabolic adaptations to physical conditioning in sportsmen. Appl Spectrosc 2002;56:1259-67.
Mougios V. Exercise Biochemistry. United States: Human Kinetics; 2006.
National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am J Kidney Dis 2002;39:S1-266.
Banfi G, Del Fabbro M. Serum creatinine values in elite athletes competing in 8 different sports: Comparison with sedentary people. Clin Chem 2006;52:330-1.
Saengsirisuwan V, Phadungkij S, Pholpramool C. Renal and liver functions and muscle injuries during training and after competition in thai boxers. Br J Sports Med 1998;32:304-8.
Marfell-Jones M, Olds T, Stewart A, Carter J. International Standards for Anthropometric Assessment. Potchefstroom: ISAK; 2006.
Bloomfield J, Polman R, Butterly R, O'Donoghue P. Analysis of age, stature, body mass, BMI and quality of elite soccer players from 4 European leagues. J Sports Med Phys Fitness 2005;45:58-67.
Helgerud J, Engen LC, Wisloff U, Hoff J. Aerobic endurance training improves soccer performance. Med Sci Sports Exerc 2001;33:1925-31.
Dunford M. Sports Nutrition: A Practice Manual for Professionals. Manhattan Island Marathon Swim NYC. USA: American Dietetic Association; 2006.
Bangsbo J, Gollnick PD, Graham TE, Saltin B. Substrates for muscle glycogen synthesis in recovery from intense exercise in man. J Physiol 1991;434:423-40.
Baxmann AC, Ahmed MS, Marques NC, Menon VB, Pereira AB, Kirsztajn GM, et al.
Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol 2008;3:348-54.
Kreider RB. Journal of the international society of sports nutrition: A new era begins. J Int Soc Sports Nutr 2007;4:1.
Moore D, McCabe G. Inference for distributions. Introduction to the Practice of Statistics. London: Blackheath High School, Vanbrugh Park; 1993. p. 498-574.
Frankel OH, Bennett E. Genetic Resources in Plants-Their Exploration and Conservation. Oxford and Edinburgh: Blackwell Scientific Publications; 1970.
Neumayr G, Pfister R, Hoertnagl H, Mitterbauer G, Getzner W, Ulmer H, et al.
The effect of marathon cycling on renal function. Int J Sports Med 2003;24:131-7.
Nagashima K, Mack GW, Haskell A, Nishiyasu T, Nadel ER. Mechanism for the posture-specific plasma volume increase after a single intense exercise protocol. J Appl Physiol (1985) 1999;86:867-73.
Raastad T, Glomsheller T, Bjøro T, Hallén J. Changes in human skeletal muscle contractility and hormone status during 2 weeks of heavy strength training. Eur J Appl Physiol 2001;84:54-63.
Fallon KE, Sivyer G, Sivyer K, Dare A. The biochemistry of runners in a 1600 km ultramarathon. Br J Sports Med 1999;33:264-9.
Guder WG, Heidland A. Urine analysis. Report on the workshop conference of the German Society for Clinical Chemistry and the Society of Nephrology in Würzburg, October 25-26, 1985. J Clin Chem Clin Biochem 1986;24:611-20.
Vikse BE, Vollset SE, Tell GS, Refsum H, Iversen BM. Distribution and determinants of serum creatinine in the general population: The Hordaland Health Study. Scand J Clin Lab Invest 2004;64:709-22.
Swaminathan R, Major P, Snieder H, Spector T. Serum creatinine and fat-free mass (lean body mass). Clin Chem 2000;46:1695-6.
Fraser CG. Biological variation: From principles to practice. Amer Assoc for Clinical Chemistry; 2001.
Nikolaidis P. Familial aggregation and maximal heritability of exercise participation: A cross-sectional study in schoolchildren and their nuclear families. Sci Sports 2011;26:157-65.
World Health Organization. Physical Status: The Use of and Interpretation of Anthropometry, Report of a WHO Expert Committee; 1995.
Turcotte LP. Role of fats in exercise. Types and quality. Clin Sports Med 1999;18:485-98.
Houtkooper LB, Going SB, Sproul J, Blew RM, Lohman TG. Comparison of methods for assessing body-composition changes over 1 y in postmenopausal women. Am J Clin Nutr 2000;72:401-6.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]