The European Journal of Public Health Advance Access originally published online on December 6, 2008
The European Journal of Public Health 2009 19(1):46-51; doi:10.1093/eurpub/ckn122
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Social inequalities |
BMI, lipid profile, physical fitness and smoking habits of young male adults and the association with parental education
Tonje Holte Stea1, Margareta Wandel2, Mohammad Azam Mansoor3, Solveig Uglem2 and Wenche Frølich1
1 Norwegian School of Hotel Management, University of Stavanger, N-4036 Stavanger, Norway
2 Department of Nutrition, Institute for Basic Medical Sciences, University of Oslo, N-0316 Oslo, Norway
3 Department of Natural Sciences, Agder University, N-4604 Kristiansand, Norway
Correspondence: Tonje Holte Stea, Gamle Aarosvei 52, 4640 Soegne, Norway, tel: +47 41 10 26 41, fax: +47 38 14 13 01, e-mail: tonje.h.stea{at}uia.no
Received February 21, 2008, accepted October 31, 2008
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Abstract |
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Background: Few studies have focused on the potential relationship between parental educational level and cardiovascular risk factors among young male adults. The aim of this study was to investigate cardiovascular disease risk factors among young men and whether body mass index (BMI), serum lipids, physical fitness and smoking habits were related to paternal and maternal education.
Methods: In this cross-sectional study 750 18- to 26-year-old male recruits participated.
Results: Linear regression analyses showed that the paternal education was inversely associated with BMI (P = 0.035) and the concentration of total cholesterol (P = 0.003) and low-density lipoprotein (LDL) (P = 0.014). Running performance was inversely related to cigarette smoking (P = 0.022) and the concentration of triacylglycerol (P = 0.001). BMI was positively related to the concentration of LDL (P = 0.002), total cholesterol/high-density lipoprotein (HDL) ratio (P < 0.001) and inversely related to the concentration of HDL (P < 0.001), running performance (P < 0.001) and muscular strength (P = 0.011). Recruits with low BMI, both high and low fitness, had a significantly better lipid profile than recruits with high BMI and low fitness (P 
0.016). A lower concentration of triacylglycerol (P 0.001) and a higher concentration of HDL (P = 0.034) were further shown among recruits with high BMI/high fit compared to recruits with high BMI/low fit.
Conclusions: High paternal educational level was associated with a lower BMI and a better lipid profile among young adult men. Furthermore, men with low BMI, both high and low fit, had a better lipid profile than those with high BMI/low fit. Men with high BMI/high fit had a better lipid profile that those with high BMI/low fit.
Keywords: body mass index (BMI), parental education, physical fitness, serum lipids, young men
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Introduction |
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In most European countries, studies among children and young adults during the last years indicate a general trend of a less healthy lifestyle and increased prevalence of overweight and obesity.1 The increased prevalence of adiposity may be accompanied by a clustering of cardiovascular risk factors, even at young age.2 Clinical data indicate that elevated concentrations of total cholesterol, triacylglycerol and low-density lipoprotein (LDL) are risk factors for cardiovascular events.3,4 High-density lipoprotein (HDL) is reported to have an anti-atherogenic effect, and the concentration of total cholesterol and total cholesterol/HDL ratio have been demonstrated to be efficient markers for predicting coronary disease.3,5,6
The relationships between socio-economic factors and health indicators have previously been well established.7 Several studies have demonstrated that the groups with low educational level may have an increased risk of cardiovascular disease, partly due to an unhealthy lifestyle compared with the groups with high educational level.7,8 Even in early life, low parental education may contribute to an individual's overall risk of developing cardiovascular disease in midlife.9
The relationship between educational level and overweight/obesity may differ in various settings, according to economics and cultural traits.10,11 In a study among 18- to 19-year old American and Norwegian males, a significant social gradient was shown for parental education in the American sample, whereas overweight did not differ by parental educational level in the Norwegian sample.12 The authors suggested that the results may reflect the major differences both in adolescent health indicators and socioeconomic patterning which was demonstrated between the Norwegian and the American sample.12 Contrary to the results from the Norwegian sample in the latter mentioned study, Swedish studies showed that low parental educational level was associated with overweight among 18-year-old male recruits in the military service.13,14 In the studies mentioned above, mothers’ education had been selected as an indicator of socioeconomic status. Since fathers may act as role models for young adult men, examining the relationship between both paternal and maternal education and overweight/obesity, as well as other risk factors for development of cardiovascular disease among young men, is of interest.
Leino et al.15 showed that parental education was inversely associated with total cholesterol and LDL in serum, especially in female subjects. The finding that socio-economic status, measured as occupation of mothers, was positively related to physical fitness in children and adolescents may have implications for a trend towards overweight for both boys and girls.16
Parental education has also been demonstrated to be inversely associated with smoking habits among adolescents.17 However, few studies have investigated the relationship between parental education and smoking habits among young male adults and whether both paternal and maternal educational level is related to smoking habits of young men.
The aim of this study was to investigate biological and behavioural risk factors of cardiovascular disease in young Norwegian men and whether BMI, serum lipids, physical fitness and smoking habits were related to paternal and maternal educational level.
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Participants
Male recruits (18- to 26-years-old) are enrolled in the Norwegian National Guard twice a year. Eligible participants included two consecutive enrolments at Vaernes military centre (Camp A)—January 2004 (n = 231) and July 2004 (n = 432)—where 541 (82%) recruits agreed to participate in the study. In August 2005, 237 newly enrolled male recruits in the Norwegian Army, Heggelia (Camp B) were asked to participate, and 209 (88%) agreed. The overall participation rate was 83%.
Study design
Data collection was carried out by trained research staff in the military camps during the first week after enrolment in the military service. Blood samples were taken, and information on age, smoking habits and parental education were collected using a self-administered questionnaire. Body weight, height and physical fitness levels were also measured. The Ministry of Health and Care Services, Regional Committee for Medical Research Ethics and the Data Inspectorate approved the study protocol. Written consents were obtained from the recruits prior to participation in this study.
Measurements
Current smoking status was measured as a dichotomous variable (yes/no). Continuous variables included current age of recruits, number of cigarettes smoked per day and number of years of smoking. Parental education was reported as a four-level categorical variable: (i)elementary school, (ii)high school, (iii)college/university (1–3 years) and (iv)college/university (>3 years). As Norwegian men usually complete their military service just after high school, the educational level of the recruits was not considered useful for the purposes of this study. Body weight in kilograms was measured after an overnight fast and height was measured to the nearest centimetre. Two physical examinations, a 3000 m run and a muscular strength test, were conducted. According to military requirements, 15 min was the maximum time allowed to obtain an approved result from the 3000 m run. Muscular strength was measured as number of push-ups, sit-ups and lift-ups. Results of the muscular strength tests were categorized on a 6-point scale. Grade 6 was the highest possible score, whereas grade 2 was the lowest score required for approved performance according to the military guidelines. To obtain grade 2, the recruits had to accomplish 16 push-ups, 20 sit-ups and 4 lift-ups.
Blood samples were collected after an overnight fast (
12 h) from resting individuals. The participants were also restricted from drinking any liquids and using tobacco for 12 h before blood sampling. The blood samples were centrifuged within 4 h at 2000g for 10 min at 4°C and serum was transferred to 1 ml blood collection tubes. The serum samples were immediately frozen at – 20°C and sent to Stavanger University Hospital in Norway and then stored at –70°C until they were analysed. Total serum cholesterol, triacylglycerol and HDL were measured with enzymatic procedures (Roche, Mannheim, Germany) at Stavanger University Hospital. Concentration of LDL was calculated by using the Friedewald formula (total cholesterol – HDL – 0.46 x triacylglycerol).18 Coefficients of variation for the measured serum lipids were: <0.3% for total cholesterol; <0.7% for triacylglycerol; and <2% for HDL.
Statistical analyses
All statistical analyses were conducted with SPSS, version 15.0 (SPSS Inc., Chicago, IL, USA). Data are presented as means and corresponding 95% confidence intervals (CIs). Linear regression models were used to identify and adjust for other factors (age, physical health variables and parental education) that were associated with variation in BMI, concentration of serum lipids and physical fitness among the recruits. The Norwegian military guidelines have described the requirements to pass the physical tests, however all results were included in the analyses. Due to unequal group sizes, parental education was merged into a 2-level categorical variable for regression analyses; (i) elementary and high school (low), (ii) college/university (high). Standard regression coefficients (β) and P-values were given for all of the independent variables. Median cut-points for BMI and the results from both tests of fitness (mean grade) were used to determine the classification of the recruits in to four cross-tabulated groups; low BMI/high fit; low BMI/low fit; high BMI/high fit and high BMI/low fit. Differences in the concentration of serum lipids across the groups were assessed by ANOVA. Post hoc analyses were analysed by the Bonferonni multiple comparison tests.
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Table 1 shows characteristics of the study sample. A total of 34.8% of the recruits were overweight or obese according to the definition by World Health Organization.1 Using the military's evaluation criteria for physical fitness, 60.7% of the recruits obtained approved results on the 3000 m run (
15 min), and 52.2% on the test of muscular strength (grade 2). There were no significant differences between the two military camps with regard to concentration of serum lipids, physical fitness levels of the recruits or parental educational levels. In Camp A, however, mean age was higher (P < 0.001), BMI was lower (P = 0.014), there was a higher prevalence of smokers (P = 0.004) and a higher consumption of cigarettes among the smokers (P = 0.002) compared with Camp B. In addition, the smokers in Camp A reported having been a smoker for a longer period of time than did smokers in Camp B (P = 0.007).
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A multivariate linear regression analysis with BMI as the dependent variable showed an inverse association with high educational level compared with low educational level of the fathers (table 2; P = 0.035). Furthermore, BMI was inversely related to running performance (P
0.001) and muscular strength (P = 0.011). Running performance was significantly and positively related to muscular strength (P 0.001) and inversely related to smoking (P = 0.022) and BMI (P 0.001). Muscular strength showed a significant positive relationship with running performance (P 0.001) and a significant inverse relationship with BMI (P = 0.011). Smoking cigarettes and the number of cigarettes smoked per day was not significantly associated with paternal or maternal education after adjusting for age, physical fitness and BMI (table not shown, linear regression, R2 = 72.9%). Furthermore, the results from multivariate linear regressions showed an inverse relationship between, paternal education and the concentration of both total cholesterol (P = 0.003) and LDL (0.014) (table 3). The regression analyses also showed that the concentration of triacylglycerol was inversely related with running performance (P = 0.001). In addition, the concentration of HDL was inversely associated with BMI (P 0.001) whereas LDL was positively associated with BMI (P = 0.002). Furthermore, total cholesterol/HDL ratio was positively associated with BMI (not shown: P < 0.001).
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The study also showed a significantly lower concentration of total cholesterol, triacylglycerol and LDL and a higher concentration of HDL among the recruits with low BMI/high fitness compared to recruits with high BMI/low fitness (table 4, P
0.001 for all). The recruits with low BMI/low fitness had also a significantly lower concentration of total cholesterol (P = 0.016), triacylglycerol (P 0.001) and LDL (P = 0.001) and a significantly higher concentration of HDL (P 0.001) than the recruits with high BMI/low fitness. A lower concentration of triacylglycerol (P 0.001) and a higher concentration of HDL (P = 0.034) was further shown among recruits with high BMI/high fitness compared to recruits with high BMI/low fitness.
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Discussion |
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The Norwegian military is probably one of the best-suited institutions to reach a broad selection of young Norwegian men. Military service is compulsory in Norway and men from different social classes and all geographical areas of the country were represented in this study. The participation rates in both military camps were high. Results are therefore likely to reflect health status of young Norwegian men in general. Data were collected during the first days after enrolment, thus characteristics will reflect physical fitness levels, BMI and health status of the recruits in the period prior to military service. It is also relevant to discuss the accuracy and validity of the reported levels of parental education in the present study. Lien et al.19 demonstrated that the accuracy level of adolescents’ (13–15 years old) proxy reports on their parents’ educational level was fair. A large meta-analysis demonstrated a strong relationship between accuracy of reports of parents’ educational level and the age of child informants.20 Little information is available on the accuracy of young adults’ reports of parental socioeconomic status. Since young adults are older, more accurate reports on parents educational status from questionnaires would be expected compared with reports from children and adolescents.
In contrast to our study, previous studies have mainly reported parental education as a collective predictor of health status, rather than reporting separate effects of maternal and paternal education. In many cultures, most children from split families live together with their mothers. Therefore, some studies have selected the mothers’ education as an indicator of SES. In Norway, joint custody is usually preferred by the parents after the break-up. This arrangement assures that both parents have regular contact with their children, and thus both fathers and mothers will act as role models for their children.
The present study investigated the relationship between the parental education and cardiovascular disease risk factors in young male adults. An inverse relationship was shown between the paternal education and both BMI and the concentration of total cholesterol and LDL. In accordance with the results from regression analyses in our study, Paavola et al.21 observed no association between parental socioeconomic status, including parental educational level, or social mobility and smoking habits among adolescents and adults between 13 and 28 years old. Other studies, however, have demonstrated an inverse association between young adult smoking and parental education after adjusting for age.17,22
The present study showed no relationship between parental education and physical fitness. On the other hand, Bellisle et al.23 demonstrated that families of higher socioeconomic strata (income and education of parents) were more devoted to childrens’ sports activities. Information on the relationship between parental education and physical fitness, however, is scarce.
Results from our study suggest that BMI and concentrations of total cholesterol and LDL of young male adults are significantly inversely related to the educational level of the fathers but not the education level of the mothers. Goodman et al.24 demonstrated that low parental education, measured collectively, was associated with high BMI, high LDL and low HDL concentration among high school students. This latter study, however, did not investigate gender differences among the students. A higher prevalence of overweight was also observed among 18-year-old males from low-educated families compared with those from high-educated families.25 The study by Kark and Rasmussen14 also demonstrated a significant inverse relationship between BMI of 18-year-old men and maternal educational levels, whereas paternal educational levels were not examined.
The results of the present study showed an inverse association between both BMI and the results on the physical tests and between running performance and smoking of cigarettes. BMI level was also inversely associated with the concentration of HDL and positively associated with LDL concentration. In addition, running performance was inversely associated with the concentration of triacylglycerol. Despite normal BMI and lipid profiles of most recruits, the participants of the present study showed fairly low scores on both physical tests. While the mean level of BMI was within the normal range according to WHO (18.5–25 kg/m2),1 the distribution was positively skewed (long upper tail). Compared with young American males in the same age group,26 a lower mean BMI (–1.6 kg/m2) and a lower prevalence of obesity (–7.3%) was shown in the present study. The purpose of calculating BMI is to categorize fatness. It should be noted, however that BMI could potentially misclassify people as being overweight due to high levels of muscle mass.27 The low scores on both physical tests among the recruits in this study, however, indicate that a high BMI was most likely related to a high level of body fat rather than high levels of muscle mass.
The present study also showed a more favourable lipid profile among the Norwegian recruits than that of 674 young American men (20–29 years) in the National Health and Nutrition Examination Surveys (NHANES, 1999–2002).28 Compared with the NHANES study, the present study demonstrated a lower mean concentration of total cholesterol (–0.77 mmol/l), LDL (–0.67 mmol/l) and triacylglycerol (–0.38 mmol/l) and a higher concentration of HDL (0.17 mmol/l). According to Iribarren et al.29 young adults with low concentrations of total cholesterol, which was also shown in the present study, have a lipoprotein profile characterized by low atherogenic potential.
Similar to our study, previous studies have shown that smoking status might be associated with physical fitness levels.30,31 Smoking has also been correlated with high concentration of triacylglycerol and low concentration of HDL.32 This latter mentioned meta-analysis also reported an inverse correlation between exercise and the concentration of both total cholesterol and triacylglycerol and positive correlation with the concentration of HDL.32 Another noticeable contribution from our study was the significant relationship between the running performance and the concentration of triacylglycerol, which was still significant after adjusting for parental education, age, smoking habits and BMI.
The study also showed that recruits with low BMI, both high and low fitness, had a significantly better lipid profile than recruits with high BMI and low fitness. Furthermore, our findings suggest that men with high BMI and high fitness had a significantly better lipid profile that those with high BMI and low fitness. Similar to the results from the present study, Eisenmann et al.33 demonstrated a significant different concentration in triacylglycerol and LDL between low BMI/high fit and high BMI/low fit adolescents. This latter study, however showed no other significant differences in serum lipid concentration across the four cross-tabulated BMI and fitness groups.
In summary, a relatively high prevalence of overweight/obesity and low physical fitness was shown among the recruits whereas the concentrations of serum lipid were within the normal range among most of the young men participating in the present study. Furthermore, high fitness was related to a better lipid profile among recruits with high BMI. The present study also showed that low paternal educational was also associated with high level of cardiovascular risk factors. Our findings indicate that young adult males, especially those with high BMI and low parental education, should be considered as important target groups of health promotion efforts.
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Norwegian Research Council.
Conflicts of interest: None declared.
Key points
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Acknowledgements |
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The authors thank the personnel at The Norwegian Home Guard Training Centre Vaernes for support and positive attitude regarding data collection. We also want to thank Ole Kristensen and Stavanger University Hospital for positive attitude regarding analyses of serum lipids.
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References |
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1 World Health Organization. Obesity: preventing and managing the global epidemic. In: Report of a WHO Consultation on Obesity. Geneva 3–5 June 1997, Geneva: WHO, 1998.
2 Freedman DS, Dietz WH, Srinivasan SR, Berenson GS. The relation of overweight to cardiovascular risk factors among children and adolescents: the Bogalusa Heart Study. Pediatrics (1999) 103:1175–82.
3 Sharrett AR, Ballantyne CM, Coady SA, et al. Atherosclerosis Risk in Communities Study Group. Coronary heart disease prediction from lipoprotein cholesterol levels, triglycerides, lipoprotein(a), apolipoproteins A-I and B, and HDL density subfractions: The Atherosclerosis Risk in Communities (ARIC) Study. Circulation (2001) 104:1108–13.
4 Greenland P, Knoll MD, Stamler J, et al. Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. JAMA (2003) 290:891–7.
5 Conroy RM, Pyorala K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J (2003) 24:987–1003.
6 Kannel WB. The Framingham Study: ITS 50-year legacy and future promise. J Atheroscler Thromb (2000) 6:60–6.[Medline]
7 Kivimäki M, Lawlor DA, Smith GD, et al. Socioeconomic position, co-occurrence of behavior-related risk factors, and coronary heart disease: the Finnish Public Sector Study. Am J Public Health (2007) 97:874–9.
8 Strand BH, Tverdal A. Can cardiovascular risk factors and lifestyle explain the educational inequalities in mortality from ischaemic heart disease and from other heart diseases? 26 year follow up of 50 000 Norwegian men and women. Eur J Epidemiol (2004) 58:705–9.
9 Karlamangla AS, Singer BH, Williams DR, et al. Impact of socioeconomic status on longitudinal accumulation of cardiovascular risk in young adults: the CARDIA Study (USA). Soc Sci Med (2005) 60:999–1015.[CrossRef][Web of Science][Medline]
10 Delva J, O’Malley PM, Johnston LD. Racial/ethnic and socioeconomic status differences in overweight and health-related behaviors among American students: national trends 1986–2003. J Adolesc Health (2006) 39:536–45.[CrossRef][Web of Science][Medline]
11 Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychol Bull (1989) 105:260–75.[CrossRef][Web of Science][Medline]
12 Friestad C, Pirkis J, Biehl M, Irwin CE. Socioeconomic patterning of smoking, sedentary lifestyle, and overweight status among adolescents in Norway and the United States. J Adolesc Health (2003) 33:275–8.[CrossRef][Web of Science][Medline]
13 Rasmussen F, Johansson M, Hansen HO. Trends in overweight and obesity among 18-year-old males in Sweden between 1971 and 1995. Acta Paediatr (1999) 88:431–7.[CrossRef][Web of Science][Medline]
14 Kark M, Rasmussen F. Growing social inequalities in the occurrence of overweight and obesity among young men in Sweden. Scand J Public Health (2005) 33:472–7.
15 Leino M, Raitakari OT, Porkka KV, et al. Associations of education with cardiovascular risk factors in young adults: the Cardiovascular Risk in Young Finns Study. Int J Epidemiol (1999) 28:667–75.
16 Kristensen PL, Wedderkopp N, Møller NC, et al. Tracking and prevalence of cardiovascular disease risk factors across socio-economic classes: a longitudinal substudy of the European Youth Heart Study. BMC Public Health (2006) 6:20–8.[CrossRef][Medline]
17 Lowry R, Kann L, Collins JL, Kolbe LJ. The effect of socioeconomic status on chronic disease risk behaviours among US adolescents. JAMA (1996) 276:792–7.
18 Friedewald W, Levy R, Fredrickson D. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem (1972) 18:499–502.[Abstract]
19 Lien N, Friestad C, Klepp KI. Adolescents’ proxy reports of parents' socioeconomic status: How valid are they? J Epidemiol Community Health (2001) 55:731–7.
20 Looker ED. Accuracy of proxy reports of parental status characteristics. Sociol Educ (1989) 62:257–76.[CrossRef]
21 Paavola M, Vartiainen E, Haukkala A. Smoking from adolescence to adulthood: The effects of parental and own socioeconomic status. Eur J Public Health (2004) 14:417–21.
22 Kestilä L, Koskinen S, Martelin T, et al. Influence of parental education, childhood adversities, and current living conditions on daily smoking in early adulthood. Eur J Public Health (2006) 16:617–26.
23 Bellisle F, Rolland-Cachera MF, Committee KSA. Three consecutive (1993, 1995, 1997) surveys of food intake, nutritional attitudes and knowledge, and lifestyle in 1000 French children, aged 9–11 years. J Hum Nutr Diet (2007) 20:241–51.[Medline]
24 Goodman E, McEwen BS, Huang B, et al. Social inequalities in biomarkers of cardiovascular risk in adolescence. Psychosom Med (2005) 67:9–15.
25 Rasmussen F, Johansson M, Hansen HO. Trends in overweight and obesity among 18-year-old males in Sweden between 1971 and 1995. Acta Paediatr (1999) 88:431–7.[CrossRef][Web of Science][Medline]
26 Flegal K, Troiano R. Changes in the distribution of body mass index of adults and children in the US population. Int J Obes Relat Metab Disord (2000) 24:807–18.[CrossRef][Web of Science][Medline]
27 Kyle UG, Schutz Y, Dupertuis YM, Pichard C. Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index. Nutrition (2003) 19:597–604.[CrossRef][Web of Science][Medline]
28 Carroll MD, Lacher DA, Sorlie PD, et al. Trends in serum lipids and lipoproteins of adults, 1960-2002. JAMA (2005) 294:1773–81.
29 Iribarren C, Belcher JD, Jacobs DR, et al. Relationship of lipoproteins, apolipoproteins, triglycerides and lipid ratios to plasma total cholesterol in young adults: the CARDIA Study. Coronary Artery Risk Development in Young Adults. J Cardiovasc Risk (1996) 3:391–6.[Medline]
30 Conway TL, Cronan TA. Smoking, exercise, and physical fitness. Prev Med (1992) 21:723–34.[CrossRef][Web of Science][Medline]
31 Barnekow-Bergkvist M, Hedberg G, Janlert U, Jansson E. Adolescent determinants of cardiovascular risk factors in adult men and women. Scand J Public Health (2001) 29:208–17.
32 Hata Y, Nakajima K. Life-style and serum lipids and lipoproteins. J Atheroscler Thromb (2000) 7:177–97.[Medline]
33 Eisenmann JC, Welk GJ, Wickel EE, Blair SN. Combined influence of cardiorespiratory fitness and body mass index on cardiovascular disease risk factors among 8-18 year old youth: The Aerobics Center Longitudinal Study. Int J Pediatr Obes (2007) 2:66–72.[Medline]
34 Rustad P, Felding P, Franzson L, et al. The Nordic Reference Interval Project 2000: recommended reference intervals for 25 common biochemical properties. Scand J Clin Lab Invest (2004) 64:271–284.[CrossRef][Web of Science][Medline]
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