Received: 19 December 2015
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Revised: 9 June 2016
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Accepted: 28 July 2016
DOI 10.1002/ajhb.22903
American Journal of Human Biology
ORIGINAL RESEARCH ARTICLE
Associations between body composition, nutrition, and physical activity in young adults María Correa-Rodríguez | Blanca Rueda-Medina | Emilio Gonz alez-Jimenez | Jacqueline Schmidt-RioValle Faculty of Health Sciences, University of Granada, Av. Ilustracion S/N, Granada 18007, Spain Correspondence Correa-Rodríguez María, Faculty of Health Sciences. Av. Ilustracion, S/ N, 18007 Granada, Spain. Email:
[email protected].
Abstract Objective: The aim of the present study is to investigate the associations between total energy, macronutrient intakes, and physical activity (PA) and body composition by assessing body fat mass, fat-free mass, and BMI in a population of young adults. Methods: The study population consisted of 605 young Spanish adults (median age 20.38 ± 2.67). Body composition, including fat mass and fat-free mass, was calculated with body composition analyzer. Daily energy and macronutrient intakes were measured using a 72-h recall method. The International PA Questionnaire was used to assess PA and sedentary time. Linear regression analyses were performed to test the possible associations between nutrition, PA factors, and body composition. Results: Linear regression analyses revealed that BMI has a significant positive association with protein intake (P = .004, B = 0.088, 95% CI 0.028–0.149) and an inverse association with carbohydrate intake (P = 0.034, B = 20.027, 95% CI 20.053 – 20.002). Protein intake also demonstrated a significant association with fat-free mass, but the size of the effect was smaller (P = .027, B = 96.965, 95% CI 11.250– 182.679). There was evidence of a positive association between total PA and moderate-to-vigorous PA (P < .001, B = 15.630, 95% CI 6.989 224.270) and fat-free mass (P < .001, B = 20.208, 95% CI 9.694 230.723). When fat mass was used as the outcome variable, there was no evidence of any association with the PA, total energy, and macronutrient intakes variables analyzed. Conclusions: Our findings suggest that PA variables were consistently associated with body composition, specifically fat-free mass. Dietary factors also have influence over body composition; we showed that protein intake is significantly associated with fat-free mass and BMI. KEYWORDS
nutrition, physical activity, body composition, fat mass, young adults
1 | INTRODUCTION Excessive body weight is a growing health problem worldwide. It is a well-known risk factor in cardiovascular disease, diabetes, hypertension, and cancers, among other conditions (Kopelman, 2007). In fact, the World Health Organization considers obesity as one of the leading threats to future public health (Guilbert, 2003). American Journal of Human Biology 2016; 00-00
The etiological mechanisms behind excess body weight have not yet been fully elucidated. Although it is important to consider the genetic contribution, both physical activity (PA) and energy intake have been considered the main contributors influencing body weight (Miller et al., 1990; Pereira-Lancha et al., 2012). It is generally accepted that being physically active and following a balanced diet are the major factors involved in controlling body weight. However,
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the associations between daily intake, PA, and body composition remain poorly understood (Summerbell et al., 2009). Some evidence points to a connection between total energy and macronutrient intakes and weight gain, but previous studies have reported different results (Bowen et al., 2015; Drenowatz et al., 2015; Zhou et al., 2015). Both positive and negative associations have been observed between average carbohydrate and protein intakes and body composition (Bowen et al., 2015; Koppes et al., 2009; Soenen and Westerterp-Plantenga, 2010; Vinknes et al., 2011). These seemingly contradictory results may result from differences in study design, such as use of different potential confounders, and/or differences in methodology, such as measurement of body composition variables with different instruments. The association between PA and body weight has been demonstrated in observational and interventional studies, but evidence concerning the effects of PA on body composition is still inconclusive (Summerbell et al., 2009). Longitudinal studies have reported a decrease in body weight in subjects who increase their PA levels (Hamer et al., 2013; Littman et al., 2005). However, other studies have indicated that moderate-to-vigorous PA (MVPA) may not contribute to weight loss because it may also be associated with changes in body composition such as an increase in fat-free mass (Deere et al., 2012). However, the majority of the literature has evaluated body composition by assessing only body mass index (BMI). The role of body composition measurements other than BMI during early adulthood has not been thoroughly investigated. Only a few studies have been conducted, and these produced conflicting results (Bowen et al., 2015; Drenowatz et al., 2015; Zhou et al., 2015). Therefore, continuous measurement of body composition (fat mass and fat-free mass) as well as BMI would provide data to further our understanding of the associations between nutrition, PA, and body composition. In addition, most studies have included individuals from a wide age range, while only one featured a study population consisting of young adults (Drenowatz et al., 2015). In this context, the aim of this study was to investigate the associations between total energy, macronutrient intakes and PA, and body composition by assessing body fat mass, fat-free mass, and BMI in a population of young adults. 2 | METHODS 2.1 | Subjects Six hundred and five healthy individuals aged 18–25 (69.3% females and 30.7% males) were recruited from seven centers of public education located in the main districts of Granada (Spain). All the subjects who participated in this study were of European descent and of a medium socio-economic level.
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A member of the research team visited the subjects at their academic centers and explained the objectives and characteristics of the study. Subject inclusion criteria included good health (not undergoing medical treatment related to managing body weight) and aged between 18 and 25 years. Subjects with major acute or chronic conditions who had made significant lifestyle changes in the previous months that could affect body weight were excluded. Written informed consent was obtained from all participants and the study was approved by the local ethics committee at the University of Granada and conducted in accordance with the Declaration of Helsinki. 2.2 | Anthropometric measurements Body weight (kg), fat mass (g), and fat-free mass (g) were measured twice, without shoes and in light clothes, to the nearest 0.11 kg using a body composition analyzer (TANITA BC-418MA®). A Harpenden stadiometer (Holtain 602VR®) was used for height measurements. Each participant was asked to stand erect with his or her back, buttocks, and heels in continuous contact with the vertical height rod of the stadiometer and head orientated in the Frankfurt plane. The horizontal headpiece was then placed on top of the head of the participant to measure their height. Height was measured twice without shoes to the nearest 0.5 cm. The averages of the two values for each measurement were used in the analysis. Anthropometric measurements were performed in the morning after a 12-h fast and 24-h abstention from exercise. BMI was calculated as weight over height squared (kg/ m2). The same trained research assistant performed all the measurements. 2.3 | Total energy and macronutrient intakes Daily nutrient intake was assessed using a 72-h diet recall interview considering intakes on Thursday, Friday, and Saturday, to capture weekly variations in weekdays and weekend. In an in-person interview with well-trained investigators, individuals were asked to recall all food consumed in the preceding 72 h, including foods eaten outside the home, nutrition supplements, and beverages. To improve the accuracy of the food descriptions, standard household measures and pictorial food models were employed during the interviews to define amounts as needed. Completed food records were analyzed using a computerized nutrient analysis program (Nutriber 1.1.5). Daily energy intakes and the percentages of carbohydrates, proteins, and fat were calculated. 2.4 | Physical activity A self-administered questionnaire was used to assess PA (the International Physical Activity Questionnaire-IPAQ). The questionnaire has proven to be a valid and reliable
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Descriptive characteristics of the study population
TABLE 1
Females
Males
Total
N (%)
419 (69.3)
186 (30.7)
605
Age
20.35 ± 2.68
20.45 ± 2.64
20.38 ± 2.67
Height (m)
1.63 ± 0.06
1.75 ± 0.06**
1.67 ± 0.08
59.44 ± 10.06
73.06 ± 13.04**
63.63 ± 12.72
BMI (kg/m )
22.15 ± 3.57
23.64 ± 3.74**
22.61 ± 3.68
Fat mass (g)
15405.96 ± 7994.45
11697.84 ± 7214.81**
14265.95 ± 7943.93
Fat-free mass (g)
44275.17 ± 3481.33
61185.48 ± 7576.59**
49474.04 ± 9325.07
Daily energy intake (kcal/day)
1990.33 ± 1275.12
2139.40 ± 716.35
2036.47 ± 1133.52
Proteins (% energy intake)
16.90 ± 4.75
17.14 ± 4.81
16.97 ± 4.77
Fat (% energy intake)
30.84 ± 10.73
30.17 ± 10.18
30.63 ± 10.56
Carbohydrates(% energy intake)
48.36 ± 11.51
50.25 ± 11.02
48.94 ± 11.39
38.74 (0-246.20)
61.67 (0-243.50) **
45.88 (0-246.20)
18.74 (0-200.00)
42.83 (0-243.67)**
26.17 (0-243.67)
7.26 ± 3.38
6.59 ± 3.42*
7.05 ± 3.41
Weight (kg) 2
a
Total PA (MET-h) MVPA (MET-h)
a
Sedentary time (h/day)
Data are shown as mean ± SD. *P < .05 between females and males. **P < .001 between females and males. BMI: bone mass index; PA: physical activity; MVPA: moderate-to-vigorous physical activity; MET: metabolic equivalent of task, representing energy expenditure per day. a MET-h are expressed as mean and range.
instrument for measuring PA in the European adult population (Craig et al., 2003). It was used to calculate the total hours of vigorous PA, moderate PA, and walking over the last 7 days. A MET-h was derived by multiplying the respective total hours by the Metabolic Equivalent of Task (MET) value for vigorous PA (MET = 8.0), moderate PA (4.0), and walking (3.3), and then adding all three (Craig et al., 2003). MVPA (MET-h) was calculated by summing vigorous PA and moderate PA while total PA (MET-h) was calculated by summing vigorous PA, moderate PA, and walking. Sedentary time (hours/day), excluding sleep, was also estimated. 2.5 | Statistical analysis SPSS Statistic version 21.0 (SPSS, Chicago, IL) was used for all the analyses. P-values < .05 were considered to be statistically significant. Variables were expressed as mean and standard deviations (SD) where normally distributed, and as median (interquartile range) where skewed. Differences in anthropometric measurements, between energy and macronutrient intakes and PA according to gender were assessed by independent t-tests. The outcome variables for body composition were fat mass (g), fat-free mass (g), and BMI (kg/m2). The following dietary exposure variables were assessed: total energy intake (kcal/day), and percent of energy from fat, car-
bohydrates, and protein. The PA exposure variables were total activity (MET-min), time spent performing MVPA (METmin), and sedentary time (h/day). Linear regression models were performed to examine associations between each of the outcome variables (fat mass, fat-free mass, and BMI) and between total energy, macronutrient intakes, and PA variables. The model was adjusted for age and sex. Analyses of total energy and macronutrient consumption were additionally adjusted for total activity, while analyses of PA measurements were adjusted for energy intake. Furthermore, when fat mass and fat-free mass were examined as separate outcomes, fat mass was adjusted for fat-free mass, and fat-free mass was adjusted for fat mass. Full model values are shown. 3 | RESULTS The participants’ characteristics are summarized separately for men and women (Table 1). The mean BMI for the study population was 22.61 ± 3.68 kg/m2. Based on BMI classification, the majority of the subjects in this study (71.8%) were of normal weight (66.1% of male subjects and 74.3% of females). Mean BMI was within the normal range for both women and men, but men showed a significantly higher mean BMI than women (P < .001; Table 1). As expected, significant differences were observed between males and females with respect to
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TABLE 2
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Association between total BMI (kg/m2) and diet and PA variables BMI
Independent variables
B (95% CI)
b
P-value
Daily energy intake (kcal/day)
20.0007 (2)
20.022
0.590
Proteins (% energy intake)
0.088 (0.028. 0.149)
0.114
0.004
Fat (% energy intake)
20.002 (20.030. 0.025)
20.007
0.863
Carbohydrates (% energy intake)
20.027 (20.053. 20.002)
20.085
0.034
Total PA (MET-h)
0.002(20.004. 0.009)
0.031
0.445
MVPA(MET-h)
0.004 (0.001. 0.016)
0.044
0.023
Sedentary time (h/day)
20.022 (20.108. 0.065)
20.020
0.624
B (95% CI): unstandardized regression coefficient (95% confidence interval); b: standardized regression coefficient (beta weight); PA: physical activity; MET: metabolic equivalent of task, representing energy expenditure per day; MVPA: moderate-to-vigorous physical activity.
height, weight, fat mass, and fat-free mass (P < .001). Regarding total energy and macronutrient intakes, the mean percentages of macronutrients were in concordance with current dietary reference intake for both sexes (Institute of Medicine, 2005). In terms of gender, the reported energy intake was higher in men than women, but there was no evidence of any significant differences. Similarly, no significant differences were observed between genders when comparing macronutrient intake. Finally, men had significantly higher reported levels of total PA and MVPA than women (P < .001), and also reported lower levels of sedentary time than women (P < .05). In the adjusted models, there was evidence of a significant positive association between protein intake and BMI, and an inverse association between carbohydrate intake and BMI (Table 2). Dietary outcome from each macronutrient remained significant after adjusting the model for total energy intake. MVPA was significantly associated with BMI following adjustments for age and sex (Table 2). TABLE 3
When considering fat-free mass (g), protein intake also revealed a significant association, but the size of the effect was smaller (P = .027; Table 3). There was evidence of a positive association between the two PA variables, total PA and MVPA, and fat-free mass after adjusting for age and sex (P < .001 and P < .001, respectively). In contrast, when fat mass was used as the outcome variable, there was no significant association with any of the dietary and PA components after adjusting for age and sex (Table 4). Note that sedentary time was not significantly associated with any of the body composition variables. 4 | DISCUSSION This study explores the associations between total energy, macronutrient intakes, and PA variables and body composition by assessing BMI, fat-free mass, and fat mass in a population of young adults. Based on a sample of 605 young adults, significant positive associations with fat-free mass and BMI were
Association between fat-free mass (g) and diet and PA variables Fat-free mass
Independent variables
B (95% CI)
b
P-value
Daily energy intake (kcal/day)
20.166 (20.531. 0.200)
20.020
0.374
Proteins(% energy intake)
96.965 (11.250. 182.679)
0.050
0.027
Fat (% energy intake)
15.195 (223.711. 54.101)
0.017
0.443
Carbohydrates (% energy intake)
233.617 (269.669. 2.435)
20.041
0.068
Total PA (MET-h)
15.630 (6.989. 24.270)
0.081
0.000
MVPA (MET-h)
20.208 (9.694. 30.723)
0.087
0.000
Sedentary time (h/day)
9.418 (2113.098. 131.934)
0.003
0.880
B (95% CI): unstandardized regression coefficient (95% confidence interval); b: standardized regression coefficient (beta weight); PA: physical activity; MET: metabolic equivalent of task. representing energy expenditure per day; MVPA: moderate-to-vigorous physical activity.
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TABLE 4
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Association between fat mass (g) and diet and PA variables Fat mass
Independent variables
B (95% CI)
b
P-value
Daily energy intake (kcal/day)
20.163 (20.714. 0.388)
20.023
0.561
Proteins(% energy intake)
107.930 (221.459. 237.320)
0.065
0.102
Fat (% energy intake)
212.980 (271.620. 45.660)
20.017
0.664
Carbohydrates (% energy intake)
234.800 (289.202. 19.601)
20.050
0.209
Total PA (MET-h)
0.865 (212.277. 14.006)
0.005
0.897
MVPA (MET-h)
1.510 (214.528. 17.548)
0.008
0.853
Sedentary time (h/day)
263.787 (2248.530. 120.957)
20.027
0.498
B (95% CI): unstandardized regression coefficient (95% confidence interval); b: standardized regression coefficient (beta weight); PA: physical activity; MET: metabolic equivalent of task, representing energy expenditure per day; MVPA: moderate-to-vigorous physical activity.
observed for protein intake, whereas an inverse association was reported for carbohydrate intake. Furthermore, total PA and MVPA were significantly associated with fat-free mass, whereas MVPA showed an association with BMI after adjusting for age and sex. Firstly, regarding between total energy and macronutrient consumption, significant associations have been observed between protein intake and fat-free mass and BMI. Dietary protein intake has been positively associated with fatfree mass in previous cross-sectional and longitudinal studies (Asp et al., 2012; Houston et al., 2008; Meng et al., 2009). It has been postulated that inadequate protein intake may be associated with a decrease in fat-free mass because dietary protein can affect muscle mass by stimulating muscle protein synthesis (Wolfe et al., 2008). Nevertheless, other studies suggest the importance of protein intake as a potential factor in obesity (Vinknes et al., 2011). Secondly, linear regression analysis performed on carbohydrate intake and BMI revealed a significant inverse association. In concordance with our results, previous studies have reported inverse associations between carbohydrate intake and body composition variables, suggesting that diets rich in carbohydrates help to maintain an appropriate weight (Atlantis et al., 2008; Vinknes et al., 2011). This seems to be due to the fact that carbohydrate-rich diets tend to passively reduce the ingestion of foods with a high fat content. As in another study, we failed to observe a significant association between fat mass and carbohydrates (Koppes et al., 2009). Underreporting of dietary food intakes may affect evidence of the actual association with body composition. Nevertheless, we estimated the misreporting level in our study population using the Goldberg cut-off method (Black, 2000) and obtained lower and upper cut-offs of 1.36 and 1.43, respectively. The calculated ratio between the mean value of the reported energy intake and the estimated BMR was 1.36 near to the lower cut-off value but within the cut-off range.
This means that the potential level of underreporting in our population is not relevant. Thus, underreporting of food intake is unlikely to explain the lack of association between the dietary components and fat mass in our study. With respect to PA variables, MVPA showed a significant association with BMI in young adults. These findings are in line with several articles that have observed a significant association between PA and BMI (Kim and Lee, 2009; Li et al., 2010; Santos et al., 2010). Similarly, linear regression analysis revealed that total PA and MVPA were significantly associated with fat-free mass. These results suggest that an increase in PA may not necessarily reduce BMI, but it may induce changes in body composition, mainly in fatfree mass (Lee, 2005; Ross and Janssen, 2001). Furthermore, the lack of association reported between PA variables and fat mass could mean that the significant association between BMI and MVPA is caused by the association with fat-free mass. Data from the present and previous studies suggest that high levels of PA and MVPA could be an effective preventive strategy that maximizes fat-free mass in early adulthood (Bann et al., 2014; Jimenez-Pavon et al., 2013). By contrast, our findings showed that sedentary time was not significantly associated with any of the body composition variables after adjustments for age and sex. One possible explanation for this finding is that our results may reflect the different lifestyles of the population assessed, misreporting of sedentary time, and overestimation of PA variables. A limitation of the present study is its cross-sectional design that cannot infer causality. As we mentioned previously, it also has some limitations inherent to the assessment of PA using a self-administered questionnaire. Data on PA was accumulated from self-reports and therefore might be prone to overestimation (Wareham and Rennie, 1998). Regarding dietary intake, the literature supports the use of 72-h recall as a pertinent method for assessing nutrient intake
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since it collects better data on the typical or average diet (Poslusna et al., 2009). However, evidence of underreporting of food intake in self-administered questionnaires has been reported previously (Livingstone and Black, 2003; Poslusna et al., 2009). In our study, the 72-h recall was interviewerdriven. Additionally, well-trained investigators asked study subjects to recall all food intakes and, in order to improve the accuracy of the food descriptions, used standard household measures and pictorial food models. Body composition measurements were performed using a body composition analyzer (TANITA BC-418MA). As previous findings showed, bioelectrical impedance analysis (BIA) can be used to measure body composition and is a significantly more cost-effective method than dual-energy X-ray absorptiometry (DXA; Beeson et al., 2010). Additionally, BIA provided good agreement with DXA for measurements of fat mass and fat-free mass (Beeson et al., 2010). The differences in measurement protocols could be explain some of the inconsistent findings with previous studies. Another limitation could be that the target subjects were young adults with similar demographic characteristics, which may limit the generalizability of the results to other populations. This study involved a population of well-characterized healthy young individuals of European descent with a medium socio-economic level. As we mentioned previously, our analysis was carefully controlled with confounding factors such as age, sex, PA, and energy intake. However, we cannot exclude the possibility that other lifestyle factors may still confound the observed associations in this study, as well as explain the lack of consistency across other studies. In summary, our findings suggest that PA variables were consistently associated with body composition, specifically fat-free mass. Dietary variables also play an important role in weight management; we evidenced that protein intake is significantly associated with fat-free mass and BMI. Furthermore, our results revealed an inverse association between carbohydrate intake and BMI. Therefore, lifestyle choices during early adulthood might have an impact on body composition, mainly by inducing changes in fat-free mass. Additional longitudinal studies are needed to report causality of associations. ACKNOWLEDGMENTS Correa-Rodríguez M is a predoctoral fellow from the Ministerio de Educacion, Cultura y Deporte (Programa de Formaci on del Profesorado Universitario). A UT HO R C O NT RI B U T I ON S Correa-Rodríguez M monitored data collection, wrote the statistical analysis plan, cleaned and analyzed the data, and drafted and revised the article. Schmidt-RioValle Jacqueline analyzed the data, and drafted and revised the article.
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Rueda-Medina B and Gonzalez-Jimenez Emilio analyzed the data and revised the draft article. CONFLICT OF INTEREST The authors declare that they have no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. R EFE RE NC ES Asp, M. L., Richardson, J. R., Collene, A. L., Droll, K. R., & Belury, M. A. (2012). Dietary protein and beef consumption predict for markers of muscle mass and nutrition status in older adults. The Journal of Nutrition, Health & Aging, 16, 784–790. Atlantis, E., Martin, S. A., Haren, M. T., Taylor, A. W., & Wittert, G. A. (2008). Lifestyle factors associated with agerelated differences in body composition: The Florey Adelaide Male Aging Study. The American Journal of Clinical Nutrition, 88, 95–104. Bann, D., Kuh, D., Wills, A. K., Adams, J., Brage, S., & Cooper, R. (2014). Physical activity across adulthood in relation to fat and lean body mass in early old age: Findings from the Medical Research Council National Survey of Health and Development, 1946-2010. American Journal of Epidemiology, 179, 1197–1207. Beeson, W. L., Batech, M., Schultz, E., Salto, L., Firek, A., Deleon, M., . . . Cordero-Macintyre, Z. (2010). Comparison of body composition by bioelectrical impedance analysis and dual-energy X-ray absorptiometry in Hispanic diabetics. International Journal of Body Composition Research, 8, 45–50. Black, A. E. (2000). Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. International Journal of Obesity and Related Metabolic Disorders 24, 1119–1130. Bowen, L., Taylor, A. E., Sullivan, R., Ebrahim, S., Kinra, S., Krishna, K. R., . . . Kuper, H. (2015). Associations between diet, physical activity and body fat distribution: A cross sectional study in an Indian population. BMC Public Health, 15, 281. Craig, C. L., Marshall, A. L., Sjostrom, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., . . . Oja, P. (2003). International Physical Activity Questionnaire: 12 Country Reliability and Validity. Medicine & Science in Sports & Exercise, 35, 3508–1381. Deere, K., Sayers, A., Davey Smith, G., Rittweger, J., & Tobias, J. H. (2012). High impact activity is related to lean but not fat mass: findings from a population-based study in
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