Influence of the level of physical activity on physical fitness, lipid profile and health outcomes in overweight/obese adults with similar nutritional status

Science & Sports (2017) 32, 278—285 Disponible en ligne sur ScienceDirect www.sciencedirect.com ORIGINAL ARTICLE Influence of the level of physical activity on physical fitness, lipid profile and health outcomes in overweight/obese adults with similar nutritional status Influence du niveau d’activité physique sur la condition physique, la composition corporelle et le profil lipidique dans une population d’adultes en surpoids ou obèses T. Mu˜ noz-Veraa, B. Sa˜ nudoa, B. del Pozo-Cruzb, J. del Pozo-Cruza, G. Lopez-Lluchc, A.J. Sánchez-Olivera,∗ aDepartamento de Educación Física y Deporte, Facultad de Ciencias de la Educación, Universidad de Sevilla, Pirotécnia s/n, N-41012, Sevilla, Spain bDepartment of Sport and Exercise Science, University of Auckland, Auckland, New Zealand cCentro Andaluz de Biologia del Desarrollo, Universidad Pablo de Olavide, Sevilla, Spain Received 30 June 2015; accepted 27 May 2016 Available online 14 April 2017 KEYWORDS Cardiorespiratory fitness; Muscle strength; Weight management; Metabolic syndrome Summary Objective. — To determine the influence of the level of physical activity on physical fitness, body composition and lipid profile and further explore the associations between fitness with these health outcomes in a population of overweight/obese adults. Methods. — Forty overweight/obesity participants with a caloric intake between a negative balance of 500 kcal/day and their estimated required energy intake were classified according to their level of physical activity. Anthropometric (weight and height), body composition (waist- to-hip ratio, percentage of body fat and fat-free mass), clinical measurements (blood pressure), physical fitness (cardiorespiratory fitness — 6MWT —, handgrip strength and lower-limb muscle strength — MVC —) and lipid profile (total cholesterol — TC —, low — LDL — and high-density lipoprotein cholesterol — HDL —, triglyceride — TG —, and plasma glucose) were determined. ∗Corresponding author. E-mail address: [email protected] (A.J. Sánchez-Oliver). http://dx.doi.org/10.1016/j.scispo.2016.05.006 0765-1597/© 2017 Elsevier Masson SAS. All rights reserved. Influence of physical activity in overweight/obese adults 279 Results. — Significant differences in MVC and fat-free mass were found between participants with different physical level. Significant associations between all the body composition measures and the strength-related variables (handgrip and MVC) in both physical activity categories also were found. In addition, associations between triglycerides, LDL and total cholesterol with muscle strength and cardiorespiratory fitness were observed only in the low physical activity group. Moreover, MVC explained up to 39% of the variance in metabolic syndrome in those with a moderate level of physical activity. Conclusions. — Correlations between physical fitness (especially in strength) and health risk factors depending on the level of physical activity were found. © 2017 Elsevier Masson SAS. All rights reserved. MOTS CLÉS Aptitude cardiorespiratoire ; Force musculaire ; Gestion du poids ; Syndrome métabolique Résumé Objectif. —Déterminer l’influence du niveau d’activité physique sur la condition physique, la composition corporelle et le profil lipidique, ainsi que préciser les associations entre aptitude physique et ces paramètres biologiques dans une population d’adultes en surpoids ou obèses. Méthodes. — Quarante participants en surpoids ou obèses recevant des apports caloriques compris entre leurs besoins énergétiques et une restriction de 500 kcal/jour ont été classés en fonction de leur niveau d’activité physique, et comparés sur le plan des données anthropométriques (poids et taille), de la composition corporelle (rapport taille/hanche, pourcentage de graisse corporelle et masse maigre), de la pression artérielle, de la condition physique (test de marche de 6 minutes, aptitude cardiorespiratoire, la force de préhension et de la force musculaire des membres inférieurs), du profil lipidique (cholestérol total, cholestérol LDL et HDL, triglycérides et glycémie). Résultats. —Des différences significatives au niveau de la force musculaire des membres inférieurs cholestérol et de la masse maigre s’observent entre les groupes de niveau d’activité physique différent. Des associations significatives entre toutes les mesures de la composition corporelle et les variables liées à la force (préhension et force musculaire des membres inférieurs) sont observées dans les deux catégories d’activité physique. Une association des paramètres lipidiques (triglycérides, cholestérol LDL et cholestérol total) avec la force musculaire l’aptitude cardiorespiratoire n’a été observée que dans le groupe à faible activité physique. Par ailleurs, la force musculaire des membres inférieurs explique jusqu’à 39 % de la variance du syndrome métabolique chez les sujets ayant un niveau modéré d’activité physique. Conclusions. — Des corrélations différentes entre la condition physique (en particulier la force musculaire) et les facteurs de risque pour la santé ont été trouvés en fonction du niveau d’activité physique. La force musculaire des membres inférieurs est un important déterminant statistique du syndrome métabolique chez les sujets à faible niveau d’activité physique. © 2017 Elsevier Masson SAS. Tous droits r´ eserv´ es. 1. Introduction Metabolic syndrome is characterized by the co-occurrence of central obesity, hypertension, dyslipidemia and hyperglycemia, which represent significant risk factors for cardiovascular disease [1]. This cluster of related metabolic abnormalities is strongly linked with overweight and obesity and is affecting a wide range of people in all age groups. According to the World Health Organization, by 2015, an estimate of ∼2.3 billion adults worldwide are expected to be overweight (body mass index [BMI] ≥ 25 kg/m2) and at least 700 million obese (BMI ≥ 30 kg/m2). Therefore, this pandemic is one of the main public health challenges, and identifying risk factors and strategies for prevention and treatment of these conditions are an important public health issue. Obesity results from an imbalance between nutritional intake and energy expenditure and it is well established that physical activity levels and diet, either independently or in combination, are associated with this disease. We have now evidence that low physical activity levels and excessive energy intake are associated with obesity [2—5] and therefore higher levels of physical activity are associated with several metabolic consequences and a lower incidence of obesity [6]. Thus, changes in lifestyle such as modifications in diet and exercise are considered to be the cornerstones of obesity management [7,8]. Physical activity is thought to positively influence energy balance increasing energy expenditure [6] but also has positive effects on the metabolism of skeletal muscle and insulin sensitivity [8]. Even low levels of physical activity may contribute to maintain a favorable metabolic profile [9], inducing the clearance of triglycerides [10]. Furthermore, changes in physical activity and fitness are inversely correlated with changes in body weight, body mass index (BMI), waist circumference and abdominal adiposity [11—13] and a dose—response relationship has been reported between the amount of exercise and changes in visceral adipose tissue in obese participants [13,14]. In addition, handgrip strength, considered a popular marker of nutritional 280 T. Mu˜ noz-Vera et al. status has recently been associated with different measures of obesity [15]. It seems that physically active individuals are considerably less likely to be obese than physically inactive and unfit individuals. However, these associations are not completely consistent in the literature [16] and several authors have reported a lack of association between physical activity and the lipid profile, plasma glucose, or resting metabolic rate [17]. While numerous studies have reported the influence of physical activity and fitness on different health outcomes even after controlling for age [18], others have not found this interaction considering the independent association of these parameters [19]. Although the influence of physical exercise on adult obesity is well known, the information when this population is controlled by a constant nutritional intake is scarce. Also if we consider the complex relationships between the level of physical activity on adults with overweight or obesity [20] and the need of understanding the effects of these changes in energy balance on key health outcomes, therefore, the aim of the current crosssectional study was to assess the association of the physical fitness, body composition and lipid profile on a group of overweight/obese adults with constant nutrition intake and to examine whether different levels of physical activity present an independent relationship with body composition and lipid profile. As Kaino et al. [21] have recently described that physical activity is more important than energy intake in the prevention of a clustering of metabolic risk factors (elevated fasting plasma glucose levels, blood pressure or triglyceride levels and reduced high-density lipoprotein cholesterol), we hypothesized that controlling the nutritional status, the participants with higher levels of physical activity will present a better fitness and metabolic profile. 2. Materials and methods 2.1. Participants Participants were recruited from a local outpatient clinic (Seville, Spain). Inclusion criteria include both males and females older than 18 years with BMI greater than or equal to 25 kg/m2. Exclusion criteria include history or evidence of advanced cardiovascular (including SBP > 160 mmHG and DBP > 100 mmHG), renal or hepatic diseases. Participants with type 1 or 2 diabetes or those on medication known to affect type 2 diabetes and cardiovascular risk factors (lipid-lowering, antihypertensive, hypoglycemic, hormone replacement therapy, or insulin), also were excluded. Only those with a similar nutritional status were selected. Out of 150 potentially eligible participants with similar nutrition patterns, 40 volunteers with overweight/obesity gave their written informed consent after receiving detailed information about the aims and study procedures. These participants had a caloric intake between a negative balance of 500 kcal/day and the required estimated energy intake. The authors declare that all experiments on human subjects were conducted in accordance with the Declaration of Helsinki and also certify that this study was approved by the Ethics Committee of the University of Seville. 2.2. Assessments of current physical activity status and daily nutritional intake To obtain information on physical activity level, the International Physical Activity Questionnaire (IPAQ) was used [22]. The short (self-administered, seven items), last-week version of the IPAQ was administered, asking about the time spent being physically active in the last 7 days. Total energy and activity-specific energy expenditure are quantified in terms of metabolic equivalents/day (METs (metabolic equivalents) · h/day). Physical activities were computed as minutes of sitting, walking, moderate-intensity, and vigorous-intensity activities and also defined on the basis of the intensity of physical activity as light (< 3.0 METs), moderate to vigorous (≥ 3.0 METs), and vigorous (≥ 6.0 METs) intensity. Daily nutritional intake was assessed on three consecutive days (Sunday, Monday and Tuesday) and during the winter months (January, February and March) to control for seasonal variation in dietary intake. Percent energy from protein, carbohydrates and fat of total energy intake were recorded. Requirements were calculated by adding the estimated energy expenditure from physical activity to basal metabolism. 2.3. Anthropometric, body composition and clinical measurements Body weight (kg) and height (m) were measured using standardized procedures and equipment, and BMI (kg/m2) was calculated. Waist circumference was measured to the nearest 0.1 cm at the point midway between the costal margin and crest in the mid-auxiliary line, as was hip circumference at the widest point around the greater trochanter. The waist-to-hip ratio (WHR) was calculated. Bioelectrical impedance was used to determine body composition using Bodystat©1500 (Bodystat Ltd, Douglas, Isle of Man, UK), which is a four terminal single frequency (500 ␮A at 50 kHz) analyzer. Resistance and reactance were measured between the right wrist and the right ankle and total body fat, fat-free mass, and their percentages were estimated from the equations proposed by Kyle et al. [23]. Further, skeletal muscle mass was calculated with Janssen’s equation [24]. As the bioelectrical impedance analysis system methodology for determining percent body fat is sensitive to hydration status, participants were asked to fast within 4 hours of the test, not exercise within 12 hours of the test and avoid alcohol or diuretics before testing. The device was tested for validity and reliability with results showing good to excellent fat and fat-free mass test—retest reliability [25]. Blood pressure was monitored using the Omron electronic blood pressure equipment (Omron BF-306, Omron Healthcare Europe BV, Hoofddorp, The Netherlands). The blood pressure of the participants was measured three times with at least 2—3 min between successive measurements. All measurements were taken in a quiet room after the participants had been sitting in a chair for 5 min. The mean of the three measurements was used for the analysis. Influence of physical activity in overweight/obese adults 281 2.4. Physical fitness Cardiorespiratory fitness was estimated using the distance results from the six-minute walk test (6MWT) [26]. The walk test was done in a corridor with a length of 70 meters. Standardized instructions were given to walk as far as possible but comfortable pace over a course in six minutes. Subjects were not encouraged during the test but were notified as to the amount of time remaining when within the last minute of the test. The distance walked in 6 minutes was recorded in meters. Handgrip strength was assessed in both hands by using a hand dynamometer (TKK 5401 Grip-D; Smedley, Takei, Tokyo, Japan) with all participants reporting that they were right-handed. Lower-limb strength was assessed by means of maximal voluntary contraction (MVC) of the knee extensors. MVC was measured by a load cell (Model 333A) connected to an A/D converting system (MuscleLabTM, Ergotest AS, Langesund, Norway). Participants sat upright on a high-backed chair, the hips and legs firmly secured, with the knee positioned at 90◦to the thigh, and the arms folded across the chest. Muscle tests for the rectus femoris were performed three times lasting 5 s and separated by 60 s. 2.5. Biochemical and hormonal assays Blood samples from an antecubital vein were made in fasting conditions. The blood was collected in a vacutainer tube containing tetraacetic diamine ethylene acid (EDTA). The collected venous blood samples were immediately placed in ice. Plasma from the venous blood samples was separated by centrifugation (3000 g, 10 min, 4◦C) and the aliquots of plasma were stored at —80 ◦C for use in subsequent chemical analyses. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL), triglyceride (TG), and plasma glucose were determined using the Reflotron system (Boehringer- Mannheim, West Germany). Low-density lipoprotein (LDL) was determined indirectly by using Friedewald’s formula: LDL = TC — HDL + (TG/5). 2.6. Data analysis Data are presented as means ± standard deviation (SD), unless otherwise stated. One-way ANOVA was performed to test the differences between the level of physical activity concerning the body composition, physical fitness and lipid profile variables. The association between physical activity and physical fitness, body composition and lipid profile were analyzed using Pearson’s correlation coefficients. Partial correlations adjusted for weight were also tested. The participants were divided into those with or without a clustering of Metabolic Syndrome defined as the presence of at least three of the following non-essential risk factors: (1) elevated triglycerides (≥ 150 mg/dl) and/or reduced HDL cholesterol (< 50 mg/dl), (2) elevated Blood pressure (SBP ≥ 130 and/or DBP ≥ 85 mmHg), (3) elevated fasting plasma glucose (≥ 100 mg/dl) and (4) waist circumference > 102 cm in men and > 88 cm in women. Binary logistic regression was used to predict the occurrence of metabolic syndrome from physical fitness (Cardiorespiratory fitness, handgrip strength and lower-limb strength) as continuous variables, alone and in combination. All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS Version 19.0, IBM Incop®). Alpha value was set at .05 level for all test performed. 3. Results In Table 1, are summarized subject demographic data and anthropometric measurements for all participants. Out of the 40 participants, 70% (n = 28) were women and 58% (n = 23) were classified as obese by NIH BMI standards (≥ 30 kg/m2). Table 2 highlights the physical fitness characteristics of the total sample categorized according to their level of physical activity. Forty-five percent of the participants presented a low prevalence of physical activity and the remaining 55% had moderate prevalence of physical activity. Differences were observed in MVC in favor to those performing the greater levels of physical activity. When the body composition was compared based on the physical activity categories (Table 3), significant differences were observed in fat-free mass (P = 0.050) and muscle mass (P = 0.044). Non-significant differences were detected in the percentage of body fat, weight or WHR. Table 4 presents the results of the lipid profile and cardiovascular risk factors according to the physical activity level. Non-significant differences between the physical activity categories were found. However, those with the lower level of physical activity reported 6% more cholesterol and 11% more triglycerides. Differences in cardiovascular risk factors between both physical activity categories were not observed. The strength of the association between physical activity, physical fitness and health outcomes (body composition and the lipid profile) are summarized in Table 5. In low physical activity group MVC (r = 0.662, P < 0.005) and in both low and moderate physical activity groups, handgrip strength (r = 0.651 to 0.835, P < 0.001) adjusted by weight were associated with fat-free mass and were negatively associated with the percentage of body fat Table 1 Descriptive characteristics of participants (n = 40). Variable Mean (SD) Gender Women (%) 70 Men (%) 30 Age (years) 35 (7.86) Height (m) 1.66 (0.09) Weight (kg) 87.89 (17.88) Body mass index (kg/m2) 31.62 (4.78) Obese (%) 58 Overweight (%) 42 Waist circumference (cm) 94.03 (12.44) Hip circumference (cm) 113.25 (8.89) Waist-to-hip ratio 0.83 (0.09) Systolic blood pressure (mmHg) 124.60 (14.90) Diastolic blood pressure (mmHg) 81.73 (11.61) Data are reported as mean (SD) or percentage. 282 T. Mu˜ noz-Vera et al. Table 2 Assessment of the physical fitness according to the physical activity level. Total (n = 40) Low physical activity (n = 18) Moderate physical activity (n = 22) P-value Handgrip strength test right hand (kg) 31.88 (12.13) 29.80 (12.10) 33.57 (12.16) 0.334 Handgrip strength test left hand (kg) 35.81 (34.04) 28.5 (10.07) 32.21 (12.16) 0.306 Maximal voluntary isometric contraction (n) 505.14 (206.09) 430.88 (155.07) 565.90 (225.43) 0.038* 6MWT (m) 607.16 (75.73) 605.2 (67.64) 608.77 (83.32) 0.884 Data are reported as mean (SD). 6MWT: six-minute walk test distance. *P < 0.05. Table 3 Assessment of body composition according to the physical activity level. Total (n = 40) Low physical activity (n = 18) Moderate physical activity (n = 22) P-value Weight (kg) 87.89 (17.87) 83.22 (15.27) 91.71 (19.25) 0.137 Body mass index (kg/m2) 31.62 (4.79) 31.17 (5.25) 31.99 (4.46) 0.600 Waist-to-hip ratio 0.83 (0.09) 0.82 (0.08) 0.84 (0.10) 0.445 Percentage body fat (%) 35.86 (5.54) 35.00 (5.82) 36.56 (6.62) 0.381 Fat-free mass (kg) 55.54 (6.97) 53.39 (5.97) 57.29 (7.36) 0.050* Muscle mass (kg) 26.88 (2.77) 25.95 (2.77) 27.64 (2.60) 0.044* Skeletal Muscle index (kg/m2) 9.70 (0.22) 9.70 (0.25) 9.70 (0.19) 0.964 Data are reported as mean (SD). *P < 0.05. Table 4 Assessment of the lipid profile and cardiovascular risk factors according to the physical activity level. Total (n = 40) Low physical activity (n = 18) Moderate physical activity (n = 22) P-value Triglycerides (mg/dl) 114.62 (44.16) 121.93 (46.00) 108.97 (42.89) 0.370 Total cholesterol (mg/dl) 179.23 (28.87) 185.38 (23.43) 173.95 (32.47) 0.222 LDL cholesterol (mg/dl) 104.71 (31.58) 109.63 (25.49) 101.27 (35.46) 0.456 HDL cholesterol (mg/dl) 50.35 (17.62) 48.13 (15.78) 51.94 (19.04) 0.530 Cardiovascular risk factors TC/HDL 3.94 (1.47) 4.06 (1.05) 3.85 (1.75) 0.674 HDL/LDL 2.43 (1.35) 2.53 (0.92) 2.37 (1.60) 0.730 TC: total cholesterol; HDL: high-density lipoprotein cholesterol; LDL: low-density lipoprotein. Data are reported as mean (SD). (r = —0.694 to —0.772, P < 0.001 for handgrip strength and r = —0.438 to —0.581, P < 0.05). In addition, in the group with the lower levels of physical activity, associations between handgrip strength and MVC with BMI and fatfree mass were showed. Triglycerides and total cholesterol were also negatively associated with these two fitness outcomes. Finally, an inverse association (r = —0.751, P < 0.001) between 6MWT and total cholesterol and between 6MWT and LDL (r = —0.702, P < 0.001) was found in this group. In the group, achieving moderate levels of physical activity additional associations between handgrip strength and SBP were observed. Finally, the results of the binary logistic regression models showed that MVC was significantly associated with metabolic syndrome in those participants with a moderate level of physical activity, and explained up to 39% of the variance in metabolic syndrome in this group (OR = 0.993, 95% CI = 0.987—0.999, P = 0.034). 4. Discussion This study aimed to determine the influence of the level of physical activity on physical fitness, body composition Influence of physical activity in overweight/obese adults 283 Table 5 Partial correlations between body composition and lipid profile with physical fitness across physical activity levels adjusted for weight. Handgrip strength test right hand Handgrip strength test left hand Maximal voluntary isometric contraction 6MWT Low PA Moderate PA Low PA Moderate PA Low PA Moderate PA Low PA Moderate PA Age —0.058 —0.234 —0.150 —0.203 —0.333 —0.276 —0.680 0.082 BMI 0.887†—0.447 0.859†—0.334 0.616*—0.332 —0.439 0.256 SBP 0.515 0.641*0.512 0.556 0.324 0.487 —0.363 0.565 DBP 0.244 —0.034 0.105 0.172 —0.106 0.146 —0.272 0.206 Waist —0.348 0.207 —0.474 0.180 —0.506 0.582 —0.625 0.548 Hip —0.773*—0.677*—0.694*—0.614*—0.541*—0.770†—0.285 —0.411 WHR 0.216 0.376 0.076 0.329 —0.037 0.691*—0.266 0.558 Body fat (%) —0.772†—0.694†—0.722†—0.752†—0.581*—0.438*—0.314 —0.353 Fat-free mass (kg) 0.835†0.651†0.813†0.660†0.662*0.352 0.375 0.469* Muscle mass (kg) 0.848†0.631*0.826†0.640*0.688*0.412 0.384 0.504* Glucose —0.046 0.421 0.022 0.321 0.233 —0.012 —0.469 —0.073 Triglycerides —0.897†—0.332 —0.902†—0.361 —0.785†—0.266 —0.340 —0.128 Total cholesterol —0.758*—0.342 —0.739*—0.351 —0.652*—0.433 —0.751*0.273 HDL cholesterol —0.381 —0.041 —0.302 —0.142 —0.082 —0.168 0.135 —0.460 LDL cholesterol —0.189 —0.223 —0.235 —0.179 —0.357 —0.244 —0.702*0.456 TC/HDL 0.037 —0.320 —0.023 —0.215 —0.199 —0.045 -0.384 0.325 LDL/HDL 0.065 —0.318 0.013 —0.213 —0.161 —0.058 —0.421 0.332 PA: physical activity; BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; WHR: waist-to-hip ratio; TC: total cholesterol; HDL: high-density lipoprotein cholesterol; LDL: low-density lipoprotein. Data are reported as mean (SD). *P < 0.05. †P < 0.01 and lipid profile and explored the associations between fitness with these health outcomes and the presence of metabolic syndrome in a population of overweight/obese adults (48% of the participants had metabolic syndrome). To our knowledge, this is the first study that has assessed participants with a similar nutrition intake due to the discrepancies among findings related to the relative combined contributions of the levels of physical activity with the lipid profile. We found significant differences in MVC but also in fat-free mass between participants with different physical level. Significant associations between all the body composition outcomes and the strength-related variables (handgrip and MVC) in both physical activity categories were found. Also associations between triglycerides, LDL and total cholesterol with muscle strength and cardiorespiratory fitness were observed only in the low physical activity group. Finally, MVC explained up to 39% of the variance in metabolic syndrome in those with a moderate level of physical activity. These findings may suggest that increasing physical activity is beneficial for improving physical fitness and body composition. Although it is beyond the scope of the current study, Kaino et al. [21] recently suggested that interventions aimed at increasing physical activity rather than focusing on decreasing energy intake may be more important to prevent the clustering of metabolic risk factors. The cross-sectional design of the study precludes any establishment of causality; however, considering that in the current study all participants had a similar nutrition intake, one may hypothesized that the differences may be attributed to the level of physical activity. Future intervention studies focused on enhancing physical fitness should carefully look at this hypothesis. Results of epidemiological studies are consistent in that those who are physically active had a better fitness and consequently a lower BMI and adipose tissue [12,13]. However, non-significant differences in the percentage of body fat were observed in the current study which is consistent with Irving at al. [27] in that vigorous exercise but not moderate exercise has been shown to reduce total body fat. By contrast, our results (greater levels of physical activity reporting 24% more strength in the lower-limb) are in line with previous studies reporting significant interactions between physical activity and lower-limb muscle strength [28] and can be attributable to the greater muscle mass (15%) reported in the moderate physical activity group. Therefore, our results may be in line of previous reports suggesting that physical activity improves muscle strength and muscle mass and has a key role in the management strategy for obesity [28]. On the other hand, our results contrast with those found in the literature regarding association between physical activity and the lipid profile [29]. However, the fact that the participants in the current study had a similar nutrition intake may, at least in part, explain this lack of association. Despite this, while non-significant changes in the lipid profile according to the level of physical activity were found, which contrast with the results reported by Dancy et al. [30] indicating that active people are less likely to have hyper-triglyceridemia and low HDL cholesterol concentrations, significant associations between physical fitness and these outcomes were observed. It is believed that exercise 284 T. Mu˜ noz-Vera et al. has a favorable influence on blood lipid and lipoprotein levels [6] and a major finding of the present study was that triglycerides and total cholesterol levels were negatively associated to the muscle strength and LDL and cholesterol were also inversely associated with 6MWT. While previous studies indicated that physical activity does not significantly reduce total cholesterol or LDL [17], this study seems to indicate significant associations between fitness and lipid profile. Numerous associations between physical fitness, body composition and the lipid profile were found when both levels of physical activity were compared and adjusted for weight. Handgrip strength and MVC were both associated with fat-free mass and were significantly negatively associated with the percentage of body fat which was also accompanied with reductions in hip circumference and BMI in those with the lower levels of physical activity. These results are consistent with Ohkawara et al. [13] who suggested that the level of physical activity was related to decreased visceral adipose tissue and support the idea that relatively modest increases in physical activity might improve BMI status. This is an important finding as central body and waist circumference can be modulated with physical activity and even fewer skeletal muscle contractions may reduce the clearance of triglycerides [10,31]. Additionally, results of epidemiological studies suggest that physical fitness can predict insulin resistance in overweight or obese subjects [32]. Both abdominal obesity and disorders of lipid and carbohydrate metabolism were defined as metabolic syndrome and have been suggested to predispose individuals to cardiovascular disease and type II diabetes [33,34]. These abnormalities were also associated with lower estimated cardiorespiratory fitness, and more self-reports of poor to fair health status and therefore fitness may be important to reduce this association [9]. Previous studies have reported the associations between lifestyle factors and metabolic syndrome [2,3] and another notable finding in our study is that physical fitness, specifically lower-limb muscle strength, was highly associated with a cluster of metabolic abnormalities. It was suggested that the cardiorespiratory fitness level might partially explain this syndrome, with higher cardiorespiratory fitness associated with more favorable metabolic health even among obese individuals [9]. In our study, cardiorespiratory fitness was not associated with metabolic syndrome; however adds a new variable to the puzzle, suggesting that MVC could explain up to 39% of the variance in those subjects who had a moderate level of physical activity which have important implications for obesity management. Several limitations are noted. The cross-sectional design limited any interpretation of causality. Another limitation of the study was the relatively small sample size. The single week of activity measures in the current study could bias the estimate of habitual activity in either direction and therefore would under/overestimate the association between activity and certain biochemical and anthropometric measures. Further, the IPAQ significantly underestimated sitting and overestimated time spent in other physical activity intensities. Finally, due to the limitations of measurement methods selected for use in this trial (percent body fat via bioelectrical impedance and cardiorespiratory fitness via 6MWT) the results should be interpreted with caution. 5. Conclusions In conclusion, this study extends our understanding of relationships between physical activity and physical fitness, body composition and lipid profile in overweight/obese adults; thus, showing what further large studies might to show. The correlation between physical fitness (lower-limb and handgrip strength and 6MWT) and health risk factors (serum lipids, triglycerides, total cholesterol, BMI, body fat and fat-free mass) was determined. The findings from this study have an important health implication due to have identified the association between muscle strength (MVC) and metabolic syndrome, which may help professionals to develop potential interventions to prevent these abnormalities. Disclosure of interest The authors declare that they have no competing interest. References [1] Alberti KG, Zimmet P, Shaw J. Metabolic syndrome — a new world-wide definition. A consensus statement from the international diabetes federation. Diabet Med 2006;23:469—80. [2] Roberts CK, Barnard RJ. Effects of exercise and diet on chronic disease. J Appl Physiol 2005;98:3—30. [3] Lakka TA, Laaksonen DE. Physical activity in prevention and treatment of the metabolic syndrome. Appl Physiol Nutr Metab 2007;32:76—88. [4] Janssen I, Ross R. Vigorous intensity physical activity is related to the metabolic syndrome independent of the physical activity dose. Int J Epidemiol 2012;41:1132—40. [5] Ayabe M, Kumahara H, Morimura K, Ishii K, Sakane N, Tanaka H. Very short bouts of non-exercise physical activity associated with metabolic syndrome under free-living conditions in Japanese female adults. Eur J Appl Physiol 2012;112:3525—32. [6] Kesaniemi YK, Danforth Jr E, Jensen MD, Kopelman PG, Lefebrvre P, Reeder BA. Dose-response issues concerning physical activity and health: an evidence-based symposium. Med Sci Sports Exerc 2001;33:351—8. [7] Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med 2011;364:2392—404. [8] Eckardt K, Taube A, Eckel J. Obesity-associated insulin resistance in skeletal muscle: role of lipid accumulation and physical inactivity. Rev Endocr Metab Disord 2011;12:163—72. [9] Hageman PA, Pullen CH, Hertzog M, Boeckner LS, Walker SN. Associations of cardiorespiratory fitness and fatness with metabolic syndrome in rural women with prehypertension. J Obes 2012;2012:618—728. [10] Hamburg NM, McMackin CJ, Huang AL, Shenouda SM, Widlansky ME, Schulz E, et al. Physical inactivity rapidly induces insulin resistance and microvascular dysfunction in healthy volunteers. Arterioscler Thromb Vasc Biol 2007;27:2650—6. [11] Yoshioka M, Ayabe M, Yahiro T, Higuchi H, Higaki Y, St-Amand J, et al. Long-period accelerometer monitoring shows the role of physical activity in overweight and obesity. Int J Obes 2005;29:502—8. Influence of physical activity in overweight/obese adults 285 [12] Sui X, LaMonte MJ, Laditka JN, Hardin JW, Chase N, Hooker SP, et al. Cardiorespiratory fitness and adiposity as mortality predictors in older adults. JAMA 2007;298:2507—16. [13] Ohkawara K, Tanaka S, Miyachi M, Ishikawa-Takata K, Tabata I. A dose-response relation between aerobic exercise and visceral fat reduction: systematic review of clinical trials. Int J Obes 2007;31:1786—97. [14] Molenaar EA, Massaro JM, Jacques PF, Pou KM, Ellison RC, Hoffmann U, et al. Association of lifestyle factors with abdominal subcutaneous and visceral adiposity: the Framingham heart study. Diabetes Care 2009;32:505—10. [15] Keevil VL, Luben R, Dalzell N, Hayat S, Sayer AA, Wareham NJ, et al. Cross-sectional associations between different measures of obesity and muscle strength in men and women in a British cohort study. J Nutr Health Aging 2015;19(1):3—11. [16] Strasser B. Physical activity in obesity and metabolic syndrome. Ann N Y Acad Sci 2013;1281:141—59. [17] Kraus WE, Houmard JA, Duscha BD, Knetzger KJ, Wharton MB, McCartney JS, et al. Effects of the amount and intensity of exercise on plasma lipoproteins. N Engl J Med 2002;347:1483—92. [18] Woolf K, Reese CE, Mason MP, Beaird LC, Tudor-Locke C, Vaughan LA. Physical activity is associated with risk factors for chronic disease across adult women’s life cycle. J Am Diet Assoc 2008;108:948—59. [19] Rauner A, Mess F, Woll A. The relationship between physical activity, physical fitness and overweight in adolescents: a systematic review of studies published in or after 2000. BMC Pediatr 2013;1:13—9. [20] Maher CA, Mire E, Harrington DM, Staiano AE, Katzmarzyk PT. The independent and combined associations of physical activity and sedentary behavior with obesity in adults: NHANES 2003- 06. Obesity 2013;21(12):E730—7. [21] Kaino W, Daimon M, Sasaki S, Karasawa S, Takase K, Tada K, et al. Lower physical activity is a risk factor for a clustering of metabolic risk factors in non-obese and obese Japanese subjects: the Takahata study. Endocr J 2013;60:617—28. [22] IPAQ Research Committee. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ): short and long forms (online); 2005. http://www.ipaq.ki.se/scoring.pdf. [23] Kyle UG, Genton L, Karsegard L, Slosman DO, Pichard C. Single prediction equation for bioelectrical impedance analysis in adults aged 20—94 y. Nutrition 2001;17:248—53. [24] Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol 2000;89:465—71. [25] Meeuwsen S, Horgan GW, Elia M. The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr 2010;29:560—6. [26] Rikli RE, Jones CJ. Development and validation of a functional fitness test for community-residing older adults. J Aging Phys Act 1999;17:127—59. [27] Irving BA, Davis CK, Brock DW, Weltman JY, Swift D, Barrett EJ, et al. Effect of exercise training intensity on abdominal visceral fat and body composition. Med Sci Sports Exerc 2008;40:1863—72. [28] Rolland Y, Lauwers-Cances V, Pahor M, Fillaux J, Grandjean H, Vellas B. Muscle strength in obese elderly women: effect of recreational physical activity in a cross-sectional study. Am J Clin Nutr 2004;79:552—7. [29] Slentz CA, Houmard JA, Johnson JL, Bateman LA, Tanner CJ, McCartney JS, et al. Inactivity, exercise training and detraining, and plasma lipoproteins. STRRIDE: a randomized, controlled study of exercise intensity and amount. J Appl Physiol 2007;103:432—42. [30] Dancy C, Lohsoonthorn V, Williams MA. Risk of dyslipidemia in relation to level of physical activity among Thai professional and office workers. Southeast Asian J Trop Med Public Health 2008;39:932—41. [31] Bey L, Hamilton MT. Suppression of skeletal muscle lipoprotein lipase activity during physical inactivity: a molecular reason to maintain daily low-intensity activity. J Physiol 2003;551:673—82. [32] Racette SB, Evans EM, Weiss EP, Hagberg JM, Holloszy JO. Abdominal adiposity is a stronger predictor of insulin resistance than fitness among 50—95 year olds. Diabetes Care 2006;29:673—8. [33] Hu G, Qiao Q, Tuomilehto J, Balkau B, Borch-Johnsen K, Pyorala K, et al. Prevalence of the metabolic syndrome and its relation to all-cause and cardiovascular mortality in nondiabetic European men and women. Arch Intern Med 2004;164:1066—76. [34] Nguyen NT, Magno CP, Lane KT, Hinojosa MW, Lane JS. Association of hypertension, diabetes, dyslipidemia, and metabolic syndrome with obesity: findings from the national health and nutrition examination survey, 1999 to 2004. J Am Coll Surg 2008;207:928—34.