Relationship between Physical Activity, Oxidative Stress, and Total Plasma Antioxidant Capacity in Spanish Children from the GENOBOX Study

antioxidants Article Relationship between Physical Activity, Oxidative Stress, and Total Plasma Antioxidant Capacity in Spanish Children from the GENOBOX Study Francisco Jesús Llorente-Cantarero 1,2,† , Francisco Javier Aguilar-Gómez 3,† , Rosaura Leis 2,4 , Gloria Bueno 2,5,6, Azahara I. Rupérez 6, Augusto Anguita-Ruiz 2,7,8 , Rocío Vázquez-Cobela 2,4, María Dolores Mesa 7,8 , Luis A. Moreno 2,6 ,Ángel Gil 2,7,8 , Concepción María Aguilera 2,7,8,* and Mercedes Gil-Campos 2,3   Citation: Llorente-Cantarero, F.J.; Aguilar-Gómez, F.J.; Leis, R.; Bueno, G.; Rupérez, A.I.; Anguita-Ruiz, A.; Vázquez-Cobela, R.; Mesa, M.D.; Moreno, L.A.; Gil, Á.; et al. Relationship between Physical Activity, Oxidative Stress, and Total Plasma Antioxidant Capacity in Spanish Children from the GENOBOX Study. Antioxidants 2021, 10, 320. https://doi.org/10.3390/ antiox10020320 Academic Editor: Barbara Tavazz Received: 1 February 2021 Accepted: 18 February 2021 Published: 20 February 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Specific Didactics, Faculty of Education, Maimónides Institute of Biomedicine Research of Córdoba (IMIBIC), University of Córdoba, 14071 Córdoba, Spain; [email protected] 2CIBEROBN, (Physiopathology of Obesity and Nutrition) Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain; [email protected] (R.L.); [email protected] (G.B.); [email protected] (A.A.-R.); cobela.r[email protected] (R.V.-C.); [email protected] (L.A.M.); [email protected] (Á.G.); mer[email protected] (M.G.-C.) 3Metabolism and Investigation Unit, Reina Sofía University Hospital, Maimónides Institute of Biomedicine Research of Córdoba (IMIBIC), University of Córdoba, 14004 Córdoba, Spain; [email protected] 4Unit of Investigation in Nutrition, Growth and Human Development of Galicia, Pediatric Department, University of Santiago de Compostela, Clinic University Hospital of Santiago, Instituto de Investigación Sanitaria de Santiago (IDIS), 15706 Santiago de Compostela, Spain 5Pediatric Endocrinology Unit, Clinic University Hospital Lozano Blesa, Facultad de Medicina, Universidad de Zaragoza, 50009 Zaragoza, Spain 6 GENUD Research Group, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria (IIS) Aragón, University of Zaragoza, 50009 Zaragoza, Spain; [email protected] 7Department of Biochemistry and Molecular Biology II, Institute of Nutrition and Food Technology “José Mataix”, Center of Biomedical Research, University of Granada, Armilla, 18016 Granada, Spain; [email protected] 8Instituto de Investigación Biosanitaria ibs Granada, 18014 Granada, Spain *Correspondence: [email protected]; Tel.: +34-9-5824-1000 (ext. 20314) † These authors contributed equally to this work. Abstract: The World Health Organization has recommended performing at least 60 min a day of moderate-to-vigorous physical activity (MVPA) and reducing sedentarism in children and adolescents to offer significant health benefits and mitigate health risks. Physical fitness and sports practice seem to improve oxidative stress (OS) status during childhood. However, to our knowledge, there are no data regarding the influence of objectively-measured physical activity (PA) and sedentarism on OS status in children and adolescents. The present study aimed to evaluate the influence of moderate and vigorous PA and sedentarism on OS and plasma total antioxidant capacity (TAC) in a selected Spanish population of 216 children and adolescents from the GENOBOX study. PA (light, moderate, and vigorous) and sedentarism (i.e., sedentary time (ST)) were measured by accelerometry. A Physical Activity-Sedentarism Score (PASS) was developed integrating moderate and vigorous PA and ST levels. Urinary 8-hydroxy-20-deoxyguanosine (8-OHdG) and isoprostane F2α(F2-IsoPs), as markers of OS, were determined by ELISA; and TAC was estimated by colorimetry using an antioxidant kit. A higher PASS was associated with lower plasma TAC and urinary 8-OHdG and F 2 -IsoPs, showing a better redox profile. Reduced OS markers (8-OHdG and F 2 -IsoPs) in children with higher PASS may diminish the need of maintaining high concentrations of antioxidants in plasma during rest to achieve redox homeostasis. Keywords: physical activity; accelerometry; oxidative stress; plasma total antioxidant capacity; 8-hydroxy-20-deoxyguanosine; isoprostane F2α Antioxidants 2021,10, 320. https://doi.org/10.3390/antiox10020320 https://www.mdpi.com/journal/antioxidants Antioxidants 2021,10, 320 2 of 14 1. Introduction Physical activity improves cardiorespiratory fitness and strengthens the musculoskeletal system, helping to maintain proper body composition, both in children and adolescents [ 1 , 2 ]. Physical activity practice also seems to reduce lipid peroxidation and improve the antioxidant defense system, resulting in the maintenance of redox homeostasis [3]. The concept of “oxidative stress” has been defined as “an imbalance between oxidants and antioxidants in favor of the oxidants, leading to a disruption of redox signaling and molecular damage” [ 4 ]. Urinary isoprostanes and 8-hydroxy-2 0 -deoxyguanosine (8-OHdG), among other body-oxidized compounds, are well-recognized biomarkers of oxidative stress, which have been found to be increased in several situations, including obesity, type 2 diabetes, and cardiovascular diseases [ 5 ]. F2-8-iso-prostaglandin F2 α , also known as isoprostane F 2α (F 2 -IsoPs), is generated by free radical-induced peroxidation of arachidonic acid and is currently regarded as one of the most reliable biomarkers of in vivo oxidative stress [ 6 ]. In addition, 8-OHdG is considered to be a biomarker of generalized cellular oxidative stress [ 7 ]; it can be easily determined in urine with good reproducibility and recovery of untimed samples [8]. Two subsystems integrate the antioxidant defense system: the first is related to the activity of several enzymes such as glutathione peroxidases, glutathione reductase, glutathione S-transferase, superoxide dismutases, and catalase; the second is formed by non-enzymatic antioxidants such as uric acid, tocopherols, ascorbate, glutathione and ubiquinone [ 9 ]. The non-enzymatic component can be chemically measured in blood plasma by facing it to several oxidizing substances, obtaining the non-enzymatic antioxidant capacity (NEAC), total antioxidant status, or, henceforth, total antioxidant capacity (TAC). The use of indices of global redox status, such as plasma TAC, may be more appropriate than the comparison of single biomarkers to evaluate oxidative stress. In this context, TAC seems a useful parameter for assessing the global redox status in children and adolescents [10]. Regardless of dietary modifications, physical activity interventions seem to improve body mass index in children and adolescents [ 11 , 12 ]. Similarly, the practice of physical activity, both in adults and children, has been associated with an increase in antioxidants and a reduction of pro-oxidants [ 13 ]. Additionally, obesity has been related to an imbalanced redox status [ 14 ]. However, it is unclear whether the effect of exercise on redox status is mediated or not by changes in body weight status. The effect of acute and chronic physical activity practice on oxidative stress responses in children and adolescents has been recently reviewed [ 15 ]. Acute exercise seems to induce a relevant, but transient, increase in markers of oxidative stress. In contrast, regular exercise appears to be associated with increased antioxidants and reduced systemic oxidative biomarkers, even independently of body weight status. However, the studies included in that review are heterogeneous in terms of the type of exercise, intensity, and time of physical activity practiced [15]. The World Health Organization (WHO) and other international institutions have recommended that children and adolescents should perform at least an average of 60 min per day of moderate to vigorous-intensity, mostly aerobic activities across the week, in order to achieve several positive outcomes regarding cardiovascular, metabolic, and musculoskeletal health [ 16 ]. However, the impact of physical activity, especially in terms of time and intensity, on redox status, has not been accurately described for children and adolescents. In fact, to our knowledge, evaluation of the redox status of children according to objectively-measured physical activity by accelerometry has not been investigated. We hypothesized that physical activity practice, especially moderate-to-vigorous physical activity (MVPA), and sedentary time could be related to higher plasma levels of TAC in children and adolescents, showing a better global redox status. For this purpose, we aimed to compare plasma levels of TAC and biomarkers of oxidative stress (urinary F 2 -IsoPs and 8-OHdG) according to a Physical Activity-Sedentarism Score (PASS), characterized by Antioxidants 2021,10, 320 3 of 14 moderate and vigorous physical activity and sedentary time measured by accelerometry, in a cross-sectional sample of Spanish children from the GENOBOX study. 2. Materials and Methods 2.1. Population The present work was part of the GENOBOX study. GENOBOX is a case-control, multicenter study carried out in a total of 1444 children (706 males and 738 females), aged 3 to 17 years Spanish children during 2012–2015. Detailed inclusion and exclusion criteria as well as informed consent and approval by the local Ethics Committees of the three Spanish Hospitals (Hospital Universitario Reina Sofía, Córdoba; Hospital Clínico Universitario, Santiago de Compostela; and Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain; Code IDs: Córdoba 01/2017, Santiago 2011/198, Zaragoza 12/2010) where children were recruited have been reported elsewhere [ 14 ]. A subsample of 216 children (111 boys) was selected based on the following inclusion criteria for the present study: Caucasian children and adolescents aged 6 to 14 with body composition measured by bioelectrical impedance analysis, valid blood measurements including sex hormones (follicle-stimulating hormone, luteinizing hormone, testosterone in boys, and estradiol in girls), and accurate data from an accelerometry standardized protocol, as well as measured plasma levels of TAC and urinary F 2 -IsoPs and 8-OHdG. Within the subsample of 216 subjects, a subgroup of 74 children (33 boys) also had measures of plasma carotenes, retinol, and tocopherols. 2.2. Clinical and Anthropometric Examination Medical history and a physical exam including the evaluation of sexual maturity according to Tanner’s five-stages were assessed and confirmed with sexual hormone measurements. Anthropometric measurements were taken by a single examiner, and details have been previously reported [17]. 2.3. Blood Sampling Blood samples were drawn from the antecubital vein between 08:00 and 09:30 h after an overnight fast. Routine blood tests (Glucose (CV = 3.0%), plasma insulin (CV = 2.6%), follicle-stimulant hormone (CV = 3.6%); luteinizing hormone (CV = 3.1%), testosterone (CV = 2%), and estradiol (CV = 1.8%) were measured as previously reported using automated analyzers [ 17 ] The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated based on the published equation: HOMA-IR = fasting glucose (mmol) × fasting insulin (mU/mL)/22.5 [18]. Plasma TAC was determined by colorimetry using a commercial antioxidant assay kit (Cat no. 709001, Cayman Chemical, Ann Arbor, MI, USA). Urinary F 2 -IsoPs was determined by using a commercial competitive ELISA kit (EA85 Oxford Biomedical Research, Oxford, MI, USA) (CV): 14.13%). Urinary 8-OHdG was also determined by using a commercial competitive ELISA kit (KOG-200S/E JaICA, Fukuroi, Japan) (CV: 5.73%). The concentrations were normalized by urinary creatinine and expressed in ng/mg of creatinine. Creatinine concentration in urine samples was determined with a colorimetric kit (Ref. 1001115, Spinreact, Barcelona, Spain) (CV: 2.89%). Measurement of plasma concentrations of retinol, carotenes, and tocopherols were determined after extraction with 1-propanol by ultra-high-pressure liquid chromatography coupled to mass spectrometry UHPLC-MS as reported elsewhere [14]. 2.4. Accelerometry ActiGraph GT3X and GT3X+ accelerometers (ActiGraph; Pensacola, FL, USA) were used to assess physical activity levels in this study. Accelerometers were placed over the right iliac crest and held in place using an adjustable elastic belt for 24 h a day with a minimum of 8 h of monitoring per day for at least 3 days (at least one weekend day). It was programmed for 15 epochs (period of 15 s), as previously recommended [ 19 ]. Accelerometry data were processed using the Actilife v6.13.3 program (ActiGraph; Pensacola, FL, USA) Antioxidants 2021,10, 320 4 of 14 replacing, as a missing data code before further analysis, all negative counts and periods of 20 min or more of consecutive zero counts were replaced [ 20 ]. The output generated by the ActiGraph GT3X+ included the total volume of physical activity and each physical activity intensity as defined by the cut-points of counts per minute (CPMs) based in the classification by Evenson et al. [21]. 2.5. Statistical Analysis All continuous variables were tested for normality using the Shapiro–Wilk and Kolmogorov tests; the variables following a non-normal distribution were square-root (TAC, 8-OHdG, F 2 -IsoPs, fat body mass (FM), moderate and vigorous physical activity) or logarithm transformed (sedentary time). The homogeneity of variances was estimated using Levene’s test. Differences between pre-pubertal and pubertal children were analyzed by two-independent-sample t-tests or Mann–Whitney U tests. χ2 tests were applied to categorical variables expressed in percentage. Principal component analysis (PCA) was performed to investigate the relationships among body mass index, body composition, peripheral tissue insulin resistance—as a risk feature of metabolic syndrome—physical activity levels, and oxidative stress and TAC in the 210 children. Extraction of the initial set of uncorrelated components was accomplished with the principal factor method, and then Varimax orthogonal rotation of components was used to facilitate interpretation. High loading values indicate a stronger relationship between a factor and an observed variable. Factor loadings lower than 0.359 (critical factor, p< 0.001) revealed marginal correlations. To estimate the overall physical activity and sedentarism levels, a composite activity score of sedentary time, moderate and vigorous intensities was calculated (i.e., PASS). For this purpose, quartiles of each variable were designed, being the minutes of moderate and vigorous physical activity higher for the fourth quartile compared to the first one, and the opposite for sedentary time. Each subject obtained 1 to 4 points according to the quartile of each variable (e.g., 1 point for being in Q1 for moderate physical activity or 4 points for being in Q4 for sedentary time). By summing the points of the three variables, the PASS was obtained for each subject. The PASS ranged from 3 (all variables in the first quartile) to 12 (all variables in the fourth quartile); a higher score indicated a higher active habit. Based on this score, four groups of PASS were established: very low active (VLA; a score of 3), low active (LA; score from 4 to 6), moderately active (MA; score from 7 to 9), and high active (HA; score from 10–12). MANOVA was used to test the difference between the four groups across several outcome variables/outcomes simultaneously and Box’s test looks at the assumption of equal covariance matrices. Pillai’s trace value lower than 0.05 was considered statistically significant for the MANOVA test. Differences between the four groups based on the PASS were later analyzed using univariant ANCOVA (UNIANCOVA), adjusting for age and/or body mass index (BMI) z-score; pairwise differences were assessed by post hoc analyses to determine differences between experimental groups. For those variables not following normality, a Kruskal–Wallis test was used to evaluate differences between groups. Values in the descriptive tables and results are expressed as means and standard deviations. Differences were considered significant when p< 0.05. All statistical procedures were conducted using SPSS (IBM SPSS Statistics, Version 25.0. Armonk, NY, USA). 3. Results General demographic, anthropometric, physical activity, and peripheral insulin resistance variables in prepubertal and pubertal selected children within the GENOBOX study are shown in Table 1. Within the 216 subjects, 105 were at prepubertal and 111 at the pubertal stage. There were no differences between prepubertal and pubertal groups regarding BMI z-score and percentages of FM and fat free mass (FFM). No differences were also found for moderate physical activity, but it was closed to statistical significance. At the same time, HOMA-IR and sedentary time were significantly higher for those at pubertal status. Antioxidants 2021,10, 320 5 of 14 Table 1. General demographic, anthropometric, physical activity, and peripheral insulin resistance variables in prepubertal and pubertal Spanish children from the GENOBOX study. Variables All Participants (216) Prepubertal (105) Pubertal (111) p-Value Age (years) 10.8 ±2.2 9.4 ±1.7 12.1 ±1.7 <0.001 Weight (kg) 50.3 ±17.3 41.8 ±12 58.2 ±17.9 <0.001 Height (m) 1.47 ±0.13 1.38 ±0.10 1.55 ±0.10 <0.001 BMI (kg/m2)22.9 ±5.2 21.7 ±4.5 23.9 ±5.6 0.003 BMI z-score 1.15 ±2.14 1.11 ±2.38 1.15 ±2.18 0.886 FM (kg) 15.2 ±9.3 12.8 ±7 17.3 ±10.6 0.001 FM (%) 27.8 ±9.9 27.9 ±9.3 27.6 ±10.4 0.836 FFM (kg) 35.1 ±10.2 29.4 ±6.4 40.4 ±10.3 <0.001 FFM (%) 71.6 ±11.1 71.5 ±10.4 71.7 ±11.8 0.887 Normal weight (%) 34.9 14.9 20.2 0.342 * Overweight (%) 23 11.5 11.5 0.342 * Obesity (%) 42.1 22.6 19.2 0.342 * HOMA-IR 2.91 ±1.81 2.32 ±1.54 3.40 ±1.83 <0.001 ST (min/d) 482 ±97 467 ±103 495 ±89 0.018 PA Moderate (min/d) 38 ±14 40 ±13 36 ±15 0.050 PA Vigorous (min/d) 15 ±10 14 ±8 16 ±11 0.251 MVPA (min/d) 53 ±21 54 ±20 52 ±23 0.494 BMI: body mass index; FM: fat mass; FFM: fat-free mass; HOMA-IR: homeostasis model assessment-insulin resistance; ST: sedentary time; PA: physical activity; MVPA: mean moderate-vigorous physical activity. Data are shown as mean ± SD. Student’s t-test for parametric analysis and U de Mann–Whitney for non-parametric analysis was used to compare variables between prepubertal and pubertal stages. * represents pvalue for Chi-square test. From the eight items included in the PCA (physical activity, body composition, insulin resistance, oxidative stress, and total plasma antioxidant capacity), three principal components were extracted (Table 2), which explained 64.4% of the total variance (30% of the variance was explained by the first factor, an additional 21% by the second factor, and another 13% by the third factor) (Table 3). The first principal component, termed “metabolic risk” showed a positive correlation between HOMA-IR, FM, and FFM, and humble correlations for sedentary time (negative) and MVPA (positive). The second component, termed “oxidative stress”, included correlations among urinary F 2 -IsoPs, 8-OHdG, and sedentary time. The third component named “physical activity” included a positive correlation between FFM and MVPA, and negative with sedentary time. TAC did not reach the critical value to be included in any of the components. The differences between PASS levels for the variables included in the PCA are presented in Table 4and Figures 1–3. Children with a higher PASS allocated in the high active group had a lower BMI z-score than those in the moderately active and low active groups. Figure 1depicts the differences between the PASS levels for the variables with a higher factor loading of the “metabolic risk” component (FM, FFM, and HOMA-IR). Children in the high active group showed the lowest levels of FM and HOMA-IR. No differences were found between groups for FFM. The influence of PASS on redox status, assessed by plasma TAC, and urinary F 2 -IsoPs and 8-OHdG, is represented in Figure 2. TAC’s plasma level was lower for those children in the high active group compared to those in the moderately active and low active groups (UNIANCOVA p= 0.035, Figure 2a). Regarding the evaluation of the oxidative stress status, children in the high active group showed lower urine levels of 8-OHdG (Figure 2b) and F 2 -IsoPs (Figure 2c) than the rest of the groups (UNIANCOVA p= 0.005 and p= 0.036, respectively). Antioxidants 2021,10, 320 6 of 14 Table 2. Principal component analysis for the GENOBOX study extracted from physical activity, body composition, peripheral tissue insulin resistance, oxidative stress, and total plasma antioxidant capacity variables. Variables Component matrix a Factor * Metabolic Risk Oxidative Stress Physical Activity HOMA-IR 0.831 FM (kg) 0.804 FFM (kg) 0.798 0.388 TAC (mM) 0.356 8-OHdG (ng/mL) 0.859 F2-IsoPs (ng/mL) 0.831 MVPA (min/d) −0.377 0.771 ST (min/d) 0.366 0.387 −0.422 HOMA-IR: homeostasis model assessment-insulin resistance; FM: fat mass; FFM: fat-free mass; TAC: plasma total antioxidant capacity; 8-OHdG: urinary 8-hydroxy-2 0 -deoxyguanosine; F 2 -IsoPs: urinary F2 α -isoprostanes; MVPA: mean moderate-vigorous physical activity; ST: sedentary time. a Extraction of the initial set of uncorrelated components was accomplished with the principal factor method, and then the Varimax orthogonal rotation of components was used to facilitate interpretation. The number of components retained was based on Scree plot analysis and eigenvalues greater than 1 (with the components accounting for more of the total variance than any single variable). * Factor loading is the product-moment correlation (a measure of linear association) between an observed variable and an underlying factor. A significant loading factor was defined as a value greater than 0.359 (p< 0.01). Table 3. Eigen values and percentages of variance associated with each linear component (factor) before extraction, after extraction, and after rotation, in the principal component analysis for the GENOBOX study of children relating physical activity, body composition, and peripheral tissue insulin resistance to risk factors for oxidative stress and total plasma antioxidant capacity. Component Total Variance Explained Initial Eigenvalues Sums of Loads Squared from Extraction Sums of Loads Squared of Rotation Total % of variance % Accumulated Total % of Variance % Accumulated Total % of Variance % Accumulated 1 2.402 30.029 30.029 2.402 30.029 30.029 2.232 27.901 27.901 2 1.694 21.175 51.204 1.694 21.175 51.204 1.634 20.424 48.325 3 1.063 13.293 64.496 1.063 13.293 64.496 1.294 16.171 64.496 The first value in the row gives the proportion of variance (the degree of spread in the data set) explained by body composition and tissue peripheral resistance; the second value, the proportion explained by oxidative stress; and the third value, the proportion explained by the level of MVPA and sedentarism. Table 4. Differences in body mass index (BMI) z-score, moderate-vigorous physical activity and sedentary time between groups of Physical Activity-Sedentarism Score (PASS) of the Spanish children from the GENOBOX study. Variables Physical Activity-Sedentarism Score Levels p-Value p-Value for Trend Very Low Active (12) Low Active (65) Moderate Active (82) High Active (57) BMI z-score 1.25 ±1.4 a,b 1.57 ±2.1 a,b 1.41 ±1.9 a,b 0.51 ±2.9 a,c 0.046 0.029 PASS 3 a5.27 ±0.78 b8±0.84 c10.88 ±0.81 d<0.001 <0.001 MVPA (min/d) 22 ±5a34 ±11 b55 ±12 c76 ±17 d<0.001 <0.001 ST (min/d) 601 ±70 a517 ±92 b471 ±95 c397 ±54 d<0.001 <0.001 BMI: body mass index; PASS: Physical Activity-Sedentarism Score; PA: physical activity; MVPA: moderate-vigorous physical activity; ST: Sedentary time. Data are shown as mean ± SD. One-way ANOVA test was used to compare variables among the different PASS levels. No matching superscript letters (a, b, c, d) indicate significant differences (p< 0.05) by pairwise post hoc test adjusted for age and/or BMI z-score to determine which experimental groups differed from each other. Antioxidants 2021,10, 320 7 of 14 Figure 1. Fat mass, fat-free mass and HOMA-IR according to Physical Activity-Sedentarism Score (PASS) groups in Spanish children from the GENOBOX study. FM: fat mass; FFM: fat-free mass; HOMA-IR: homeostasis model assessment-insulin resistance; VLA: very low active; LA: low active; MA: moderately active; HA: high active. Differences between PASS levels were analyzed using one-way ANCOVA, adjusting for age and/or BMI z-score; pairwise differences were assessed by post hoc analyses to determine differences between experimental groups. For those variables not following normality, a Kruskal–Wallis test was used to evaluate differences between groups. FM: # Differences between the HA and VLA of (p= 0.003). HOMA-IR (UNIANCOVA, p=0.005): † Differences between HA vs. LA and VLA (p= 0.018 and p= 0.010, respectively); * Differences between A vs. LA and VLA (p= 0.014 and p= 0.010, respectively). pfor trend for HOMA-IR (p= 0.001) and FM (p= 0.044). Finally, the plasma concentration of some components of the non-enzymatic antioxidant defense system, retinol, beta-carotene, and tocopherols were measured in a subgroup of our study. Differences in the plasma levels of these biomarkers, according to the PASS groups, are presented in Figure 3. Children in the low active group showed the highest values of retinol (UNIANCOVA, p= 0.002) compared to those in other groups. Regarding tocopherols (UNIANCOVA, p= 0.022), children in the high active group showed lower levels compared with children with very low active and moderately active groups. Antioxidants 2021,10, 320 8 of 14 Antioxidants 2021, 10, 320 8 of 14 Figure 2. (a)Total plasma antioxidant capacity, (b) urinary 8-hydroxy-2’-deoxyguanosine and (c) F 2α -Isoprostanes by Physical Activity-Sedentarism Score (PASS) groups in Spanish children from the GENOBOX study. TAC: total plasma antioxidant capacity; 8-OHdG: 8-hydroxy-2’- deoxyguanosine; F 2 -IsoPs: F 2α -isoprostanes; PA: physical activity; VLA: very low active; LA: low active; MA: moderately active; HA: high active. Differences between PASS levels were analyzed using one-way ANCOVA, adjusting for age and/or BMI z-score; pairwise differences were assessed by post hoc analyses to determine differences between experimental groups. For those variables not following normality, a Kruskal–Wallis test was used to evaluate differences between groups. * Differences with HA score. p for trend for TAC (p = 0.020). Finally, the plasma concentration of some components of the non-enzymatic antioxidant defense system, retinol, beta-carotene, and tocopherols were measured in a subgroup of our study. Differences in the plasma levels of these biomarkers, according to the PASS groups, are presented in Figure 3. Children in the low active group showed the Figure 2. ( a )Total plasma antioxidant capacity, ( b ) urinary 8-hydroxy-2’-deoxyguanosine and ( c ) F 2α -Isoprostanes by Physical Activity-Sedentarism Score (PASS) groups in Spanish children from the GENOBOX study. TAC: total plasma antioxidant capacity; 8-OHdG: 8-hydroxy-2’-deoxyguanosine; F 2 -IsoPs: F 2α -isoprostanes; PA: physical activity; VLA: very low active; LA: low active; MA: moderately active; HA: high active. Differences between PASS levels were analyzed using one-way ANCOVA, adjusting for age and/or BMI z-score; pairwise differences were assessed by post hoc analyses to determine differences between experimental groups. For those variables not following normality, a Kruskal–Wallis test was used to evaluate differences between groups. * Differences with HA score. pfor trend for TAC (p= 0.020). Antioxidants 2021,10, 320 9 of 14 Antioxidants 2021, 10, 320 9 of 14 highest values of retinol (UNIANCOVA, p = 0.002) compared to those in other groups. Regarding tocopherols (UNIANCOVA, p = 0.022), children in the high active group showed lower levels compared with children with very low active and moderately active groups. Figure 3. (a) Plasma retinol, (b) beta-carotene, and (c) tocopherols levels according to the Physical- Activity-Sedentarism Score (PASS) levels in Spanish children from the GENOBOX study. PA: physical activity; VLA: very low active; LA: low active; MA: moderately active; HA: high active. Differences between PASS levels were analyzed using one-way ANCOVA, adjusting for age and/or BMI z-score; pairwise differences were assessed by post hoc analyses to determine differences between experimental groups. For those variables not following normality, a Kruskal– Wallis test was used to evaluate differences between groups. * Differences with HA; # Differences with VLA; † Differences MA. p for trend for retinol (p = 0.043) and tocopherols (p = 0.040). Figure 3. ( a ) Plasma retinol, ( b ) beta-carotene, and ( c ) tocopherols levels according to the Physical- Activity-Sedentarism Score (PASS) levels in Spanish children from the GENOBOX study. PA: physical activity; VLA: very low active; LA: low active; MA: moderately active; HA: high active. Differences between PASS levels were analyzed using one-way ANCOVA, adjusting for age and/or BMI zscore; pairwise differences were assessed by post hoc analyses to determine differences between experimental groups. For those variables not following normality, a Kruskal–Wallis test was used to evaluate differences between groups. * Differences with HA; # Differences with VLA; † Differences MA. pfor trend for retinol (p= 0.043) and tocopherols (p= 0.040). 4. Discussion The influence of objectively measured physical activity and sedentary time on redox status of children and adolescents has been scarcely described so far. In the present analysis, a high PASS, characterized by a high time spent on moderate and vigorous physical activity,