Aging has been associated with increased generation of reactive oxygen species (ROS), limited antioxidant capacity, and decreased functioning of repair systems (19). This imbalance in pro/antioxidant status has been implicated in the incidence of such civilization diseases like CHD, hypertension, or diabetes (30,37). It has also been suggested that sarcopenia and declining physical performance may be triggered by ROS that have accumulated during lifetime (14).
Living organisms have developed a complex antioxidant network to counteract ROS. Nonenzymatic antioxidants such as albumin, ascorbic acid, α-tocopherol, β-carotene, uric acid, bilirubin, and flavonoids constitute an important aspect of this network (31). Antioxidant molecules protect from free radicals by preventing the formation of radicals, scavenging them, or promoting their decomposition (37). The large number of different antioxidants in serum makes it difficult to measure each element separately. Therefore, several methods have been developed and used to determine the total antioxidant capacity (TAC) of various physiological bodily fluids such as plasma, serum, tears, or saliva. TAC may be important in monitoring the clinical status of patients because it has been reported to be decreased in cancer or diabetes (30).
Physical activity (PA) is an indispensable and recommended element of a healthy lifestyle and prevention of many diseases (17). A systematically implemented and adequately dosed training process, through adaptive changes in the human body, prevents cardiovascular and metabolic diseases and leads to delay in the decrease of physical performance (23). One of the mechanisms through which PA exerts its beneficial influence might be via increasing the antioxidant defense system (12,26). Concentration of ROS increases during exercise secondary to the increased oxygen uptake (19). The effect of exercise on the TAC may depend on the preferred type of PA or training period. Acute endurance exercise causes the significant generation of both ROS and antioxidant enzyme levels in several tissues (19,39). Chronic training has been suggested to induce positive adaptation to the antioxidant defense system (12).
The relationship of the antioxidant defense system to PA/fitness status data is so far inconclusive, usually on the basis of the limited number of studied subjects and different methodologies. It is not clear whether PA is able to counteract the age-related accumulation of oxidative stress by enhancing the antioxidant defense system and thereby preventing functional decline and the incidence of civilization diseases. Recently, we developed a promising new method to assess TAC that could be useful in clinical studies (10). This method measures the total activity of circulating low–molecular weight antioxidants as the ability of deproteinized serum with acetonitrile to decompose a 2,2-diphenyl-1-picryl-hydrazyl (DPPH) radical. It was found to exhibit good compliance with the ferric reducing ability of serum (FRAS) (10), a modification of the ferric reducing ability of plasma (FRAP) method commonly used for TAC measurement (2). Therefore, in the present study, we used the two established TAC methods (DPPH test and FRAS test) to assess their relationship to age, comprehensive PA/fitness data, and risk factors of cardiovascular and metabolic diseases in a large age-heterogeneous population of relatively healthy men.
MATERIAL AND METHODS
The study was conducted in a group of 422 males age 19.2–89.8 (56.5 ± 11.6) yr who attended the Healthy Men Center of the Medical University of Lodz (Poland) during a period of 2 yr. All participants were relatively healthy community-dwelling men able and willing to visit the outpatient clinic as well as to take part in the multiple examinations. All the men were either sedentary or involved in recreational noncompetitive endurance sports; for example, running, cycling, swimming, volleyball, and basketball (23). They participated in the physical examination and laboratory tests. They were free from known malignant diseases, any important disability, or dementia. Ninety-four men had arterial hypertension, 29 had diabetes mellitus, and 74 were treated for hypercholesterolemia. Apart from salt, glucose, and cholesterol limitations, none of the subjects was following a special diet. All the subjects were informed of the purpose of the study. The study had been approved by the ethics committee, and written informed consent was obtained from all the subjects.
Protocol and measures
The examinations took place in the Department of Geriatrics and the Department of Preventive Medicine. Laboratory measurements were performed both in the Department of Preventive Medicine and in the Department of Clinical Physiology, Medical University of Lodz. The subjects were asked to report to the center between 8:00 and 9:00 a.m. after overnight fasting for a minimum of 12 h (they could consume a light supper without animal-derived fats on the previous day, but no other particular dietary instructions were given), after overnight rest, refraining from physical exercises, smoking, and alcohol for at least 12 h before laboratory measurements. After fasting blood drawing, all the participants were given a light breakfast, and a multidimensional assessment was performed on each subject. Smoking habits, alcohol consumption, and usual dietary habits were assessed with the World Health Organization Countrywide Integrated Noncommunicable Diseases Intervention Program (CINDI) questionnaire (36).
Anthropometric data were collected by standard methods. Height and weight were measured, and the body mass index (BMI) (kg·m−2) was calculated. Skinfold measurements were taken at four sites: triceps, biceps, below the scapula, and above the ileum. The percentage of body fat was estimated from skinfold measurements according to Durnin and Womersley (11). Measurements of waist and hip circumference were taken, and waist-to-hip ratio (WHR) was calculated as an index of visceral obesity.
During the standard medical interview, the level of energy expenditure concerning the habitual leisure time PA (LTPA) was estimated. On the basis of the amount of hours earmarked for weekly recreational sport activities (kcal·wk−1) according to the tables of Fox et al. (13), the value of energy expenditure was calculated. On the basis of the current LTPA, men were divided into two groups of energy expenditure: 0–1000 kcal·wk−1 (inactive) and >1000 kcal·wk−1 (active). Current PA was also assessed by two popular PA questionnaires: the Seven-Day Recall PA Questionnaire (32) and the Stanford Usual Activity Questionnaire (32). These questionnaires were chosen because of their high validity demonstrated in older individuals (3). The Seven-Day Recall Moderate (activities with energy expenditure of 4 kcal·min−1), Hard (activities with energy expenditure of 6 kcal·min−1), Very Hard (activities with energy expenditure of 10 kcal·min−1), and Total scores (total energy expenditure over past week (kcal·kg−1·wk−1)) and the Stanford Moderate (six habitual moderate activities) and Hard (five habitual intensive activities) indices were calculated and used for further comparisons. Historical PA was assessed according to Kriska and Caspersen (24) in individuals older than 40 (n = 393). This questionnaire has been recently shown to be reasonably valid during a substantial period in older women (40). A total of 128 men deemed themselves not able to recall accurately historical LTPA data. These 128 men did not differ as to age, current PA, and fitness level from those 265 subjects included in the analysis. Historical PA was assessed for seven different periods of life: the 12- to 34-yr-old, 35- to 49-yr-old, and ≥50-yr-old periods; the last 5, 10, and 20 yr of life; and the whole period of life from the age of 12 yr to the day of the examination. All PAs were summed up according to hours per week, weeks of activity during the month, months of activity during the year, and years of activity during a period. The estimated number of hours during a period was divided by the number of years. Therefore, all the seven measures of historical PA are expressed as hours per year for a given period.
Physical fitness (aerobic capacity)
The graded submaximal exercise test was carried out on a Monark type 818E (Stockholm, Sweden) bicycle ergometer with 30-W increments every 3 min to achieve at least 85% of maximal age-predicted HR (220 − age). HR (continuous ECG tracing) was regressed against the three last workloads. The resultant linear regression equation was used to calculate the aerobic capacity index, i.e., physical working capacity at 85% of the maximal HR (
Equation (Uncited)Image Tools
was calculated by interpolating the workload–HR regression line at the point of 85% of the maximal age-predicted HR (16). This methodology, even with a lower (
Equation (Uncited)Image Tools
) exercise test intensity level, has been proposed as a useful measure of aerobic power for epidemiological studies (16).
Equation (Uncited)Image Tools
was expressed as absolute values (PWC (W)) and relative to body mass (PWC per kilogram (W·kg−1)). From the initial number of 422 men, 60 participants failed to achieve at least 85% of maximal age-predicted HR (because of chest pain, electrocardiogram ST segment changes, arrhythmias, general or muscle fatigue) during the exercise test.
Fasting blood samples were drawn from the antecubital vein: for measurements of TAC, into Vacuette tubes (Greiner Bio-One GmbH, Kremsmunster, Austria) with sodium heparin (200 mg·L−1), or for other tests, into siliconized tubes. Enzymatic methods were used to determine serum total cholesterol (TC; CORMAY Liquick Cor-CHOL, Lomianki, Poland), triglycerides (TG; CORMAY Liquick Cor-TG), glucose (CORMAY Liquick Cor-GLUCOSE), and uric acid concentrations (CORMAY Liquick Cor-UA). HDL cholesterol (HDL-C) was measured by the precipitation method (CORMAY HDL). LDL cholesterol (LDL-C) was estimated using the Friedewald formula.
Total antioxidant capacity
The measurements of blood serum TAC were performed using two spectrophotometric methods: the FRAS method originally described by Benzie and Strain (2) with some modifications (10) and the DPPH method (10,33). To get reliable data, all individual results were calculated as a mean from three separate measurements. The mean coefficient of variation across the triplicate measurements (n = 30) calculated for TAC-DPPH and TAC-FRAS was 0.049 and 0.018, respectively. In addition, both TAC assessments were done in parallel using the same laboratory equipment (spectrophotometer) and within the same time frame ensuring the duration of serum samples storage at −80°C for no longer than 30 d (10,33). The FRAS test measures TAC determined by nonenzymatic antioxidants; the main contributors are ascorbic acid and uric acid, whereas plasma proteins (e.g., albumins) and low–molecular weight thiols (e.g., reduced glutathione) have very low activity in this method (5). FRAS-TAC values are expressed in millimoles per liter of formed FeCl2 (mmol FeCl2·L−1). The DPPH test consists of scavenging free radical DPPH (a relatively stable compound in alcoholic solution with a peak absorbance at λ = 517 nm (A517)) by a complex of antioxidants in the assayed sample. A decline of absorbance values equivalent to the percentage of DPPH reduction expresses the level of the DPPH-TAC. The precise methodology of both tests has been described elsewhere (10).
Data were verified for normality of distribution and equality of variances. A one-way ANOVA with Bonferroni post hoc testing and the Kruskal–Wallis test were used for comparisons between age and LTPA groups. Pearson or Spearman correlations were used to determine the relationships between variables. Multiple linear regression with a backward stepwise technique was used to select variables that independently predict TAC levels in the whole examined group. TAC-FRAS and TAC-DPPH values were normalized using a log transformation for the purpose of statistical analyses. The results are presented as the mean ± SD. The level of significance was set at P ≤ 0.05 for all the analyses.
All the examined participants were grouped according to decades. Age, selected anthropometric and biochemical characteristics, blood pressure, HR, and TAC values in the various age groups as compared with the youngest group are presented in Table 1. Body mass was lower in the two oldest groups compared with the youngest one. Height decreased, whereas WHR increased, with age. Glucose level was higher in the 60- to 69.9-yr group compared with the youngest one. TC and LDL-C increased in all the older men in comparison with the ≤39.9-yr group, whereas HDL-C was highest in the oldest group. Systolic blood pressure (SBP) was higher in the two oldest groups, whereas diastolic blood pressure (DBP) was higher in the three middle age groups compared with the youngest one. There were no differences for both TAC-FRAS and TAC-DPPH between all the age groups.
PA and fitness measures in the various age groups as compared with the youngest group are presented in Table 2. Current LTPA was lower in the two oldest groups compared with the youngest one. In contrast, the Stanford Moderate score reflecting everyday PA-related behavior was higher in older men. Physical fitness measures were already lower in the 40- to 49.9-yr group than with the youngest men and declined further with advancing age. Physical fitness was strongly (P < 0.001) related to the majority of PA measures (r = 0.48, r = 0.27, and r = 0.38 for correlations of
Equation (Uncited)Image Tools
with current LTPA, Seven-Day Recall Very Hard score, and Stanford Hard score, respectively) (not shown in the table).
Correlations between TAC and selected anthropometric, biochemical, blood pressure, HR, PA and fitness characteristics are shown in Table 3. Both TAC measures were positively related to anthropometric overweight/obesity measures, TG, TC/HDL-C ratio, uric acid, SBP, and DBP and negatively related to HDL-C. TAC-FRAS was inversely related to the majority of PA and fitness measures. Both TAC-FRAS and TAC-DPPH were negatively correlated to
Equation (Uncited)Image Tools
Table 4 shows mean comparisons of cardiovascular and metabolic disease risk profile measures, TAC-FRAS, and TAC-DPPH in relation to current LTPA level. Current LTPA favorably influenced risk profile measures; differences were statistically significant for the majority of them (BMI, waist circumference, WHR, percentage of body fat, HDL-C, TG, TC/HDL-C ratio, and resting HR). However, TAC-DPPH did not differ between categories of LTPA, whereas TAC-FRAS was even lower in the high-LTPA group. Similar associations were observed when cardiometabolic risk profile and TAC (Table 3) were compared with other measures of current PA (indices of the Seven-Day Recall PA Questionnaire and the Stanford Usual Activity Questionnaire).
Table 5 shows average LTPA during different life periods assessed by the historical PA questionnaire and correlation coefficients of anthropometric and biochemical measures to LTPA during different life periods. BMI, waist circumference, WHR, percentage of body fat, TG, TC/HDL-C, and uric acid were negatively, whereas HDL-C was positively, related to several historical PA indices. Spearman correlation coefficients with TAC had a generally negative sign but were not significantly related to PA during different life periods (P > 0.05).
Only 15.1% of the whole studied population were smokers; the majority of them were occasional smokers. We were not able to evidence any significant difference in TAC between smoking and nonsmoking men (P = 0.77 for TAC-FRAS and P = 0.97 for TAC-DPPH). Most of the men (65.7%) declared alcohol consumption during the previous week. No association was found between frequency or amount of alcohol consumption during the last week and TAC. No important relationships between dietary habits (average weekly consumption of CHO, animal-derived fats, dairy produces, meat, fish, vegetables, fruits, sweets, and fruit juices) and TAC were observed. Medication (antihypertensives, oral antidiabetics, statins) had no influence on TAC.
Multiple linear regression (backward stepwise technique) was used with all the independent variables influencing TAC in bivariate analyses (with P < 0.05). Around half (45.9%) of the variability in TAC-FRAS was dependent on the concentration of uric acid and physical fitness (
Equation (Uncited)Image Tools
Equation (Uncited)Image Tools
Uric acid and SBP contributed to TAC-DPPH and explained together 13.6% of TAC-DPPH variance:
Equation (Uncited)Image Tools
When uric acid levels were not used in the model, then TAC-FRAS was predicted favorably by BMI and TG but negatively by
Equation (Uncited)Image Tools
Equation (Uncited)Image Tools
HDL-C (negatively) and SBP (positively) were independent contributors to TAC-DPPH variance:
Equation (Uncited)Image Tools
This is the first study assessing simultaneously the potential influence of current and historical PA on TAC in a large age-heterogeneous population of men. We used three different measures of current PA, a well-validated questionnaire for historical PA together with the two leading measures of TAC, and a set of the most important risk factors for cardiovascular and metabolic diseases, including exercise testing with aerobic fitness measurements. Our results show that both current and historical PA counteract the age-related deterioration of the cardiometabolic diseases risk profile. Nevertheless, this effect is not detectable for TAC. In fact, an adverse relationship between PA/fitness and TAC was observed. Furthermore, in this population of men, TAC did not decline with age and was directly related to overweight/obesity and laboratory markers of metabolic syndrome.
One would expect that systematic PA will lead, in addition to increased physical capacity and protective effects on the cardiovascular system, to greater protective effects of redox compounds on the human system. Available data from the literature discussing the influence of physical exercise on antioxidant capacity are ambiguous and often do not determine precisely the volume of PA. Some studies show the effects of moderate PA on increasing TAC or activity of antioxidant enzymes (12,26). For example, both single intense exertion (half-marathon run) and regular endurance training were connected with higher antioxidant capacity (7,8). Cesari et al. (6) also found a positive correlation between the concentration of antioxidants in the blood serum and the level of physical performance in the elderly. The results of other studies do not confirm any positive relation between PA and TAC (21,22,35). There is also strong evidence that a high-intensity single exertion or exercise training period may lead to a reduction in antioxidant capacity (34) and an increased concentration of malondialdehyde in blood (12).
Discrepancies in the available literature are probably associated with the variety of techniques for TAC determination and depend on whether TAC is the result of a single exercise or training process. It is recommended to use at least two methods of determining TAC because of the differences in the tests used for investigation (31,33). The TAC-DPPH method analyzes the ability to reduce the radical cation and determines the decrease in absorbance, whereas the TAC-FRAS assay measures the formed ferrous ions by increased absorbance (33). DPPH, used in TAC-DPPH assay, is a relatively stable free radical. TAC-FRAS measures the ferric reducing ability of a sample, and there are no free radicals or oxidants applied in the assay (5). The correlation between the used methods (r = 0.44, P < 0.0001) is higher than among others allowing the assessment of TAC, e.g., FRAP and OXY-ADSORBENT test (Diacron, Italy) (r = 0.22) (37) and FRAP and oxygen radical absorbance capacity (r = 0.35) (5). In the present study, we evaluated the relationship of long-term PA of varying volume to the actual TAC of blood serum by FRAS and DPPH methods. A generally negative correlation was observed between TAC and the current level of PA or fitness. This association was similar across the whole examined adult lifespan. Current PA data were further corroborated with historical PA. Historical PA had a favorable effect on actual BMI, WHR, waist circumference, percentage of body fat, and laboratory markers of metabolic syndrome, including uric acid levels. In contrast, no relationship with actual TAC could be evidenced. Therefore, regular PA and better fitness do not associate with better TAC throughout the adult lifespan of men.
Advancing age and several diseases have been associated with increased oxidative stress and an impaired antioxidant defense system (1,26,27,30). In our male subjects, age had no influence on TAC. These results, although contradicting the common understanding of free radicals in aging research, are in agreement with a few previous studies (20,22,35). In one population study involving 1600 subjects, a positive relationship was found between antioxidant potential and age in women (35). In a second study examining 2828 subjects from the Framingham Heart Study, an inverse relationship was found between age and urinary creatinine–indexed levels of 8-epi-PGF2 (a marker of systemic oxidative stress) (20). Therefore, from the present and previous studies involving larger populations, it seems that in free-living relatively healthy subjects, TAC does not decrease with advancing age. Several explanations are plausible for why regular PA and better fitness as well as advancing age had no expected effect on TAC levels. One possible explanation may be based on the concept of hormesis that has been recently extended to the ROS-generating effects of exercise (15). Moderate exercise has been shown to increase expression of mitochondrial antioxidant enzymes, being recognized per se as an antioxidant (15). Therefore, one may assume that subjects with regular PA had lower exposition of tissue structures to endogenous oxidants and it is not necessary to maintain high levels of circulating low–molecular weight antioxidants. Uric acid contributes to about half of total plasma antioxidant activity (10). A regular moderate exercise training program may decrease serum levels of uric acid (25). Also, in the present study, physically active men were characterized by a lower concentration uric acid. These may explain why regular PA and better fitness were associated with lower TAC values. We did not observe significant differences of serum uric acid levels across the subjects’ age. Moreover, aerobic fitness was previously described to decrease the mitochondrial rate of hydrogen peroxide production and to attenuate age-related DNA damage (28). These can be explanations of similar TAC values in subgroups with different ages noted in our study.
TAC data from our study was directly related to common cardiometabolic risk factor measures. Positive correlations of TAC with blood pressure, indices of overweight/obesity, and laboratory indices of metabolic syndrome together with a negative correlation with HDL-C were found. One plausible explanation for these findings may be related to PA/fitness data. TAC of blood serum, determined by both the FRAS and DPPH methods, reached higher values in participants with lower PA and fitness levels. Less physically active subjects were characterized by higher values of anthropometric overweight/obesity indicators (BMI, WHR, waist circumference, and percentage of body fat) and higher values of blood pressure. Physically active men were characterized by a lower concentration of TG, TC, and uric acid but higher levels of HDL-C. Another explanation may be related to oxidant/antioxidant balance in different diseases. The risk of CHD increases with obesity and physical inactivity (18). Lower levels of TAC in the course of many diseases may be due to the depletion of the antioxidant barrier as an effect of long-term oxidative stress (30). In early stages, in the presence of risk factors, the antioxidant defense system may increase its activity in response to sustained oxidative stress. Similar observations have been reported in some earlier studies (29,35,37). Vassalle et al. (37) observed higher TAC in patients with hypertension compared with peers with normal blood pressure. In a population study involving 1600 subjects, a positive relationship was found between antioxidant potential and BMI in women (35). On the other hand, in one study, an inverse relationship between body fat or central fat and TAC was found in both males and females (9).
Among laboratory findings of particular interest are the correlations found for uric acid. Uric acid is the strongest antioxidant of human serum, which prevents oxidative inactivation of endothelial enzymes (cyclooxygenase, angiotensin-converting enzyme) and preserves the ability of the endothelium to mediate vascular dilatation in the face of oxidative stress (10). On the other hand, uric acid is considered to be an independent risk factor of cardiovascular diseases (38). Hyperuricemia is associated with several components of metabolic syndrome (4). Uric acid levels have been found to be not correlated or only weakly associated with PA, those associations being mediated by obesity measures (4,25). Therefore, the two-directional activity of uric acid, as both a potent antioxidant and a cardiovascular risk factor, requires further studies.
Several shortcomings of the present study should be acknowledged. This is a cross-sectional study performed in either sedentary or endurance-trained men. Patterns of declines may be different in longitudinal comparisons. Our subjects were more physically active and healthy than a random sample would be. Active well-educated subjects in good health are more prone to participate in such studies and especially to undergo exercise testing. Nevertheless, they may overestimate recent and either forget to report or underestimate historical PA. This may explain lower PA reported in early as compared with advanced adulthood. Correlation coefficients of TAC to selected patients characteristics were relatively modest and reached higher values for TAC-FRAS (Table 3). Although both TAC-DPPH and TAC-FRAS measure total antioxidant activity of serum, they reflect somewhat different physiological properties. Some loss of serum antioxidants related to serum deproteinization with acetonitrile in the TAC-DPPH method and higher coefficient of variation across the triplicate measurements noted for TAC-DPPH could explain these differences (10). We used fasting glucose concentration as the most important measure of CHO metabolism and cardiometabolic risk factor. However, fasting insulin concentration and glycated hemoglobin are other important indicators of insulin sensitivity and CHO metabolism status.
In conclusion, men with disadvantageous risk factor values for cardiovascular and metabolic diseases (BMI, WHR, waist circumference, high blood pressure, low HDL-C) showed higher TAC. PA, both actual and historical, has a positive effect on the elimination of risk factors of cardiovascular disease but does not affect the increase in TAC, as revealed by the FRAS and DPPH methods. Age has no influence on TAC. Therefore, present data strongly suggest that the known beneficial effects of PA alleviating the age-related decline in health and performance do not seem to be directly mediated through increased serum antioxidant status.
This study was supported by grant 2 P05D 070 30 from the Ministry of Education and Science and the State Committee for Scientific Research.
The authors thank E. Rębowska, M.Sc., for assistance in collecting the samples and Mrs. B. Lipowska for technical supervision during the analysis.
The authors have no conflicts of interest to declare.
The authors state that the results of the present study do not constitute endorsement by the American College of Sports Medicine.
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