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Archives of Endocrinology and Metabolism

Print version ISSN 2359-3997On-line version ISSN 2359-4292

Arch. Endocrinol. Metab. vol.61 no.2 São Paulo Mar./Apr. 2017  Epub Nov 24, 2016

http://dx.doi.org/10.1590/2359-3997000000227 

Original Article

Evaluation of salivary oxidative parameters in overweight and obese young adults

Eduardo Ottobelli Chielle1 

Jeferson Noslen Casarin1 

1Departamento de Ciências da Saúde, Laboratório de Bioquímica Clínica, Universidade do Oeste de Santa Catarina (Unoesc), São Miguel do Oeste, SC, Brasil

ABSTRACT

Background

Obesity is characterized by a deposition of abnormal or excessive fat in adipose tissue, and is linked with a risk of damage to several metabolic and pathological processes associated with oxidative stress. To date, salivary oxidative biomarkers have been minimally explored in obese individuals. Thus, the aim of this study was to assess the concentrations of salivary oxidative biomarkers (ferric-reducing antioxidant power, uric acid, sulfhydryl groups) and lipid peroxidation in obese and overweight young subjects.

Materials and methods

Levels of lipid peroxidation, ferric-reducing antioxidant power, uric acid, and SH groups were determined in the saliva and serum of 149 young adults, including 54 normal weight, 27 overweight, and 68 obese individuals. Anthropometric measurements were also evaluated.

Results

Salivary levels of ferric-reducing antioxidant power, sulfhydryl groups, and lipid peroxidation, as well as serum levels of ferric-reducing antioxidant power, uric acid, and lipid peroxidation were higher in obese patients when compared with individuals with normal weight. There were correlations between salivary and serum ferric-reducing antioxidant power and salivary and serum uric acid in the obese and normal-weight groups.

Conclusions

Our results indicate that the increase in salivary levels of ferric-reducing antioxidant power, sulfhydryl groups, and lipid peroxidation, and serum levels of ferric-reducing antioxidant power, uric acid, and lipid peroxidation could be related to the regulation of various processes in the adipose tissue. These findings may hold promise in identifying new oxidative markers to assist in diagnosing and monitoring overweight and obese patients.

Key words: Obesity; biomarkers; oxidative stress; saliva; antioxidants

INTRODUCTION

Reactive oxygen species (ROS) and reactive nitrogen species (RNS) play an important role in cell signaling and metabolic pathways in physiological conditions. On the other hand, decreased antioxidant levels and/or increased production of reactive metabolites may disrupt homeostatic processes and lead to oxidative damage (1,2). Oxidative/nitrosative stress may have a serious impact on cell viability and induce cellular responses leading to cell death (3). Many studies have shown a connection between oxidative molecular damage and pathophysiological mechanisms associated with severe diseases such as atherosclerosis (4), neurodegenerative disorders (5), and diabetes, and an important role in the etiopathogenesis of inflammatory diseases (6). There is also a relationship between oxidative stress and aging processes (7).

Obesity is associated with an increase in oxidative stress, defined as an increased load of free radicals comprised of ROS and RNS generated during cellular metabolism (8). These free radicals are chemically reactive molecules that may damage cellular proteins, membranes, and DNA. The increase in oxidative stress is considered to be involved in the pathogenesis of insulin resistance and type 2 diabetes (T2DM) associated with obesity (9,10). Data from studies with cell culture systems have shown that products of oxidative stress impair insulin-mediated translocation of GLUT4 in myotubes and adipocytes and suppress the transcription of the insulin gene in β cells and adiponectin in adipocytes (11,12).

On the other hand, most cells have an adequate protective system to circumvent harmful oxidative events. This system is composed of antioxidant enzymes (superoxide dismutase, catalase, and many other peroxidases) and nonenzymatic antioxidants, including the glutathione (SH groups), uric acid, ascorbate, and ferric-reducing antioxidant power (FRAP) (13). Was observed a progressive increase TBARS levels in accordance with the increase in body weight and progressive decrease in SH groups and FRAP according to body weight gain (14). The increased oxidative stress in vascular walls is involved in the pathogenesis of atherosclerosis, hypertension and induces damage to cell structures, including membranes, proteins, and ADN and this contributes to disorders cardiometabolic (15).

The major antioxidant capacity toward named oxidants is represented by numerous thiol groups (SH groups) in intracellular and extracellular compartments. At the same time, thiols are also the major targets for ROS and RNS. It is well known that the serum SH groups levels are modified in many diseases, including obesity, indicating that SH groups content is a useful biochemical marker of “in vivo” oxido-reduction reactions (16). Membrane SH groups are liable to be modified by different oxidants or alkylating agents, increasing their membrane permeability to different ions, such as Ca++, which can promote excitotoxicity, by RNS and ROS that lead to lipid peroxidation, increasing the levels of TBARS and decreased concentration of SH groups in membrane proteins (17). Thiol-containing molecules with SH bonds such as glutathione (GSH) suppress oxidative damage and involve the maintenance of the cell redox status. In healthy tissues, these antioxidants work in cooperative to maintain the pro oxidant–antioxidant balance and prevent tissue damage and disease (18).

The saliva also has antioxidant properties attributed to its composition of enzymatic (mostly peroxidase system) and nonenzymatic compounds (uric acid, glutathione, sialic acid), which may be determined in salivary samples (19,20). Sampling of human saliva is an attractive means to diagnose and monitor diseases since the saliva may be collected easily and noninvasively. Analysis of the saliva may also be beneficial to predict the progression of future diseases (14,21,22). As a clinical tool, saliva has many advantages over serum, including ease of collection, storing, and shipping. Saliva is composed of organic and inorganic elements and represents an important fluid in the oral cavity that has been used as a sample to diagnose and control the treatment of systemic diseases and disorders (23). Measurement of oxidative biomarkers in the saliva to examine numerous clinical conditions has increased over the last decade (24).

Thus, considering the fact that obesity is a chronic state that involves diverse pathways such as oxidative stress, inflammation, and endothelial dysfunction, the aim of this study was to assess the salivary and serum levels of oxidative biomarkers in obese and overweight subjects and analyze the correlation between salivary and serum parameters.

MATERIALS AND METHODS

Study population

Participants were recruited from January to August 2014 in São Miguel do Oeste located in south of Brazil. The protocol of the study was approved by the Ethics Committee of the University of West Santa Catarina (UNOESC, Nº 219.091) and all participants provided writ- ten informed consent. Experiments were performed in 149 subjects. A total of 54 normal weight subjects with gender-matched healthy volunteers served as a control group (32 females and 22 males). The subjects with increased weight were divided in two subgroups, matching for sex, age, and body mass index, and were enrolled as follows: 1) 27 overweight subjects (17 females and 10 males); 2) 68 obese young subjects (41 females and 27 males). The participants were non-smokers and were not using any medications, as shown in Table 1.

Table 1 Baseline characteristics of study participants 

Groups

Normal weight Overweight Obese
N 54 27 68
Male/Female 22/32 10/17 27/41
Age (years) 21.0 (19.8-24.0) 24.0 (21.0-26.0) 25.0 (22.0-27.0)
Weight (kg) 60.1 ± 9.4 77.2 ± 7.0a 97.7 ± 16.0ab
Height (cm) 167.8 ± 7.3 167.0 ± 8.3 166.8 ± 10.5
BMI (kg/m2) 20.9 (19.3 – 22.6) 28.1 (26.5 – 28.7)a 34.1 (32.4 – 37.5)ab
NC (cm) 36.0 ± 4.3 36.0 ± 3.3 38.8 ± 3.5ab
WC (cm) 72.3 ± 6.8 87.7 ± 6.3a 104.2 ± 13.7ab
HC (cm) 95.7 ± 6.2 107.1 ± 5.5a 117.8 ± 8.9ab
SBP (mmHg) 120.9 ± 11.7 126.3 ± 11.2 136.7 ± 14.2ab
DBP (mmHg) 72.0 (67.8 – 80.0) 81.0 (73.0 – 89.0)a 86.0 (77.0 – 93.0)a
Body fat (%) 25.3 (18.9 – 28.9) 33.3 (27.4 – 36.8)a 38.7 (34.8 – 41.6)ab
Fat body mass (kg) 14.9 (12.7 – 17.9) 24.2 (21.0 – 28.3)a 36.1 (31.1 – 40.7)ab

Data are expressed as means ± SD or median (interquartile ranges). Data were processed for analysis for One-way ANOVA followed by Tukey’s test or Kruskal Wallis test followed by Dunn’s Multiple Comparison Test. BMI: body mass index; NC: neck circumference; WC: waist circumference; HC: hip circumference; SBP: systolic blood pressure; DBP: diastolic blood pressure.

a p < 0.0001 compared to normal weight group.

b p < 0.0001 compared to overweight group.

Anthropometric measurements

All measures were taken in the Anthropometry Laboratory at the University of West Santa Catarina, São Miguel do Oeste, SC (Table 1). Standing height (H, cm) was measured to the nearest 0.1 cm using a wall-mounted stadiometer (Charder model HM-210D). Weight (W, kg) was measured to the nearest 0.1 kg using a calibrated electronic scale (Toledo model 2124). BMI was calculated as W/H2 (kg/m2). Waist circumference (WC), neck circumference (NC) and hip circumference (HC) was measured in centimeters with a flexible tape to the nearest 0.1 cm. For AC the tape was applied above the iliac crest with the subject standing with the abdomen relaxed, arms at sides and feet together. NC for the participant remained in the same position and tape was placed on half of the neck on the hyoid bone. The percentages of fat and fat weight were determined by bioimpedance (Biodynamics Model 450). Systolic and diastolic pressure (SBP, DBP) was measured in the individual after being seated and resting for 10 minutes, with a digital apparatus and were expressed in mmHg. All measurements were taken on the left side of the body, according to standardized procedures by Weiner and Lourie (11). During the anthropometric measurements, all participants were barefoot and clothed appropriately.

Indices and classifications

According to the World Health Organization, underweight was defined as BMI < 18.5 kg/m2, normal weight as BMI 18.5–24.9 kg/m2, overweight as BMI 25–29.9 kg/m2, and obesity as a BMI > 30 kg/m2 (12), all without comorbidities. According to Gallagher and cols. (13) % fat ≥ 20% (males) and % fat ≥ 33% (females) are the cut-points adopted to define over fatness, corresponding to overweight classification using BMI in a population of young adults. According to the National Institute for Health and Clinical Excellence guidelines, WC ≥ 102 cm for men and ≥ 88 cm for women are prerequisite risk factors for the diagnosis of the metabolic syndrome, as Waist-Hip Ratio (WHR) ≥ 0.9 for males and ≥ 0.8 females (25).

Saliva sampling

A well defined and standardized protocol was used for collection, storage, and processing of all the samples under the exactly same conditions. Unstimulated saliva samples were collected in the morning after fasting for at least 8 h. Brushing teeth, smoking, eating or drinking anything but water for at least 60 min prior to sampling were prohibited. For the collection of salivary samples, patients were asked to put and keep the cotton swab under tongue for Laboratory analyzes 3 min and then to place it back directly into the plastic container according to the manufacture’s instruction (Salivette tubes, Sarstedt, Nümbrecht, Germany). The collections were made in the laboratory under the guidance and supervision of researchers. Immediately after collecting the saliva samples were centrifuged for 10 min at 1100g, fractionated in eppendorf and were stored at –20ºC until analysis (19). Approximately 200 µL of thawed saliva samples was processed for enzymatic and biochemical tests. To avoid the effects of protein degradation, the samples that had been thawed were not reused.

Laboratory methods

Determination of Ferric-Reducing Antioxidant Power (FRAP): salivary and serun FRAP levels were measured according to Singh and cols., (20). Two hundred μL of prewarmed 37ºC FRAP reagent (1 volume of 3 mol/L acetate buffer, pH 3.6 + 1 vol of 10 mmol/L 2,4,6-tripyridyl-S-triazine in 40 mmol/L HCl + 1 vol of 20 mmol/L FeCl3) was mixed with 20 μL of saliva. Absorbance was read at 593 nm. Ferrous sulphate was used as standard and the concentration of FRAP was expressed in μmol/L. All other chemicals were obtained from Sigma Chemical Co. (St. Louis, MO, USA).

Determination of Thiobarbituric Acid Reactive Substances (TBARS): lipid peroxidation was estimated in plasma and salivary by measurement of thiobarbituric acid reactive substances (TBARS) according to the method of Lapenna and cols. (23), using 1% phosphoric acid and 0.6% thiobarbituric acid (TBA). The reaction product was measured spectrophotometrically at 532 nm and the results were expressed in nmol TBARS/mL.

Determination of Protein thiol groups (SH groups): protein thiol groups were assayed in salivary and plasma by the method of Boyne and Ellman (24) which consist of the reduction of 5.5’-dithio(bis-nithrobenzoic) acid (DTNB) in pH 7.0, measured at 412 nm. The results were expressed in nmol P-SH/mL.

Statistical analysis

The data were analyzed using Statistica 6.0 software (StatSoft, Tulsa, OK, USA). Data are expressed as means ± SD or median (interquartile ranges). The Kolmogorov-Smirnov test was used to examine the distribution of variables. Comparisons of baseline data among the groups were performed using One-way ANOVA followed by Tukey’s test or Kruskal Wallis test followed by Dunn’s Multiple Comparison Test. Spearman or Pearson correlation coefficients were calculated to describe crude associations between variables (bivariate correlation) and the effect of potential confounding factors was tested in multivariate linear regression models. A p-value of < 0.05 was considered statistically significant.

RESULTS

The baseline characteristics of the study participants are described in Table 1. As expected, systolic blood pressure (SBP) and diastolic blood pressure (DBP), weight, body mass index (BMI), neck, hip and waist circumferences, body fat percentage, and body fat mass were higher in obese when compared with normal-weight subjects (p < 0.0001).

Levels of salivary FRAP and SH groups were significantly elevated in the obese group when compared with the normal-weight group (p < 0.05 and p < 0.0001, respectively). There were no significant differences in levels of salivary uric acid among the groups. The degree of lipid peroxidation, measured by levels of salivary TBARS, were significantly higher in the obese group compared with the normal-weight group (p < 0.0001), as shown in Table 2.

Table 2 Concentration of salivary antioxidants and lipid peroxidation in the groups studied 

Groups

Normal weight Overweight Obese
N 54 27 68
Uric acid (mg/dL) 1.7 (1.4 – 2.4) 1.8 (1.4 – 2.6) 1.9 (1.4 – 2.3)
FRAP (mmol/L) 0.37 (0.29 – 0.46) 0.38 (0.35 – 0.48) 0.45 (0.35 – 0.59)a
SH groups (nmol P-SH/mL) 15.1 ± 8.9 20.9 ± 8.7b 22.22 ± 8.8b
TBARS (mmol/L) 20.7 ± 7.2 23.4 ± 7.9 28.2 ± 11.6b

Data are expressed as means ± SD or median (interquartile ranges). Normality was assessed by Kolmogorov-Smirnov test. Data were processed for analysis, where One-way ANOVA followed by Tukey’s test, and Kruskal-Wallis test followed by Dunn’s Multiple Comparison Test.

ap < 0.05, bp < 0.0001 compared to normal weight group.

Serum levels of FRAP, uric acid, and TBARS were significantly elevated in the obese group when compared with the normal-weight group (p < 0.05, p < 0.0001, and p < 0.05, respectively). In contrast, levels of SH groups were significantly decreased in the obese group when compared with the normal-weight group (p < 0.0001), as shown in Table 3.

Table 3 Concentration of serum antioxidants and lipid peroxidation in the groups studied 

Groups

Normal weight Overweight Obese
N 54 27 68
Uric acid (mg/dL) 3.8 ± 1.0 4.2 ± 1.0 4.7 ± 1.7b
FRAP (mmol/L) 0.89 ± 0.20 0.98 ± 0.25 1.05 ± 0.22a
SH groups (nmol P-SH/mL) 143.6 ± 21.7 123.2 ± 25.1 105.4 ± 24.2bc
TBARS (mmol/L) 5.1 (4.6 – 6.5) 6.0 (5.0 – 7.4) 6.6 (5.4 – 8.3)a

Data are expressed as means ± SD or median (interquartile ranges). Normality was assessed by Kolmogorov-Smirnov test. Data were processed for analysis, where One-way ANOVA followed by Tukey’s test, and Kruskal-Wallis test followed by Dunn’s Multiple Comparison Test.

a p < 0.05, bp < 0.0001 compared to Normal Weight Group, cp < 0.0001 compared to Overweight Group.

Correlation analyses were performed between antioxidant parameters and salivary and serum lipid peroxidation levels among the study groups. As shown in Table 4, there were significant correlations between salivary and serum levels of uric acid (r = 0.5162, p < 0.0001) and FRAP (r = 0.4361, p = 0.001) in the normal-weight group. This positive correlation was also observed in the obese group (r = 0.4205 and p = 0.0004 for uric acid, and r = 0.4482 and p = 0.0001 for FRAP), as shown in Table 5. No other positive correlations were observed in the other groups and parameters.

Table 4 Correlation between antioxidants salivary and serum in normal weight group 

Saliva Serum p and r value
Uric Acid (mg/dL) 1.7 (1.4 – 2.4) 3.8 ± 1.0 r = 0.5162 p < 0.0001
FRAP (mmol/L) 0.37 (0.29 – 0.46) 0.89 ± 0.20 r = 0.4361 p = 0.001

Data are expressed as means ± SD or median (interquartile ranges). Normality was assessed by Kolmogorov-Smirnov test. Data were processed for analysis by Pearson or Spearman correlation.

Table 5 Correlation between antioxidants salivary and serum in group obese 

Saliva Serum p and r value
Uric Acid (mg/dL) 1.9 (1.4 – 2.3) 4.7 ± 1.7 r = 0.4205 p = 0.0004
FRAP (mmol/L) 0.45 (0.35 – 0.59) 1.05 ± 0.22 r = 0.4482 p = 0.0001

Data are expressed as means ± SD or median (interquartile ranges). Normality was assessed by Kolmogorov-Smirnov test. Data were processed for analysis by Pearson or Spearman correlation.

DISCUSSION

Sampling of human saliva is an attractive means to diagnose and monitor diseases since the saliva may be collected easily and noninvasively. Saliva has been widely used to study a variety of molecules and biochemical substances. Salivary analysis may also be helpful in predicting the development of future diseases (26,27). In the present study, we investigated antioxidant parameters and markers of lipoperoxidation in the saliva of healthy young adults with obesity and overweight to verify if the excessive body fat would influence these parameters. We then correlated the results in the saliva with those in the serum.

The nonenzymatic parameters evaluated in this study were selected based on their specific antioxidant properties (28): (i) the FRAP test quantifies the general capacity of the saliva to chelate and inactivate metal ions (mainly Fe2+/3+) involved in the formation of highly reactive ROS/RNS, such as hydroxyl radicals (using the Fenton reaction); (ii) uric acid is both a preventive (chelating) antioxidant and a scavenger of free radicals that have already been produced; and (iii) SH groups are considered the nonenzymatic antioxidant frontline in most body fluids, and their measurement accurately identifies the redox (pro/antioxidant) balance in the saliva.

TBARS are products of lipid peroxidation often used to determine the balance between oxidation and antioxidation (29). The present data demonstrated an increase in TBARS concentration (p = 0.0002) in the saliva and serum (p < 0.05) in obese patients compared with non-obese individuals. These results suggest an increased generation of reactive species with potential to cause damage to cell membranes. Physiologically, an increase in O2 and H2O2 production can increase the levels of TBARS. Obesity associated with a dyslipidemic profile, characterized by high levels of triglycerides and low-density lipoprotein (LDL) and low levels of high-density lipoprotein (HDL), promotes lipid peroxidation through formation of free radicals and ROS. Lipid oxidation generates some products, including TBARS, F2-isoprostanes, and MDA, which are used as markers of oxidative stress (30).

SH groups (mixed disulfides of proteins and low-molecular-weight thiols) are very early products of protein oxidation generated during oxidative stress, formed a few seconds after oxygen radicals are generated. As a result, the assessment of the extent and specificity of this process during oxidative stress is one of the best measurements of the primary effects of oxygen radicals. S-thiolation has been correlated in many cases with changes in protein function. Hence, the estimation of salivary levels of protein thiol indicates the status of the oxidative stress (31,32). Many studies have observed changes in protein thiols, including alterations in glutathione levels in various diseases in which oxidative stress occurs (33,34). Oxidative stress is considered to be involved in the pathogenesis of insulin resistance associated with obesity (35).

In this study, salivary protein thiols were significantly increased (p < 0.0001) in obese patients when compared with normal-weight individuals. Similar results have been reported in studies measuring salivary and serum levels of antioxidants (peroxidase, superoxide dismutase, salivary total antioxidant status) in patients with type 1 diabetes mellitus. Increased antioxidant levels have been observed in these patients, indicating a compensation of the antioxidant systems against oxidative stress (36).

Corroborating with the above, FRAP levels were significantly higher (p < 0.05) in the saliva of patients in the obese group. This may reflect a compensation of the obese body against oxidative stress to neutralize reactive species since several paths generating oxidative stress are activated in obesity (37). Studies in obese diabetic subjects, compared with normal men and women matched for weight, have shown higher oxidative stress in obese individuals (12), as evidenced in this study by the increased production of TBARS.

In the acute phase of obesity, levels of antioxidants appear to be increased, but as the obesity becomes chronic, the antioxidant reserve depletes over time (9). In addition to a deficiency in antioxidants in individuals with obesity and excessive fat tissue, other mechanisms may contribute to the increased oxidative stress: hyperglycemia, increased muscle activity to bear the excessive weight, hypertension, chronic inflammation, endothelial production of EROS, and hyperleptinemia (37).

We observed no significant differences in uric acid levels in the saliva among the studied groups, although this compound has been noted to increase with increases in BMI. However, we observed that the serum of patients in the obese group had a greater concentration of uric acid. Uric acid, one of the largest hydrophilic antioxidants in the body, inhibits the action of free radicals on organic molecules, such as those that make up the cell membrane and the genetic material (26). However, the sharp increase in the concentration of uric acid appears to be a protective factor against oxidative stress, whereas its chronic increase is associated with a risk of chronic diseases (38).

Data from a series of studies have shown a strong and independent correlation between serum uric acid and insulin resistance in subjects with metabolic syndrome. Evidence has also shown that serum uric acid is a strong predictor of future development of diabetes (39,40). In this context, an important result of the present study was to detect higher concentrations of uric acid in obese individuals.

Oxidative stress occurring in obesity contributes to the oxidation of proteins in the plasma; these proteins may then undergo functional changes, in particular, loss of metabolic, enzymatic, and immunological properties (8). Protein thiol groups are the most susceptible to oxidation. Among proteins with antioxidant properties, powerful nonenzymatic antioxidants stand out such as glutathione, which has an important antioxidant action (7). Plasma thiol groups serve as antioxidants capable of removing oxidants responsible for initiating peroxidation, the main responsible for oxidative damage to proteins (40).

Our results demonstrate a significant plasma reduction in SH groups in the obese group compared with the normal-weight group. Since the serum levels of TBARS were significantly higher in the obese group, this effect was probably due to a requirement for neutralization of ROS, which are produced in greater amounts in this group. Plasma SH groups are antioxidants that scavenge oxidants that initiate peroxidation and are quantitatively the major manifestation of oxidative protein damage (18). In obesity, the increase in carbonyl protein and a concomitant reduction in plasma thiol groups may be a possible mechanism contributing to atherosclerosis, insulin resistance, and hypertension (41). These data demonstrate a consumption of thiol groups to combat overproduction of free radicals and ROS produced during oxidative stress.

The results showed a progressive increase in serum FRAP levels in relation to BMI, in addition to significantly higher levels in obese volunteers. This reinforces our line of thought in which we consider this compound to be produced in greater amounts to offset the increased oxidative stress in the obese group since FRAP levels indicate the total plasma antioxidant capacity.

The second objective of this study was to correlate the salivary and serum parameters. We found a significant positive correlation between salivary and serum levels of uric acid and FRAP in the obese and normal-weight group, suggesting that measurement of salivary antioxidants could be used in the future to assess the antioxidant status of obese patients.

In conclusion, the results of this study showed that obese young patients presented significant changes in salivary biomarkers when compared normal-weight volunteers. Changes in salivary and serum levels of FRAP, SH groups, uric acid, and TBARS in obese subjects suggest major changes in oxidative status in these individuals. These findings are particularly important since they show an oxidative and inflammatory imbalance frequently observed in obesity. These results could help determine the pathways involved in obesity-related oxidative stress that are relevant to the development of obesity in young individuals. The results demonstrated a good correlation between salivary and serum biomarkers. There was a good replication in the results and maybe in the future, salivary can be used instead of blood to measure these oxidative biomarkers. Hence, we conclude that in addition to physical characteristics, levels of salivary FRAP and SH groups are indicators of oxidative stress in young obese individuals. Salivary analysis may prove to be a noninvasive, patient-friendly technique to assess the antioxidant status in these cases.

Acknowledgments

The work had the financial support of the Brazilian National Research Council (CNPq), FAPE – UNOESC (Research Support Fund). The authors wish to thank the University of West of Santa Catarina (UNOESC), SC, Brazil, for support in this study.

REFERENCES

1. Arana C, Cutando A, Ferrera MJ, Gómez-Moreno G, Worf CV, Bolaños MJ, et al. Parameters of oxidative stress in saliva from diabetic and parenteral drug addict patients. J Oral Pathol Med. 2006;35(9):554-9. [ Links ]

2. Haidari M, Ali M, Gangehei L, Chen M, Zhang W, Cybulsky MI. Increased oxidative stress in atherosclerosis-predisposed regions of the mouse aorta. Life Sci. 2010;87(3-4):100-10. [ Links ]

3. Pazdro R, Burgess JR. The role of vitamin E and oxidative stress in diabetes complications. Mech Ageing Dev. 2010;131(4):276-86. [ Links ]

4. Sohal RS, Mockett RJ, Orr WC. Mechanisms of aging: an appraisal of the oxidative stress hypothesis. Free Radic Biol Med. 2002;33(5):575-86. [ Links ]

5. Al-Aubaidy HA, Jelinek HF. Oxidative DNA damage and obesity in type 2 diabetes mellitus. Eur J Endocrinol. 2011;164(6):899-904. [ Links ]

6. Maddux BA, See W, Lawrence JC Jr, Goldfine AL, Goldfine ID, Evans JL. Protection against oxidative stress-induced insulin resistance in rat L6 muscle cells by mircomolar concentrations of alpha-lipoic acid. Diabetes. 2001;50(2):404-10. [ Links ]

7. Furukawa S, Fujita T, Shimabukuro M, Iwaki M, Yamada Y, Nakajima Y, et al. Increased oxidative stress in obesity and its impact on metabolic syndrome. J Clin Invest. 2004;114(12):1752-61. [ Links ]

8. Rudich A, Tirosh A, Potashnik R, Hemi R, Kanety H, Bashan N. Prolonged oxidative stress impairs insulin-induced GLUT4 translocation in 3T3-L1 adipocytes. Diabetes. 1998;47(10):1562-9. [ Links ]

9. Rodrıguez de Sotillo D, Velly AM, Hadley M, Fricton JR. Evidence of oxidative stress in temporomandibular disorders: a pilot study. J Oral Rehabil. 2011;38(10):722-8. [ Links ]

10. Lee YH, Wong DT. Saliva: an emerging biofluid for early detection of diseases. Am J Dent. 2009;22(4):241-8. [ Links ]

11. Weiner JS, Lourie JA. Practical human biology. London: Academic Press, 1981. [ Links ]

12. James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic. Obes Res. 2001;9 Suppl 4:228S-33S. [ Links ]

13. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr. 2000;72(3):694-701. [ Links ]

14. Chielle EO, Bonfanti G, De Bona KS, Moresco RN, Moretto MB. Adenosine deaminase, dipeptidyl peptidase-IV activities and lipid peroxidation are increased in the saliva of obese young adult. Clin Chem Lab Med. 2015;53(7):1041-7. [ Links ]

15. Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C; National Heart, Lung, and Blood Institute; American Heart Association. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Arterioscler Thromb Vasc Biol. 2004;24(2):e13-8. [ Links ]

16. Calabrese V, Bella R, Testa D, Spadaro F, Scrofani A, Rizza V, et al. Increased cerebrospinal fluid and plasma levels of ultraweak chemiluminescence are associated with changes in the thiol pool and lipid-soluble fluorescence in multiple sclerosis: the pathogenic role of oxidative stress. Drugs Exp Clin Res. 1998;24(3):125-31. [ Links ]

17. Staron A, Makosa G, Koter-Michalak M. Oxidative stress in erythrocytes from patients with rheumatoid arthritis. Rheumatol Int. 2012;32(2):331-4. [ Links ]

18. Ramprasath T, Senthil Murugan P, Prabakaran AD, Gomathi P, Rathinavel A, Selvam GS. Potential risk modifications of GSTT1, GSTM1 and GSTP1 (glutathione-S-transferases) variants and their association to CAD in patients with type-2 diabetes. Biochem Biophys Res Commun. 2011;407(1):49-53. [ Links ]

19. Sereg M, Toke J, Patócs A, Varga I, Igaz P, Szücs N, et al. Diagnostic performance of salivary cortisol and serum osteocalcin measurements in patients with overt and subclinical Cushing’s syndrome. Steroids. 2011;76(1-2):38-42. [ Links ]

20. Singh N, Bhardwaj P, Pandey RM, Saraya A. Oxidative stress and antioxidant capacity in patients with chronic pancreatitis with and without diabetes mellitus. Indian J Gastroenterol. 2012;31(5):226-31. [ Links ]

21. Rahim MA, Rahim ZH, Ahmad WA, Hashim OH. Can saliva proteins be used to predict the onset of acute myocardial infarction among high-risk patients? Int J Med Sci. 2015;12(4):329-35. [ Links ]

22. Zhou L, Wang K, Li Q, Nice EC, Zhang H, Huang C. Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives. Expert Rev Proteomics. 2016;13(4):367-81. [ Links ]

23. Lapenna D, Ciofani G, Pierdomenico SD, Giamberardino MA, Cuccurullo F. Reaction conditions affecting the relationship between thiobarbituric acid reactivity and lipid peroxides in human plasma. Free Radic Biol Med. 2001;31(3):331-5. [ Links ]

24. Boyne AF, Ellman GL. A methodology for analysis of tissue sulfhydryl components. Anal Biochem. 1972;46(2):639-53. [ Links ]

25. Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56(5):303-7. [ Links ]

26. Vincent HK, Taylor AG. Biomarkers and potential mechanisms of obesity-induced oxidant stress in humans. Int J Obes (Lond). 2006;30(3):400-18. [ Links ]

27. Skrha J, Sindelka G, Kvasnicka J, Hilgertová J. Insulin action and fibrinolysis influenced by vitamin E in obese Type 2 diabetes mellitus. Diabetes Res Clin Pract. 1999;44(1):27-33. [ Links ]

28. Erdeve O, Siklar Z, Kocaturk PA, Dallar Y, Kavas GO. Antioxidant superoxide dismutase activity in obese children. Biol Trace Elem Res. 2004;98(3):219-28. [ Links ]

29. Sautin YY, Johnson RJ. Uric acid: the oxidant-antioxidant paradox. Nucleosides Nucleotides Nucleic Acids. 2008;27(6):608-19. [ Links ]

30. Tsouli SG, Liberopoulos EN, Mikhailidis DP, Athyros VG, Elisaf MS. Elevated serum uric acid levels in metabolic syndrome: an active component or an innocent bystander? Metabolism. 2006;55(10):1293-301. [ Links ]

31. Yoo TW, Sung KC, Shin HS, Kim BJ, Kim BS, Kang JH, et al. Relationship between serum uric acid concentration and insulin resistance and metabolic syndrome. Circ J. 2005;69(8):928-33. [ Links ]

32. Rho YH, Woo JH, Choi SJ, Lee YH, Ji JD, Song GG. Association between serum uric acid and the Adult Treatment Panel III-defined metabolic syndrome: results from a single hospital database. Metabolism. 2008;57(1):71-6. [ Links ]

33. Himmelfarb J, McMonagle E. Albumin is the major plasma protein target of oxidant stress in uremia. Kidney Int. 2001;60(1):358-63. [ Links ]

34. Himmelfarb J, McMonagle E, McMenamin E. Plasma protein thiol oxidation and carbonyl formation in chronic renal failure. Kidney Int. 2000;58(6):2571-8. [ Links ]

35. Uzun H, Konukoglu D, Gelisgen R, Zengin K, Taskin M. Plasma protein carbonyl and thiol stress before and after laparoscopic gastric banding in morbidly obese patients. Obes Surg. 2007;17(10):1367-73. [ Links ]

36. Lubrano V, Balzan S. Enzymatic antioxidant system in vascular inflammation and coronary artery disease. World J Exp Med. 2015;5(4):218-24. [ Links ]

37. Cejvanovic V, Asferg C, Kjær LK, Andersen UB, Linneberg A, Frystyk J, et al. Markers of oxidative stress in obese men with and without hypertension. Scand J Clin Lab Invest. 2016 Sep 26:1-6. [ Links ]

38. Shani M, Vinker S, Dinour D, Leiba M, Twig G, Holtzman EJ, et al. High normal uric acid levels are associated with an increased risk of diabetes in lean, normoglycemic healthy women. J Clin Endocrinol Metab. 2016;101(10):3772-8. [ Links ]

39. Wang ZN, Li P, Jiang RH, Li L, Li X, Li L, et al. The association between serum uric acid and metabolic syndrome among adolescents in northeast China. Int J Clin Exp Med. 2015;8(11):21122-9. [ Links ]

40. Xue B, Tan JB, Ning F, Sun JP, Zhang KY, Liu L, et al. Association between serum uric acid and prevalence of type 2 diabetes diagnosed using HbA1c criteria among Chinese Adults in Qingdao, China. Biomed Environ Sci. 2015;28(12):884-93. [ Links ]

41. Mori T, Ogawa S, Cowely AW Jr, Ito S. Role of renal medullary oxidative and/or carbonyl stress in salt-sensitive hypertension and diabetes. Clin Exp Pharmacol Physiol. 2012;39(1):125-31. [ Links ]

Received: December 25, 2015; Accepted: September 12, 2016

Correspondence to: Eduardo Ottobelli Chielle Rua Oiapoc, 211 89900-000 – São Miguel do Oeste, SC, Brasil eduardochielle@yahoo.com.br

Disclosure: no potential conflict of interest relevant to this article was reported.

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