draft · 2019. 4. 5. · draft 4 50 introduction 51 bodybuilding is a sport in which competitors...

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Oxidative stress, inflammatory, psychological markers and severity of respiratory infections are negatively affected during the pre-contest period in amateur bodybuilders

Journal: Applied Physiology, Nutrition, and Metabolism

Manuscript ID apnm-2018-0430.R2

Manuscript Type: Article

Date Submitted by the Author: 06-Sep-2018

Complete List of Authors: de Moraes, Wilson; Universidade Catolica de Brasilia, Post-Graduation Program on Physical Educationde Moura, Felipe; Universidade Federal do Ceara, Post-Graduation Program in Biotecnology (RENORBIO)Costa Moraes, Thamires; Faculdade LS, Department of Health SciencesSousa, Luis; University of California Davis, Department of Internal Medicine; Universidade de Sao Paulo Instituto de Ciencias Biomedicas, Department of Physiology and BiophysicsRosa, Thiago; Federal University of São Paulo, Medicine; Universidade Catolica de Brasilia, Post-Graduation Program in Physical EducationSchoenfeld, Brad; Department of Health Sciences, Lehman College, Bronx , NY , USA.Maia, Fernanda; Universidade Estadual do Ceara Centro de Ciencias da Saude, Department of Health SciencesPrestes, Jonato; Catholic University of Brasilia, Physical Education

Keyword: dietary intake < energy regulation, sports nutrition < nutrition, athletes, inflammation, dietary analysis < nutrition, stress < stress

Is the invited manuscript for consideration in a Special

Issue? :Not applicable (regular submission)

https://mc06.manuscriptcentral.com/apnm-pubs

Applied Physiology, Nutrition, and Metabolism

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1 Oxidative stress, inflammatory, psychological markers and severity of respiratory infections are

2 negatively affected during the pre-contest period in amateur bodybuilders

3

4 Wilson Max Almeida Monteiro de Moraes1, Felipe Carmo de Moura2, Thamires Cristina da Costa

5 Moraes3; Luís Gustavo Oliveira de Sousa4, Thiago dos Santos Rosa1, Brad J. Schoenfeld5, Fernanda

6 Maria Machado Maia6, Jonato Prestes1

7

8 1 Post-Graduation Program on Physical Education, Catholic University of Brasilia (UCB), Brasília,

9 Federal District, Brazil

10 2 Post-Graduation Program in Biotecnology (RENORBIO), Ceara Federal University, Ceara, Brazil

11 3 Department of Health Sciences, Faculdade LS, Brasilia, Federal District, Brazil

12 4 Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of Sao Paulo,

13 Sao Paulo, Brazil

14 5 Department of Health Sciences, CUNY Lehman College, Bronx, NY, United States

15 6 Department of Health Sciences, Ceara State University, Ceara, Brazil

16

17 e-mails: [email protected], [email protected], [email protected],

18 [email protected], [email protected], [email protected],

19 [email protected], [email protected]

20

21 Corresponding author:

22 Wilson Max Almeida Monteiro de Moraes

23 Wilson Max de Moraes, PhD. Post-Graduation Program on Physical Education,

24 Catholic University of Brasilia - Q.S. 07, Lote 01, EPTC – Bloco G. Zip code: 71966-

25 700 – Taguatinga – Federal District, Brazil. Phone/ fax: (+55) 618531019821

26 e-mail: [email protected]

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mailto:[email protected]

mailto:[email protected]

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28 Abstract

29 We examined whether off-season (OffS) and pre-contest (PreC) periods affect blood oxidative stress,

30 inflammation, immunological and psychological markers in twenty bodybuilders. The athletes

31 completed food intake (3-d record), physical activities, mood states (POMS), recovery-stress (RESTQ-

32 sport), Wisconsin Upper Respiratory Symptom Survey (WURSS-21), and blood were obtained for

33 biochemistry analyses. Almost all athletes were in a positive energy balance during the OffS, while

34 bodybuilders presented markedly restricted energy intake (~45%) leading to a loss of weight (-9%) and

35 fat mass (-45%), with a corresponding preservation of fat free mass in PreC. Protein intake was high

36 during both periods, while lipid and carbohydrate intake were reduced ~50% in PreC. Almost all

37 athletes consumed 100% of the RDA for micronutrients in OffS and 45% and 75% of the athletes had

38 intakes bellow the RDA for vitamin A and E in PreC. Oxidative damage to lipids (TBARS), protein

39 carbonyl, TBARS/ total antioxidant capacity ratio increased in PreC (32%, 27%, 60%), accompanied

40 by an increase in plasma TNF-α (4x), and WURSS-21 scores (25%). There were no significant changes

41 in serum antioxidant catalase, glutathione reductase and superoxide dismutase, nor in IL-1β and

42 immunoglobulin’s. In PreC, POMS showed positive changes in vigor (-20%), and negative effects on

43 fatigue (23%), as well as total mood disturbance (35%) and alterations in RESTQ-sport for general and

44 sport stress (34 and 50%, respectively) and sport recovery (-23%). Thus, PreC negatively affects

45 nutrient intake, which may exacerbate oxidative stress, inflammation and psychological status, as well

46 as the severity of respiratory infections in bodybuilders.

47

48 Keywords: dietary intake; sports nutrition; athletes; inflammation; dietary analysis; stress;

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50 Introduction

51 Bodybuilding is a sport in which competitors are judged by their muscular appearance,

52 presenting characteristics that include extreme muscular development, very low levels of body fat, and

53 symmetrical physiques (Helms et al., 2014). Bodybuilders generally employ two distinct phases in their

54 preparation: "Off season” (OffS), whereby the main focus is on maximizing muscle hypertrophy and

55 body mass gain, achieved primarily through altered training volumes combined with a hypercaloric diet

56 and positive energy balance (Spendlove et al., 2015; De Moraes et al., 2017); and a pre-competitive

57 period (PreC), lasting from 9 to 26 weeks before the competition (Rossow et al., 2013; Mitchell et al.

58 2018), when athletes focus on reducing body fat stores while maintaining fat-free mass levels specific

59 to competing in a given weight class. In this phase, bodybuilders enter a negative energy balance,

60 usually brought about by elevations in training volume, inclusion of aerobic activity and the use of

61 restrictive diets (Bamman et al., 1993; Newton et al., 1993; Helms et al., 2014; Spendlove et al., 2015).

62 It has been demonstrated that the combined effects of severe energy restriction and intensified

63 exercise training during rapid weight loss increase oxidative stress (Rankin et al., 2006; Yanagawa et

64 al., 2010; Tsai et al., 2011), which is known to initiate damage to cell components, an increase in

65 inflammation markers, as well as an impairment in immune function (Plunkett et al., 2010; Tsai et al.,

66 2011).

67 With regard to immune function, evidence shows that the deleterious effects of energy

68 restriction-induced rapid weight loss in athletes is associated with a lower concentration of serum

69 immunoglobulin (Ig) (Ohta et al., 2002; Umeda et al., 2004), and despite one study that found no major

70 alterations in serum Ig levels between professional bodybuilders in OffS and controls non-athletes

71 (Naghib et al., 2012), most research examining the effects of rapid weight loss and biomarkers of

72 oxidative stress and Ig levels has been conducted in combat sports (Ohta et al., 2002; Umeda et al.,

73 2004; Finaud et al., 2006; Yanagawa et al., 2010). While combat sports may involve short-term (eg, 7–

74 21d) weight-cutting strategies, the PreC period in bodybuilding is unique in that caloric restriction is

75 more prolonged (9 or more weeks) (Helms et al., 2014) and to our knowledge, no previous study has

76 investigated the effects of bodybuilding competition preparation on oxidative stress.

77 In addition, athletes who are energy restricted represent a population potentially exposed to

78 multiple micronutrient deficiencies since the diet can fail to meet vitamin and mineral requirements

79 including antioxidants (e.g., vitamins A, C, E). This, in turn, can diminish the ability of the body’s

80 antioxidant defense system to cope with an increase in exercise-induced reactive oxygen species, thus

81 increasing stress and fatigue (Plunkett et al., 2010). Ultimately, these detriments may predispose

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82 athletes to episodes of infections and training interruption by exacerbating inflammation and

83 immunosuppression (Gleeson, 2016). Although some studies involving bodybuilders have reported that

84 vigorous training regimens and dietary restrictive practices are associated with negative outcomes such

85 as compromised force-generating capacity (Mäestu et al., 2010; Rossow et al., 2013), elevations in

86 cortisol, and reductions in testosterone levels (Mäestu et al., 2010), the effects on blood biomarkers of

87 oxidative stress during a preparation period are currently unknown.

88 Another phenomenon observed in preparation for bodybuilding competitions is an alteration in

89 mood states. Although research indicates associated negative mood alterations (Newton et al., 1993;

90 Rossow et al., 2013), the data in these studies were collected during different periods prior to

91 competition. Moreover, the diets comprised different macronutrient proportions, which have been

92 shown to differentially affect mood states (Helms et al., 2015). Thus, the effect of OffS and PreC

93 training on mood states needs further investigation.

94 The aim of this study was to describe the habitual food intake of amateur bodybuilders,

95 evaluating the adequacy of nutrient intake in relation to current recommendations and determine

96 whether intake is associated with oxidative stress markers, mood states, recovery, and perceived stress.

97 A second purpose was to assess whether oxidative stress was associated with serum immunoglobulin

98 and upper respiratory tract infection (URTI) severity. Consequences on anthropometric parameters,

99 body composition, and inflammation-related markers were examined. We hypothesized that PreC

100 training and nutritional approaches would increase oxidative stress and inflammation accompanied by a

101 reduction in Ig levels with enhanced severity of upper respiratory tract infections (URTI) and negative

102 alterations in psychological parameters.

103

104 Methods

105 Subjects

106 We contacted the President and Vice President of the Local Federation of Bodybuilding and

107 Fitness and were granted permission to advertise to recruit member athletes. Thirty-three male

108 bodybuilders were interviewed, and twenty were selected for data collection during both OffS and PreC

109 phases. The inclusion criteria were: aged 20-35 years; attainment of least one championship and in

110 preparation for a nationally recognized competition. Smokers, diabetics, those using vitamin-mineral

111 supplements, and those who did not train regularly (less than 3 times a week) during the data collection

112 phase were excluded from participation. The research study was approved by the Research Ethics

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113 Committee of Ceara State University (process 04463980-5) in accordance with the Helsinki

114 Declaration, and all participants provided informed written consent before participation.

115

116 Anthropometric data

117 Body weight and height measurements were carried out on a Plenna® scale and Stadiometer

118 (exata, Brazil), respectively. Waist circumference was measured using a nylon measuring tape (WCS,

119 Brazil) and skinfolds were obtained using a scientific skinfold caliper (Lange, EUA) with a precision of

120 1mm and a constant pressure of 10g/mm². Measurements were taken across seven sites including the

121 triceps brachii, biceps brachii, suprailiac, abdominal, mid-thigh, subscapular, and medial calf. All

122 measurements were performed by the same evaluator, and the average of three measurements was

123 considered for analysis. Body density was calculated using the Petroski (1995) method, and percentage

124 body fat was calculated using the Siri (1961) equation.

125

126 Estimated energy expenditure and dietary data

127 Subjects recorded all their physical activities in activity diaries over a four-day period and total

128 energy expenditure was calculated as the sum of energy expenditure for each activity obtained by

129 multiplying the amount of time spent in that activity by the corresponding metabolic equivalents

130 (Ainsworth et al., 2000). Physical activity level was determined by dividing total energy expenditure by

131 the basal metabolic rate calculated with the Cunningham equation (Cunningham, 2000). Energy

132 balance was estimated by subtracting total energy intake from energy expenditure.

133 A 3-day food record (2 nonconsecutive weekdays and 1 weekend day) and a food frequency

134 questionnaire with food consumed regionally (Henriques, 2000) were used to estimate habitual nutrient

135 intake with DietWin® software (Porto Alegre, Brazil). Carbohydrate, protein, and fat intakes were

136 compared with current recommendations for bodybuilders (Helms et al., 2014), saturated,

137 monounsaturated and polyunsaturated fats were compared with Brazilian guidelines (Santos et al.,

138 2013), and fiber and micronutrients were compared with the values recommended by the Dietary

139 Reference Intake (DRI) (Institute of Medicine, 2000).

140

141 Psychological measurements

142 Mood was evaluated by the Profile of Mood States (POMS) questionnaire, previously translated

143 and validated for the Brazilian population (Peluso, 2003). POMS comprises 6 dimensions: Tension,

144 depression, anger, fatigue and confusion (negative dimension) and vigor (positive dimension), each

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145 consisting of 4 items. The instrument comprises 65 items rated on a 5-point Likert-type scale (ranging

146 from 0 = “no” to 4 = “extremely”). Participants answered the questionnaire as to, “How have you been

147 feeling during the past week, including today?” Total mood disturbance was determined by the sum

148 score of the negative dimension subtracting the score of the positive dimension vigor and then adding

149 the value “100” (Morgan et al., 1987).

150 The Recovery Stress Questionnaire for Athletes (RESTQ-Sport), translated and validated to

151 Brazilian Portuguese (Costa and Samulski, 2005), was assessed for measurements of simultaneous

152 frequency of the actual stress with the frequency of recovery-associated activities. RESTQ-Sport

153 includes 76 items distributed in 19 scales (10 related to stress and nine related to recovery). Each scale

154 contains four items evaluated by a 6-point Likert-type (ranging from 0 = “never” to 6 = “always”).

155 Final scores were calculated as the sum of the stress-related scales (ΣS) and recovery (ΣR), and the

156 difference between ΣR and ΣS.

157

158 Questionnaire of Symptoms of Upper Respiratory Tract Infection (URTI)

159 The Portuguese version of the WURSS-2120 questionnaire [25] was applied in each training

160 period. The severity of reported symptoms was rated on the following 7-point Likert-type scale: 1 (very

161 lightly), 3 (lightly), 5 (moderately) and 7 (severely). When a symptom was absent, the item was filled

162 in with 0 (zero). A general symptom score was calculated as the sum of total severity scores (ten

163 questions about symptoms and the nine involving limitations) and collectively considered as “severity”

164 (Moreira et al., 2009).

165

166 Blood Collection and Biochemical Analysis

167 10 ml of venous blood was collected in heparinized tubes from each subject after a 10-12 hour

168 fast and 48 hours after the last training session during OffS (12-16 weeks previous to competition) and

169 PreC (3-5 weeks previous to competition). Plasma was immediately separated by centrifugation (15

170 minutes at 3000 rpm) and used to determine creatine kinase-MB (CK), lipid peroxidation, carbonyl

171 protein (CP), total antioxidant capacity (TAC) and pro-inflammatory cytokine concentration. After this

172 procedure, red blood cells were centrifuged (5 minutes at 3000 rpm) in a saline phosphate buffer (PBS)

173 5mM containing NaCl 0.9%, then washed three times and diluted in water (1:1 v/v) for antioxidant

174 enzymes assays glutathione reductase (GR), catalase (CAT) and total superoxide dismutase (SOD) as

175 well as measurement of IgA, IgG, IgM.

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176 CK was analyzed by the enzymatic method with commercial kits (Bioclin, Brazil). Lipid

177 peroxidation (damage to lipids) was determined by thiobarbituric acid reactive substances (TBARS)

178 and the results were calculated according to the standard curve made with MDA at 4Μm as previously

179 described (Maia et al., 2014; De Moraes et al., 2017). For CP determination, 100μL aliquots of plasma

180 were mixed in 600 μL of 10mM DNPH or 2M HCl (sample blank) and the tubes were kept at room

181 temperature in the dark for 60 min. Then 600μl of 20% TCA was added to the tubes, which were kept

182 at room temperature in the dark for 10 min and centrifuged at 11000g for 5 min. The supernatant was

183 discarded and the precipitate was washed three times with ethanol/ ethyl acetate (1:1), with incubations

184 of 10 min, RT and centrifugation at 15000g for 5 min at 4°C. The precipitated proteins were dissolved

185 with 900μL of 6.0M guanidine, prepared in 20mM KH2PO4, under continuous agitation at 37°C for 60

186 min. Afterward, the tubes were centrifuged at 15000g for 10min at 4°C and absorbance of supernatant

187 was read in 360nm against 6.0M guanidine solution. For the determination of total proteins, the tube

188 supernatant blank was read at 280nm using bovine serum albumin as standard. The concentration of

189 carbonyl protein was calculated using the molar absorptivity coefficient 22000L.mol-1.cm-1 and results

190 expressed in μmol/mg (Levine et al., 1990).

191 For TAC measurement, we utilized a commercial kit from Randox Laboratories (Randox

192 Laboratories Ltd, Ireland). In this essay, metmyoglobin reacts with H2O2 to form ferrylmyoglobin. A

193 chromogen (2,2’-azinodi-(ethylbenzthiazoline sulfonate (ABTS) was incubated with the

194 ferrylmyoglobin to produce the radical cation species ABTS·+. This has a relatively stable blue-green

195 color measured at 600 nm and results were expressed as mmol/L of trolox equivalent.

196 GR was measured in accordance with the protocol of Smith et al. (1988), in which hemolysate

197 diluted (1:20) samples were added to an incubation medium containing 0.2M KH2PO4, 2mM EDTA at

198 pH 7.0, 50μl 2mM NADPH and 250μl 3mM DTNB. A 50 μl volume of 20mM GSSG was added to

199 initiate the reaction. Formation of 5,5 'Thiobis 2-nitrobenzoic acid (TNB) was monitored at 412nm.

200 The following equation was used to calculate the enzymatic activity: E = 100 x A / [Hb], where E is the

201 activity of the enzyme in international units (IU)/ hemoglobin (in grams); A is the number of units of

202 enzyme in the sample, calculated by the equation: ΔA/ 13.600 x Vh/ Vc, where ΔA is the absorbance

203 difference at 412nm/ minute; 13.600 is the extinction coefficient of TNB at 412nm; Vh is the volume

204 of the hemolysate in cuvette and Vc is the total volume of cuvette and [Hb] is the hemoglobin

205 concentration of the hemolysate (g/dl);

206 For CAT determination, the samples were added to 50mM phosphate buffer and 10mM H2O2.

207 The drop in absorbance values was followed at 240nm. The enzymatic activity was calculated by the

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208 following equation: (2.3/Δt).(a/b).(logA1/A2), where a is the volume of hemolysate in cuvette and b is

209 the total volume of cuvette; A1 is the absorbance value at t = 0 and A2 is the absorbance value at the

210 final time (Aebi, 1984).

211 SOD activity was based on the nitroblue tetrazolium (NBT) reduction as per the method of

212 Ewing and Janero (1995), which involves the reduction of O2•- radicals by NTB following a linear first

213 order kinetic during 5 min. In this assay, the samples were added to 50mM phosphate buffer and

214 0.1mM EDTA, 62mM NBT, and 98mM NADH. The reaction was initiated with the addition of 33mM

215 PMS in 50mM phosphate buffer, pH 7.4, containing 0.1 mM EDTA. The absorbance was monitored at

216 560nm and the results were expressed in unit (U)/gHb, whereby one unit of SOD activity is defined as

217 the amount of enzyme required to inhibit the reduction of NBT by 50% under the specified conditions.

218 Serum immunoglobulins IgG, IgM, and IgA were measured by the nephelometry method in a

219 Behring nephelometer with commercial kits (Boclin, Brazil). The cytokines interleukin-1β (IL-1β) and

220 TNF-α on plasma were analyzed as inflammatory marker by ELISA specific kit according to the

221 manufacturer's instructions (Quantikine, R&D System, Minneapolis, MN, USA) with a detection limit

222 of 3.91pg/ml for IL-1β and 15.6pg/ml for TNF-α. All the biochemical measurements were performed in

223 duplicate. Although it was not our goal to compare athletes with non-athletes, we included one control

224 group paired for age with healthy individual’s non-athletes to better understand the biochemical results

225 when appropriate.

226

227 Statistical analyses

228 Data were analyzed with SigmaStat for Windows (Systat Software Inc., version 3.5, San Jose,

229 CA, USA). The data related to anthropometry, food intake, biochemistry and psychological parameters

230 during the two training phases were tested for normality by the Shapiro-Wilk normality and compared

231 by either a paired Student’s t test or Wilcoxon signed rank test. All values were expressed as mean and

232 standard deviation (SD). The significance threshold was set at p≤0.05. Simple linear regression analysis

233 was used to investigate associations between variables.

234

235 Results

236 Anthropometric and training characteristics

237 The mean reported time of training and participation in competitions was 11.0±3.6 years and

238 5.6±2.6 competitions, respectively. The anthropometric characteristics of bodybuilders (age 28.10±3.94

239 years) is presented in Table 1 (test-retest coefficient exceeding 0.85 for each measurement). Most of

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240 the parameters in PreC were different compared with the OffS period, except for height, fat free mass

241 (FFM), arm muscle circumference (AMC), arm muscle area (AMA), and forearm circumference.

242 The training characteristics employed by athletes (Table 1) indicate that total training volume

243 was higher in the PreC and that this finding extends to both resistance training and aerobic exercises.

244 Regarding the use of pharmacological agents, 11 bodybuilders in OffS, and 15 in PreC,

245 respectively, reported anabolic steroids usage at the time of data collection. Two bodybuilders used

246 pharmacological agents to increase lipolysis (ephedrine) and this usage increased in PreC, as 14

247 athletes reported consumption of clenbuterol and/ or ephedrine. In addition, two athletes in OffS, and

248 five athletes in PreC, reported the use of growth hormone.

249 Drugs that were taken in the OffS are as follows: oxymetholone (n=3), methandrostenolone

250 (n=4), testosterone cypionate (n=5), testosterone propionate (n=9), testosterone phenilpropionate (6),

251 nandrolone decanoate (n=6), testosterone suspension (n=6), trenbolone acetate (n=8), boldenone (n=8).

252 The following drugs were taken in the PreC period: stanozolol (n=11), oxandrolone (n=4), testosterone

253 propionate (n=14), nandrolone decanoate (n=12), undecanoato de testosterone (n=6), testosterone

254 phenilpropionate (3), trenbolone acetate (n=8), trenbolone enanthate (n=6). Total dosages ranged from

255 40 to 800 mg of the various steroids combinations per day.

256

257 Estimated energy expenditure and food intake analysis

258 During the OffS, athletes consumed on average 43% more calories compared with the PreC and

259 presented in general, a positive energy balance (see Table 2). In contrast, during the PreC all athletes

260 maintained an energy deficit and 95% had a deficit exceeding 500kcal/day.

261 Regarding protein consumption, the mean reported daily intake during the OffS was

262 237.4±61.1g, corresponding to 23.6±5.2% of total energy intake. During PreC, protein intake decreased

263 slightly to 205.0 ±54.6g corresponding to 36.6±9.7% of the total energy intake.

264 The carbohydrate intake of the athletes during the OffS was 560.1±89.8g, corresponding to

265 54.1±9.6% of the total energy intake. During the PreC, the athletes reported a carbohydrate intake of

266 277.1±82.3g, corresponding to 48.6±10.6% of the total energy intake; this represents a reduction in

267 intake of ~50% PreC compared to OffS.

268 The mean reported fat intake during OffS was 1.1±0.4g/kg/day (22.4±8.9% of total energy

269 intake), decreasing to 0.48±0.2g/kg/day (14.8±5 of the total energy intake) during the PreC.

270 The mean daily intake of micronutrients is presented in Table 3. Vitamins C and E, and zinc and

271 manganese intake showed a significant reduction in PreC. When compared to DRI values, zinc,

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272 manganese and copper intake were above RDA values for 100% of the athletes in OffS, while 90% of

273 the athletes had intakes above the RDA for zinc and copper and 80% of bodybuilders had intakes above

274 the RDA for manganese in PreC. In the OffS, 100% of the individuals had intakes above the RDA for

275 vitamin E, 90% had intakes above the RDA for vitamin C, and 70% had intakes above the RDA for

276 vitamin A. However, 15% of the athletes had intakes below the RDA for vitamin C, 45% of the athletes

277 had intakes below the RDA for vitamin A, and 75% had intakes below the RDA for vitamin E in PreC.

278

279 Psychological measurements

280 Results related to psychological status are presented in Table 4. During the OffS and PreC,

281 responses obtained for the POMS were distinct, as observed in the “iceberg profile” (high score on

282 vigor, low scores on depression, tension, fatigue, confusion and anger) noted during the OffS. In

283 contrast, during PreC athletes displayed worsened mood states (vigor) (-20%) and total mood states

284 (35%). There were also significant negative changes in specific mood states scores, such as higher

285 levels of fatigue (23%) and a tendency toward increases in depression (19%; p=0.08).

286 The results of RESTQ-sport are also shown in Table 4. Findings suggest a higher level of stress

287 and a lower level of perceived recovery in PreC. In addition, PreC was associated with a significant

288 increase in scores of fatigue, lack of energy and emotional exhaustion (61.9, 47.7 and 73.1%

289 respectively), as well as a reduction in well-being and sleep quality scores (-33.6 and -33.5%,

290 respectively).

291

292 Oxidative stress and inflammatory markers, immunoglobulin and symptoms of upper

293 respiratory severity

294 Blood antioxidant enzymes, oxidative and damage markers measured during the OffS and PreC

295 periods are shown in Figure 1. There was no effect of training phase on antioxidant enzymes CAT, GR

296 or SOD (Fig. 1A, 1B, 1C). Oxidative damage markers TBARs and carbonyl protein (Fig. 1D, 1E) were

297 increased during PreC (32% and 27%, respectively), accompanied by a reduced TAS (-26%) (Fig. 1F)

298 and mainly TBARS/TAC (60%) (Fig. 1G), as well as an increase in CK values (45%) (Fig. 1H).

299 TNF-α was increased fourfold during the PreC (Fig. 2A), but there were no major alterations in

300 IL-1β levels between training phases (Fig. 2B). In regard to symptoms of URTI, the results suggest that

301 athletes were most likely to acquire infections of the upper respiratory tract during the PreC (Fig. 2C).

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302 In regard to serum Igs, results showed that IgG was reduced ~8% in comparison to OffS,

303 showing a trend toward a decrease in PreC (p=0.08) (Fig. 2D) without major changes in IgA and IgG

304 levels (Fig. 2E, 2F).

305

306 Discussion

307 Findings of the present study confirm our initial hypothesis that bodybuilding athletes employ

308 greater exercise training overloads and restrictive diets during the pre-contest period, and that these

309 strategies were accompanied by increased oxidative stress and inflammation. This, in turn, may

310 exacerbate the severity of upper respiratory tract infections and result in negative alterations of mood

311 states and perceived balance stress/ recovery.

312 Despite these unfavorable outcomes, the athletes in our study maintained their FFM values

313 during the PreC period, in contrast to previous research showing that an energy deficit induces FFM

314 loss (Rossow et al., 2013; Robinson et al., 2015). The high protein intake reported by athletes in our

315 study may have contributed to the preservation of FFM, as well as their reported use of anabolic agents.

316 These findings are consistent with previous findings in bodybuilders, whereby fat mass was

317 significantly reduced in PreC and FFM was preserved (Bamman et al., 1993; Mäestu et al., 2010;

318 Mitchell et al., 2018).

319 Bodybuilding is a sport with specific weight categories, and competing athletes often need to

320 “cut weight” in the weeks leading up to competition to improve their appearance through maximal fat

321 loss, and then qualify for entry in a weight class close enough to their fat reduced-bodyweight (Helms

322 et al., 2014). It is well documented that this weight reduction is generally achieved by increasing

323 energy expenditure (training volume) and restricting daily caloric intake (Mäestu et al., 2010; Rossow

324 et al., 2013). Bodybuilders in the present study presented markedly restricted energy intake (~45%)

325 while only modestly increasing energy expenditure (~7%), thereby promoting a daily caloric deficit of

326 -1355.51kcal. The energy reduction was achieved mainly through a lower energy intake per meal

327 (−46.7%), while daily meal frequency was not substantially altered (−4.1%). Although the athletes

328 increased their total training volume during the PreC period, the level of physical activity did not

329 increase significantly, suggesting that they spent less energy on activities other than exercise training.

330 Together, these data indicate that weight loss was primarily achieved via energy restriction.

331 Restrictive dietary practices have been associated with loss of muscle strength and negative

332 alterations in mood (Mäestu et al., 2010; Rossow et al., 2013), while studies that investigated the

333 effects of energy restriction on oxidative stress markers have found conflicting results. Some studies

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334 found no modification of oxidative stress (Finaud et al., 2006; Merrells et al., 2008; Reljic et al., 2015)

335 or a reduction (Pons et al., 2018), whereas others reported an increase (Rankin et al., 2006; Yanagawa

336 et al., 2015). This discrepancy may be related to factors such as the duration of the energy restriction,

337 the biomarkers utilized for the measure of oxidative stress, and the training load employed during the

338 energy restriction. It is well-established that athletes undergo physiologic adaptations that enhance

339 antioxidant defense systems, which in turn help to minimize oxidative stress at rest and in response to

340 acute exercise conditions. However, insufficient energy intake may compromise nutrient intake

341 (Merrells et al., 2008; Plunkett et al., 2010), which in turn may negatively impact an athlete’s

342 endogenous antioxidant adaptive response.

343 In the present study, there was no significant increase in activity of the antioxidant enzymes

344 GR, CAT or SOD during the PreC, while increases in oxidative stress markers TBARS and PC

345 occurred in parallel with a decrease in TAC. These findings suggest that an improvement of the

346 antioxidant system does not occur to the same extent as oxidative damage propagates during this

347 training phase, and corroborates the hypothesis that oxidative stress is higher following the PreC

348 period in bodybuilders. Notably, the level of physical activity did not increase significantly despite an

349 increase in total training volume during PreC. Thus, it is unlikely that training overload was a primary

350 determinant in the limited increase in the activity of antioxidant enzymes.

351 During the PreC period, several factors resultant to calorie restriction may have contributed

352 to the attenuated antioxidant response and the increase in markers of oxidative damage. For one, the

353 high rate of deficiency in monounsatured and, more so, polyunsatured fats (PUFA) during the PreC

354 period. Copious intake of PUFA in the diet has been associated with reduced oxidative stress during

355 energy restriction in athletes (Finaud et al., 2006). PUFAs may act as a free radical scavenger,

356 increasing RNAm for antioxidant enzymes, and may stimulate vitamin E incorporation into membranes

357 to protect against lipid peroxidation (Venkatraman et al., 1998). In addition, PUFA intake was

358 associated with antioxidant enzymes CAT (r=0.701) and GR (r=0.613), and inversely associated with

359 TBARs (r=-0.867) and PC (r=-0.702) during PreC.

360 Another important finding was the lower reported carbohydrate intake during the PreC (3.5g/kg

361 body weight), which is in concordance with that observed in studies where mean intakes were between

362 2.5 to 3.0 g/kg body weight over 26 weeks (Rossow et al., 2013), and 2.6 to 3.8g/kg body weight over

363 16 weeks (Mitchell et al., 2018), probably reflecting a contemporary practice of lower carbohydrate

364 consumption for achieving the necessary caloric deficit while consuming adequate protein and fat in

365 the weeks prior to a competition.

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366 A critical issue regarding micronutrient consumption was the limited intake of vitamin A and E

367 in PreC. Most athletes presented a micronutrient intake below the UL cutoff point, indicating that

368 vitamin and mineral intake was not excessive. A systematic review of the dietary practices of

369 bodybuilders (Spendlove et al., 2015) showed that several micronutrients were consumed in excess

370 (>1000% above the RDA or above the UL). However, when supplements were excluded from the

371 analysis, micronutrient intake was below RDA values. The results of the present study are in accord

372 with this finding because the use of vitamin-mineral supplements was an exclusion criteria and the

373 intake of vitamins A and E was negatively affected by energy restriction.

374 Vitamin E displayed a positive correlation with CAT (r=0.723) and GR (r=0.60) and an inverse

375 correlation with TBARs (r=-0.789). The absorption and utilization of vitamin E highly depends on the

376 presence of dietary lipids, particularly PUFAs, which had very limited consumption in PreC.

377 Bodybuilders in the present study reported an intake of fruits and vegetables equivalent to 1.3 and 2.8

378 servings/day, respectively, in PreC. These data are lower compared to those reported by Vega and

379 Jackson (1996), where mean intakes were 3.4 for fruits and 3.6 servings/day for vegetables. Moreover,

380 75% of the athletes consumed less than 2 servings/ day of fruits and 85% of the athletes consumed less

381 than 4 servings/ day of vegetables. This may explain, at least in part, the low antioxidant intake.

382 It is likely that the marginal intake of some micronutrients in short periods of time is

383 insufficient to induce deficiency of a micronutrient, but may lead to immunosuppression and

384 inflammation, thereby predisposing athletes to episodes of infections and corresponding training

385 interruption (Plunkett et al., 2010; Glesson et al., 2016). In the present study, this finding is supported

386 by a greater URTI severity, as indicated by WURSS-21 total score. Importantly, TNF-α levels

387 dramatically increased during the PreC, suggesting a heightened inflammatory response. Plunkett et al.

388 (2010) also reported higher baseline TNF-α concentrations (38-fold) in athletes after they restricted

389 fruit and vegetables (1-2 sizes/day) for 2 weeks compared to a period with no restriction, highlighting

390 the negative consequences of short-term reductions in exogenous antioxidants, even in the absence of

391 clinical manifestation of micronutrient deficiency.

392 On the other hand, the serum concentrations of Igs did not explain the increased URTI severity

393 in PreC. Otha et al. (2002) reported a significant post-competition reduction in the serum levels of IgG,

394 IgA and IgM (measured 5 days after the event) in judokas who performed rapid weight loss during the

395 pre-competition period. Similarly, Umeda et al. (2004) reported reductions in serum IgG and IgM 7

396 days post-competition in judokas who created an energy deficit from combined exercise and caloric

397 restriction. These findings suggest that immunosuppression occurs in the post-competition recovery

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398 period rather than immediately before competition. Because data from our study were collected 3 to 5

399 weeks prior to competition, there is a possibility that an increase in immunoglobulin could not be

400 detected. It is interesting to note that in the studies with judokas, the majority of weight loss occurred in

401 the week prior to competition, indicating rapid and aggressive weight loss, which may have

402 exacerbated the reduction in serum immunoglobulin levels.

403 In addition to the negative physiological effects observed in PreC, a worsening in mood states

404 was also noted. Bodybuilders in OffS exhibited higher vigor scores with low levels of negative mood

405 scores (tension, depression, anger, fatigue and confusion). This is characterized by an “iceberg profile”,

406 which has been proposed to be a predictor of performance (Morgan et al., 1987). However, the iceberg

407 profile was not maintained in PreC, similar to previously reported results in bodybuilders 6 weeks prior

408 to competition (Newton et al., 2003) and one month before competition (Rossow et al., 2013).

409 Moreover, RESTQ-sport results indicated deleterious alterations in stress/recovery balance and

410 reflected the adverse psychological effects of energy restriction associated with training overload.

411 These findings, together with the higher levels of CK observed in PreC, reinforce the athletes had a

412 poor recovery of exercise training demands in this phase.

413 The present study had several limitations. Firstly, data were collected at only one time-point in

414 each training phase whereby diets were assessed over a 3-day period; this may not reflect dietary

415 practices over a longer period of time, particularly with respect to micronutrient intake (Burke, 2015).

416 Second, plasma concentrations of micronutrients were not measured. However, it is interesting to note

417 that serum antioxidant micronutrient levels were not associated with dietary intake in athletes

418 (Machefer et al., 2007; Reljic et al., 2015). Third, the fact that some athletes reported anabolic steroid

419 usage may have influenced the results, limiting comparisons with more recent investigations conducted

420 in “drug-free” athletes (Rossow et al., 2013; Robinson et al., 2015; Mitchell et al., 2018).

421 Further studies assessing long-term diets and endeavoring to track dietary changes from

422 emerging strategies such as "carbocycles” or “refeed days" are needed to extend our results to a larger

423 population of bodybuilders (Mitchell et al., 2017). Additional studies are also need to explore the use of

424 vitamin and mineral supplements in competitive bodybuilders, as well as to investigate salivary

425 immunoglobulin levels because their stronger correlation with the frequency and severity of URTI

426 compared to blood levels (Glesson et al., 2016).

427 Our findings suggest that athletes and professionals involved with competitive bodybuilding

428 should consider a slower weight loss (0.5 to 1% of body weight/ week) and implement strategies to

429 obtain adequate nutritional intake, which has been shown to result in better outcomes as FFM

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430 preservation and resting metabolic rate (Helms et al., 2014; Mitchell et al., 2018). Athletes consuming

431 suboptimal amounts of micronutrients or with high baseline levels of oxidative stress and/or low

432 antioxidants may benefit from a greater intake of fruits and vegetables (Plunkett et al., 2009), as well

433 micronutrient supplementation (within physiological doses) (Helms et al., 2014) to optimize

434 antioxidant status.

435 In summary, the present study provides evidence that bodybuilders restrict energy intake during

436 PreC, which is associated with an increase in oxidative stress markers during the PreC period, impaired

437 up regulation in antioxidant enzymes, and decreased total plasma antioxidant capacity. These outcomes

438 may have been negatively affected by reduced polyunsaturated fat, and vitamin intake, particularly

439 vitamin E. Furthermore, the PreC period induced adverse psychological effects, higher inflammation

440 and greater severity of infections of the upper respiratory tract. These findings suggest that the

441 deleterious effects of energy restriction are not confined to combat athletes, but also occur in sports

442 involving long term weight-cutting strategies for competition, such as bodybuilding.

443

444 Conflict of interests

445 The authors declare no conflict of interests.

446

447 Acknowledgements

448 The authors are grateful to Alexandre Pagnani (in memorian) and José da Páscoa Neto, President and

449 Vice-president of Brazilian Confederation of Bodybuilding and Fitness, respectively, for assistance

450 during the study. Also, they thank Galton Moreira for assistance in creatine kinase and

451 immunoglobulin’s analysis.

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581 Figure legends

582 Figure 1. Glutatione reductase (GR) (A), Catalase (B), total superoxide dismutase (SOD) (C),

583 malondhialdeyde (MDA) (D), protein carbonils (E), total antioxidant capacity (TEAC) (E), TBARS/

584 TEAC ratio (F), creatine kinase (G) in bodybuilders during Off season (OffS) or pre contest period

585 (PreC). Data are presented as mean±SE. n=20/ per group for all parameters. * p<0.05 vs. OffS.

586 Figure 2. Tumor necrosis factor (TNF-α) (A), interleukin 1-beta (1β) (B), URTI severity (C),

587 Immunoglobulin M (IgM) Immunoglobulin A (IgA), Immunoglobulin G (IgG) in bodybuilders during

588 Off season (OffS) or pre contest period (PreC). Data are presented as mean±SE or median values

589 (interquartile intervals). n=20/ per group for all parameters with exception of TNF-α (n=17). * p<0.05

590 vs. OffS.

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Table 1. Anthropometric and training characteristics of bodybuilders in Off season and Pre-

contest phases. Values are expressed as mean and standard deviation.

Anthropometric Parameter Off season Pre-contest

Percentage

change (%)

Height (cm) 173.1 ± 0.1 173.1 ± 0.1 -

Weight (kg) 86.8 ± 10.3 78.7 ± 6.3* -9.3

BMI (kg/m²) 27.8 ± 3.2 25.5 ± 2.1* -8.3

Body fat (%) 17.3 ± 3.5 10.5 ± 2.0* -39.3

Fat mass (kg) 14.9 ± 5.5 8.2 ± 1.8* -45.0

FFM (kg) 70.4 ± 9.2 71.5 ± 8.3 1.6

FFM/ Fat mass 5.2 ± 1.2 9.0 ± 1.2* 73.1

Σ skinfolds (mm) 104.7 ± 27.1 52.5 ± 11.1* -49.9

AMC (cm) 35.2 ± 2.5 34.9 ± 2.3 -0.9

AMA (mm²) 99.1 ± 16.6 102.8 ± 14.8 3.7

Circunferences

Chest “expired” (cm) 112.3 ± 7.0 109.1 ± 6.0 -2.8

Hip (cm) 102.1 ± 4.9 99.0 ± 2.5 -3.0

Abdomen (cm) 87.6 ± 7.5 81.6± 4.5* -6.8

Thigh (cm) 63.3 ± 4.2 60.7 ± 2.9 -4.1

Calf (cm) 40.3 ± 2.6 39.1 ± 1.6 -3.0

Arm (cm) 42.2 ± 2.6 41.1 ± 2.3 -2.6

Forearm (cm) 35.0 ± 2.3 34.2 ± 3.1 -2.3

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DraftAMC: arm muscle circumference

AMA: arm muscle area

FFM: Fat-free mass

*: p≤0.05; Student's t-test

Training characteristics

Resistance training

Days/ week 4.5 ± 0.5 5.4 ± 0.4* 20.0

Minutes/ day 57.3 ± 10.2 68.8 ± 16.7* 20.1

Minutes/ week 257.9 ± 43.3 371.5 ± 57.0* 44.1

Aerobic exercise

Days/ week 0.5 ± 0.7 4.1 ± 0.4* 740.0

Minutes/ day 5.7 ± 13.2 39.5 ± 16.7* 593.0

Minutes/ week 2.9 ± 31.3 161.9 ± 37.0* 5721.1

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Table 2. Energy intake and expenditure, energy balance, physical activity level and macronutrient intake of bodybuilders in Off season and

Pre-contest phases. Values are expressed as mean and standard deviation (SD).

Off-season Pre-contest

Energy intake (Kcal/d) 4047.6 ± 626.3 2277.2 ± 397.2*

Energy expenditure (Kcal/d) 3511.3 ± 342.0 3732.7 ± 247.6

Energy balance (Energy intake -

expenditure) 536.3 ± 433.1 -1355.5 ± 390.4*

Physical activity level 1.8 ± 0.2 1.8 ± 0.1

Recommendations Mean intake ± SDAdequacy

N (%)Mean intake ± SD

Adequacy

N (%)

Proteins (g/Kg fat free mass) † 2.3-3.1 3.5 ± 0.6 4 (20) 2.9 ± 0.6 15 (75)

Carbohydrates (g/kg body weight)

† remaining 6.5 ± 1.1 - 3.5 ± 1.1* -

Lipids (% total energy) † 15-30% 22.4 ± 8.9 11 (55) 14.8 ± 5.5* 5 (25)

Saturated fats (% total energy) ‡ <10% 5.7 ± 2.2 18 (90) 4.5 ± 2.1 20 (100)

Monounsatured fats (% total remaining 6.3 ± 2.1 - 4.7 ± 1.7 -

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†: Helms et al. (2014)

‡: Santos et al. (2013)

§: Institute of Medicine (2000)

energy) ‡

Polyunsatured fats (% total energy)

‡ 6-10% 5.2 ± 1.6 3 (15) 3.5 ± 1.5 -

Fibers (g) § 38 51.2 ± 14.9 18 (90) 32.1 ± 10.0 7 (35)

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Table 3. Micronutrient intake of bodybuilders in Off Season and Pre-contest in relation to

nutritional recommendations of dietary reference intake. Data analyzed by paired t-test and

shown as mean±SD or analyzed by Wilcoxon signed rank test and shown as

median values (interquartile intervals).

TE: tocopherol equivalent

†: Recommended Dietary Allowance (RDA)

‡: Adequate intake (AI)

*: p≤0.05 versus Off Season;

Mean intake ± SD

Micronutrients RDAOff Season Pre contest

Vitamin A (µg) † 900 1491.8 ± 687.3 1096.8 ± 819.8

Vitamin C (mg) † 90 274.1 (156.6- 459.5) 144.7 (117.4-254.6) *

Vitamin E (mg TE) † 15 31.6 (28.5-37.3) 10.0 (3.5-14.5) *

Zinc (mg) † 11 21.1 ± 10.3 14.1 ± 6.4 *

Copper (µg) † 900 3002.8 ± 1016.5 2444.7 ± 1144.6

Manganese (mg) & 2.3 7.2 (5.4-8.7) 4.1 (2.7-5.1) *

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Table 4. Psychological profiles (POMS and REST-Q) of bodybuilders in off season and

pre-contest periods. Values are presented as mean and standard deviation (SD).


Mood states Off-season Pre contestPercentage

change (%)

Tension 39.1 ± 4.4 41.7 ± 4.9 6.6

Depression 36.3 ± 5.9 43.3 ± 5.6 19.3

Anger 42.6 ± 4.9 48.9 ± 6.2 14.9

Vigor 65.8 ± 7.7 52.7 ± 6.3* -19.9

Fatigue 39.1 ± 7.9 48.2 ± 6.3* 23.1

Confusion 35.3 ± 6.7 41.1 ± 6.9 16.4

Total mood state 126.6 ± 8.2 171.1 ± 9.3* 35.2

Recovery-Stress Questionnaire Off-season Pre contestPercentage

change (%)

General stress 11.3 ± 0.7 15.2 ± 0.6* 34.1

General recovery 19.7 ± 0.9 14.8 ± 1.4 -24.9

Sport stress 4.5 ± 0.3 6.8 ± 0.5* 50.1

Sport recovery 17.4 ± 0.8 13.4 ± 1.0* -23.0

Ʃ stress scales 15.9 ± 0.3 21.9 ± 0.3* 38.7

Ʃ recovery scales 36.3 ± 0.4 28.7 ± 0.6* -21.0

Ʃ recovery scales – Ʃ stress scales 20.4 ± 0.6 6.7 ± 0.4* 67.4

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Draft

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Draft

Figure 1. Glutatione reductase (GR) (A), Catalase (B), total superoxide dismutase (SOD) (C), malondhialdeyde (MDA) (D), protein carbonils (E), total antioxidant capacity (TEAC) (E), TBARS/ TEAC ratio

(F), creatine kinase (G) in bodybuilders during Off season (OffS) or pre contest period (PreC). Data are presented as mean±SE. n=20/ per group for all parameters. * p<0.05 vs. OffS.

305x224mm (300 x 300 DPI)

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Draft

Figure 2. Tumor necrosis factor (TNF-α) (A), interleukin 1-beta (1β) (B), URTI severity (C), Immunoglobulin M (IgM) Immunoglobulin A (IgA), Immunoglobulin G (IgG) in bodybuilders during Off season (OffS) or pre

contest period (PreC). Data are presented as mean±SE or median values (interquartile intervals). n=20/ per group for all parameters with exception of TNF-α (n=17). * p<0.05 vs. OffS.

261x200mm (300 x 300 DPI)

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draft · 2019. 4. 5. · draft 4 50 introduction 51 bodybuilding is a sport in which competitors...

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