metabolomics approach in the investigation of metabolic changes in

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METABOLOMICS APPROACH IN THE INVESTIGATION OF METABOLIC CHANGES IN OBESE MEN AFTER 24 WEEKS OF COMBINED TRAINING Renata Garbellini Duft, Alex Castro, Ivan Luiz Padilha Bonfante, Diego Trevisan Brunelli, Mara Patrícia Traina Chacon-Mikahil, and Cláudia Regina Cavaglieri J. Proteome Res., Just Accepted Manuscript • Publication Date (Web): 11 May 2017 Downloaded from http://pubs.acs.org on May 12, 2017

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Journal of Proteome Research

METABOLOMICS APPROACH IN THE INVESTIGATION OF METABOLIC CHANGES IN OBESE MEN AFTER 24 WEEKS OF COMBINED TRAINING

Renata G. Duft1, Alex Castro1, Ivan L. P. Bonfante1, Diego T. Brunelli1, Mara P. T. Chacon-Mikahil1, *Cláudia R. Cavaglieri1 1

University of Campinas–Exercise Physiology Laboratory

*Corresponding Author: Cláudia Regina Cavaglieri Email: [email protected] Address: Av. Érico Veríssimo, 701 – Cidade Universitária “Zeferino Vaz”. Barão Geraldo – Campinas – SP – Brazil. ZIP CODE: 13.083-851. Telephone: ++ 55 19 35216781

Running Title: Metabolic Changes after Combined Training in Obese Men

Conflict of interest The authors declare no conflict of interest.

1 ACS Paragon Plus Environment


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Abstract Obesity is associated with comorbidities related to metabolic disorders, due to excess of adipose tissue. Physical exercise has a major role in the prevention of obesity. Combined training (CT), especially, has been shown to improve markers of health. In this study, we used 1H NMR-based metabolomics to investigate changes in the metabolism of obese men, after 24 weeks of CT. Twenty-two obese (Body Mass Index 31 ± 1.4 kg/m²), middle-aged men (48.2 ± 6.1 years) were randomly assigned to a control group (CG, n = 11) or CT group (n = 11). The CT was performed three times a week (resistance and aerobic training) for 24 weeks. Blood samples were collected before and after experimental period. There was an improvement in body composition and physical fitness indices after CT training. Multivariate PCA and PLS-DA models showed a distinct separation between groups. Twenty metabolites with importance for projection (VIP) higher > 1.0 were identified, and four classified as best discriminators (tyrosine, 2-oxoisocaproate, histidine, pyruvate). Some metabolites were correlated with strength, VO2peak, fat and lean body mass, waist circumference and insulin. In conclusion, 24 weeks of CT was effective for functional improvements and metabolic changes in obese middle-aged men.

Keywords: metabolic profile, physical exercise, obesity, biomarkers, nuclear magnetic resonance, metabolomics


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1. Introduction Obesity has become one of the largest global health problems. It is accompanied by an increase in comorbidities, such as type 2 diabetes, cardiovascular disease

(CVD),

cancer,

hypertension,

depression,

cerebrovascular

accident,

hypertriglyceridemia, hyperlipidemia, coronary heart disease and others 1. These comorbidities are associated with metabolic disorders, due to excess of adipose tissue, an endocrine organ which has functions of producing and secreting adipokines. These adipokines act controlling metabolic functions. However, the excess of body fat cause a dysregulation in adipokines secretion, and the development of a low-grade systemic inflammatory state, which is related to insulin resistance, atherosclerosis, and noncommunicable chronic diseases (NCDs) 1,2. In this regard, physical exercise has a significant role in the prevention of NCDs, such as obesity. Regular exercise increases energy expenditure, improves body composition, and has a significant anti-inflammatory effect 2. The most recommended exercise program includes the implementation of both aerobic and resistance training (AT; RT) (i.e. Combined training (CT)), which chronically can improve health, decrease systemic inflammation and improve physiological aspects of both types of activities performed 3–5. For a better understanding of the mechanisms by which obesity physical exercise

9–12

6-8

and

change the metabolism, metabolomics has been used, for

identification and quantification of metabolites, reproducing a screening of the physiological organism conditions

6,13–15

. The use of targeted metabolomics to quantify

metabolites allow the characterization of candidate biomarkers, which would be potential biomarkers used as diagnostic tools for diseases screening, prognosis, and prediction of therapeutic interventions to obesity

7,16,17

. Metabolomics approach

simultaneously measures several molecules, facilitating the study of metabolic networks and pathways, which is restricted to reductionist analysis with the dosage of



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specific biochemical markers 18,19. Some studies have already used metabolomics to investigate metabolic pathways and metabolites related to obesity. For example, in the review of Rauschert et. al, 2014

20

, many metabolites showed different levels in obese individuals in

comparison with lean subjects. Some researchers are using metabolomics to investigate metabolic responses in differents exercises protocols

10,12,21–24

. However,

there are few studies using metabolomics to elucidate the metabolic profile of obese individuals after a prolonged training period, especially when combining aerobic and resistance exercises within the same training session 9,25. In the study of Kuhl et. al 9, the authors found significant correlations between one metabolite and insulin sensitivity after a protocol of circuit CT for 12 weeks. In Glynn et. al

25

, the investigators performed six months of CT to investigate the

relationship between branched-chain amino acids (BCAA) metabolism and insulin sensitivity in overweight individuals. They found a decrease in BCAAs concentrations, as a predictor of insulin sensitivity improvement after the training period. This study, however, did not use a control group. Also, the post moment blood collection was performed after 24 and 36 hours of the last session and may have some acute residue, instead of only chronic responses. Besides that, both studies have used CT protocols to investigate only the glycemic homeostasis. The metabolites were not associated with functional variables of the training, or with the changes in body composition. Therefore, we based our investigation on the lack of randomized controlled studies using CT for prolonged periods to investigate clinical markers and the use of metabolomics approach performing a global analysis of the metabolism. The aims of this study were to investigate the physical fitness, body composition and metabolic changes promoted by exercise in obese middle-aged men, after 24 weeks of CT, and to examine the association of metabolic changes with measures of cardiometabolic risks related to obesity.


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2. Experimental Section All the procedures described in this manuscript were approved by the Research Ethics Committee of the University of Campinas, Brazil (CAAE.000 1181.0.146-11),

and

the

protocol

has

a

registered

Clinical

Trial

(ACTRN12615001000594). The procedures were conducted according to the Helsinki Declaration of 1964 (last review in 2008). All the volunteers signed an informed consent document. Also, this study is part of a big project, and other results were already presented 4.

2.1. Subjects Twenty-two middle-aged men with age between 40 and 60 years, grade 1 obesity (Table 1) volunteered to participate in the study. As inclusion criteria, they should not be involved in a regular program of physical exercise in the last twelve months, according to the physical activity questionnaire 26 and considered inadequately active by the international physical activity questionnaire (IPAQ)27. The sample size was calculated based on moderate effect sizes (ƒ = 0,3) observed in previous studies with similar design

4,5

and assuming Type I error rate (α) of 5% to ensure at least 80%

of statistical power (1-β) in the analyses 28,29. The exclusion criteria involved the presence of any pathology evidenced by general cardiological physical examination and ergometry. The presence of complicating factors that could be risk factors or hinder the regular practice of the physical activity proposed, including coronary artery disease, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, osteoarticular diseases. The use of any medication that could interfere with the physiological responses to the tests or training. The disapproval in the initial clinical assessment, the discontinuance from training, with a frequency less than 85% of the sessions, or absence in more than two



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consecutive sessions. Before the beginning of the training, the selected subjects were randomly assigned in: combined training group (CT, n = 11) and control group (CG, n = 11), which did not perform any physical exercise.

2.2. Anthropometry and Body Composition Anthropometric measurements were performed before and after the training period. Weight was measured using a calibrated standard scale (Filizola) with a precision of 0.1 kg. The height was measured using a wall stadiometer with an accuracy of 0.1 cm. Body mass index was calculated by dividing body weight (kg) / height squared (m2). Subcutaneous skinfold thickness was measured at the chest, abdomen, and thigh using a skinfold caliper (Lange, Cambridge Scientific Industries, Cambridge, MD, USA) always by the same researcher 4. All measures were made in triplicate. Percentage of body fat was estimated from body density (Jackson and Pollock equation 30) using the Siri equation 31.

2.4. Functional Assessments For the muscle strength assessment, the one-repetition maximum (1-RM) test was performed on the bench press, leg press and barbell curls using NakaGym equipment (SP, Brazil). The test protocol was conducted according to the descriptions in Brunelli et al. 4. For the warm-up, subjects performed ten repetitions at 50% of their estimated 1-RM, followed by 3 min of rest. After this, subjects performed three repetitions at 70% of their estimated 1-RM, and 3 min of recovery to start the test. A maximum of 5 trials to find the highest weight for the 1RM were performed, increasing the weights progressively, with 3-5 min of rest between trials. Subjects were tested and retested with 48-h rest between them, and the value of the highest load obtained was used. For the cardiorespiratory assessment, the volunteers performed a maximum


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effort protocol in a treadmill (Quinton, model TM55, USA) with continuous gas exchange collection breath-by-breath (CPX Ultima, Medgraphics, USA). The test protocol consisted of 2 min warm-up at 4 km.h-1, followed by an increment of 0.3 km.h-1 each 30 s until exhaustion

32

. A 4 min recovery period at 5 km.h-1 was performed after the

exhaustion with a gradual reduction in speed. The highest average 30 s mean values were considered as the peak oxygen consumption (VO2peak). For the dietary intake assessment, dietary records were given to the subjects to be filled, always before assessments periods (weeks 0, 8, 16 and 24). Volunteers informed all ingested food during the three days prescribed (different and nonconsecutive days, being two weekdays and one weekend day). The problems of under or over self-report in dietary records were reduced by some meetings with volunteers and nutritionist, to explain the importance of writing the truth in their records to have a data reliability in the study. The records were analyzed using the software Diet Pró, version 5i, by experienced nutritionists. The food intake assessment was carried out from the average data of three records

33,34

. Each month, the subjects of the control

group were monitored being reminded to keep their eating patterns and to report possible changes in the routine of their daily life and use of medications.

2.4. Experimental Protocol The CT protocol was performed using general recommendations previous studies carried out by our laboratory

4,32,1

3

and

. It consisted of RT (30 minutes) and

AT (30 minutes) conducted in the same session (approximately 60 minutes), for 24 weeks, three times a week (Monday, Wednesday, and Fridays), always under professional supervision. Subjects always performed the RT protocol first. The training period was divided into three stages (S1, S2, and S3), each consisting of 8 wk of training. In the first stage, the RT consisted of 6 exercises (leg press, leg extension, leg curl, bench press, lateral pulldown, and arm curl), three sets of 10 RM and 1-min rest



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between sets. After this, the subjects performed AT (30 min of walking or running) on an athletic track, with varying intensities 50%–85% of VO2peak. In the second stage (S2), the training has the same duration and exercises, varying intensities and recovery period. The RT was performed with three sets of 8 RM and 1m30 s of rest between sets. In the AT, there was an adjustment in the training zone as described in Brunelli et. al, 2015 4, but the intensities continue corresponding to 50%–85% of VO2peak. In S3, the RT consisted of 6 RM and 1m30 s of rest between sets. In the AT, there was a new adjustment in the training zone, with intensities corresponding to 50%–85% of VO2peak. The RT workloads changes were made every week on Friday. The subjects were encouraged to perform the maximum number of repetitions as possible in the last set of each exercise, without change the range of motion and execution velocity. The workloads were increased for each exercise, by repetitions performed over the number of the training protocol, being 0.5 kg for upper body and 1 kg for lower body 4. Postintervention period assessments were performed 72 h after the last session.

2.3. Blood Collection and Biochemical Analysis Blood samples were collected from the antecubital vein in dry tubes (8 mL), Vacutainer® brand (Becton Dickinson Ltd, Oxford, UK) prior and after the training period. The samples were centrifuged at 956 g for 10 minutes, and approximately 4 mL of serum were stored at -80 °C. All samples were collected at the same time interval (between 7:00 and 9:00 in the morning), after 12 hours overnight fast, and 72 after the last training or evaluation session. The serum glucose concentrations were analyzed using an automatic chemistry analyzer and a commercially available kit (Laborlab, SP, Brazil). The serum insulin levels were determined by electrochemiluminescence, using commercially available kits (Roche Diagnostics GmbH, IN, USA).


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2.4. Metabolomics Analysis 2.4.1 Sample Preparation Serum samples were centrifuged at 20 817 g for 45 minutes at the temperature of 4 °C, with the use of 3kDa filter (Amico Ultra). The filtered solution (200 µL) was transferred to a standard 5 mm NMR tube (Wilmad). We added 340 µL of MilliQ H2O, 60 µL of phosphate buffer, pH 7.4 (Monobasic Sodium Phosphate, NaH2PO4 H2O- 137.99 g/mol; Dibasic Sodium Phosphate, Na2HPO3- 141.96 g/mol), for the pH standardization,

containing

0.5

mM

TSP

(3-(trimethyl-silyl)-

2,2',3,3‘tetradeuteropropanoic acid, in D2O), for internal chemical shift reference 35,36.

2.3.2 Acquisition of Spectra and Quantification of Metabolites The nuclear magnetic resonance spectroscopy (1H NMR) was carried out in 600 MHz spectrometer, Varian Inova (Agilent Technologies Inc., Santa Clara, CA, USA), equipped with a triple cold probe. A constant temperature of 298 K (25 °C) was used. A total of 256 scans were collected, with an acquisition time of 4 seconds and relaxation delay intervals between scans of 1.5 s

24

. After acquired all spectrum, the

phase adjustment, baseline correction, spectral calibration, and quantification was conducted by software Chenomx NMR Suite 7.6 (Chenomx Inc., Edmonton, AB, Canada) 37.

2.5. Statistical Analysis Multivariate statistical analyses were undertaken to analyze the spectral data, as principal component analyses (PCA), and partial least squares discriminant analysis (PLS-DA). We carried out the data analysis in MetaboAnalyst 3.0 software. To decrease variability of metabolites concentration values between and within groups, the fold change (FC, post divided by pre values) were calculated to increase the power of



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analysis. A logarithmic transformation (Log10) was applied for FC values, for greater symmetry between the data distribution curves, and the auto-scaling technique for standardization

38

. The PCA was applied to examine differentiation in overall metabolic

profile between changes in CG and CT groups. After that, PLS-DA was conducted to the classifying of the group's segregation, and identify the most important metabolites that explain the changes in metabolic profile between groups. Metabolites with variable importance for projection (VIP) higher > 1.0 were classified as the most important metabolites in the model segregation. Reduced PLS-DA models were tested, with these metabolites, until it was obtained a model with greater predictive capacity using the fewest possible variables. The robustness and quality of the models were reported by permutation tests (one hundred permutations) and cross-validation (R2Y and Q2). Univariate analyses were conducted to compare specific changes in each dependent variable arising from training, between the CG and CT groups. For metabolites, were considered for analysis only the ones who presented VIP score higher than 1. The data distribution and homogeneity of variances were tested by the Shapiro-Wilk Test and Levene's test. For the analyze of dependent variables between and within groups was used Repeated Measures ANOVA two-way [group (CG and CT) x time (Pre and Post training)]. In the case of statistically significant differences between groups in pre-data, the pre-data was included as a covariate in the statistical model by ANCOVA analysis. Exceptionally for analyzing the metabolites, a natural logarithmic transformation (log10) was used before the analyses to improve the approximation to a Gaussian distribution, and after back transformed for clarity. Whenever a significant F-value was obtained, a Sidak adjustment was performed for multiple comparison purposes. For comparisons of the changes between groups after the intervention, the FC values were used and analyzed by Independent T-Test or Mann-Whitney U Test and after values were adjusted by false discovery rate

39

. Also,

Pearson’s Correlation Coefficient followed by simple linear regression were used to


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analyze associations between significant metabolites and clinical parameters changes. These analyses were carried using the PASW statistics software version 18.0 (SPSS Inc., Chicago, USA). The level of statistical significance for all analyses was 95% (p< 0.05). Finally, the quantitative Metabolite Set Enrichment Analysis (MSEA) was carried out using FC values, and graphs of metabolic pathways of each metabolite belongs were generated

40

. These analyses were carried using the online platform

MetaboAnalyst 3.0 41.

3. Results 3.1. Nutritional, Body Composition and Functional Aspects The dietary pattern was maintained throughout the study in both groups. No significant differences were presented in calories and macronutrient intake between pre and post moments of both groups. The comparisons within each group (pre vs. post) showed no significant differences in the CG. However, the CT group showed a significant reduction in the percentage of fat mass (p< 0.01), waist circumference (p= 0.03), and glycemia (p= 0.01). The percentage of lean body mass had a significant increase (p< 0.01), as well as VO2peak (p< 0.01) and total strength (p< 0.01) (Table 1). No significant differences in weight and BMI were observed after 24 weeks of training. In the comparisons between groups, only total strength (p 1.0 (tyrosine, histidine, 2- oxoisocaproate, pyruvate, phenylalanine, isoleucine, choline, betaine, carnitine, lysine, glucose, creatinine, ornithine, valine, alanine, leucine, glutamine, asparagine, 2aminobutyrate, lactate). They were classified as the most important metabolites in the segregation of the model. Four metabolites with highest VIP score were selected as the most influential in the separation of metabolic changes (tyrosine, histidine, 2oxoisocaproate, and pyruvate). A specific moments comparison (pre and post) between metabolites of both groups and FC between groups are presented in Table 2. All metabolites showed a significant difference in FC comparison between CG and CT. Tyrosine also showed a difference at CT post moment compared to CG. Pyruvate showed a difference between CT and CG post-moments, and a significant increase in the post-CT group compared to pre-moment. Isoleucine and 2-amynobutyrate presented a significant decrease in post-moment compared to pre in CG group. Finally, the Quantitative Metabolite Enrichment Analysis was performed, and the metabolic pathways of these four metabolites (tyrosine, histidine, 2-oxoisocaproate and pyruvate) were represented in Figure 5.

3.3. Correlations Moderate correlations coefficients between 20 VIP score metabolites, and functional variables (strength, VO2peak, waist circumference, fat and lean body mass, glycemia, and insulin) were showed in table 3. Eight metabolites showed a positive correlation with strength [tyrosine (r= 0.53 / p= 0.01), histidine (r= 0.55 / p< 0.01), 2-


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oxoisocaproate (r= 0.52 / p= 0.01), pyruvate (r= 0.34 / p= 0.04), betaine (r= 0.56 / p< 0.01), glucose (r= 0.50 / p= 0.02), creatinine (r = 0.44 / p= 0.04), asparagine (r= 0.54 / p< 0.01)). Three of them positive correlated with VO2peak [tyrosine (r= 0.62 / p< 0.01), phenylalanine (r= 0.43 / p= 0.04), and glucose (r= 0.42 / p= 0.05)]. Waist circumference negatively correlated with four metabolites [phenylalanine (r= -0.44 / p= 0.04), betaine (r= -0.55 / p< 0.01), glucose (r = -0.53 / p= 0.01) and creatinine (r= -0.49 / p= 0.02)]. Three metabolites negatively correlated with fat mass [pyruvate (r= -0.67 / p< 0.01), glucose (r= -0.46 / p= 0.03), and glutamine (r= -0.47 / p= 0.02)], and two metabolites correlated with lean body mass [pyruvate (r= 0.56 / p< 0.01) and glutamine (r= 0.48 / p= 0.02)]. Glycemia did not correlate with metabolites, and insulin had a negative correlation with six metabolites [tyrosine (r= -0.60 / p< 0.01), histidine (r= -0.47 / p= 0.02), phenylalanine (r= -0.46 / p= 0.03), choline (r= -0.46 / p= 0.03), ornithine (r= -0.47 / p= 0.02) and glutamine (r= -0.46 / p= 0.03)].

4. Discussion This study aimed to investigate changes in the metabolism of obese, middleaged men, after 24 weeks of CT. At the completion of the experimental period, significant differences were observed in metabolic profiles between CG and CT. Twenty metabolites with higher VIP score were selected as most important in groups segregation. They correlated with functional variables such as strength, VO2peak, fat and lean body mass, waist circumference and insulin. Among these metabolites, four were classified as best discriminators and associated with obesity and exercise. The functional variables as total strength, VO2peak and percentage of lean body mass significantly increased after training period (Table 1). The glycemia, waist circumference and percentage of fat mass significantly decreased after 24 weeks of CT. These results possibilities to classify this training protocol as an excellent tool for health improvement.



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The 20 VIP metabolites that most influenced the segregation of groups in PLSDA model is showed in Figure 3. Coincidentally, all of them had higher levels in CT group, and four metabolites (tyrosine, 2-oxoisocaproate, histidine, and pyruvate) were classified as the most important. We performed associations between metabolites and their metabolic pathways to understanding the role of physical exercise in the obese population, using the Human Metabolome Database 42. Tyrosine is a nonessential amino acid, synthesized from phenylalanine. It is part of catecholamine biosynthesis and phenylalanine metabolism (Figure 5). Lipolysis is stimulated by catecholamines via β1 and β2 adrenergic receptors, however, when catecholamines stimulate α2 adrenoreceptors, an antilipolytic effect is observed. Obesity can modify the sensitivity of the α and β receptors, and the lipolytic effect in adipose tissue. The density of α2 adrenoreceptor is higher, while the β2-adrenergic and catecholamine levels in plasma of obese subjects are lower at rest

43,44

. Our study

showed an increase in tyrosine and catecholamine pathways after 24 weeks of CT. Tyrosine also showed a correlation with total strength and VO2peak (Table 3), suggesting the importance of this metabolite to the functional effects elicited by the combined training protocol. The biosynthesis of proteins metabolic pathway is the second most important pathway. We observed an increase in total strength, lean body mass, which may be related to hypertrophy and increased protein synthesis 47. Another essential metabolite that explains the group's variation is 2oxoisocaproate, which is part of the degradation pathway of branched-chain amino acids (BCAAs) valine, isoleucine, and leucine (Figure 5). Obese individuals have higher BCAAs concentrations and degradation products in blood

48

. Physical exercise also

induces an increase in metabolites products of the BCAAs degradation

49

, such as 2-

oxoisocaproate. A study by MCCORMACK et al.50 showed that elevated levels of BCAAs in the bloodstream are associated with obesity and insulin resistance. Our study showed a significant decrease in glycemia that can be related to the improved in


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insulin resistance. This resistance is associated with the effects of insulin, such as suppression of proteolysis, leading to an increase in muscle breakdown and release of BCAAs into the bloodstream. The leucine transamination produces 2-oxoisocaproate, that can be interconverted into leucine or continue the metabolic pathway following two paths. The first one is the mitochondrial oxidation into acetoacetate and then into acetyl-CoA. The second path occurs in the liver where it is oxidized in the cytosol to 3hydroxy-3-methylbutyrate (HMB)

51

. This HMB metabolite shows some ergogenic

benefits in the literature, such as increased lean body mass and muscle hypertrophy, effects on the repair of tissue injury and even lipolytic effects

52–54

. The 2-

oxoisocaproate correlated with total strength, confirming this importance in a probable muscle hypertrophy. Histidine is the third metabolite that contributes in the distinction of CG and CT groups. It is an amino acid that can be converted into histamine, an important neurotransmitter involved in the immune system

55

. It is part of histidine metabolism

pathway, ammonia recycling and protein biosynthesis (Figure 5). In a study by Niu et al. (2012)

56

, histidine appeared as a biomarker for obesity, and presented low plasma

levels in obese women, compared to non-obese, healthy women. A negative association with inflammation and oxidative stress were presented in this study. Low levels of plasma histidine have been associated with protein-energy wasting, inflammation, oxidative stress and increased mortality in patients with chronic kidney disease

57

. Our study showed an increase in histidine levels after 24 weeks of CT,

suggesting that our training protocol acted as a protection against obesity inflammatory effects in obese people. In our previous studies with this same exercise protocol 4,5,32,58, the CT showed anti-inflammatory effects with an increase in adiponectin and IL-15 and a decrease C-reactive protein, resistin and leptin (inflammatory cytokines) 4. Pyruvate is the last metabolite found as an indicator of physical training changes affected by obesity. It participated in several metabolic pathways (Figure 5). In



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glycolysis pathway, pyruvate is the final product from glucose. In the aerobic metabolism, it is converted into Acetyl-CoA, which will condense with oxaloacetate to form citrate, the first compound in the citric acid cycle. In anaerobiosis, it will form lactate, an energy substrate for tissues and organs

59,60

. Obesity causes a disorder in

pyruvate metabolism, because of the impaired regulation of carbohydrate metabolism, leading to a metabolic inflexibility. This inflexibility is an inability of switching from fat to carbohydrate oxidation during exercise or post-prandial. A high percentage of fat mass contribute to a dysfunction in pyruvate dehydrogenase activity and the flux of pyruvate into citric acid cycle

62

. Pyruvate demonstrated correlations with lean body

mass, total strength, and a negative correlation with fat mass (Table 3). Studies are showing that exercise can restore the activity of pyruvate dehydrogenase

62–64

. In CT

group, there was an increase in lean body mass, strength, and a decrease in fat mass, what could increase the enzyme activity and the flux of pyruvate. Other metabolites presented positive and negative correlations with functional variables (Table 3). Glutamine positively correlated with lean body mass, and negatively with fat mass. This metabolite has positive effects in supplementation for increase fat oxidation and lean body mass

65

. Phenylalanine, betaine, and creatinine

showed negative correlations with waist circumference. These metabolites had an increase in concentrations after the training period, and it is related to cardiometabolic risks in the obese population because of the waist circumference significant decrease after 24 weeks of CT. Six amino acids tyrosine, histidine, phenylalanine, glutamine, ornithine, choline presented a negative correlation with insulin. The concentrations increased after the training period observed in Table 2, influencing directly in the insulin decrease as seen in Table 1. Due to the high sensitivity of this technique, the existence of external factors that affect the metabolome is common. The volunteers were obese and already exhibited associated pathology (overweight, dyslipidemia). Studies in the literature


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show that different types of diseases (obesity, diabetes, cardiovascular diseases) can affect the mechanisms involved in the metabolic pathways, increasing or decreasing the regulatory and enzymatic activity, interfering with the concentration of the final product, with different responses for each group studied 6. Other factors could also have influenced the metabolome, such as the stress, environmental factors, and diet. Although, the dietary pattern has been maintained during the experimental period. This study proposed a CT based on recommendations of the minimum duration and intensity necessary to promote health benefits 3, and with the possibility of performing this training in the daily lives of this population. The twenty-four weeks of training were completed in a controlled manner with professional supervise in all sessions, controlling the frequency, intensity, and performance during the sessions. A limitation of our study is the assessment of body fat using skinfold thickness measurements, not a standard gold method for the evaluation of body composition.

5. Conclusion The twenty-four weeks of CT improved the body composition, physical fitness and glycemic homeostasis in obese middle-aged men. Combined aerobic and strength training altered the basal metabolic profile of obese individuals, as evident by changes in twenty metabolites. Tyrosine, histidine, 2-oxoisocapoate, and pyruvate were the main discriminators of changes in metabolic profile following combined training. The metabolic changes were correlated with functional and biochemical parameters such as strength, VO2peak, the percentage of fat mass and lean body mass, waist circumference and insulin plasma concentration. The results of our study show the efficacy of a CT program to induce favorable metabolic changes in obese individuals. We also demonstrate the importance of metabolomics as a tool for studying metabolism in this population.



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Acknowledgments We thank the financial support of Fapesp (São Paulo Research Foundation) and CNPq (National Council for Scientific and Technological Development). We are grateful to LNBio (National Laboratory of Bioscience) for the infrastructure provided for the conduction of experiments and staff for help us. The authors thank Espaço da Escrita – Coordenadoria Geral da Universidade – UNICAMP – for the language services provided. References (1)

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Table 1. Descriptive Variables CG (n = 11)

Variables

CT (n = 11)

Pre

Post

Pre

Post

Age (years)

47.5 ± 6.2

47.5 ± 6.2

48.6 ± 5.5

48.6 ± 5.5

Weight (kg)

95.8 ± 10.6

96.0 ± 10.4

94.0 ± 7.8

93.3 ± 8.8

Height (m)

1.77 ± 0.1

1,77 ± 0.1

1.73 ± 0.1

1.73 ± 0.1

BMI (Kg/m²)

30.53 ± 1.0

30.62 ± 1.5

31.3 ± 1.9

31.0 ± 1.3

Glycemia (mg/dL)

99.1 ± 10.1

99.0 ± 12.7

93.1 ± 9.6

87.1 ± 14.2*

Insulin (µU/mL)

6.6 ± 5.5

4.3 ± 3.8

3.8 ± 2.0

3.9 ± 2.6

102.4 ± 4.8

103.9 ± 6.3

101.6 ± 6.7*

Waist Circumference (cm) 101.5 ± 4.6 Lean Body Mass (%)

68.6 ± 5.1

65.3 ± 6.2

63.5 ± 6.1

69.5 ± 5.5*

Fat Mass (%)

32.82 ± 5.5

31.4 ± 5.1

36.4 ± 6.1

28.9 ± 7.0*

29.1 ± 4.7

29.0 ± 4.1

28.2 ± 4.7

31.5 ± 5.0*

-1

-1

VO2peak (ml.kg .min ) Total Strength (kg)

433.0 ± 84.5 426.7 ± 82.4 423.1 ± 67.4 505.3 ± 67.1*†

* Significant Difference from Pre (p < 0.05). † Significant Difference from CG (p < 0.05).

Table 2. Metabolites with VIP score > 1.0 for fold change values (Post/Pre) and metabolites concentrations (mM) Pre and Post intervention, for Control Group (CG) and Combined Training group (CT). Metabolites (mM)

CG

CT Post

FCd

0.031 ± 0.007

0.033 ± 0.006†

1.08 ± 0.15†

0.93 ± 0.10

0.028 ± 0.005

0.030 ± 0.005

1.09 ± 0.16†

0.003 ± 0.001

0.98 ± 0.16

0.003 ± 0.001

0.003 ± 0.001

1.03 ± 0.28†

0.027 ± 0.018

0.90 ± 0.37

0.021 ± 0.010

0.031 ± 0.011*†

1.56 ± 0.67†

Pre

Post

FC

Tyrosineb

0.029 ± 0.004

0.025 ± 0.003

0.85 ± 0.02

Histidineb

0.030 ± 0.005

0.027 ± 0.004

2-Oxoisocaproateb 0.003 ± 0.000 Pyruvatea

0.030 ± 0.013

Pre


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Phenylalanineb

0.98 ± 0.18

0.024 ± 0.003

0.027 ± 0.006

1.11 ± 0.13†

0.031 ± 0.006 0.028 ± 0.004* 0.92 ± 0.24

0.029 ± 0.004

0.031 ± 0.005

1.06 ± 0.08†

Cholinec

0.003 ± 0.001

0.003 ± 0.001

1.18 ± 0.74

0.002 ± 0.001

0.003 ± 0.001

1.31 ± 0.43†

Betaineb

0.017 ± 0.005

0.018 ± 0.006

1.15 ± 0.55

0.015 ±0.003

0.018 ± 0.006

1.31 ± 0.72†

Carnitineb

0.013 ± 0.002

0.013 ± 0.002

0.97 ± 0.12

0.013 ± 0.003

0.013 ± 0.003

1.10 ± 0.18†

Lysineb

0.054 ± 0.008

0.051 ± 0.009

0.95 ± 0.18

0.056 ± 0.008

0.059 ± 0.009

1.05 ± 0.13†

Glucoseb

1.700 ± 0.301

1.658 ± 0.282

0.93 ± 0.08

1.584 ± 0.181

1.420 ± 0.488

0.91 ± 0.31†

Creatinineb

0.031 ± 0.005

0.030 ± 0.005

0.95 ± 0.10

0.029 ± 0.004

0.031 ± 0.004

1.08 ± 0.14†

Ornithineab

0.016 ± 0.006

0.014 ± 0.005

0.95 ± 0.28

0.017 ± 0.004

0.018 ± 0.004

1.33 ± 0.42†

Valineb

0.106 ± 0.016

0.099 ± 0.010

0.94 ± 0.13

0.099 ± 0.014

0.104 ± 0.012

1.05 ± 0.09†

Alanineb

0.174 ± 0.021

0.159 ± 0.025

0.91 ± 0.10

0.167 ± 0.042

0.166 ± 0.037

1.01 ± 0.16†

Leucineb

0.055 ± 0.008

0.052 ± 0.007

0.96 ± 0.16

0.051 ± 0.009

0.051 ± 0.008

0.99 ± 0.08†

Glutamineb

0.191 ± 0.027

0.180 ± 0.031

0.94 ± 0.12

0.181 ± 0.028

0.195 ± 0.023

1.09 ± 0.19†

Asparagineb

0.024 ± 0.006

0.019 ± 0.005

0.86 ± 0.30

0.020 ± 0.005

0.022 ± 0.003

1.17 ± 0.38†

0.008 ± 0.002 0.007 ± 0.001* 0.93 ± 0.49

0.007 ± 0.001

0.008 ± 0.002

1.16 ± 0.24†

0.930 ± 0.329

0.788 ± 0.196

0.802 ± 0.239

1.02 ± 0.19†

Isoleucinec

2-Aminobutyrateb Lactatec

0.025 ± 0.003

0.024 ± 0.001

0.764 ± 0.213

0.86 ± 0.27

Values are a mean ± standard deviation and fold change (FC). Repeated Measures ANOVA Twoa

Way was used for comparisons between and within-groups. groups analyzed by ANCOVA (adjusted data by pre-values). Test.

c

b

Comparisons between and within

FC values analyzed by Student's t-

FC values analyzed by Mann-Whitney Test. d P – values were adjusted by false discovery

rate. * Significant difference from Pre (p < 0.05). † Significant difference from CG (p < 0.05).

Table 3. Correlations coefficients between metabolites and functional variables. Metabolites

Strength

VO2peak

Waist Circumference

Fat Mass

Lean Body Mass

Tyrosine

0.53 *

0.62 *

-0.32

-0.34

0.26

-0.22

-0.60 **

Histidine

0.55 **

0.41

-0.38

-0.31

0.29

-0.25

-0.47 *

2- oxoisocaproate

0.52 *

0.41

-0.36

-0.27

0.16

-0.41

-0.40

Pyruvate

0.44 *

0.37

-0.38

-0.67 **

0.56 **

-0.27

-0.18

Phenylalanine

0.30

0.43 *

-0.44 *

-0.34

0.26

0.00

-0.46 *

Isoleucine

0.37

0.23

-0.13

-0.21

0.18

0.10

-0.24

Choline

0.36

0.24

-0.41

-0.39

0.36

-0,15

-0.46 *

Betaine

0.56 **

0.21

-0.55 **

-0.38

0.39

-0.09

-0.38

Carnitine

0.35

0.31

-0.39

-0.27

0.23

-0.25

-0.21

Lysine

0.32

0.25

-0.11

-0.15

0.11

0.08

-0.27

Glycemia Insulin



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 33

Glucose

0.50 *

0.42 *

-0.53 *

-0.46 *

0.37

-0.02

-0.22

Creatinine

0.44 *

0.31

-0.49 *

-0.33

0.37

-0.01

-0.35

Ornithine

0.42

0.41

-0.41

-0.15

0.12

-0.15

-0.47 *

Valine

0.26

0.21

-0.05

-0.21

0.18

0.21

-0.22

Alanine

0.28

0.22

-0.27

-0.13

0.15

-0.19

-0.37

Leucine

0.20

0.28

-0.11

-0.08

-0.02

0.09

-0.28

Glutamine

0.41

0.15

-0.38

-0.47 *

0.48 *

-0.33

-0.46 *

Asparagine

0.54 **

0.41

-0.36

-0.29

0.33

-0.22

-0.41

2 Aminobutyrate

0.22

0.16

-0.14

-0.26

0.12

-0.06

0.05

Lactate

0.23

0.19

-0.12

-0.01

0.01

0.28

-0.35

r: Pearson Correlation Coefficient, P-values for Pearson Correlation: *(p

metabolomics approach in the investigation of metabolic changes in

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