Effect of Training Exercise on Urinary Brain-derived ... - SAGE Journals

Article

Effect of Training Exercise on Urinary Brain-derived Neurotrophic Factor Levels and Cognitive Performances in Overweight and Obese Subjects: A Pilot Study

Psychological Reports 2017, Vol. 120(1) 70–87 ! The Author(s) 2016 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0033294116679122 journals.sagepub.com/home/prx

Angelo Russo Healthy Lifestyle Institute, Centro Universitario Ricerca Interdipartimentale Attivita Motoria (C.U.R.I.A.MO.), University of Perugia, Perugia, Italy

Livia Buratta Department of Philosophy, Social, University of Perugia, Perugia, Italy

Roberto Pippi, Cristina Aiello, Claudia Ranucci, and Elisa Reginato Healthy Lifestyle Institute, Centro Universitario Ricerca Interdipartimentale Attivita Motoria (C.U.R.I.A.MO.), University of Perugia, Perugia, Italy

Valerio Santangelo Department of Philosophy, Social, University of Perugia, Perugia, Italy

Pierpaolo DeFeo Healthy Lifestyle Institute, Centro Universitario Ricerca Interdipartimentale Attivita Motoria (C.U.R.I.A.MO.), University of Perugia, Perugia, Italy

Claudia Mazzeschi Department of Philosophy, Social, University of Perugia, Perugia, Italy

Corresponding Authors: Claudia Mazzeschi, Department of Philosophy, Social, Human & Educational Sciences, University of Perugia, Italy, Piazza G. Ermini 1, Perugia 06123, Italy. Email: [email protected] Angelo Russo, Healthy Lifestyle Institute, Centro Universitario Ricerca Interdipartimentale Attivita Motoria (C.U.R.I.A.MO.), University of Perugia, Perugia 06123, Italy. Email: [email protected]

Russo et al.

71

Abstract Exercise-mediated, brain-derived neurotrophic factor induction benefits health and cognitive functions. The multifaceted interplay between physical activity, urinary brain-derived neurotrophic factor levels and cognitive functioning has been largely neglected in previous literature. In this pilot study, two bouts of training exercise (65% and 70% of heart rate reserve) influenced urinary brain-derived neurotrophic factor levels and cognitive performances in 12 overweight and obese participants. Percent heart rate reserve, expenditure energy, brain-derived neurotrophic factor urinary levels and cognitive performances were measured before and after the exercise. No significant variations in energy expenditure were observed, while differences of heart rate reserve between two groups were maintained. Both bouts of training exercise induced a similar reduction in urinary brain-derived neurotrophic factor levels. Only visuo-spatial working memory capacity at 65% of heart rate reserve showed a significant increase. These findings indicate a consistent effect of training exercise on urinary brain-derived neurotrophic factor levels and cognitive factors in overweight and obese participants. Keywords Brain-derived neurotrophic factor, physical activity, cognitive functioning, working memory

Introduction Neurotrophins are secreted proteins that stimulate and check neurogenesis and neuroprotection processes in the central nervous system (CNS), playing a key role in neuronal plasticity (Huang & Reichardt, 2001). Neurotrophic growth factor was the first member of neurotrophin family to be discovered (Huang & Reichardt, 2001; Levi-Montalcini, Meyer, & Hamburger, 1954), and is widely expressed in the central and peripheral nervous system, in particular in the hippocampus, cerebral cortex and amygdala (Hohn, Leibrock, Bailey, & Barde, 1990) as well as peripheral tissues such as intestine, bladder, thymus, pancreas and blood (Maisonpierre et al., 1991). BDNF is secreted by contracting muscle; therefore, it can be considered a ‘‘myokine’’ that plays an important role in repair, regeneration and differentiation of muscle (Willand et al., 2016). The brain-derived neurotrophic factor (BDNF) is translated as a precursor protein (pro-BDNF, 28 kDa), subsequently cleaving to generate the mature protein (m-BDNF, 13 kDa) (Yang et al., 2009). The precursor protein (pro-BDNF) binds the p75 receptor with high affinity, activating different pathways that mediate neuronal cell death via apoptosis (Koshimizu et al., 2009); in contrast to the mature protein (m-BDNF) which binds the TrkB receptor, promoting neuronal survival and influencing the morphogenesis of CNS neurons. The BDNF is involved in various nervous system functions such as growth, differentiation and survival of neurons and synaptic plasticity (Gonzalez,

72

Psychological Reports 120(1)

Moya-Alvarado, Gonzalez-Billaut, & Bronfman, 2016; Shang et al., 2016); in addition, it is associated with cognitive functions such as learning and memory as demonstrated in mice (Chen et al., 2016). Because BDNF promotes the survival and the growth of neurons, endogenous changes in its level contribute to the pathogenesis of several neurological diseases. In humans, a decreased synthesis and secretion of BDNF in the brain has been reported in various mental diseases such as Parkinson’s disease and Alzheimer’s disease (Diniz & Teixeira, 2011; Wang, Liu, Zhang, Soares, & Zhang, 2016). BDNF is able to cross blood– brain barrier in both directions. In the serum of humans and rats, it has been observed that BDNF levels are about 100-fold higher than in plasma; these differences are due to the BDNF internalization in the platelets that release it after the coagulation process (Fujimura et al., 2002). Recent findings have demonstrated that BDNF is also involved in energy metabolism regulating the energy homeostasis, the body weight control and the feeding behavior (Noble, Billington, Kotz, & Wang, 2011). Obesity is a metabolic disease correlated with cognitive dysfunctions including deterioration of learning, memory and processing speed. This disease is associated with low BDNF serum levels compared with healthy individuals (Li, Lang, & Cheng, 2016); however, these findings seem to be controversial because other studies report higher BDNF serum levels probably through a compensatory mechanism to supply better neuroprotection due to pathophysiologic conditions (Monteleone et al., 2004). It has been demonstrated that physical activity gives benefits to health and cognitive functions (Mueller et al., 2015). In particular, aerobic exercise has been shown to increase the circulating BDNF levels. These findings suggest that physical exercise, influencing BDNF synthesis, can result in neuroprotective stimulating neurogenesis and brain synaptogenesis, thus ameliorating cognitive functions (Ruscheweyh et al., 2011). To date, few studies have evaluated the effect of physical activity on BDNF levels in obese individuals and a dependence on age has been observed. In fact, while Lee in 2014 observed a significant increase of serum BDNF levels after a 12-week program of regular aerobic exercise in young obese participants, compared to young healthy individuals (Lee et al., 2014), Babaei in 2013 and Damirchi in 2014 reported decreased BDNF serum levels after aerobic training in middle-aged subjects with metabolic syndrome (Babaei, Azali Alamdari, Soltani Tehrani, & Damirchi, 2013; Damirchi, Tehrani, Alamdari, & Babaei, 2014). BDNF is also synthesized by non-neural cells of the bladder (Oddiah, Anand, McMahon, & Rattray, 1998), and it can be measured in urine (Koven & Collins, 2014). Recently, it has been observed that individuals with overactive bladder (OAB) have higher urinary BDNF levels than healthy individuals (AntunesLopes, Carvalho-Barros, Cruz, Cruz, & Martins-Silva, 2011; Wang, Han, Chen, Ma, & Hai, 2014). Moreover, Ozdemir, Dincel, Berdeli, and Mir (2016) observed that pharmacologic therapy with oxybutynin for a period of

Russo et al.

73

three and six months in children affected by OAB significantly reduced urinary BDNF levels. To our knowledge, no study has evaluated the effect of physical activity on urinary BDNF levels in overweight and obese participants, and only one study has investigated this link in healthy participants (Collins & Koven, 2014). In Collins and Koven (2014), a positive association between urinary BDNF levels and physical activity was observed, demonstrating that urine samples can be used as a measure of exercise-mediated changes of peripheral BDNF (Collins & Koven, 2014). The link between physical activity, urinary BDNF levels and cognitive functioning in obesity has yet to be explored. The aim of this study was to evaluate whether urinary BDNF can be used as non-invasive tool to investigate improvements in metabolic and cognitive functions mediated by physical activity in overweight and obese individuals. The effect of two bouts of exercise, performed in different sessions at different intensities of heart rate reserve (HRR) and alternating aerobic and resistance training, was investigated on urinary BDNF levels and cognitive functions in individuals affected by overweight and obesity. We expected that both urinary BDNF levels and cognitive performance would be positively associated with training exercise and intensity training.

Method Participants This study involved a volunteer group of 12 adult Italian participants with overweight or obesity who were referred to a multidisciplinary lifestyle intervention program at the Lifestyle Institute of the University of Perugia, CURIAMO (Centro Universitario di Ricerca Interdipartimentale Attivita` MOtoria). The inclusion criteria were body mass index (BMI) 28, age between 30 and 70 years; exclusion criteria were concomitant diseases contraindicating physical exercise, alcohol consumption, heart failure, pulmonary disease and smoking. The mean age of the participants was 51.4 years (SD ¼ 13.4); 66.7% were women and 33.3% were men. All participants enrolled at the Lifestyle Institute of the University of Perugia (CURIAMO) followed an intervention model (described by De Feo et al., 2011) that included a medical examination by an endocrinologist for the measurement of anthropometric and biochemical parameters (Table 1); an interview by a psychologist and a supervised and individualized exercise program (groups of five to six participants) of 26 sessions (two per week) of structured indoor exercise, described above in the training protocol. The study was approved by the local Ethics Committee (CEAS Umbria Region, HREC no. 1/10/1633) (De Feo et al., 2011). All procedures were

74

Psychological Reports 120(1) Table 1. Anthropometric and biochemical parameters in overall group (n ¼ 12).

BMI (kg/m2) Weight (kg) Waist circumference (cm) LDL (mg/dl) HDL (mg/dl) Total Cholesterol (mg/dl) Triglycerides, mg/dl

M

SD

35.36 93.34 107.75 135.1 48.17 215.72 161.91

7.21 25.04 15.33 38.2 12.45 41.91 40.15

Note: Data are shown as means and standard deviations (SD). LDL: low-density lipoprotein; HDL: high-density lipoprotein; BMI: body mass index.

in accordance with the Helsinki declaration of 1975 as revised in 1996. Full informed consent was given by all participants at the beginning of the treatment.

Measures Anthropometric and biochemical measurements. Participants were weighed before the analysis and each participant’s weight was measured and recorded. BMI (kg/m2) was calculated as ratio between weight (kg) and square height (m2), while waist circumference (WC) was measured in all participants using standard techniques (Lohman, Roche, & Martorell, 1988). Biochemical parameters (low-density lipoprotein, high-density lipoprotein, total cholesterol and triglycerides) were recorded in agreement with the values of laboratory analysis reported by participants. Training measurements. The energy expenditure was measured during two training sessions at different intensities (65% and 70% of HRR) by Bodymedia SenseWear Armband, a portable multisensory device able to measure several parameters such as galvanic skin response, heat flux and skin temperature (Jakicic et al., 2004). These data were combined with sex, age, body weight, and height to estimate both energy expenditure and intensity training using algorithms developed by the SenseWear professional software, Version 8.0. The maximum dynamic force of legs and arms was conducted by Brzycki 1-RM prediction equation using isotonic machines such as leg press, leg extension, lat machine and chest press machine (Technogym, Cesena, Italy) (do Nascimento et al., 2007).

Russo et al.

75

Urinary BDNF measurement. Urinary BDNF concentration (pg/ml) was measured by an enzyme-linked immunosorbent assay (Biorbyt, Explore Bioreagent, UK) according to manufacturer’s instructions. Briefly, the samples were not diluted prior to analysis due to low urinary BDNF concentrations. Standards and samples, containing human BDNF, were added to wells of microplate coated with high-affinity antibodies. During the first incubation (90 min at 37 C), the immobilized antibodies bound the antigens. After discarding plate content, biotinylated anti-human BDNF antibody was added and incubated at 37 C for 60 min in each microtiter well to bind the antigens. After washing with phosphatebuffered saline (PBS), an avidin-biotin-peroxidase complex was incubated at 37 C for 30 min. The unbound conjugates were removed by washing with PBS buffer, and TMB substrate was added (30 min at 37 C) to visualize the peroxidase enzymatic reaction. TMB hydrolysis was catalyzed by horseradish peroxidase producing a blue color product that changed into yellow color after addition of a stop solution (sulfuric acid, H2SO4 0.2 M). The yellow color intensity was directly proportional to urinary BDNF concentration present in each sample. Using the values obtained from standard, a dose–response curve of absorbance unit (optical density at 450 nm) vs. concentration was generated; subsequently, the urinary BDNF concentration (pg/ml) for each sample was determined. Subsequently, total BDNF concentrations (pg/ml) were normalized on creatinine concentrations (mg/dl), measured by Jaffe Colorimetric Method, and the new values were expressed as BDNF/Cr (pg/mg). For each participant, urinary BDNF levels were compared to normal levels before and after training exercise. Cognitive tests. Digit memory test sequences of digits were presented starting with a length of two digits (Woods et al., 2011). Two trials were presented at each increasing list length; the assessment ended when the participant failed to report either trial at one sequence length or when the maximal list length was reached (nine digits forward, eight backward). The total number of lists reported correctly was combined across forward span and backward span to produce the final score. The change location test consisted of a variable number of colored squares, according to variations in memory load (Gold et al., 2006). These included sub-span conditions (i.e., easy, with 2 or 4 squares) and supra-span conditions (i.e., hard, with 6 or 8 squares). These were presented on a computer screen for 500 ms; after a short retention interval of 1000 ms, the squares were presented again, but one of them had now changed in color. The individuals were required to keep in mind the spatial configuration of the squares and to indicate which of them changed. Individuals completed 24 trials for each memory load condition.

76


Procedures In the group presented before, using a quasi-experimental study design, BDNF urinary levels and cognitive performances were measured before and after two training sessions carried out at different intensities (65%–70% of HRR). Training protocol. The exercise program was performed in the gym with a maximum attendance of six participants per group. Before beginning the training period, all the people enrolled participated in a test session. The participants wore heart rate monitors and they worked on target zone, calculated using the Karvonen’s formula according to resting and maximum heart rates. The workout for muscular strength used isotonic machines for training of the lower and upper limbs, with the gradual increase from 50% of 1 repetition maximum (RM). The aerobic workout was performed using ergometers for cardiovascular work (treadmill, cycloergometer and arm ergometer) gradually increasing the intensity from 50% HRR. The HRR range increased 5% every three weeks up to 70%. The workout for muscular strength used isotonic machines for training of the trunk, lower and upper limbs, with a load corresponding to 50% of 1 RM at the beginning, evaluated for each exercise before starting the training period. Each participant performed two sets of 20 repetitions for each exercise and 45 s of resting between sets and exercises; gradually, loads at the machine increased by 2.5/5 kg every three weeks, according to the physical fitness of the participants. Sample collection. BDNF urinary concentration was determined through two collections of urine samples: the first was performed 2 h before the training session while the second collection was conducted 1 h from the end of the training. After collection, urine samples were stored at 4 C for a period no longer than 3 h. Subsequently, the samples were centrifuged for 10 min at 3000 r/min and the supernatants were aliquoted in 1.5 ml Eppendorf tubes and stored at 80 C before analysis of BDNF and creatinine concentrations. Cognitive assessment. Two cognitive tests assessing both verbal and visuo-spatial short-term memory capacity (i.e., the ‘‘digit memory’’ and ‘‘change location’’ test, respectively; (Luck & Vogel, 2013)) were administered 30 min before and just after each training exercise.

Statistical analysis In order to evaluate the normal distribution of data, the Kolmogorov-Smirnov test was used. According to the normal distribution of the sample, a two-tailed paired t-tests and an analysis of variance (ANOVA) to analyze differences between pre-training and post-training sessions in gender and BMI subgroups

Russo et al.

77

were applied. Data were analyzed using the SPSS 11.5 statistical package (SPSS Inc., Chicago, IL, USA). Differences were considered significant at p .05.

Results Table 1 shows anthropometrics and biochemical parameters of the group. The mean BMI of the whole sample allowed collocation of the participants in a severely obese group, according to the World Health Organization standard. WC and fasting triglycerides of all participants were higher than the limits indicated by International Diabetes Federation for the identification of ‘‘Central obesity’’ (Alberti, Zimmet, & Shaw, 2005). The ANOVA computed showed no differences of anthropometrics and biochemical measurements between pre-training and post-training sessions in gender and BMI subgroups (data not shown). Table 2 displays the training parameters both at 65% and 70% of HRR. While the differences of HRR between two groups were maintained with a large effect size (p ¼ .01, d ¼ 1.0) according to Cohen (1988), relating to Energy Expenditure (kJ), no significant variations were observed. Urinary BDNF levels, normalized with urinary creatinine concentration, were expressed as BDNF/Cr (pg/mg). For each subject, a comparison of urinary BDNF levels before and after the acute training exercise, respectively, at 65% and 70% of HRR, was performed. At 65% of HRR, after training exercise the urinary BDNF concentration was not measurable in 4 of 12 participants because it was under the detection limit. Significant variations in urinary BDNF normalized levels were observed after training exercise. At baseline (pre-training), the mean urinary BDNF level was 49.32 pg/mg 33.48 vs. 32.26 pg/mg 24.45 of post-training, t ¼ 2.45, p ¼ .04, d ¼ 0.58, according to Cohen (1988) (Figure 1(a)). Also at 70% of HRR, a significant reduction of urinary BDNF levels posttraining exercise was observed. At baseline, the mean urinary BDNF level was

Table 2. Training exercise parameters. Factors

65% HRR

70% HRR

t

Resting heart rate, (bpm) 68.00 5.48 68.00 5.48 / % Heart rate reserve, (bpm) 61.00 6.62 66.80 4.78* 3.28 Energy expenditure, (kJ) 2124.90 631.28 2127.20 553.62 0.02 MET’s average 3.85 0.35 3.74 0.57 0.64

p n.s 0.01 n.s n.s

Cohen’s d n.s 1 n.s n.s

Note. Data are shown as means standard deviations. Statistical significance was considered at *p .05.

78


52.88 pg/mg 24.78, while post-training it decreased to 33.32 pg/mg 18.68, t ¼ 2.72, p ¼ .02, with a large significant effect size (d ¼ 0.89) (Figure 1(b)). The ANOVA computed showed no differences on BDNF urinary levels between pre-training and post-training sessions in gender and BMI subgroups (data not shown). Regarding cognitive performances, both change location test (visuo-spatial working memory capacity) and digit memory test (verbal working memory capacity) of the participants before and after each of two training exercises were compared (65% and 70% of HRR). In the change location test, a significant increase in participants’ performance was found after 65% of HRR intensity training with a moderate significant effect size, specifically for the supra-span hard conditions (1.45 0.53 pre-training vs. 1.88 0.71 post-training, t ¼ 2.43, p ¼ .03, d ¼ 0.69) (Figure 2(a)). By contrast, at 70% of HRR intensity training, there were no significant differences (1.71 0.48 pre-training vs. 1.83 0.89 post-training, t ¼ 0.63, p ¼ .54) (Figure 2(b)). For the sub-span easy conditions,

Figure 1. Comparison of means of urinary BDNF levels. Normalized levels (pg/mg) before and after training session, respectively, ‘‘pre-training’’ and ‘‘post-training’’, at 65% (panel A) or 70% (panel B) of HRR in a sample of overweight and obese participants. Statistical significance was considered at *p .05. Data are shown as mean standard deviation.

Russo et al.

79

Figure 2. Participants’ performance in terms of K score (number of objects stored in memory) before and after training session at 65% (panel A) or 70% (panel B) of HRR in a sample of overweight and obese individuals considering the supra-span conditions (6 and 8 squares) at the change location test. Statistical significance was considered at *p .05. Data are shown as mean standard deviation.

the analysis did not reveal any differences in the two training sessions (65% HRR: 1.43 0.24 pre-training vs. 1.45 0.28 post-training, t ¼ 0.32, p ¼ .75; 70% HRR: 1.49 0.18 pre-training vs. post-training 1.51 0.22, t ¼ 0.31, p ¼ .76). Also in the digit memory test, the comparison did not reveal any significant change after training both at 65% (14.50 4.17 pre-training vs. 14.92 3.23 post-training, t ¼ 0.47, p ¼ .65) and 70% (16.50 4.44 pretraining vs. 15.42 4.01 post-training, t ¼ 1.74, p ¼ .11) of HRR. The ANOVA computed showed no differences of cognitive performances between pre-training and post-training sessions in gender and BMI subgroups (data not shown).

Discussion This is the first report that investigates in parallel the effect of an acute session of training exercise both on urinary BDNF levels and cognitive performances in

80


individuals affected by overweight and obesity, evaluating in addition the possible linking of these two parameters with training intensity (respectively, 65% and 70% of HRR). Before and during physical activity with this specific type of individual, we had carefully considered the physiological effects of training exercise on the cardiovascular system and on skeletal muscle, paying particular attention to avoiding potential risks associated with cardiovascular injuries. Several participants reported that they did not feel confident with high rates of perceived exertion and naturally preferred to train at moderate intensities (Piana et al., 2013). A successful exercise program should be proposed at a moderate intensity and with a low perceived effort because overweight and obese participants who have low self-efficacy, poor mood status and are not familiar with high-intensity workouts could dropout (Mazzeschi et al., 2012). For these kinds of participants, the optimal strategy to promote physical activity is open-air, group exercise sessions at moderate intensity but for many hours, using socialization and pleasant surroundings as elements for long-term adherence (De Feo, 2013). Physical activity is an important factor that gives benefits to health and cognitive functions. In humans, a positive association between BDNF levels and physical activity has been well demonstrated (Huang & Reichardt, 2001). In mice, increased BDNF levels induced by physical activity are correlated with increased neurogenesis, learning and memory (Chen et al., 2016). In addition, there is some evidence showing that the increased BDNF secretion might be dependent on exercise intensity (Rasmussen et al., 2009). Few studies have assayed in obese participants the effect of training exercise on BDNF serum levels (Babaei et al., 2013; Damirchi et al., 2014; Lee et al., 2014), and no reports have been performed on urine. The aim of this study was to explore the effect of two bouts of training exercise, performed in different sessions at different intensity of HRR alternating aerobic and resistance training, on urinary BDNF levels and on cognitive functions in individuals affected by overweight and obesity. The findings revealed that while urinary BDNF levels decreased after both sessions of training exercise (65%–70% of HRR), regarding cognitive performance only the change location test of HRR (visuo-spatial working memory capacity), but not digit memory test (verbal working memory capacity), showed a significant increase. A hypothesis of decreased urinary BDNF levels could be due to the pathologic conditions of the individuals. Some studies have demonstrated higher levels of serum BDNF in obese individuals than healthy people, perhaps through a compensatory mechanism (Monteleone et al., 2004). It is possible that in our study the exercise results in improved health conditions, inducing a reduction of peripheral BDNF as it is less necessary. Ozdemir (2016) also observed significant reduced urinary BDNF levels in children affected by OAB after pharmacologic therapy with oxybutynin, assuming that an improvement in health conditions could be linked to a reduction of urinary BDNF. There were no differences in

Russo et al.

81

anthropometrics and biochemical measurements, BDNF urinary levels and cognitive performances between pre-training and post-training sessions in gender and BMI subgroups, according to ANOVAs. However, significant changes may not have been observed due to the limited number of participants. With regard to the relationship between physical activity and cognitive performances, the literature is somewhat inconsistent. While some studies show that physical activity can increase cognitive functioning (Murray & Russoniello, 2012), other studies failed to provide similar evidence (Luft, Takase, & Darby, 2009). To our knowledge, only one study has investigated the relationship between physical activity and cognitive functions in overweight and obese participants showing that a long period of physical activity (four months) improves cognitive performance (Drigny et al., 2014). The current results are in agreement with the latter study. In fact, we showed that moderate physical activity can affect cognitive functioning in these individuals. Additionally, here we extend these results further, showing a cognitive improvement even after a short training exercise program (i.e., one single training session). More specifically, the current findings highlight an HRR-dependent increment of visuo-spatial (but not verbal) WM capacity in post- as compared to pre-training sessions. The specificity of this cognitive enhancement, involving visuo-spatial but no verbal working memory, appears to rule out the contribution of a general learning effect, highlighting a selective effect of physical training of cognition. This result may be viewed in the light of some evidence showing a strong relationship between physical activity and the increase of the hippocampal volume (Erickson et al., 2011). In the hippocampus, there are the ‘‘place-cells’’ that are specialized in coding and memorization of visuo-spatial information. In fact, place-cell activity increases whenever an individual is in a specific location in the environment, thus forming a spatial-related map, as has been demonstrated (Eichenbaum, Dudchenko, Wood, Shapiro, & Tanila, 1999; Moser, Rowland, & Moser, 2015). Importantly, the hippocampus has also been shown to integrate visuospatial information at short temporal latencies (i.e., during working memory performance) (Santangelo & Macaluso, 2013). Thus, we can argue that moderate physical activity and BDNF might contribute to increasing hippocampal functioning, with a consequent effect on visuo-spatial working memory capacity. In conclusion, the present study extends the numerous studies that have demonstrated that physical activity induces a change in urinary BDNF levels, along with an improvement in cognitive function, in this case observed in visuospatial working memory capacity. However, this pilot study also suffers from some objective limitations. First, the limited number of participants that joined the study; in addition, the effect was analyzed after a single session of training exercise without performing analysis of long intervention. Finally, the study lacks a control group (healthy individuals) not performing the training exercise in between the two testing phases. Unfortunately, this may limit overall

82


understanding of the interplay between physical activity, BDNF urinary levels, and cognitive functions in overweight and obese participants. Future research will have the important role of confirming the current findings after overcoming these potential limitations. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This study was supported by Lifestyle Institute of the University of Perugia, CURIAMO (Centro Universitario di Ricerca Interdipartimentale Attivita` Motoria).

References Alberti, K. G., Zimmet, P., & Shaw, J. (2005). The metabolic syndrome – a new worldwide definition. The Lancet, 366, 1059–1062. doi: 10.1016/S0140-6736(05)67402-8 Antunes-Lopes, T., Carvalho-Barros, S., Cruz, C. D., Cruz, F., & Martins-Silva, C. (2011). Biomarkers in overactive bladder: A new objective and noninvasive tool? Advances in Urology, 2011, 382431. doi: 10.1155/2011/382431 Babaei, P., Azali Alamdari, K., Soltani Tehrani, B., & Damirchi, A. (2013). Effect of six weeks of endurance exercise and following detraining on serum brain derived neurotrophic factor and memory performance in middle aged males with metabolic syndrome. The Journal of Sports Medicine and Physical Fitness, 53(4), 437–443. Cohen J. (1988) Statistical Power Analysis for the Behavioral Sciences. New York, NY: Routledge Academic. Chen, J., Li, C. R., Yang, H., Liu, J., Zhang, T., Jiao, S. S., . . . Xu, Z. Q. (2016). proBDNF attenuates hippocampal neurogenesis and induces learning and memory deficits in aged mice. Neurotoxicity Research, 29(1), 47–53. doi: 10.1007/s12640-015-9568-2 Collins, L. R., & Koven, N. S. (2014). Urinary BDNF-to-creatinine ratio is associated with aerobic fitness. Neuroscience Letters, 559, 169–173. doi: 10.1016/j.neulet. 2013.12.004 Damirchi, A., Tehrani, B. S., Alamdari, K. A., & Babaei, P. (2014). Influence of aerobic training and detraining on serum BDNF, insulin resistance, and metabolic risk factors in middle-aged men diagnosed with metabolic syndrome. Clinical Journal of Sport Medicine, 24(6), 513–518. doi: 10.1097/JSM.0000000000000082 De Feo, P. (2013). Is high-intensity exercise better than moderate-intensity exercise for weight loss?. Nutrition, metabolism, and cardiovascular diseases, 23(11), 1037–1042. doi: 10.1016/j.numecd.2013.06.002 De Feo, P., Fatone, C., Burani, P., Piana, N., Pazzagli, C., Battistini, D., . . . Mazzeschi, C. (2011). An innovative model for changing the lifestyles of persons with obesity and/ or Type 2 diabetes mellitus. Journal of Endocrinological Investigation, 34(10), e349–354. doi: 10.3275/7857

Russo et al.

83

Diniz, B. S., & Teixeira, A. L. (2011). Brain-derived neurotrophic factor and Alzheimer’s disease: Physiopathology and beyond. Neuromolecular Medicine, 13(4), 217–222. doi: 10.1007/s12017-011-8154-x Do Nascimento, M. A., Serpeloni Cyrino, E., Nakamura, F. Y, Romanzini, M., Cardoso Pianca, H.J., & Queiro´ga, R. (2007). Validation of the Brzycki equation for the estimation of 1-RM in the bench press. Rev Bras Med Esporte 13, 40e–42e. doi:10.1590/ S1517-86922007000100011 Drigny, J., Gremeaux, V., Dupuy, O., Gayda, M., Bherer, L., Juneau, M., . . . Nigam, A. (2014). Effect of interval training on cognitive functioning and cerebral oxygenation in obese patients: A pilot study. Journal of Rehabilitation Medicine, 46(10), 1050–1054. doi: 10.2340/16501977-1905 Eichenbaum, H., Dudchenko, P., Wood, E., Shapiro, M., & Tanila, H. (1999). The hippocampus, memory, and place cells: Is it spatial memory or a memory space? Neuron, 23(2), 209–226. Erickson, K. I., Voss, M. W., Prakash, R. S., Basak, C., Szabo, A., Chaddock, L., . . . Kramer, A. F. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 3017–3022. doi: 10.1073/pnas.1015950108 Fujimura, H., Altar, C. A., Chen, R., Nakamura, T., Nakahashi, T., Kambayashi, J., . . . Tandon, N. N. (2002). Brain-derived neurotrophic factor is stored in human platelets and released by agonist stimulation. Thrombosis and Haemostasis, 87(4), 728–734. Gold, J. M., Fuller, R. L., Robinson, B. M., McMahon, R. P., Braun, E. L., & Luck, S. J. (2006). Intact attentional control of working memory encoding in schizophrenia. Journal of Abnormal Psychology, 115(4), 658–673. doi: 10.1037/0021-843X.115.4.658 Gonzalez, A., Moya-Alvarado, G., Gonzalez-Billaut, C., & Bronfman, F. C. (2016). Cellular and molecular mechanisms regulating neuronal growth by brain-derived neurotrophic factor (BDNF). Cytoskeleton (Hoboken), 73(10), 612–628. doi: 10.1002/cm.21312 Hohn, A., Leibrock, J., Bailey, K., & Barde, Y. A. (1990). Identification and characterization of a novel member of the nerve growth factor/brain-derived neurotrophic factor family. Nature, 344(6264), 339–341. doi: 10.1038/344339a0 Huang, E. J., & Reichardt, L. F. (2001). Neurotrophins: Roles in neuronal development and function. Annual Review of Neuroscience, 24, 677–736. doi: 10.1146/ annurev.neuro.24.1.677 Jakicic, J. M., Marcus, M., Gallagher, K. I., Randall, C., Thomas, E., Goss, F. L., . . . Robertson, R. J. (2004). Evaluation of the SenseWear Pro Armband to assess energy expenditure during exercise. Medicine and Science in Sports and Exercise, 36(5), 897–904. Koshimizu, H., Kiyosue, K., Hara, T., Hazama, S., Suzuki, S., Uegaki, K., . . . Kojima, M. (2009). Multiple functions of precursor BDNF to CNS neurons: Negative regulation of neurite growth, spine formation and cell survival. Molecular Brain, 2, 27. doi: 10.1186/1756-6606-2-27 Koven, N. S., & Collins, L. R. (2014). Urinary brain-derived neurotrophic factor as a biomarker of executive functioning. Neuropsychobiology, 69(4), 227–234. doi: 10.1159/ 000362242

84


Lee, S. S., Yoo, J. H., Kang, S., Woo, J. H., Shin, K. O., Kim, K. B., . . . Kim, Y. I. (2014). The effects of 12 weeks regular aerobic exercise on brain-derived neurotrophic factor and inflammatory factors in juvenile obesity and type 2 diabetes mellitus. Journal of Physical Therapy Science, 26(8), 1199–1204. doi: 10.1589/jpts.26.1199 Levi-Montalcini, R., Meyer, H., & Hamburger, V. (1954). In vitro experiments on the effects of mouse sarcomas 180 and 37 on the spinal and sympathetic ganglia of the chick embryo. Cancer Research, 14(1), 49–57. Li, B., Lang, N., & Cheng, Z. F. (2016). Serum levels of brain-derived neurotrophic factor are associated with diabetes risk, complications, and obesity: A cohort study from Chinese patients with type 2 diabetes. Molecular Neurobiology, 53(8), 5492–5499. doi: 10.1007/s12035-015-9461-2 Lohmann, T. G., Roche, A. F. & Martorell, R. (1988). Anthropometric standardization reference manual. Champaign, IL: Human Kinetics. Luck, S. J., & Vogel, E. K. (2013). Visual working memory capacity: From psychophysics and neurobiology to individual differences. Trends in Cognitive Sciences, 17(8), 391–400. doi: 10.1016/j.tics.2013.06.006 Luft, C. D., Takase, E., & Darby, D. (2009). Heart rate variability and cognitive function: Effects of physical effort. Biological Psychology, 82(2), 164–168. doi: 10.1016/ j.biopsycho.2009.07.007 Maisonpierre, P. C., Le Beau, M. M., Espinosa, R. III, Ip, N. Y., Belluscio, L., de la Monte, S. M., . . . Yancopoulos, G. D. (1991). Human and rat brain-derived neurotrophic factor and neurotrophin-3: Gene structures, distributions, and chromosomal localizations. Genomics, 10(3), 558–568. Mazzeschi, C., Pazzagli, C., Buratta, L., Reboldi, G. P., Battistini, D., Piana, N., . . . De Feo, P. (2012). Mutual interactions between depression/quality of life and adherence to a multidisciplinary lifestyle intervention in obesity. The Journal of Clinical Endocrinology and Metabolism, 97(12), E2261–2265. doi: 10.1210/jc.2012-2364 Monteleone, P., Tortorella, A., Martiadis, V., Serritella, C., Fuschino, A., & Maj, M. (2004). Opposite changes in the serum brain-derived neurotrophic factor in anorexia nervosa and obesity. Psychosomatic Medicine, 66(5), 744–748. doi: 10.1097/ 01.psy.0000138119.12956.99 Moser, M. B., Rowland, D. C., & Moser, E. I. (2015). Place cells, grid cells, and memory. Cold Spring Harbor Perspectives in Biology, 7(2), a021808. doi: 10.1101/ cshperspect.a021808 Mueller, K., Moller, H. E., Horstmann, A., Busse, F., Lepsien, J., Bluher, M., . . . Pleger, B. (2015). Physical exercise in overweight to obese individuals induces metabolic- and neurotrophic-related structural brain plasticity. Frontiers in Human Neuroscience, 9, 372. doi: 10.3389/fnhum.2015.00372 Murray, N. P., & Russoniello, C. (2012). Acute physical activity on cognitive function: A heart rate variability examination. Applied Psychophysiology and Biofeedback, 37(4), 219–227. doi: 10.1007/s10484-012-9196-z Noble, E. E., Billington, C. J., Kotz, C. M., & Wang, C. (2011). The lighter side of BDNF. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology, 300(5), R1053–1069. doi: 10.1152/ajpregu.00776.2010 Oddiah, D., Anand, P., McMahon, S. B., & Rattray, M. (1998). Rapid increase of NGF, BDNF and NT-3 mRNAs in inflamed bladder. Neuroreport, 9(7), 1455–1458.

Russo et al.

85

Ozdemir, K., Dincel, N., Berdeli, A., & Mir, S. (2016). Can Urinary Nerve Growth Factor and Brain-Derived Neurotrophic Factor be used in the Diagnosis and Follow-Up of Voiding Dysfunction in Children? Urol J, 13(3), 2690–2696. Piana, N., Battistini, D., Urbani, L., Romani, G., Fatone, C., Pazzagli, C., . . . De Feo, P. (2013). Multidisciplinary lifestyle intervention in the obese: Its impact on patients’ perception of the disease, food and physical exercise. Nutrition, metabolism, and cardiovascular diseases, 23(4), 337–343. doi: 10.1016/j.numecd.2011.12.008 Rasmussen, P., Brassard, P., Adser, H., Pedersen, M. V., Leick, L., Hart, E., . . . Pilegaard, H. (2009). Evidence for a release of brain-derived neurotrophic factor from the brain during exercise. Experimental Physiology, 94(10), 1062–1069. doi: 10.1113/expphysiol.2009.048512 Ruscheweyh, R., Willemer, C., Kruger, K., Duning, T., Warnecke, T., Sommer, J., . . . Floel, A. (2011). Physical activity and memory functions: An interventional study. Neurobiology of Aging, 32(7), 1304–1319. doi: 10.1016/ j.neurobiolaging.2009.08.001 Santangelo, V., & Macaluso, E. (2013). Visual salience improves spatial working memory via enhanced parieto-temporal functional connectivity. The Journal of Neuroscience, 33(9), 4110–4117. doi: 10.1523/JNEUROSCI.4138-12.2013 Shang, Y., Wang, X., Shang, X., Zhang, H., Liu, Z., Yin, T., . . . Zhang, T. (2016). Repetitive transcranial magnetic stimulation effectively facilitates spatial cognition and synaptic plasticity associated with increasing the levels of BDNF and synaptic proteins in Wistar rats. Neurobiology of Learning and Memory, 134(Pt B), 369–378. doi: 10.1016/j.nlm.2016.08.016 Wang, L. W., Han, X. M., Chen, C. H., Ma, Y., & Hai, B. (2014). Urinary brain-derived neurotrophic factor: A potential biomarker for objective diagnosis of overactive bladder. International Urology and Nephrology, 46(2), 341–347. doi: 10.1007/s11255-0130540-x Wang, Y., Liu, H., Zhang, B. S., Soares, J. C., & Zhang, X. Y. (2016). Low BDNF is associated with cognitive impairments in patients with Parkinson’s disease. Parkinsonism & Related Disorders, 29, 66–71. doi: 10.1016/j.parkreldis.2016.05.023 Willand, M. P., Rosa, E., Michalski, B., Zhang, J. J., Gordon, T., Fahnestock, M., . . . Borschel, G. H. (2016). Electrical muscle stimulation elevates intramuscular BDNF and GDNF mRNA following peripheral nerve injury and repair in rats. Neuroscience, 334, 93–104. doi: 10.1016/j.neuroscience.2016.07.040 Woods, D. L., Kishiyamaa, M. M., Lund, E. W., Herron, T. J., Edwards, B., Poliva, O., . . . Reed, B. (2011). Improving digit span assessment of short-term verbal memory. Journal of Clinical and Experimental Neuropsychology, 33(1), 101–111. doi: 10.1080/ 13803395.2010.493149 Yang, J., Siao, C. J., Nagappan, G., Marinic, T., Jing, D., McGrath, K., . . . Hempstead, B. L. (2009). Neuronal release of proBDNF. Nature Neuroscience, 12(2), 113–115. doi: 10.1038/nn.2244.

Author Biographies Angelo Russo has a degree in Biotechnology at the University of Perugia and PhD student at the University of Perugia. He works at Healthy Lifestyle

86


Institute C.U.R.I.A.M.O, University of Perugia, Italy, and collaborates at the EUROBIS project. Livia Buratta, studied Pedagogy at the University of Perugia. She actually studies Psychology at the University of Perugia. Since 2011 she collaborates with Psychological Unit of the University Centre C.U.R.I.A.Mo. and of the EOROBIS Project. Roberto Pippi, Master’s degree in Sciences and Techniques of Preventive and Adaptive Physical Activity, is now Physical Activity Coordinator at C.U.R.I.A.Mo. Center and PhD candidate in Translational Medicine And Surgery at University of Perugia. Cristina Aiello has a Master’s degree in Sciences and Techniques of Preventive and Adaptive Physical Activity and is PhD candidate in Translational Medicine and Surgery at University of Perugia. She works at Healthy Lifestyle Institute C.U.R.I.A.Mo., University of Perugia, Italy. Claudia Ranucci, Dietitian and nutrition specialist, is PhD candidate in Translational Medicine and Surgery at University of Perugia. She works at Healthy Lifestyle Institute C.U.R.I.A.Mo., University of Perugia, Italy, and collaborates at the EUROBIS project. Elisa Reginato has a degree in Medicine at the University of Perugia and specialization in Clinical Nutrition at the University of Perugia. She works at Healthy Lifestyle Institute C.U.R.I.A.Mo., University of Perugia, Italy, and collaborates at the EUROBIS project. Valerio Santangelo, Associate Professor in Cognitive Psychology at the University of Perugia; he investigated Attention and Memory processes using a variety of experimental methods, including psychophysical measures, computational modeling, ERPs, and fMRI. He published more than 40 peer-reviewed papers and book chapters, including publication in high-impact journals, such as Neuron and The Journal of Neuroscience. Pierpaolo De Feo, Associate Professor in Endocrinology sector; he has done important research in the field of pathophysiology of hypoglycemia defense mechanisms of insulin in normal subjects and in patients with Type 1 diabetes. He is the founder of the University Center Research Interdepartmental Motor Activity (C.U.R.I.A.Mo.), Healthy Lifestyle Institute, University of Perugia and of the EUROBIS project.

Russo et al.

87

Claudia Mazzeschi, PhD in Developmental Psychology, is Full Professor in Psychology at the University of Perugia, Director of the Department of Philosophy and Human Sciences, President of the Master Course Degree in Psychology; Coordinator of the Academic Research Committee of the Department; Member of the PhD board in Human Sciences; Coordinator of the Psychological Unit of the University Centre C.U.R.I.A.Mo. and of the EUROBIS Project.