Printer Friendly

Use of murinometrics indices and bioelectrical impedance (BIA) in the determination of experimental obesity in oophorectomized rats/Uso de indices murinometricos e impedancia bioeletrica (BIA) na determinacao de obesidade experimental em ratas ooforectomizadas.

Introduction

Obesity is a disease and a risk factor for various cardiovascular, metabolic, orthopedic and psychosocial disorders (Brandalize & Leite, 2010; Grundy, 2004; Vaidya, 2006), with high and still increasing prevalence and incidence in the world (Kelly, Yang, Chen, Reynolds, & He, 2008). One of the criteria generally used for identification and monitoring is to determine the increase in body weight by body mass index (BMI) (Collaboration, 2009). However, obesity is in fact characterized by the differential increase in body fat, which is not only an energy reserve, but an endocrine organ (Ahima & Flier, 2000; Wellen & Hotamisligil, 2003). In this way, it has aroused discussion, about not only its absolute amount but also its relative participation in body composition and topographic distribution (Goodpaster, Thaete, & Kelley, 2000).

It is known that, in menopause, the female body undergoes metabolic changes that can activate subsequent changes in health condition. The change in gynecoid to android fat distribution (Raskin, Pinto-Neto, Paiva, Raskin, & Martinez, 2000; Tremollieres, Pouilles, & Ribot, 1996) can cause excessive accumulation of visceral adipose tissue and trigger chronic inflammation (Abu-Taha et al., 2009; Pfeilschifter, Koditz, Pfohl, & Schatz, 2002).

In this context, it is important to use animal models in order to simulate and evaluate specific experimental conditions of postmenopausal obesity. Experimental oophorectomy triggers changes in metabolism that are compatible with the development of postmenopausal excess body fat by deprivation of estrogen in female rats (Ignacio et al., 2009; Torrezan et al., 2008).

Most articles describing animal models monitor body weight as a standard of development evaluation. In the early the last century, Donaldson (1915) showed the importance of direct methods for analyzing the partition of body weight and indirect methods with wider applicability in vivo. Among the indirect methods of estimating body composition, measures and metric indices are widely used, because they are non-invasive, having greater clinical applicability and therefore convenient tools in monitoring body composition (Bernardis & Patterson, 1968; Dahms & Glass, 1982; Novelli et al., 2007; Simson & Gold, 1982).

Bioelectric techniques have been proposed as low cost and less invasive indirect methods for assessing more precisely body composition in experimental animals (Angeloco et al., 2012; Hall, Lukaski, & Marchello, 1989; Rutter, Hennoste, Ward, Cornish, & Thomas, 1998), as well as several indices, which, in this case, are called murinometric (from Latin murinae--rodent and Greek metri--measure). These indices are methodologically similar to anthropometric measures in the human body and are very discussed in relation to their efficiency in defining the framework of obesity in rats (Dahms & Glass, 1982; Simson & Gold, 1982). Regarding the use of these indices, Novelli et al. (2007) showed that body mass index (BMI) has a greater sensitivity than the Lee Index as obesity determination index for experimental animals. However, no studies were found comparing murinometric indices with bioelectric measures, or evaluating the effectiveness of the analysis in experimental models of postmenopausal obesity. Thus, the objective was to evaluate and discuss the use of bioimpedance (BIA) to identify the obesity framework in oophorectomized female rats.

Material and methods

Animals

Ten young adult Wistar rats (8 weeks) weighing between 130-160 g, from the Animal House of the Universidade Federal do Piaui (UFPI) were kept at 22-25[degrees]C, 12 hours light/dark cycle, and received water and food ad libitum (LABCIL COBAIAS 6522). All procedures related to the use of animals were performed according to standards of the Brazilian Society of Science in Laboratory Animals (SBCAL) and according to the Normative Resolutions of the National Council for Animal Experimentation Control (CONCEA). The research project was approved by the Ethics Committee on Animal Experimentation of Universidade Federal do Piaui (017-2009).

Experimental groups

The animals were randomly assigned to the control group (N = 5), in which the animals were subjected to false oophorectomy surgery, and the oophorectomy group (N = 5) underwent a bilateral oophorectomy.

Surgical procedures

Surgical procedures were performed under intraperitoneal anesthesia (ketamine 60 mg [kg.sup.-1] and xylazine 8 mg [kg.sup.-1]), confirmed by testing sensory responses to pressure stimulus. Midline incision of 1.5 cm in length was made in the skin and subcutaneous tissue on the animal's back below the last rib, bilaterally, the ovaries were exposed and isolated with suture thread #5, followed by excision and replacement of the fallopian tubes and suture. After disinfection with iodine solution (Friezol, Sao Paulo, Brazil), analgesic administration and anti-inflammatory treatment with Pencivet Plus[R] (Intervet, Cotia, Sao Paulo, Brazil) intramuscularly at a dose 1 ml [kg.sup.-1] was provided. The control group underwent the entire surgical procedure except excision of the ovaries.

Murinometric and bioelectrical evaluation

After 4 weeks, the animals were weighed on an analytical balance (Marte/AD500) under anesthesia and the naso-anal length (NAL), the length from nose to anus, was measured with a stadiometer, with the animal in the prone position. The Body Mass Index (BMI) and Lee index were calculated according to the following equations:

BMI = Weight/[NAL.sup.2] Lee Index = [cube root of Weight]/NAL x 1000

For bioelectrical impedance analysis, dual electrodes were used, subdermal needle type (26 x 10, stainless steel), with 1 cm between needles. The animals were kept in left lateral decubitus and the electrodes were introduced in the upper intramuscular region (thigh) in the foreleg and back of the right hemisphere. After set, the distance between the electrodes (DBE) was determined using inextensible millimeter measuring tape. The resistance at 50 KHz ([R.sub.50]) and reactance (Xc) were determined using a tetrapolar bioimpedance analyzer at 50 kHz Model BIA-101Q (RJL Systems Clinton Township, MI, USA), which emits an alternating current of 800 [micro]A at 50 KHz.

The formulas for the determination of body composition were used in accordance to Cornish, Ward, and Thomas (1992) and Ilagan, Bhutani, Archer, Lin, and Jen (1993). The following equations enable the determination of total body water (TBW) and fat-free mass (FFM):

TBW = 309.9 x [L.sup.2]/Zc + 30.0

FFM = 0.38 x BW + 13.8 x [L.sup.2]/[R.sub.50] + 70.9

in which L corresponds to DBE, R50 is the resistance in 50 KHz, BW is Body Weight and Zc is Impedance (Zc = [square root of ([R.sup.2] + [X.sup.2])]). Body fat was calculated as the difference between body weight and FFM and then the fat percentage was determined by proximate relationship between body fat and body weight of the animal.

Murinometric evaluation of body tissues

After dissection and removal of gastrocnemius and soleus muscles, adipose pads concerning periovarian and retroperitoneal fat were weighed on an analytical balance (Marte/AD500). The left tibia was dissected and measured with a caliper Digimess #100.150 (Sao Paulo, Sao Paulo, Brazil). The ratios for tissue weights were determined dividing tissue weight by the length of the animal tibia.

Statistical analysis

The results were presented as descriptive statistics (mean and standard error). The comparison between groups was performed by Student's t test for independent samples and Pearson correlation using GraphPad prism 6.0[R] program. Statistical significance was considered when the results presented likelihood of null hypothesis lower than 5% (p< 0.05). The post-hoc results relative to power of analysis (1-[beta] error probability) and effect size of t-test in Lee Index, Body Mass Index and Bioimpedance was calculated using the software G*Power 3.1.9.2 (Universitat Dusseldorf, Germany).

Results

Table 1 shows the murinometric and bioimpedance data demonstrating that, during the experimental period, both groups gained body weight, the oophorectomized group presented a mean weight 46.3% higher than the control group at the end of the experiment. This weight gain as a result of hormone deprivation was reflected in increased muscle weight, 39.7%, and body fat weight, with an increase of 108.9%, in the oophorectomized group.

Based on these results, with respect to relative tissue weight normalized to tibia length, only the fat weight ratio was significantly different between the groups (Table 1). This fact, added to a total dissected fat mass of about 100% higher in oophorectomized animals compared to the control group, indicates that the average weight gain of this group was mainly due to a differential increase in body fat gain.

Bioelectrical data were able to sensitively predict the gain of FFM and proportional gain of body fat in oophorectomy group compared to the control (Figure 1). In relative terms, the BIA technique has evidenced that fat percentage of this group was 72.6% higher than the control, while the changes in obesity assessment using the murinometric indices, LEE status and BMI, although significant, were relatively low, 8.4% and 32.6% respectively (Figure 1).

When analyzing the statistical power of the results, the percent variation observed in results in Figure 1 was confirmed by the post-hoc test of effect size and power (Table 2). Thereby, the accuracy of the BIA was demonstrated, as effect size and power of analysis were similar or even higher than the direct methods.

Discussion

The detection of variations in the "body complexion" due to ovarian castration in young Wistar rats was possible by the different strategies proposed here: dissection of the adipose tissue, murinometric and BIA indices. However, the approach of a bioelectrical method revealed a greater variation in fat percentage between groups than variations found using murinometric indices.

Despite the general anabolic state and tissue weight gain due to hormonal deprivation (Gavin, Cooper, Raymer, & Hickner, 2013), bioimpedance was further able to identify that the body weight gain of oophorectomized rats mainly derived from adipose tissue gain, as discussed below.

Although all indirect methods for obesity determination were able to identify differences in body weight, the sharp increase of adipose tissue observed in the oophorectomized group, was best determined using BIA, while murinometric indices apparently do not distinguish well between body weight and composition. Murinometric indices exhibited similar responses for the body weight gain in animals, but with different sensitivity, as shown by Novelli et al. (2007). The Lee index showed only a small difference (8.4%) between control and oophorectomized rats, although metabolic disorder was already settled in the oophorectomized group accompanied by a substantial increase in body fat, as also commonly described for this hormone deprivation model (Ignacio et al., 2009; Torrezan et al., 2008). Again, the BMI presented a greater distinction between the groups, thereby representing a higher detection capability of metabolic disorders related to obesity (Novelli et al., 2007) than the Lee index.

In comparison with the post-mortem tissue dissection, we observed that the BIA was able to predict the increase in absolute body lean and fat mass of the oophorectomized animals, reflecting the data obtained from the dissection of muscle and adipose tissue (Table 1). These results were also confirmed by the analysis of power of the measurements (Table 2). These analyses show that BIA obtained a greater effect size and a smaller possibility of error in hypothesis testing in relation to BMI and Lee index. Angeloco et al. (2012) failed to validate BIA as a co-equal method for chemical fractionation as a direct method, since they disregarded the distance between the electrodes in the prediction of FFM and Fat Mass contemplated by Cornish et al. (1992), Ilagan et al. (1993) and Rutter et al. (1998), and successfully applied in our study. Our results also corroborated the capability in detecting changes in body composition by BIA, as already described by Skalicky, Narath, and Viidik (2001) and Narath, Skalicky, and Viidik (2001), who also considered the distance between the electrodes.

Nevertheless, murinometric measures in animal models are still widely used because of their simplicity, such as division of specific tissue weight by body weight, body length and/or long bone length (Dantas et al., 2010). We used murinometric indices in order to determine the differential gain of fat content relative to body lean mass in body weight composition, but these indices are also utilized in more sophisticated applications such as for obesity prediction indices (Simson & Gold, 1982; White et al., 2013). In this context, the Lee Index (Bernardis & Patterson, 1968) was considered inadvisable by Novelli et al. (2007) to estimate the fat mass and obesity in rats. These authors also claimed that BMI applied to rats reliably estimates the body fat content, and normal weight, "eutrophism", would be determined as the range of 0.45 to 0.68 g [cm.sup.-2]. Applying this range to our data, both control and oophorectomized animals would be considered as eutrophic with BMIs of 0.46 g [cm.sup.-2] and 0.61 g [cm.sup.-2] (Figure 1). But dissection of body fat and BIA revealed a fat percentage approximately 2 times higher in oophorectomized animals. These findings demonstrate that the BMI range proposed by Novelli et al. (2007) is not suitable to predict changes in body composition and is also a disadvantage the use of BMI in rats, thus requiring further discussion. We conclude that bioimpedance has advantages over murinometric techniques in terms of evaluating body composition and obesity rates.

Conclusion

Murinometric indices as techniques for determining body fat and obesity in oophorectomized rats, as well as other models already studied, should be used with caution as they may underestimate the differential gain of fat tissue in these animals. In this sense, the BIA, as an indirect method to determine body composition, was able to detect the increase of adipose tissue, in this post-menopausal obesity model. Additionally, it revealed that this increase in body fat occurred within a BMI range proposed as eutrophic.

Doi: 10.4025/actascibiolsci.v38i4.31714

Acknowledgements

The authors acknowledge the financial support by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Capes), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Fundacao de Amparo a Pesquisa do Estado do Piaui (FAPEPI) and Fundacao Cearense de Apoio ao Desenvolvimento Cientifico e Tecnologico (FUNCAP). The authors also acknowledge the contribution of Katrin Radloff for the detailed writing review.

References

Abu-Taha, M., Rius, C., Hermenegildo, C., Noguera, I., Cerda-Nicolas, J.-M., Issekutz, A. C., ... Sanz, M.-J. (2009). Menopause and ovariectomy cause a low grade of systemic inflammation that may be prevented by chronic treatment with low doses of estrogen or losartan. The Journal of Immunology, 183(2), 1393-1402.

Ahima, R. S., & Flier, J. S. (2000). Adipose tissue as an endocrine organ. Trends in Endocrinology & Metabolism, 11(8), 327-332.

Angeloco, N., Rodrigues, L., Deminice, R., Leme, I. d. A., Lataro, R. C., & Jordao, A. A. (2012). Bioelectrical impedance analysis and anthropometry for the determination of body composition in rats: effects of high-fat and high-sucrose diets. Revista de Nutricao, 25(3), 331-339.

Bernardis, L., & Patterson, B. (1968). Correlation between "Lee index" and carcass fat content in weanling and adult female rats with hypothalamic lesions. Journal of Endocrinology, 40(4), 527-528.

Brandalize, M., & Leite, N. (2010). Orthopedic alterations in obese children and adolescents. Fisioterapia em Movimento, 23(2), 283-288.

Collaboration, P. S. (2009). Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. The Lancet, 373(9669), 1083-1096.

Cornish, B., Ward, L., & Thomas, B. (1992). Measurement of extracellular and total body water of rats using multiple frequency bioelectrical impedance analysis. Nutrition Research, 12(4), 657-666.

Dahms, W. T., & Glass, A. R. (1982). Correlation of percent body fat with body specific gravity in rats. The Journal of Nutrition, 112(2), 398-400.

Dantas, E. M., Pimentel, E. B., Goncalves, C. P., Lunz, W., Rodrigues, S. L., & Mill, J. G. (2010). Effects of chronic treadmill training on body mass gain and visceral fat accumulation in overfed rats. Brazilian Journal of Medical and Biological Research, 43(5), 515-521.

Donaldson, H. H. (1915). The rat: reference tables and data for the albino rat (Mus norvegicus albinus) and the Norway rat (Mus norvegicus). Philadelphia, PA: The Wistar Institute for Anatomy and Biology.

Gavin, K. M., Cooper, E. E., Raymer, D. K., & Hickner, R. C. (2013). Estradiol effects on subcutaneous adipose tissue lipolysis in premenopausal women are adipose tissue depot specific and treatment dependent. American Journal of Physiology-Endocrinology and Metabolism, 304(11), E1167-E1174.

Goodpaster, B. H., Thaete, F. L., & Kelley, D. E. (2000). Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus. The American Journal of Clinical Nutrition, 71(4), 885-892.

Grundy, S. M. (2004). Obesity, metabolic syndrome, and cardiovascular disease. The Journal of Clinical Endocrinology & Metabolism, 89(6), 2595-2600.

Hall, C., Lukaski, H., & Marchello, M. (1989). Estimation of rat body composition using tetrapolar bioelectrical impedance analysis. Nutrition Reports International, 39(3), 627-33.

Ignacio, D. L., Frankenfeld, T. G., Fortunato, R. S., Vaisman, M., Werneck-de-Castro, J. P. S., & Carvalho, D. P. (2009). Regulacao da massa corporea pelo estrogenio e pela atividade fisica. Arquivos Brasileiros de Endocrinologia e Metabologia, 53(3), 310-317.

Ilagan, J., Bhutani, V., Archer, P., Lin, P., & Jen, K. (1993). Estimation of body composition changes during weight cycling by bioelectrical impedance analysis in rats. Journal of Applied Physiology, 74(5), 20922098.

Kelly, T., Yang, W., Chen, C., Reynolds, K., & He, J. (2008). Global burden of obesity in 2005 and projections to 2030. International Journal of Obesity, 32(9), 1431-1437.

Narath, E., Skalicky, M., & Viidik, A. (2001). Voluntary and forced exercise influence the survival and body composition of ageing male rats differently. Experimental Gerontology, 36(10), 1699-1711.

Novelli, E., Diniz, Y., Galhardi, C., Ebaid, G., Rodrigues, H., Mani, F., ... Novelli Filho, J. (2007). Anthropometrical parameters and markers of obesity in rats. Laboratory Animals, 41(1), 111-119.

Pfeilschifter, J., K ditz, R., Pfohl, M., & Schatz, H. (2002). Changes in proinflammatory cytokine activity after menopause. Endocrine Reviews, 23(1), 90-119.

Raskin, D. B. F., Pinto-Neto, A. M., Paiva, L. H. S. C., Raskin, A., & Martinez, E. Z. (2000). Factors related to obesity and android pattern of body fat distribution in climacteric women. Revista Brasileira de Ginecologia e Obstetricia, 22(7), 435-441.

Rutter, K., Hennoste, L., Ward, L. C., Cornish, B. H., & Thomas, B. J. (1998). Bioelectrical impedance analysis for the estimation of body composition in rats. Laboratory Animals, 32(1), 65-71.

Simson, E. L., & Gold, R. M. (1982). The lee obesity index vindicated? Physiology & Behavior, 29(2), 371-376.

Skalicky, M., Narath, E., & Viidik, A. (2001). Housing conditions influence the survival and body composition of ageing rats. Experimental Gerontology, 36(1), 159-170.

Torrezan, R., Gomes, R. M., Ferrarese, M. L., Melo, F. B.-H. d., Ramos, A., Mathias, P. C., & Scomparin, D. X. (2008). O tratamento com isoflavonas mimetiza a acao do estradiol no acumulo de gordura em ratas ovariectomizadas. Arquivos Brasileiros de Endocrinologia e Metabologia, 52(9), 1489-1496.

Tremollieres, F. A., Pouilles, J.-M., & Ribot, C. A. (1996). Relative influence of age and menopause on total and regional body composition changes in postmenopausal women. American Journal of Obstetrics and Gynecology, 175(6), 1594-1600.

Vaidya, V. (2006). Obesity, Mental Health. Journal of American Medical Association, 296(10), 1294-1299.

Wellen, K. E., & Hotamisligil, G. S. (2003). Obesity-induced inflammatory changes in adipose tissue. The Journal of Clinical Investigation, 112(12), 1785-1788.

White, P. A., Cercato, L. M., Araujo, J. M., Souza, L. A., Soares, A. F., Barbosa, A. P. O., ... Camargo, E. A. (2013). Modelo de obesidade induzida por dieta hiperlipidica e associada a resistencia a acao da insulina e intolerancia a glicose. Arquivos Brasileiros de Endocrinologia e Metabologia, 57(5), 339-345.

Received on May 4, 2016.

Accepted on September 30, 2016.

Alex Soares Marreiros Ferraz (1) *, Ruan Carlos Macedo de Moraes (2), Naiza Arcangela Ribeiro de Sa (3), Francisco Teixeira Andrade (4), Maria do Carmo de Carvalho e Martins (4) and Vania Marilande Ceccatto (3)

(1) instituto de Educacao Fisica e Esportes, Universidade Federal do Ceara, Campus do Pici, Av Mister Hull, s/n, Parque Esportivo, Bloco 320, 60455-760, Fortaleza, Ceara, Brazil. (2) Programa de Pos-graduacao em Fisiologia Humana, Instituto de Ciencias Biomedicas, Universidade de Sao Paulo, Sao Paulo, Sao Paulo, Brazil. (3) Programa de Pos-graduacao em Ciencias Fisiologicas, instituto Superior de Ciencias Biomedicas, Universidade Estadual do Ceara, Fortaleza, Ceara, Brazil. (4) Departamento de Biofisica e Fisiologia, Universidade Federal do Piaui, Teresina, Piaui, Brazil. * Author for correspondence. E-mail: ferrazalex@hotmail.com

Caption: Figure 1. Body composition index (mean [+ or -] standard error) in oophorectomy and control groups. (*) = significant difference to control (p < 0.05). (BMI = body mass index; %G = body fat percentage).
Table 1. Murinometric and Bioelectrical data (mean [+ or -] standard
error) of animals and body tissue in oophorectomy and control groups.
(*) = significant difference to control (p < 0.05). (TBW = total body
water; FFM = fat-free mass).

                                  Control

Murinometric data

  Initial body weight (g)   138.1 [+ or -] 4.90
  Final body weight (g)     175.6 [+ or -] 4.30
  Muscle weight (g)          1.16 [+ or -] 0.03
  Fat weight (g)             5.87 [+ or -] 0.98
  Muscle/tibia length       23.55 [+ or -] 2.57
    ratio (x [10.sup.-3])
  Fat/tibia length          157.4 [+ or -] 26.66
    ratio (x [10.sup.-3])

Bioelectrical data

  Resistance (ohms)         360.0 [+ or -] 28.64
  Reactance (ohms)          86.60 [+ or -] 2.50
  Impedance (ohms)          370.5 [+ or -] 27.96
  TBW (g)                   140.4 [+ or -] 9.96
  FFM (g)                   142.7 [+ or -] 1.94

Fat weight (g)              32.89 [+ or -] 2.43

                                 Oophorectomy        P value

Murinometric data

  Initial body weight (g)    148.2 [+ or -] 1.10     0.0800
  Final body weight (g)     256.6 [+ or -] 7.67 *    <0.0001
  Muscle weight (g)          1.62 [+ or -] 0.05 *    <0.0001
  Fat weight (g)            12.26 [+ or -] 0.64 *    0.0013
  Muscle/tibia length        30.16 [+ or -] 3.94     0.1643
    ratio (x [10.sup.-3])
  Fat/tibia length          306.3 [+ or -] 18.10 *   0.0033
    ratio (x [10.sup.-3])

Bioelectrical data

  Resistance (ohms)          367.3 [+ or -] 14.56    0.8414
  Reactance (ohms)           83.50 [+ or -] 3.43     0.4781
  Impedance (ohms)           376.6 [+ or -] 14.86    0.8634
  TBW (g)                    152.4 [+ or -] 14.25    0.4997
  FFM (g)                   173.9 [+ or -] 3.50 *    <0.0001

Fat weight (g)              82.40 [+ or -] 4.20 *    <0.0001

Table 2. Post-hoc results relative to the power of analysis
(1 - [beta] error probability) and effect size of t-test in LEE Index,
Body Mass Index and Fat calculated by Bioimpedance, dissecation and
fat/tibia length ratio.

Analysis                 Effect Size     Power

Lee Index                 2.322834     0.8938870
Body Mass Index           3.078142     0.9883145
Bioimpedance % G          7.330463     1.0000000
Dissected Fat             7.720680     1.0000000
Fat/Tibia length ratio    6.534833     1.0000000
COPYRIGHT 2016 Universidade Estadual de Maringa
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Ferraz, Alex Soares Marreiros; de Moraes, Ruan Carlos Macedo; de Sa, Naiza Arcangela Ribeiro; Andrad
Publication:Acta Scientiarum. Biological Sciences (UEM)
Date:Oct 1, 2016
Words:3690
Previous Article:The family Bignoniaceae in the Environmental Protection Area Serra Branca, Raso da Catarina, Jeremoabo, Bahia, Brazil/A familia Bignoniaceae na APA...
Next Article:Cd, Cu, and Mn from Uruguay River Basin in Uruguaiana, RS, Brazil, and their toxicological potential for human leukocyte/Cd, Cu e Mn da Bacia do Rio...
Topics:

Terms of use | Privacy policy | Copyright © 2022 Farlex, Inc. | Feedback | For webmasters |