SciELO - Scientific Electronic Library Online

 
vol.35 número1En búsqueda de electrodos óptimos para el uso de la bioimpedancia eléctrica. Una evaluación necesariaPrevalencia de anemia y riesgo de hemoconcentración durante los 3 trimestres de embarazo y factores de riesgo índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • No hay articulos similaresSimilares en SciELO
  • En proceso de indezaciónSimilares en Google

Compartir


Nutrición Hospitalaria

versión On-line ISSN 1699-5198versión impresa ISSN 0212-1611

Nutr. Hosp. vol.35 no.1 Madrid ene./feb. 2018

https://dx.doi.org/10.20960/nh.1284 

Trabajos Originales

Correlation between skinfold thickness and bioelectrical impedance analysis for the evaluation of body composition in patients on dialysis

Correlación entre la plicometría y el análisis de bioimpedancia eléctrica para la evaluación de la composición corporal en pacientes en diálisis

Ana-Laura Flores-García1  , Carmen-Alicia Sánchez-Ramírez1  , Oscar-Alberto Newton-Sánchez1  2  , Fabián Rojas-Larios1  2 

1Medicine Faculty. Universidad de Colima. Mexico

2Hospital Regional Universitario. Health Services of the State of Colima. Mexico

Abstract

Introduction:

Patients on dialysis have important changes in body composition.

Objectives:

To determine the correlation between skinfold thickness (SKF) and bioimpedance analysis (BIA) for estimating fat mass (FM) and lean body mass (LBM) in patients undergoing hemodialysis (HD) and peritoneal dialysis (PD).

Methods:

Cross-sectional study. We included 50 patients under dialysis treatment. To measure SKF, we used the Lange(r) skinfold caliper (Beta Technology, California, USA) and we carried out the impedance analysis with the Bodystat Quadscan 4000(r) (Quadscan, Isle of Man, UK). The measurements were performed post-hemodialysis. The PD patients were measured with and without peritoneal dialysate and body weight was corrected for peritoneal fluid. We determined the Pearson's correlation coefficient between SKF and BIA for estimating FM and LBM. We also evaluated the influence of age, sex, diuretic use, dialysis vintage, extracellular water (ECW), and intracellular water (ICW) through a multivariate regression analysis.

Results:

Of the 50-patient total, 29 were men (58%) and patient mean age was 46.3 ± 16.5 years. The correlation between SKF and BIA was r = 0.784 (p < 0.001) for FM and r = 0.925 (p < 0.001) for LBM. Age and sex influenced the variability of FM, whereas sex, age, and ECW influenced the variability of LBM, both evaluated through the SKF and BIA methods.

Conclusion:

SKF and BIA are useful methods in clinical practice. The strong and statistically significant correlations between the two methods show they are interchangeable. Age, sex, ECW, and ICW influence the variability of FM and LBM.

Key words: Fat mass; Lean body mass; Skinfold thickness; Bioelectrical impedance analysis; Dialysis

Resumen

Introducción:

los pacientes en tratamiento con diálisis presentan cambios importantes en la composición corporal.

Objetivos:

determinar la correlación entre la plicometría y el análisis de bioimpedancia eléctrica (BIE) para la estimación de la masa grasa (MG) y la masa magra (MM) en pacientes sometidos a hemodiálisis (HD) y diálisis peritoneal (DP).

Métodos:

diseño transversal-analítico. Se incluyeron 50 pacientes en tratamiento con diálisis. Se utilizó el plicómetro Lange(r) (Beta Technology, California, USA) para la medición de pliegues cutáneos y la BIE fue realizada con el Bodystat Quadscan 4000(r) (Quadscan, Isle of Man, UK). Las mediciones fueron realizadas poshemodiálisis. Los pacientes en DP fueron medidos con y sin líquido peritoneal y el peso corporal fue corregido. Determinamos el coeficiente de correlación de Pearson entre la plicometría y la BIE en la estimación de la MG y la MM. Se evaluaron otras variables como edad, sexo, uso de diuréticos, tiempo en tratamiento de diálisis, agua extracelular (AEC) e intracelular (AIC) a través de un análisis de regresión multivariada.

Resultados:

veintinueve pacientes (58%) son del sexo masculino; la edad promedio de 46,3 ± 16,5 años. Se obtuvo una correlación significativa y positiva entre la plicometría y la BIE [r = 0,784 (p < 0,001) para MG y r = 0,925 (p < 0,001)] para MM. La edad y el sexo influyeron en la variabilidad de la MG, mientras que el sexo, la edad y el AEC influyeron en la variabilidad de la MM, evaluados con ambos métodos.

Conclusiones:

la plicometría y la BIE son métodos útiles en la práctica clínica. La correlación que se obtuvo entre los dos métodos muestra que son intercambiables. Por otro lado, variables como la edad, el sexo, el agua AEC y AIC se identificó que influyen en la variabilidad de la MG y MM.

Palabras clave: Masa grasa; Masa magra; Pliegues cutaneous; Bioimpedancia eléctrica; Diálisis

INTRODUCTION

Nutritional status is one of the most important factors influencing the quality life of patients on hemodialysis (HD) and peritoneal dialysis (PD). Poor nutritional status leads to a high risk of morbidities, hospitalization, catabolic stress, and mortality 1) (2.

Patients on dialysis have important changes in body composition because of decreased protein and/or energy intake, chronic inflammation, physical inactivity, concurrent acute or chronic conditions, illness, and catabolism induced by the dialysis process. Previous studies have shown that changes in body composition occur after dialysis treatment, with a significant decrease in lean body mass (LBM) and an increase in body weight and fat mass (FM), whereas other studies state that FM and body weight decrease over time 3) (4) (5) (6) (7.

Total body weight can be divided into the compartments of FM and LBM. The FM represents an essential energetic reserve and 50% is situated in the subcutaneous tissue. The LBM includes minerals, proteins, glycogen, extracellular water (ECW) and intracellular water (ICW) 8.

Dual-energy X-ray absorptiometry (DXA) is considered the gold standard in the assessment of body composition in patients on dialysis. However, this method is not always available and is both expensive and impractical. There are various methods for estimating body composition in patients on dialysis, one of which is bioelectrical impedance analysis (BIA). BIA measures impedance and resistance with a small electrical current as it travels through the body's water pool. In addition, BIA divides total body water (TBW) into ECW and ICW. Another available and practical method for estimating FM is skinfold thickness (SKF). It measures specific skinfolds and uses the Durnin and Womersley formulas to estimate density and FM. Both BIA and SKF are methods that have shown significant correlations with the gold standard 1) (9) (10) (11) (12) (13) (14 .

We consider the adequate assessment of body composition with practical and available methods and tools in patients on dialysis to be very important. Therefore the aims of this study were to determine the correlation between SKF and BIA for estimating FM and LBM in HD and PD patients, and to analyze the influence of the variables that can affect body composition.

METHODS

A cross-sectional study was performed in thirty-eight patients undergoing HD and 12 patients undergoing PD at two dialysis units in Colima, Mexico, were included.

The inclusion criteria were: age ≥ 18 years old, on HD or continuous ambulatory PD or automated PD treatment for at least 2 months. Exclusion criteria were patients with pacemakers, patients with metallic implants, amputees, and pregnant women.

The measurements were performed on the HD patients after dialysis, in accordance with previous methodologies. For the PD patients, the measurements were taken during a visit to the outpatient clinic. For practical reasons, measurements were performed with intraperitoneal fluid and body weight was corrected (body weight minus 2 kg corresponding to the peritoneal fluid). Body composition was not affected by intraperitoneal dialysate because the trunk contributes to less than 10% of total body impedance 9) (15) (16) (17) (18) (19) (20.

ANTHROPOMETRIC MEASUREMENTS

Height, body weight, and mid-upper arm circumference (MUAC) were measured in all patients. MUAC was assessed using Seca 201(r) (Hamburg, Deutschland) non-stretchable metric tape. The Seca 700(r) Mechanical Column Scale (Hamburg, Deutschland) was used to measure height and body weight, applying the previously described standard techniques 21.

Four SKF -biceps, triceps, subscapular, and suprailiac- were measured 3 times using a Lange(r) skinfold caliper (California, USA) and then averaged. The logarithm of the sum of the four SKF was calculated, as well as body density (D) according to age and sex. The Durnin and Womersley equations were used to calculate FM and LBM with the formulas 22: FM = body weight (kg) - [(4.95/D)-4.5)] and LBM = body weight (kg) - FM.

BODY COMPOSITION ANALYSIS

To determine ICW, ECW, FM, and LBM, we used a Bodystat Quadscan 4000(r) (Isle of Man, UK) multi-frequency body composition analyzer. The measurements were carried out with the patient in the supine position for 5 minutes, with the arms parallel to and separated from the trunk and the legs apart. Two electrodes were placed on the hand and wrist and another two on the foot and ankle. The electrodes were placed on the non-access site of the body of the HD patients and on the right side of the PD patients 19) (20) (23) (24.

STATISTICAL METHODS

The Kolmogorov-Smirnov test was used to test for the distribution normality of the variables. Descriptive analyses were presented as mean ± standard deviation (SD), frequencies, and percentages. We determined the Pearson's correlation coefficient between SKF and BIA for estimating FM and LBM. We also evaluated the influence of age, sex, diuretic use, ECW, and HD and PD vintage on FM and LBM through a multivariate regression analysis. Stepwise regression and backward elimination (automatic procedure) were carried out.

Statistical significance was accepted as p < 0.05 and all statistical tests were two-tailed. The statistical analyses were performed using the IBM SPSS version 20 program (IBM, Chicago, IL).

ETHICS STATEMENT

Written informed consent was obtained from each patient before participation. The present study was structured according to the ethical requirements of the Declaration of Helsinki and approved by the local Ethics and Health Research Committee (2014/SR/CLIN/PED/96).

RESULTS

We evaluated fifty patients and their mean age was 46.3 ± 16.5 years. Demographic, clinical, and body composition characteristics of the patients undergoing dialysis treatment are shown in Table I.

The patients with PD presented with greater body weight, FM, LBM, ECW, and ICW. FM measured by the two methods, ECW, and body weight showed statistically significant differences betwzeen the HD and DP patients (p < 0.05).

Table I Demographic, clinical, and body composition characteristics in patients undergoing dialysis treatment (n = 50) 

HD: hemodialysis; PD: peritoneal dialysis; CAPD: continuous ambulatory PD; APC: automated PD; FM: fat mass; LBM: lean body mass; ECW: extracellular water. 1Body weight post-dialysis; 2Body weight corrected in patients with peritoneal fluid; 3Statistically significant. *Measured with SKF; **Measured with BIA. Data are presented as mean ± standard deviation. p < 0.05 statistical significance.

PEARSON'S CORRELATION BETWEEN SKF AND BIA FOR ESTIMATING FAT MASS AND LEAN BODY MASS

Figure 1 show the correlation between SKF and BIA for estimating FM and LBM. They were positive and statistically significant and the SKF and BIA correlation was highest for evaluating LBM.

Figure 1 A. Correlation between SKF and BIA for estimating FM. B. Correlation between SKF and BIA for estimating LBM (SKF: skinfold thickness; BIA: bioelectrical impedance analysis). 

MULTIVARIATE REGRESSION ANALYSIS

We analyzed the degree of influence of the variables that can influence FM and LBM measured by both methods and the results are shown in Table II.

Regarding Table II, the variables of sex and age influenced the variability of FM and LBM to different degrees when evaluated by each of the two methods. Sex, age, and ECW influenced the variability of LBM, and the BIA estimate had the greatest influence on the variability of LBM.

Table II Predictors of FM and LBM according to SKF and BIA (multivariable regression) 

FM: fat mass; LBM: lean body mass; SKF: skinfold thickness; BIA: bioimpedance analysis; ECW: extracellular water; ICW: intracellular water. CI: confidence interval. p < 0.05 statistical significance.

DISCUSSION

The nutritional status of patients undergoing HD and PD is a survival indicator. Patients, whose FM is lower than the normal range, have been reported to have a higher risk of mortality due to catabolic stress. Patients on dialysis have changes in body composition, such as greater FM, lower LBM, and an altered hydration status. These changes can directly affect nutritional status, making body composition measurement an important issue. Through body composition evaluation, adequate nutritional status can be maintained, resulting in a better quality of life 25) (26) (27.

Many methods have been used for assessing body composition in patients undergoing dialysis, but the ideal method should be a noninvasive one with reproducible results, as well as being low cost and easily available. DXA is considered the gold standard, but in relation to clinical practice, it is not always available in dialysis care units or in primary care units, given that it requires a trained staff and a specific area for taking the measurements, in addition to its high cost. Therefore, alternative methods that meet the criteria of an ideal method, such as SKF or BIA, are being used. Both methods have shown a statistically significant correlation with the gold standard, which is why we decided on the two-compartment model and analyzed the correlation between SKF and BIA for estimating FM and LBM in dialysis patients 8) (26) (28) (29.

Our results showed high and statistically significant correlations between SKF and BIA for estimating FM and LBM, with a stronger correlation for the LBM evaluation.

SKF and BIA are methods for estimating FM and LBM that can be available at all dialysis care units, nutrition departments, and primary care units. They are reproducible, low-cost, noninvasive, and can be used by health professionals with a minimum of training. Kamimura et al. analyzed FM in 30 Brazilian HD patients using DXA, BIA, and SKF. They reported excellent and statistically significant correlations with BIA and SKF. Lamarca et al. correlated DXA with BIA and SKF for estimating LBM in 102 Brazilian HD patients. Both correlations were statistically significant, and the correlation between DXA and BIA was superior. Unlike our study, Kamimura et al. and Lamarca et al. analyzed only one body composition compartment and correlated the measuring methods with the gold standard. They did not correlate BIA and SKF with two compartments, as we did. Bravo et al also correlated DXA with BIA and SKF for estimating FM and LBM in 20 Mexican HD patients. They found high and statistically significant correlations, but did not estimate body composition with those methods in PD patients. To the best of our knowledge, ours is the first study conducted on Mexican patients that correlates the two methods in the assessment of FM and LBM in HD and PD patients. In regard to previous studies on method correlations in DP patients, Dong et al reported a statistically significant correlation between SKF and DXA in the analysis of LBM in 60 Korean PD patients. Another relevant study by Chow et al found a statistically significant correlation between SKF and BIA for estimating FM in 60 Chinese PD patients 14) (19) (23) (25) (26) (30) (31) (32.

In the multivariate regression analysis of our study, we showed the influence of the variables of age and sex on the variability of FM assessed through SKF. Only age influenced the variability of fat mass evaluated by BIA. LBM was influenced by sex, age, and ECW, when estimated using BIA and by sex and ECW, when using SKF. In general, age, sex, and ECW influenced different grades of body composition variability, regardless of the measuring method employed.

Similar to what occurs in the healthy population, body composition suffers changes with age. Once the individual reaches 30 years of age, LBM decreases from 1 to 1.5% per year, and there is a simultaneous increase in FM, mainly in the trunk area. There are also many differences in relation to sex, given that women have more FM and less LBM than men 10) (29) (33) (34) (35) (36.

ECW influenced the variability of LBM, which can be explained by the fact that LBM contains protein mass and minerals and ECW and ICW depend on hydration status, thus affecting LBM by underestimating or overestimating it. It is important to keep in mind that over-hydration can increase SKF, resulting in an overestimation of FM and % of fat. In addition, catabolic stress can increase oncotic pressure and permeability and produce an increase in capillary filtration and interstitial volume, resulting in tissular edema 37) (38.

Dialysis vintage was also analyzed in the multivariate regression analysis and did not present statistical significance. However, reports in the literature state that patients have important changes in body composition associated with dialysis vintage and the most significant changes present in the first year of treatment, mainly as increased FM and decreased LBM 1) (3) (4.

Diuretic use was another variable analyzed in the multivariate regression, but it did not correlate with the body composition compartments.

SKF and BIA are useful methods in clinical practice for estimating FM and LBM in dialysis patients. Our results showed high and statistically significant correlations, signifying that the methods are interchangeable and offer an alternative in the evaluation of dialysis patients when DXA is not available. SKF and BIA can be used in dialysis care units and/or primary and secondary care units to identify early changes in body composition that affect the nutritional status of these patients. Sex and age influenced the variability of FM, whereas age, sex, and ECW influenced the variability of LBM.

REFERENCES

1. Choi SJ, Kim NR, Hong SA, et al. Changes in body fat mass in patients after starting peritoneal dialysis. Perit Dial Int 2011;31(1):67-73. [ Links ]

2. Caetano C, Valente A, Oliveira T, et al. Body Composition and Mortality Predictors in Hemodialysis Patients. J Ren Nutr 2016;26(2):81-6. [ Links ]

3. Broers NJH, Cuijpers ACM, Van Der Sande FM, et al. The first year on haemodialysis: A critical transition. Clin Kidney J 2015;8(3):271-7. [ Links ]

4. Marcelli D, Brand K, Ponce P, et al. Longitudinal Changes in Body Composition in Patients After Initiation of Hemodialysis Therapy: Results From an International Cohort. J Ren Nutr 2016;26(2):72-80. [ Links ]

5. Keane D, Gardiner C, Lindley E, et al. Changes in Body Composition in the Two Years after Initiation of Haemodialysis: A Retrospective Cohort Study. Nutrients 2016;8(11):702. [ Links ]

6. Su CT, Yabes J, Pike F, et al. Changes in anthropometry and mortality in maintenance hemodialysis patients in the HEMO study. Am J Kidney Dis 2013;62(6):1141-50. [ Links ]

7. Gallar-Ruiz P, Di-Gioia MC, Lacalle C, et al. Composición corporal en pacientes en hemodiálisis?: relación con la modalidad de hemodiálisis , parámetros inflamatorios y nutricionales. Nefrologia 2012;32(4):467-76. [ Links ]

8. Jiménez EG. Composición corporal?: estudio y utilidad clínica. Endocrinol Nutr 2012;60(2):69-75. [ Links ]

9. Broers NJH, Martens RJH, Cornelis T, et al. Body Composition in Dialysis Patients: A Functional Assessment of Bioimpedance Using Different Prediction Models. J Ren Nutr 2015;25(2):121-8. [ Links ]

10. Van Biesen W, Claes K, Covic A, et al. A multicentric , international matched pair analysis of body composition in peritoneal dialysis versus haemodialysis patients. Nephrol Dial Transplant 2013;28 (10):2620-8. [ Links ]

11. Lee SY, Gallagher D. Assessment methods in human body composition. Curr Opin Nutr Clin Metab Care 2008;11(5):566-72. [ Links ]

12. Alvero R, Cabañas M, Herrero A, et al. Protocolo de valoración de la composición corporal para el reconocimiento médico-deportivo. Documento de consenso del Grupo Español de Cineantropometría (GREC) de la Federación Española de medicina del Deporte (FEMEDE). Versión 2010. AMD 2010;27(139):330-43. [ Links ]

13. Espinosa-Cuevas MA, Correa-Rotter R. Alteraciones renales y nutrición. In: Casanueva E, Flores-Quijano ME. Nutriologia Medica. 3rd ed. Médica Panamericana; 2008. pp. 501-38. [ Links ]

14. Kamimura MA, Avesani CM, Cendoroglo M, et al. Comparison of skinfold thicknesses and bioelectrical impedance analysis with dual-energy X-ray absorptiometry for the assessment of body fat in patients on long-term haemodialysis therapy. Nephrol Dial Transplant 2003;18(1):101-5. [ Links ]

15. Rymarz A, Bartoszewicz Z, Szamotulska K, et al. The Associations Between Body Cell Mass and Nutritional and Inflammatory Markers in Patients With Chronic Kidney Disease and in Subjects Without Kidney Disease. J Ren Nutr 2016;26(2):87-92. [ Links ]

16. Noori N, Kovesdy CP, Dukkipati R, et al. Survival predictability of lean and fat mass in men and women undergoing maintenance hemodialysis. Am J Clin Nutr 2010;92(5):1060-70. [ Links ]

17. Pellicano R, Pellicano R, Strauss BJ, et al. Body composition in home haemodialysis versus conventional haemodialysis: a cross-sectional, matched, comparative study. Nephrol Dial Transplant 2010;25(2):568-73. [ Links ]

18. Bataille S, Serveaux M, Carreno E, et al. The diagnosis of sarcopenia is mainly driven by muscle mass in hemodialysis patients. Clin Nutr 2016:1-7. [ Links ]

19. Lamarca F, Carrero JJ, Rodrigues JC, et al. Prevalence of sarcopenia in elderly maintenance hemodialysis patients: the impact of different diagnostic criteria. J Nutr Health Aging 2014;18(7):710-7. [ Links ]

20. van Biesen W, Williams JD, Covic AC, et al. Fluid status in peritoneal dialysis patients: The European body composition monitoring (EuroBCM) study cohort. PLoS One 2011;6(2):e17148. [ Links ]

21. Frisancho A. Anthropometric Standards for the Assessment of Growth and Nutritional Status. Ann Arbor, MI: University of Michigan Press; 1990. [ Links ]

22. Durnin JV, Womersley J. Body fat assessed from total body density and its estimation from skinfold thickness?: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 1973;32(1):77. [ Links ]

23. Chow VC, Lee C, Ho EH, et al. Nutritional assessment of continuous ambulatory peritoneal dialysis patients by bioelectrical impedance. Perit Dial Int 2003;23(S2):55-7. [ Links ]

24. Beberashvili I, Azar A, Sinuani I, et al. Geriatric nutritional risk index, muscle function, quality of life and clinical outcome in hemodialysis patients. Clin Nutr 2016 35(6):1522-9. [ Links ]

25. Molfino A, Don BR, Kaysen GA. Comparison of bioimpedance and dual-energy X-ray absorptiometry for measurement of fat mass in hemodialysis patients. Nephron Clin Pract 2012;122(3-4):127-33. [ Links ]

26. Carter M, Zhu F, Kotanko P, et al. Assessment of Body Composition in Dialysis Patients by Arm Bioimpedance Compared to MRI and 40 K Measurements. Blood Purif 2009;27(4):330-7. [ Links ]

27. Oreopoulos A, Noori N, Golden S. Comparing Body Composition Assessment Tests in Long-term Hemodialysis Patients. Am J Kidney Dis 2011;55(5):885-96. [ Links ]

28. Wang Z, Pierson R, Heymsfield S. The five-level model: a new approach to organizing. Am J Clin Nutr 1991;56(1):19-28. [ Links ]

29. Morishita Y, Kubo K, Haga Y, et al. Skeletal Muscle Loss Is Negatively Associated With Single-Pool Kt/V and Dialysis Duration in Hemodialysis Patients. Ther Apher Dial 2014;18(6):612-7. [ Links ]

30. Vogt BP, Ponce D, Costa J, Caramori T. Anthropometric Indicators Predict Metabolic Syndrome Diagnosis in Maintenance Hemodialysis Patients. Nutr Clin Pract 2016;31(3):368-74. [ Links ]

31. Ramírez AMB, Ramos AC, Hurtado GF. Composición corporal en pacientes con insuficiencia renal crónica y hemodiálisis. Nutr Hosp 2010;25(2):245-9. [ Links ]

32. Dong J, Li Y, Xu R, et al. Novel equations for estimating lean body mass in peritoneal dialysis patients. Perit Dial Int 2015;35(22):743-52. [ Links ]

33. Chumlea WC, Guo SS, Kuczmarski RJ, et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes Relat Metab Disord 2002;26(12):1596-609. [ Links ]

34. Leal VO, Mafra D, Fouque D, et al. Use of handgrip strength in the assessment of the muscle function of chronic kidney disease patients on dialysis: a systematic review. Nephrol Dial Transplant 2011;26(4):1354-60. [ Links ]

35. Yamada Y, Watanabe Y, Ikenaga M, et al. Comparison of single- or multifrequency bioelectrical impedance analysis and spectroscopy for assessment of appendicular skeletal muscle in the elderly. J Appl Physiol 2013;115(6):812-8. [ Links ]

36. Stenvinkel P, Carrero JJ, Walden F Von, et al. Muscle wasting in end-stage renal disease promulgates premature death: established , emerging and potential novel treatment strategies. Nephrol Dial Transplant 2016;31(7):1-8. [ Links ]

37. Yamada Y, Schoeller DA, Nakamura E, et al. Extracellular Water May Mask Actual Muscle Atrophy During Aging. J Gerontol A Biol Sci Med Sci 2010;65(5):510-6. [ Links ]

38. Reed RK, Rubin K. Transcapillary exchange?: role and importance of the interstitial fluid pressure and the extracellular matrix. Cardiovasc Res 2010;87(2):211-7. [ Links ]

Received: May 15, 2017; Accepted: May 29, 2017

Correspondence: Carmen Alicia Sánchez Ramírez. Facultad de Medicina. Universidad de Colima. Mexico e-mail: calicesr26@hotmail.com

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License