bioelectrical impedance measurements. Aviation Space and Environmental Medicine, 59,

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1 MS ID: Authors: Macias et al Title: Body fat measurement by bioelectrical impedance versus air displacement plethysmography: a validation to design bioelectrical impedance equations in Mexican Adults. Corresponding Author: Mauro E Valencia Response to reviewer s comments Major Issues Reviewer: the sample in which the equation was generated is entirely to small, you performed it only in 78. Since this is a type of study that will be generalized to Mexicans the sample must be much larger. Response: Sample size for BIA validation equations using reference methods such as isotopic dilution (Deuterium or Oxygen-18), body density or using three and four compartment models in adults of this age range is quite variable (Lukasky et al 1, 25 male and 28 female, years age: R 2 =0.97: SEE= 1.61); (Kyle 2, 201 male and 141 female, one equation for both, R 2 =0.97; SEE=1.72); An article by Dioum 3 et al 2004 on body composition prediction equations for African women presents a table with published BIA analysis of different sample sizes, for males, females or both. According to Guo & Chumlea 4 in their chapter: Statistical methods for the developing and testing of predictive equations in body composition studies, it is not possible to determine in advance, the sample size necessary to derive an accurate predictive equations. In general, the larger the sample size, the more precise and accurate the predictive equation is. The sample size required to achieve accuracy of an equation on cross-validation, depends on the relationship between the response variable and the predictor variables, the number of predictor variables and the variance of the response variable. If the dependent and predictor variables are 1 Lukaski HC and Bolonchuk WW Estimation of body fluid volumes using tetrapolar bioelectrical impedance measurements. Aviation Space and Environmental Medicine, 59, Kyle UG, Genton L, Karsegard L, Slosman D, Pichard C. Single prediction equation for bioelectrical impedance analysis in adults aged years. Nutrition, 2000; 17: Dioum A, Gartner A, Cisse AS, Delpeuch F, Maire B, Wade S, Schutz Y. Validity of impedance-based equations for the prediction of total body water as measured by deuterium dilution in Africa women. Am J Clin Nutr, 2005; 81: Guo S and Chumlea C, in : Statistical methods for the development and testing of predictive equations Roche A, Heymsfield S, Lohman T, Eds. Human body composition. USA, Human Kinetics, 1996:

2 highly correlated, only a small sample is necessary to develop an accurate prediction equation 5. For regression equations, statistical power refers to the probability that the increase in the R 2 due to the addition of another variable is significant. Although our cross validated sample of 78 appears to be small, it was enough to detect a correlation value greater than 0.90 at α=0.05 and a power of We agree that the sample may have the problem of generalizability because it does not include Mexicans from all the country; however this fact is discussed in the conclusions as a one of the limitations of our study. Reviewer: And on this note, was any effort made to quantify the amount of Mexican blood quantum (i.e. how much blood is Mexican?). Did they have to be 100% Mexican to participate and if so, would they have relevance to Mexicans who are not pure blood? These sorts of questions will arise when using this equation, thus a much larger sample size is warranted and needed. Response: Yes, 100% Mexicans, born and living in Mexico all their lives and ancestry going back several generations, from very dark to fare skin. No pure Indian subjects (by self identification or who spoke any Indian dialect were included). Our subjects have Spanish ancestry and regional Indian blood (Mestizos) from Yaquis, Mayos, Pimas, Seris, which are the main tribes in this region. In this context it is difficult to understand the term Mexican pure blood. The subjects are Mexicans from Sonora, a State from Northwest Mexico. The issue was acknowledged in the discussion as a potential limitation of the equation from this study. These types of regional equations are important alternatives to those used by the manufacturers or from other populations published in the literature. As an example, if we analyze the kg of fat free mass measured by ADP in all 156 subjects and compare them to those calculated by the BIA Manufacturer s software, the difference is kg (fat free mass), versus (kg fat free mass), with our cross validated equation. 5 Guo SS, Chumlea, WC Cockram DB. Use of statistical methods to estimate body composition. Am J Clin Nutr 1996, 64(3)

3 I included one of the questions from the minor essential revisions concerns that best fit in this section. Reviewer: To my knowledge Mexican equations do exist and are published in Vivian Hayward s body composition book. Another group (Conlisk 1992 AJCN) generated equations in Hispanic -- would this sample be different than yours? In the literature there are hundred of equations from different countries and regions, in mainly in the US, Europe and Asia; however to our knowledge there are no Universal equations for each country. We understand that this is a modest contribution but done accordingly to international methodology. At present there are no reported equations developed from Mexicans living in Mexico, For clarification, the term Hispanic used in US studies can be any population from Latin America, Caribbean countries including Europeans (Spain). Our report is a cross sectional study that represents Mexicans from this region at the present time, reflecting their genetic make-up and environment. Reviewer: Why did you only use FFM as the predicted variable, why not % fat or even fat mass? Response: The main reason for doing this is that Bioimpedance, in theory, measures total body water, the most important and abundant component of fat free mass. In bioimpedance prediction equations, the best single predictor of either total body water or fat free mass is the combined variable, Resitance 2 /Height, also called the resistance index. Plenty of examples of this can be found in Roche, Heysmfield and Lohman s Human Body Composition. This resistance index in the case of Bioimpedance is a very poor predictor of body fat (see data scatter plots from the Fels Longitudinal Study 6 reported by Guo 7 ) Similar to many reports, we used a 2 Compartment Model : Total Body Mass = Fat Mass + Fat free mass, where either compartment can be calculated by difference from total body mass. In our laboratory we have validated air 6 Roche AF. Growth, maturation and body composition: the Fels Longitudinal Study New York: Cambridge University Press, Guo SS, Chumlea WC, and Cockram DB. Use of statistical methods to estimate body composition. Am J Clin Nutr, 1996; 64: s.

4 displacement plethysmography (BOD POD) using a four compartment model in elderly Mexican subjects 8 Reviewer: I am shocked that only one equation was generated. I would have thought (most every other study shows this) that a regression equation would be generated for males and another equation for females, can you please clarify this issue and explain this -- I was quite surprised to see this. When large samples are available, this can be one of the ways to develop predictive equations, however, sex, as in many studies of this age group, was evaluated as an independent or predictor variable. This is a normal procedure when generating predictive equations. For each population and age group, sex as a predictor or independent variable may or may not enter a given model. In our study, the multiple variable selection procedure did not include sex as significant predictor. In spite of this, the equation had a high R 2 and low standard error. Forcing sex to be included in the model as independent variable with such a small insignificant coefficient degrades the model and is not recommended. In a previous study we did in 133 elderly subjects (Mexicans, Cubans and Chileans) sex was a significant predictor in the equation 9 as was ethnicity (country). Independent or predictor variables behave differently for different groups in their association to the dependent variable (fat free mass or total body water) Please consult reference 3 from Dioum et al, in the present document and in Table 2, you will find 11 published equations Reference numbers: 12, 13, 16, 17, 22, 29, and 32, where both male and female were used to develop BIA equations, and sex as a predictor or independent variable did not contribute to the model and hence was not included. There are many more examples but I hope this list illustrates the point. 8 Aleman-Mateo H, Huerta RH, Esparza-Romero J, Mendez RO, Urquidez R, Valencia ME. Body composition by the four-compartment model: validity of the BOD POD for assessing body fat in Mexican elderly. Eur J Clin Nutr; Valencia ME, Aleman-Mateo H, Salazar G, Hernandez Triana M.Body composition by hydrometry (deuterium oxide dilution) and bioelectrical impedance in subjects aged >60 y from rural regions of Cuba, Chile and Mexico. Int J Obes Relat Metab Disord Jul;27(7):

5 Minor Issues: 1. a was added between as and standard. 2. Reviwer: You state that it is free of bias but you present no statistical data to support this. Response: In the methods section, on page 6, last sentence of the second paragraph we stated that bias was examined using the Bland and Altman analysis and provided the reference. The evidence is provided on page 8, last sentence of the 1 st paragraph: where we stated: Furthermore, there was no significant association (p>0.05) between the mean differences and the mean of the two measurements (FFM obtained by ADP and the new validated equation). Bias occurs when these two variables, based upon linear regression analysis show a significant association. The dispersion diagram is presented in Figure 2, which is not correctly cited at the end of this paragraph. This has now been corrected. 3. Reviwer: Page 3 the first line does not make sense to me. Response: The paragraph was changed to: Body composition has become extremely important in the evaluation of nutritional status. 4. Reviwer: Page 3 second paragraph first line I think it should be re-written. Response: The line was changed to: Prediction of body compartments from bioelectrical impedance and anthropometric measurements requires proper validation techniques as well as solid reference methods. 5. Reviwer: Page 3 last sentence. To my knowledge Mexican equations do exist and are published in Vivian Heywards body composition book. Another group (Conlisk, 1992 AJCN) generated equations in Hispanic -- would this sample be different than yours? Response: This was given in the previous section 6. Reviwer: Page 4 I would try not to say BOD POD -- I would keep it to ADP Page 4 you say the bathing cap -- actually it is a swimming cap. Response: Both comments were addressed and proper changes were made been in the manuscript

6 7. Reviewer: Page 4 what is the CV of ADP in your laboratory -- if you have it please add. Response: The reproducibility of the BOD-POD was tested in 91 duplicate measurements in the same subject during the same session. Mean body densities of groups 1 and 2, were ± and ± 0.019, respectively and the mean difference in body difference was The coefficient of variation for both groups was 1.89%. 10. In terms of % body fat the technical error of measurement was % fat units, assessed as (Σd 2 /2n) 1/2, where d is the difference between repeated measurements and n is the number of paired repeated measurements. (This paragraph has been added in the method section) 8. Reviewer: Pag 10 the last paragraph. You state different studies find different things in %FM between ADP and DEXA etc -- you are correct but it is consistent, that is to say there are gender effects, this is to say it tend to underestimate in males and over underestimate in females relative to HW. I think it beneficial you try to more clearly deleniate this in that paragraph. Response: The paragraph was modified based on a more thorough review. The comparison is discussed in relation to hydrostatic weighing and four compartment models only. This is the new text and references: A recent and thorough review in the assessment of body composition using air displacement plethysmography 11 analyzed the validity of the ADP relative to hydrostatic weighing (HW) in male and female adults, age years. Mean group differences between ADP and HW measurements ranged from -4% to 1.9 % body fat; 5 of the 12 studies showed no significant difference between the two methods. Of the other 7 studies that did show a significant mean difference, the direction of the differences was inconsistent. Percent fat measured by ADP explained % of the variance in % body fat measured by HW and the reported SEEs ranged from 1.8 to 2.3 % and ideally the 10 Nayeli Macías, Ana María Calderón de la Barca, Adriana V. Bolaños., Heliodoro Alemán, Julián Esparza, and Mauro E. Valencia. Body Composition in Mexican Adults by Air Displacement Pletismography (ADP) with the Bod-Pod and Deuterium Oxide Dilution Using Infrared Spectroscopy (IRS-DOD). Food and Nutrition Bulletin, 2002; 23 (3 suppl): David A Fields, Michael I Goran, and Megan A McCrory. Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am J Clin Nutr Mar;75(3): Review.

7 range should be < 2.5 % body fat. These authors state that, whether the sex of the subject systematically affects results from ADP or HW remains to be determined. Because males tend to be leaner than females, it is difficult to determine whether the significant effect of sex reported in some studies are due to an effect of sex per se or to body fatness. Plotting the results from different studies as sex-specific means, in Bland-Altman fashion, they observed an upward trend with no overlap in mean % body fat between females and males. Therefore they conclude that in their analysis as in the individual studies, it is impossible to separate the confounding effects of subject sex and % body fat. Even though many studies consider the ADP as a reliable and valid technique applicable in a wide range of subjects including children and elderly, more validation studies are required using a 4 Compartment model as a reference gold standard rather than HW. Other studies 12 suggest that that it is important to test its accuracy in more heterogeneous samples, or in individuals outside the average % fat range, specifically lean individuals, and insist in looking at possible gender bias. In our laboratory we have previously validated ADP using a 4 Compartment model in elderly subjects 8 as well as in children and adolescents (studies in progress). 9. Reviewer: Figure 2 -- I am not sure that is done correctly. In this case you have a criterion method (i.e. ADP) thus technically you would do a plot where on the X axis is ADP and on the Y axis is the difference of the new equation- ADP. A Bland-Altman is used only if you don t have a criterion method, in this case you do. Response: According to Bland and Altman 12 the plot of differences against the mean allows investigating any possible relationship between the measurement error and the true value. In most situations, even when you have a reference method we do not know the true value and the mean of the two measurements is the best estimate we have. They also indicate that it would be a mistake to plot the difference against either value separately because the difference will be related to each, a well known statistical artifact. Some studies do compare to a single value (the standard method) but most studies follow Bland & Altman recommendation as we did. 12 Bland JM, Altman DG: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986, 1(8476):

8 10. Reviewer: Quality of written English: Needs language corrections before being accepted Response. Unless the reviewer specifies the language problems it would be impossible to comply. This manuscript was edited by a professional scientific editing service called San Francisco Edit before submission. Reviewer: Missing legends to Figures Response: Missing legends to Figures have been added at the end of the reference list