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Body Mass Index (BMI),
New Vision and Perspectives.

Galo Narváez P, MD; Ximena Narváez, MD

Laboratorio de Evaluaciones Morfofuncionales, LABEMORF, Quito, Ecuador

   The Body Mass Index, it is a simple but objective anthropometrics index of the nutritional status of the population. The following factor had a significant impact on BMI: the weigh-height in the intrauterine growth, the birth weight-height (15), the socioeconomic status, seasonal changes in food availability and demands on activity. This was a major issue emerged at the first meeting of the International Dietary Energy Consultancy Group (IDECG), held in Guatemala in 1987 (13).

   It is relatively inexpensive, easy to collect and to analyze (8,9). The weight and the height from which is derived what is, (BMI Kg/m2 = weight/height2) can readily be incorporated into regional and national survey. It could be used for the purpose of nutritional surveillance (39,45) or for inter-regional or inter-countries comparison as well as longitudinal comparison within the same region or country (17). In other words, it is a valid and standardized variable for this type of programs.

   The classic and traditional classification of obesity, it has been applied almost of exclusive way in adult and with objections and limitations for children and adolescent in growth and development ages, prepubertal and pubertal children; and especially in population with sports activity (18,19).

   On account of these limitations, was recommended the BMI for the age as indicative the best for the employment in the adolescence, by that: a) It incorporates the information required on the age; b) has been validated as indicative of the total body fat in the superior percentiles (Klebanoff M. A. and cabbage) (15); and c - provides continuity with the indicators of the adults (5). On the other hand thanks to all this, today we counted with reference data of great quality, tables OMS (7). Even though the BMI has not been validated fully as indicative of the thinness or the malnutrition in the adolescents (6,10,19,20), constitutes an index of the body mass, applicable in both extreme (6).

   Furthermore, the application of the BMI as valuation or qualification criterion in groups of adolescents with sports activity, it is yet discrepancies motive (7,8,9).

The objective of this work is two fold: 1) to study and to analyze thoroughly, the variability of the BMI in adolescent populations of the Argentina, with and without physical activity in relationship to population highly trained; and 2) in children to accomplish a comparative study with a population apparently different or at least with socioeconomic realities and different ethnic origin from the Argentina (11).

   Were incorporated to this study 22266 subject between 6 - 53 years, of both sexes distributed in:

   1) Group A = 2895 Argentine children 21,22,27 (1513 male and 1382 women); and 3690 Ecuadoran children (1,33,34,35,36,37,38) (2262 male and 1428 women) between 6 - 12 years distributed by ages as is shown in Table Nº 1.

Table Nº 1.

   2) Group B1 = 885 adolescent and adult, top rank athletes (622 male and 263 women) (28,29,30,31,32), representative of 33 sports and 30 Pan American countries. Table Nº 2 B2= 444 adolescent and adult (315 male and 129 women) (40,41) of white race, black and yellow, of 29 countries and representative of 18 South American, Pan American and world levels sports. Table Nº 3.

Table Nº 2

Table Nº 3

   3) Group C = 600 adolescent of both sexes (355 male and 245 women) (2,3), of regional competitive sports, without arriving to the high performance. Table Nº 4

Table Nº 4

   4) Group D = 13294 Argentine adolescents with scholastic sports activity (7630 male and 6564 women) (2,3), between 11-17 years, Table Nº 5.

Table Nº 5

   5) Group E = 458 male Argentine adults, belonging to a national safety force. Table Nº 6.

Table Nº 6

   To achieve a homogeneous treatment and to avoid the bias, were categorized the studied variables, through the transformation technique of continuous quantities into discreet. Were codified in four groups according to the statistical technique of Quartiles (I = 0-25; II = 26-50; III = 51-75; and IV 76 -100 centiles) . This method permits to have equal number of cases in each quartile and to assume the rectangularity (4). The general model is shown what is in table Nº 7.

Table Nº 7

   The statistic treatment was central trend measures, dispersion and percentile values. Was applied Kolmogorov - Smirnov test for to prove normal distribution of the samples and Mann-Whitney, Kruskal-Wallis to test that the samples were originating of the same populations. Furthermore, Variance Analysis ANOVA to study possible differences between groups was used.

   Through cross tables we calculated the dependency of the variables with x2 and the degree of association with the correlation coefficient (r of Pearson).

   In the group A = children, the ANOVA, establishes significant differences of BMI between countries, alone when this present as covariate in the model, the fat component (sum of triceps and sub scapular skinfold thicknesses). The country - age effect, is NS in male. Alone in women is made slightly significant in presence of the fat component. The age component on the BMI is very significant by the logic allometric of the BMI = WEIGHT (Kg) / HEIGHT (m2). Figure Nº 1-2.

Figure Nº 1

Figure Nº 2

   In the group B1 = top rank athletes, in male the ANOVA establishes for the BMI, no difference between countries neither type of sports. It is very significant F = 22.524 p<0.000 for the age; Lean Body Mass (LBM) F = 13.679 p<0.000; F = 23.843 p<0.000 for SSk and moderate F = 2.317 p<0.043 for the interaction Muscular Mass (Musc. M.) - LBM and F = 1.934 p<0.045 for the interaction Musc. M. - SSk.

   In women the age was NS; moderated significance F = 9.261 p<0.003 for type of sport; F = 6.891 p<0.009 for country; and F = 5.351 p<.002 for Musc. M. Very significant F = 19.894 p<0.000 for LBM and F = 13.167 p<0.000 for the SSk.

   Was found very low influence of the relationship trunk length (TL) - length of inferior members (LIM) on the BMI. TL vs. BMI r = 0.30 p<0.001 ( significant by the n of the sample). Furthermore, the country - sport factor, was made weakly significant, alone in male, when was added the fat factor.

   In the group B2 = top rank athletes, of both sexes. The ANOVA shows difference NS for origin country, either for the three races in the two sexes; weak significance F = 4.506 p<0.35 for the type of sports only in male; for the age, moderate difference F = 4.151 p<0.043 in males and F = 4.222 p<0.043 in females. While for the LBM, was very significant the difference F = 8.969 p<0.000 alone in male. The interaction Musc. M - LBM was significant in males F = 2.403 p< 0.21 and F = 4.37 P<0.001 in females.

   All the variables were transformed by natural logarithms and null hypothesis (variances homogeneity) was to accept by to applied Levene test F = 1.647 NS.

   Group C = 600 adolescent of both sexes, of competitive sports, without arriving to the high performance. It was found very significant correlations in males r = 0.69 p<0.001 of the BMI 21.3±2.7 Kg/m2, with sum of skinfolds 19.0±9.1 mm. In females r = 0.68 p<0.001 BMI = 21.1± 2.8 Kg/m2, SSk = 26.5 ±10.2 mm. The bone-free muscle cross-sectional area (CSA) (Calculus in Appendix A) of leg (255.3±44.1 cm2) r = 0.65 p<0.001; and arm (79.2±15.7 cm2) r = 0.65 p<0.001. On the other hand, the correlation of BMI with LIM was low significance and negative sign (r = - 0.11 p<0.006). The correlation with LT was slightly highest r = 0.18 p<0.001.

   In the ANOVA design, were considered as principal factors 1) SSk; and 2) CSA of Leg and Arm. As covariate sex, age and type of sport. The results were very significant for the principal factors p<0.001; and NS for the covariates. However, it was found interaction among CSA of leg and SSk.

   In the group D = general population of adolescents, with ages between 13-16 years, was demonstrated that BMI is scanty modified by the TL r = 0.26 p<0.001,and much less by the LIM r = - 0.06. Both are dependent of the age and the sex, something which is explained by the growth velocity and the sexual dimorphism. This group also was split into four Quartiles as is shown in table N° 6 for the age and in N° 7 for the BMI. The analysis of covariance (ANCOVA), demonstrated that the variable more significant for the males was the SSk F = 16819 p<0.0000. In decrement order was the age F = 74.6 p<0.000; and the TL with F = 16.8 p<0.000 . The adjust model for those covariates had R2 = 0.76 p<0.000. For the females SSk F = 187.04 p<0.0000; age F = 62.9 p<0.000. TL was NS and R2 = 0.76.

   In the group E = adult with physical activity (members of a safety force), the ANOVA does not establish differences between the four groups of age, with or without the influence of the fat component represented by the sub scapular and triceps skinfold thicknesses. Of the same way, the age factor was no significant either the interaction among age - activity. In direct contrast, the activity factor was very significant for the four established groups.

   It is important to emphasize that the sub scapular skinfold, the same as in the groups A and B, presents the level but high of differences p<0.001, something which is in according to previous projects as the report of (Karatsu and Cabbage, 1987)7.

   The study of the human biological variability, had been very favored by the advent and the massive utilization of the multivariate techniques, of statistic packages. These methods permit to try to the individuals as biological units within the population (Howells) (12) and to reduce the variability to a small number of vectors, that they can reveal the so wanted biological standard (Blackith and Reyment) (4).

   In this work have been very insistent in the utilization of diagnostic statistic methods not only for giving to our study the greater math reliability, but also for to permit that our results could be reproduced and eventually applied in similar studies.

   We had to appeal also to the conclusions of recognized studies made by world institutions as the FAO, OPS, WHO (7,42). Tables of BMI, of WHO (7) are data base validated to qualify chronic energy deficiency, undernourished and monitoring the changes in the nutritional status influenced by the socioeconomic level, seasonal changes in the foods availability and demands on physical activity.

   It was recommended the BMI as base of the anthropometric indicators of the thinness and the overweight during the adolescence (Spurr and Cabbage) (44). It is considered that the weight for the age provides few information and it is even misleading in absence of the corresponding information on the height for the age. However, the traditional methods of combined employment of the height for the age and the weight for the age to evaluate the body mass, both are complicated and have paid off biased (Kramer) (16).

   The reference data of the weight for the height have the advantage of the fact that they do not require the knowledge of the chronological age. However the relationship between the weight and the height changes much with the age (and probably with the maturity status) during the adolescence; consequently, with a given height , the corresponding weight to a particular percentile is not the same for all the ages, in such a way that the meaning of a given percentile of the weight for the height defers according to the age.

   For the same reasons, the relative weights calculated in the categories of the height during the adolescence are appropriate solely when are used in categories restricted to the age (Kramer) (16).

   The results of our study in adolescent, with significant physical activity (group C), demonstrate that the BMI is affected by the fat component as principal factor, in each one of the four Quartiles in those which were divided the BMI and the SSk. Consequently, it is not a relationship escalated with the absolute values. The dependency is linear and very significant in all the range of the BMI as of the SSk scales.

   The very significant positive relationship of the BMI with of the SSk of leg (males r = 0.76; females r = 55 p<0.001) and CSA of arm (Males r = 0.66; females r = 0.64 p<0.000), demonstrate that the muscle - skeletal growth and development process, it can be easily monitored into juvenile populations, without dread to be distorted by the action of the physical activity and/or sports competition, in significant way. The interaction found among BMI and SSk would be explained by greater subcutaneous fat in the inferior member of the women.

   The positive correlation of the BMI with the cross sectional area of net muscle-skeletal, of superior and inferior members, favor the idea that the BMI it is very close of muscular development independent of the sex, of the age, and of the type of sport. This is crucial to feed the idea of recommending the BMI as useful index of energetic supplement of the body and the variations in the body composition.

   It is certain that it is possible to find equal value of the BMI in two subject with different body composition. However it has been suggested that this index is easy of calculate, relatively independent of height and is less biased by height than weight/height [Khosla & Lowe (17), Evans & Preceding (8), Florey (9), Smalley and col.(43)]. Moreover, BMI is more sensitive to changes of energetic reserves of the fat, that of body bulk.

   The load of the age, sex, weight and type of sport as covariates, in spite of being present in the analysis, they were NS, demonstrating that contrary to other reports ( relatively long legs will reduce the values of BMI), this is not significantly modified by the linear growth of the subjects, in according with the work of Great, 1986 (7). However, since it is considered a population in high growth velocity age (peak high velocity V = 15.699±1.34 years old; and M = 15.534±1.384 years old),we used the partial correlation coefficient, in which the values were controlled by the age and the sex.

   Furthermore, it is asserted that in growth ages the permanent increases of the height and the imbalances among trunk lengths (TL) and of inferior members lengths (IML), remains reliability. In order to observe modifications of the BMI in growth age, we accomplished a follow-up of 8 months in a sub sample of 79 adolescent (58 males of 12.156 ± 0.08 years old and 21 females of 12.535±0.10 years old) in the NW region of the Argentine. In all subjects the factor "physical activity" ( two classes of physical education by week) was controlled and they were elected by be located below of the percentile 25 for height by age, according to the national growth and development tables. The results evidenced significant differences of the BMI among the two measurements 18.85± 2.73 vs. 19.21 ±2.79 Kg/m2 t = 2.779 p<0.07. However, the variability within subject is greater than observed between measurements. The age or the sex were not determinant factors, as demonstrates by ANOVA NS. This is probably due to very short observation time, and the own characteristics of the sample. [Busdrago F. and Colonel J., data not published]

   In this lap of time, the change were significant with increases of the height 144.7± 6.9 cm vs. 148.7 ± 7.6 cm t = 18.7 p<0.001; and of the weight 39.8 ± 8.6 Kg vs. 42.8 ± 9.4 Kg t = 10.34 p<0.001. It is appreciated however, that the increase of the height was made in the women to expenses of the IML r = 0.56 and in the males to expenses of TL, in spite of the low correlation found in the last r = 0.07.

   For the results obtained in the group A, the BMI can be used as an applicable index in children if in the analysis is incorporate the fat component. This finding are present also in the others age groups.

   This modifies substantially the traditional concept of its few usefulness in child (16,18). The comparison among two countries, reinforces the validity of the BMI to be applied for epidemiological uses and follow-up between regions or countries, in this age range.

   Other of the frequent cautions, it is the application of the BMI in adolescent with high training levels. The subjects can be influenced by the growth velocity; and the physical training would modify significantly the body composition and the muscle-skeletal structure.

   We study in the group B1, the interaction of the BMI with all these factors in Pan American athletes of high performance, where the training is over 12 loads by week. Furthermore, this group of 900 athletes of both sexes, was in competitive period. We consider also the influence "origin country" factor (34 countries of the American continent).

   Our results failed to demonstrate in male, the influence of the "origin country" component on the BMI, in neither type of sport. However, in the female the differences though moderate, were present. We think that the fat component, is active as characteristics own of the sex and more noticeable that in males.

   On the other hand, the body fat mass was the more determinant factor in established the differences of the BMI for the males and females, demonstrating a strong dependency.

   In spite of this, for males, we find significant correlations, r = 0.75 between BMI vs. LBM and vs. Mus. M r = 0.65 p<0.001. In females: BMI vs. LBM r = 0.74 vs. Musc. M r =0.95 p<0.001.

   This demonstrates a high degree of reliability in the use of the BMI to qualify easy and roughly, the body composition in athletes. In other words an BMI increased in a athlete, is indicative also of a greater LBM and/or muscular mass.

   In the TL and IML, alone was found a very weak correlation of the country - sport factor in males, alone when was included the fat component.

   In the B2 group, we arrived to similar conclusions, but furthermore, we can reject the race factor, since in this group we not found differences among sexes neither between the ethnic groups white, black or yellow.

   Was surprising for us the influence of the age in males considering that the age competition is similar in all subjects, except for gymnastics. The age for the B1 group is 22.4 ± 5.3 for females and of 24.1 ± 4.2 for males. For the B2 group is 21.602 ± 5.388 in females; and 21.235 ± 4.233 in males. With this consider finishes it variable that was lacking.

   With the results of the groups A, B1, B2, C and D have given satisfaction to the classic questions of fast growth, sexual dimorphism in relationship with TL,IML, and the determinant action of physical training.

   Considering that the "fat factor" has so much importance on the BMI, is design a regression model that adjustment this relationship.

Figure Nº 3

   The previous considerations permit us, that the not - incorporation of the adiposity factor (sum of triceps and sub scapular skinfold thicknesses), would produce a lost of the variability in the BMI, of the 38 % in the children; athletes adolescent 39 % ; general population of adolescents 36%; 27 % in the top rank athletes; and in adult of safety force 35 %.

   All this justifies more than enough, that we recommend the utilization of BMI corrected.

   The values of SSk were not normally distributed in spite of have made the logarithms-transformation (Kolmogorov-Smirnov p<0.000), consequently we applied in some calculations nonparametric test.

   On the basis of our more recent publications, we test the application of the allometric scaling of the BMI by the SSk, according to the following equations (Figure Nº 4):

   However, the age was a discrimination factor very significant in both sexes F = 5.26 p<0.001 in males and F = 13.67 p<0.000 in females. Consequently we discarded this procedure, and was chosen the construction of a two-way table.

   We think to give to general practice as a second option, a tool but simple of using. Was designed a two-way cross table, in which the percentiles 15 and 85 delimit the normality zone. Above of the percentile 85 is established the overweight zone and below the percentile 15 the thinness zone. These criteria though arbitrational are based on the criterion of dispersion the median. Tables 8-9-10 -11.

Table Nº 8

Table Nº 9

Table Nº 10

Table Nº 11

   The percentile tables often include the inter quartile range (IQR). It is known also as inter quartile deviation. It is half of the distance among the quartile Q1=25 and Q3=75. In a normal distribution, if from median (P50), are auditioned to each side 1 IQS, is covert approximately 50 percent of the cases; and 4 IQR covers all the distribution.

    In any variable we can see that the median more 1 IQR, are very close to the percentiles 15 downward and 85 upward. In other words, the limit of normality are fixed among these percentiles. All values what is outside of this statistical boundary (upward or under limits), are considered, sub or over valuation.

   The steps to read the table of BMI, are: to locate the age in the first column and then found the values of BMI in the first line and SSk in the second line. In accordance to the value obtained in the measurement, to locate one of the five columns, that correspond to the percentiles 5 - 15 - 50 - 85 - 95. As have established in previous paragraphs, we suggested as normal range the values among 15 - 85 percentile and the values above or below these borderline will receive the qualification of substandard or supranormal respectively.

   Would be desirable that the values of BMI and SSk are the same centil. However are frequents the situations in which the both values are located in different column. The interpretation, in this case it will have to be established by the experience and good judgments of the professional.

   We have presented evidence of the usefulness of BMI in critical periods of age, label by the growth and development process; this is during the infantile ages and the validity to be applied in different environments. Finally, we have proven the usefulness of the BMI, in general population of adolescents, with moderate, top-level sports activity and competition. Furthermore, was discarded the racial and origin factor.

   In anything case we are supporting the replacement of specific indicators internationally standardized, for the evaluation of the muscle - skeletal development. However, we insisted on discarding prejudices and limitations in the utilization of the BMI, especially for frequent use in the medical daily practice.

   We recommend the utilization of the cross tables suggested in this work, but at condition that the measurement of sub scapular and triceps skinfold thicknesses are included. We will be in possession of a fast, easy and very reliable tool of measure. Moreover, the incorporation of the SSk, adding in average approximately 35% of precision.

The purpose of this paper was accomplished a retrospective study from data banks of our Laboratory, to test the hypothesis that the BMI is a simple but objective anthropometric indicator of the nutritional status and the obesity or thinness degree. Furthermore, of the body composition, in populations submitted to physical activity and sports training.
Were evaluated 22266 subject between 6 - 53 years, of both sexes, distributed in: Group A = 2895 Argentine children, 1513 male (M) and 1382 female (F). 3690 Ecuadorian children (2262 M and 1428 F) between 6 - 12 years. Group B1 = 885 adolescent and adult, top-rank athletes (622 M and 263 F), representative of 33 sports and 30 Pan-American countries. Group B2 = 444 adolescent and adult (315 M and 129 F) of white, black and yellow race, of 29 South American, Pan-American and world sport class. Group C = 600 adolescent of both sexes (355 M and 245 F), of competitive sports, not high performance. Group D = 13294 Argentine adolescents with scholastic sports activity, (7630 M and 6564 F), between 11-17 years. Group E = 458 Argentine adult M, belonging to a national safety force.
Were tried the data through parametric and not parametric tests. The hypothesis null was rejected to the 5% of probability.
The results demonstrated difference NS between countries and races; and significant dependency of the BMI vs. fat component (triceps + sub scapular skinfolds thicknesses) (SSk) p<0.000 in all the groups. The best smoothing for the regressions plot between both variables in all the groups was quadratic model. However, to facilitate the practice use of our proposal were elaborated cross-tabs by age and sex for BMI and SSK.
We recommended the use of tables of BMI joint to SSk for qualify children, athletes and adult youths, discarding the established prejudices and demonstrating that the incorporation of the fat component improves the precision of the BMI in 35% approximately.

    We are grateful to all and every one of the collaborators of our laboratory, that through all these years have made possible to present today this paper, with a significant number of cases.


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2nd Virtual Congress of Cardiology

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