Is the 1975 Reference Man still a suitable reference?

Abstract

Background/Objective:

In 1975, a Reference Man for the estimation of radiation doses without adverse health effects was created. However, during the past few decades, considerable changes in body weight and body composition were observed, as a result of which, new in vivo technologies of body composition analysis are now available. Thus, the Reference Man might be outdated as adequate standard to assess medication and radiation doses. The objective of this study was to compare body composition of an adult population with 1975 Reference Man data, thereby questioning its value as a suitable reference.

Methods:

Body composition was assessed in 208 healthy, Caucasian subjects (105 males, 103 females) aged 18–78 years with a body mass index range of 16.8–35.0 kg/m2. Fat mass (FM) and muscle mass (MM) were assessed by dual-energy X-ray absorptiometry, organ masses (OMs) were measured by magnetic resonance imaging.

Results:

There was a considerable variance in body weight and body composition. When compared with Reference Man, great differences in body composition were found. Men and women of the study population were heavier, taller and had more FM, MM and higher masses of brain, heart and spleen. These differences did not depend on age. Relationships between body weight and body composition were investigated by general linear regression models, whereby deviations in FM, MM and heart mass disappeared, whereas differences in brain and spleen mass persisted.

Conclusions:

Our data indicate the need of a modern Reference Man and thus a recalculation of medical radiation doses and medication.

Introduction

On the basis of the increasing exposure of humans to radiation due to occupational, public and medical reasons and procedures, the ‘Task Group on Reference Man’ created a Reference Man and a Reference Woman in 1975. This was based on analyses of anatomic databases. Considering this Reference Man, lowest radiation doses were estimated for the planning and the application of medical radiation that do not cause harm in humans (Snyder et al., 1975). The Reference Man established quantified constraints, or limits, on individual doses from medical sources. The limitation of this approach is obvious with regard to the combination of data sets from a multitude of several studies from different countries and geographic zones all over the world, which included results of hundreds of patients at different times. A representativeness of a certain population or population group is therefore not given for the Reference Man. However, the statistically precise definition as average men was not the aim of the ‘Task Group on Reference Man’ (Snyder et al., 1975).

It is well known that body composition of men and women had changed since 1975 and that obesity has reached epidemic proportions (Roche, 1979; World Health Organization, 2000; Ogden et al., 2006; Wardle and Boniface, 2008; Lahti-Koski et al., 2009). This is especially true in rich countries due to changes in lifestyle, eating behaviour, living conditions and working demands. Thus, we hypothesize that the estimated radiation or medication doses based on the Reference Man and the Reference Woman, respectively, might no longer be reasonable and appropriate for a current population.

The aim of our study was to compare body composition data of the Reference Man from 1975 with recent data measured by state of the art in vivo methods in a representative healthy Caucasian population with a normal distribution of age and body mass index (BMI). On the basis of this comparison a re-evaluation of the Reference Man is intended.

Subjects and methods

The study population consisted of 208 healthy, Caucasian volunteers (103 females and 105 males) aged 18–78 years with a BMI range of 16.8–35.0 kg/m2. Participants were recruited from students and staff at the University of Kiel and by notice board postings in local supermarkets and pharmacies. All subjects were non-smokers and took no medication known to influence body composition. Subjects with splenomegaly (enlargement of the spleen >350 g) were excluded from analyses. The study protocol was approved by the local ethical committee of the Christian-Albrechts-Universität zu Kiel. Each subject provided informed written consent before participation.

Study protocol

All participants arrived at the metabolic unit of the Institute of Human Nutrition and Food Science in the morning at 0730 h after an over night fast of >8 h.

Body composition analysis

Anthropometrics

Body height was measured to the nearest 0.5 cm with subjects in underwear and without shoes (stadiometer Seca, Vogel & Halke, Hamburg, Germany). Weight was assessed by an electronic scale (TANITA, Tokyo, Japan).

Dual-energy X-ray absorptiometry

Whole body measurement by Dual-energy X-ray absorptiometry (DXA) was performed using a Hologic absorptiometer (QDR 4500A, Hologic, Bedford, MA, USA). Scans were carried out by a licensed radiological technician. Manufactures’ software (version V8.26a:3) was used for the analyses of whole body and regional bone mineral content, lean soft tissue (LST) and percentage fat mass (FM). Skeletal muscle mass (MM) was calculated from the sum of appending LST (for example, LSTarms+LSTlegs), using the formula of Kim et al. (2002).

Magnetic resonance imaging

The volumes of five internal organs (brain, heart, liver, kidneys and spleen) were measured by transversal magnetic resonance imaging (MRI) images. Scans were obtained by a 1.5T scanner (Magnetom Vision Siemens, Erlangen, Germany). Brain and abdominal organs were examined by a T1-weighted sequence (FLASH) (TR: 177.8 ms (abdominal organs); TR: 170.0 ms (brain); TE: 4.1 ms/echo). Electrocardiogram-triggered, T2-weighted turbo spin-echo ultrashot scans (HASTE) (TR: 800.0 ms; TE: 43 ms/echo) were used to examine the heart. The slice thickness ranged from 6 mm for brain (1.2 mm interslice gap) to 7 mm for the heart (2.1 mm interslice gap) and 8 mm the internal organs (2.4 mm interslice gap). Cross-selectional organ areas were determined manually using a segmentation software (SliceOmatic, version 4.3, TomoVision, Montreal, Canada). Volume data were transformed into organ masses (OMs) using the following densities: 1.036 g/cm3 for brain, 1.06 g/cm3 for heart and liver, 1.05 g/cm3 for kidneys and 1.054 g/cm3 for spleen (Duck, 1990).

Data analysis

Descriptive subject data are given as means±s.d.s and range. Statistical analyses were performed using SPSS for Windows 13.0 (SPSS, Chicago, IL, USA). Deviations between means are given as percent (Δ mean (%)), and a cut-off of <10% differences was accepted as data agreement. Influences of varying body height were analysed by comparison of within group height-tertiles in men (group 1: <1.74 m; group 2: 1.74–1.83 m; group 3: >1.83 m) and women (group 1: <1.64 m; group 2: 1.64–1.72 m; group 3: >1.72 m). Multiple stepwise regression analyses were used to estimate the explained variances in body composition parameters given as R2. Values of standardized β-coefficients and standard error of the estimate are presented for each of the developed regression models. Relationships between body composition and body mass or age are shown as general linear models, and linear regression equations were used to calculate body composition parameters. Differences between sexes were analysed using the independent t-test. All tests were two-tailed and a P-value <0.05 was accepted as the limit of significance.

Results

Comparison of body composition between Reference Man and study population

In Table 1, body composition of the study population is compared with mean data of Reference Man and Reference Woman. When compared with the reference, subjects of the study population were heavier, taller and had more FM, MM and OMs (brain, heart (in women only), spleen). No differences were found for liver and kidney masses (Δ<10%). When compared with women, men had significantly higher BMI, MM and OM and less FM (P<0.01), but sex had no effect on the difference between measured values and the reference. Considering the influence of age on body composition, a subgroup of young subjects (20–30 years) was compared with Reference Man and Reference Woman (Table 1). This approach revealed similar results, that is, higher weight, FM, MM and OM (except liver and kidney mass). Also significant sex differences in FM, MM and OM were found (P<0.01).

Table 1 Comparison of body composition between the study population (n=208) and younger subjects (n=63) (mean±s.d., range) and the Reference Man and Reference Woman from 1975 (mean)

Impact of age on variance in body composition

In Figures 1a–g, age is plotted against OM and tissue mass for men (closed circles) and women (open circles). The mean age of men and women is given as continuous vertical line, whereas the age range of Reference Man and Woman is presented by shaded areas. Mean OM and tissue mass are given as continuous (study population) or dashed horizontal (Reference Man and Woman) line. Results of OM and tissue mass showed only small differences between the ‘younger’ reference subjects and the ‘older’ study population. Thus, there was no significant influence of age on the variance of the data.

Figure 1
figure1

(ag) Age-dependent changes in masses of brain (a), heart (b), liver (c), kidneys (d), spleen (e), skeletal muscle (f) and fat mass (g) for women (open circles) and men (closed circles). Mean tissue and organ masses are shown as horizontal line for reference subjects (dashed) and for men and women of the study population (continuous).

Influence of body weight, height and age on variances on body composition

A stepwise multiple regression analysis explaining variance in body composition parameters is shown in Table 2. Weight, height and age were used as independent variables within different models. Except for brain mass, variance in OM and tissue mass was mainly explained by body weight alone in men (FM: 77%; MM: 54%; heart: 9%; liver: 43%; kidneys: 16%) and in women (FM: 81%; MM: 35%; heart: 14%; liver: 36%; kidneys: 24%; spleen: 18%). In addition, body height explained further variance in FM (men: 2%; women: 7%), MM (women: 6%), liver (women: 4%) and kidneys (men: 4%). Furthermore, age contributed significantly to explained variance in MM (5%) and spleen mass (8%) in men, and in variance in brain (4%) and spleen mass (5%) in women. No significant correlation was found between body weight and age, both in men and women (data not shown). On the basis of the significant contribution of body height to the variance in body composition (Table 2), the study population was categorized into body height-tertiles to analyse differences between actual and reference data (Table 3). When compared with tall subjects, deviations between measured data and data from Reference Man and Reference Woman with respect to weight, height and MM (and FM in men) were lower for shorter people (group 1). In addition, brain, heart and spleen mass (and kidney mass in men) of shorter subjects showed the highest agreement with the Reference Man and Reference Woman. In general, most body composition data were consistent to reference data (Δ<10%) in shorter men and women when compared with taller subjects (groups 2 and 3), showing a higher difference in weight, height, FM, MM, brain, heart and spleen mass (and liver and kidney mass in men) (Table 3). Within different height-tertiles, these findings were also true in young subjects (20–30 years) (data not shown).

Table 2 Explained variance in body composition of men and women (n=208) using a multiple stepwise regression analyses
Table 3 Comparison of body composition between the study population (n=208) (mean±s.d. (range)) and the Reference Man and Reference Woman from 1975 (mean) considering different body heights

Relationship between body mass and OM/tissue mass

The relationship between body weight and OM/tissue mass is given in Figures 2a–g. The mean body weight of Reference Man (70 kg) and Reference Woman (58 kg) is shown as dashed vertical line within the figures. A horizontal line is presenting the calculated tissue mass for the reference subjects, using the regression equations given in Table 4. For both sexes, highest R2 were found between FM and body mass (men R2=0.88; women R2=0.89; P<0.01), whereas a weak or no relationship was seen between body mass and brain mass (men R2=0.21, P<0.05; women R2=nonsignificant).

Figure 2
figure2

(ag) Weight-dependent changes in masses of brain (a), heart (b), liver (c), kidneys (d), spleen (e), skeletal muscle (f) and fat mass (g) for women (open circles) and men (closed circles). Weight of Reference Woman (58 kg) and Reference Man (70 kg) is shown as dashed vertical line, and the calculated organ mass as dashed horizontal line.

Table 4 Linear regression equations between body mass and body composition for men and women (n=208)

Calculation of OM and MM on the basis of body mass

Linear regression equations calculated from the relationship between body mass and body composition (Figures 2a–g) are presented in Table 4. On the basis of these regression equations, the masses of brain, heart, liver, kidneys and spleen, fat and muscle were calculated for a man (with a body weight of 70 kg ≈ body weight of the Reference Man) and a woman (with a body weight of 58 kg ≈ body weight of the Reference Woman), respectively. The estimated tissue masses of the study population (measured by MRI and DXA) and the reference subjects (autopsy data) with identical body weights were compared. There were considerable differences in brain and spleen mass (and MM in women) with an overestimation of these masses in reference subjects. By contrast, FM and liver mass in men and kidney mass in women were underestimated in Reference Man and Reference Woman, respectively.

Discussion

The primary purpose of this study was to compare reference data from 1975 with recent database, based on in vivo measurements of body composition in a greater group of healthy subjects. Considerable differences in body composition were found, with todays men and women being heavier, taller and having more FM and MM when compared with Reference Man and Reference Woman, respectively. Furthermore, OMs of brain, heart and spleen of the study population differed. These finding were independent of age and gender. Accounting for differences in body weight deviations in FM, MM (for men only) and heart mass disappeared, whereas differences in brain and spleen mass remained. A comparison among different height groups revealed highest agreement in body composition in shorter people, whereas taller subjects showed higher percentage deviations. The latter finding is in line with data of Heymsfield et al. (2007).

Differences in body composition between actual data and the 1975 reference subjects may be partly caused by methodical issues. Although in this study, masses of internal organs have been measured in vivo using MRI, data of the Reference Man were based on autopsy post-mortem analyses and organ weighing, that is, the organs were removed from the body followed by exclusion of remaining tissue before weighing. It is well known that during the first 15 min after extraction from the body the organ looses significant weight. On the other hand, considerable differences in in vivo organ weight estimates might be due to segmentation techniques. For example, analysing brain mass cerebrospinal fluid has been excluded by manual slice segmentation. In accordance, gallbladder, portal vein and other big blood vessels were excluded from the liver mass, which were included within post-mortem organ weighing. Thus, OMs of Reference Man and Reference Woman might not resemble metabolic active OM, but remaining fluid within the organ and thus add to systematic differences between results of the two measurement procedures.

Comparing MM as assessed in autopsy studies with MM measured by DXA also implicates method-based inaccuracies. DXA has a great precision of soft tissue composition measurement, although it includes some assumptions that should be taken into account, for example, constant attenuation of FM (Lohman and Chen, 2005). Another assumption is that DXA measurements are not affected by the anteroposterior thickness of the human body. However, previous studies found slightly overestimated fat and lean masses due to thickness <20 cm (Laskey et al., 1992). In addition, the accuracy of DXA may differ with tissue. For example, in the thorax, DXA has limits to distinguish between bone and soft tissue; thus, estimations of thoracic composition tend to be imprecise (Roubenoff et al., 1993). However, advances of the DXA technique prevail. In research and clinical settings, DXA is a non-invasive, accurate and reproducible tool for assessing body composition with minimal radiation doses superior to many other method (Slosman et al., 1992; Gately et al., 2003; Brownbill and Ilich, 2005). There are high correlations between DXA and computer tomography estimates of lean mass and MM (Visser et al., 1999).

However, there may be a small influence of different measurement techniques on deviations found in in vivo body composition and the Reference Man.

We hereby present data of a large homogeneous Caucasian study population with a wide range in age and BMI (18–78 years, 16.8–35.0 kg/m2). Owing to the limited recruitment area of our sample, we do not consider our data as representative. To get an idea we compared our data to the data set of the second national nutrition survey (NVSII), conducted by the Federal Research Centre for Nutrition and Food in Germany (Max Rubner Institute FRCfNaF, 2008). High agreements were found in BMI (men: 26.9 vs 26.4 kg/m2; women: 26.1 vs 24.4 kg/m2) and body weight (men: 84.6 vs 84.3 kg; women: 69.9 vs 68.7 kg) (Max Rubner Institute FRCfNaF, 2008). Thus, with respect to BMI our study population was similar to the representative NVSII population.

Our body composition data were also compared with previous detailed in vivo body composition studies on shorter populations. In these studies, FM, MM and OM were measured using the same in vivo methods, for example, DXA, MRI or computer tomography (Sparti et al., 1997; Gallagher et al., 1998; Bosy-Westphal et al., 2004). When compared with these previous body composition analysis data, men and women of our study population were older, had slightly higher body weights and FM compared with other populations (Sparti et al., 1997; Gallagher et al., 1998). Excluding subjects >50 years from our present analysis, weight, BMI, FM and MM were in good agreement with previous data (Sparti et al., 1997; Gallagher et al., 1998; Bosy-Westphal et al., 2004). In addition, differences in liver and kidneys masses (Gallagher et al., 1998) might be explained by methodical differences in different segmentation procedures. Contrary to previous data, in this study renal pelvis and portal vein were not included within the calculation of organ volume. Taken as a whole, we found good agreements between our estimates of body composition and the results of previous studies.

When compared with data observed in Caucasians, ethnic differences in body composition have been reported (Gasperino, 1996; Rahman et al., 2009): African Americans have more bone mass and MM, but less OM and FM than Caucasians (Aloia et al., 1999; Weinsier et al., 2001; Gallagher et al., 2006; Wu et al., 2007). These differences remained after controlling for differences in age, weight and height (Gasperino, 1996). When compared with the Reference Man from 1975, African Americans had higher FM (+0.6 to +10.8 kg) (Aloia et al., 1999; Weinsier et al., 2001; Gallagher et al., 2006). Concomitantly, MM was considerably higher (+7 kg) in black women when compared with the Reference Woman (Aloia et al., 1999). Race-dependent differences in body composition argue in favour to develop a Reference Men and a Reference Women for various ethnic groups.

In conclusion, we found considerable differences in current in vivo estimates of body composition and Reference Man and Reference Woman, with present men and women being heavier, taller and having higher FM and MM. Substantial differences were also found for OM of brain, heart and spleen, whereas no difference occurred for liver and kidney mass in both gender. Comparing subjects with identical body weight deviations in FM, MM (only in men) and heart mass disappeared, whereas differences in brain and spleen mass persisted. Considering different height groups revealed lowest deviations to reference values for shorter people (men <1.74 m; women <1.64 m).

On the basis of the considerable differences in body composition between the present results and the 1975 Reference Man, a modern Reference Man is needed as a basis to estimate accurate medical radiation doses and to calculate medication application (for example, doses of drugs).

References

  1. Aloia JF, Vaswani A, Mikhail M, Flaster ER (1999). Body composition by dual-energy X-ray absorptiometry in black compared with white women. Osteoporos Int 10, 114–119.

    CAS  Article  Google Scholar 

  2. Bosy-Westphal A, Reinecke U, Schlorke T, Illner K, Kutzner D, Heller M et al. (2004). Effect of organ and tissue masses on resting energy expenditure in underweight, normal weight and obese adults. Int J Obes Relat Metab Disord 28, 72–79.

    CAS  Article  Google Scholar 

  3. Brownbill RA, Ilich JZ (2005). Measuring body composition in overweight individuals by dual energy x-ray absorptiometry. BMC Med Imaging 5, 1.

    Article  Google Scholar 

  4. Duck FA (1990). Physical properties of tissue. Academic Press: New York.

    Google Scholar 

  5. Gallagher D, Albu J, He Q, Heshka S, Boxt L, Krasnow N et al. (2006). Small organs with a high metabolic rate explain lower resting energy expenditure in African American than in white adults. Am J Clin Nutr 83, 1062–1067.

    CAS  Article  Google Scholar 

  6. Gallagher D, Belmonte D, Deurenberg P, Wang Z, Krasnow N, Pi-Sunyer FX et al. (1998). Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass. Am J Physiol 275, E249–E258.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Gasperino J (1996). Ethnic differences in body composition and their relation to health and disease in women. Ethn Health 1, 337–347.

    CAS  Article  Google Scholar 

  8. Gately PJ, Radley D, Cooke CB, Carroll S, Oldroyd B, Truscott JG et al. (2003). Comparison of body composition methods in overweight and obese children. J Appl Physiol 95, 2039–2046.

    CAS  Article  Google Scholar 

  9. Heymsfield SB, Gallagher D, Mayer L, Beetsch J, Pietrobelli A (2007). Scaling of human body composition to stature: new insights into body mass index. Am J Clin Nutr 86, 82–91.

    CAS  Article  Google Scholar 

  10. Kim J, Wang Z, Heymsfield SB, Baumgartner RN, Gallagher D (2002). Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr 76, 378–383.

    CAS  Article  Google Scholar 

  11. Lahti-Koski M, Seppanen-Nuijten E, Mannisto S, Harkanen T, Rissanen H, Knekt P et al. (2009). Twenty-year changes in the prevalence of obesity among Finnish adults. Obes Rev 11, 171–176.

    Article  Google Scholar 

  12. Laskey MA, Lyttle KD, Flaxman ME, Barber RW (1992). The influence of tissue depth and composition on the performance of the Lunar dual-energy X-ray absorptiometer whole-body scanning mode. Eur J Clin Nutr 46, 39–45.

    CAS  PubMed  Google Scholar 

  13. Lohman TG, Chen Z (2005). Dual-Energy X-Ray Absorpiometry. In: Heymsfield SB, T.G. L, Wang Z, Going SB (eds). Human Body Composition. Human Kinetics: Champaign.

    Google Scholar 

  14. Max Rubner Institute FRCfNaF (2008). Ergebnisbericht Teil 1, Nationale Verzehrsstudie II. Max Rubner Institute FRCfNaF: Karlsruhe.

  15. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM (2006). Prevalence of overweight and obesity in the United States, 1999-2004. JAMA 295, 1549–1555.

    CAS  Article  Google Scholar 

  16. Rahman M, Temple JR, Breitkopf CR, Berenson AB (2009). Racial differences in body fat distribution among reproductive-aged women. Metabolism 58, 1329–1337.

    CAS  Article  Google Scholar 

  17. Roche AF (1979). Secular trends in human growth, maturation, and development. Monogr Soc Res Child Dev 44, 1–120.

    CAS  Article  Google Scholar 

  18. Roubenoff R, Kehayias JJ, Dawson-Hughes B, Heymsfield SB (1993). Use of dual-energy x-ray absorptiometry in body-composition studies: not yet a ‘gold standard’. Am J Clin Nutr 58, 589–591.

    CAS  Article  Google Scholar 

  19. Slosman DO, Casez JP, Pichard C, Rochat T, Fery F, Rizzoli R et al. (1992). Assessment of whole-body composition with dual-energy x-ray absorptiometry. Radiology 185, 593–598.

    CAS  Article  Google Scholar 

  20. Snyder WS, Cook MJ, Nasset ES, Karhausen LR, Howells GP, Tipton IH (1975). Report of the Task Group on Reference Man. Pergamon Press: Oxford.

    Google Scholar 

  21. Sparti A, DeLany JP, de la Bretonne JA, Sander GE, Bray GA (1997). Relationship between resting metabolic rate and the composition of the fat-free mass. Metabolism 46, 1225–1230.

    CAS  Article  Google Scholar 

  22. Visser M, Fuerst T, Lang T, Salamone L, Harris TB (1999). Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat-free mass and leg muscle mass. Health, aging, and body composition Study–Dual-Energy X-ray Absorptiometry and body composition Working Group. J Appl Physiol 87, 1513–1520.

    CAS  Article  Google Scholar 

  23. Wardle J, Boniface D (2008). Changes in the distributions of body mass index and waist circumference in English adults, 1993/1994 to 2002/2003. Int J Obes (Lond) 32, 527–532.

    CAS  Article  Google Scholar 

  24. Weinsier RL, Hunter GR, Gower BA, Schutz Y, Darnell BE, Zuckerman PA (2001). Body fat distribution in white and black women: different patterns of intraabdominal and subcutaneous abdominal adipose tissue utilization with weight loss. Am J Clin Nutr 74, 631–636.

    CAS  Article  Google Scholar 

  25. World Health Organization (2000). Obesity: preventing and managing the global epidemic. Report of a WHO consultation on Obesity. WHO Technical Report Series: Geneva.

  26. Wu CH, Heshka S, Wang J, Pierson Jr RN, Heymsfield SB, Laferrere B et al. (2007). Truncal fat in relation to total body fat: influences of age, sex, ethnicity and fatness. Int J Obes (Lond) 31, 1384–1391.

    Article  Google Scholar 

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Acknowledgements

This study was funded by DFG.

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Correspondence to M J Müller.

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Later, W., Bosy-Westphal, A., Kossel, E. et al. Is the 1975 Reference Man still a suitable reference?. Eur J Clin Nutr 64, 1035–1042 (2010). https://doi.org/10.1038/ejcn.2010.125

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Keywords

  • Reference Man
  • body composition
  • organ mass
  • magnetic resonance imaging

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