Skip to main content
Download PDF

Associations between 47 anthropometric markers derived from a body scanner and relative fat-free mass in a population-based study

BMC Public Health volume 24, Article number: 1079 (2024) Cite this article

Abstract

Background

Low relative fat free mass (FFM) is associated with a greater risk of chronic diseases and mortality. Unfortunately, FFM is currently not being measured regularly to allow for individuals therapy.

Objective

One reason why FFM is not being used may be related to additional equipment and resources, thus we aimed to identify easily accessible anthropometric markers related with FFM.

Materials and methods

We analyzed data of 1,593 individuals (784 women; 49.2%, age range 28–88 years) enrolled in the population-based Study of Health in Pomerania (SHIP-TREND 1). Forty-seven anthropometric markers were derived from a 3D optical body-scanner. FFM was assessed by bioelectrical impedance analysis (FFMBIA) or air displacement plethysmography (FFMADP). In sex-stratified linear regression models, FFM was regressed on anthropometric measurements adjusted for body height and age. Anthropometric markers were ranked according to the coefficient of determination (R2) derived from these regression models.

Results

Circumferences of high hip, belly, middle hip, waist and high waist showed the strongest inverse associations with FFM. These relations were stronger in females than in males. Associations of anthropometric markers with FFMAPD were greater compared to FFMBIA.

Conclusion

Anthropometric measures were more strongly associated with FFMADP compared to FFMBIA. Anthropometric markers like circumferences of the high or middle hip, belly or waist may be appropriate surrogates for FFM to aid in individualized therapy. Given that the identified markers are representative of visceral adipose tissue, the connection between whole body strength as surrogate for FFM and fat mass should be explored in more detail.

Peer Review reports

Introduction

Due to the ageing population in Western societies, there is a growing interest of assessing body composition in the clinical setting. Relative fat-free mass (FFM, described as % of total body weight) is directly related to cardiorespiratory fitness [1] and inversely associated with all-cause, cardiovascular, and cancer mortality [2,3,4]. Low FFM is also an inherent characteristic of malnutrition and sarcopenia [5, 6]. Physical disability and a reduced functional capacity are also related to a lack of FFM [7, 8]. In addition, a small FFM is associated with a higher prevalence of cardiovascular risk factors like hypertension [9], obesity or diabetes [3]. Overall, FFM is of growing clinical interest and needs to be assessed in clinical practice to aid in individualized therapy.

FFM can be precisely determined by imaging methods such as quantitative magnetic resonance imaging (MRI), dual-energy X-ray absorption (DXA), ordinary MRI or computed tomography scans [10, 11]. However, these methods are commonly time consuming, rely on large and partly expensive stationary hardware and often go hand in hand with radiation exposure. Considering the aforementioned limitations, these procedures are inconvenient or infeasible for practical reasons in the clinical setting and in large epidemiological studies.

Bioelectrical impedance analysis (BIA) can be considered an indirect, yet radiation free, time-efficient and portable alternative for FFM estimation [10]. Nonetheless, BIA has some limitations. BIA requires some assumptions like constant hydration status [12] and this technology may not be used in patients with implanted cardiac devices [13]. Albeit, FFM measurements based on BIA (FFMBIA) has substantial variability on the individual level, satisfactory reliability has been reported on a population level [14,15,16].

Air displacement plethysmography (ADP) may also be used to quickly assess FFM (FFMADP) [17, 18]. This method is also radiation-free and time-efficient, though not very portable. Various studies have shown high test-retest-reliability [19] of ADP in the assessment of body composition and good validity compared to DXA or BIA [20, 21].

Since the assessment of FFM, independent of method, is currently hampered by time and costs, we aimed to identify easily accessible anthropometric markers. Automatic three-dimensional optical body scans are a reliable and repeatable method to identify potential anthropometric biomarkers [22]. This process is completely radiation free and safe. Importantly, this method agrees with the reference method of manual measurements fulfilling WHO criteria [22,23,24].

Previous studies with relatively small sample sizes reported associations between three-dimensional body surface anthropometrics and data derived from BIA [23, 25] or ADP [26]. However, the study population was rather small and consisted almost exclusively of healthy men or even athletes. A systematic review summarizing associations of body scan markers with FFMADP and FFMBIA [27] reported that body scanners had a high degree of accuracy and reliability. The major limitations of the previous studies were the small sample sizes as well as the homogenous study populations which we tried to address in our analysis.

The rationale for this study was to identify easily accessible anthropometric markers of FFM to aid in individualized therapy. A prime example could be the use of resistance training to either reduce the loss or even gain fat-free mass (i.e. skeletal muscle). Especially in rural and/or economically not very successful regions of the world, the above mentioned technologies may not be available. Yet, inhabitants of these regions may also suffer from cardiovascular risk factors and/or age-induced sarcopenia. This study aims to identify potential surrogates of FFM by investigating associations of automatic body scanner-derived anthropometric measurements with FFM as determined by BIA and ADP in a large-population-based sample including 1,593 individuals aged 28–88 years to provide alternatives. Since individuals with higher BMI have also higher FFM, we decided to use FFM normalized for total body weight as an outcome to not underestimate the effect of obesity on the relation between anthropometric markers and FFM.

Materials and methods

Study population

The Study of Health in Pomerania (SHIP), conducted by the University Medicine Greifswald, has been designed as a population-based project assessing common risk factors and subclinical disorders in the adult population [28]. For the present analyses, we used data from the SHIP-TREND-1 cohort. SHIP-TREND-1 is the first follow-up of the population-based SHIP-TREND study, in which 2,507 individuals were examined between 2016 and 2019. We excluded 679 participants without BIA and 67 participants without ADP measurements. In addition, we did not include 38 participants with missing body scanner values and 130 participants with implausible BIA or ADP measurements resulting in a study population of 1,593 individuals (Fig. 1).

Fig. 1
figure 1

Flow chart on the selection process of the study population

Full size image

Anthropometric measurements

Manual anthropometric measurements included height, weight, waist- and hip circumference. Weight was measured to the nearest 0.1 kg in light clothing and without shoes using standard digital scales. Waist circumference (WC) was measured to the nearest 0.1 cm using an inelastic tape midway between the lower rib margin and the iliac crest in the horizontal plane with the subject standing comfortably with weight distributed evenly on both feet. Hip circumference (HC) was measured to the nearest 0.1 cm using an inelastic tape midway between the iliac crest and the most lateral (sideways) protruding points of the greater trochanter. Waist-to-hip ratio (WHR) was calculated as WC/HC. Body mass index (BMI) was calculated as weight [kg] divided by height to the square [m2].

3D optical body scan

Anthropometric data was measured with a three-dimensional optical body scanner (VITUS Smart XXL, Vitronic, Wiesbaden, Germany) driven by the software AnthroScan Professional (Version 3.0.7, Human Solutions GmbH, Kaiserslautern, Germany). This measuring technique is based on the optical triangulation process using four lasers and eight cameras, to date one of the most precise method of contactless capture of body shape. The apparatus allows the observer to receive deformation-free measurements, since neither the lasers nor the sensors enter into any physical contact with the study participant [29, 30].

The measurement results in a detailed three-dimensional image of the participants’ body surface and extracts a large number of standard anthropometric markers, including circumferences, lengths, distances, areas, volumes and their ratios within 10–15 s. After exclusion of markers which were highly associated with other anthropometric markers (e.g. different height measurements) and markers which cannot be measured standardized in clinical practice (e.g. distance back to the wall), we used 47 standard anthropometric markers for the present analyses (Supplementary Tables 1 and 2).

While being scanned participants wore underwear and a fabric head cap in order to reduce measurement error due to hair volume. After measuring body height twice (sitting and standing position) a so-called standard-scan allowed measuring aforementioned anthropometric markers in an upright standing position. More details on the assessment can be found in the supplement.

Bioelectrical impedance analysis

BIA was performed using a multifrequency Nutriguard-M device (Data Input GmbH, Pöcking, Germany) and the NutriPlus software (Version 5.4.1, Data Input GmbH, Pöcking, Germany). R (resistance) and Xc (reactance) were measured applying electric currents of 800 mA at 5, 50, and 100 kHz following the manufacturer’s instructions [31, 32]. Source and sensor electrodes were placed on the dorsum of hand, wrist, ankle and dorsum of foot of the right side of the body with participants in supine position [33]. Absolute FFM was calculated within the software. Relative FFM was calculated in % by dividing absolute FFM by total body weight and then multiplying by 100. The BIA examination took place on a different date than the core examination. For more details, see supplement.

Air displacement plethysmography

Air displacement plethysmography was performed using a BOD POD®(COSMED Deutschland GmbH, Werneck, Germany) device according to the manufacturer’s recommendation (COSMED Deutschland GmbH, Werneck, Germany). This method is a densitometric measuring method and can be used to determine body volume, body density, lung volume and fat mass. Absolute FFM was calculated by subtracting body weight and fat mass. Relative FFM was calculated in % by dividing absolute FFM by total body weight and then multiplying by 100.

Other clinical measurements

Non-fasting blood samples were taken and serum levels of low density lipoprotein (LDL-C) cholesterol, high density lipoprotein (HDL-C) cholesterol, triglycerides, serum glucose and glycated hemoglobin (HbA1c) were assessed by a Dimension Vista 500 analytical system (Siemens AG, Erlangen, Germany). Blood pressure was measured using a HEM 705CP device (Omron Corporation, Tokyo, Japan). Arterial hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg or antihypertensive treatment (Anatomical Therapeutic Chemical Classification System (ATC) codes C02, C03, C04, C07, C08 or C09A). Known or newly detected diabetes mellitus was defined as self-reported physician’s diagnosis or intake of glucose-lowering drugs (ATC code A10) or HbA1c concentrations of ≥ 6.5% (≥ 47.54 mmol/mol) or non-fasting serum glucose > 11.1 mmol/l.

Statistical analysis

Characteristics of the study population are reported stratified by sex as absolute numbers and percentages for categorical data and as median, 25th, and 75th percentiles for continuous data. Since individuals with greater absolute FFM also have a higher fat mass, FFM is positively related with cardiovascular risk. Hence, we used relative FFM as the outcome in our analysis [4]. Anthropometric markers were associated with FFM by sex-stratified linear regression models adjusted for age and body height. Since men and women show significant differences with regards to cardiometabolic biomarkers [34], body fat distribution [35] and body composition [36] we decided that a sex-stratified analysis is warranted. To make the effect sizes of the different anthropometric markers comparable, all anthropometric markers were z-standardized. Stratified by sex and measurement technique, anthropometric markers were ranked based on the coefficients of determination (R2). As a result, we derived for each variable a β-coefficient, a 95%-confidence interval, a R2, a p value, and a -log p value. For each sex the β-coefficients and 95%-confidence intervals for the ten variables with the highest R2 were plotted. Furthermore, we created a heatmap, in which all body scanner variables were included that were one of the ten variables most strongly associated with FFMBIA or FFMADP in men or women. In addition, we conducted a sex-stratified random forest regression, in which we included all 47 body scan markers together with age and height as explanatory variables and the respective FFM as outcome. Before applying the random forest, the data was randomly split into equally-sized training and test datasets. Before applying the final models, we optimized the hyper-parameters “number of iterations” (numit) and “number of variables to randomly investigate at each iteration” (numvars) by minimizing the out of bag (oob) and validation errors as described by Schonlau et al. [37]. Afterwards, the final random forest models with the optimized hyper-parameters were calculated and the most important variables for each setting were plotted. All statistical analyses were performed using Stata 18.0 (Stata Corporation, College Station, TX, USA).

Results

Characteristics of the study population

The study sample included 1,593 individuals (49.2% females, 28–88 years). Males were slightly older and had a higher BMI than females (Table 1). Independent of method, FFM was higher in males than in females.

Table 1 Characteristics of the study population stratified by sex
Full size table

Association of FFM assessed by BIA and ADP

Relative FFMBIA (median = 68.7; inter-quartile range (IQR) = 62.5 to 74.0) was 3% higher than FFMADP (median = 65.7; IQR = 58.5 to 72.4). The median difference was more pronounced in females (63.3% vs. 59.6%) compared to males (72.7% vs. 70.3%). In linear regression analysis FFMBIA and FFMADP were stronger associated in women compared to men (R2 = 0.70 vs. R2 = 0.43). Overall association expressed as R2 was 0.69 (Fig. 2). The correlation coefficients between FFMBIA and FFMADP were 0.66 in men and 0.84 in women.

Fig. 2
figure 2

Association between ADP relative fat-free mass (FFM) and BIA FFM

The models were stratified by sex (red = female, blue = male) and were adjusted for body height, age and time between ADP and BIA examination

Full size image

Associations between anthropometric markers and FFM

After adjustment for age and body height, the ten body scanner markers associated most strongly with FFMADP were very similar for men and women (Fig. 3). However, the order of these markers was slightly different and most of these markers were indicators of belly fat. Associations of the anthropometric markers with FFMADP were higher in women compared to men. The R2’s for the ten strongest markers for FFMADP ranged from 0.76 to 0.70 for women and from 0.70 to 0.58 for men. Calf and upper arm circumference, which are frequently used markers for FFM estimation in clinical practice, had considerably lower R2 values in both sexes and were not among the ten makers. The sex-specific associations of all body scan markers with FFMADP and FFMBIA are listed in the Supplementary Tables 1 and 2. Accounting for multiple testing, all markers with a–log(p) > 6.84 were considered as statistically significant. With this threshold we observed 42 significant markers for FFMADP and 41 FFMBIA in men. In women 42 significant markers were found for FFMADP for FFMBIA.

Fig. 3
figure 3

Body scan markers showing the strongest associations with relative fat free mass (FFM)

Ranking according to the R2. Models were adjusted for body height, age (and time between core and BIA examination [for BIA FFM]). ADP F = FFM from ADP in females, BIA F = FFM from BIA in females, ADP M = FFM from ADP in males, BIA M = FFM from BIA in males

Full size image

Correlations of body scan markers with FFMBIA (adjusted for body height, age and time between core and BIA examination) were higher in women compared to men. Nonetheless, nine of the ten body scan markers showing the strongest association with FFMBIA were the same in women and men. The body scan markers for belly fat, which were most tightly related to BIA, were also among the strongest markers for FFMADP. The R2’s for the ten strongest markers for FFMBIA ranged from 0.69 to 0.63 for women and from 0.42 to 0.32 for men.

In females, the β-coefficients for the relation between anthropometric markers and FFM were very similar independent of measurement technique. In men, however, the β-coefficients were much smaller for FFMBIA compared to FFMADP (Fig. 4). Independent of measurement technology and sex, the most strongly related anthropometric markers showed inverse relations with FFM.

Fig. 4
figure 4

β-coefficients for the ten anthropometric markers most strongly associated with relative fat free mass (FFM)

Models were adjusted for body height, age, (and time between core and BIA examination [for BIA FFM]). ADP M = FFM from ADP in males, BIA M = FFM from BIA in males, ADP F = FFM from ADP in females, BIA F = FFM from BIA in females

Full size image

Independent of measuring technique and sex, manually measured waist circumference showed a weaker association with FFM than waist circumference measured by body scanner. (Table 2) Manually measured hip circumference had a lower association with FFM than the automatically measured equivalents (“middle hip circumference” [MHC] and “high hip circumference” [HHC]) in females and males independent of measuring technique. Manually measured waist to hip ratio was only weakly associated with FFM independent of measurement technique and sex.

Table 2 Associations of manually measured anthropometric markers with relative fat free mass (FFM) adjusted for body height, age (and time between core and BIA examination [for BIA FFM])
Full size table

Further adjustment of the models for smoking status, alcohol consumption and the sports score did not change the results substantially. Exactly the same markers showed the strongest associations to FFMBIA compared to FFMADP in a very slight different order.

In sex-stratified random forest regression, the most important markers for FFMADP and FFMBIA were similar to those detected in the logistic regression analyses and represented mainly markers of central adiposity (Fig. 5). The model specifications for the random forest models were as follows: FFMADP in men: numit = 300, numvars = 25, oob error = 3.32, root mean square error = 4.17; FFMBIA in men: numit = 150, numvars = 8, oob error = 3.68, root mean square error = 4.82; FFMADP in women: numit = 150, numvars = 23, oob error = 2.96, root mean square error = 3.92; FFMBIA in women: numit = 150, numvars = 20, oob error = 2.98, root mean square error = 3.78.

Fig. 5
figure 5

Importance ranking for body scan markers for relative fat free mass (FFM) as observed in random forest regression

ADP M = FFM from ADP in males, BIA M = FFM from BIA in males, ADP F = FFM from ADP in females, BIA F = FFM from BIA in females

Full size image

Discussion

We investigated associations of anthropometric markers derived from an automated three-dimensional body scanner as well as manual measurement with FFM assessed by ADP and BIA in 1,593 individuals from Northeast Germany. We found strong inverse associations for markers of abdominal adiposity with relative FFM. Interestingly, effect estimates for markers derived automatically in the three-dimensional body scanner were much stronger compared to those measured manually. Furthermore, associations of body scan markers with FFM were stronger in women compared to men. With regards to measurement technique we found higher effect estimates for FFMADP compared to FFMBIA. Interestingly, our results highlight the potential to use of markers for central adiposity as a surrogate for FFM. This is especially surprising given that FFM is very much related to skeletal muscle mass and one would have hypothesized arm circumferences rather than markers of visceral adipose tissue [38].

By automatically collecting 47 different anthropometric sites using a three-dimensional body scanner, we were able to access a large, comprehensive set of anthropometric parameters. Importantly, the Global Leadership Initiative on Malnutrition (GLIM) recently endorsed anthropometric markers as alternatives to technology-based measurements of low muscle mass (i.e. low FFM), in settings where resources are limited [39]. We identified eleven anthropometric markers associated with FFM independent of technology. Hence, these parameters could be useful in the clinical assessment of FFM. These markers are generally considered to be surrogates of central adiposity: circumferences of belly, buttock, hip, waist, maximum belly, and depths of the abdomen and buttock. In current clinical practice the circumference of the arm and of the calf are used to estimate FFM. However, in our analysis these parameters only showed very weak associations. Thus, FFM may be related to fat accumulation in the waist, hip, and abdomen area of a person, regardless of sex or age. Since circumferences in this region can be taken relatively easy using an inelastic tape measure, the assessment will not depend on a large stationary device. We acknowledge that non-circumference anthropometric markers like “body depth” and “buttock abdomen depth” are not easily measured. The two reference points for applying the measuring tape are not on a horizontal plane. This suggests to use circumferences as convenient anthropometric markers in order to assess FFM in a clinical setting.

We show that automatically derived anthropometric markers had larger effect estimates compared to their manually measured equivalents. In addition, circumferences measured by scan were slightly different from those derived manually, which may be the result of different measuring positions. Assuming that parameters such as “high hip circumference” or “middle hip circumference” serve as the equivalent of manually measured, for instance, “hip circumference”, automatic body scanners may be superior to manual measurements or that the locations of the circumference of the automatic measurements contain more information for FFM prediction. Repp et al. [40] previously reported that the site of waist circumference measurement is important to improve the prediction of visceral adipose tissue. The exact measuring regions of aforementioned manually measured circumferences are described and defined in the ISO 7250-1:2017 standard [41]. Hence, future studies need to elucidate the most optimal site for circumference measurements.

Ng et al. [42] described three-dimensional scans as reliable methods to estimate FFM in healthy adults, albeit in a rather small study sample (n = 39). Bennett et al. [43] evaluated estimates of three-dimensional anthropometric markers and body composition (DXA scans). They reported strong association of body scanner anthropometrics and FFM based on DXA. Overall, previous studies already reported that anthropometrics may be used to estimate FFM, yet in relatively homogeneous study samples. The advantage of our study is that we included study participants representative of the general population. Hence, we extend the current knowledge by assessing the relation between anthropometrics and FFM in a very broad phenotype. We hope that this may help to promote the use of anthropometric markers as surrogates for FFM in clinical practice. This may be especially relevant for individuals living in rural or middle/low income regions with little access to technology. Health care professionals in these locations may use the identified anthropometric markers to monitor the individual response to specific treatments or interventions.

The beta-estimates for the associations between anthropometric parameters and FFM were depended on measurement technique. This was particularly seen in men, where the association between the anthropometric markers and FFM was much stronger for FFMADP than for FFMBIA. An explanation for this may be that the correlation between FFMBIA and FFMADP was substantially higher in women compared to men. Previous studies reported that in individuals with obesity the different measurement techniques for FFM provide unequal results [44, 45]. In our study population men had a higher BMI compared to women. However, this difference may not explain a systematic bias in measuring FFM by BIA since previous studies also revealed conflicting results [46,47,48,49]. Our results identified very similar anthropometric parameters related to FFM yet with different effect estimates and in a different order for each sex. These findings may be related to sex specific cardiometabolic biomarkers [34], body fat distribution [35] and body composition [36].

In our study, the men had a higher prevalence of hypertension and type 2 diabetes mellitus compared to women. The presence of chronic diseases such as hypertension or type 2 diabetes are related to chronic inflammation or malnutrition resulting in a lower FFM. In addition, these metabolic conditions are associated with greater visceral fat [50, 51]. This again highlights the interesting nature of our results. In general, individuals with a greater BMI (i.e. more visceral adipose tissue) also have higher FFM, since locomotion of the greater body weight also requires more muscle mass. Yet, our findings highlight that when FFM is adjusted for FM and thus relative FFM is used, the statement above is not true. However, we believe that relative FFM is more important compared to absolute FFM with regards to health risk. Since chronic inflammation, malnutrition and aberrant fat distribution can cause altered hydration status and BIA equations depend on the consistency of hydration status [12, 52, 53], one may speculate that the observed differences in FFMBIA and FFMADP are related to the hydration status of study participants with preexisting metabolic dysfunction.

This study has several strengths. We were able to assess a high number of anthropometric markers in a rather large study sample (n = 1,593). In addition, SHIP data uses very stringent data quality control. This is particularly related to standardization of non-invasive examination methods and data management [33]. FFMBIA and FFMADP are established in clinical, as well as in research settings with a high degree of validity on a population level [14,15,16, 20, 21].

However, we were unable to use the gold standard for FFM measurement (DXA) due to radiation exposure and practicality. Our study population consisted exclusively of Caucasians from rural northeastern Germany, which supports validity within this ethnicity but limits conclusions about other study populations. In addition, we did not test for multicollinearity between our outcomes which could be considered a limitation. However, the manually measured anthropometric data may potentially not be as accurate as automatically scanned data, since an inter-observer bias cannot be excluded entirely [54]. Distinguishing whether there is a systematic difference between methods or a measurement area adjustment is critical to the association with FFM in order to transfer our results to an outpatient setting may be part of future research.

Conclusion

Our findings suggest that anthropometric markers of visceral adiposity may be appropriate surrogates for relative FFM in the general population. The three-dimensional anthropometric markers were more strongly associated with FFMADP compared to FFMBIA potentially related to the hydration status of individuals with metabolic dysfunction. Overall, our findings support the use of anthropometrics in daily clinical practice to estimate a person’s FFM for individualized therapy.

Data availability

Data from the “Study of Health of Pomerania” are available from the University Medicine Greifswald, Germany but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available upon reasonable request at https://transfer.ship-med.uni-greifswald.de/FAIRequest/ and with permission of the University Medicine Greifswald.

References

  1. Kohler A, King R, Bahls M, Gross S, Steveling A, Gartner S, Schipf S, Glaser S, Volzke H, Felix SB, et al. Cardiopulmonary fitness is strongly associated with body cell mass and fat-free mass: the study of Health in Pomerania (SHIP). Scand J Med Sci Sports. 2018;28(6):1628–35.

    Article  CAS  PubMed  Google Scholar 

  2. Imboden MT, Kaminsky LA, Peterman JE, Hutzler HL, Whaley MH, Fleenor BS, Harber MP. Cardiorespiratory Fitness Normalized to Fat-Free Mass and Mortality Risk. Med Sci Sports Exerc. 2020;52(7):1532–7.

    Article  CAS  PubMed  Google Scholar 

  3. Abramowitz MK, Hall CB, Amodu A, Sharma D, Androga L, Hawkins M. Muscle mass, BMI, and mortality among adults in the United States: a population-based cohort study. PLoS ONE. 2018;13(4):e0194697.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lagace JC, Marcotte-Chenard A, Paquin J, Tremblay D, Brochu M, Dionne IJ. Increased odds of having the metabolic syndrome with greater fat-free mass: counterintuitive results from the National Health and Nutrition Examination Survey database. J Cachexia Sarcopenia Muscle. 2022;13(1):377–85.

    Article  PubMed  Google Scholar 

  5. Kawakami R, Tanisawa K, Ito T, Usui C, Miyachi M, Torii S, Midorikawa T, Ishii K, Muraoka I, Suzuki K, et al. Fat-Free Mass Index as a surrogate marker of appendicular skeletal muscle Mass Index for low muscle Mass Screening in Sarcopenia. J Am Med Dir Assoc. 2022;23(12):1955–61. e1953.

    Article  PubMed  Google Scholar 

  6. Cederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, Baptista G, Barazzoni R, Blaauw R, Coats A, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1–9.

    Article  CAS  PubMed  Google Scholar 

  7. Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (Sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002;50(5):889–96.

    Article  PubMed  Google Scholar 

  8. Rolland Y, Czerwinski S, Van Abellan G, Morley JE, Cesari M, Onder G, Woo J, Baumgartner R, Pillard F, Boirie Y, et al. Sarcopenia: its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Health Aging. 2008;12(7):433–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ittermann T, Werner N, Lieb W, Merz B, Nothlings U, Kluttig A, Tiller D, Greiser KH, Vogt S, Thorand B, et al. Changes in fat mass and fat-free-mass are associated with incident hypertension in four population-based studies from Germany. Int J Cardiol. 2019;274:372–7.

    Article  PubMed  Google Scholar 

  10. Lemos T, Gallagher D. Current body composition measurement techniques. Curr Opin Endocrinol Diabetes Obes. 2017;24(5):310–4.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ellis KJ. Human body composition: in vivo methods. Physiol Rev. 2000;80(2):649–80.

    Article  CAS  PubMed  Google Scholar 

  12. Sergi G, De Rui M, Stubbs B, Veronese N, Manzato E. Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res. 2017;29(4):591–7.

    Article  PubMed  Google Scholar 

  13. Chabin X, Taghli-Lamallem O, Mulliez A, Bordachar P, Jean F, Futier E, Massoullie G, Andonache M, Souteyrand G, Ploux S, et al. Bioimpedance analysis is safe in patients with implanted cardiac electronic devices. Clin Nutr. 2019;38(2):806–11.

    Article  PubMed  Google Scholar 

  14. Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol (1985). 2000;89(2):465–71.

    Article  CAS  PubMed  Google Scholar 

  15. Chien MY, Huang TY, Wu YT. Prevalence of Sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc. 2008;56(9):1710–5.

    Article  PubMed  Google Scholar 

  16. Achamrah N, Colange G, Delay J, Rimbert A, Folope V, Petit A, Grigioni S, Dechelotte P, Coeffier M. Comparison of body composition assessment by DXA and BIA according to the body mass index: a retrospective study on 3655 measures. PLoS ONE. 2018;13(7):e0200465.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Dempster P, Aitkens S. A new air displacement method for the determination of human body composition. Med Sci Sports Exerc. 1995;27(12):1692–7.

    Article  CAS  PubMed  Google Scholar 

  18. McCrory MA, Gomez TD, Bernauer EM, Mole PA. Evaluation of a new air displacement plethysmograph for measuring human body composition. Med Sci Sports Exerc. 1995;27(12):1686–91.

    Article  CAS  PubMed  Google Scholar 

  19. Tucker LA, Lecheminant JD, Bailey BW. Test-retest reliability of the Bod Pod: the effect of multiple assessments. Percept Mot Skills. 2014;118(2):563–70.

    Article  PubMed  Google Scholar 

  20. de Castro JAC, de Lima LRA, Silva DAS. Accuracy of octa-polar bioelectrical impedance analysis for the assessment of total and appendicular body composition in children and adolescents with HIV: comparison with dual energy X-ray absorptiometry and air displacement plethysmography. J Hum Nutr Diet. 2018;31(2):276–85.

    Article  PubMed  Google Scholar 

  21. Wingfield HL, Smith-Ryan AE, Woessner MN, Melvin MN, Fultz SN, Graff RM. Body composition assessment in overweight women: validation of air displacement plethysmography. Clin Physiol Funct Imaging. 2014;34(1):72–6.

    Article  PubMed  Google Scholar 

  22. Koepke N, Zwahlen M, Wells JC, Bender N, Henneberg M, Ruhli FJ, Staub K. Comparison of 3D laser-based photonic scans and manual anthropometric measurements of body size and shape in a validation study of 123 young Swiss men. PeerJ. 2017;5:e2980.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sager R, Gusewell S, Ruhli F, Bender N, Staub K. Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men. PLoS ONE. 2020;15(6):e0234552.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Nishida C, Ko GT, Kumanyika S. Body fat distribution and noncommunicable diseases in populations: overview of the 2008 WHO Expert Consultation on Waist Circumference and Waist-Hip ratio. Eur J Clin Nutr. 2010;64(1):2–5.

    Article  CAS  PubMed  Google Scholar 

  25. Takai Y, Nakatani M, Aoki T, Komori D, Oyamada K, Murata K, Fujita E, Akamine T, Urita Y, Yamamoto M, et al. Body shape indices are predictors for estimating fat-free mass in male athletes. PLoS ONE. 2018;13(1):e0189836.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wagner DR, Castaneda F, Bohman B, Sterr W. Comparison of a 2D iPad application and 3D body scanner to air displacement plethysmography for measurement of body fat percentage. J Hum Nutr Diet. 2019;32(6):781–8.

    Article  CAS  PubMed  Google Scholar 

  27. Rumbo-Rodriguez L, Sanchez-SanSegundo M, Ferrer-Cascales R, Garcia-D’Urso N, Hurtado-Sanchez JA, Zaragoza-Marti A. Comparison of body scanner and manual anthropometric measurements of body shape: a systematic review. Int J Environ Res Public Health 2021, 18(12).

  28. Volzke H, Schossow J, Schmidt CO, Jurgens C, Richter A, Werner A, Werner N, Radke D, Teumer A, Ittermann T et al. Cohort Profile Update: the study of Health in Pomerania (SHIP). Int J Epidemiol 2022.

  29. Bretschneider T, Koop U, Schreiner V, Wenck H, Jaspers S. Validation of the body scanner as a measuring tool for a rapid quantification of body shape. Skin Res Technol. 2009;15(3):364–9.

    Article  PubMed  Google Scholar 

  30. Kohler A, Filges B, Volzke H, Felix SB, Ewert R, Stubbe B, Markus MRP, Gross S, Dorr M, Ittermann T, et al. Body surface scan anthropometrics are related to cardiorespiratory fitness in the general population. Sci Rep. 2022;12(1):22185.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gomez JM, Heitmann BL, Kent-Smith L, Melchior JC, Pirlich M, et al. Bioelectrical impedance analysis–part I: review of principles and methods. Clin Nutr. 2004;23(5):1226–43.

    Article  PubMed  Google Scholar 

  32. GmbH D-I. Das B.I.A.-Kompendium 3. Ausgabe. In. Darmstadt; 2005.

  33. Völzke H, Alte D, Schmidt CO, Radke D, Lorbeer R, Friedrich N, Aumann N, Lau K, Piontek M, Born G, et al. Cohort profile: the study of health in Pomerania. Int J Epidemiol. 2011;40(2):294–307.

    Article  PubMed  Google Scholar 

  34. Lew J, Sanghavi M, Ayers CR, McGuire DK, Omland T, Atzler D, Gore MO, Neeland I, Berry JD, Khera A, et al. Sex-based differences in Cardiometabolic Biomarkers. Circulation. 2017;135(6):544–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Veilleux A, Tchernof A. Sex Differences in Body Fat Distribution. In: Adipose Tissue Biology edn. Edited by Symonds ME. New York, NY: Springer New York; 2012: 123–166.

  36. Bredella MA. Sex Differences in Body Composition. In: Sex and Gender Factors Affecting Metabolic Homeostasis, Diabetes and Obesity edn. Edited by Mauvais-Jarvis F. Cham: Springer International Publishing; 2017: 9–27.

  37. Schonlau M, Zou RJ. The random forest algorithm for statistical learning. Stata J. 2020;20:3–29.

    Article  Google Scholar 

  38. Filges B, Bahls M, Radke D, Groß S, Ewert R, Stubbe B, Markus M, Felix S, Völzke H, Dörr M et al. Body surface scan anthropometrics are associated with grip strength in the general population. Nutr Metab Cardiovasc Dis 2024.

  39. Compher C, Cederholm T, Correia M, Gonzalez MC, Higashiguch T, Shi HP, Bischoff SC, Boirie Y, Carrasco F, Cruz-Jentoft A, et al. Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition diagnosis of malnutrition. JPEN J Parenter Enter Nutr. 2022;46(6):1232–42.

    Article  Google Scholar 

  40. Repp KD, Radke D, Ittermann T, Albers M, Markus MRP, de Santa Helena ET, Friedrich N, Bulow R, Volzke H. The site of waist measurement impacts the estimation of visceral fat: results from three-dimensional photonic body scanning. Br J Nutr 2021:1–32.

  41. ISO. ISO 7250-1:2017 - Basic human body measurements for technical design. In. Switzerland; 2017.

  42. Ng BK, Hinton BJ, Fan B, Kanaya AM, Shepherd JA. Clinical anthropometrics and body composition from 3D whole-body surface scans. Eur J Clin Nutr. 2016;70(11):1265–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bennett JP, Liu YE, Quon BK, Kelly NN, Wong MC, Kennedy SF, Chow DC, Garber AK, Weiss EJ, Heymsfield SB, et al. Assessment of clinical measures of total and regional body composition from a commercial 3-dimensional optical body scanner. Clin Nutr. 2022;41(1):211–8.

    Article  PubMed  Google Scholar 

  44. Wong Vega M, Srivaths PR. Air Displacement Plethysmography Versus Bioelectrical Impedance to Determine Body Composition in Pediatric Hemodialysis patients. J Ren Nutr. 2017;27(6):439–44.

    Article  PubMed  Google Scholar 

  45. Wingo BC, Barry VG, Ellis AC, Gower BA. Comparison of segmental body composition estimated by bioelectrical impedance analysis and dual-energy X-ray absorptiometry. Clin Nutr ESPEN. 2018;28:141–7.

    Article  PubMed  PubMed Central  Google Scholar 

  46. King S, Wilson J, Kotsimbos T, Bailey M, Nyulasi I. Body composition assessment in adults with cystic fibrosis: comparison of dual-energy X-ray absorptiometry with skinfolds and bioelectrical impedance analysis. Nutrition. 2005;21(11–12):1087–94.

    Article  PubMed  Google Scholar 

  47. Day K, Kwok A, Evans A, Mata F, Verdejo-Garcia A, Hart K, Ward LC, Truby H. Comparison of a Bioelectrical Impedance Device against the Reference Method Dual Energy X-Ray Absorptiometry and Anthropometry for the Evaluation of Body Composition in Adults. Nutrients 2018, 10(10).

  48. Nunez FJ, Munguia-Izquierdo D, Petri C, Suarez-Arrones L. Field methods to Estimate Fat-free Mass in International Soccer players. Int J Sports Med. 2019;40(10):619–24.

    Article  PubMed  Google Scholar 

  49. Huang AC, Chen YY, Chuang CL, Chiang LM, Lu HK, Lin HC, Chen KT, Hsiao AC, Hsieh KC. Cross-mode bioelectrical impedance analysis in a standing position for estimating fat-free mass validated against dual-energy x-ray absorptiometry. Nutr Res. 2015;35(11):982–9.

    Article  CAS  PubMed  Google Scholar 

  50. Roubenoff R. Sarcopenia and its implications for the elderly. Eur J Clin Nutr. 2000;54(Suppl 3):S40–47.

    Article  PubMed  Google Scholar 

  51. Seidell JC, Hautvast JG, Deurenberg P. Overweight: fat distribution and health risks. Epidemiological observations. A review. Infusionstherapie. 1989;16(6):276–81.

    CAS  PubMed  Google Scholar 

  52. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gomez J, Lilienthal Heitmann B, Kent-Smith L, Melchior JC, Pirlich M, et al. Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr. 2004;23(6):1430–53.

    Article  PubMed  Google Scholar 

  53. Demirci MS, Demirci C, Ozdogan O, Kircelli F, Akcicek F, Basci A, Ok E, Ozkahya M. Relations between malnutrition-inflammation-atherosclerosis and volume status. The usefulness of bioimpedance analysis in peritoneal dialysis patients. Nephrol Dial Transpl. 2011;26(5):1708–16.

    Article  Google Scholar 

  54. Ulijaszek SJ, Kerr DA. Anthropometric measurement error and the assessment of nutritional status. Br J Nutr. 1999;82(3):165–77.

    Article  CAS  PubMed  Google Scholar 

  55. Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. 1982;36(5):936–42.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

SHIP is part of the Community Medicine Research Network of the University Medicine Greifswald, which is supported by the German Federal State of Mecklenburg-West Pomerania, the Ministry of Cultural Affairs, and the Social Ministry of the Federal State of Mecklenburg–West Pomerania. The CMR encompasses several research projects that are sharing data of the population-based Study of Health in Pomerania (SHIP; http://ship.community-medicine.de).

Open Access funding enabled and organized by Projekt DEAL.

Author information

Author notes

  1. Martin Bahls and Till Ittermann contributed equally to this work.

Authors and Affiliations

  1. Institute for Community Medicine, University Medicine Greifswald, Walther Rathenau Str. 48, D-17475, Greifswald, Germany

    Maximilian Dietzmann, Dörte Radke, Henry Völzke & Till Ittermann

  2. German Centre for Cardiovascular Research (DZHK) partner site Greifswald, Greifswald, Germany

    Marcello RP Markus, Henry Völzke, Stephan B. Felix, Marcus Dörr, Martin Bahls & Till Ittermann

  3. Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany

    Marcello RP Markus, Stephan B. Felix, Marcus Dörr & Martin Bahls

  4. Department of Internal Medicine A, University Medicine Greifswald, Greifswald, Germany

    Mats Wiese

Authors
  1. Maximilian Dietzmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dörte Radke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcello RP Markus
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mats Wiese
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Henry Völzke
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Stephan B. Felix
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marcus Dörr
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Martin Bahls
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Till Ittermann
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MD, MB and TI designed research; DR, MRM, HV, SBF, MD, MB and TI conducted research; TI conducted the statistical analyses; MD, MB and TI wrote the paper; TI had primary responsibility for final content.

Corresponding author

Correspondence to Till Ittermann.

Ethics declarations

Ethic approval

The study complied with the Declaration of Helsinki and was approved by the Ethics Committee of the University of Greifswald.

Consent to participate

All study participants gave written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dietzmann, M., Radke, D., Markus, M.R. et al. Associations between 47 anthropometric markers derived from a body scanner and relative fat-free mass in a population-based study. BMC Public Health 24, 1079 (2024). https://doi.org/10.1186/s12889-024-18611-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-024-18611-w

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us