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64 (); 29-33
doi:
10.1016/j.jor.2024.11.009

Could point-of-care bioimpedance analysis be another tool in the prevention of osteoporotic fractures?

, , , ,
a Physiology, School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland
b School of Computer Science, College of Science and Engineering, University of Galway, Ireland
c School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland

⁎Corresponding author: Louise A. Horrigan. louise.horrigan@universityofgalway.ie

Received: , Accepted: ,
Professor P K Surendran Memorial Education Foundation
Disclaimer:
This article was originally published by Reed Elsevier India Pvt. Ltd. and was migrated to Scientific Scholar after the change of Publisher.

Abstract

Abstract

A treatment gap exists in osteoporosis, with people at risk of fracture often not identified. Dual X-Ray Absorptiometry is the gold standard technique for the identification of low bone mass, but it is not always easily accessible. Bioimpedance analysis (BIA) is a non-invasive, safe and portable technology, which can provide a calculated estimate of bone mass. However, the validity of using BIA in the assessment of bone health is largely unknown. The objective of this study was to examine BIA-derived bone mass (BBM) data from a local population, with the aim of exploring its potential for use as a preliminary osteoporosis screening tool.

A convenience sample of 124 participants (40 males, 84 females) was recruited from the local population. BIA was performed on participants according to standard procedures. BBM data was analysed in relation to weight, height, sex, age and BMI. Data was analysed using ANOVA, hierarchial regression, and bivariate correlation as appropriate.

Weight was strongly associated with BBM (R2 = 0.637, p < 0.001), providing the greatest contribution to variance, of the factors examined. BBM was also positively associated with height, in a model that included weight (ΔR2 = 0.218, p < 0.001). Females had a significantly lower bone mass than males, independent of weight and height (ΔR2 = 0.055, p < 0.001). There was a small negative association of BBM with age, in a model that included weight and sex (ΔR2 = 0.011; p = 0.002). A positive correlation between BMI and BBM was found in both males (rs(38) = 0.482, p = 0.002), and females (rs(82) = 0.565, p < 0.001). The finding that BBM values are associated with factors known to be relevant to fracture risk, provides a rationale to perform further studies to investigate if BBM values could have validity for point-of-care assessment of bone health.

Keywords

Bioelectrical impedance analysis
Bioimpedance analysis
Osteoporosis
Fracture
Bone
Body composition
1

1 Introduction

Osteoporotic fractures, particularly hip and clinical vertebral fractures, are associated with high morbidity and mortality, as well as a high economic burden for society.1,2 While treatment for low bone mass and fracture-prevention strategies can be very effective, the difficulty is that only a proportion of people who require treatment actually receive it.3,4 Studies indicate a treatment gap in Ireland of 53 % for women aged 70 or older,5 and 34 % for women aged 50 or older with a risk of major osteoporotic fracture.2 While there are a number of factors contributing to the treatment gap, a lack of identification of at-risk people is a major factor.5 With the Irish population aged 50 or over expected to increase by 38 % from 2019 to 2034,2 it is imperative that this issue is addressed and that innovative approaches for the identification of at-risk people are explored.

In the identification of people at risk of osteoporotic fractures, low bone mass density (BMD) is an important prognostic risk factor,6 and Dual Energy X-Ray Absorptiometry (DXA) is currently the gold standard for the identification of low BMD and the diagnosis of osteoporosis.7 While DXA is a safe and highly versatile technology, with excellent accuracy and precision,8 a DXA scan is not always easily or immediately accessible for patients. In addition, where DXA resources are limited, it is important to make optimal use of services by determining who will benefit most from priority access.9 Given its cost/benefit ratio, DXA has been deemed to be most beneficial when used selectively where there is evidence of risk factors for fracture.10 In determining risk for fracture, several tools have been developed, including the well-established FRAX algorithmic tool.11 FRAX is utilised in Ireland, with a 60 % increase in usage between 2011 and 2019.2 In addition, various other algorithms have been developed to support the determination of fracture risk, including the Osteoporosis Self-Assessment Tool (OST) index.12 The aim of using these tools is to maximise the sensitivity and specificity of fracture risk prediction, in order to correctly identify the people who will ultimately require treatment and prevention strategies.9,13 Despite the availability of these tools however, given our aging population, it is likely that services will continue to be under pressure, whilst the economic and social burden of osteoporotic fractures persist.

In light of the prevalent crisis in this area, the aim of this study is to consider the potential addition of bioimpedance analysis (BIA) to the toolbox for the identification of people who would benefit from treatment or prevention strategies for osteoporosis. Clinical bioimpedance is a quick, non-invasive and relatively low cost technology that can be performed using a portable device in any setting. The use of bioimpedance for the estimation of lean and fat mass is well accepted to have high validity, but although software associated with many devices provides a calculated estimate of bone mass, the usefulness of this measurement is currently uncertain.14 The aim of this study is to collect BIA-derived bone mass (BBM) data from a local sample population, and to examine the data in relation to some known risk factors for fracture. This study does not purport to determine the validity of BIA for the assessment of bone mass and risk of osteoporotic fracture, but simply to open the conversation on its potential use for point-of-care assessment of bone mass, and to lay some groundwork for further studies.

2

2 Methods

2.1

2.1 Participant recruitment

Ethical approval for the study was obtained from the university research ethics committee, and informed written consent was obtained. A convenience sample of participants, from the university community and from the local public, was recruited by face-to-face contact, e-mail to various university clubs and societies, and via social media and personal messaging apps. Participants were aged at least 18. As a safety precaution, exclusion criteria included the use of an implanted electrical device. Participants were also excluded if they had ever suffered an eating disorder, or anxiety in relation to body composition. The study included data from 124 participants (40 males and 84 females).

2.2

2.2 Body composition analysis

Body composition analysis was performed in venues convenient to the participants, including the exercise physiology laboratory (university), a local centre during a public health event, and the homes of some participants. When setting up the device in each venue, the spirit level was used to ensure the device was flat on the floor, and the feet were adjusted as required.

A Tanita MC780-MA (Tanita Corporation, Tokyo, Japan) portable body composition analyser with GMON Tanita Pro Health Monitor software, version 3.4.5 (Medizin & Service GmbH, Germany), was used. This is an 8-electrode, multi-frequency (5 kHz, 50 kHz and 250 kHz) analyser, which operates with the participant in the standing position. A number of studies have used this device to research the validity of BIA for the assessment of fat and lean mass.15–17 After measuring the participant's weight, electrical current is passed through the body, and impedance is calculated from reactance and resistance to the flow of current.18 This impedance, as well as the participant's weight, height, sex and date of birth, are incorporated into algorithms by the software, to calculate many body composition parameters including an estimation of bone mass. Assessment of body composition was performed according to the manufacturer's instructions.19 With shoes and socks removed, the participant stepped onto the platform, with their toes and heels in contact with the circular electrode plates. They then took up the hand grips, with all fingers making contact with the electrodes. They were instructed to hold their arms downward, slightly out from the body. Analysis was performed within 20s, after which the participant was asked to step down from the platform. Data was saved for analysis.

2.3

2.3 Height measurement

Participants’ height was measured using a SECA 213 portable stadiometer (Seca, Hamburg, Germany), according to the standard procedure.20 Participants were asked to stand on the base of the stadiometer, with their back to the vertical measuring stand. They were instructed to put their heels together with toes slightly outward, and as far as possible, to place their heels, buttocks, scapulae and head in contact with the measuring stand. They were then asked to stand tall and to look straight ahead, with their head in the Frankfurt Horizontal Plane. The horizontal measuring bar was lowered to the crown of their head, and their height, to the nearest 0.1 cm, was recorded.

2.4

2.4 Intra-assessment precision

Separately from the study protocol, 28 participants (11 males and 17 females, aged 20–22) attended for BIA assessment on two separate occasions, with 1 week between assessments, and similar measuring conditions each day. There was no change in BBM values between assessment 1 and 2 in 18 of the participants, while the remaining 10 participants had a difference in BBM of 0.1 kg between the 2 assessments, where BBM values were generated to 1 decimal place. Mean and standard deviation for each participant across the 2 assessments were calculated, followed by the % Coefficient of Variation (%CV) for each participant. Overall %CV between the two assessments, calculated as the average of the %CV for each participant, was 1.06 %.

2.5

2.5 Data analysis

Data was exported to Excel, and statistically analysed using IBM SPSS 27 software. Tables were generated using Microsoft Word, and figures were created using GraphPad Prism 10.3.1.

Descriptive statistics of body composition parameters in males and females were expressed as mean ± s.d., and compared using independent t tests.

BIA-derived bone mass (BBM) values were categorised according to weight and sex, as suggested by the device manufacturers.21 Assumptions for ANOVA were checked, including visual assessment of plots, Shapiro-Wilk test of normality and Levene's test for homogeneity of variances. Differences between groups were analysed by two-way ANOVA, and by one-way ANOVA followed by Scheffe's post-hoc test.

Hierarchial linear regression was performed, to study associations of BBM with known risk factors. Assumptions for regression analysis were checked using SPSS, and included visual examination of plots, examination of Variable Inflation Factors, casewise diagnostics for outliers, and the Durbin-Watson test. Where any of the assumptions were violated, hierarchial regression was not performed, and simple bivariate correlation between variables was used. For bivariate correlation, where variable data failed the Shapiro-Wilk normality test (p < 0.05), the Spearman's rho test was performed, with correlation coefficient denoted as rs.

For all hypothesis testing, significance was deemed as p < 0.05.

3

3 Results

3.1

3.1 Body composition of participants

The convenience sample population in this study included over twice as many females as males, and the average age of the females was significantly greater than the average age of the males, with the oldest male being 74 and the oldest female 83. Most parameters assessed by bioimpedance analysis were significantly different between males and females (Table 1).

Table 1 Bioimpedance analysis of males and females.
MALES (N = 40) FEMALES (N = 84)
Mean ± S.D. Range Mean ± S.D. Range P
Age (yrs) 33.5 ± 17.62 18–74 44.5 ± 21.10 18–83 0.003a
Height (cm) 177.5 ± 7.35 164.5–190.2 163.7 ± 6.66 145.0–181.0 <0.001a
Weight (kg) 80.8 ± 13.36 47.5–111.9 68.7 ± 13.99 41.5–124.1 <0.001a
Fat Mass (kg) 18.5 ± 7.58 6.1–39.7 22.8 ± 9.63 8.3–64.4 0.008a
% Fat 22.3 ± 6.11 8.6–35.5 32.0 ± 7.30 17–51.9 <0.001a
Muscle Mass (kg) 59.2 ± 7.82 36.6–78.4 43.5 ± 5.66 31.4–58.0 <0.001a
% Muscle 73.8 ± 5.83 61.2–87.0 64.4 ± 6.93 45.7–78.9 <0.001a
BMI (kg/m 2 ) 25.6 ± 3.92 16.2–34.6 25.6 ± 5.10 17.7–45.0 1.0
% Water 55.4 ± 3.83 46.6–62.1 46.4 ± 5.17 33.7–61.2 <0.001a
Bone Mass (BBM) (kg) 3.10 ± 0.39 2.0–4.0 2.34 ± 0.29 1.7–3.1 <0.001a
BMR (kcal) 1853.7 ± 244.68 1196–2522 1398.3 ± 183.07 1018–1895 <0.001a
Visceral Fat Score 6.9 ± 5.00 1–19 5.4 ± 3.53 1–18 0.100
Metabolic Age 37.0 ± 19.29 12–78 43.3 ± 19.56 12–85 0.095
Phase Angle 6.5 ± 0.74 4.8–8.3 5.4 ± 0.62 4.0–7.2 <0.001a
Sarcopenic Index (kg/m 2 ) 8.51 ± 1.13 4.86–11.22 6.69 ± 0.78 5.37–9.67 <0.001a
a denotes significant difference between males and females according to independent t-test, equal variances not assumed.
3.2

3.2 Association of BIA-derived bone mass (BBM) with risk factors for fracture

3.2.1

3.2.1 Categorisation: weight and sex

When participants were categorised according to weight and sex (Fig. 1), there was a highly significant effect of both weight [F(2, 118) = 74.5; p < 0.001] and sex [F(1, 118) = 153.3; p < 0.001], with a significant weight × sex interaction [F(2, 118) = 5.0; p = 0.008]. One-way ANOVA followed by Scheffe's post-hoc test showed that BBM was significantly different between all 3 wt categories (p < 0.001) for both males and females. Regarding assumptions for ANOVA, the dependent variable residuals of 2 of the 6 groups failed the Shapiro-Wilk test of normality (p > 0.05) (females of weight 50–75 kg; males of weight >95 kg). ANOVA was performed regardless, as all other assumptions were met, including Levene's test for homogeneity of variances (p > 0.05).

BIA-derived bone mass (BBM) in males and females across 3 wt categories. Horizontal lines indicate means. Number of participants shown under each group. All groups significantly different from all others within each sex (p < 0.001).
Fig. 1 BIA-derived bone mass (BBM) in males and females across 3 wt categories. Horizontal lines indicate means. Number of participants shown under each group. All groups significantly different from all others within each sex (p < 0.001).
3.2.2

3.2.2 Regression analysis: weight, height, sex and age

All of the regression models (Table 2) show that higher weight is associated with higher BBM, with weight contributing 63.7 % of the variance in bone mass, when no other variables were included (Model 1). Model 2 shows that 85.5 % of the variance in BBM was explained by weight and height, with taller people tending to have higher BBM. Model 3 shows that 90.9 % of the variance in bone mass was explained by height, weight and sex, indicating lower BBM in females than males. With weight and height controlled for, Model 3 predicts that females would have a BBM of 0.331 kg less than males. When only weight was controlled for, the difference in BBM between males and females was even greater (Model 4), again indicating a lower BBM in females than males. When weight, height and sex were controlled for, there was no significant association of BBM with age (not shown). However, with height removed from the model, and only weight and sex included, there was a significant negative association between age and BBM (Model 5), showing that older age is associated with lower BBM. The lack of significance when height was included in the model was possibly due to the negative association between height and age in our participants (rs(122) = −0.383; p < 0.001), with younger participants being taller than older participants (not shown). Age as a variable was positively skewed, as 67.5 % of males and 40.5 % of females were aged ≤29 (not shown).

Table 2 Association of weight, height, sex and age with bone mass.
Model R2 Adj. R2 Δ R2 Δ F p B t p 95 % C.I.
1 Weight 0.637 0.634 0.637 213.92 <0.001 0.026 5.46 <0.001 0.022, 0.029
2 Weight 0.855 0.852 0.218 181.36 <0.001 0.018 13.90 <0.001 0.015, 0.020
Height 0.027 13.47 <0.001 0.020, 0.031
3 Weight 0.909 0.907 0.055 72.70 <0.001 0.017 16.71 <0.001 0.015, 0.019
Height 0.016 8.01 <0.001 0.012, 0.020
Sex −0.331 −8.53 <0.001 −0.408, −0.254
4 Weight 0.861 0.859 0.224 195.47 <0.001 0.019 16.43 <0.001 0.017, 0.022
Sex −0.524 −13.98 <0.001 −0.598, −0.450
5 Weight 0.872 0.869 0.011 9.97 0.002 0.020 17.29 <0.001 0.018, 0.023
Sex −0.486 −12.73 <0.001 −0.561, −0.410
Age −0.003 −3.16 0.002 −0.004, −0.001
3.2.3

3.2.3 Correlation: BMI

In analysis of BMI, hierarchial regression was not performed, as models failed to meet the assumptions for regression. A significant correlation between BMI and BBM was found in both males (rs(38) = 0.482, p = 0.002), and females (rs(82) = 0.565, p < 0.001), with higher BMI associated with higher BBM (Fig. 2). The slope of the trendline was steeper in males (m = 0.062) than females (m = 0.036), with the relationships between BBM and BMI reflecting higher BBM values in males than females.

Correlation between BIA-derived bone mass (BBM) and BMI in males (n = 40; p = 0.002) and females (n = 84; p < 0.001).
Fig. 2 Correlation between BIA-derived bone mass (BBM) and BMI in males (n = 40; p = 0.002) and females (n = 84; p < 0.001).
4

4 Discussion

This study collected BIA-derived bone mass (BBM) data from a local population of males and females, and demonstrated significant associations with weight, height, sex and age – variables known to be associated with BMD, and relevant to fracture risk.

The BIA device manufacturers suggest that bone mass values may be referenced to 3 different weight categories for males and females.21 Using this approach, it was found that both weight and sex had a highly significant impact on BBM. Similarly, in regression analysis, weight consistently showed a significant positive association with BBM across all 5 models. It is well-known that weight is highly associated with BMD, and for this reason, weight is included in many of the algorithmic tools for the prediction of bone mass.22 In relation to sex, males had a significantly higher BBM than females, even when weight and height were controlled for (Model 3). It is important to remember however, that BBM values are derived from fat-free mass (discussed below), which is known to be higher in males than females,23 and would also be expected to relate to total weight. Therefore, these associations cannot be taken as direct evidence that BBM is representative of actual bone mass.

The relationship between height and bone health is complex. Increased height is a risk factor for fracture.24 However, there is also some evidence that height is positively associated with BMD,25 even when loss of height due to osteoporotic deformities is not at play.26Table 2 shows that, in our participants, height was a significant predictive factor of BBM, independent of weight, with taller individuals tending to have higher BBM. As BBM values estimate total bone mass, it would be expected that a taller person may have a larger skeleton and thus greater bone mass.

Related to weight and height is BMI, but by adjusting weight for height, BMI reduces the variability due to sex and ethnicity.27 As low BMI is a well-known risk factor for osteoporotic fracture,27 as expected, there was a highly significant correlation between BMI and BBM. This was evident in both males and females, but for any given BMI, BBM tended to be higher in males than females.

While there was a significant negative association of BBM with age, it was very small (Model 5). In this study, the population sample was skewed toward the younger ages, with 36.3 % of participants aged ≤22, and only 18.5 % of participants aged >65 (not shown). Also, as all participants in the study had good mobility and participated in the assessment independently, older people may not have been fully represented here. Further studies would need to include a greater number of older participants in different settings. Also in relation to age, when height was included in the regression model, the significant effect of age disappeared (not shown), reflecting a negative correlation between height and age amongst our study participants. As this could be due to height loss resulting from vertebral deformities in older participants, it was reasonable to remove height from the model investigating age.

The concept of using BIA to screen for osteoporosis was posited previously by Patil and colleagues, who validated a BIA equation, with 91.36 % correlation with DXA, for the prediction of bone mineral content (BMC).28 A similar approach was taken by a team of researchers in Brazil, who investigated the usability of BIA for the prediction of BMC in children and adolescents with HIV. By including a range of physical and clinical characteristics, they were able to achieve a model with a higher predictive value for BMD than provided by the internal equations in the BIA device.29 The successful development of BIA for use in these specific populations indicates that there is potential for the use of BIA in the assessment of bone health.

In this study, it was observed that BBM values approximated 5 % of the fat-free mass (data not shown). This estimate of bone mass relies on the well-established association between bone mass and fat-free mass (or lean body mass), which has been demonstrated in many studies.30,31 In relation to a specific calculation of bone mass as approximately 5 % of fat-free mass (FFM), there is substantial evidence in the literature to support this approximation. Early studies based on the ashing of human cadavers, as well as densitometric calculations, reported that BMC constituted 5.6 % FFM.32 Later studies using absorptiometry techniques in living subjects generally supported this approximation, with the assumption that BMC values from DXA approximate ashed bone.33 It is likely however that a rigid calculation, such as 5 % FFM, provides only a rudimentary estimate of actual bone mass, given that there are differences due to factors such as race, gender and athleticism.33,34 For example, using DXA, Withers and colleagues reported a combined mean bone mineral mass (BMC x 1.0436 to account for loss of mineral in the ashing process) of 5.91 % (of FFM), with higher values in women than men, and sedentary women having a higher mean value than trained women (6.34 % vs 5.93 %).33 Despite these complexities however, given the established association between bone and lean body mass, a BIA-derived estimate of bone mass may be sufficiently accurate to serve as a screening tool for the identification of people at risk of osteoporotic fracture, especially if it is used in conjunction with other tools.

5

5 Conclusion

Although BIA is not currently used to screen for osteoporosis, this study in our local population suggests that it has potential in this area. BBM values were shown to be significantly positively associated with weight, height and BMI, whilst being negatively associated with age. Males had higher BBM values than females, even after controlling for weight and height. This is only an early insight, and larger studies, to include greater numbers of participants across all ages, would be imperative to progress knowledge on this area. Future studies would need to include DXA scans in conjunction with BIA, in order to test the validity of the BBM data and eventually to establish normative values for specific populations. In light of the current treatment gap, and the crisis in osteoporosis management, it is important to consider all options for inclusion in the toolbox, including bioimpedance analysis.

CRediT authorship contribution statement

Louise A. Horrigan: Conceptualization, Methodology, Project administration, Writing – original draft. Mairead Cooke: Investigation, Writing – review & editing. Jessica Diskin: Investigation, Writing – review & editing. Attracta Brennan: Formal analysis, Visualization, Writing – review & editing. John J. Carey: Supervision, Writing – review & editing.

Participant consent

Informed written consent was obtained from all participants in this study.

Ethical statement

Ethical approval for this study was granted by the University of Galway Research Ethics Committee (Ref. no. 2022.11.009) on Jan 10, 2023. Procedures were performed in compliance with relevant laws and institutional guidelines.

Funding statement

No funding was provided for this project, and there are no financial sponsors to declare.

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