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Article

Sex and Obesity-Specific Associations of Ultrasound-Assessed Radial Velocity of Sound with Body Composition

by
Simona Sulis
,
Darina Falbová
,
Radoslav Beňuš
*,
Petra Švábová
,
Alexandra Hozáková
and
Lenka Vorobeľová
*
Department of Anthropology, Faculty of Natural Sciences, Comenius University in Bratislava, 82105 Bratislava, Slovakia
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2024, 14(16), 7319; https://doi.org/10.3390/app14167319
Submission received: 29 July 2024 / Revised: 14 August 2024 / Accepted: 16 August 2024 / Published: 20 August 2024
(This article belongs to the Section Biomedical Engineering)
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Abstract

:
Background: This study aimed to investigate the association between body composition (BC) specific parameters such as fat mass (FM) and lean body mass (LBM) and bone quality in obese and non-obese young Slovak adults (18–30 years) using bioelectrical impedance analysis and quantitative ultrasound while considering the factors of sex and obesity status; Methods: BC was evaluated using bioelectrical impedance analysis (InBody 770) and bone quality by the speed of sound (SOS) in radius using QUS (Sunlight MiniOmni) in 774 young Slovak young adults aged 18–30 years, categorized by sex and obesity status (body fat %, PBF, ≥28% for men and ≥20% for women); Results: In non-obese individuals, radial SOS correlated positively with FM parameters. Significant positive correlations with LBM, including skeletal muscle mass (SMM), were observed in non-obese men but not in women. Conversely, SOS correlated negatively with FM parameters in obese individuals, significantly only in women. The correlation of SOS with LBM in obese individuals was insignificant in both sexes. Age, visceral FM, FM in the arm, and vitamin D intake were identified as the main predictors of radial SOS in young adults, except in obese men. Conclusions: Findings indicate FM benefits bone tissue in non-obese individuals, while excessive adiposity deteriorates bone quality in obese individuals, necessitating tailored evaluations and interventions based on sex, obesity status, and specific predictors.

1. Introduction

Maintaining the integrity of bone tissue is critical to its function and adaptability, particularly during skeletal growth. Behaviors associated with osteoporosis, such as physical inactivity and poor nutrition during childhood, adolescence, and young adulthood, can reduce peak bone mass and increase the risk of osteoporosis later in life [1]. A decrease in bone quantity, particularly bone mass and bone mineral density (BMD), increases the susceptibility of bones to deformity and fragility [2]. Over the last three decades, research has shown that bone quantity alone does not increase fracture rates [3,4]. Therefore, there is an increased interest in the factors that influence bone quality, including composition, structure, microdamage mechanisms, and remodeling processes. Clinical evaluation of bone fragility and fracture risk typically relies on the measurement of bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). However, recent research indicates that DXA-derived BMD correlates poorly with fracture resistance in adults and children [3]. Multisite quantitative ultrasound (mQUS), a non-invasive diagnostic tool that measures the speed of sound (SOS) in the bone, is an alternative method for assessing bone health and predicting fracture strength. This technique has evolved significantly and offers advantages over DXA, such as affordability, portability, and the ability to target specific anatomical sites without ionizing radiation. Research suggests that mQUS can detect aspects of bone quality not captured by DXA, reflecting both bone density and qualitative properties, including elasticity and trabecular microarchitecture [5,6,7]. Therefore, mQUS can potentially assess bone health and integrity [6]. In particular, radius QUS is considered a reliable method in primary healthcare settings to assess fracture risk and to perform osteoporosis pre-screening [8,9,10]. The mQUS showed promise in predicting fractures in the radius of 10 to 12-year-olds, indicating its potential as a screening tool for bone fragility. Although it is not consistent with the whole body without head (WBLH) BMD measurements from the DXA [11], it remains a valuable resource. A recent meta-analysis by Fu et al. [12] confirmed these findings and showed an association between measured SOS and the risk of hip fractures, particularly in women.
Bone mass is influenced by many factors, with two main body components being the most important determinants: fat mass (FM) and lean body mass (LBM). LBM, which represents the total body weight excluding fat, is crucial for bone health [13,14]. Skeletal muscle, which makes up a large portion of the LBM, functions as an endocrine organ that secretes muscle factors, such as insulin-like growth factor 1, fibroblast growth factor 2, and brain-derived neurotrophic factor. These substances influence bone metabolism through interactions with osteoblasts and osteoclasts [15,16]. Although a recent systematic review and meta-analysis demonstrated a positive association between LBM and BMD [17], the impact on bone quality as defined by the SOS parameter remains unanswered.
Furthermore, the conclusions regarding the relationship between FM and bone tissue properties are still not clear. Mosca et al. [18] observed that high amounts of FM had a negative effect on bone mass, while Dolan et al. [19], who stratified the samples by age, found this negative effect only in individuals under 25 years of age. In contrast, the effect was positive in older people. This finding was also confirmed by Deng [17], who pointed out that body fat (%) appears to have a negative effect on bone formation in children and adolescents. In addition, a longitudinal study of adolescents aged 11–19 years found a negative effect of FM on cortical bone strength in boys [20]. Recent evidence suggests that FM may affect bone health through inflammation, oxidative stress, and the shared origin of adipocytes and osteoblasts from mesenchymal stem cell progenitors [21]. Other studies have also suggested that it is important to consider body fat distribution when assessing the effects of FM on bone mass, as subcutaneous and visceral fat in the abdominal region may have different effects on bone tissue [22,23].
Furthermore, it can be assumed that not only age category and sex are important determinants of the relationship between bone mass and body composition (BC) parameters, but that obesity also plays an important role. In a cohort of non-obese postmenopausal women over 60 years of age from China, total FM was positively associated with BMD, with an increase in BMD observed with increasing FM [24]. When analyzing obese and non-obese subgroups in the study by Kim et al. [25], the positive correlation between FM and BMD disappeared in obese adults. In addition, FM and BMD showed a negative correlation in premenopausal obese women. The authors concluded that FM is not beneficial in obese individuals and may even be detrimental to bone health. Later, Mosca et al. [18] pointed out that the higher the PBF, the lower the BMD in male adolescents with obesity. From this, the study concluded that the decrease in bone mass in overweight, obese, and extremely obese adolescents was influenced by an increase in PBF. We therefore suggest that these different results emphasize the importance of assessing bone quality and bone tissue properties differently in obese and non-obese individuals, depending on the level of FM.
There is evidence of a correlation between the amount of bone measured by DXA BMD and BC parameters. However, current research rarely focuses on investigating the effect of BC on bone tissue properties measured by mQUS SOS in young adults, so clear conclusions in this area are lacking. Current data only suggest a lower tibial SOS in female adolescents with higher adiposity [26]. Furthermore, there are currently no studies investigating the differential effects of BC on bone quality measured by SOS, specifically in obese and non-obese young adults. However, existing literature [27] suggests that there may be different associations between bone tissue and BC in adolescents with FM within and above normal limits. Therefore, this study aimed to explore the relationships between SOS (bone tissue parameter from the transaxial mQUS) and specific bioelectrical impedance analysis (BIA) parameters (e.g., FM and LBM), taking into account sex and obesity status. Adjustments for osteoporosis-related behavioral and lifestyle factors were also included in the regression analyses.

2. Materials and Methods

2.1. Participants

The study examined 994 Slovak young adults aged 18 to 30 years, with an average age of 21.61 ± 2.32 years. A non-random selection was made for the study from 2019 to 2023, and the participants volunteered for the examinations at the Biomedical Laboratory, Department of Anthropology, Comenius University, Bratislava, Slovakia. Participants’ data (name, date of birth, and sex) were collected, and each participant was assigned an ID number to ensure anonymity. Written informed consent was obtained in accordance with the guidelines of the Declaration of Helsinki, and the study was approved by the Ethics Committee of the Faculty of Natural Sciences, Faculty of Comenius University (approval number ECH19021). Exclusion criteria included chronic or acute diseases affecting bone tissue and osteoporosis (n = 157), such as thyroiditis, Crohn’s disease, liver problems, type 1 diabetes, eating disorders, epilepsy, cancer, celiac disease, and treatments affecting the bone tissue. The final sample of 774 people was, on average, 21.60 ± 2.32 years old, including 504 women (mean age 21.26 ± 2.19) and 270 men (mean age 22.23 ± 2.41) (Figure 1).

2.2. Questionnaire

A standardized and validated questionnaire (modified WHO expert questionnaire from 2014—STEPwise approach to surveillance—instrument version 3.2 [28] in Slovak language) was used to collect baseline characteristics and sociodemographic information; the data collected were subjective in nature, as they were based on the perceptions, memories and judgments of the participants. Lifestyle variables were collected by self-report and personal interview. Data on smoking status, alcohol drinking, coffee consumption, sweetened beverage consumption, physical activity, calcium, and vitamin D intake were all collected by a “yes” or “no” question. To obtain more in-depth information on smoking, alcohol consumption, coffee drinking, and sweetened beverage consumption, a question on the frequency of consumption was asked with seven response categories: daily, 5–6 days per week, 3–4 days per week, 1–2 days per week, 1–3 days per month, less than once a month, and never. For further analysis, three categories of physical activity were defined based on the World Health Organization minimum activity guidelines for adults aged 18–64. Tier 0 includes individuals who do not meet the minimum guidelines, engage in less than 300 min per week of moderate activity or 150 min per week of vigorous activity, and only occasionally participate in activities, such as walking to work. Tier 1 includes individuals who engage in 150–300 min of moderate or 75–150 min of vigorous activity per week or more than 300 min of moderate or 150 min of vigorous activity per week, but only one to two times a week. Tier 2 included individuals who regularly engaged in more than 300 min of moderate or 150 min of vigorous activity per week and training approximately three times weekly [29]. The questions on physical activity were included in Step 1 of the STEPS Instrument, which distinguishes between moderate and vigorous activity.

2.3. Body Composition Analysis

The InBody 770 analyzer uses segmental multi-frequency bioelectrical impedance analysis to measure body composition by sending low-level electrical currents through the body and assessing the resistance in various tissues. It measures LBM and FM for total body, trunk, and arm both in percentage and kilograms, FFMI, FMI, phase angle, visceral FM in squared cm, PBF in percentage, as well as total body BCM, SMM, and FFM in kilograms in both sexes (Biospace Co., Seoul, Republic of Korea) as shown in validation studies with DXA, where a 98% correlation between the two techniques was achieved [30]. To obtain reliable results, the measurements were performed under controlled conditions. Participants rested in the morning without being physically active in the eight hours before the measurement, did not consume any significant amount of water or food in the three hours before the examination, stood barefoot on the pedal plate electrode, and held the hand electrode at a 15° angle to avoid contact between the arm and torso. The InBody 770 integrated scale measured the body weight to the nearest 0.1 kg.
The study sample was categorized by sex and obesity status (PBF ≥ 28 for women; PBF ≥ 20 for men) using the cutoff points provided by the InBody 770 analyzer in the data output. We used the PBF to define obesity instead of the traditional BMI. The PBF provides a more accurate assessment of obesity than BMI, which cannot distinguish between SMM and FM, potentially categorizing muscular individuals as overweight or obese and individuals with low muscle mass as healthy [31].

2.4. Anthropometric Analyses

Trained anthropologists have measured anthropometric data using internationally validated techniques [32]. Height was measured with a Sieber and Hegner anthropometer with an accuracy of 0.5 cm, with participants standing upright, feet together and barefoot. Hip and waist circumferences were measured with a tape measure (Seca, Hamburg, Germany). The participants stood upright with a relaxed abdomen, feet together, and arms crossed in front of their chest. The operator, standing in front of the proband, applied the tape measure to the narrowest part of the abdomen, taking care not to compress the tissue, and measured by standing on the proband’s right side. The measurements were taken at the end of normal exhalation. During the hip measurement, the subject also stood with arms crossed over the chest and feet together. The operator stood on the right side of the proband and placed a measuring tape on the maximum circumference of the buttocks. Body mass index (BMI) was calculated by dividing body weight in kilograms by the square of height in meters. The waist-to-hip ratio (WHR) was calculated by dividing the waist circumference by the hip circumference.

2.5. Radius Quantitative Ultrasound

Bone quality was assessed by quantitative ultrasonography (QUS) with a Sunlight MiniOmni device (BeamMed, Inc., Plantation, FL, USA). The hand-held probe probed the optimal measurement point in the distal third of the left radius (non-dominant), and three repeated measurements were performed to ensure accuracy. The device measured the speed of sound (SOS) through the radius, with higher SOS values indicating increased bone density and providing information on cortical thickness, microstructure, and elasticity of the bone. QUS is a nonionizing technology that measures the time between the transmission and reception of ultrasound pulses as they move beneath the bone surface. The probe head contains the transmitting and receiving crystals, which are positioned at a precise distance from each other. For cross-bone measurements, this distance is adjusted based on the thickness of the site. The transmitted pulse passes through soft tissue and enters the bone at a critical angle. This is defined by Snell’s laws, which dictate the wave refraction at the interface between soft tissue and bone. With this method, the influence of the tissue on the bone measurements is completely eliminated. This has been confirmed by tests with horses, in which the differences between the bone measurements with and without tissue were minimal. This results in highly precise and repeatable measurements, making the method ideal for monitoring disease progression and an effective screening tool.

2.6. Statistical Analysis

Statistical analyses were performed using Jamovi (version 2.3) and SPSS (version 20) for Windows, with a p-value < 0.05 considered statistically significant. Descriptive statistics and assessment of normality using the Kolmogorov–Smirnov test were performed for the study sample categorized by sex and obesity status. The means and standard deviations were calculated for continuous variables (age, SOS, BC, and anthropometric data) and percentages for categorical variables (behavioral and lifestyle data). Differences in categorical variables between non-obese and obese individuals were tested using Pearson’s chi-square test in contingency tables for both sexes. Statistical comparisons between the obese and non-obese groups in normally distributed continuous data were analyzed using the independent Sample T-test, whereas non-normally distributed continuous data were assessed using the Mann–Whitney U test in both sexes. Based on the normality assessment, Spearman or Pearson’s correlation was used to analyze the correlation between continuous variables. Forward linear regression analyses were used to examine the association between SOS and the following independent variables: age, smoking, alcohol and sweetened beverage consumption, coffee consumption, Ca and vitamin D intake, physical activity, BMI, PBF, FM (%) in the arm and trunk, and visceral FM. Only predictors with a p-value of less than 0.05 were associated with the SOS parameter.

3. Results

The baseline characteristics of the entire sample are shown in Table 1, which was initially divided into men and women and then into obese and non-obese groups, which was pre-planned given the literature on the topic. It encompasses the age, SOS bone parameter, anthropometric, and BC parameters. A statistically significant differences between obese and non-obese men were observed in the following anthropometric and BC variables: weight (kg), BMI, WHR, PBF, FM (kg), FM in the arm and trunk (kg, %), visceral FM (cm2), LBM in the arm and trunk (%) and phase angle (with a p-value of less than 0.001 for all parameters). Similar results were also found when comparing obese and non-obese women for the following variables: age, weight, BMI and WHR, PBF (p < 0.001; %), FM, FM in the arm and trunk, visceral F, FFM (p = 0.009; kg), FFMI, LBM (p < 0.001; kg), LBM in the arm, LBM in the trunk, SMM (p < 0.001; kg) and BCM. SOS averaged 4034 ± 113.98 m/s in non-obese men and 4024.7 ± 107.64 m/s in obese men (p = 0.550), whereas SOS values averaged 4071.3 ± 106.53 m/s in non-obese women and 4073.3 ± 102.76 m/s in obese women.
The analysis of the frequency of smokers, consumers of coffee, alcohol, and sweetened beverages, users of vitamin D and Ca supplements, and physically active individuals in obese and non-obese groups in both sexes is documented in Table 2. A statistically significant result was only observed for the lifestyle parameter, physical activity, in the male group, while, as expected, there were more regular exercisers (tier 1 and 2) in the non-obese group (54% vs. 26%). Among women, statistically significantly more smokers (28% vs. 16%) and less vitamin D and Ca consumers (24% vs. 34%, 8% vs. 16%, respectively) were analyzed in the obese women group. Finally, significantly more women in the non-obese group performed regular physical activity (tier 2) compared to the obese group (26% vs. 16%).
The correlation analysis documented in Figure 2 revealed a significant positive correlation between SOS and the following parameters in non-obese men: LBM (kg; r = 0.15, p = 0.043), LBM in the trunk, FFM (r = 0.18, p = 0.015; kg), SMM (r = 0.17, p = 0.017; kg), BCM, BMI, FM (r = 0.19, p = 0.007; kg), FM in the trunk, FM in the arm (r = 0.15, p = 0.038; kg; r = 0.14, p = 0.048; %), visceral FM (r = 0.24, p < 0.001; cm2), FMI, body weight and height; this indicates that higher values of the above body composition variables are associated with better bone tissue quality. However, no significant correlation was observed between SOS and BC parameters in obese men. Nevertheless, it is worth noting that, with the exception of LBM % at the trunk, LBM % at the arm, FFMI, SMM (kg), BCM (kg), phase angle (°) and weight (kg), the correlation coefficient was negative in obese men; this means that lower values of these body composition parameters are associated with higher SOS values and thus better bone quality; specifically, the correlation between SOS and BMI (r = −0.05; kg/m2), WHR (r = −0.15), FM (r = −0.05; kg), FM in the trunk(r = −0.05; % and kg), FM in the arm (r = −0.05; kg; r = −0.06; %), PBF (r = −0.06; %), FMI (r = −0.03), FFM (r = −0.03; kg) and visceral FM (r = −0.07; cm2).
Similarly, in non-obese women, significant positive correlations were observed between SOS and FM in the trunk, arm (r = 0.12, p = 0.032; %), PBF (r = 0.13, p = 0.023; %), and FMI, suggesting that higher values of FM-associated parameters were found in women with higher SOS. The only significant negative correlation found was with height (r = −0.12, p = 0.038; cm); thus, individuals with a higher height had a lower SOS. In contrast, a significant negative correlation was observed between SOS and BC parameters in obese women, particularly FM (r = −0.16, p = 0.025; kg), FM in the trunk, FM in the arm (r = −0.16, p = 0.023; %; r = −0.15, p = 0.032; kg), FMI, visceral FM (r = −0.15, p = 0.028; cm2) and between SOS and anthropometric parameters such as weight and BMI (r = −0.16, p = 0.025; kg/m2); indicating lower bone quality in those with higher values of FM-associated parameters (Figure 3).
Linear regression analysis revealed significant predictors of SOS (m/s), adjusted for age and behavioral and lifestyle factors, in non-obese men, non-obese women, and obese women but not in obese men (Table 3). The Durbin–Watson test revealed no autocorrelation. Among non-obese men, age (p = 0.001; B = 11.613) and visceral FM (p = 0.003; B = 1.546; cm2) were significant predictors of SOS (m/s). The R2 value in the regression model with SOS (m/s) as the dependent variable was 0.110, emphasizing that the independent variables explained 11% of the variability in SOS (m/s) and confirmed the effects of age (y) and visceral FM (cm2). In obese men, none of the independent variables we selected—age, percentage of FM in the arm, vitamin D intake, and visceral FM—were significant predictors of SOS. In non-obese women, age (p < 0.001; B = 16.634), percentage of FM in the arm (p = 0.038; B = 0.637), and vitamin D intake (p = 0.043; B = 26.057) were significant predictors. The R2 value in the regression model with SOS (m/s) as the dependent variable was 0.156. The results indicated that the independent variables were responsible for 15.6% of the variation in SOS (m/s) and confirmed the impact of age, percentage of fat in the arms, and vitamin D consumption on SOS (m/s). Finally, in obese women, only age (p < 0.001; B = 13.463) and visceral FM (p = 0.014; B = −0.406; cm2) were significant predictors. The R2 value of the regression model, with SOS (m/s) as the dependent variable, was 0.105. Thus, the independent variable accounted for 10.5% of the variation in SOS (m/s) and corroborated the effect of age (y) and visceral FM (cm2). Notably, the B coefficient for visceral FM was positive in non-obese men, whereas it was negative in obese women, suggesting an inverse relationship in this group. However, other factors, including smoking status, alcohol consumption, coffee drinking, sweetened beverage consumption, physical activity, calcium intake, BMI, PBF, and FM, were not significant predictors in any category. These results emphasize the different effects of age and FM distribution on bone tissue quality in the different groups according to sex and obesity status.

4. Discussion

4.1. Fat Mass and Bone Tissue

The relationship between FM and bone health in the context of obesity is complex and multifaceted. In particular, visceral FM has been associated with lower BMD and higher fracture risk, which may be due to factors such as systemic inflammation and changes in bone-regulating hormone levels [21,33]. In detail, the negative effect of high visceral FM on bone tissue is likely due to its association with increased inflammatory cytokines, which alter bone metabolism, and its influence on circulating levels of specific adipokines such as E-selectin and adiponectin, which may mediate the negative relationship between visceral FM and BMD [34,35]. In addition, Wang et al. [36] discovered that visceral FM was related to decreased BMD in 320 Chinese women aged 19–86 years, supporting our results regarding the SOS parameter. According to a community-based study of 4865 men and women aged 45–70 years, higher visceral FM was associated with lower BMD, even in women with normal BMI (≤24.9 kg/cm2), suggesting that non-obese individuals are also affected by the negative effects of visceral FM on bone health [37], which is not consistent with our findings. Considering that our results showed an interesting trend of higher SOS with a higher amount of visceral fat in the non-obese.
The available literature provides mixed results. Some studies show an association between obesity and increased bone mineral density, suggesting that the effect of excess body fat on bone health may be site-specific and influenced by mechanical loading [33,38]. On the other hand, several previous studies have conversely shown that excessive accumulation of adipose tissue, particularly visceral fat, may negatively affect bones through inflammation, oxidative stress, and altered endocrine function [39,40].
Thus, the above data highlight the importance of controlling fat distribution to optimize bone tissue quality, particularly in obese individuals, and suggest that interventions should focus on reducing excess FM while considering the potential benefits of moderate fat content. The relationship between total FM and BMD varies across demographic categories, including age, ethnicity, and bone sites. In adults, the impact of FM on BMD appears to exhibit sex-specific differences [41]. In postmenopausal women, a positive correlation was observed between FM and BMD at various bone sites, including the spine, femur, and total body. This finding suggests that higher FM may be associated with higher BMD [42]. However, in older men, higher total adiposity correlates with lower cortical BMD, whereas a higher percentage of subcutaneous adipose tissue is associated with higher cortical and trabecular BMD [43].
Contrary to these findings, Yao et al. [44] showed that total body fat was positively associated with BMD in younger girls but inversely associated with BMD in older boys, with abdominal adiposity being associated with lower BMD in both older boys and girls. Yerges-Armstrong et al. [43] reported that different fat deposits may have different associations with bone mass, with total adiposity correlating with lower cortical BMD, while a higher percentage of subcutaneous adipose tissue is associated with higher cortical and trabecular BMD. Interestingly, Douchi [45] found that upper-body fat distribution rather than total adiposity was associated with lumbar spine BMD in premenopausal women, which may suggest a similar effect in non-obese young adults. In contrast, Hage et al. [46] found that trunk fat mass had no effect on BMD of the non-dominant arm in adolescent girls, which may indicate a different relationship between non-weight-bearing sites and weight-bearing sites, such as the hip.
In addition, a study of postmenopausal women in Malaysia found that total adiposity was inversely associated with total BMD. However, regional associations varied, and no differences were found between Malay, Chinese, and Indian ethnicities [47]. The lack of ethnic differences in the association between adiposity and BMD in a multi-ethnic cohort [47] suggests that this relationship may be consistent across different populations.
To summarise, obesity is thought to protect against osteoporosis. However, recent research has shown the detrimental effects of excessive visceral FM on bone tissue. This is supported by studies showing a negative correlation between visceral FM and BMD, regardless of total FM [35,48]. The exact mechanisms by which visceral adiposity affects bone health are not yet fully understood. Further research is needed to understand the interaction between adipose tissue and bone quality, particularly in the context of an increasingly obese population [21].

4.2. Fat-Free Mass and Bone Tissue

In the four groups studied (obese and non-obese women and men), a significant positive correlation between SOS in the radius and LBM parameters was observed only in non-obese men, emphasizing the positive effect of lean mass on bone quality. In particular, total LBM, LBM in the trunk, SMM, and BCM were all positively correlated with SOS, supporting the notion that lean mass contributes to bone tissue quality in non-obese men. In contrast, radial SOS was negatively correlated with LBM in obese men, suggesting that SOS tends to decrease with higher LBM; however, the correlation was not significant. However, the differential effect of LBM on bone tissue between obese and non-obese men suggests that lean mass favors bone quality more in the absence of high-fat mass. Furthermore, the correlation of SOS with most LBM-associated parameters in women was insignificantly negative in both the obese and non-obese groups.
Our results in men are consistent with those of previous studies that showed a positive association between LBM and bone tissue characteristics. For example, LBM significantly predicted SOS in young Chinese adults [49]. The existence of an interaction between bone and muscle was also evidenced by the bone loss due to SMM decline investigated in the study by Chen et al. [50], who found that the reduction of bone mass in the limbs of older adults was positively associated with loss of skeletal muscle. In addition, the study reported that individuals with a low skeletal muscle mass index (SMI) had a higher prevalence of osteoporosis and lower bone mass at various sites than the control group. Interestingly, Chen et al. [50] focused on the correlation between bone mass and SMM in older adults, while Taniguchi et al. [51] also supported the notion of an association between osteoporosis and SMM loss, particularly in community-dwelling older women. However, Taniguchi et al. [51] found no significant association between osteoporosis and muscle strength, suggesting that the loss of muscle mass rather than muscle strength may be more closely related to bone density. Boot et al. [52] reported that peak BMD and peak LBM are reached in late adolescence to early adulthood, with a significant association between BMD and fracture risk, suggesting that LBM may play a role in bone strength. Patalong-Wójcik et al. [53] also found a significant association between muscle strength, which is closely related to LBM, and bone mineralization in young adult women. Finally, Zhu et al. [54] showed that handgrip strength, which is an indicator of muscle mass, is strongly correlated with LBM and moderately correlated with BMD. However, it is important to note that although these studies show a positive correlation, the relationship between LBM and bone tissue characteristics may be complex and influenced by various factors such as age, sex, and health status. For example, Li et al. [16] pointed out that the diabetic subgroup did not show the same robust association as other subgroups, and Arden and Spector [55] suggested that the genetic component of BMD is not significantly reduced after adjustment for lean mass and muscle strength, suggesting that other genetic factors may also play a role in determining BMD.
Literature indicates that LBM positively influences bone density and strength through endocrine interactions between muscle and bone, muscle contraction, and physical activity, including specific exercise regimens, which are closely related to mechanical bone stress [56,57,58]. Mechanical loading has recently been considered as the main factor influencing bone strength, as the muscles associated with bones produce small levers that require significant pressure to generate torque. The mechanical loading from these forces stresses the bones [59,60]. Moreover, muscle strength, which is closely related to LBM, has been found to be directly related to BMD, suggesting that the interaction between muscle and bone is mediated by both mechanical and endocrine pathways [57]. Recent studies have proposed the bone-muscle unit hypothesis, according to which skeletal muscle influences bone formation and density through mechanical loading, which primarily promotes osteocyte signaling pathways [61,62]. These pathways involve both cellular and molecular activities, including the production of circulating components such as osteocalcin, the hormone produced exclusively by mature osteoblasts and osteocytes, which plays a role in bone calcification [63]. Additionally, the relationship between muscle strength and BMD appears to be influenced by sex and age, with variations observed in response to hormonal treatments in transgender individuals [64] and in the context of age-related hormonal changes [65]. The results of these studies demonstrate a positive association between lean body mass and bone tissue characteristics, particularly BMD. This relationship is observed in different populations and suggests that LBM is an important determinant of bone health. However, the complexity of the relationship requires further investigation to fully understand the underlying mechanisms and the influence of other factors.

4.3. Study Limitations

The main limitation of this study was its cross-sectional design, which prevented causal inference. The demographic composition of the participants may affect the generalizability of the results, although this is also an advantage, as the majority of participants were young and healthy, minimizing the influence of confounding factors such as comorbidities or menopause in women, which could affect the quantity and quality of bone tissue in older individuals. Self-reported environmental factors, such as physical activity, alcohol consumption, calcium intake, and vitamin D, are subjective and may not be adequately accounted for. This study also lacked comprehensive dietary data, detailed information on dietary supplementation, and blood levels of calcium and vitamin D, which could shed light on whether the observed associations were mediated by diet quality and specific nutrients. In addition, the sample size, particularly among men, was a limitation. However, further studies are required to confirm these findings. Finally, measuring BMD, FM, and LBM at a single time point could not account for long-term changes; therefore, a longitudinal study is required to better understand these interactions over time.

5. Conclusions

Our study indicates that in non-obese individuals, higher levels of FM are associated with better bone tissue quality, as shown by the positive correlations between FM and SOS. Conversely, in obese individuals, particularly in obese women, excessive adiposity has a negative effect on bone quality, as evidenced by significant negative correlations between FM and SOS. This suggests that the influence of FM on bone quality varies greatly depending on the level of adiposity. Furthermore, our results emphasize the positive role of LBM in improving bone health in men, especially in non-obese men, in whom LBM, SMM, and BCM correlated positively with SOS. However, these positive effects were not found in obese men, where no significant correlations between LBM and SOS were observed. Regression analysis revealed that age and visceral FM were significant predictors of SOS in both non-obese men and obese women, albeit with opposite effects, with visceral FM positively predicting SOS in non-obese men but negatively predicting SOS in obese women. In non-obese women, the percentage of FM in the arm and vitamin D intake were also found to be significant predictors of SOS, emphasizing the need for sex- and obesity-specific interventions to maintain or improve bone quality. These findings underscore the importance of tailored approaches to assess and improve bone tissue quality, particularly considering the differential effects of FM and LBM depending on obesity status and sex. Future research should focus on exploring additional factors, such as lifestyle and dietary habits, which could further influence bone health outcomes in different populations.

Author Contributions

Conceptualization, L.V., D.F. and S.S.; methodology, L.V.; formal analysis, S.S., L.V. and P.Š.; investigation, S.S., L.V., D.F. and A.H.; resources, L.V., D.F. and R.B.; data curation, S.S., L.V., D.F. and A.H.; writing—original draft preparation, S.S.; writing—review and editing, L.V., P.Š., A.H. and D.F.; visualization, L.V. and S.S; supervision, L.V. and R.B.; project administration, L.V.; funding acquisition, L.V. and D.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Cultural and Educational Grant Agency (KEGA 046UK-4/2023) of the Ministry of Education, Science, Research and Sport of the Slovak Republic.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Faculty of Natural Sciences of Comenius University (approval number ECH19021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The research data are not shared.

Conflicts of Interest

The authors declare no conflict of interest.

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Applsci 14 07319 g001
Figure 1. Flow diagram of the inclusion criteria and exclusion criteria.
Figure 1. Flow diagram of the inclusion criteria and exclusion criteria.
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Figure 2. Correlation of anthropometric and body composition parameters with SOS in men. * Marks statistical significance p < 0.05. ** Marks statistical significance p < 0.01. Abbreviations: LBM, Lean Body Mass; FFMI, Fat-Free Mass Index; FFM, Fat-Free Mass; SMM, Skeletal Muscle Mass; BCM, Body Cell Mass; FM, Fat Mass; PBF, Percent Body Fat; FMI, Fat Mass Index; WHR, Waist to Hip Ratio; BMI, Body Mass Index; r, correlation coefficient.
Figure 2. Correlation of anthropometric and body composition parameters with SOS in men. * Marks statistical significance p < 0.05. ** Marks statistical significance p < 0.01. Abbreviations: LBM, Lean Body Mass; FFMI, Fat-Free Mass Index; FFM, Fat-Free Mass; SMM, Skeletal Muscle Mass; BCM, Body Cell Mass; FM, Fat Mass; PBF, Percent Body Fat; FMI, Fat Mass Index; WHR, Waist to Hip Ratio; BMI, Body Mass Index; r, correlation coefficient.
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Figure 3. Correlation of anthropometric and body composition parameters with SOS in women. * Marks statistical significance p < 0.05. Abbreviations: LBM, Lean Body Mass; FFMI, Fat-Free Mass Index; FFM, Fat-Free Mass; SMM, Skeletal Muscle Mass; BCM, Body Cell Mass; FM, Fat Mass; PBF, Percent Body Fat; FMI, Fat Mass Index; WHR, Waist to Hip Ratio; BMI, Body Mass Index; r, correlation coefficient.
Figure 3. Correlation of anthropometric and body composition parameters with SOS in women. * Marks statistical significance p < 0.05. Abbreviations: LBM, Lean Body Mass; FFMI, Fat-Free Mass Index; FFM, Fat-Free Mass; SMM, Skeletal Muscle Mass; BCM, Body Cell Mass; FM, Fat Mass; PBF, Percent Body Fat; FMI, Fat Mass Index; WHR, Waist to Hip Ratio; BMI, Body Mass Index; r, correlation coefficient.
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Table 1. Baseline sex and obesity-specific SOS, anthropometric, and body composition characteristics of the study sample.
Table 1. Baseline sex and obesity-specific SOS, anthropometric, and body composition characteristics of the study sample.
MenWomen
Non-Obese
(N = 190)		Obese
(N = 80)		pNon-Obese
(N = 296)		Obese
(N = 208)		p
MeanSDMeanSD MeanSDMeanSD
Age (y)22.152.4422.432.340.313 b21.022.1121.602.270.003 *b
Weight (kg)74.2710.5087.7916.29<0.001 *a55.366.5468.6012.18<0.001 *a
Height (cm)180.877.34180.287.280.546 a167.026.14166.245.990.158 a
BMI (kg/m2)22.662.4926.884.16<0.001 *a19.801.9024.733.87<0.001 *b
WHR0.800.050.820.05<0.001 *a0.730.050.740.06<0.001 *b
PBF (%)13.53.6225.95.52<0.001 *a22.23.7534.55.20<0.001 *a
FM (kg)10.123.3423.379.94<0.001 *b12.392.9524.117.85<0.001 *b
FM arm (%)66.6432.11255.70229.61<0.001 *b79.5319.24184.1587.58<0.001 *b
FM arm (kg)0.430.211.671.51<0.001 *b0.800.201.830.89<0.001 *b
FM trunk (%)109.9844.50267.8594.23<0.001 *b101.5629.38211.8567.14<0.001 *b
FM trunk (kg)5.032.1012.234.54<0.001 *a5.721.7011.833.82<0.001 *b
FMI3.090.997.152.89<0.001 *b4.441.038.712.73<0.001 *b
FFM (kg)63.888.8063.948.760.958 a42.844.8144.096.150.009 *a
FFMI19.562.0019.751.800.477 a15.351.2516.061.48<0.001 *a
LBM (kg)60.738.3960.847.950.928 a40.454.5141.965.34<0.001 *a
LBM arm (%)105.6410.6298.009.49<0.001 *a96.048.2693.6810.150.004 *a
LBM arm (kg)3.570.703.530.630.641 a1.930.352.140.42<0.001 *a
LBM trunk (%)103.905.8797.565.67<0.001 *a100.634.7995.644.97<0.001 *a
LBM trunk (kg)28.003.7528.043.620.941 a18.352.0719.632.60<0.001 *a
SMM (kg)36.625.6236.375.000.739 a23.392.8724.293.33<0.001 *a
Visceral FM (cm2)40.1217.21101.5842.93<0.001 *b51.2713.20110.6342.31<0.001 *b
BCM (kg)42.255.7942.155.490.888 a27.853.1328.873.66<0.001 *a
Phase (°)6.410.616.040.55<0.001 *a5.160.505.180.450.609 b
SOS (m/s)4034119.824024.7107.640.550 a4071.3106.534073.3102.760.840 a
* Marks statistical significance p < 0.05. a Indicates normal data distribution according to Kolmogorov–Smirnov. b Indicates non-normal data distribution according to Kolmogorov–Smirnov. Abbreviations: SD, Standard Deviation; BMI, Body Mass Index; WHR, Waist to Hip Ratio; PBF, Percent Body Fat; FM, Fat Mass; FFM, Fat-Free Mass; FMI, Fat Mass Index; FFMI, Fat-Free Mass Index; LBM, Lean Body Mass; SMM, Skeletal Muscle Mass; BCM, Body Cell Mass; SOS, Speed of Sound.
Table 2. Baseline sex and obesity-specific behavioral and lifestyle characteristics of the study sample.
Table 2. Baseline sex and obesity-specific behavioral and lifestyle characteristics of the study sample.
Men Women
Non-ObeseObese Non-ObeseObese
%%p  a%%p  a
Smoking26280.77116280.001 *
Alcohol92880.23391920.605
Coffee73620.064791790.869
Sweetened beverages88910.47185900.070
Vit. D intake28210.25534240.019 *
Calcium intake16150.8701680.005 *
PA, tier 02947<0.001 *54660.012 *
PA, tier 117272018
PA, tier 254262616
* Marks statistical significance p < 0.05. p a value of statistical significance (χ2 test). Abbreviations: Vit., vitamin; PA, physical activity; tier 0, individuals who do not meet the minimum guidelines, engage in less than 300 min of moderate or 150 min of vigorous activity per week, and participate in occasional activities, such as walking to work; tier 1, individuals who engage in 150–300 min of moderate or 75–150 min of vigorous activity per week or more than 300 min of moderate or 150 min of vigorous activity per week, but only one to two times a week; tier 2, individuals who regularly engage in more than 300 min of moderate or 150 min of vigorous activity per week and training approximately thrice weekly.
Table 3. Association of SOS with FM—associated parameters in various body segments after adjustment for age, behavioral, and lifestyle factors.
Table 3. Association of SOS with FM—associated parameters in various body segments after adjustment for age, behavioral, and lifestyle factors.
Dependent VariablesPredictorsUnstandardized BStandardized ꞵ95% CI for BSE for BpR2Adjusted R2Durbin–WatsonT
Non-obese men
SOS (m/s)Age11.6130.2334.624–18.6023.5420.001 *0.1100.0991.8280.994
Visceral FM1.5460.2180.550–2.5430.5050.003 *0.994
The least significant variables: smoking status, alcohol consumption, coffee drinking, sweetened beverages consumption, physical activity, Ca intake, vitamin D intake, BMI, PBF, FM in the trunk (%), and FM in the arm (%)
Non-obese women
SOS (m/s)Age16.6340.32811.034–22.2342.845<0.001 *0.1560.1471.7150.937
FM arm, %0.6370.1150.037–1.2380.3050.038 *0.967
Vitamin D26.0570.1160.815–51.29912.8240.043 *0.910
The least significant variables: smoking status, alcohol consumption, coffee drinking, sweetened beverages consumption, physical activity, Ca intake, vitamin D intake, BMI, PBF, FM in the trunk (%), and visceral FM
Obese women
SOS (m/s)Age13.4630.2967.469–19.4570.296<0.001 *0.1140.1051.7081.000
Visceral FM−0.406−0.166−0.728–0.084−0.1660.014 *1.000
The least significant variables: smoking status, alcohol consumption, coffee drinking, sweetened beverages consumption, physical activity, Ca intake, vitamin D intake, BMI, PBF, FM in the trunk (%), and FM in the arm (%)
Note: * Marks statistical significance p < 0.05. Abbreviations: B, beta coefficient; CI, Confidence Interval; p, value of statistical significance (linear regression analysis, forward method); R2, coefficient of determination; SE, Standard Error; SOS, speed of sound; T, tolerance (collinearity statistics), BMI, Body Mass Index; PBF, Percent Body Fat; FM, Fat Mass.
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Sulis, S.; Falbová, D.; Beňuš, R.; Švábová, P.; Hozáková, A.; Vorobeľová, L. Sex and Obesity-Specific Associations of Ultrasound-Assessed Radial Velocity of Sound with Body Composition. Appl. Sci. 2024, 14, 7319. https://doi.org/10.3390/app14167319

AMA Style

Sulis S, Falbová D, Beňuš R, Švábová P, Hozáková A, Vorobeľová L. Sex and Obesity-Specific Associations of Ultrasound-Assessed Radial Velocity of Sound with Body Composition. Applied Sciences. 2024; 14(16):7319. https://doi.org/10.3390/app14167319

Chicago/Turabian Style

Sulis, Simona, Darina Falbová, Radoslav Beňuš, Petra Švábová, Alexandra Hozáková, and Lenka Vorobeľová. 2024. "Sex and Obesity-Specific Associations of Ultrasound-Assessed Radial Velocity of Sound with Body Composition" Applied Sciences 14, no. 16: 7319. https://doi.org/10.3390/app14167319

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