Relationships among Physical Activity Bone Mineral Density and Body Composition in Obese and Athletes
Article information
Abstract
Background
Osteoporosis is a significant global public health issue, increasingly affecting younger individuals and placing substantial economic burdens on society. Risk factors vary, with non-modifiable ones like age and ethnicity, as well as modifiable factors including corticosteroid use, caffeine intake, and reduced exercise. This study examines the relationship between bone density, body components, and physical activity (PA) in enhancing bone health, particularly in obese athletes.
Methods
The 66 participants aged 18 to 30 were classified into two groups: 34 obese and 32 athletes. Measured parameters included body composition through bioelectrical impedance analysis, and bone mineral density (BMD) via quantitative ultrasound, while PA was assessed using the International PA Questionnaire.
Results
Our findings revealed a significant positive correlation between BMD and PA (r=0.284, P=0.023). Additionally, PA demonstrated strong negative correlations with body mass index (BMI), fat mass, and visceral fat (r=−0.738, r=−0.733, and r=−0.704 respectively, all P<0.001). In contrast, no significant correlation was observed between PA and lean mass (r=0.065, P=0.609). BMD was negatively associated with BMI and visceral fat, while a robust correlation between basal metabolic rate and lean mass was evident.
Conclusions
A study comparing athletes involved in high-impact sports indicated that these athletes maintained adequate BMD for their chronological age (Z-score≥−2.0). Moreover, a significant difference in BMD was observed when comparing the athletes to the obese group(P=0.018).
INTRODUCTION
Osteoporosis is a global public health issue, and it is prevalent in Thailand as well, posing a significant economic burden on society.[1] There was a significant increase in the crude prevalence of osteoporosis among individuals undergoing bone mineral density (BMD) measurements at a tertiary care referral center in Northeastern Thailand from 2003 to 2022.[2] Osteoporosis is defined as “low bone mass and microarchitectural deterioration of bone tissue, leading to enhanced bone fragility and a consequent increase in fracture risk.”[3] Several risk factors associated with osteoporosis, which can be categorized into non-modifiable factors such as age, race, and sex. Modifiable risk factors include using corticosteroid medications, caffeine consumption from tea and coffee, and smoking. Additionally, two often-overlooked risk factors include increased body fat and decreased physical activity (PA).[4] A positive correlation between body mass index (BMI) and BMD has been reported. A higher BMI is associated with the production of estrogen from adipose tissue, which benefits bone health by helping to maintain calcium levels and bone strength.[5] However, when separating body composition into lean mass and fat mass, study outcomes vary. While fat mass shows a positive relationship with BMD, this relationship lacks significance concerning BMI. Conversely, lean mass is positively correlated with BMD more than fat mass. Moreover, it has been found that BMD per kilogram of total body mass in the obese is lower when compared with a normal-weight individual and obese individuals experience more rapid bone mass loss.[6–8] Overweight and obesity indicated by an excessive accumulation of body fat result from an imbalance of energy intake (diet) and PA.[9,10] Physical inactivity is a modifiable risk factor for osteoporosis, and increasing PA at any point throughout the lifespan positively affects bone health, while reductions in PA can result in bone loss. [11] Therefore, this study aims to explore the relationship between BMD and body components in obese individuals, as well as to investigate and compare the relationship between PA as these are modifiable risk factors that are directly related to BMD in obese and athletes.
METHODS
1. Study sample
This study was approved by the Human Research Ethics Committee of Science in accordance with the compliance with the Declaration of Helsinki, The Belmont Report, CIOMS Guidelines, and the International Practice (ICH-GCP) Project No. 66AH141 COA No. 001/2567. The target population was males and females aged between 18 and 30 years in Thailand. Participants were categorized into 2 groups including obese individuals and athlete. The participants in obese group, contained BMI values more than 25 kg/m2 and the athletic group contained BMI in the normal range (18.5–24.9 kg/m2). The inclusion criteria for athletes, was that they have to participate in sports, that exert high impact loading on the skeleton, or weight-bearing exercise, such as football, volleyball, boxing, jogging, or physical fitness training at least 3 hr per week. Participants who had musculoskeletal injuries or fractures within the last 3 months, or taken medication that could affects bones within 1 year before joining the study were excluded. All participants provided written consent. The total trial group consisted of 66 participants (34 obese: 19 males, 15 females, and 32 athletes: 23 males, 9 females).
2. Body composition measurement
Body composition was determined by using the bioelectrical impedance analysis model Tanita MC-780 MA with high-frequency current (50 kHz, 90 μA) and eight electrodes that allow the current to flow into the upper and lower limbs (tetrapolar).
3. BMD measurement
BMD was assessed using the Achilles Quantitative Ultrasound (QUS) device from GE Healthcare. The two primary parameters measured in QUS are broadband ultrasound attenuation (BUA), expressed in decibels per megahertz, and speed of sound (SOS), measured in meters per second. BUA reflects the attenuation of ultrasound as it interacts with bone structures, primarily influenced by absorption in cortical bone and scattering in cancellous bone, and is presented on a logarithmic scale across a frequency range of 0.1 to 1 MHz. SOS quantifies how fast the ultrasound signal travels through bone, independent of attenuation effects. QUS measurements are typically taken at the non-dominant heel. Research shows a strong correlation (r=0.888) between SOS and BMD at the same site, indicating that SOS is closely associated with bone mineralization. In contrast, BUA is more affected by the structural characteristics of trabecular bone, such as porosity. Notably, SOS measurements tend to exhibit higher precision in cortical bone than in trabecular bone due to the greater SOS waves in cortical tissue. Similarly, the precision of BUA measurements is generally lower than that of SOS using the same devices.[12–14] Results from QUS can be reported as absolute values or as T-scores and Z-scores, which are compared to a normative reference database that conforms to the World Health Organization (WHO) criteria for BMD diagnosis. This study, conducted on young adults, adhered to WHO guidelines that classify Z-scores below −2.0 as indicative of inadequate BMD for chronological age.[15–17] To calculate Z-score, the BMD is automatically compared to a selectable built-in Asian reference database of the same age and sex as the participants.[18] QUS has several advantages, including the ability to evaluate various bone properties such as density, microarchitecture, and elasticity. Additionally, QUS devices are smaller, portable, and more cost-effective than dual energy X-ray absorptiometry (DXA) scanners, requiring no specialized training for operation and avoiding exposure to ionizing radiation.[12]
4. Estimation of PA level
Finding out the type and quality of PA was determined by using the International Physical Activity Questionnaire (IPAQ). PA was measured by counting the sum of time spent in vigorous recreational activity and moderate recreational activity during 7 days for each subject and averaging this into their daily activity time. The classification of PA level is estimated by the intensity of exercise based on the metabolic equivalent task (MET). To estimate the energy expenditure, PA data obtained from the IPAQ was computed for MET-min per week, calculated as the MET intensity multiplied by the min for each activity over the seven-day period. A simple, easy-to-use spreadsheet (the IPAQ Short From) was used for automatic scoring in this study.[19] After calculation of the total MET score, the participants were divided into various categories as follows: Category 1 (Low PA): <600 MET-min/week, Category 2 (Moderate PA): ≥600 to <3,000 MET-min/week and Category 3 (High PA): ≥3,000 MET-min/week.
5. Statistical analysis
Descriptive statistics were indicated by mean, standard deviation (SD) and frequency of the sample characteristics and type of sport the athletic subjects participated in. The independent samples t-test was used for comparison of means and Spearman Rank correlation coefficients were used to assess the relationship between total BMD (Z-score), lean mass, fat mass, BMI, basal metabolic rate (BMR), visceral fat, and PA. The correlation coefficient ranges from −1 to 1. A value of −1 indicates a perfect negative correlation between two variables, while a value of 1 denotes a perfect positive correlation. Correlation strengths can be categorized as follows: weak correlations range from 0.1 to 0.29, moderate correlations from 0.30 to 0.49, and strong correlations from 0.5 to 1.00. All analyses were conducted using a 95% confidence level (P<0.05). The data were exported to IBM SPSS statistics version 28.0.1.1(15) for analysis.
RESULTS
Our study includes 32 athletes and 34 obese students enrolled at Thammasat University, Rangsit campus aged between 18- to 30-year-old. The descriptive data by sex and group categories were shown in Table 1 and 2 respectively. The MET-min duration of PA level was divided into three groups (high, moderate, and low), as shown in Table 3.
Study sample descriptive data by sex
Study sample descriptive data by group
Categorical score-wise distribution of participants for physical activity level in athlete and obese
In comparing descriptive data shows higher body weight, BMI, total body water, extracellular water, BMR, fat mass, and visceral fat in obese group. In contrast, T-score, Z-score and MET-min shows lower than athletes’ group. Our study shows, that the BMD was weak positively correlated with PA (r=0.284, P=0.023). The correlations between PA, BMD, and body composition parameters in athletes and obese groups are shown in Table 4 and 5. The correlation analyses showed that PA was weak and positively correlated with BMD. A strong negative correlation was found between PA and BMI, fat mass, and visceral fat respectively (r=−0.738, −0.733, and −0.704, P<0.001). Conversely, no correlation was found between PA and lean mass (r=0.065, P=0.609). The BMD was moderately and negatively associated with BMI and visceral fat (r=−0.300, P=0.016; r=−0.297, P=0.017). The highest correlation between BMR and lean mass was observed (r= 0.986, P<0.001). Significant correlations were found between BMI and visceral fat (r=0.945, P<0.001).
Assessment of bone mineral density status, body composition, and physical activity level in athlete and obese
The correlations among physical activity bone mineral density and body composition in obese and athletes
DISCUSSION
To investigate the relationship among PA, BMD, and body composition in both obese individuals and athletes, our study employed QUS to measure BMD. Although the WHO designates DXA as the gold standard for diagnosing osteoporosis,[20] stand-alone QUS is not recommended for initiating treatment decisions or for follow-up assessments. Additionally, QUS may not be suitable for screening early postmenopausal women for low axial or peripheral BMD. [21] Nevertheless, calcaneal QUS has been shown to effectively predict proximal femoral BMD in middle-aged and elderly populations, as well as lumbar BMD in women. As a screening method for osteoporosis, calcaneal QUS demonstrates good specificity, making it a viable option as a prescreening tool. This approach could potentially reduce the need for DXA screenings, particularly in resource-limited settings, by identifying patients at low risk of fractures that do not require further screening, thereby reserving DXA for those at the highest risk.[22] It is known that BMD tends to decrease after reaching peak bone mass due to the complex interplay of factors including changes in sex hormones, nutrition, and mechanical loading on bones. Modifiable behaviors such as smoking, dietary choices, and PA significantly contribute to the development of osteoporosis in older adults.[23] Physical inactivity, particularly due to prolonged bed rest or exposure to reduced gravity, can alter bone turnover and mineral homeostasis.[11] PA has been shown to promote bone health, aligning with previous studies that report varying effects of PA on BMD in females and males. However, in obese populations, the relationship between BMD and PA often appears to be nonsignificant.[24] In contrast to these findings, our study suggests that BMD increases with higher levels of PA (r=0.284, P=0.023), although the weak correlation may be attributed to the limited number of participants. Historically, obesity has been associated with improved bone integrity, leading to suggestions of an osteoprotective effect. However, more recent evidence indicates that obesity may also correlate with poor bone quality and a heightened risk of fractures. [25] The association between obesity and fracture risk is complex and appears to vary by skeletal site, with potential differences between men and women.[26] The risks of hip and wrist fracture were reduced by 25% (N=8; relative risk [RR], 0.75; 95% confidence interval [CI], 0.62–0.91; P=0.003; I2=95%) and 15% (N=2 studies; RR, 0.85; 95% CI, 0.81–0.88), respectively, while ankle fracture risk was increased by 60% (N=2 studies; RR, 1.60; 95% CI, 1.52–1.68) in postmenopausal women with obesity compared with those without obesity.[27] Conversely, high BMI was associated with an increased risk of humerus and elbow fractures. Furthermore, although numerous studies consistently report higher areal BMD in individuals with obesity, it seems that altered bone quality may play a significant role in determining fracture risk within this population.[28] Osteoporosis did not significantly increase the odds of ankle fractures thus, suffering an ankle fracture does not automatically warrant further osteoporosis assessment. These data suggest that mechanical factors may be more important than bone density in determining ankle fractures in obese individuals. Similarly, the extra fat mass near the hip could play an important role in hip fracture protection in obese individuals.[29] Several studies have been published showing a positive association between obesity and bone health. [30] The scientific literature shows how weight bearing increases bone density by acting also at the cellular level. Studies conducted on animals show that osteocytes are particularly sensitive to biomechanical stress. They die by apoptosis in the absence of loading, while when the shear stress signal is picked up by the osteocytes, they do not undergo apoptosis, and their secretion of sclerostin is suppressed. At the same time, the action of the osteoclasts is repressed, and osteoblastic differentiation is stimulated. [31–38] In addition, the increase in BMD that has been found in obesity also seems to be linked to the action of estrogens. It is widely demonstrated that estrogens have an important effect on bone metabolism, stimulating bone formation and reducing its resorption.[23] Even though a wide literature shows that a higher BMD is correlated with obesity in part of BMI our findings reveal that this association is not significant.[39] After adjusting for body weight and BMI, we observed a negative association between BMD and BMI. In our study, BMD showed a significant positive correlation with PA, a negative correlation with BMI and visceral fat, and no significant association with body composition metrics such as fat mass, lean mass, and BMR. These results suggest that maintaining a higher level of PA could be beneficial for preventing future risks of osteoporosis. Moreover, our study confirms that among various body composition variables and baseline anthropometric characteristics, BMI and visceral fat consistently serve as significant negative independent contributors to BMD in both athletes and obese groups aged 18 to 30 years. A study comparing athletes involved in high-impact sports indicated that these athletes maintained adequate BMD for their chronological age (Z-score≥−2.0). Moreover, a significant difference in BMD was observed when comparing the athletes to the obese group (P=0.018). This study underscores the importance of addressing PA and body composition in mitigating osteoporosis risk, particularly among the obese population. Notably, no correlation between lean mass and BMD was found. Given the association of obesity with higher mortality and increased risks of cardiovascular disease, diabetes, cancer, and BMD our study focused on assessing fat mass, lean mass, and visceral fat instead of BMI to better understand their correlation.
Notes
Funding
This work was supported by the financial support provided by the Faculty of Allied Health Science Research Fund, Thammasat University (Contract No. 66AH141).
Ethics approval and consent to participate
This study was approved by the Human Research Ethics Committee of Science in accordance with the compliance to The Declaration of Helsinki, The Belmont Report, CIOMS Guidelines and The International Practice (ICH-GCP) Project No. 66AH141 COA No. 001/2567.
Conflict of interest
No potential conflict of interest relevant to this article was reported.