Age-related bone mass and body composition dynamics in female cynomolgus monkeys: dual-energy X-ray absorptiometry insights for osteoporosis etiology

Introduction

Osteoporosis is defined as a systemic skeletal disorder characterized by reduced bone mass and deterioration of bone microarchitecture, leading to increased bone fragility and a heightened risk of fractures (1). Fragility fractures, the most severe consequence of osteoporosis, result in a decline in health-related quality of life, increased healthcare costs, and a significant societal burden. With the aging global population, osteoporosis has become increasingly prevalent among the elderly, with an estimated 9 million incident fragility fractures occurring annually worldwide (2). Consequently, research into anti-osteoporosis drugs has gained prominence, necessitating the use of animal models to assess the safety and efficacy of new drugs in preclinical settings. Various species are employed as osteoporosis models, including mice, rats, rabbits, sheep, dogs, and nonhuman primates (3,4). Among these, nonhuman primates, particularly female cynomolgus monkeys, are often utilized due to their skeletal characteristics, which closely resemble those of women, especially in terms of hormonal patterns (5), menstrual cycles (6), natural menopause (7).

Quantitative detection of bone mineral density (BMD) is indispensable for osteoporosis research. A variety of methods are available for measuring BMD, including dual-energy X-ray absorptiometry (DXA), quantitative computed tomography (QCT), quantitative ultrasound (QUS), and magnetic resonance imaging (MRI) (8). According to the World Health Organization (WHO) criteria, osteoporosis is diagnosed in postmenopausal women and in men aged 50 years and older if the T-score of the lumbar spine, total hip, or femoral neck is −2.5 or less. As for females prior to menopause and in males younger than age 50 years, a Z-score of −2.0 or lower is defined as “below the expected range for age” (9). The International Society for Clinical Densitometry (ISCD) recommends thresholds of 80 mg/cm³ for osteoporosis in clinical practice with single-slice QCT of the spine trabecular BMD (10). Among these, DXA is widely used for diagnosing osteoporosis due to its precision, ease of operation, and minimal radiation risk. The underlying principle of DXA measurement involves differentiating bone mineral from soft tissue based on their respective attenuation under high- and low-energy irradiation. Consequently, DXA is also employed to assess body composition, encompassing lean mass (LM) and fat mass (FM).

Since the 1980s, numerous studies have investigated the age-related changes in bone mass among nonhuman primates, yielding inconsistent results regarding the age at which peak bone mass is achieved (11-14). The majority of these studies have focused on rhesus monkeys as subjects, while a subset has utilized cynomolgus monkeys (15,16). In those investigations, a decrease in lumbar BMD was rarely observed in nonhuman primates. In a pivotal study by Krueger et al. in 1999 (17), the central region-of-interest (CROI) analysis technique was employed to minimize the impact of osteoarthritis (OA) on BMD measurements in rhesus monkeys. This approach demonstrated that CROI analysis revealed lower bone density in older monkeys compared to standard analysis. Building on this technique, we have applied it to measure BMD in the CROI (BMD_CROI) of the lumbar spine in cynomolgus monkeys to determine whether BMD_CROI decreases with age, accounting for the minimized influence of OA.

A multitude of factors have been identified as influencing BMD, encompassing genetic factors, nutritional factors, lifestyle factors, hormonal influences, chronic diseases, medication use, and body composition (18). The relationship between body composition, specifically LM and FM, and BMD is widely accepted, yet the primary contributing factor to BMD and the nature of FM’s contribution—whether positive or negative—remains a subject of debate in human studies (19-21). There is a paucity of research focusing on these correlations in nonhuman primates. Black et al. (11) reported a positive association between LM and BMD at four skeletal sites (total body, lumbar spine, midradius, and distal radius) in males and with the distal radius in females. That study involved a relatively small sample of 20 premenopausal female and 29 male rhesus monkeys, which may limit the robustness of the evaluation.

The objectives of our study were to investigate the age-related changes in bone mass and body composition in female cynomolgus monkeys and to examine the associations between body composition and BMD across different life cycle stages. This research aims to provide valuable insights that can be extrapolated to human studies, contributing to our understanding of the factors influencing bone health and body composition. We present this article in accordance with the ARRIVE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-877/rc).

Methods

A total of 112 healthy female cynomolgus monkeys, aged between 1 and 25 years, were enrolled in this study and categorized into four age groups: juvenile (1–4 years old, n=35), youth (5–10 years old, n=25), middle-aged (11–19 years old, n=28), and elderly (≥20 years old, n=24), as detailed in Table 1. Inclusion criteria: (I) female cynomolgus monkeys born and raised naturally in the breeding area; (II) no obvious skeletal deformities. If the experimental subjects present any of the following conditions, they will be excluded from the experiment: (I) obvious skeletal deformities; (II) obvious osteophytes or scoliosis in elderly crab-eating macaques; (III) obvious abnormalities found during the DXA scan that affect the measurement of bone mass and body composition. These monkeys were sourced from Guangdong Landau Biotechnology Co., Ltd., China, an institution holding the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International accreditation. All animal experiments were performed under a project license (No. LDIACUC2018-0004) granted by the Ethics Committee of Guangdong Landau Biotechnology Co., Ltd., in compliance with international Association for Assessment and Accreditation of Laboratory Animal Care guidelines for the care and use of animals. A protocol was prepared before the study without registration. The animals were maintained on a diet of fruits, commercial monkey chow, and water, with ad libitum access. They were housed outdoors in social group cages, each consisting of one male, several females, and their offspring. Throughout the study, none of the subjects exhibited clinical disorders or pregnancy. Prior to DXA scanning, all participants were weighed and fasted for 8–12 hours. The animals were sedated using intramuscular ketamine hydrochloride, followed by intravenous pentobarbital sodium for the scanning procedure.

Total body DXA scans were conducted using the Lunar iDXA system (GE Healthcare, Madison, WI, USA) in the anteroposterior (AP) position (Figure 1). Adhering to the manufacturer’s guidelines, daily quality assurance scans were performed utilizing an aluminum spine phantom. The enCORE software (version 16.0, small animal mode, GE Healthcare) automatically analyzed total body composition, including total body bone mineral content (BMC_TB), total body LM (LM_TB), and total body FM (FM_TB). In contrast, data for subcranial bone mass and lumbar spine BMD (BMDs) were derived from manual region of interest (ROI) delineation (Figure 2, Figure 3A). Unlike humans, cynomolgus monkeys typically possess seven lumbar vertebrae. Furthermore, prior precision analysis conducted at our group (22) on various lumbar spine combinations identified the L1–4 segment as yielding the highest precision. Given this finding and the inclusion of a subset of female monkeys exhibiting only six lumbar vertebrae in the current study, the L2–4 segment was selected for BMDs measurements. The counting standard for the lumbar vertebrae is to consider the last lumbar vertebra as the 7th lumbar vertebra and to count upwards from there. BMDs delineations were performed using manually delineated ROIs via a customized analysis protocol. ROIs were drawn using multi-vertex polygons with tangential adjustments to closely trace the vertebral margins. Subcranial BMD ROIs were defined with the inferior border of the occipital soft tissue serving as the boundary. To mitigate the random error associated with manual ROI delineation at the lumbar spine, three replicate assays were conducted, and the average values were taken as the definitive values for the BMD parameters.

Figure 1 Schematic diagram of anteroposterior total body DXA in cynomolgus monkey. (A) Total body bone mineral density map; (B) total body body composition map. DXA, dual-energy X-ray absorptiometry.

Figure 2 Schematic diagram of anteroposterior subcranial DXA in cynomolgus monkey. (A) Subcranial bone mineral density map; (B) subcranial body composition map. Subcranial BMD ROIs were defined with the inferior border of the occipital soft tissue serving as the boundary. BMD, bone mineral density; DXA, dual-energy X-ray absorptiometry; ROI, region of interest.

Figure 3 Schematic diagram of anteroposterior spine DXA in cynomolgus monkey. (A) Delineated of DXA lumbar spine (L2–4) BMD. ROIs were drawn using multi-vertex polygons with tangential adjustments to closely trace the vertebral margins. (B) Delineated of BMD_CROI. BMD_CROI data were acquired by placing a square CROI of 0.67 cm² (0.82 cm × 0.82 cm) within L2–4. BMD_CROI, bone mineral density in central region of interest; BMDs, spine bone mineral density; CROI, central region of interest; DXA, dual-energy X-ray absorptiometry; ROI, region of interest.

Given the smaller size of cynomolgus monkeys compared to the rhesus monkeys in Krueger et al.’s study (17), CROI data were acquired by placing a square CROI of 0.67 cm² (0.82 cm × 0.82 cm) within L2–4. This square was rotated by 45° and manually centered within the vertebral endplates and edges (Figure 3B). All DXA measurements and analyses were carried out by a highly trained operator to ensure consistency throughout the study. The in vivo precision of all parameters was assessed through repeated scans in 30 subjects, with precision expressed as the root-mean-square standard deviation (RMS-SD) and the coefficient of variation (RMS-CV%). Our group has previously reported on the precision study of partial parameters in DXA scanning for female cynomolgus monkeys (22). The RMS-SD (RMS-CV%) of BMD_CROI was 0.009 g/cm² (1.11%).

Statistical analyses were performed using Statistical Product and Service Solutions (SPSS, version 26.0) and the R programming environment. Initially, a normal distribution test was conducted for all measured parameters to determine their distribution characteristics. For parameters that exhibited a normal distribution, a one-way analysis of variance (ANOVA) followed by Bonferroni’s multiple comparison test was employed to assess differences among the groups. In contrast, for parameters that did not follow a normal distribution, the Kruskal-Wallis H test was utilized. To explore the age-related trends in total body bone mass, subcranial bone mass, BMDs, BMD_CROI, and body composition parameters, non-linear regression analysis was conducted using a generalized additive model (GAM). Spearman’s rank correlation analysis was applied to investigate the relationships between whole body and subcranial bone mass, BMDs, BMD_CROI, and body composition. The standardized regression coefficient (r) was used to evaluate the relative contribution of various parameters to BMD. A statistical significance threshold of P<0.05 was set to determine where differences or correlations were considered statistically significant.

Results

Descriptive statistics for bone mass and body composition in female cynomolgus monkeys across various age groups are presented in Table 2. The BMD_TB and BMC_TB were significantly higher than subcranial BMD and subcranial BMC in different age groups, with statistically significant differences (P<0.001). BMD_TB demonstrated a high positive correlation with subcranial BMD, with correlation coefficients ranging from 0.947 to 0.990 (P<0.001), while BMC_TB also showed a high positive correlation with subcranial BMC, with correlation coefficients ranging from 0.961 to 0.996 (P<0.001). The proportion of bone mineral content (BMC) in the head region relative to total BMC was between 0.21% and 0.33%, and this proportion was negatively correlated with age (r=−0.379, P<0.001).

Age change trend of bone mass and body composition in female cynomolgus monkeys

GAM was employed to perform nonlinear regression analysis on the aging trends of bone mass and body composition parameters in female cynomolgus monkeys, as illustrated in Figure 4. The analysis revealed that bone mass parameters in female cynomolgus monkeys aged ≤10 years increased with age, with peak bone mass occurring at the age of 10 years. For monkeys >10 years, BMDs remained relatively stable with respect to age, while BMD_TB and subcranial BMD exhibited a slight increasing trend in advanced age. Regarding BMC_TB, subcranial BMC, and BMD_CROI, these parameters remained stable in old age after an initial decrease to a certain level during middle age.

Figure 4 Age variation trend of bone mass and muscle tissue in female cynomolgus monkey. BMC_TB, total body bone mineral content; BMD_CROI, bone mineral density in central region of interest; BMDs, spine bone mineral density; BMD_TB, total body bone mineral density; Subcranial BMC, subcranial bone mineral content; Subcranial BMD, subcranial bone mineral density.

Cumulative bone loss rate of lumbar vertebrae in female cynomolgus monkeys after peak bone mass

As depicted in Table 3, the age range of 8 to 12 years is identified as the period during which female cynomolgus monkeys maintain their peak bone mass. Consequently, the average values of BMDs and BMD_CROI within this age range were considered representative of peak bone mass. Subsequently, the cumulative bone loss rate was calculated for the elderly group. The findings indicated that the cumulative bone loss rates for BMDs and BMD_CROI in elderly female cynomolgus monkeys were 1.8% and −10.0%, respectively.

Spearman correlation analysis of body composition and bone mass in female cynomolgus monkeys

The Spearman correlation analysis results for bone mass and body composition parameters among female cynomolgus monkeys of different age groups are presented in Table 4. The analysis revealed that LM was positively correlated with all six bone mass and body composition parameters in the juvenile group, BMD_TB, BMC_TB and Subcranial BMD in youth group, and BMC_TB, subcranial BMD and subcranial BMC in middle-aged group (r=0.389–0.917, P<0.05). There were significant positive correlations between FM and all six parameters in the youth group (r=0.426–0.736, P<0.05), and total body and subcranial bone mass in the middle-aged group (r=0.523–0.686, P<0.01), while there was no correlation between body composition and bone mass in the elderly group.

Multiple regression analysis of bone mass and body composition

Stepwise multiple regression analysis was conducted to develop multiple regression equations that assess the contributions of LM, FM, and age to whole-body and subcranial bone mass, BMDs, and BMD_CROI. The subjects were categorized into two groups based on the age at peak bone mass, with 10 years of age serving as the threshold (Table 5). The pre-peak bone mass group included subjects aged 10 years or younger, while the post-peak bone mass group included subjects older than 10 years.

In the pre-peak bone mass group (aged ≤10 years), LM and FM both positively contributed to whole-body and subcranial bone mass, BMDs, and BMD_CROI (P<0.01), with LM having a greater impact on bone mass than FM. In contrast, in the post-peak bone mass group (aged >10 years), FM had a more significant positive contribution to total-body and subcranial bone mass than LM, while neither LM nor FM had a significant contribution to BMDs and BMD_CROI.

Discussion

The findings of this study indicate that female cynomolgus monkeys (Macaca fascicularis) achieve peak bone mass at the age of 10 years, after which there is a decline in BMC_TB, subcranial BMC, and BMD_CROI in advanced age. The age-related changes in subcranial bone mass closely parallel those observed in whole-body bone mass, with BMDs maintaining a plateau following the peak at age 10 until senescence. Female cynomolgus monkeys typically attain sexual maturity around 4 years of age and exhibit regular menstrual cycles of approximately 29 days, akin to human females. They subsequently undergo natural menopause between 15 and 20 years of age (23). Consequently, based on the physiological stages of female cynomolgus monkeys, we categorized them into four age groups: the prepubertal group (≤4 years old), the premenopausal group (≤10 years old), the post-peak bone mass group (11–19 years old), and the postmenopausal group (≥20 years old). Studies by Jayo et al. (15) and Chen et al. (16) suggest that the peak bone age in female cynomolgus monkeys is 9 years old. Our findings indicate a later peak bone mass age compared to these studies, which may be attributed to advancements in modern breeding techniques and improvements in the living conditions of cynomolgus monkeys, thereby prolonging the period of bone mass accumulation.

It is widely accepted that BMD significantly declines post-menopause due to estrogen depletion and advancing age. However, studies in non-human primates have produced inconsistent findings. Similar to human studies, some research has noted a decrease in BMD in postmenopausal or elderly female non-human primates with age (11-13,15), with certain studies (11,13) observing this decrease specifically in the forearm BMD, not in the lumbar spinal BMD. Conversely, several studies have failed to find a correlation between BMD reduction and age in female non-human primates (16,24). In our study, only BMC_TB in middle-aged individuals decreased with age, while BMDs in older age did not show a significant decrease, and BMC_TB exhibited a slight increasing trend in older age. This outcome might be attributed to reduced bone mass in regions outside the lumbar spine. Unlike humans, who are bipedal, non-human primates primarily move by quadrupedal locomotion or forelimb suspension (25). Consequently, compared to humans, the forearms of non-human primates bear a greater portion of the body’s weight, potentially leading to a more pronounced decrease in forearm BMD compared to spinal BMD. Additionally, due to the retro-anterior overlapping projection in DXA scans, osteophytes may be included in the BMD measurements, potentially offsetting the decrease in lumbar bone density. The subsequent phase of our research involved the use of QCT to measure volumetric BMD in female cynomolgus monkeys, a method that is not influenced by calcifications, osteophytes, or spinal deformities (26).

In this research, we adopted the CROI methodology from Krueger’s study, which was specifically designed to minimize the impact of OA on BMD measurements by excluding vertebral facets, endplates, and disc spaces. Considering the smaller body size of cynomolgus monkeys compared to rhesus monkeys, we selected an appropriately scaled ROI for our measurements, ensuring that the precision in this region is comparable to standard analytical procedures. As is the case with older humans, spinal OA can also develop in older female cynomolgus monkeys, potentially affecting the accuracy of lumbar BMD measurements. Our study’s findings indicate that BMD measurements within the CROI can mitigate the influence of spinal OA and are more sensitive in detecting bone mass loss compared to traditional BMD measurements, with a cumulative bone loss rate of −10.0% for BMD_CROI versus 1.8% for BMDs. Furthermore, cynomolgus monkeys may serve as a valuable model for investigating the pathogenesis of spinal OA, given the noticeable development of OA in their elderly population.

Given the small stature of children, the bone mass of the skull constitutes a significant portion of the total body bone mass. Consequently, BMD in human children and adolescents is commonly assessed using densitometry (27). In a similar context, we present for the first time the subcranial BMD data for female cynomolgus monkeys, who also exhibit a petite frame. Our findings demonstrate a strong correlation between subcranial BMD and BMD_TB, with the age-related trends of subcranial BMD closely mirroring those of BMD_TB. The correlations between bone mass and body composition were found to be minimally variable. Therefore, we can infer that subcranial BMD is a reliable indicator that can accurately reflect the true skeletal status of female cynomolgus monkeys. Consequently, we suggest that subcranial BMD parameters could be incorporated into bone mass experiments using cynomolgus monkeys as a model organism to reduce the potential influence of individual differences in cranial BMD on experimental outcomes.

To date, there have been limited studies examining the aging trends of body composition in non-human primates. Our study provides foundational data on the body composition of female cynomolgus monkeys across various age groups and analyzes the associated age-related trends. The findings indicate that for LM, the juvenile period is characterized by rapid growth, whereas the young adult period is marked by slower growth. This pattern aligns with the observed increase in bone mass, suggesting a close relationship between body composition and bone mass.

Currently, the relationship between body composition and BMD in humans has been a subject of extensive research. However, it remains uncertain which factor, LM and FM, exerts a more significant influence on BMD. There is also debate over whether FM affects BMD in a favorable or unfavorable manner. Some studies have demonstrated a positive correlation between LM and BMD, suggesting that LM is the most potent predictor of BMD (28). In contrast, other studies have shown a positive correlation between FM and BMD (29). Furthermore, several studies have indicated that LM is more strongly correlated with BMD in premenopausal women, while FM is more strongly correlated in postmenopausal women (19,20). However, research has also shown that both visceral and subcutaneous fat can be detrimental to bone health in both premenopausal and postmenopausal women, with severe obesity in young women potentially increasing the risk of vertebral fractures (21). These findings suggest that the relationship between body composition and BMD may vary across different body weights and age groups.

In a study using 20 premenopausal female rhesus monkeys and 29 male rhesus monkeys, Black et al. (11) found that LM was positively correlated with total body and regional bone mass, while FM was negatively correlated with the middle and distal radius. Our study, employing Spearman correlation analysis and multiple linear regression analysis, revealed that both LM and FM were positively correlated with BMD in various parts of the body before the age of peak bone mass (≤10 years old), with LM contributing more to BMD than FM. In contrast, in older individuals, FM contributed more to BMD than LM. A study focusing on human children, adolescents, and adult women (4–20 years old) have shown that muscle strength is the primary influencing factor of bone strength, while the static load caused by FM does not affect bone strength (30), aligning with the results of our study.

The mechanism by which LM affects BMD is not entirely clear, but several hypotheses have been proposed. Firstly, there is a genetic correlation between muscle tissue and bone, with muscle cells and osteoblasts sharing common genes derived from mesenchymal cells and being directly linked. Secondly, muscle tissue can act on bone tissue through mechanical gravity load. According to mechanostat (31) and muscle-bone unit theory (32), muscle tissue and bone are considered a single entity, with muscle tissue influencing the skeleton through dynamic mechanical contraction load, and the skeleton inducing bone reconstruction to adapt to the mechanical stimulation of muscle, leading to increased bone strength and mass. There are a few human studies have examined the impact of LM on BMD. Sun et al. (33), by examining the genetic and environmental correlation of total muscle tissue content and skeletal geometric parameters in 4,489 subjects from 512 families, found a high genetic correlation between muscle and skeleton (r=0.28–0.69). Recently, Villa et al. (34) reported a high prevalence of osteoporosis (20% lumbar, 18% femoral) and pre-sarcopenia (24%) in young anorexic women, with skeletal muscle mass index significantly correlating with lumbar and femoral BMD, highlighting the critical role of LM in bone health.

The action mechanism of FM on bone mass may involve several points. Firstly, FM can act on bone tissue through static gravity load. Secondly, adipose tissue, outside of the sexual glands, is a primary source of estrogen secretion, a major hormonal regulator of bone metabolism in both women and men. Direct estrogen effects on osteocytes, osteoclasts, and osteoblasts lead to the inhibition of bone remodeling, decreased bone resorption, and maintenance of bone formation, respectively. Estrogen also modulates osteoblast/osteocyte and T-cell regulation of osteoclasts (35). Furthermore, bone-active hormones influenced by adiposity, such as leptin, have been shown to promote the proliferation and differentiation of osteoblasts, thereby increasing bone mass (36).

Our study acknowledges several limitations inherent to its design. Firstly, we did not include cynomolgus monkeys under 1 year of age in our study, as they are unable to undergo scanning independently without their mothers. However, the first year of life is a critical period of rapid growth in monkeys, and thus, the exclusion of this age group may have impacted our findings. Secondly, the subjects were categorized into four broad age groups rather than being selected on a per-year basis. While this approach allowed us to observe age variation trends and conduct correlation analyses between bone mass and body composition, the accuracy of these data could be enhanced with a larger sample size. Specifically, to ensure robust statistical power, it is recommended that the number of female cynomolgus monkeys per age group exceeds five per year of age.

Conclusions

Our study establishes a novel reference dataset for BMD and body composition in female cynomolgus monkeys across different ages using DXA. Our findings demonstrate that BMD_CROI is a sensitive indicator for detecting age-related spinal bone loss with minimal confounding from OA. Furthermore, the age-specific contributions of LM and FM to BMD provide critical insights into the etiology of osteoporosis. This dataset enhances the utility of this primate model for researching osteoporosis, sarcopenia, and obesity, and for supporting preclinical drug development.

Acknowledgments

We would like to thank Guangdong Landau Biotechnology Co. Ltd. for providing the female cynomolgus monkeys used in this study. We also extend our thanks to their veterinarians for their professional support and cooperation throughout the experimental period.

Footnote

Reporting Checklist: The authors have completed the ARRIVE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-877/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-877/dss

Funding: This study was financially supported by the National Natural Science Foundation of China (No. 81871383) and Science and Technology Projects in Guangzhou (No. 2024A03J0828).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-877/coif). All authors report that this study was financially supported by the National Natural Science Foundation of China (No. 81871383) and Science and Technology Projects in Guangzhou (No. 2024A03J0828). Y.L. is an employee of Guangdong Landau Biotechnology Co., Ltd. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All animal experiments were performed under a project license (No. LDIACUC2018-0004) granted by the Ethics Committee of Guangdong Landau Biotechnology Co., Ltd., in compliance with International Association for Assessment and Accreditation of Laboratory Animal Care guidelines for the care and use of animals.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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