The influence of physical activity intensity on bone mineral density and trabecular bone score in young adults with overweight and obesity.

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Published in final edited form as: Int J Obes (Lond). 2025 Jul 3;49(9):1840–1846. doi: 10.1038/s41366-025-01833-2 The influence of physical activity intensity on bone mineral density and trabecular bone score in young adults with overweight and obesity Horacio Sanchez-Trigo 1, ✉ , David Wing 2, 3 , Shadia J Mansour-Assi 2, 4 , Jeanne F Nichols 2, 3 , Britta A Larsen 2 , Borja Sañudo 1 , Natalie M Yarish 5 , Sonia Jain 6 , Job G Godino 2, 3, 4 Author information Article notes Copyright and License information 1 Physical Education and Sports Department, University of Seville, Seville, Spain. 2 Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA. 3 Exercise and Physical Activity Resource Center, University of California, San Diego, CA, USA. 4 Laura Rodriguez Research Institute, Family Health Centers of San Diego, San Diego, CA, USA. 5 Exercise Science, Old Dominion University, Norfolk, VA, USA. 6 Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, USA. AUTHOR CONTRIBUTIONS HST and JGG conceived the study and led the manuscript preparation. DW, SJM-A, and JFN contributed to the study design, coordinated and participated extensively in the experimental phase, and supported data interpretation. BAL and BS assisted with the implementation of the experimental procedures and contributed to drafting the manuscript. NMY and SJ provided statistical expertise and contributed to the analysis and interpretation of the data. All authors reviewed, edited, and approved the final version of the manuscript. ✉ Correspondence and requests for materials should be addressed to Horacio Sanchez-Trigo. fstrigo@us.es Issue date 2025 Sep. Reprints and permission information is available at http://www.nature.com/reprints PMC Copyright notice PMCID: PMC13235530  NIHMSID: NIHMS2173157  PMID: 40610757 The publisher's version of this article is available at Int J Obes (Lond) Abstract INTRODUCTION: This study examines how physical activity intensity, body mass index (BMI), and grip strength interact with bone health indicators—specifically areal bone mineral density (BMD) and Trabecular Bone Score (TBS)—in young adults with overweight and obesity. This research aims to clarify these relationships to inform effective physical activity interventions for this demographic. METHODS: Data from 638 young adults (aged 18–35 years) participating in a weight loss randomized clinical trial was analyzed. Multiple Linear Regression (MLR) was utilized to assess the impact of physical activity, BMI, and grip strength on BMD and TBS. RESULTS: Significant correlations were found between femoral neck BMD and age (β = −0.0056, p < 0.001), BMI (β = 0.0140, p < 0.001), grip strength (β = 0.2865, p < 0.001), light physical activity (β = 0.0002, p < 0.01) and moderate-to-vigorous physical activity (MVPA) (β = 0.0006, p < 0.01) physical activity intensities. Total hip BMD showed similar associations. Analyses of spine BMD and mean TBS highlighted the positive impacts of BMI and grip strength, with light physical activity also benefiting TBS. DISCUSSION: The study confirms the beneficial effects of light activity and MVPA on femoral neck and total hip BMD, challenging the traditional perception that higher body weight positively impacts bone health by revealing a negative correlation between BMI and TBS. Grip strength’s consistent association with better bone health outcomes underlines the critical role of muscular strength. INTRODUCTION Osteoporosis, a systemic skeletal disorder characterized by reduced bone mass and deterioration of bone tissue, leads to increased bone fragility and susceptibility to fractures [ 1 ]. Osteoporosis is a significant public health issue with direct healthcare costs, such as fracture treatment and post-fracture care, and indirect costs, including loss of productivity and decreased quality of life [ 2 ]. Thus, understanding and addressing factors that contribute to bone health are essential to reduce the burden of this disease [ 3 ]. Among known behavioral strategies currently recommended to maintain and enhance bone health, particularly bone mass, physical activity plays a pivotal role. Impact exercise and resistance training are particularly beneficial as they stimulate bone formation [ 4 ] by exerting mechanical stress on the skeleton which, in turn, activates bone-forming cells and enhances bone strength [ 5 ]. The impact of physical activity on bone health, however, varies according to body mass index (BMI). In normal-weight individuals (BMI ≤ 25 kg/m 2 ), the benefits of physical activity on bone health are well-documented and generally linear [ 6 ]. In contrast, the relationship between physical activity and bone health in individuals with overweight and obesity (BMI > 25 kg/m 2 ) is more complex [ 7 ]. Excess body weight has been traditionally thought to be protective against osteoporosis due to higher bone density observed in individuals with overweight [ 8 ]. This may be explained by increased body weight that exerts greater mechanical loading on the skeleton, thereby promoting bone formation and increasing bone mass [ 9 ]. However, emerging evidence suggests that the quality of bone in this population may not be optimal as chronic inflammation and altered hormonal environment associated with obesity can mitigate these benefits [ 10 – 12 ]. This underscores the need for a nuanced understanding of how different intensity levels of physical activity are associated to bone health in individuals with overweight and obesity, focusing on both the quantity and quality of bone mass. BMD is a critical indicator of bone health, as it is widely recognized for its role in predicting fracture risk. BMD measurement, typically conducted using dual-energy X-ray absorptiometry (DXA) [ 13 ], quantifies bone mineral content in relation to the scanned bone area, thus providing a surrogate measure of bone strength. In addition, TBS has emerged as a significant, albeit less studied, indicator of bone quality. The importance of TBS lies in its potential to provide insights into the bone’s microarchitectural integrity, an aspect not directly assessed by BMD [ 14 ]. TBS is a textural index that evaluates pixel gray-level variations in DXA images, reflecting trabecular microarchitecture [ 15 ]. This innovative approach allows for the assessment of bone quality independent of bone density [ 16 ]. TBS enhances the prediction of fracture risk, particularly in cases where BMD alone may not fully capture an individual’s fragility [ 17 , 18 ]. In 2021, the American Medical Association (AMA) approved dedicated Category 1 Current Procedural Terminology (CPT) codes for TBS which signifies its growing integration and recognition in regular clinical practice of fracture risk assessment and bone health management. This study seeks to explore the associations between physical activity intensity and bone health, considering both bone quantity (measured by BMD) and quality (measured by TBS), in young adults with overweight and obesity. Current research predominantly focuses on these factors in the general population or in older adults, with less attention paid to this relationship in the context of young adults with overweight and obesity. Addressing this gap will not only contribute to the broader understanding of bone health in the context of obesity, but also aid in the formulation of more effective public health strategies and clinical guidelines tailored to the needs of young adults with overweight and obesity. METHODS Population This study used baseline measures from a randomized controlled trial titled Social Mobile Approaches to Reducing Weight (SMART) 2.0 [ 19 ]. The primary goal of SMART 2.0 was to evaluate the effectiveness of an evidence-based, multimodal intervention aimed at achieving significant weight loss in young adults with overweight and obesity over a 24-month period. The 638 participants enrolled in SMART 2.0 were aged 18–35 years and affiliated with university and college campuses in San Diego, CA. Eligibility criteria included overweight or obesity (BMI > 25 and <40 kg/m 2 ) and desire to lose weight. Exclusion criteria included obesity-related comorbidities, recent cardiovascular events, ongoing treatment for malignancy or eating disorders, plans for weight loss surgery or enrollment in other weight loss programs, recent significant weight loss, and current or planning pregnancy. The SMART 2.0 study was approved by the Human Research Protections Programs at UCSD (protocol #181862). Written, informed consent to participate was obtained from all participants. Measures Independent variables include demographic and physiological factors such as sex, age, BMI, grip strength normalized by body weight, alongside accelerometer-derived data quantifying daily minutes spent in various physical activity intensities—specifically, sedentary, light, and moderate to vigorous categories [ 20 , 21 ]. Dependent variables include femoral neck BMD, total hip BMD, spine BMD, and mean TBS measured at baseline. Measures were collected by trained staff with detailed methodologies outlined in the study protocol [ 19 ]. Participants included in the analysis had complete data for all independent and dependent variables and met the criteria of recording at least 4 days of physical activity, accumulating a minimum of 3000 min of valid data, as recommended by best practice guidelines [ 22 ]. Days with less than 600 min of wear time were excluded. Time spent during sleep was not included in the analyses. Statistical analysis Multiple Linear Regression (MLR) was used in our statistical analyses. The model’s coefficients were estimated using the least squares method, which minimizes the sum of the squared differences between the observed and predicted values. The MLR model was evaluated using two primary metrics: the coefficient of determination (R 2 ) and the Mean Squared Error (MSE) [ 23 ]. The model’s validity was confirmed using Pearson’s product coefficient for linearity, the Durbin–Watson test for autocorrelation, the Jarque–Bera test for residual normality, the Goldfeld-Quandt test for homoscedasticity, and the Variance Inflation Factor for multicollinearity [ 24 ]. The Python package sklearn ( https://scikit-learn.org/ ) was used to build the MLR models. The level of significance for all tests was set a priori at 0.05. A comparative analysis was performed on these MLR models to evaluate the impact of various predictors (like sex, age, BMI, activity levels, and grip strength normalized by body weight) on the previous bone health indicators (femoral neck BMD, total hip BMD, lumbar spine BMD, and mean TBS). The predictor variables were standardized using Z-score normalization for consistent measurement scales and comparability. Finally, to assess whether adherence to the World Health Organization’s (WHO) physical activity guidelines [ 25 ] is associated with better bone health, we conducted unpaired T-tests to compare BMD and mean TBS between participants who met these physical activity recommendations and those who did not. RESULTS Descriptive statistics for all independent and dependent variables, stratified by gender are shown in Table 1 . Table 1. Descriptive statistics. Men ( N = 220) Women ( N = 301) Mean SD Min Max Mean SD Min Max Femoral neck BMD (g/cm 2 ) 0.998 0.156 0.659 1.562 0.927 0.115 0.622 1.340 Total hip BMD (g/cm 2 ) 1.097 0.142 0.740 1.630 1.040 0.111 0.730 1.450 Total spine BMD (g/cm 2 ) 1.065 0.120 0.740 1.561 1.076 0.113 0.763 1.490 Mean TBS 1.44 0.08 1.22 1.64 1.46 0.08 1.22 1.69 Age (years) 22.9 4.3 18.0 35.0 23.3 4.7 18.0 35.0 BMI (kg/m 2 ) 30.1 3.6 25.1 40.0 30.4 3.8 25.0 39.9 Grip Strength (kg/body mass) 0.51 0.10 0.26 0.77 0.39 0.07 0.23 0.64 Sedentary time (min/day) 547.8 89.2 266.2 794.5 537.8 83.2 321.2 781.0 Light PA (min/day) 249.8 67.9 71.0 494.7 266.8 72.6 113.3 497.6 MVPA (min/day) 50.9 27.9 5.9 211.7 42.8 23.3 1.5 135.3 Open in a new tab SD Standard Deviation, BMD Bone Mineral Density, TBS Trabecular Bone Score, BMI Body Mass Index, PA Physical activity MVPA Moderate to vigorous physical activity. The outcomes of the MLR models for each dependent variable are detailed in the following sections. Femoral neck BMD The MLR model for femoral neck BMD, which included 517 participants, explained 23% of the variance (R 2 = 0.23) and 22% after adjusting for the number of predictors (R 2 = 0.22). The regression coefficients can be found in Supplementary Table 1 . The analysis revealed significant associations of younger age (β = −0.0056, p ≤ 0.001), higher BMI (β = 0.0140, p ≤ 0.001), and greater grip strength (β = 0.2865, p = < 0.001) with increased Femoral neck BMD. Being female was negatively associated with Femoral neck BMD (β = −0.0390, p = 0.0058). Additionally, higher levels of light (β = 0.0002, p = 0.0095) and moderate to vigorous physical activity (MVPA) (β = 0.0006, p = 0.0097) positively correlated with Femoral neck BMD, whereas sedentary time did not show a significant association ( p = 0.8085). Total hip BMD The MLR model for femoral neck BMD included 517 participants and explained 19% of the variance (R 2 = 0.19), decreasing to 18% after adjusting for the number of predictors (R 2 = 0.18). The regression coefficients can be found in Supplementary Table 2 . Key observations from the model indicate that younger age (β = −0.0033, p = 0.005) and higher BMI (β = 0.0133, p = <0.001) were significantly associated with increased total hip BMD. Being female showed a tendency toward a negative association with total hip BMD, albeit with a p -value marginally above the conventional significance threshold (β = −0.0258, p = 0.0539). Higher grip strength (β = 0.2802, p ≤ 0.001), higher amounts of light physical activity (β = 0.0002, p = 0.0486), and greater amounts of MVPA (β = 0.0005, p = 0.0156) were also positively correlated with total hip BMD, while sedentary time was not a significant predictor ( p = 0.9097). Lumbar spine BMD The MLR model for lumbar spine BMD included 518 participants and explained 5.4% of the variance (R 2 = 0.054), decreasing to 4.1% after adjusting for the number of predictors (R 2 = 0.041). The regression coefficients for the analysis are summarized in Supplementary Table 3 . The findings show that higher BMI (β = 0.0069, p ≤ 0.001) and greater grip strength (β = 0.1765, p = 0.0103) were significantly associated with higher lumbar spine BMD. Age, sedentary time, light physical activity, and MVPA did not significantly influence lumbar spine BMD. Mean TBS A total of 400 participants had complete data to be included in the MLR model for mean TBS. This model accounted for 9.6% of the variance in mean TBS (R 2 = 0.096), and 8% after adjusting for the number of predictors (R 2 = 0.080). The regression coefficients are outlined in Supplementary Table 4 .The results indicate that being female (β = 0.0332, p = 0.0008) and larger amounts of light physical activity (β = 0.0002, p = 0.005) were significantly associated with higher mean TBS, while higher BMI (β = −0.0030, p = 0.0094) was significantly associated with lower mean TBS. Grip strength also showed a significant positive association (β = 0.1363, p = 0.0127). However, age, sedentary time, and MVPA were not significantly associated with mean TBS. Model comparison Figure 1 displays a bar chart of the standardized coefficients, illustrating the relative impact of each predictor on bone health. Fig. 1. Comparative analysis of model regression coefficients. Open in a new tab Statistically significant features ( p -values less than 0.05) are marked with an asterisk. BMD Bone Mineral Density, TBS Trabecular Bone Score, BMI Body Mass Index, PA Physical activity, MVPA Moderate to vigorous physical activity. Key observations from the study’s comparison reveal several impactful relationships between predictors and bone health outcomes: 1) BMI consistently exhibited a positive effect on bone quantity as measured by BMD, though it did not significantly affect bone quality, as indicated by the TBS. 2) Age was found to negatively influence both femoral neck and total hip BMD, yet it had a negligible effect on spine BMD and mean TBS. 3) Regarding the impact of sex, being female was associated with negative effects on femoral neck BMD, but it had a slightly positive impact on spine BMD and mean TBS. 4) Grip strength demonstrated a uniformly positive influence across all bone health outcomes. 5) Light physical activity and MVPA were positively associated with several indicators of bone health. Compliance with physical activity recommendations To determine if adherence to the World Health Organization’s (WHO) physical activity guidelines correlates with improved bone health, we performed unpaired T-tests comparing BMD and mean TBS between 81 participants who met the guideline (≥150 min/week of MVPA or ≥75 min/week of vigorous physical activity) and 436 who did not. The results showed statistically significant improvements in femoral neck BMD ( p = 0.002) and total hip BMD ( p = 0.007) for the adherent group. However, spine BMD and mean TBS did not show significant differences ( p = 0.761 and p = 0.193, respectively). DISCUSSION In this study we aimed to investigate how physical activity intensity and muscular strength interrelate and collectively impact key bone health indicators, namely BMD measured at the femoral neck, total hip, and lumbar spine, as well as mean TBS in young adults with overweight and obesity. Our findings demonstrate meaningful associations between predictors and bone health outcomes. For instance, each 0.1 kg/body mass increase in grip strength is associated with approximately a 0.029 g/cm 2 higher femoral neck BMD and a 0.028 g/cm 2 higher total hip BMD, highlighting the substantial impact of muscular strength on bone density. Similarly, each additional 10 min per day of moderate-to-vigorous physical activity (MVPA) corresponds to an approximate increase of 0.006 g/cm 2 in femoral neck BMD and 0.005 g/cm 2 in total hip BMD. Additionally, each increase of 1 kg/m 2 in BMI is related to an increase of ~0.014 g/cm 2 in femoral neck BMD. Although sedentary time showed negligible and non-significant effects, each additional 10 min/day spent in light physical activity was positively associated with increments of roughly 0.002 g/cm 2 in femoral neck BMD and total hip BMD. These clinically meaningful interpretations, derived directly from the absolute coefficients provided in Supplementary Tables 1 – 4 , facilitate practical application and enhance the clinical relevance of our results. In exploring the impact of physical activity intensity on bone health we found that, while both light activity and MVPA were positively correlated with femoral neck and total hip BMD, these activities did not significantly impact lumbar spine total BMD. This distinction aligns with previous research [ 26 ], which also observed differential association of physical activity with various BMD sites and specifically did not find associations between activity and bone density at the lumbar spine. Similarly, a meta-analysis of randomized controlled trials [ 27 ] suggested that lumbar spine BMD might be less responsive to exercise than BMD measured at the femur. However, this finding contrasts with studies that have identified a positive correlation between increased physical activity and lumbar spine BMD [ 28 ]. In combination, these differing results both across studies, and across regions (i.e. hip vs. spine) indicate that the relationship between physical activity and BMD is multifaceted, and the impact of different types of physical activities on specific body regions may vary. Further research is needed to better understand the mechanisms underlying this relationship and to clarify the specific effects of physical activity on spine BMD in young adults with overweight and obesity. The findings from the current study did not show a significant association between sedentary behavior on BMD, which is contrary to common assertions in the literature [ 29 , 30 ]. However, the evidence on this topic remains mixed. For instance, a study found that among young adult and middle-aged women, sedentary time was not a significant predictor of femoral or spinal BMD. However, for older adult females, sedentary time was a notable predictor of femoral, but not spinal BMD [ 28 ]. This suggests distinct differences between what affects the accumulation (or resorption) of spine and hip BMD. Previous studies indicate that sedentary time does not independently affect bone structure, BMD, or bone strength (assessed by HR-pQCT) in either gender [ 31 ], nor is there a link between sedentary time and lumbar spine BMD in men or women [ 32 ]. This divergence prompts a reevaluation of the role of sedentary time in bone health among young individuals with overweight and obesity. While previous research has indicated beneficial effects of physical activity on TBS [ 7 , 33 ], the specific impact of activity intensity on TBS in young adults with overweight and obesity remains less clear. A distinctive finding of our study is the positive influence of light physical activity on TBS, suggesting that even activities of lower intensity, which might be more practical for individuals with overweight and obesity, can positively affect bone quality. This insight is crucial for formulating realistic and effective physical activity guidelines that are appropriately tailored to this group’s needs and abilities. Our findings also align with the broader benefits of meeting current WHO physical activity recommendations, showing a positive correlation with higher femoral neck and total hip BMD. However, these benefits did not extend to spine BMD and mean TBS, indicating the nuanced effects of physical activity on different aspects of bone health. This distinction underscores an important consideration: the factors that enhance bone quality may differ from those that increase bone quantity, suggesting the need for varied intervention strategies. In light of our results, further research into how low-intensity physical activities specifically benefit bone quality in young adults with overweight and obesity is warranted. This could contribute to the development of customized physical activity guidelines that not only consider bone density but also the quality of bone microarchitecture. Such guidelines would underscore the intricate relationship between physical activity and bone health in this demographic, emphasizing the need for personalized approaches to physical activity recommendations. A critical observation from our study is that, although BMI positively affects BMD [ 34 ], a higher BMI is associated with lower TBS in our sample. This finding echoes the results of previous studies which reported similar disagreement between bone density and markers of bone quality in populations with obesity [ 35 – 38 ]. The traditional view that increased body weight is advantageous for bone health due to enhanced mechanical loading has been increasingly questioned in recent research that shows how obesity can interfere with bone metabolism through mechanical, hormonal, and inflammatory factors [ 11 , 39 – 41 ]. Even though individuals with obesity often have normal or higher BMD values, they still face an elevated risk of fractures [ 42 , 43 ]. Thus, while individuals with a higher BMI may also have a higher BMD, it might not necessarily mean that the microarchitecture of the bone is also improved, as evidenced by the lower TBS scores. Overall, these findings underscore the need for a deeper investigation into how obesity-related factors might differentially impact bone density and quality. Across all measured bone health outcomes, grip strength consistently demonstrated a positive association with bone health. Given grip strength’s strong correlation with strength metrics across the entire body [ 44 ], these results suggests that muscular strength is a vital determinant of bone health, aligning with a growing body of research [ 45 – 47 ]. Incorporating grip strength into regular health assessments could significantly enhance the early detection of individuals at risk of poor bone health, allowing for prompt interventions like strength training and muscle-building exercises. Given that DXA scans are not routinely performed on younger individuals, it could be advisable to utilize grip strength clinically as a predictive tool for bone health. This approach should include the development of age-specific cut points that indicate deteriorating bone health. Establishing these benchmarks could help guide clinical decisions and tailor interventions such as strength training and muscle-building exercises. Our study is not without limitations. One of the primary limitations lies in its cross-sectional design, which naturally limits the ability to make definitive causal inferences. Given the exploratory nature of this secondary analysis, multiple comparisons were performed without formal adjustments, increasing the possibility of false-positive findings. Therefore, our findings should be interpreted cautiously, and further research is needed to confirm these associations. Another limitation pertains to the use of accelerometry to objectively assess physical activity. While accelerometry is a widely accepted method for measuring physical activity levels, it has some limitations. These include the inability to capture certain types of activities accurately, such as swimming or cycling, and difficulties in assessing the intensity of some movements [ 48 , 49 ]. Additionally, because accelerometry most accurately typifies ambulation-based activities, key exercise modalities expected to impact bone health, most notably resistance training, are unlikely to be well captured. This represents a significant limitation, as we did not collect data on participants’ participation in resistance training or their historical exercise patterns. Although lifestyle factors such as smoking and alcohol consumption are recognized to negatively affect bone health, preliminary analyses revealed no significant associations in our sample. This is likely due to the young age (18–35 years) and generally low consumption levels among our participants—for instance, 83% consumed fewer than one alcoholic drink per day, and 95% of smokers smoked fewer than five cigarettes daily. To further explore potential residual confounding, we conducted sensitivity analyses explicitly including smoking and alcohol as covariates. Results remained highly consistent, with no substantial changes in the magnitude, direction, or significance of our findings. Therefore, we did not include these factors as covariates in our primary analyses. However, we acknowledge that their exclusion might slightly limit the generalizability of our findings, and further studies with populations exhibiting broader smoking and drinking behaviors may provide additional insights. Finally, while TBS is a valuable tool for fracture risk assessment, it is important to note that its measurements may have limitations in individuals with high BMI, as excessive adipose tissue can influence the accuracy of trabecular bone quality assessment [ 50 ]. This study’s findings may have significant implications for clinical practice aimed at enhancing bone health among young adults with overweight and obesity. A deeper understanding of how physical activity, BMI, grip strength, and various bone health indicators interact can help shape more customized clinical guidelines and public health initiatives. Interventions should extend beyond mere weight loss to emphasize the comprehensive benefits of physical activity alongside muscle-strengthening exercises for bone health. Public health campaigns and programs should be developed to more broadly educate the public on the comprehensive nature of bone health, highlighting that all levels of physical activity —including light activities— may contribute positively to maintaining and improving bone structure. Future research should aim to deepen our understanding of bone health in young adults with overweight and obesity by focusing on a variety of critical areas, as highlighted by our study. Longitudinal studies are imperative to delineate causal links between physical activity, BMI, grip strength, and bone health, advancing beyond the correlations established by cross-sectional analyses. Additionally, it is important to examine other influencing factors on bone health, including dietary habits, hormone levels, genetic factors, and socio-economic backgrounds. Delving into the precise mechanisms by which physical activity and grip strength affect bone health at the molecular, hormonal, and cellular levels will shed light on targeted intervention strategies. It is also vital to assess how these influences differ among various demographic segments, taking into account ethnicity and age, to uncover population-specific trends in bone health. Furthermore, evaluating the efficacy of different intervention approaches, such as varied physical activity regimens and lifestyle changes, is crucial for devising effective strategies to bolster bone health in this demographic. These research directions will contribute to a more comprehensive and nuanced understanding of bone health in young adults with overweight and obesity, guiding the development of targeted prevention and management strategies. Our study provides a groundwork for this exploration, offering key insights into the relationships between physical activity intensity, BMI, grip strength, and bone health, and underscoring the complexity of these interactions. CONCLUSIONS Light physical activity and MVPA positively correlate with femoral neck and total hip BMD, while the impact on spine BMD is less pronounced among young adults with overweight and obesity. Additionally, light physical activity shows a beneficial association with TBS, suggesting that even moderate exercise can positively influence bone quality in this demographic. The study also highlights an inverse relationship between BMI and TBS, challenging conventional perceptions that higher body weight uniformly benefits bone health. This finding emphasizes the complexity of factors affecting bone quality and the need for a nuanced understanding of bone health in individuals with overweight and obesity. Grip strength emerged as a positive predictor of bone health, reinforcing its value as a reliable, straightforward marker for identifying individuals at risk of poor bone health and guiding intervention strategies. These insights underscore the importance of personalized physical activity regimens that consider the specific needs and capabilities of young adults with overweight and obesity, aiming not only to enhance bone mass but also to improve bone quality. Future research should continue to explore these relationships longitudinally and in diverse populations to refine our understanding and inform more targeted and effective bone health strategies. Supplementary Material SUPPLEMENTARY MATERIALS NIHMS2173157-supplement-SUPPLEMENTARY_MATERIALS.pdf (228.1KB, pdf) Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41366-025-01833-2 . FUNDING The study is supported by a grant from the National Institute of Health’s National Heart Lung and Blood Institute: R01HL136769. This work was carried out during a research stay financed by the University of Sevilla. Footnotes COMPETING INTERESTS The authors declare no competing interests. ETHICS APPROVAL AND CONSENT TO PARTICIPATE All methods were performed in accordance with the relevant guidelines and regulations. Ethical approval for the SMART 2.0 study was obtained from the Human Research Protections Program at the University of California, San Diego (UCSD) under protocol #181862. Written informed consent to participate was obtained from all participants prior to enrollment. No identifiable images of participants are included in this publication. Therefore, separate written informed consent for publication of images was not required. DATA AVAILABILITY The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request, in line with Springer Nature’s Type 3 data policy. REFERENCES 1. Kanis JA. Diagnosis and clinical aspects of osteoporosis. In: Pocket Reference to Osteoporosis. Cham: Springer International Publishing; 2018. pp. 11–20. [ Google Scholar ] 2. Harvey N, Dennison E, Cooper C. Osteoporosis: impact on health and economics. Nat Rev Rheumatol. 2010;6:99–105. [ DOI ] [ PubMed ] [ Google Scholar ] 3. Pouresmaeili F, Kamalidehghan B, Kamarehei M, Goh YM. A comprehensive overview on osteoporosis and its risk factors. Ther Clin Risk Manag. 2018;14:2029–49. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. 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