Associations of Visceral Adiposity Index and Lipid Accumulation Product with bone health status in men aged 50 and over: a cross-sectional analysis of Bushehr Elderly Health (BEH) Program

Associations of Visceral Adiposity Index and Lipid Accumulation Product with bone health status in men aged 50 and over: a cross-sectional analysis of Bushehr Elderly Health (BEH) Program | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations of Visceral Adiposity Index and Lipid Accumulation Product with bone health status in men aged 50 and over: a cross-sectional analysis of Bushehr Elderly Health (BEH) Program Shahrzad Mohseni, Kazem Khalagi, Alireza Amanollahi, Noushin Fahimfar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8908074/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The association of obesity- related markers such as Visceral Adiposity Index (VAI) and Lipid Accumulation Product (LAP) and bone health is controversial. This study aimed to evaluate this association among men using data from of the second recruitment of the Bushehr Elderly Health (BEH) program. Methods A cross-sectional analysis was conducted on 851 men aged 50 years and older on the data from BEH program. Demographic information, laboratory data, bone mineral density (BMD) and trabecular bone score (TBS) were collected. Participants with a T-score ≤ − 2.5 at the lumbar spine, total hip, or femoral neck were classified as having osteoporosis, while others were not. Low BMD (osteopenia/osteoporosis), was defined as a T-score ≤ − 1 at any of these sites. Associations of adiposity measures and bone health were investigated using linear and logistic regression analyses. Results The mean age of the participants was 62.9 ± 0.2 years and 151 (17.7%) were diagnosed with osteoporosis. Compared to individuals without osteoporosis, those with osteoporosis had lower mean of VAI (2.3 vs. 2.7, p = 0.001) and LAP (44.5 vs. 61.6, p < 0.001). After adjusting for known and measured confounders, VAI exhibited a positive association with BMD at total hip and LAP exhibited a negative association with TBS. Each one unit increases in VAI was associated with 0.0192 gr/cm 2 increase in lumbar spine BMD, while one-unit increase in LAP was associated with 0.0011 gr/cm 2 decrease in TBS. Moreover, in logistic regression analysis, VAI was related with lower odds of having low BMD (odds ratio = 0.725, p = 0.041). Conclusion These findings highlighted that some adiposity parameters may serve as valuable markers in evaluating bone health in older adult men. A deeper understanding of these associations may improve clinical approaches for the prevention and management of osteoporosis in this population. Bone mineral density Trabecular bone score Lipid accumulation product Visceral adiposity index Osteoporosis Iran Introduction Osteoporosis, characterized by decreased bone mineral density (BMD) and microstructural deterioration, is affecting one in three women and one in five men over age 50 worldwide [ 1 ]. In Iran specifically, a meta-analysis including 10 studies aged over 50 reported a prevalence of osteoporosis at 38% for women, and 25% for men [ 2 ]. This condition raises bone fragility and resulted in an increased risk of fractures [ 3 ], resulted in significant poor patients’ quality of life and financial burden. Osteoporosis constitutes a significant health concern in men, primarily owing to its underdiagnosis and undertreatment relative to women[ 4 ]. The reduction of testosterone levels critically accelerates bone loss, whereas increased adiposity exacerbates hormonal imbalance by promoting the aromatization of testosterone to estrogen [ 5 ]. Dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis diagnosis, but its use in wide-scale screening is restricted by its limited access as well as high cost [ 6 ]. As a result, it is crucial to find a biomarker that is affordable and practical, for early screening and osteoporosis risk evaluation. It has been shown that obesity has a complex relationship with BMD status[ 7 – 9 ]. Several epidemiological studies have been examined the relationship between bone health status and conventional obesity indices such as body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR). However, these metrics predominantly reflect the total body fat rather than visceral fat [ 10 ]. It has been shown that visceral fat has a strong association with bone metabolism [ 11 ]. In recent decade, some novel indicators including the visceral adiposity index (VAI) and lipid accumulation product (LAP) have gained attention for their potential associations with osteoporosis and bone quality measures. VAI incorporates BMI, WC, triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) and reflects visceral fat accumulation[ 12 ]. Likewise, LAP that is determined by TG and WC, provides the degree of lipid overaccumulation [ 13 ]. The present evidence on the relationship between these indices and bone health is controversial, and is still scarce, notably among Iranian populations. Moreover, to our knowledge the relationship of LAP and VAI with trabecular bone score (TBS), an indicator of bone microarchitecture quality, has not been evaluated by the previous research. As the incidence of osteoporosis is higher in women aged 50 years and older, most studies have focused on female populations, with comparatively less attention to men. Nonetheless, emerging evidence suggests that osteoporosis-related complications may occur more frequently in men than in women[ 14 ]. The objective of the present study was to assess the association of LAP and VAI with BMD and TBS among men aged of 50 and more utilizing data from the Bushehr Elderly Health (BEH) Program. Material and methods Study population A cross-sectional analysis was conducted on 851 men aged 50 years and older using data from the second recruitment of the Bushehr Elderly Health (BEH) program (PoCOsteo study). The BEH program has conducted by the Endocrinology and Metabolism Research Institute (EMRI) at Tehran University of Medical Sciences and Persian Gulf Marine Biotechnology Research Center (PGTMRC) at Bushehr University of Medical Sciences. The primary objective of the BEH program was to determine the epidemiology of non-communicable diseases (NCDs) and their risk factors among the elderly population [ 15 , 16 ]. The second wave of recruitment of the BEH program, implemented between 2018 and 2019, and introduced 2,000 new participants aged 50 years and older into the BEH cohort. The study population consisted of residents of Bushehr city, who were selected using a multi-stage stratified cluster sampling method [ 15 , 17 ]. All participants signed a written informed consent. Comprehensive details of the study protocol and methodology have been published previously [ 17 ]. Official researchers accessed to the dataset in June 2025, at which time the analysis phase began. The authors did not have access to the personal and identifying information of the participants when analyzing the data. This study was conducted in accordance with the Declaration of Helsinki. Participants with incomplete and invalid data were excluded from the analyses. The protocol of the current study was approved by the Ethics Committee of the Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences (ID code: IR.TUMS.EMRI.REC.1403.072). Data collection Information on age, medical history, education level, tobacco smoking (cigarettes or water pipes) and medication use were collected through a comprehensive, valid questionnaire by trained interviewers. The height and weight of individuals were measured by a fixed stadiometer and a digital scale after wearing light clothes and bared foot. Waist circumferences was measured using a flexible and fixed elastic band at the midpoint between the last rib and the iliac crest. BMI was calculated by dividing weight in kilograms by the square of height in meters. After overnight fasting for 8–12-hour, 20 ml of venous blood sampling were obtained. Commercial assay kits (Pars Azmoon, Karaj, Iran) were utilized to determine fasting plasma glucose (FPG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), blood urea nitrogen (BUN), alkaline phosphatase (ALP), and serum 25-hydroxy vitamin D3. Bone mineral density (BMD) of the lumbar spine (L1-L4), total hip, and femoral neck as well as trabecular bone score (TBS) were evaluated using a dual-energy X-ray absorptiometry (DXA) system (Hologic, Bedford, MA, USA). Definition of terms Current smokers included those who regularly or occasionally smoked cigarettes, as well as those who used pipes or hookah. Based on WHO definitions of osteoporosis, participants were categorized according to their T-score values [ 18 ]. The reference population for T-score calculation is the bone mass at each site in young Caucasian women. Those with a T-score of 2.5 standard deviation (SD) or more below the reference mean (− 2.5 and lower) in at least spine, total hip, or femoral neck were classified as having osteoporosis, while the remainder were considered to have no osteoporosis. Furthermore, we defined low BMD, which includes both osteopenia and osteoporosis, as having a T-score that is one standard deviation or more below the reference mean (-1 or lower) at each site. The VAI and LAP were calculated as follows: LAP for male = [WC (cm) − 65] × TG (mmol/l) [ 13 ] VAI for male= [WC (cm)/ 39.68 + (1.88 × BMI (kg/m 2 ))] × [TG (mmol/L)/1.03) × (1.31/HDL-C (mmol/ L))][ 19 ] Statistical analyses The comparison of studied variables between the outcome groups—including demographic, laboratory, clinical, and anthropometric indices—was conducted using independent t-tests and chi-square tests. To assess the associations between the predictors (VAI and LAP) and the outcomes of interest (osteoporosis, low BMD, TBS L1–L4, and lumbar spine, total hip, and femoral neck BMDs), logistic or linear regression analyses were performed. For each outcome, two models were estimated: one unadjusted (crude) and one adjusted. The adjusted models accounted for potential confounders such as age, BUN, ALP, TG, HDL-C, LDL-C, FBS, BMI, cholesterol, vitamin D, education, and smoking status. All statistical assumptions were evaluated throughout the analyses. Group comparisons were checked for normality and homogeneity of variances, while modeling procedures assessed multicollinearity among predictors and the behavior of residuals in linear regression. Variance Inflation Factor (VIF) was used to test collinearity between variables and residual model also had a mean of zero and a standard deviation of 1. All analyses were conducted at a significance level of 0.05 using Stata software, and figures were generated with the ggplot2 package in R. Results The baseline characteristics of study population are presented in Table 1. The study population consisted of 851 individuals (the mean age of 62.9± 0.2 years), of whom 151 (17.7%) were diagnosed with osteoporosis. Compared to individuals without osteoporosis, those with osteoporosis were older (66.5 vs. 62.1 years), had a lower BMI (24.2 vs. 26.9 kg/m 2 ), had higher levels of education, had a greater prevalence of current smoking (36.4% vs. 25.5%), had a lower waist circumference (93.1 cm vs. 99.9 cm), had higher level of ALP (246.5 vs. 216.2 IU/L) and exhibited lower levels of TG, VAI and LAP. Table 1. Baseline characteristics of the participants based on osteoporosis status Association of VAI and LAP with BMD status and TBS Table 2 presents the associations of the anthropometric indices with BMD status and TBS. In the simple linear regression model, a positive association of VAI and LAP with BMD status at all sites was found. A negative significant association was found between LAP and TBS in simple linear regression model. After adjusting for known and measured confounders, all of the associations were lost except for VAI, which exhibited a positive association with BMD at total hip and LAP, which exhibited a negative association with TBS. Each one unit increases in VAI was associated with 0.0192 gr/cm 2 increase in lumbar spine BMD, while one-unit increase in LAP was associated with 0.0011 gr/cm 2 decrease in TBS. Table 2. The association of VAI and LAP with bone mineral density and TBS among study participants Outcome Exposure Crude model Adjusted model * ß (%95 CI) p-value ß (%95 CI) p-value Lumbar spine (L1-L4) BMD VAI 0.012(0.003, 0.021) 0.007 0.003(-0.0054, 0.0114) 0.482 LAP 0.0008(0.0005, 0.0011) <0.001 0.00002(-0.0003, 0.0004) 0.884 Total hip BMD VAI 0.015(0.007, 0.022) <0.001 0.0192(0.0034, 0.035) 0.017 LAP 0.0008(0.0006, 0.0010) <0.001 0.00003(-0.0002, 0.0003) 0.803 Femoral neck BMD VAI 0.013(0.006, 0.019) <0.001 0.0032(-0.0031, 0.0097) 0.318 LAP 0.0007(0.0005, 0.0009) <0.001 -0.00001(-0.0002, 0.0002) 0.900 TBS (L1-L4) VAI -0.0002(-0.005, 0.005) 0.949 0.001(-0.003, 0.006) 0.645 LAP -0.0004(-0.0005, -0.0002) <0.001 -0.0011(-0.0016, -0.0007) <0.001 *Adjusted for Age, BUN, ALP, FBS, LDL-C, Cholesterol, vitamin D, education, smoking, BMI (except for VAI), HDL-C (except for VAI). Abbreviations: CI, Confidence interval; VAI, Visceral adiposity index; LAP, lipid accumulation product Association of VAI and LAP with osteoporosis and low BMD The associations of VAI and LAP with osteoporosis and low BMD are presented in Table 3. In the crude model, both anthropometric indices demonstrated a statistically significant protective association with the both osteoporosis and low BMD (OR < 1). However, after adjusting for potential confounders, only the association between VAI and low BMD remained significant (OR = 0.725, p = 0.041). On the other hand, higher VAI was related with lower odds of having low BMD. Table 3. Logistic regression analysis assessed the association of LAP and VAI with osteoporosis and low BMD Outcome Exposure Crude model Adjusted model * OR (%95 CI) p-value OR (%95 CI) p-value Osteoporosis VAI 0.763(0.645, 0.902) 0.002 0.863(0.723, 1.03) 0.102 LAP 0.985(0.978, 0.990) <0.001 0.996(0.990, 1.004) 0.385 Low BMD VAI 0.888(0.795, 0.993) 0.036 0.725(0.533, 0.987) 0.041 LAP 0.992(0.989, 0.996) <0.001 1.002(0.997, 1.007) 0.404 *Adjusted for Age, BUN, ALP, FBS, LDL-C, Cholesterol, vitamin D, education, smoking, BMI (except for VAI), HDL-C (except for VAI). Abbreviations: CI, Confidence interval; OR, Odds ratio; VAI, Visceral adiposity index; LAP, lipid accumulation product Discussion In this large population-based cross-sectional study of the older adults in the Iran using BEH program data, the results revealed that those with osteoporosis had significantly lower BMI and waist circumference compared to those without osteoporosis. Furthermore, VAI and LAP values was lower in osteoporotic group compared to those without it. After comprehensive adjustment for potential confounders, higher VAI level was positively associated with greater BMD at total hip and the lower odds of having low BMD. Conversely, it was shown that higher LAP was associated with lower TBS. Compared to traditional obesity indices, LAP and VAI are more sensitive and accurate in assessing cardiovascular disorders and metabolic health. Despite numerous studies, the association between visceral adiposity and bone health remains controversial. Some recent studies have shown that higher visceral fat level is harmful for bone health and is a risk factor for osteoporosis or low BMD [ 20 ], while some others proposed that it is protective against osteoporosis[ 21 , 22 ]. Chen et all. [ 23 ] found a negative linear relationship between adiposity and osteoporosis among men over 50 years. Another study by Tian et al. [ 22 ] showed the protective effect of adiposity against osteoporosis in both men and women. Evans et al. [ 24 ] reported a higher BMD in obese men and women of 55–75 years compared to those with normal weight, that reflects possible protective role of obesity in preventing age-related bone loss. On the other hand, Jiao et al. [ 25 ] reported an inverse relationship between obesity-related parameters and BMD among male participants. These discrepancies are attributed to variations in study population, study design and controlling for confounding variables. In the present study, the positive association observed between VAI and hip BMD, as well as its protective effect against low BMD, can be explained by the greater mechanical loading exerted by a heavier body on the skeleton that promotes bone remodeling processes and ,in turn, resulted in higher bone density as an adaptive reaction [ 26 ]. Obesity and BMD exhibited a complex relationship that influenced by multiple factors [ 27 ]. The mechanisms linking obesity and bone health mainly involve two aspects: mechanical and metabolic. Obesity increases mechanical loading on bones, which protects bone modeling, density, and structure, potentially explaining the higher BMD seen in obese individuals[ 28 ]. On the other hand, visceral adipose tissue releases prominent proinflammatory cytokines, including tumor necrotizing factor- α (TNF-α) and interlukin-1 and − 6 which aggravate a systemic inflammatory response, disrupt metabolic homeostasis, and adversely affect bone health[ 29 ]. Adipocytokines such as leptin and adiponectin have direct effects on skeletal metabolism. The association between leptin and BMD has been reported to demonstrate both positive and negative correlations[ 30 ]. Leptin which is secreted by fat cells may inhibit bone breakdown by raising osteoprotegerin levels that block osteoclast formation[ 31 ]; however, through its regulation of the central nervous system, it may simultaneously inhibit bone formation[ 30 ]. Similarly, adiponectin that is secreted by adipocytes, impacts visceral fat and bone metabolism with dual effect [ 32 , 33 ]. Adiponectin enhances proliferation and differentiation of human osteoblasts, through the mitogen-activated protein kinase (MAPK) signaling pathway [ 34 ]. Conversely, by upregulating RANKL and suppressing osteoprotegerin production in osteoblasts, it indirectly assists osteoclastogenesis leading to reduced BMD [ 35 ]. Given the dualistic roles of these factors, which may yield both beneficial and adverse effects on bone health, their overall effects remain controversial. Hence, further research is essential to thoroughly reveal the complex interplay between visceral fat and bone. To the best of our knowledge this was the first study evaluating the association between some obesity-related indices and TBS. Our result revealed a negative association LAP and TBS. BMD assesses solely the quantity of bone and does not offer information regarding bone quality. The TBS, derived from DXA images, reflects the structural integrity of bone microarchitecture and serves as a superior indicator for evaluating bone quality; with low TBS values indicates degradation of bone microarchitecture, which contribute to an increased risk of fractures, to some extent independent of BMD[ 36 ]. Consistent with our findings, Lv et al.[ 37 ] found that visceral adiposity, have negative effects on bone microstructure among healthy Chinese men (mean age: 58.78 years). Visceral adiposity induces low-grade systemic inflammation, progressive insulin resistance, and other metabolic disorders [ 38 , 39 ]. A cross-sectional analysis on 1229 Korean male aged 50 or older, revealed that subjects with low TBS values were more insulin-resistant than those with high TBS [ 40 ]. This association may be attributed to the formation of advanced glycosylation products (AGEs) in the bone matrix under the condition of insulin resistance state, which impairs bone deformation capacity and resistance, as well as increases fragility [ 41 ]. However, further longitudinal studies are needed to clarify these relationships and to elucidate potential causal pathways. Our study had several advantages. It included a large community-based sample with a focus on men aged 50 years and older. Additionally, we applied a broadly important confounding factors, which contributes to the robustness of the findings. Our results provide a foundation for further longitudinal cohort studies. However, the cross-sectional design precludes causal implication and reverse causality cannot be excluded. However, the cross-sectional design limits causal inferences, and the possibility of reverse causality cannot be ruled out. Conclusion In conclusion, this large-sample study revealed a significant association between some adiposity measures and bone health in men aged 50 years and above. Our findings revealed that VAI is significantly associated with higher total hip BMD as well as lower odds of having low BMD. On the other hand, it was demonstrated that LAP had negative relationship with TBS values. These findings underscore a complex interplay between adiposity and bone health. Future research should validate these findings and investigate the underlying mechanisms, through longitudinal studies. A deeper understanding of these associations may improve clinical approaches for the prevention and management of osteoporosis in aging male population. Declarations Ethical approval This study was conducted in accordance with the Declaration of Helsinki. Participants with incomplete and invalid data were excluded from the analyses. The protocol of the current study was approved by the Ethics Committee of the Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences (ID code: IR.TUMS.EMRI.REC.1403.072). Funding Not applicable. Availability of data and materials The data used in this study are from the Bushehr Elderly Health (BEH) program, a population-based cohort study owned and governed by the Ministry of Health and Medical Education of the Islamic Republic of Iran through the Persian Cohort and Biobank regulations. Due to ethical and legal restrictions on sharing potentially identifiable human data and national regulations, the raw dataset cannot be made publicly available. However, the de-identified minimal dataset underlying the results of this study is available upon reasonable request to the project administrator. Acknowledgements The present study ( ethic code: IR.TUMS.EMRI.REC.1403.072), was a secondary analysis of data from the Bushehr Elderly Health (BEH) Program (ethic code: IR.TUMS.EMRI.REC.1394.0036). Author Contributions SHM: writing (original draft, and editing); KK : methodology, validation; AA : Formal analysis; NF: validation, supervision; MPS: conceptualization; supervision; IN and AO: Project administration. All authors reviewed the manuscript and approved the final version. Conflict of Interest The authors declared that they have no conflict of interest. Human Ethics and Consent to Participate declarations Not applicable . Clinical trial number Not applicable References Amin U, McPartland A, O’Sullivan M, Silke C. An overview of the management of osteoporosis in the aging female population. Women's Health. 2023;19:17455057231176655. Fahimfar N, Hesari E, Mansourzadeh MJ, Khalagi K, Sanjari M, Hajivalizadeh S, et al. 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Adiponectin stimulates RANKL and inhibits OPG expression in human osteoblasts through the MAPK signaling pathway. J Bone Miner Res. 2006;21(10):1648–56. doi: 10.1359/jbmr.060707. Leslie WD, Binkley N, McCloskey EV, Johansson H, Harvey NC, Lorentzon M, et al. FRAX Adjustment by Trabecular Bone Score with or Without Bone Mineral Density: The Manitoba BMD Registry. J Clin Densitom. 2023;26(3):101378. doi: 10.1016/j.jocd.2023.101378. Lv S, Zhang A, Di W, Sheng Y, Cheng P, Qi H, et al. Assessment of Fat distribution and Bone quality with Trabecular Bone Score (TBS) in Healthy Chinese Men. Sci Rep. 2016;6:24935. doi: 10.1038/srep24935. Favaretto F, Bettini S, Busetto L, Milan G, Vettor R. Adipogenic progenitors in different organs: Pathophysiological implications. Rev Endocr Metab Disord. 2022;23(1):71–85. doi: 10.1007/s11154-021-09686-6. Longo M, Zatterale F, Naderi J, Parrillo L, Formisano P, Raciti GA, et al. Adipose Tissue Dysfunction as Determinant of Obesity-Associated Metabolic Complications. Int J Mol Sci. 2019;20(9). doi: 10.3390/ijms20092358. Kim JH, Choi HJ, Ku EJ, Kim KM, Kim SW, Cho NH, et al. Trabecular bone score as an indicator for skeletal deterioration in diabetes. J Clin Endocrinol Metab. 2015;100(2):475–82. doi: 10.1210/jc.2014-2047. Napoli N, Chandran M, Pierroz DD, Abrahamsen B, Schwartz AV, Ferrari SL, et al. Mechanisms of diabetes mellitus-induced bone fragility. Nat Rev Endocrinol. 2017;13(4):208–19. doi: 10.1038/nrendo.2016.153. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8908074","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594735141,"identity":"74967d06-61a5-484e-92ef-fe9fb168f818","order_by":0,"name":"Shahrzad Mohseni","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shahrzad","middleName":"","lastName":"Mohseni","suffix":""},{"id":594735142,"identity":"15fa3ce7-489d-45e2-8892-5e8c0d51f03a","order_by":1,"name":"Kazem Khalagi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kazem","middleName":"","lastName":"Khalagi","suffix":""},{"id":594735143,"identity":"0778c8b3-01b3-4fbe-9779-e82f7eb74c8a","order_by":2,"name":"Alireza Amanollahi","email":"","orcid":"","institution":"Iran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Amanollahi","suffix":""},{"id":594735144,"identity":"e952cdf7-64f1-442e-826c-0bbd28765aed","order_by":3,"name":"Noushin Fahimfar","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Noushin","middleName":"","lastName":"Fahimfar","suffix":""},{"id":594735146,"identity":"2166620f-409d-453c-bb65-2f0fc386edea","order_by":4,"name":"Mahnaz Pejman Sani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYNACAxsGNhSBBMJa0iBaDhCvheEwhDqAVxEU6M5uf/i5oOC8PZ908wPmj23b7BnYDz9geLgHtxazO2eMpWcY3E5skzlmwHCw7XZiA0+aAUPCMzxabuQwSPMY3E5gk0gAawH6IgfoFzxONLuR/vg3j8E5ezaJ9A8gLfYM/G8IaUkwA9pygLFNIgdsC2ODBCFb7pwxs+YxSE4Eaik4cOYc0FMSzwwO4NVyu/3xbZ4/dvbyM9I3Pqgou23Pz5/88OEPPFoYJJDYYHVsDITiRwKv7CgYBaNgFIwCIAAAVbFQ4LttJZIAAAAASUVORK5CYII=","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mahnaz","middleName":"Pejman","lastName":"Sani","suffix":""},{"id":594735149,"identity":"3155f5da-e9ef-45c8-8a85-e93930298f64","order_by":5,"name":"Iraj Nabipour","email":"","orcid":"","institution":"Bushehr University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Iraj","middleName":"","lastName":"Nabipour","suffix":""},{"id":594735150,"identity":"53fbd99f-63b9-45a6-bb37-8512c38215fb","order_by":6,"name":"Afshin Ostovar","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Afshin","middleName":"","lastName":"Ostovar","suffix":""}],"badges":[],"createdAt":"2026-02-18 10:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8908074/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8908074/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103506158,"identity":"ca280dd6-44b4-4858-896f-39156eca0d64","added_by":"auto","created_at":"2026-02-26 13:34:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":897061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8908074/v1/05c45430-54de-46eb-bd18-28ef1e290e35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssociations of Visceral Adiposity Index and Lipid Accumulation Product with bone health status in men aged 50 and over: a cross-sectional analysis of Bushehr Elderly Health (BEH) Program\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis, characterized by decreased bone mineral density (BMD) and microstructural deterioration, is affecting one in three women and one in five men over age 50 worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Iran specifically, a meta-analysis including 10 studies aged over 50 reported a prevalence of osteoporosis at 38% for women, and 25% for men [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This condition raises bone fragility and resulted in an increased risk of fractures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], resulted in significant poor patients\u0026rsquo; quality of life and financial burden.\u003c/p\u003e \u003cp\u003eOsteoporosis constitutes a significant health concern in men, primarily owing to its underdiagnosis and undertreatment relative to women[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The reduction of testosterone levels critically accelerates bone loss, whereas increased adiposity exacerbates hormonal imbalance by promoting the aromatization of testosterone to estrogen [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis diagnosis, but its use in wide-scale screening is restricted by its limited access as well as high cost [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a result, it is crucial to find a biomarker that is affordable and practical, for early screening and osteoporosis risk evaluation.\u003c/p\u003e \u003cp\u003eIt has been shown that obesity has a complex relationship with BMD status[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Several epidemiological studies have been examined the relationship between bone health status and conventional obesity indices such as body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR). However, these metrics predominantly reflect the total body fat rather than visceral fat [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It has been shown that visceral fat has a strong association with bone metabolism [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In recent decade, some novel indicators including the visceral adiposity index (VAI) and lipid accumulation product (LAP) have gained attention for their potential associations with osteoporosis and bone quality measures. VAI incorporates BMI, WC, triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) and reflects visceral fat accumulation[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Likewise, LAP that is determined by TG and WC, provides the degree of lipid overaccumulation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The present evidence on the relationship between these indices and bone health is controversial, and is still scarce, notably among Iranian populations. Moreover, to our knowledge the relationship of LAP and VAI with trabecular bone score (TBS), an indicator of bone microarchitecture quality, has not been evaluated by the previous research.\u003c/p\u003e \u003cp\u003eAs the incidence of osteoporosis is higher in women aged 50 years and older, most studies have focused on female populations, with comparatively less attention to men. Nonetheless, emerging evidence suggests that osteoporosis-related complications may occur more frequently in men than in women[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The objective of the present study was to assess the association of LAP and VAI with BMD and TBS among men aged of 50 and more utilizing data from the Bushehr Elderly Health (BEH) Program.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA cross-sectional analysis was conducted on 851 men aged 50 years and older using data from the second recruitment of the Bushehr Elderly Health (BEH) program (PoCOsteo study). The BEH program has conducted by the Endocrinology and Metabolism Research Institute (EMRI) at Tehran University of Medical Sciences and Persian Gulf Marine Biotechnology Research Center (PGTMRC) at Bushehr University of Medical Sciences. The primary objective of the BEH program was to determine the epidemiology of non-communicable diseases (NCDs) and their risk factors among the elderly population [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The second wave of recruitment of the BEH program, implemented between 2018 and 2019, and introduced 2,000 new participants aged 50 years and older into the BEH cohort. The study population consisted of residents of Bushehr city, who were selected using a multi-stage stratified cluster sampling method [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e All participants signed a written informed consent. Comprehensive details of the study protocol and methodology have been published previously [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Official researchers accessed to the dataset in June 2025, at which time the analysis phase began. The authors did not have access to the personal and identifying information of the participants when analyzing the data.\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki. Participants with incomplete and invalid data were excluded from the analyses. The protocol of the current study was approved by the Ethics Committee of the Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences (ID code: IR.TUMS.EMRI.REC.1403.072).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eInformation on age, medical history, education level, tobacco smoking (cigarettes or water pipes) and medication use were collected through a comprehensive, valid questionnaire by trained interviewers. The height and weight of individuals were measured by a fixed stadiometer and a digital scale after wearing light clothes and bared foot. Waist circumferences was measured using a flexible and fixed elastic band at the midpoint between the last rib and the iliac crest. BMI was calculated by dividing weight in kilograms by the square of height in meters. After overnight fasting for 8\u0026ndash;12-hour, 20 ml of venous blood sampling were obtained. Commercial assay kits (Pars Azmoon, Karaj, Iran) were utilized to determine fasting plasma glucose (FPG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), blood urea nitrogen (BUN), alkaline phosphatase (ALP), and serum 25-hydroxy vitamin D3.\u003c/p\u003e \u003cp\u003eBone mineral density (BMD) of the lumbar spine (L1-L4), total hip, and femoral neck as well as trabecular bone score (TBS) were evaluated using a dual-energy X-ray absorptiometry (DXA) system (Hologic, Bedford, MA, USA).\u003c/p\u003e\n\u003ch3\u003eDefinition of terms\u003c/h3\u003e\n\u003cp\u003eCurrent smokers included those who regularly or occasionally smoked cigarettes, as well as those who used pipes or hookah. Based on WHO definitions of osteoporosis, participants were categorized according to their T-score values [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The reference population for T-score calculation is the bone mass at each site in young Caucasian women. Those with a T-score of 2.5 standard deviation (SD) or more below the reference mean (\u0026minus;\u0026thinsp;2.5 and lower) in at least spine, total hip, or femoral neck were classified as having osteoporosis, while the remainder were considered to have no osteoporosis. Furthermore, we defined low BMD, which includes both osteopenia and osteoporosis, as having a T-score that is one standard deviation or more below the reference mean (-1 or lower) at each site.\u003c/p\u003e \u003cp\u003eThe VAI and LAP were calculated as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eLAP for male = [WC (cm)\u0026thinsp;\u0026minus;\u0026thinsp;65] \u0026times; TG (mmol/l) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVAI for male= [WC (cm)/ 39.68 + (1.88 \u0026times; BMI (kg/m\u003csup\u003e2\u003c/sup\u003e))] \u0026times; [TG (mmol/L)/1.03) \u0026times; (1.31/HDL-C (mmol/ L))][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eThe comparison of studied variables between the outcome groups\u0026mdash;including demographic, laboratory, clinical, and anthropometric indices\u0026mdash;was conducted using independent t-tests and chi-square tests. To assess the associations between the predictors (VAI and LAP) and the outcomes of interest (osteoporosis, low BMD, TBS L1\u0026ndash;L4, and lumbar spine, total hip, and femoral neck BMDs), logistic or linear regression analyses were performed. For each outcome, two models were estimated: one unadjusted (crude) and one adjusted. The adjusted models accounted for potential confounders such as age, BUN, ALP, TG, HDL-C, LDL-C, FBS, BMI, cholesterol, vitamin D, education, and smoking status.\u003c/p\u003e \u003cp\u003eAll statistical assumptions were evaluated throughout the analyses. Group comparisons were checked for normality and homogeneity of variances, while modeling procedures assessed multicollinearity among predictors and the behavior of residuals in linear regression. Variance Inflation Factor (VIF) was used to test collinearity between variables and residual model also had a mean of zero and a standard deviation of 1. All analyses were conducted at a significance level of 0.05 using Stata software, and figures were generated with the ggplot2 package in R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics of study population are presented in Table 1. The study population consisted of 851 individuals (the mean age of 62.9\u0026plusmn; 0.2 years), of whom 151 (17.7%) were diagnosed with osteoporosis. Compared to individuals without osteoporosis, those with osteoporosis were older (66.5 vs. 62.1 years), had a lower BMI (24.2 vs. 26.9 kg/m\u003csup\u003e2\u003c/sup\u003e), had higher levels of education, had a greater prevalence of current smoking (36.4% vs. 25.5%), had a lower waist circumference (93.1 cm vs. 99.9 cm), had higher level of ALP (246.5 vs. 216.2 IU/L) and exhibited lower levels of TG, VAI and LAP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eBaseline characteristics of the participants based on osteoporosis status\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"695\" height=\"642\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of VAI and LAP with BMD status and TBS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the associations of the anthropometric indices with BMD status and TBS. In the simple linear regression model, a positive association of VAI and LAP with BMD status at all sites was found. A negative significant association was found between LAP and TBS in simple linear regression model. After adjusting for known and measured confounders, all of the associations were lost except for VAI, which exhibited a positive association with BMD at total hip and LAP, which exhibited a negative association with TBS. Each one unit increases in VAI was associated with 0.0192 gr/cm\u003csup\u003e2\u003c/sup\u003e increase in lumbar spine BMD, while one-unit increase in LAP was associated with 0.0011 gr/cm\u003csup\u003e2\u0026nbsp;\u003c/sup\u003edecrease in TBS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eThe association of VAI and LAP with bone mineral density and TBS among study participants\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted model\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026szlig; (%95 CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026szlig; (%95 CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLumbar spine\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(L1-L4) BMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.012(0.003, 0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.003(-0.0054, 0.0114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.0008(0.0005, 0.0011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.00002(-0.0003, 0.0004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal hip BMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.015(0.007, 0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.0192(0.0034, 0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.0008(0.0006, 0.0010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.00003(-0.0002, 0.0003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemoral neck BMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.013(0.006, 0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.0032(-0.0031, 0.0097)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.0007(0.0005, 0.0009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e-0.00001(-0.0002, 0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTBS (L1-L4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e-0.0002(-0.005, 0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.001(-0.003, 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e-0.0004(-0.0005, -0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e-0.0011(-0.0016, -0.0007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 684px;\"\u003e\n \u003cp\u003e*Adjusted for Age, BUN, ALP, FBS, LDL-C, Cholesterol, vitamin D, education, smoking, BMI (except for VAI), HDL-C (except for VAI).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Abbreviations: CI, Confidence interval; VAI, Visceral adiposity index; LAP, lipid accumulation product\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of VAI and LAP with osteoporosis and low BMD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations of VAI and LAP with osteoporosis and low BMD are presented in Table 3. In the crude model, both anthropometric indices demonstrated a statistically significant protective association with the both osteoporosis and low BMD (OR \u0026lt; 1). However, after adjusting for potential confounders, only the association between VAI and low BMD remained significant (OR = 0.725, p = 0.041). On the other hand, higher VAI was related with lower odds of having low BMD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eLogistic regression analysis assessed the association of LAP and VAI with osteoporosis and low BMD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"676\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted model\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (%95 CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (%95 CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOsteoporosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.763(0.645, 0.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.863(0.723, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.985(0.978, 0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.996(0.990, 1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow BMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.888(0.795, 0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.725(0.533, 0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.992(0.989, 0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1.002(0.997, 1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 676px;\"\u003e\n \u003cp\u003e*Adjusted for Age, BUN, ALP, FBS, LDL-C, Cholesterol, vitamin D, education, smoking, BMI (except for VAI), HDL-C (except for VAI).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Abbreviations: CI, Confidence interval; OR, Odds ratio; VAI, Visceral adiposity index; LAP, lipid accumulation product\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large population-based cross-sectional study of the older adults in the Iran using BEH program data, the results revealed that those with osteoporosis had significantly lower BMI and waist circumference compared to those without osteoporosis. Furthermore, VAI and LAP values was lower in osteoporotic group compared to those without it. After comprehensive adjustment for potential confounders, higher VAI level was positively associated with greater BMD at total hip and the lower odds of having low BMD. Conversely, it was shown that higher LAP was associated with lower TBS.\u003c/p\u003e \u003cp\u003eCompared to traditional obesity indices, LAP and VAI are more sensitive and accurate in assessing cardiovascular disorders and metabolic health. Despite numerous studies, the association between visceral adiposity and bone health remains controversial. Some recent studies have shown that higher visceral fat level is harmful for bone health and is a risk factor for osteoporosis or low BMD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while some others proposed that it is protective against osteoporosis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Chen et all. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] found a negative linear relationship between adiposity and osteoporosis among men over 50 years. Another study by Tian et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] showed the protective effect of adiposity against osteoporosis in both men and women. Evans et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported a higher BMD in obese men and women of 55\u0026ndash;75 years compared to those with normal weight, that reflects possible protective role of obesity in preventing age-related bone loss. On the other hand, Jiao et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported an inverse relationship between obesity-related parameters and BMD among male participants. These discrepancies are attributed to variations in study population, study design and controlling for confounding variables.\u003c/p\u003e \u003cp\u003eIn the present study, the positive association observed between VAI and hip BMD, as well as its protective effect against low BMD, can be explained by the greater mechanical loading exerted by a heavier body on the skeleton that promotes bone remodeling processes and ,in turn, resulted in higher bone density as an adaptive reaction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Obesity and BMD exhibited a complex relationship that influenced by multiple factors [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The mechanisms linking obesity and bone health mainly involve two aspects: mechanical and metabolic. Obesity increases mechanical loading on bones, which protects bone modeling, density, and structure, potentially explaining the higher BMD seen in obese individuals[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. On the other hand, visceral adipose tissue releases prominent proinflammatory cytokines, including tumor necrotizing factor- α (TNF-α) and interlukin-1 and \u0026minus;\u0026thinsp;6 which aggravate a systemic inflammatory response, disrupt metabolic homeostasis, and adversely affect bone health[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Adipocytokines such as leptin and adiponectin have direct effects on skeletal metabolism. The association between leptin and BMD has been reported to demonstrate both positive and negative correlations[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Leptin which is secreted by fat cells may inhibit bone breakdown by raising osteoprotegerin levels that block osteoclast formation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; however, through its regulation of the central nervous system, it may simultaneously inhibit bone formation[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, adiponectin that is secreted by adipocytes, impacts visceral fat and bone metabolism with dual effect [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Adiponectin enhances proliferation and differentiation of human osteoblasts, through the mitogen-activated protein kinase (MAPK) signaling pathway [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Conversely, by upregulating RANKL and suppressing osteoprotegerin production in osteoblasts, it indirectly assists osteoclastogenesis leading to reduced BMD [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Given the dualistic roles of these factors, which may yield both beneficial and adverse effects on bone health, their overall effects remain controversial. Hence, further research is essential to thoroughly reveal the complex interplay between visceral fat and bone.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge this was the first study evaluating the association between some obesity-related indices and TBS. Our result revealed a negative association LAP and TBS. BMD assesses solely the quantity of bone and does not offer information regarding bone quality. The TBS, derived from DXA images, reflects the structural integrity of bone microarchitecture and serves as a superior indicator for evaluating bone quality; with low TBS values indicates degradation of bone microarchitecture, which contribute to an increased risk of fractures, to some extent independent of BMD[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Consistent with our findings, Lv et al.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] found that visceral adiposity, have negative effects on bone microstructure among healthy Chinese men (mean age: 58.78 years). Visceral adiposity induces low-grade systemic inflammation, progressive insulin resistance, and other metabolic disorders [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A cross-sectional analysis on 1229 Korean male aged 50 or older, revealed that subjects with low TBS values were more insulin-resistant than those with high TBS [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This association may be attributed to the formation of advanced glycosylation products (AGEs) in the bone matrix under the condition of insulin resistance state, which impairs bone deformation capacity and resistance, as well as increases fragility [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, further longitudinal studies are needed to clarify these relationships and to elucidate potential causal pathways.\u003c/p\u003e \u003cp\u003eOur study had several advantages. It included a large community-based sample with a focus on men aged 50 years and older. Additionally, we applied a broadly important confounding factors, which contributes to the robustness of the findings. Our results provide a foundation for further longitudinal cohort studies. However, the cross-sectional design precludes causal implication and reverse causality cannot be excluded. However, the cross-sectional design limits causal inferences, and the possibility of reverse causality cannot be ruled out.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this large-sample study revealed a significant association between some adiposity measures and bone health in men aged 50 years and above. Our findings revealed that VAI is significantly associated with higher total hip BMD as well as lower odds of having low BMD. On the other hand, it was demonstrated that LAP had negative relationship with TBS values. These findings underscore a complex interplay between adiposity and bone health. Future research should validate these findings and investigate the underlying mechanisms, through longitudinal studies. A deeper understanding of these associations may improve clinical approaches for the prevention and management of osteoporosis in aging male population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Participants with incomplete and invalid data were excluded from the analyses. The protocol of the current study was approved by the Ethics Committee of the Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences (ID code: IR.TUMS.EMRI.REC.1403.072).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are from the Bushehr Elderly Health (BEH) program, a population-based cohort study owned and governed by the Ministry of Health and Medical Education of the Islamic Republic of Iran through the Persian Cohort and Biobank regulations. Due to ethical and legal restrictions on sharing potentially identifiable human data and national regulations, the raw dataset cannot be made publicly available. However, the de-identified minimal dataset underlying the results of this study is available upon reasonable request to the project administrator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003eethic code: IR.TUMS.EMRI.REC.1403.072), was a secondary analysis of data from the Bushehr Elderly Health (BEH) Program (ethic code: IR.TUMS.EMRI.REC.1394.0036). \u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSHM:\u0026nbsp;\u003c/strong\u003ewriting (original draft, and editing); \u003cstrong\u003eKK\u003c/strong\u003e: methodology, validation; \u003cstrong\u003eAA\u003c/strong\u003e: \u0026nbsp;Formal analysis;\u003cstrong\u003e\u0026nbsp;NF:\u0026nbsp;\u003c/strong\u003evalidation,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003esupervision;\u003cstrong\u003e\u0026nbsp;MPS:\u003c/strong\u003e\u0026nbsp; conceptualization; supervision; \u003cstrong\u003eIN and AO:\u0026nbsp;\u003c/strong\u003eProject administration. All authors reviewed the manuscript and approved the final version.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmin U, McPartland A, O\u0026rsquo;Sullivan M, Silke C. An overview of the management of osteoporosis in the aging female population. 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BMC Public Health. 2020;20(1):285. doi: 10.1186/s12889-020-8379-4.\u003c/li\u003e\n\u003cli\u003eChen PJ, Lu YC, Lu SN, Liang FW, Chuang HY. Association Between Osteoporosis and Adiposity Index Reveals Nonlinearity Among Postmenopausal Women and Linearity Among Men Aged over 50 Years. J Epidemiol Glob Health. 2024;14(3):1202\u0026ndash;18. doi: 10.1007/s44197-024-00275-9.\u003c/li\u003e\n\u003cli\u003eEvans AL, Paggiosi MA, Eastell R, Walsh JS. Bone density, microstructure and strength in obese and normal weight men and women in younger and older adulthood. J Bone Miner Res. 2015;30(5):920\u0026ndash;8. doi: 10.1002/jbmr.2407.\u003c/li\u003e\n\u003cli\u003eJiao Y, Sun J, Li Y, Zhao J, Shen J. Association between Adiposity and Bone Mineral Density in Adults: Insights from a National Survey Analysis. Nutrients. 2023;15(15). doi: 10.3390/nu15153492.\u003c/li\u003e\n\u003cli\u003eYou L, Temiyasathit S, Lee P, Kim CH, Tummala P, Yao W, et al. Osteocytes as mechanosensors in the inhibition of bone resorption due to mechanical loading. Bone. 2008;42(1):172\u0026ndash;9. doi: 10.1016/j.bone.2007.09.047.\u003c/li\u003e\n\u003cli\u003eFassio A, Idolazzi L, Rossini M, Gatti D, Adami G, Giollo A, et al. The obesity paradox and osteoporosis. Eat Weight Disord. 2018;23(3):293\u0026ndash;302. doi: 10.1007/s40519-018-0505-2.\u003c/li\u003e\n\u003cli\u003eSinghal V, Reyes KC, Pfister B, Ackerman K, Slattery M, Cooper K, et al. Bone accrual in oligo-amenorrheic athletes, eumenorrheic athletes and non-athletes. Bone. 2019;120:305\u0026ndash;13. doi: 10.1016/j.bone.2018.05.010.\u003c/li\u003e\n\u003cli\u003eGautier A, Bonnet F, Dubois S, Massart C, Grosheny C, Bachelot A, et al. Associations between visceral adipose tissue, inflammation and sex steroid concentrations in men. Clin Endocrinol (Oxf). 2013;78(3):373\u0026ndash;8. doi: 10.1111/j.1365-2265.2012.04401.x.\u003c/li\u003e\n\u003cli\u003eRinonapoli G, Pace V, Ruggiero C, Ceccarini P, Bisaccia M, Meccariello L, et al. Obesity and Bone: A Complex Relationship. Int J Mol Sci. 2021;22(24). doi: 10.3390/ijms222413662.\u003c/li\u003e\n\u003cli\u003eLiu X, Liang Y, Xia N, Liu W, Yang Q, Wang C. Decrease in leptin mediates rat bone metabolism impairments during high-fat diet-induced catch-up growth by modulating the OPG/RANKL balance. 3 Biotech. 2021;11(2):103. doi: 10.1007/s13205-021-02658-2.\u003c/li\u003e\n\u003cli\u003eRussell M, Mendes N, Miller KK, Rosen CJ, Lee H, Klibanski A, et al. Visceral fat is a negative predictor of bone density measures in obese adolescent girls. J Clin Endocrinol Metab. 2010;95(3):1247\u0026ndash;55. doi: 10.1210/jc.2009-1475.\u003c/li\u003e\n\u003cli\u003eAgbaht K, Gurlek A, Karakaya J, Bayraktar M. 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J Clin Densitom. 2023;26(3):101378. doi: 10.1016/j.jocd.2023.101378.\u003c/li\u003e\n\u003cli\u003eLv S, Zhang A, Di W, Sheng Y, Cheng P, Qi H, et al. Assessment of Fat distribution and Bone quality with Trabecular Bone Score (TBS) in Healthy Chinese Men. Sci Rep. 2016;6:24935. doi: 10.1038/srep24935.\u003c/li\u003e\n\u003cli\u003eFavaretto F, Bettini S, Busetto L, Milan G, Vettor R. Adipogenic progenitors in different organs: Pathophysiological implications. Rev Endocr Metab Disord. 2022;23(1):71\u0026ndash;85. doi: 10.1007/s11154-021-09686-6.\u003c/li\u003e\n\u003cli\u003eLongo M, Zatterale F, Naderi J, Parrillo L, Formisano P, Raciti GA, et al. Adipose Tissue Dysfunction as Determinant of Obesity-Associated Metabolic Complications. Int J Mol Sci. 2019;20(9). doi: 10.3390/ijms20092358.\u003c/li\u003e\n\u003cli\u003eKim JH, Choi HJ, Ku EJ, Kim KM, Kim SW, Cho NH, et al. Trabecular bone score as an indicator for skeletal deterioration in diabetes. 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Nat Rev Endocrinol. 2017;13(4):208\u0026ndash;19. doi: 10.1038/nrendo.2016.153.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bone mineral density, Trabecular bone score, Lipid accumulation product, Visceral adiposity index, Osteoporosis, Iran","lastPublishedDoi":"10.21203/rs.3.rs-8908074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8908074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe association of obesity- related markers such as Visceral Adiposity Index (VAI) and Lipid Accumulation Product (LAP) and bone health is controversial. This study aimed to evaluate this association among men using data from of the second recruitment of the Bushehr Elderly Health (BEH) program.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional analysis was conducted on 851 men aged 50 years and older on the data from BEH program. Demographic information, laboratory data, bone mineral density (BMD) and trabecular bone score (TBS) were collected. Participants with a T-score\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;2.5 at the lumbar spine, total hip, or femoral neck were classified as having osteoporosis, while others were not. Low BMD (osteopenia/osteoporosis), was defined as a T-score\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;1 at any of these sites. Associations of adiposity measures and bone health were investigated using linear and logistic regression analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean age of the participants was 62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 years and 151 (17.7%) were diagnosed with osteoporosis. Compared to individuals without osteoporosis, those with osteoporosis had lower mean of VAI (2.3 vs. 2.7, p\u0026thinsp;=\u0026thinsp;0.001) and LAP (44.5 vs. 61.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for known and measured confounders, VAI exhibited a positive association with BMD at total hip and LAP exhibited a negative association with TBS. Each one unit increases in VAI was associated with 0.0192 gr/cm\u003csup\u003e2\u003c/sup\u003e increase in lumbar spine BMD, while one-unit increase in LAP was associated with 0.0011 gr/cm\u003csup\u003e2\u003c/sup\u003e decrease in TBS. Moreover, in logistic regression analysis, VAI was related with lower odds of having low BMD (odds ratio\u0026thinsp;=\u0026thinsp;0.725, p\u0026thinsp;=\u0026thinsp;0.041).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings highlighted that some adiposity parameters may serve as valuable markers in evaluating bone health in older adult men. A deeper understanding of these associations may improve clinical approaches for the prevention and management of osteoporosis in this population.\u003c/p\u003e","manuscriptTitle":"Associations of Visceral Adiposity Index and Lipid Accumulation Product with bone health status in men aged 50 and over: a cross-sectional analysis of Bushehr Elderly Health (BEH) Program","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:34:23","doi":"10.21203/rs.3.rs-8908074/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f398556-2020-49f4-8ee2-2700da87dcff","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T18:54:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:34:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8908074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8908074","identity":"rs-8908074","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}