Causal relationship between type 2 diabetes and BMD: a Mendelian randomization study

Causal relationship between type 2 diabetes and BMD: a Mendelian randomization study | 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 Causal relationship between type 2 diabetes and BMD: a Mendelian randomization study Xiao-Cheng Jiang, Huan Li, YangLiang Ren, Ting Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3850790/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 Summary: When Mendelian randomization (MR) studies were used to investigate the causal relationship between type 2 diabetes and BMD at different sites, there was no causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD, or forearm BMD. Purpose: The purpose of this study was to assess the causal relationship between type 2 diabetes and BMD in the lumbar spine, femoral neck, and forearm. Methods: Based on the aggregated statistical data of a large published genome-wide association study. The IVW method, the MR-Egger method, the WM method, the Simple Mode method, and the Weighted Mode method were used to evaluate the causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD and forearm BMD. In addition, sensitivity analysis was performed using MR-Egger regression, Cochran's Q test and MR-PRESSO Global test to ensure the robustness of the results. Results： The results of the inverse variance weighted (IVW) analysis for type 2 diabetes and lumbar BMD showed an odds ratio (OR) of 1.070997 (95% confidence interval [CI]: 0.9839422 to 1.165754), with a p-value of 0.11279766. Similarly, the IVW analysis for type 2 diabetes and femoral neck BMD showed an OR of 1.041797 (95% CI: 0.9657858 to 1.123791), with a p-value of 0.28944290. For type 2 diabetes and forearm BMD, the IVW analysis resulted in an OR of 1.102443 (95% CI: 0.9433071 to 1.288424), with a p-value of 0.22012100. Heterogeneity tests for type 2 diabetes and lumbar BMD, femoral neck BMD, and forearm BMD did not identify any outlier variables. Sensitivity analyses confirmed the robustness of the results, and no pleiotropic effects were observed. Conclusions： There was no causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD, or forearm BMD. type 2 diabetes Lumbar bone mineral density Femoral Neck bone mineral density Forearm bone mineral density Mendelian Randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Osteoporosis (OP) is a progressive systemic skeletal disease that involves the loss of bone mass, destruction of bone microfibrils, loss of bone quality, and an increased risk of fracture. It is commonly associated with aging and has been identified as a significant public health concern in many countries. This is due to its subtle symptoms, vulnerability to fractures, and its impact on disability and mortality [ 1 ]. type 2 diabetes and osteoporosis are common chronic diseases in the elderly, often co-existing clinically. Previous studies have considered osteoporosis as a chronic complication of type 2 diabetes. However, recent cohort studies have shown a higher prevalence of osteoporosis in patients with type 2 diabetes compared to the general population, suggesting that type 2 diabetes may be one of the factors causing osteoporosis [ 2 ]. Despite this, there is still controversy regarding the relationship between type 2 diabetes and bone mineral density. Most current studies aim to correlate the two, but fail to perform causal analyses. Mendelian randomization (MR) is a method that utilizes genetic variation as an instrumental variable to minimize bias in causality estimates and enhance causal inference. In this study, we applied the concept of Mendelian randomization to investigate the causal relationship between type 2 diabetes and bone mineral density at various sites. Materials and methods Study design This study employed two-sample Mendelian randomization to investigate the causal relationship between type 2 diabetes and bone mineral density in the lumbar spine, femoral neck, and forearm. type 2 diabetes was considered as the exposure variable, while bone mineral density, encompassing lumbar spine, femoral neck, and forearm, served as the outcome variable. Data sources Single nucleotide polymorphisms (SNPs) were utilized as instrumental variables, with type 2 diabetes serving as the exposure factor. The outcome variables included lumbar spine, femoral neck, and forearm bone mineral density. Relevant datasets from genome-wide association studies (GWAS) were collected through the website ( https://gwas.mrcieu.ac.uk ). The type 2 diabetes data were derived from a GWAS analysis that encompassed 48,286 cases and 250,671 controls of European ancestry. The outcome dataset consisted of GWAS data for lumbar spine bone mineral density, femoral neck bone mineral density, and forearm bone mineral density, which were obtained from the 2015 publication by Zheng et al. This dataset contained 10,582,867 SNPs in 28,498 samples for lumbar spine bone mineral density, 10,586,900 SNPs in 32,735 samples for femoral neck bone mineral density, and 9,955,366 SNPs in 8,143 samples for forearm bone mineral density. The population included in the outcome dataset was of European descent, and precautions were taken to avoid duplicate data samples from different sources. Ethical approval was not required for this study, as it involved a secondary analysis of publicly available data.Summary information is given in Table 1 . Table 1 Details of the GWASs included in the Mendelian randomization. Consortium Exposure or outcome Sample size SNPs (n) Population ebi-a-GCST007515 Type 2 diabetes 298,957 190,486 European ieu-a-982 Lumbar spine BMD 28,498 10,582,867 European ieu-a-980 Femoral neck BMD 32,735 10,586,900 European ieu-a-977 Forearm BMD 8,143 9,955,366 European Instrumental variables Genome-wide statistically significant SNPs with independent and highly correlated exposure factors and outcome variables were selected as instrumental variables. We used single nucleotide polymorphisms (SNPs) as instrument variables (IVs) that met three strict assumptions: 1) the instrument variables were strongly correlated with the exposure group; 2) the instrument variables were not associated with the outcome; and 3) the instrument variables were not associated with any potential confounders independent of the outcome (SNPs with confounders were detected and manually removed using the Phenoscanner tool) (Additional file 4). The type 2 diabetes with genome-wide significance parameter was set to P < 5×10 − 8, the linkage disequilibrium parameter (r2) to 0.001, and genetic distances to 10MB from the type 2 diabetes data were sub-screened for instrumental variables without chain effects. Instrumental variables associated with lumbar BMD, femoral neck BMD, and forearm BMD were excluded from the screened instrumental variables, respectively (P < 0.05). Finally, SNPs with palindromic coding and those associated with potential confounders were excluded ( http://www.phenoscanner.medschl.http://cam.ac.uk/ ). Mendelian randomization analysis In this study, Mendelian randomization analysis was performed using inverse-variance weighted (IVW), MR-Egger, weighted median (WM), Simple Mode, and Weighted Mode. The statistical F value was set to > 10 and calculated as F = beta2/se2; R2 was calculated as R2 = 2×MAF×(1-MAF)×beta2. All analyses were implemented using the TwoSampleMR (version 0.5.7) package in R (version 4.3.1). Sensitivity analysis Heterogeneity tests were performed using the IVW method and MR-Egger method. A test P-value less than 0.05 indicated the presence of heterogeneity among SNPs, while a P-value greater than 0.05 indicated no heterogeneity. Sensitivity analyses were conducted using the one-by-one exclusion method to investigate the impact of individual SNPs on causal associations. Multiplicity analysis was performed using the MR_pleiotropy_test function. A test P-value less than 0.05 indicated the presence of multiplicity, while a P-value greater than 0.05 indicated the absence of multiplicity. Table 2 Reliability test of MR analysis results. Exposure Outcome MR-Pleiotropy Test Cochrane Q Test (IVW) MR-PRESSO Global Test P-value P-value P-value Type 2 diabetes Lumbar spine BMD 0.9243475 0.30647320 0.3254 Femoral neck BMD 0.4042280 0.21369830 0.2223 Forearm BMD 0.9189454 0.05724749 0.0598 Results Initially, we selected strongly associated SNPs (F-statistic > 10) that satisfied the genome-wide significance P-value setting and the linkage disequilibrium test. Then, we excluded SNPs with palindromic coding and associated with potential confounders (Additional file 4). And by outlier test (MR-PRESSO), we found that the P-values were all greater than 0.05. Finally, the screened SNPs were analyzed as IVs for MR analysis (Look at Fig. 1 of the flowchart). Type 2 diabetes and lumbar BMD analysis results A total of 26 SNPs were included as instrumental variables after screening for type 2 diabetes (Additional file 1). The results of Mendelian randomization showed consistent findings across various analysis methods, including the IVW method, MR-Egger method, Simple Mode method, and Weighted Mode method. The IVW method indicated an odds ratio (OR) of 1.070997 (95% CI: 0.9839422 to 1.165754) with a p-value of 0.11279766, suggesting no causal association between type 2 diabetes and lumbar BMD (Fig. 2 and Fig. 3 ). The heterogeneity test yielded p-values of 0.3064732 for the IVW method and 0.2592281 for the MR-Egger method, indicating no heterogeneity. Sensitivity analysis revealed that no individual SNPs had a significant impact on the estimation of causal association. Furthermore, the MR-Egger-intercept showed a p-value of 0.9243475 for type 2 diabetes, suggesting no evidence of horizontal pleiotropy. (Table 2 , Fig. 4 , and Fig. 5 ) Type 2 diabetes and femoral neck BMD analysis results A total of 26 SNPs were included as instrumental variables for screening type 2 diabetes (Additional file 2). The results of Mendelian randomization indicated that the IVW method, MR-Egger method, WM method, Simple Mode method, and Weighted Mode method analysis all yielded consistent results. The IVW method showed an odds ratio (OR) of 1.041797 (95% CI: 0.9657858 to 1.123791) with a p-value of 0.2894429, suggesting no causal relationship between type 2 diabetes and femoral neck BMD (Fig. 2 and Fig. 3 ). The heterogeneity test yielded p-values of 0.2136983 for the IVW method and 0.2052822 for the MR-Egger method, indicating no heterogeneity. Sensitivity analysis revealed no significant impact of any individual SNP on the estimation of causal association. The MR-Egger-intercept yielded a p-value of 0.404228 for type 2 diabetes, indicating no evidence of horizontal pleiotropy (Table 2 , Fig. 4 , and Fig. 5 ). type 2 diabetes and forearm BMD analysis results A total of 29 SNPs were included as instrumental variables after screening for type 2 diabetes (Additional file 3). The results of Mendelian randomization showed that the IVW method, MR-Egger method, WM method, Simple Mode method, and Weighted Mode method analysis results were consistent. Specifically, the IVW results indicated an odds ratio (OR) of 1.102443 (95% CI: 0.9433071 to 1.288424) with a p-value of 0.220121, suggesting no causal relationship between type 2 diabetes and forearm BMD (Fig. 2 and Fig. 3 ). The tests for heterogeneity yielded p-values of 0.05724749 for the IVW method, 0.04419279 for the MR-Egger method, and 0.0598 for the MR-PRESSO.Global.Test, indicating no significant heterogeneity. Sensitivity analysis revealed no SNPs that significantly influenced the causal association estimates when using the case-by-case exclusion method. The MR-Egger-intercept showed a p-value of 0.9189454 for type 2 diabetes, indicating no evidence of horizontal pleiotropy. (Table 2 , Fig. 4 , and Fig. 5 ) Discussion Observational studies are commonly used to investigate the relationship between phenotype and disease [ 3 ]. Prior to birth, human chromosomes are determined. Genome-wide association studies (GWAS) aim to identify sequence variants across the entire human genome, while Mendelian randomization (MR) employs disease-associated single nucleotide polymorphisms (SNPs) from GWAS data as instrumental variables for analysis. SNPs, following the principle of random assignment and being unaffected by the environment, can effectively minimize bias and help establish causal effects between exposure and outcomes, unlike observational studies such as cohort studies [ 4 ]. Therefore, this study did not find any causal relationship between genetically predicted type 2 diabetes and BMD in the lumbar spine, femoral neck, and forearm. Furthermore, sensitivity analysis using forest plots and leave-one-out tests did not reveal any significant effects of individual SNPs on the results. Both the MR-Egger-intercept and MRPRESSO tests also did not detect any horizontal polymorphisms, which enhances the reliability of this article. This study is believed to be the first to comprehensively determine the causal relationship between type 2 diabetes and BMD (BMD) at different sites. Previous studies have conducted Mendelian randomization (MR) studies in East Asian populations, which confirmed a causal relationship between type 2 diabetes and higher BMD, but no causal relationship with decreased BMD. However, these studies did not differentiate between different sites of BMD [ 5 ]. In contrast to the conclusion of another MR study, which suggested that type 2 diabetes is causally associated with femoral neck BMD but not with lumbar spine BMD, our results are inconsistent with this finding [ 6 ]. Although the study design is robust, it does not seem to have accounted for single nucleotide polymorphisms (SNPs) with palindromic coding and their association with potential confounders such as BMI, rheumatoid arthritis, and other factors. Jia et al. [ 7 ] conducted an observational study which revealed a positive association between higher body mass index and higher BMD T-scores, indicating a lower risk of osteoporosis. Similarly, Andreoli et al. [ 8 ] found that obesity significantly reduced the risk of osteoporosis. Moreover, Ouyang et al. discovered a significant positive correlation between body mass index and BMD in adolescents aged 8–19 years [ 9 ]. These findings suggest that the genetic variation in the Ahmad et al. study might have been pleiotropic, violating two key assumptions in Mendelian randomization. These assumptions include the absence of a relationship between genetic variation and confounding factors, as well as the influence of genetic variation on the outcome solely through risk factors. Consequently, the inconsistent results obtained in Ahmad et al.'s study could be attributed to these factors. Additionally, varying data sources can also contribute to inconsistent findings. In relation to type 2 diabetes and osteoporosis, numerous observational studies have indicated that individuals with type 2 diabetes face a higher risk of fractures compared to those without diabetes. However, this increased risk does not appear to be linked to BMD, but rather to bone strength (10–13). Some previous studies have even found that individuals with type 2 diabetes have either unchanged or higher BMD when compared to those without diabetes (14). On the other hand, it has been discovered that individuals with type 2 diabetes experience altered body composition, impaired bone healing, the accumulation of microfractures and cortical pores due to the loss of transverse trabecular junctions (resulting in a decreased flexion ratio), increased cortisol secretion, peripheral activation and sensitization (known as the 'cortisol milieu'), and an overall impairment of osteoblast activity. These factors may contribute to decreased bone turnover and poor bone microarchitecture (17). In addition to diabetes, other factors that may increase the risk of fractures in diabetic patients include a higher likelihood of falls due to complications like neuropathy, vision loss, and balance issues resulting from long-term poor glycemic control. Sarcopenia and the side effects of antidiabetic medications also contribute to this increased risk. One specific medication, thiazolidinediones (TZDs) such as pioglitazone and rosiglitazone, which activate the peroxisomal peroxisome proliferator-activated receptor gamma (PPARg), has been found to have a negative impact on bone metabolism despite its positive effects on glycemic control. By activating PPARg, TZDs promote adipogenesis and hinder osteoclastogenesis, leading to decreased bone formation and increased bone resorption. This ultimately results in weaker bone biomechanical properties. 18–20 . In conclusion, the precise mechanism by which type 2 diabetes impacts osteoporosis is still uncertain. Further studies are needed to examine whether it influences BMD (BMD) and bone strength through other factors that affect glucose metabolism. Mendelian randomization is a powerful tool for studying the relationship between exposure and outcome to establish causality. It offers several advantages over randomized controlled trials, such as overcoming potential confounding and reverse causation effects, as well as avoiding unnecessary resource wastage. This study stands out for its utilization of genetic data from a large-scale GWAS study on type 2 diabetes and BMD at three specific sites: the lumbar spine, femoral neck, and forearm. This approach enhances the statistical efficacy of the causal associations. Additionally, the distribution of genetic variants across chromosomes and the negligible impact of potential gene-gene interactions further strengthen the assessment. The article has some limitations that should be considered. Firstly, the genetic variants studied are all from European populations, so further validation is necessary to apply the findings to other populations and races. Secondly, the use of Mendelian randomization assumes a linear relationship between type 2 diabetes and BMD, and it cannot be used if this linear relationship does not exist. Thirdly, Mendelian randomization does not provide insights into the biological mechanisms underlying the observed associations. Lastly, due to the unavailability of detailed data on BMD, factors such as age and gender could not be included for deeper analysis. Therefore, future studies should consider using larger sample sizes or randomized controlled studies to obtain more accurate and comprehensive results. Conclusion From a genetic perspective, there is no causal relationship between type 2 diabetes and BMD in the lumbar spine, femoral neck, or forearm. Declarations Acknowledgements The authors want to acknowledge all the participants and investigators of the GWASs involved in the present study for generously sharing the data. Authors’ contributions X.J and H.L designed the study. Y.R analyzed the data and prepared the original draft. T.W revised and edited the paper. All authors have read and agreed to the published version of the manuscript. Declarations Funding No funding. Availability of data and materials In our study, we utilized publicly available summary data from GWAS. The pooled statistics for Type 2 diabetes, Lumbar spine BMD, Femoral neck BMD and Forearm BMD can be accessed at https://gwas.mrcieu.ac.uk. Ethics approval and consent to participate Not applicable. We conducted an analysis using data from publicly available online databases. No administrative permissions were required to access the data. Consent for publication Not applicable. Competing interests Xiao-Cheng Jiang, Huan Li, Yang-Liang Ren, and Ting Wang declare that they have no conflict of interest. References Wu D, Cline-Smith A, Shashkova E, Perla A, Katyal A, Aurora R. T-Cell Mediated Inflammation in Postmenopausal Osteoporosis. Front Immunol. 2021;12:687551. Russo V, Chen R, Armamento-Villareal R, Hypogonadism. Type-2 Diabetes Mellitus, and Bone Health: A Narrative Review. Front Endocrinol (Lausanne). 2021;11:607240. Wang X, Ji X. Sample Size Estimation in Clinical Research: From Randomized Controlled Trials to Observational Studies. Chest. 2020;158(1S):12–S20. Bowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Res Synth Methods. 2019;10(4):486–96. Huang G, Chen X, Chen Y et al. Causal relationship between type 2 diabetes and BMD: a Mendelian randomization study in an East Asian population. Osteoporos Int. 2023 Jun 12. Ahmad OS, Leong A, Miller JA, et al. A Mendelian Randomization Study of the Effect of Type-2 Diabetes and Glycemic Traits on BMD. J Bone Miner Res. 2017;32(5):1072–81. Jia L, Cheng M. Correlation analysis between risk factors, BMD and serum osteocalcin, CatheK, PINP, β-crosslaps, TRAP, lipid metabolism and BMI in 128 patients with postmenopausal osteoporotic fractures. Eur Rev Med Pharmacol Sci. 2022;26(21):7955–9. Andreoli A, Bazzocchi A, Celi M, et al. Relationship between body composition, body mass index and BMD in a large population of normal, osteopenic and osteoporotic women. Radiol Med. 2011;116(7):1115–23. Ouyang Y, Quan Y, Guo C, et al. Saturation Effect of Body Mass Index on BMD in Adolescents of Different Ages: A Population-Based Study. Front Endocrinol (Lausanne). 2022;13:922903. Eller-Vainicher C, Cairoli E, Grassi G, et al. Pathophysiology and Management of type 2 diabetes Bone Fragility. J Diabetes Res. 2020;2020:7608964. Napoli N, Chandran M, Pierroz DD, IOF Bone and Diabetes Working Group, et al. Mechanisms of diabetes mellitus-induced bone fragility. Nat Rev Endocrinol. 2017;13(4):208–19. Moayeri A, Mohamadpour M, Mousavi SF, et al. Fracture risk in patients with type 2 diabetes and possible risk factors: a systematic review and meta-analysis. Ther Clin Risk Manag. 2017;11:455–68. Khosla S, Samakkarnthai P, Monroe DG, et al. Update on the pathogenesis and treatment of skeletal fragility in type 2 diabetes. Nat Rev Endocrinol. 2021;17(11):685–97. Cipriani C, Colangelo L, Santori R, et al. The Interplay Between Bone and Glucose Metabolism. Front Endocrinol (Lausanne). 2020;11:122. Jang M, Kim H, Lea S, et al. Effect of duration of diabetes on BMD: a population study on East Asian males. BMC Endocr Disord. 2018;18(1):61. Cui R, Zhou L, Li Z, et al. Assessment risk of osteoporosis in Chinese people: relationship among body mass index, serum lipid profiles, blood glucose, and BMD. Clin Interv Aging. 2016;11:887–95. Compston J. type 2 diabetes and bone. J Intern Med. 2018;283(2):140–53. Gimble JM, Robinson CE, Wu X, Kelly KA, Rodriguez BR, Kliewer SA, Lehmann JM, Morris DC. Peroxisome proliferator-activated receptor-gamma activation by thiazolidinediones induces adipogenesis in bone marrow stromal cells. Mol Pharmacol. 1996;50(5):1087–94. Zhang YS, Zheng YD, Yuan Y, et al. Effects of Anti-Diabetic Drugs on Fracture Risk: A Systematic Review and Network Meta-Analysis. Front Endocrinol (Lausanne). 2021;12:735824. Kalaitzoglou E, Fowlkes JL, Popescu I, et al. Diabetes pharmacotherapy and effects on the musculoskeletal system. Diabetes Metab Res Rev. 2019;35(2):e3100. Additional Declarations No competing interests reported. Supplementary Files Additionalfiles.docx 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. We do this by developing innovative software and high quality services for the global research community. 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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-3850790","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268395862,"identity":"83dd678e-bb17-4570-9e20-f4f2e1cba85a","order_by":0,"name":"Xiao-Cheng Jiang","email":"","orcid":"","institution":"Jintang First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Cheng","middleName":"","lastName":"Jiang","suffix":""},{"id":268395863,"identity":"1eb85c59-32a2-4d29-89f0-32d9810816a1","order_by":1,"name":"Huan Li","email":"","orcid":"","institution":"Jintang First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Li","suffix":""},{"id":268395864,"identity":"62f6d115-8c5c-42b6-93a9-6d1642e72604","order_by":2,"name":"YangLiang Ren","email":"","orcid":"","institution":"Jintang First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"YangLiang","middleName":"","lastName":"Ren","suffix":""},{"id":268395865,"identity":"9c4fa09a-e9f7-4260-a931-b5bf999d5b67","order_by":3,"name":"Ting Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACNmbGxgcfKtjq+dmbDxCnhY+9udlwxhm+BMmeYwnEaZHjOd4mzdsml2BwI8eASIdJJDYb8LCZ5RkcyPl44w2DnZxuA2EtjQ8keNKKJQ+c3Ww5hyHZ2OwAMbYYSBxj7DvYu02ah+FA4jYitLRJJBj8Z2w4zPOMSC08B9skDiSwJU44xsNGpBb2xmbDhgNsxpI9bMaWcwyI8It8M/vDx3//scnxyz9+eONNhZ0cQS0oQIKHyKhB1kKqjlEwCkbBKBgRAAAMAkN4nLi1/AAAAABJRU5ErkJggg==","orcid":"","institution":"Jintang First People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-01-10 15:45:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3850790/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3850790/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50055536,"identity":"d2e9c436-bc19-49bf-aef6-90e6c8b9381d","added_by":"auto","created_at":"2024-01-23 17:40:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":303012,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Mendelian randomization study design. 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Type 2 diabetes and Forearm BMD.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-3850790/v1/42abe3ef87b08e69c5b46557.png"},{"id":50055532,"identity":"732dfb47-57c2-4338-b0d2-ae6577fa54de","added_by":"auto","created_at":"2024-01-23 17:40:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111820,"visible":true,"origin":"","legend":"\u003cp\u003eThe leave-one-out-sensitivity forest plot of genetic associations between Type 2 diabetes and BMD. A. Type 2 diabetes and lumbar BMD; B. Type 2 diabetes and Femoral neck BMD; C. Type 2 diabetes and Forearm BMD.\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-3850790/v1/c679b2d18ff6ee914cb9c8e6.png"},{"id":63439057,"identity":"7c2c339d-4586-496e-896a-eea969b321c9","added_by":"auto","created_at":"2024-08-28 07:03:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1063586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3850790/v1/c892828a-b59e-4a26-a98b-67cf7d1f37f7.pdf"},{"id":50056008,"identity":"622137ce-b4c3-46b7-a4dc-3b0f49d3116f","added_by":"auto","created_at":"2024-01-23 17:48:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40710,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-3850790/v1/5000c3c5dbba64d876c7a8d4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between type 2 diabetes and BMD: a Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis (OP) is a progressive systemic skeletal disease that involves the loss of bone mass, destruction of bone microfibrils, loss of bone quality, and an increased risk of fracture. It is commonly associated with aging and has been identified as a significant public health concern in many countries. This is due to its subtle symptoms, vulnerability to fractures, and its impact on disability and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003etype 2 diabetes and osteoporosis are common chronic diseases in the elderly, often co-existing clinically. Previous studies have considered osteoporosis as a chronic complication of type 2 diabetes. However, recent cohort studies have shown a higher prevalence of osteoporosis in patients with type 2 diabetes compared to the general population, suggesting that type 2 diabetes may be one of the factors causing osteoporosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite this, there is still controversy regarding the relationship between type 2 diabetes and bone mineral density. Most current studies aim to correlate the two, but fail to perform causal analyses.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a method that utilizes genetic variation as an instrumental variable to minimize bias in causality estimates and enhance causal inference. In this study, we applied the concept of Mendelian randomization to investigate the causal relationship between type 2 diabetes and bone mineral density at various sites.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design\u003c/h2\u003e\n \u003cp\u003eThis study employed two-sample Mendelian randomization to investigate the causal relationship between type 2 diabetes and bone mineral density in the lumbar spine, femoral neck, and forearm. type 2 diabetes was considered as the exposure variable, while bone mineral density, encompassing lumbar spine, femoral neck, and forearm, served as the outcome variable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eData sources\u003c/h2\u003e\n \u003cp\u003eSingle nucleotide polymorphisms (SNPs) were utilized as instrumental variables, with type 2 diabetes serving as the exposure factor. The outcome variables included lumbar spine, femoral neck, and forearm bone mineral density. Relevant datasets from genome-wide association studies (GWAS) were collected through the website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk\u003c/span\u003e\u003c/span\u003e). The type 2 diabetes data were derived from a GWAS analysis that encompassed 48,286 cases and 250,671 controls of European ancestry. The outcome dataset consisted of GWAS data for lumbar spine bone mineral density, femoral neck bone mineral density, and forearm bone mineral density, which were obtained from the 2015 publication by Zheng et al. This dataset contained 10,582,867 SNPs in 28,498 samples for lumbar spine bone mineral density, 10,586,900 SNPs in 32,735 samples for femoral neck bone mineral density, and 9,955,366 SNPs in 8,143 samples for forearm bone mineral density. The population included in the outcome dataset was of European descent, and precautions were taken to avoid duplicate data samples from different sources. Ethical approval was not required for this study, as it involved a secondary analysis of publicly available data.Summary information is given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetails of the GWASs included in the Mendelian randomization.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConsortium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure or outcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSNPs (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eebi-a-GCST007515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e298,957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190,486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eieu-a-982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLumbar spine BMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,582,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eieu-a-980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemoral neck BMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32,735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,586,900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eieu-a-977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForearm BMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,955,366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eInstrumental variables\u003c/h2\u003e\n \u003cp\u003eGenome-wide statistically significant SNPs with independent and highly correlated exposure factors and outcome variables were selected as instrumental variables. We used single nucleotide polymorphisms (SNPs) as instrument variables (IVs) that met three strict assumptions: 1) the instrument variables were strongly correlated with the exposure group; 2) the instrument variables were not associated with the outcome; and 3) the instrument variables were not associated with any potential confounders independent of the outcome (SNPs with confounders were detected and manually removed using the Phenoscanner tool) (Additional file 4). The type 2 diabetes with genome-wide significance parameter was set to P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8, the linkage disequilibrium parameter (r2) to 0.001, and genetic distances to 10MB from the type 2 diabetes data were sub-screened for instrumental variables without chain effects. Instrumental variables associated with lumbar BMD, femoral neck BMD, and forearm BMD were excluded from the screened instrumental variables, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Finally, SNPs with palindromic coding and those associated with potential confounders were excluded (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.http://cam.ac.uk/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eMendelian randomization analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, Mendelian randomization analysis was performed using inverse-variance weighted (IVW), MR-Egger, weighted median (WM), Simple Mode, and Weighted Mode. The statistical F value was set to \u0026gt;\u0026thinsp;10 and calculated as F\u0026thinsp;=\u0026thinsp;beta2/se2; R2 was calculated as R2\u0026thinsp;=\u0026thinsp;2\u0026times;MAF\u0026times;(1-MAF)\u0026times;beta2. All analyses were implemented using the TwoSampleMR (version 0.5.7) package in R (version 4.3.1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n \u003cp\u003eHeterogeneity tests were performed using the IVW method and MR-Egger method. A test P-value less than 0.05 indicated the presence of heterogeneity among SNPs, while a P-value greater than 0.05 indicated no heterogeneity. Sensitivity analyses were conducted using the one-by-one exclusion method to investigate the impact of individual SNPs on causal associations. Multiplicity analysis was performed using the MR_pleiotropy_test function. A test P-value less than 0.05 indicated the presence of multiplicity, while a P-value greater than 0.05 indicated the absence of multiplicity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReliability test of MR analysis results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Pleiotropy Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCochrane Q Test (IVW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-PRESSO Global Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eType 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLumbar spine BMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9243475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30647320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemoral neck BMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4042280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21369830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForearm BMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9189454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05724749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eInitially, we selected strongly associated SNPs (F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10) that satisfied the genome-wide significance P-value setting and the linkage disequilibrium test. Then, we excluded SNPs with palindromic coding and associated with potential confounders (Additional file 4). And by outlier test (MR-PRESSO), we found that the P-values were all greater than 0.05. Finally, the screened SNPs were analyzed as IVs for MR analysis (Look at Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of the flowchart).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eType 2 diabetes and lumbar BMD analysis results\u003c/h2\u003e \u003cp\u003eA total of 26 SNPs were included as instrumental variables after screening for type 2 diabetes (Additional file 1). The results of Mendelian randomization showed consistent findings across various analysis methods, including the IVW method, MR-Egger method, Simple Mode method, and Weighted Mode method. The IVW method indicated an odds ratio (OR) of 1.070997 (95% CI: 0.9839422 to 1.165754) with a p-value of 0.11279766, suggesting no causal association between type 2 diabetes and lumbar BMD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The heterogeneity test yielded p-values of 0.3064732 for the IVW method and 0.2592281 for the MR-Egger method, indicating no heterogeneity. Sensitivity analysis revealed that no individual SNPs had a significant impact on the estimation of causal association. Furthermore, the MR-Egger-intercept showed a p-value of 0.9243475 for type 2 diabetes, suggesting no evidence of horizontal pleiotropy. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eType 2 diabetes and femoral neck BMD analysis results\u003c/h2\u003e \u003cp\u003eA total of 26 SNPs were included as instrumental variables for screening type 2 diabetes (Additional file 2). The results of Mendelian randomization indicated that the IVW method, MR-Egger method, WM method, Simple Mode method, and Weighted Mode method analysis all yielded consistent results. The IVW method showed an odds ratio (OR) of 1.041797 (95% CI: 0.9657858 to 1.123791) with a p-value of 0.2894429, suggesting no causal relationship between type 2 diabetes and femoral neck BMD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The heterogeneity test yielded p-values of 0.2136983 for the IVW method and 0.2052822 for the MR-Egger method, indicating no heterogeneity. Sensitivity analysis revealed no significant impact of any individual SNP on the estimation of causal association. The MR-Egger-intercept yielded a p-value of 0.404228 for type 2 diabetes, indicating no evidence of horizontal pleiotropy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003etype 2 diabetes and forearm BMD analysis results\u003c/h2\u003e \u003cp\u003eA total of 29 SNPs were included as instrumental variables after screening for type 2 diabetes (Additional file 3). The results of Mendelian randomization showed that the IVW method, MR-Egger method, WM method, Simple Mode method, and Weighted Mode method analysis results were consistent. Specifically, the IVW results indicated an odds ratio (OR) of 1.102443 (95% CI: 0.9433071 to 1.288424) with a p-value of 0.220121, suggesting no causal relationship between type 2 diabetes and forearm BMD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The tests for heterogeneity yielded p-values of 0.05724749 for the IVW method, 0.04419279 for the MR-Egger method, and 0.0598 for the MR-PRESSO.Global.Test, indicating no significant heterogeneity. Sensitivity analysis revealed no SNPs that significantly influenced the causal association estimates when using the case-by-case exclusion method. The MR-Egger-intercept showed a p-value of 0.9189454 for type 2 diabetes, indicating no evidence of horizontal pleiotropy. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eObservational studies are commonly used to investigate the relationship between phenotype and disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Prior to birth, human chromosomes are determined. Genome-wide association studies (GWAS) aim to identify sequence variants across the entire human genome, while Mendelian randomization (MR) employs disease-associated single nucleotide polymorphisms (SNPs) from GWAS data as instrumental variables for analysis. SNPs, following the principle of random assignment and being unaffected by the environment, can effectively minimize bias and help establish causal effects between exposure and outcomes, unlike observational studies such as cohort studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, this study did not find any causal relationship between genetically predicted type 2 diabetes and BMD in the lumbar spine, femoral neck, and forearm. Furthermore, sensitivity analysis using forest plots and leave-one-out tests did not reveal any significant effects of individual SNPs on the results. Both the MR-Egger-intercept and MRPRESSO tests also did not detect any horizontal polymorphisms, which enhances the reliability of this article. This study is believed to be the first to comprehensively determine the causal relationship between type 2 diabetes and BMD (BMD) at different sites. Previous studies have conducted Mendelian randomization (MR) studies in East Asian populations, which confirmed a causal relationship between type 2 diabetes and higher BMD, but no causal relationship with decreased BMD. However, these studies did not differentiate between different sites of BMD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In contrast to the conclusion of another MR study, which suggested that type 2 diabetes is causally associated with femoral neck BMD but not with lumbar spine BMD, our results are inconsistent with this finding [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although the study design is robust, it does not seem to have accounted for single nucleotide polymorphisms (SNPs) with palindromic coding and their association with potential confounders such as BMI, rheumatoid arthritis, and other factors. Jia et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] conducted an observational study which revealed a positive association between higher body mass index and higher BMD T-scores, indicating a lower risk of osteoporosis. Similarly, Andreoli et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] found that obesity significantly reduced the risk of osteoporosis. Moreover, Ouyang et al. discovered a significant positive correlation between body mass index and BMD in adolescents aged 8\u0026ndash;19 years [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings suggest that the genetic variation in the Ahmad et al. study might have been pleiotropic, violating two key assumptions in Mendelian randomization. These assumptions include the absence of a relationship between genetic variation and confounding factors, as well as the influence of genetic variation on the outcome solely through risk factors. Consequently, the inconsistent results obtained in Ahmad et al.'s study could be attributed to these factors. Additionally, varying data sources can also contribute to inconsistent findings.\u003c/p\u003e \u003cp\u003eIn relation to type 2 diabetes and osteoporosis, numerous observational studies have indicated that individuals with type 2 diabetes face a higher risk of fractures compared to those without diabetes. However, this increased risk does not appear to be linked to BMD, but rather to bone strength (10\u0026ndash;13). Some previous studies have even found that individuals with type 2 diabetes have either unchanged or higher BMD when compared to those without diabetes (14). On the other hand, it has been discovered that individuals with type 2 diabetes experience altered body composition, impaired bone healing, the accumulation of microfractures and cortical pores due to the loss of transverse trabecular junctions (resulting in a decreased flexion ratio), increased cortisol secretion, peripheral activation and sensitization (known as the 'cortisol milieu'), and an overall impairment of osteoblast activity. These factors may contribute to decreased bone turnover and poor bone microarchitecture (17). In addition to diabetes, other factors that may increase the risk of fractures in diabetic patients include a higher likelihood of falls due to complications like neuropathy, vision loss, and balance issues resulting from long-term poor glycemic control. Sarcopenia and the side effects of antidiabetic medications also contribute to this increased risk. One specific medication, thiazolidinediones (TZDs) such as pioglitazone and rosiglitazone, which activate the peroxisomal peroxisome proliferator-activated receptor gamma (PPARg), has been found to have a negative impact on bone metabolism despite its positive effects on glycemic control. By activating PPARg, TZDs promote adipogenesis and hinder osteoclastogenesis, leading to decreased bone formation and increased bone resorption. This ultimately results in weaker bone biomechanical properties.\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e. In conclusion, the precise mechanism by which type 2 diabetes impacts osteoporosis is still uncertain. Further studies are needed to examine whether it influences BMD (BMD) and bone strength through other factors that affect glucose metabolism.\u003c/p\u003e \u003cp\u003eMendelian randomization is a powerful tool for studying the relationship between exposure and outcome to establish causality. It offers several advantages over randomized controlled trials, such as overcoming potential confounding and reverse causation effects, as well as avoiding unnecessary resource wastage. This study stands out for its utilization of genetic data from a large-scale GWAS study on type 2 diabetes and BMD at three specific sites: the lumbar spine, femoral neck, and forearm. This approach enhances the statistical efficacy of the causal associations. Additionally, the distribution of genetic variants across chromosomes and the negligible impact of potential gene-gene interactions further strengthen the assessment. The article has some limitations that should be considered. Firstly, the genetic variants studied are all from European populations, so further validation is necessary to apply the findings to other populations and races. Secondly, the use of Mendelian randomization assumes a linear relationship between type 2 diabetes and BMD, and it cannot be used if this linear relationship does not exist. Thirdly, Mendelian randomization does not provide insights into the biological mechanisms underlying the observed associations. Lastly, due to the unavailability of detailed data on BMD, factors such as age and gender could not be included for deeper analysis. Therefore, future studies should consider using larger sample sizes or randomized controlled studies to obtain more accurate and comprehensive results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFrom a genetic perspective, there is no causal relationship between type 2 diabetes and BMD in the lumbar spine, femoral neck, or forearm.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors want to acknowledge all the participants and investigators of the GWASs involved in the present study for generously sharing the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.J and H.L designed the study. Y.R analyzed the data and prepared the original draft. T.W revised and edited the paper. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, we utilized publicly available summary data from GWAS. The pooled statistics for Type 2 diabetes,\u0026nbsp;Lumbar spine BMD, Femoral neck BMD\u0026nbsp;and\u0026nbsp;Forearm BMD\u0026nbsp;can be accessed at https://gwas.mrcieu.ac.uk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. We conducted an analysis using data from publicly available online databases. No administrative permissions were required to access the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiao-Cheng Jiang, Huan Li, Yang-Liang Ren, and Ting Wang declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu D, Cline-Smith A, Shashkova E, Perla A, Katyal A, Aurora R. T-Cell Mediated Inflammation in Postmenopausal Osteoporosis. Front Immunol. 2021;12:687551.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusso V, Chen R, Armamento-Villareal R, Hypogonadism. Type-2 Diabetes Mellitus, and Bone Health: A Narrative Review. Front Endocrinol (Lausanne). 2021;11:607240.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Ji X. Sample Size Estimation in Clinical Research: From Randomized Controlled Trials to Observational Studies. Chest. 2020;158(1S):12\u0026ndash;S20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Res Synth Methods. 2019;10(4):486\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang G, Chen X, Chen Y et al. Causal relationship between type 2 diabetes and BMD: a Mendelian randomization study in an East Asian population. Osteoporos Int. 2023 Jun 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad OS, Leong A, Miller JA, et al. A Mendelian Randomization Study of the Effect of Type-2 Diabetes and Glycemic Traits on BMD. J Bone Miner Res. 2017;32(5):1072\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia L, Cheng M. Correlation analysis between risk factors, BMD and serum osteocalcin, CatheK, PINP, β-crosslaps, TRAP, lipid metabolism and BMI in 128 patients with postmenopausal osteoporotic fractures. Eur Rev Med Pharmacol Sci. 2022;26(21):7955\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreoli A, Bazzocchi A, Celi M, et al. Relationship between body composition, body mass index and BMD in a large population of normal, osteopenic and osteoporotic women. Radiol Med. 2011;116(7):1115\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuyang Y, Quan Y, Guo C, et al. Saturation Effect of Body Mass Index on BMD in Adolescents of Different Ages: A Population-Based Study. Front Endocrinol (Lausanne). 2022;13:922903.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEller-Vainicher C, Cairoli E, Grassi G, et al. Pathophysiology and Management of type 2 diabetes Bone Fragility. J Diabetes Res. 2020;2020:7608964.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNapoli N, Chandran M, Pierroz DD, IOF Bone and Diabetes Working Group, et al. Mechanisms of diabetes mellitus-induced bone fragility. Nat Rev Endocrinol. 2017;13(4):208\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoayeri A, Mohamadpour M, Mousavi SF, et al. Fracture risk in patients with type 2 diabetes and possible risk factors: a systematic review and meta-analysis. Ther Clin Risk Manag. 2017;11:455\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhosla S, Samakkarnthai P, Monroe DG, et al. Update on the pathogenesis and treatment of skeletal fragility in type 2 diabetes. Nat Rev Endocrinol. 2021;17(11):685\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCipriani C, Colangelo L, Santori R, et al. The Interplay Between Bone and Glucose Metabolism. Front Endocrinol (Lausanne). 2020;11:122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang M, Kim H, Lea S, et al. Effect of duration of diabetes on BMD: a population study on East Asian males. BMC Endocr Disord. 2018;18(1):61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui R, Zhou L, Li Z, et al. Assessment risk of osteoporosis in Chinese people: relationship among body mass index, serum lipid profiles, blood glucose, and BMD. Clin Interv Aging. 2016;11:887\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCompston J. type 2 diabetes and bone. J Intern Med. 2018;283(2):140\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGimble JM, Robinson CE, Wu X, Kelly KA, Rodriguez BR, Kliewer SA, Lehmann JM, Morris DC. Peroxisome proliferator-activated receptor-gamma activation by thiazolidinediones induces adipogenesis in bone marrow stromal cells. Mol Pharmacol. 1996;50(5):1087\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang YS, Zheng YD, Yuan Y, et al. Effects of Anti-Diabetic Drugs on Fracture Risk: A Systematic Review and Network Meta-Analysis. Front Endocrinol (Lausanne). 2021;12:735824.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalaitzoglou E, Fowlkes JL, Popescu I, et al. Diabetes pharmacotherapy and effects on the musculoskeletal system. Diabetes Metab Res Rev. 2019;35(2):e3100.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"type 2 diabetes, Lumbar bone mineral density, Femoral Neck bone mineral density, Forearm bone mineral density, Mendelian Randomization","lastPublishedDoi":"10.21203/rs.3.rs-3850790/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3850790/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eSummary:\u003c/strong\u003eWhen Mendelian randomization (MR) studies were used to investigate the causal relationship between type 2 diabetes and BMD at different sites, there was no causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD, or forearm BMD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e The purpose of this study was to assess the causal relationship between type 2 diabetes and BMD in the lumbar spine, femoral neck, and forearm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Based on the aggregated statistical data of a large published genome-wide association study. The IVW method, the MR-Egger method, the WM method, the Simple Mode method, and the Weighted Mode method were used to evaluate the causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD and forearm BMD. In addition, sensitivity analysis was performed using MR-Egger regression, Cochran's Q test and MR-PRESSO Global test to ensure the robustness of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults：\u003c/strong\u003eThe results of the inverse variance weighted (IVW) analysis for type 2 diabetes and lumbar BMD showed an odds ratio (OR) of 1.070997 (95% confidence interval [CI]: 0.9839422 to 1.165754), with a p-value of 0.11279766. Similarly, the IVW analysis for type 2 diabetes and femoral neck BMD showed an OR of 1.041797 (95% CI: 0.9657858 to 1.123791), with a p-value of 0.28944290. For type 2 diabetes and forearm BMD, the IVW analysis resulted in an OR of 1.102443 (95% CI: 0.9433071 to 1.288424), with a p-value of 0.22012100. Heterogeneity tests for type 2 diabetes and lumbar BMD, femoral neck BMD, and forearm BMD did not identify any outlier variables. Sensitivity analyses confirmed the robustness of the results, and no pleiotropic effects were observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions：\u003c/strong\u003eThere was no causal relationship between type 2 diabetes and lumbar BMD, femoral neck BMD, or forearm BMD.\u003c/p\u003e","manuscriptTitle":"Causal relationship between type 2 diabetes and BMD: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 17:40:40","doi":"10.21203/rs.3.rs-3850790/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":"d0d2f2cb-96aa-48fc-8309-a66caa82a720","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-28T06:54:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-23 17:40:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3850790","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3850790","identity":"rs-3850790","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}