Unveiling the Etiology of Osteoporosis Onset: A Mendelian Randomization Investigation

doi:10.21203/rs.3.rs-4979145/v1

Objective

Osteoporosis (OP) is a prevalent systemic metabolic disorder characterized by a reduction in total bone mass and a deterioration of bone microarchitecture. These changes result in significantly increased bone fragility, which predisposes patients to a higher risk of fractures. As a consequence, OP severely impacts patients' quality of life and imposes a considerable economic burden on society. With the ongoing global demographic shift towards an aging population, it is crucial to gain a deeper understanding of the pathogenesis of OP and to develop effective therapeutic strategies. This study aims to identify potential causal risk factors associated with OP by examining genetic variations, with the goal of providing new insights for the prevention and management of the disease.

Methods

We commenced our investigation by developing a comprehensive search protocol. Subsequently, we conducted a systematic search across several Chinese databases, including the China National Knowledge Infrastructure (CNKI), the Chinese Biomedical Literature Database (CBM), the Wanfang Database, and VIP Information (CSTJ), as well as international databases such as The Cochrane Library, PubMed, Embase, and Web of Science. This thorough search was executed electronically to ensure a complete collection of the most current literature and data related to OP, thereby minimizing the risk of oversights. Following this, we established rigorous inclusion and exclusion criteria for literature selection, which was followed by a meticulous review and synthesis of the existing literature. This process enabled us to accurately identify a range of potential etiological risk factors associated with OP. To substantiate the association between these factors and the disease, we incorporated extensive outcome data from the Finnish database, which included 3,203 OP cases and 209,575 controls, as well as the UK Biobank database, which comprised 5,266 cases and 331,893 controls.The inclusion of these robust datasets enhances the statistical rigor and reliability of our findings. We employed a two-sample bidirectional Mendelian randomization(MR) approach, utilizing genetic variation as an instrumental variable. This method mitigates the influence of confounding factors and reverse causality, thus enabling a more thorough exploration of the genetic relationship between hypothesized risk factors and OP risk. To assess heterogeneity in our results, we applied Cochran's Q test and employed the MR-Egger and MR-PRESSO regression techniques to investigate the potential for pleiotropy. To ensure the homogeneity of our research data and guard against pleiotropy, we calculated the impact quantification index (ORSD) for each risk factor's effect on OP risk across varying degrees of genetic variation. This calculation offers substantial evidence for a deeper understanding of the disease's etiology. Furthermore, to rigorously control the accuracy of our research outcomes, we implemented the False Discovery Rate (FDR) correction and the Bonferroni correction methods. These approaches mitigate the risk of false positives in multiple hypothesis testing, thereby preserving the scientific integrity and credibility of our conclusions.

Results

Through rigorous analysis, we identified several factors associated with OP in the Finnish database. Notably, primary biliary cholangitis, type 1 diabetes, seropositive rheumatoid arthritis, and seronegative rheumatoid arthritis exhibited positive correlations with OP. In contrast, type 2 diabetes demonstrated an inverse relationship with the condition. Biochemical indicators, including Dickkopf-related protein 1 and sex hormone-binding globulin levels, were positively associated with OP. Socioeconomic factors, such as higher education levels and years of schooling, showed negative correlations with OP. Lifestyle habits, including drinking frequency, as well as biochemical indicators like oxalate levels, also displayed negative associations. Furthermore, specific population characteristics, such as the relative body size of 10-year-old male children, along with physical indicators like body mass index and systolic blood pressure, were inversely related to OP risk.In the UK Biobank data, factors such as menopausal status, celiac disease, irritable bowel syndrome, systemic lupus erythematosus, education level, and environmental exposures like PM2.5 exhibited positive correlations with OP. Conversely, menopausal age, dietary factors including non-oily fish consumption, and pulse pressure were found to be negatively associated with the disease. Post-hoc corrections employing the Bonferroni method revealed significant positive correlations between seropositive rheumatoid arthritis and type 1 diabetes with OP in the Finnish database, along with negative correlations for menopausal age and pulse pressure in the UK Biobank. Following the application of the False Discovery Rate (FDR) correction, the Finnish database indicated additional positive associations with OP for primary biliary cholangitis, irritable bowel syndrome, type 1 diabetes, seropositive rheumatoid arthritis, and sex hormone-binding globulin levels. Furthermore, type 2 diabetes and systolic blood pressure were confirmed to have negative correlations with OP. In the UK Biobank, the negative associations for menopausal age and pulse pressure remained consistent.

Conclusion

These findings, derived from a genetic variation perspective, effectively exclude certain previously implicated pathogenic risk factors for OP while highlighting others. This distinction is pivotal as it enhances our understanding of the disease's etiology. The implications of our study are profound, providing valuable insights that could significantly inform the development of preventive and therapeutic strategies for OP.

Health sciences/Diseases

Health sciences/Medical research

Health sciences/Pathogenesis

Health sciences/Risk factors

Osteoporosis (OP) is a prevalent chronic metabolic bone disorder that predominantly affects the elderly and postmenopausal women. It is characterized by reduced bone mass, compromised bone microarchitecture, and an increased susceptibility to fractures. As the aging population grows and life expectancy rises, OP is becoming a significant global health challenge. This condition and its associated fractures not only diminish the quality of life for affected individuals but also impose a considerable economic burden on nations and societies.Data indicate that in the United States(1), 10.3% of adults aged 50 and older suffer from OP. In China, the standardized prevalence among individuals over 50 is significantly higher, affecting 6.46% of men and a striking 29.13% of women. Globally, the incidence of disability attributed to low bone density has risen dramatically, increasing from 8,588,936 in 1990 to 16,647,466 in 2019. Concurrently, mortality due to low bone density has nearly doubled, rising from 207,367 in 1990 to 437,884 in 2019. These figures underscore the substantial burden that OP imposes on the global health landscape.A prospective study forecasts a concerning escalation in the incidence of osteoporosis-related fractures in China(2), projecting that by 2035, the occurrence of such fractures, along with the associated economic costs, will double from current figures. If left unaddressed, this trend is predicted to culminate by 2050 in an astonishing 5.99 million fractures, with the financial burden soaring to a staggering 25.43 billion US dollars. These figures not only signal an impending public health crisis but also underscore the imperative for strategic and preemptive interventions to mitigate the socioeconomic impact of OP.A further study indicates that(3), in 2017, the direct economic burden of OP treatment in Australia amounted to a significant 3.44 billion US dollars. Within this total, expenditures related to the management of osteoporotic fractures comprised a substantial 68%. Notably, the costs associated with the treatment of hip fractures alone represented an impressive 43% of the overall fracture management expenses. When compared to figures from 2007, it is clear that the total expenditure has tripled. Globally, OP currently affects over 200 million individuals.Projections indicate that by 2025(4), the financial burden of treating osteoporotic fractures will rise to at least 25 billion US dollars. The etiology of OP is inherently complex, involving a myriad of factors, including genetics, hormonal influences, physiological mechanisms, environmental exposures, and lifestyle choices. Investigating this multifaceted pathogenesis and identifying more effective diagnostic and therapeutic strategies has become imperative. Such efforts not only promise to enhance patient well-being and alleviate the strain on healthcare systems but also contribute significantly to the scientific foundation for disease prevention and postponement, ultimately advancing human health.

Throughout the historical trajectory of research on OP, numerous observational studies have been conducted, all aimed at accurately identifying the etiologies of this condition. These studies are designed to aggregate comprehensive data and perform exhaustive analyses, utilizing meticulous observations of a wide array of cases alongside control samples. Their objective is to explore the causal factors and various influences associated with OP, thereby establishing a solid foundation for preventive and therapeutic strategies. However, the inherent limitations of observational studies necessitate careful consideration. Firstly, such studies are constrained by the intrinsic characteristics and context of the subjects being examined, which can obscure access to underlying or complex causal relationships. Additionally, the observer's subjective biases, perceptual limitations, and challenges in controlling temporal and situational factors may introduce discrepancies in the data. Furthermore, the scope of observation and the scale of sampling can limit the generalizability of findings, hindering the representation of the broader population. Ultimately, the quantification of observational data is often problematic, and the lack of rigorous methodological controls typically found in experimental designs can undermine the scientific validity and reliability of the conclusions drawn.

Given the inherent limitations and constraints of observational studies, there is a pressing need for novel and precise methodologies for exploration and investigation. The advancing field of genomics has highlighted the critical role that genetic factors play in the pathogenesis of diseases, as demonstrated by a growing body of research. In this context, Mendelian randomization (MR) has emerged as a groundbreaking statistical approach rooted in genome-wide association studies (GWAS).MR has increasingly emerged as a pivotal tool for elucidating the causal nexus between exposure variables and disease outcomes(5, 6). This method utilizes genetic variation as an instrumental variable, applying Mendel's laws of independent assortment to structure disease research and conduct data analysis. By doing so, MR provides a robust framework for substantiating causal disease hypotheses. Furthermore, MR functions as an analytical approach that identifies causal relationships with target diseases through genetic variations that influence risk factors, commonly referred to as 'genetic instruments'(7, 8).Most notably, genetic variations are established prior to birth and thus precede disease onset. The utilization of MR effectively mitigates confounding biases and reverse causality that can distort associative findings. Consequently, the evidence linking gene-proxied exposures to disease outcomes, as demonstrated through MR, can substantiate the presence of a causal relationship. This is particularly compelling as it provides a means to infer causality from observational data. In this MR study, we begin with a retrospective analysis, encompassing all literature on OP risk factors from the inception of the database up to July 2024. By systematically cataloging and assessing these risk factors through the lens of genetic variation, we aim to perform a comprehensive synthesis and induction. Our goal is to furnish clinical research with valuable genetic and statistical evidence, thereby enhancing the foundation for subsequent investigative endeavors.

Data sources of exposure factors and outcomes

Initially, we developed a comprehensive search strategy and conducted extensive literature searches across a wide range of databases. Our search included the CBM database, Wanfang database, VIP database, CNKI database, Clinical Trials database, Cochrane Library, Embase database, PubMed database, and the Web of Science database. The temporal scope of our search extended from the inception of each database to July 2024, and we also examined relevant references to ensure thoroughness.The Chinese subject terms included: "osteoporosis", "bone loss", "reduced bone density", "decreased bone density", "influencing factors", "etiology", "related factors", "risk factors", "case-control study", "case-control", "cohort study", "cohort". English subject terms include: "osteoporosis", "Osteoporoses", "Osteoporosis, Age-Related", "Osteoporosis, Age Related", "Age-Related Osteoporosis", "Age-Related Osteoporoses", "Age Related Osteoporosis", "Osteoporoses, Age-Related", "Bone Loss, Age-Related", "Age-Related Bone Loss", "Age-Related Bone Losses", "Bone Loss, Age Related", "Bone Losses, Age-Related", "Osteoporosis, Senile", "Osteoporoses, Senile", "Senile Osteoporoses", "Senile Osteoporosis", "Osteoporosis, Involutional", "Osteoporosis, Post-Traumatic", "Osteoporosis, Post Traumatic", "Post-Traumatic Osteoporoses", "Post-Traumatic Osteoporosis", "Factor, Risk", "Factors, Risk", "Risk Factor", "Risk Factors", "Case-Control Studies", "Case-Control Study", "Studies, Case-Control", "Study, Case-Control", "Case-Comparison Studies", "Case Comparison Studies", "Case-Comparison Study", "Studies, Case-Comparison", "Study, Case-Comparison", "Case Control Studies", "Case Control Study", "Studies, Case Control", "Study, Case Control", "Cohort Study", "Studies, Cohort", "Study, Cohort", "Cohort Studies". Building upon the foundation of a thorough review of the existing literature, we established and implemented rigorous inclusion and exclusion criteria to identify the risk factors for OP, as previously documented. The literature screening process was conducted by three independent reviewers who adhered to the predefined criteria. In cases of disagreement, a fourth reviewer was appointed to mediate and make a final determination. Additionally, to ensure a comprehensive evaluation of osteoporotic risk factors, we imposed no restrictions based on gender, race, nationality, or sample size. To facilitate the management of the literature, we utilized EndNote X9 and NoteExpress 3.9.0, effectively organizing and synthesizing the collected data.

In our research, we have carefully selected large-scale GWAS involving individuals of European ancestry. The GWAS data related to our exposure indicators have been sourced from several esteemed databases, including the European Bioinformatics Institute, the GWAS Catalog, the FinnGen database, and the UK Biobank. For the outcome indicators, we specifically focused on GWAS data concerning OP from the FinnGen and UK Biobank databases. These datasets comprise 3,203 cases and 209,575 controls from the FinnGen database, as well as 5,266 cases and 331,893 controls from the UK Biobank database. This extensive array of data facilitates a thorough investigation into the genetic foundations of OP within the context of our study.

Selection of Genetic Instrumental Variables

Our study rigorously adheres to the guidelines established by the STROBE-MR checklist(9), thereby ensuring methodological rigor in our investigation(We submit the specific content in the form of Supplementary Material 1.). Utilizing a two-sample MR analysis, we have thoroughly evaluated the potential risk factors for OP identified through extensive meta-analyses. This approach has enabled us to verify the causal relationships between these factors and OP through the lens of genetic variation. is a robust genetic epidemiological tool that employs instrumental variables (IVs) to explore the causal links between exposures and outcomes, effectively addressing confounding factors. This methodological strategy is particularly advantageous in the context of our research, as it facilitates the discernment of causality amid the complex interplay of genetic and environmental influences. Figure 1 illustrates the three fundamental assumptions that effective instrumental variables must satisfy in MR causal inference(10). First, there must be a direct association between genetic variation and the exposure of interest, indicating that genetic variation directly influences the occurrence of the exposure. Second, genetic variation should be independent of all confounding factors related to both the exposure and the outcome. This implies that the relationship between genetic variation and the outcome is not confounded by other factors and remains unaffected by such external influences. Lastly, while genetic variation may influence the outcome, this effect should be mediated solely through the exposure. In other words, the pathway of influence from genetic variation to outcome is indirect, operating exclusively through its impact on the exposure variable.

For each exposure factor, we implemented a rigorous screening protocol to identify eligible instrumental variables. In alignment with prior MR studies, we established a significance threshold of P < 5×10⁻⁸ for MR analysis, which ensured the selection of single nucleotide polymorphisms (SNPs) with a strong association with the exposure of interest. This criterion takes into account the collective evidence from both previous research and the present study, thereby meeting the methodological requirements of MR studies. Furthermore, to ensure the independence of instrumental variables across different exposure phenotypes, we utilized a linkage disequilibrium (LD)-based clustering approach(11).To eliminate SNPs with high linkage disequilibrium (LD), we applied an r² threshold of 0.8. We excluded SNPs with unresolvable allele inconsistencies and those exhibiting repetitive palindromic patterns. The SNPs that passed this rigorous screening were designated as instrumental variables for subsequent MR analysis. To assess potential weak instrument bias, we calculated the F statistic, a metric that quantifies the strength of the instrumental variable.The F statistic is calculated using the formula(12, 13): F = R²(N - K − 1) / [K(1 - R²)], where R² represents the variance in exposure explained by the instrumental variable (IV), N denotes the sample size, and K indicates the number of IVs. Single nucleotide polymorphisms (SNPs) with an F statistic below 10 were classified as weak IVs and subsequently excluded to reduce bias. These meticulous steps and rigorous hypothesis testing ensure the scientific integrity and precision of our research, thereby providing robust support for a comprehensive investigation into the potential risk factors associated with OP.

Statistical Analysis

This study employs a two-sample bidirectional MR analysis to investigate the potential risk factors associated with OP. In the initial phase of forward analysis, we identify the reported potential risk factors from our literature review as exposure factors, with OP designated as the outcome. This methodology enables us to examine the genetic variation perspective on how these risk factors may influence the onset of OP, either by increasing or decreasing the risk. Following the completion of the forward analysis, we promptly conduct a reverse MR analysis to reinforce the reliability of our conclusions. The reverse analysis mirrors the forward analysis in terms of settings and data utilization but reverses the presumed causality, treating OP as the exposure and the potential risk factors as the outcome. This bidirectional analytical strategy aims to reveal any non-causal associations and further validate our preliminary findings.

We employed R software for data analysis, specifically version 4.4.1. Our analysis primarily utilized the TwoSampleMR package, version 0.6.6(14), and incorporated a suite of six methods(15–20): MR Egger, Weighted Median, Inverse Variance Weighted (IVW), Simple Mode, Weighted Mode, and Maximum Likelihood, to investigate potential risk factors for OP. In instances of discordant results(21), we prioritized the IVW method and its associated p-value to substantiate the correlation between exposure and outcome, given its generally higher efficiency and precision. A p-value below 0.05 was considered indicative of a significant correlation. To further reinforce the robustness of our conclusions, we conducted supplementary analyses using both the Weighted Median method and the MR-Egger method.The Weighted Median method can accommodate up to 50% invalid instrumental variables, thereby mitigating concerns regarding their validity to some extent. In contrast, the MR-Egger method enhances adaptability by recognizing that not all instrumental variables are equally effective, which allows for the evaluation of causal effects across a broader range of conditions. Consequently, when the findings from these three analytical methods align and support one another, our conclusions become more compelling and reliable. Following this, we apply False Discovery Rate (FDR) correction(22)and Bonferroni correction(23), both of which aim to control the error rate in multiple comparisons. While FDR correction provides a flexible balance between error rate and statistical power, Bonferroni correction offers a more stringent approach to error rate control. An FDR-corrected p-value below 0.1 indicates a potential causal relationship, while a value below 0.05 suggests a significant causal link(23, 24). The Bonferroni correction establishes a more rigorous threshold by dividing the significance level by the number of comparisons, which corresponds to the number of potential exposure factors. A corrected p-value threshold of 1.61 × 10⁻⁴ is considered statistically significant, with findings yielding p-values below this threshold interpreted as indicating a significant correlation.

We utilize Cochran's Q test, along with the corresponding P value, to evaluate the heterogeneity among the instrumental variables. This test is proficient in identifying potential heterogeneity in effect sizes. A P value from Cochran's Q test that is less than 0.05 indicates significant heterogeneity. Nevertheless, the presence of heterogeneity does not inherently undermine the effectiveness of the Inverse Variance Weighted (IVW) model(18). The selection of the model is additionally guided by the I² statistic, which measures consistency across studies. If the I² value exceeds 50%, suggesting considerable heterogeneity, we employ the IVW estimate derived from the random effects model.Conversely, if the I² value falls below this threshold, we employ the fixed effects model. To minimize the potential impact of horizontal pleiotropy, we apply the MR-Egger intercept test and the MR-PRESSO method, each accompanied by their respective P values. A significant intercept term, indicated by a P value of less than 0.05, suggests the presence of horizontal pleiotropy. In the absence of such significance, we infer that horizontal pleiotropy does not act as a confounding factor. Additionally, we enhance our analysis with visual tools such as scatter plots, funnel plots, and forest plots. Scatter plots enable the visualization of quantitative correlation trends between variables, facilitate the identification of outliers, and allow for an assessment of their influence on the analysis. Funnel plots are utilized to evaluate potential biases and heterogeneity among the instrumental variables. Conversely, forest plots display the effect sizes and confidence intervals of each study, providing a visual representation of heterogeneity. Collectively, these methods fortify the robustness of our analysis and the reliability of our conclusions regarding the potential risk factors for OP.

Ethical Approval

This study exclusively analyzes pre-existing data and documentation from published research that has already received approval from ethical review committees. Consequently, no further ethical approval is required for the current investigation.

In our quest to explore the causal links between various potential pathogenic risk factors and OP, we undertook a rigorous screening process. Our literature retrieval involved an extensive search across multiple databases, resulting in the following yields: 476 articles from the Chinese Biomedical Literature Database (CBM), 1151 from the Wanfang Database, 82 from the VIP Database, 402 from CNKI, 1457 from the Cochrane Library, 536 from the Embase database, 987 from the PubMed database, and a substantial 8673 from the Web of Science database. After identifying 2188 duplicates across these databases, we initiated our screening process. The preliminary screening, based on titles, resulted in the retention of 83 Chinese articles and the exclusion of 393; for English literature, 4420 articles were retained and 6683 were excluded. Subsequent screening through abstracts and keywords further narrowed down the selection to 1078 English and 66 Chinese articles. The final, meticulous full-text review led to the retention of 636 English and 56 Chinese articles. Additionally, we conducted a search for included GWAS data in the IEU OpenGWAS database. Following this series of rigorous screenings, we identified a total of 310 potential risk factors, as detailed in Supplementary Table 1.

Upon meticulous examination of the myriad risk factors identified in our search, several themes emerged with notable frequency. Content related to calcium intake was referenced eight times, encompassing terms such as 'calcium intake,' 'dietary calcium intake,' and 'calcium nutritional intake,' thereby underscoring the widely recognized significance of calcium for bone health. Similarly, vitamin D receptor-related content appeared eight times, prominently featuring terms like 'vitamin D receptor gene' and 'BsmI in vitamin D receptor gene,' which highlight the pivotal role of the vitamin D receptor in OP research. Physical activity-related content also emerged with frequency, appearing eight times and including phrases like 'sedentary lifestyle' and 'low activity,' emphasizing the profound influence of physical activity levels on overall health. Insulin-like growth factor-related content was noted seven times, primarily focusing on 'insulin-like growth factor-1,' reflecting its growing research interest within the OP domain. Tobacco-related content was observed six times, with references to 'tobacco' and 'passive tobacco behavior,' demonstrating a broad awareness of tobacco's detrimental effects on health. Corticosteroid-related content was mentioned five times, covering terms such as 'corticosteroids' and 'glucocorticoids,' indicating the relevance of hormonal factors in both the etiology and treatment of OP. Lastly, menopause-related content also emerged five times, featuring aspects such as 'menopause time' and 'female menopause age,' which indicate the importance of estrogen changes as a significant contributor to OP.

Subsequently, we performed a two-sample MR analysis, with the statistical results presented in Supplementary Table 2. Our thorough investigation identified several factors significantly correlated with the onset of OP, as summarized in Table 1. In the Finnish database, numerous factors exhibited positive associations with OP.Notably, primary biliary cholangitis showed a positive correlation (P = 0.002504, OR = 1.069789, 95% CI 1.024003 to 1.117621), as did irritable bowel syndrome (P = 0.003587, OR = 2.868974, 95% CI 1.411509 to 5.831355) and elevated levels of Dickkopf-related protein 1 (P = 0.033762, OR = 1.212191, 95% CI 1.014875 to 1.447870). Type 1 diabetes also exhibited a positive correlation (P = 0.000002, OR = 1.053891, 95% CI 1.031156 to 1.077126), as did serum-positive rheumatoid arthritis (P = 0.000001, OR = 1.123461, 95% CI 1.071752 to 1.177664) and sex hormone-binding globulin levels (P = 0.001329, OR = 1.171618, 95% CI 1.063605 to 1.290599). Additionally, serum-negative rheumatoid arthritis displayed a positive association (P = 0.012022, OR = 1.087250, 95% CI 1.018538 to 1.160596). Conversely, an education level of college graduate was negatively correlated with OP (P = 0.042833, OR = 0.549692, 95% CI 0.308051 to 0.980882), as was drinking frequency (P = 0.032657, OR = 0.737570, 95% CI 0.557847 to 0.975196) and oxalate levels (P = 0.022389, OR = 0.904926, 95% CI 0.830569 to 0.985939). The relative body shape of 10-year-old boys showed a negative correlation (P = 0.046615, OR = 0.642499, 95% CI 0.415549 to 0.993400), as did type 2 diabetes (P = 0.000224, OR = 0.884198, 95% CI 0.828244 to 0.943932), body mass index (P = 0.038583, OR = 0.826736, 95% CI 0.690354 to 0.990060), and diabetes (P = 0.004771, OR = 0.088821, 95% CI 0.016529 to 0.477302). Furthermore, higher education levels, measured in years of education, were negatively associated with OP (P = 0.006496, OR = 0.928166, 95% CI 0.879653 to 0.979355), as was systolic blood pressure (P = 0.002446, OR = 0.682680, 95% CI 0.533304 to 0.873896).

In our analysis of the UK Biobank, several factors exhibited significant correlations with OP. Menopausal status demonstrated a positive association with the condition (P = 0.045661, OR = 1.018174, 95% CI 1.000347 to 1.036318). Additionally, celiac disease (P = 0.009117, OR = 1.000604, 95% CI 1.000150 to 1.001058), irritable bowel syndrome (P = 0.046915, OR = 1.006719, 95% CI 1.000091 to 1.013391), and systemic lupus erythematosus (P = 0.035667, OR = 1.000896, 95% CI 1.000060 to 1.001732) each showed positive correlations with OP. The level of education, measured in years, also indicated a positive relationship (P = 0.038985, OR = 1.000682, 95% CI 1.000034 to 1.001331), as did PM2.5 exposure (P = 0.035282, OR = 1.045726, 95% CI 1.003087 to 1.090178). Conversely, menopausal age was found to be negatively correlated with OP (P = 0.00000383, OR = 0.998979, 95% CI 0.998546 to 0.999412). The consumption of non-oily fish revealed a negative association (P = 0.014164, OR = 0.935658, 95% CI 0.887237 to 0.986721), as did pulse pressure (P = 0.000095, OR = 0.994963, 95% CI 0.992443 to 0.997490). To visually represent these associations, we employed a forest plot, which is presented in Figure 2, illustrating the relationship between each factor and OP. The p-values derived from the inverse variance weighting method were utilized to gauge the strength of association and statistical significance. The OR values correspond to the odds ratio for each standard deviation increase in the gene-predicted risk factors. Furthermore, we have summarized the number of single nucleotide polymorphisms (SNPs) considered in our statistical analysis, along with the results of horizontal pleiotropy tests, providing a comprehensive overview of the genetic factors implicated in OP risk.

Table 1. Pathogenic risk factors for OP in MR analysis.

exposure	outcome	nsnp	IVW_Pval	FDR_adjustP	OR	95%CI	F	MR-egger	MR-PRESSO
Educational attainment (college completion)	Osteoporosis（FinnGen）	200	0.042833	0.27512	0.549692	0.308051―0.980882	46.043392	0.074782	0.778
Primary biliary cholangitis	Osteoporosis（FinnGen）	39	0.002504	0.041819	1.069789	1.024003―1.117621	101.243645	0.429624	0.141
Alcohol intake frequency.	Osteoporosis（FinnGen）	95	0.032657	0.247898	0.73757	0.557847―0.975196	53.425103	0.146194	0.119
Irritable bowel syndrome	Osteoporosis（FinnGen）	4	0.003587	0.049914	2.868974	1.411509―5.831355	31.674052	0.485354	0.417
Oxalate (ethanedioate) levels	Osteoporosis（FinnGen）	140	0.022389	0.178045	0.904926	0.830569―0.985939	41.428953	0.99957	0.076
Dickkopf-related protein 1 levels	Osteoporosis（FinnGen）	9	0.033762	0.23493	1.212191	1.014875―1.447870	76.055758	0.698845	0.884
Comparative body size at age 10, Males	Osteoporosis（FinnGen）	51	0.046615	0.278024	0.642499	0.415549―0.993400	67.637364	0.529862	0.453
Type 2 diabetes	Osteoporosis（FinnGen）	173	0.000224	0.007491	0.884198	0.828244―0.943932	80.395849	0.880038	0.534
Type 1 diabetes	Osteoporosis（FinnGen）	85	0.000002	0.000099	1.053891	1.031156―1.077126	274.805955	0.143879	0.553
Seropositive rheumatoid arthritis	Osteoporosis（FinnGen）	11	0.000001	0.000071	1.123461	1.071752―1.177664	200.249593	0.934539	0.567
Sex hormone-binding globulin levels	Osteoporosis（FinnGen）	371	0.001329	0.027748	1.171618	1.063605―1.290599	192.676265	0.405961	0.285
Body mass index	Osteoporosis（FinnGen）	362	0.038583	0.257732	0.826736	0.690354―0.990060	68.309699	0.617831	0.054
Diabetes diagnosed by doctor	Osteoporosis（FinnGen）	66	0.004771	0.061292	0.088821	0.016529―0.477302	72.232493	0.084623	0.431
Educational attainment (years of education)	Osteoporosis（FinnGen）	207	0.006496	0.077482	0.928166	0.879653―0.979355	44.206188	0.074281	0.865
Systolic blood pressure	Osteoporosis（FinnGen）	200	0.002446	0.045392	0.68268	0.533304―0.873896	88.61366	0.946083	0.682
Seronegative rheumatoid arthritis	Osteoporosis（FinnGen）	6	0.012022	0.133849	1.08725	1.018538―1.160596	86.164291	0.180935	0.391
Menopause (age at onset)	osteoporosis （UKB Bank）	77	0.000004	0.000105	0.998979	0.998546―0.999412	78.253424	0.720845	0.299
Had menopause	osteoporosis （UKB Bank）	12	0.045661	0.220246	1.018174	1.000347―1.036318	68.44226	0.412217	0.181
Celiac disease	osteoporosis （UKB Bank）	15	0.009117	0.074757	1.000604	1.000150―1.001058	285.817019	0.870153	0.303
Irritable bowel syndrome	osteoporosis （UKB Bank）	5	0.046915	0.213724	1.006719	1.000091―1.013391	32.737853	0.864906	0.495
Systemic lupus erythematosus	osteoporosis （UKB Bank）	4	0.035667	0.188692	1.000896	1.000060―1.001732	47.135626	0.885067	0.725
Educational attainment (years of education)	osteoporosis （UKB Bank）	211	0.038985	0.199798	1.000682	1.000034―1.001331	44.801959	0.154222	0.083
Non-oily fish consumption	osteoporosis （UKB Bank）	5	0.014164	0.096785	0.935658	0.887237―0.986721	36.339112	0.101326	0.171
Pulse pressure	osteoporosis （UKB Bank）	257	0.000095	0.001421	0.994963	0.992443―0.997490	64.792212	0.318058	0.093
Particulate matter air pollution (pm2.5)	osteoporosis （UKB Bank）	4	0.035282	0.192875	1.045726	1.003087―1.090178	32.773471	0.430645	0.086

Subsequent to our initial analysis, we applied the FDR correction to filter out potentially spurious correlations and identified several factors with strong, statistically significant associations in the Finnish database. Primary biliary cholangitis exhibited a robust correlation with OP (P = 0.002504, FDR_P = 0.041819). Similarly, irritable bowel syndrome demonstrated a strong correlation (P = 0.003587, FDR_P = 0.049914). Type 2 diabetes, already significant in the initial analysis, maintained its strong association with OP (P = 0.000224, FDR_P = 0.007491). Notably, type 1 diabetes showed an even more pronounced correlation (P = 0.000002, FDR_P = 0.000100). Serum-positive rheumatoid arthritis also had a strong correlation (P = 0.000001, FDR_P = 0.000071), as did the level of sex hormone-binding globulin (P = 0.001329, FDR_P = 0.027748) and systolic blood pressure (P = 0.002446, FDR_P = 0.045392). In the UK Biobank database, menopausal age continued to show a strong correlation with OP (P = 0.000004, FDR_P = 0.000105), and pulse pressure also revealed a strong correlation (P = 0.000095, FDR_P = 0.001421). These findings underscore the impact of both metabolic and cardiovascular factors in the context of OP risk.

In the final phase of our statistical analysis, we implemented a more stringent Bonferroni correction to our dataset. With the corrected threshold for statistical significance established at P = 1.61×10⁻⁴, we identified several factors that exhibited strong correlations with OP. Within the Finnish database, type 1 diabetes demonstrated a highly significant correlation with OP (P = 0.000002), as did serum-positive rheumatoid arthritis (P = 0.000001). These correlations highlight the potential role of autoimmune and metabolic conditions in the pathogenesis of OP. Similarly, in the UK Biobank database, menopausal age revealed a strong correlation with OP (P = 0.000004), aligning with the established impact of hormonal changes on bone health. Additionally, pulse pressure showed a significant correlation with OP (P = 0.000095), indicating a possible connection between cardiovascular dynamics and bone density. These findings, which have withstood the rigorous Bonferroni correction, offer valuable insights into the multifactorial nature of OP and may inform future research and clinical practice.

To explore the possibility of reverse causality, we conducted a reverse MR analysis. This analytical approach aims to determine whether OP may itself trigger a series of physiological changes. Supplementary Table 3 provides a detailed summary of the findings and conclusions from this reverse MR study. The application of this method enhances our understanding of the pathological mechanisms associated with OP and serves as a robust tool for examining potential causal pathways in the disease's progression. By challenging the traditional unidirectional perspective on causality, this approach improves our capacity to reveal the intricate interplay of factors contributing to OP.

Utilizing MR studies, we employed genetic variations as proxies for potential exposure factors to assess their causal effects on OP. Our investigation provided substantial evidence linking various factors to an increased risk of this condition. In our analysis of the Finnish database, we identified several predictors of OP, including low educational attainment, infrequent alcohol consumption, diminished relative body shape in children, hypoglycemia, low body mass index (BMI), limited years of education, low systolic blood pressure, and conditions such as irritable bowel syndrome, primary biliary cholangitis, elevated levels of Dickkopf-related protein 1, type 1 diabetes, and both serum-positive and serum-negative rheumatoid arthritis. Additionally, high levels of sex hormone-binding globulin were recognized as a risk factor. Similarly, our examination of the UK Biobank data revealed that early menopause, low intake of non-oily fish, reduced pulse pressure, exposure to PM2.5 pollution, irritable bowel syndrome, systemic lupus erythematosus, increased years of education, menopausal status, celiac disease, and specific hemodynamic factors such as pulse pressure and menopausal age are associated with OP risk. These findings offer a comprehensive overview of the multifaceted risk factors for OP, underscoring the complexity of its etiology and the necessity for a multidisciplinary approach to prevention and treatment.

To enhance the reliability of our research findings, we applied both False Discovery Rate (FDR) correction and the more stringent Bonferroni correction to account for multiple comparisons. Our analysis of the Finnish database revealed significant correlations between the onset of OP and various factors, including primary biliary cholangitis, irritable bowel syndrome, type 2 diabetes, type 1 diabetes, serum-positive rheumatoid arthritis, sex hormone-binding globulin levels, and systolic blood pressure. In parallel, the UK Biobank database indicated that menopausal age and pulse pressure were strongly correlated with the onset of OP. Notably, even after applying the more stringent Bonferroni correction, the Finnish database continued to demonstrate robust correlations with OP for type 1 diabetes and serum-positive rheumatoid arthritis. Similarly, in the UK Biobank, the strong correlations for menopausal age and pulse pressure remained evident. These refined results, having undergone rigorous statistical scrutiny, provide a valuable reference for further investigation into the pathogenesis and risk factors associated with OP, thereby guiding future research directions and potential intervention strategies.

Through a thorough exploration of the literature across various databases, we conducted a comprehensive analysis of previously reported pathogenic risk factors for OP. Simultaneously, we investigated correlations at the genetic variation level, aiming to uncover potential etiological factors of the disease. By employing rigorous research methodologies and adopting a novel perspective, our study successfully excluded numerous pathogenic risk factors that were previously considered from a genetic variation standpoint. Additionally, it illuminated a range of risk factors that had either been controversial or overlooked in the past. The findings of our research significantly enhance the current understanding of OP and provide valuable insights for clinical research initiatives and the formulation of public health policies. We are confident that the contributions of this study will be instrumental in refining prevention and treatment strategies for OP, ultimately leading to improved patient outcomes and enhanced public health.

In this study, we utilized large-scale GWAS data to conduct a comprehensive exploration of various pathogenic risk factors associated with OP from a genetic variation perspective. We performed an extensive analysis of a wide range of risk factors previously identified in the literature as potentially linked to the disease. Our analysis encompassed multiple dimensions, including socioeconomic status, lifestyle habits, specific diseases, biochemical markers, reproductive health factors, and environmental exposures. Following this, we employed MR analysis to evaluate these factors. To our knowledge, this represents the first study to examine the pathogenic risk factors related to OP, as suggested by earlier observational studies, through the lens of genomics and GWAS data.

Socioeconomic factors and air pollution have subtle yet profound effects on the risk of OP. Our analysis, which utilized the Finnish database, identified low education levels, fewer years of education, and exposure to PM2.5 as significant risk factors. These findings highlight the complex relationship between socioeconomic status, the social environment, and bone health. The rationale behind this association is multifaceted(25-27):Individuals with lower socioeconomic status often face limited access to high-quality healthcare. Additionally, they are more likely to experience increased life stress, live in unstable housing and poor air quality conditions, and engage in unhealthy dietary habits. The interplay of these factors can negatively impact the skeletal system, thereby significantly elevating the risk of OP.

Lifestyle habits and health status have complex and varied effects on the risk of OP. Contrary to the traditional belief that alcohol consumption is harmful to health, our study, in agreement with previous research(28), demonstrates that moderate alcohol intake among elderly women is linked to significantly lower serum levels of the bone remodeling marker osteocalcin and reduced urinary levels of the bone resorption marker. These findings imply a decrease in bone remodeling activity, which may contribute to an increase in bone density.Additionally, serum parathyroid hormone levels are significantly lower in individuals who consume alcohol, which may further contribute to reduced bone resorption. Conversely, a study involving 14,237 participants found no significant correlation between drinking frequency and vertebral deformation, particularly among elderly women. However, regular moderate alcohol consumption seems to be associated with a reduced risk of disease(29). Furthermore, low body mass index (BMI) and blood sugar levels, which are directly linked to nutritional and metabolic status, have also been identified as risk factors.The association between childhood body shape and the risk of OP in adulthood highlights the necessity of adopting a life-cycle approach to bone health management. In the realm of diabetes, a phenomenon referred to as the 'diabetic bone paradox' has emerged(30), wherein individuals with type 2 diabetes (T2D) do not demonstrate significantly lower bone mineral density (BMD) yet are at an elevated risk of fractures. This paradox can be attributed to a myriad of complex factors, including changes in bone microstructure, the effects of hyperinsulinemia and hyperglycemia, the accumulation of advanced glycation end products (AGEs), and the presence of comorbidities associated with T2D. Notably, even after controlling for potential confounders, a significant correlation between T2D and higher BMD measurements, coupled with an increased fracture risk, remains evident.Longitudinal studies on type 1 diabetes have demonstrated a diminished responsiveness of bone remodeling to mechanical stimuli(31), characterized by decreased rates of both bone formation and resorption that correlate with the severity of neuropathy and the levels of diabetes control. Notably, trabecular bone formation and resorption at the distal radius in patients with type 1 diabetes are lower compared to healthy controls. Furthermore, bone formation shows a positive correlation with nerve conduction amplitude and a negative correlation with glycosylated hemoglobin levels.The utilization of vertebral bone quality (VBQ) scores has identified both increased VBQ and low body mass index (BMI) as significant risk factors for low bone mineral density(32). Furthermore, an analysis of data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018(33), which included 1,557 adolescents, revealed a significant negative correlation between the adolescent body shape index (ABSI) and femoral bone mineral density (BMD). This correlation exhibited variations based on factors such as age, race, family income, and specific detection sites. Collectively, these findings underscore the complex interplay between lifestyle factors and health status in relation to OP risk. Factors such as alcohol consumption, diabetes status, BMI, childhood body shape, and specific indicators of bone quality are all intricately associated with OP risk to varying degrees. This understanding offers valuable insights for further elucidating the etiology of OP and provides a crucial scientific foundation for the development of more targeted prevention and treatment strategies. Therefore, continued in-depth research into these interactions, alongside the optimization of lifestyle habits and health status, is essential for mitigating OP risk and safeguarding public bone health.

Chronic conditions such as irritable bowel syndrome (IBS), primary biliary cholangitis (PBC), and rheumatoid arthritis have been recognized as significant contributors to the increased risk of OP, highlighting the complex relationship between long-term health issues and the maintenance of bone health. A retrospective cohort study conducted on the Korean population revealed that(34) individuals with IBS have substantially elevated risk ratios for OP and osteoporotic fractures, recorded at 1.476 and 1.427, respectively. This finding reinforces the strong association between IBS and compromised bone health. Additionally, a comprehensive systematic review has indicated that the risk of OP among IBS patients is significantly higher than in the non-IBS population, with a pooled risk ratio rising to 1.95(35).These findings highlight a concerning trend and underscore the necessity for integrated bone health management strategies specifically designed for patients with irritable bowel syndrome (IBS) in clinical practice. Primary biliary cholangitis (PBC), a condition closely associated with an increased risk of OP, poses an even greater challenge to bone health. Research indicates that patients with PBC are three to four times more likely to develop OP compared to the general population, with a significant majority (80%-90%) of this high-risk group being female(35-37).The susceptibility of women to bone health issues may be exacerbated by their unique physiological characteristics. A key factor in the progression of bone disease is the malabsorption of fat-soluble vitamins, particularly vitamin D, associated with primary biliary cholangitis (PBC). This malabsorption, combined with the significant depletion of vitamin D levels in affected patients, creates a detrimental cycle that further undermines bone health. Additionally, the intricate relationship between rheumatoid arthritis and OP warrants attention. Numerous studies have demonstrated a substantial correlation between these two conditions(38-40), indicating that OP frequently coexists with rheumatoid arthritis. Importantly, patients with serum-positive rheumatoid arthritis face an increased risk of both systemic and localized bone loss(41-43).This insight provides a novel perspective on the comprehensive management of rheumatoid arthritis, highlighting the importance of interdisciplinary collaboration in improving patient outcomes. In summary, these findings not only deepen our understanding of the intricate relationships between chronic diseases and OP but also establish a scientific foundation for the early identification, intervention, and holistic management of at-risk populations within clinical settings. As research advances, we expect the emergence of more precise and effective strategies for prevention and treatment, designed to alleviate the negative impacts of these conditions on bone health and to enhance the overall quality of life for affected individuals.

Ultimately, our study established that premature menopause, menopausal status, and menopausal age are critical risk factors for OP, highlighting the close relationship between women's reproductive health and bone health. This association has been supported by numerous existing studies. The underlying mechanism is attributed to the sharp decline in estrogen levels following menopause. Estrogen not only serves as a vital foundation for maintaining bone health in women but also plays a key role in regulating calcium metabolism. The postmenopausal reduction in estrogen levels places the skeletal system under dual pressures(44-46):accelerated bone resorption leads to exacerbated bone loss and a diminished capacity for calcium absorption and reabsorption, resulting in further decreases in bone strength. When compounded by the cumulative effects of unhealthy lifestyle habits and potential genetic predispositions, these factors become intertwined in postmenopausal women, significantly increasing their risk of OP. Consequently, enhancing the holistic management of women's reproductive and bone health is of paramount importance for the prevention and treatment of OP. Special attention should be given to monitoring and modulating estrogen levels while actively promoting the adoption of healthy lifestyles to mitigate the risk of OP.

As with all prior MR studies, excluding the effects of horizontal pleiotropy and establishing direct causal links presents complex challenges. To address these issues, we employed methodologies such as the Weighted Median, Simple Mode, and Weighted Mode for horizontal pleiotropy analysis, applying stringent exclusion criteria to screen genetic variants. These approaches are designed to yield unbiased estimates of causal effects. In sensitivity analyses, the effects of most characteristics significantly or suggestively associated with the risk of OP onset were found to be consistent, providing robust support for the inferred causal relationships. However, there are differences in the estimation of causal effects between the Random Effects Maximum Likelihood approach and the MR-Egger regression. The Maximum Likelihood method fully accounts for the uncertainty associated with exposure factors and potential sample overlap in two-sample MR studies, whereas the MR-Egger regression may exhibit a less pronounced detection of causal effects. Furthermore, we employed both the MR-Egger and MR-PRESSO methods to verify the presence of horizontal pleiotropy(47).The MR-Egger method provides a robust estimate of causal effects by analyzing the direct influence of genetic variation while assessing the aggregate non-zero impact of indirect pathways on the outcomes. In contrast, the MR-PRESSO method emphasizes data quality optimization, identifies and excludes outliers, and enhances the robustness of causal inference. Although these two methods have distinct focuses, they collectively offer substantial support for MR analysis. Furthermore, we acknowledge that the cases and controls in this study may partially overlap with those in previous MR analyses and recognize the potential for confounding factors. To mitigate the likelihood of false positives in our results, we employed not only the False Discovery Rate (FDR) correction but also a more stringent Bonferroni correction.

Our study reaffirms established connections while introducing a comprehensive suite of preventive strategies against OP. These strategies include reducing obesity, stabilizing blood glucose levels, maintaining healthy and consistent blood pressure, enhancing educational attainment, and moderating alcohol consumption. By addressing metabolic disorders, we can positively influence the nutritional support and metabolic functionality of osteoblasts, thereby restoring bone metabolic equilibrium. In summary, the insights derived from our research are crucial for identifying key areas for current and future primary prevention initiatives within public health. Our findings highlight the risk factors associated with the onset of OP and provide significant contributions to its prevention and treatment. We aspire that these revelations will pave the way for innovative treatment paradigms, potentially transforming the management and prevention of OP.

This study primarily focuses on individuals of European descent, and the generalizability of its findings to other ethnic groups requires further investigation. The limitations of our study include a restricted sample size and potential biases arising from uneven group distribution. Although stringent thresholds and multiple corrections have strengthened the rigor of our analysis, there remains a possibility that true associations may be overlooked in smaller samples due to insufficient statistical significance. Additionally, the subtle effects of genetic variations may reduce statistical power, thereby increasing the risk of type I errors. It is unfortunate that some observed associations between exposure factors and OP were relatively weak, suggesting that these factors may only marginally influence OP risk. Such minor changes may lack substantial clinical or statistical relevance in practical applications. Nevertheless, we acknowledge the potential influence of uncontrolled confounding factors that could obscure or diminish the true associations. The pathogenesis of OP continues to be fraught with uncertainties. There is an urgent need for large-scale, multi-center, and meticulously designed studies to address these knowledge gaps and to elucidate the interplay between various factors and OP more precisely. Such research efforts will provide robust scientific evidence to inform effective prevention and treatment strategies.

Our investigation has undertaken a thorough and systematic exploration of various potential pathogenic factors and their intricate associations with the risk of OP onset. By examining genetic variation, we have analyzed the risk factors identified in published observational studies, excluding many while highlighting those that increase the likelihood of OP. This process has deepened our understanding of OP pathogenesis and established a foundation for future research endeavors. From a genetic perspective, we have dismissed certain genetic variations previously associated with OP, thereby clarifying the role of genetic factors in the disease's etiology. This lays the groundwork for precision medicine and the development of personalized prevention strategies. Our rigorous statistical analysis has strengthened the scientific evidence supporting the causal links between identified risk factors and OP, enhancing the credibility of our conclusions. These findings provide novel insights and tools for investigating the pathological processes of OP, equipping clinicians and researchers with the necessary support to develop preventive strategies and refine treatment plans. The research outcomes are distilled into proactive, practical recommendations aimed at guiding the prevention, early diagnosis, and comprehensive management of OP. These recommendations emphasize general preventive measures, including lifestyle modifications and nutritional supplementation, alongside personalized intervention plans for at-risk groups to mitigate disease risk. Furthermore, the dissemination of these scientific recommendations is expected to alleviate the burden on public health systems, improve patient quality of life, and contribute to broader goals of enhancing social health and well-being.

Acknowledgements

We extend our sincere gratitude to the European Bioinformatics Institute, the GWAS Catalog database, the Finnish database (FinnGen), the UK Biobank database, and the IEU OpenGWAS database for their invaluable contribution in providing and sharing GWAS data. Our appreciation also goes out to the anonymous reviewers for their insightful and constructive feedback.

Authors’ contributions

Author Wei Yang contributed to the conception and design of this study. Zhenhua Li supervised the research process. Wei Yang, Peng Yang, Xiuzhen Han, and Miao Cui conducted literature search and screening. Wei Yang conducted data analysis. Wei Yang wrote the manuscript. Wei Yang, Miao Cui, Xiuzhen Han, and Peng Yang revised and polished the manuscript. All authors revised and approved the final manuscript.

Funding

This study was funded by the Science and Technology Development Plan Project of Jilin Province, China (202512JC010274271).

Conflict of interest

The authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Availability of data and materials

All the GWAS summary statistics of this study can be obtained by querying the IEU OpenGWAS database at the website (https://gwas.mrcieu.ac.uk/).

Ethics approval and consent to participate

For this study, all our GWAS data are sourced from published statistical data, so we did not seek ethical approval.

Author details

1. Changchun University of Chinese Medicine, Changchun 130117, Jilin, China. 2. South China Normal University, Guangzhou 510006, Guangdong, China. 3. Changchun University of Chinese Medicine, Changchun 130117, Jilin, China. 4. Jiangsu Province Hospital of Traditional Chinese Medicine, Nanjing 210004, Jiangsu, China.

Zheng M, Wan Y, Liu G, Gao Y, Pan X, You W, et al. Differences in the prevalence and risk factors of osteoporosis in chinese urban and rural regions: a cross-sectional study. BMC Musculoskelet Disord. 2023;24(1):46. https://dx.doi.org/10.1186/s12891-023-06147-w
Si L, Winzenberg TM, Jiang Q, Chen M, Palmer AJ. Projection of osteoporosis-related fractures and costs in China: 2010–2050. Osteoporos Int. 2015;26(7):1929-37. https://dx.doi.org/10.1007/s00198-015-3093-2
Tatangelo G, Watts J, Lim K, Connaughton C, Abimanyi-Ochom J, Borgström F, et al. The Cost of Osteoporosis, Osteopenia, and Associated Fractures in Australia in 2017. J Bone Miner Res. 2019;34(4):616 − 25. https://dx.doi.org/10.1002/jbmr.3640
Gao Y, Chen N, Fu Z, Zhang Q. Progress of Wnt Signaling Pathway in Osteoporosis. Biomolecules. 2023;13(3). https://dx.doi.org/10.3390/biom13030483
Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89-98. https://dx.doi.org/10.1093/hmg/ddu328
Davey Smith G, Paternoster L, Relton C. When Will Mendelian Randomization Become Relevant for Clinical Practice and Public Health? Jama. 2017;317(6):589 − 91. https://dx.doi.org/10.1001/jama.2016.21189
Verduijn M, Siegerink B, Jager KJ, Zoccali C, Dekker FW. Mendelian randomization: use of genetics to enable causal inference in observational studies. Nephrol Dial Transplant. 2010;25(5):1394-8. https://dx.doi.org/10.1093/ndt/gfq098
Tezuka K, Tezuka Y, Maejima A, Sato T, Nemoto K, Kamioka H, et al. Molecular cloning of a possible cysteine proteinase predominantly expressed in osteoclasts. J Biol Chem. 1994;269(2):1106-9.
Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. Jama. 2021;326(16):1614-21. https://dx.doi.org/10.1001/jama.2021.18236
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj. 2018;362:k601. https://dx.doi.org/10.1136/bmj.k601
Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015;31(21):3555-7. https://dx.doi.org/10.1093/bioinformatics/btv402
Feng R, Lu M, Xu J, Zhang F, Yang M, Luo P, et al. Pulmonary embolism and 529 human blood metabolites: genetic correlation and two-sample Mendelian randomization study. BMC Genom Data. 2022;23(1):69. https://dx.doi.org/10.1186/s12863-022-01082-6
Wei T, Zhu Z, Liu L, Liu B, Wu M, Zhang W, et al. Circulating levels of cytokines and risk of cardiovascular disease: a Mendelian randomization study. Front Immunol. 2023;14:1175421. https://dx.doi.org/10.3389/fimmu.2023.1175421
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7. https://dx.doi.org/10.7554/eLife.34408
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304 − 14. https://dx.doi.org/10.1002/gepi.21965
Xue H, Shen X, Pan W. Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects. Am J Hum Genet. 2021;108(7):1251-69. https://dx.doi.org/10.1016/j.ajhg.2021.05.014
Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481-7. https://dx.doi.org/10.1038/ng.3538
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512 − 25. https://dx.doi.org/10.1093/ije/dyv080
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985-98. https://dx.doi.org/10.1093/ije/dyx102
Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30(7):543 − 52. https://dx.doi.org/10.1007/s10654-015-0011-z
Lin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet. 2021;17(11):e1009922. https://dx.doi.org/10.1371/journal.pgen.1009922
Zhou L, Gao H, Zhang J, Xu Q, Wang Q, Wang L, et al. Metabolic syndrome and cancer risk: A two-sample Mendelian randomization study of European ancestry. Int J Surg. 2024. https://dx.doi.org/10.1097/js9.0000000000001926
Cornish AJ, Law PJ, Timofeeva M, Palin K, Farrington SM, Palles C, et al. Modifiable pathways for colorectal cancer: a mendelian randomisation analysis. Lancet Gastroenterol Hepatol. 2020;5(1):55–62. https://dx.doi.org/10.1016/s2468-1253(19)30294-8
Hubert A, Achour D, Grare C, Zarcone G, Muntaner M, Hamroun A, et al. The relationship between residential exposure to atmospheric pollution and circulating miRNA in adults living in an urban area in northern France. Environ Int. 2023;174:107913. https://dx.doi.org/10.1016/j.envint.2023.107913
Kraus DA, Medibach A, Behanova M, Kocijan A, Haschka J, Zwerina J, et al. Nutritional Behavior of Patients with Bone Diseases: A Cross-Sectional Study from Austria. Nutrients. 2024;16(12). https://dx.doi.org/10.3390/nu16121920
Cheng B, Pan C, Cai Q, Liu L, Cheng S, Yang X, et al. Long-term ambient air pollution and the risk of musculoskeletal diseases: A prospective cohort study. J Hazard Mater. 2024;466:133658. https://dx.doi.org/10.1016/j.jhazmat.2024.133658
Zhao H, Fan L, Yi X, Zhu L, Liu X, Hou J, et al. Effect modification of socioeconomic status on the association of exposure to famine in early life with osteoporosis in women. J Hum Nutr Diet. 2023;36(4):1349-58. https://dx.doi.org/10.1111/jhn.13164
Rapuri PB, Gallagher JC, Balhorn KE, Ryschon KL. Alcohol intake and bone metabolism in elderly women. Am J Clin Nutr. 2000;72(5):1206-13. https://dx.doi.org/10.1093/ajcn/72.5.1206
Naves Diaz M, O'Neill TW, Silman AJ. The influence of alcohol consumption on the risk of vertebral deformity. European Vertebral Osteoporosis Study Group. Osteoporos Int. 1997;7(1):65–71. https://dx.doi.org/10.1007/bf01623463
Li GF, Zhao PP, Xiao WJ, Karasik D, Xu YJ, Zheng HF. The paradox of bone mineral density and fracture risk in type 2 diabetes. Endocrine. 2024;85(3):1100-3. https://dx.doi.org/10.1007/s12020-024-03926-w
Walle M, Duseja A, Whittier DE, Vilaca T, Paggiosi M, Eastell R, et al. Bone remodeling and responsiveness to mechanical stimuli in individuals with type 1 diabetes mellitus. J Bone Miner Res. 2024;39(2):85–94. https://dx.doi.org/10.1093/jbmr/zjad014
Wang Y, Song N, Zhang J, Li J, Li R, Wang L. Systematic evaluation of vertebral bone quality score as an opportunistic screening method for BMD in spine surgery patients. Eur Spine J. 2024;33(8):3261-7. https://dx.doi.org/10.1007/s00586-024-08284-9
Lin R, Tao Y, Li C, Li F, Li Z, Hong X, et al. Central obesity may affect bone development in adolescents: association between abdominal obesity index ABSI and adolescent bone mineral density. BMC Endocr Disord. 2024;24(1):81. https://dx.doi.org/10.1186/s12902-024-01600-w
Lee SY, Hwang HR, Yi YH, Kim JM, Kim YJ, Lee JG, et al. Association between Irritable Bowel Syndrome and Risk of Osteoporosis in Korean Premenopausal Women. Med Princ Pract. 2021;30(6):527 − 34. https://dx.doi.org/10.1159/000517909
Wongtrakul W, Charoenngam N, Ungprasert P. The association between irritable bowel syndrome and osteoporosis: a systematic review and meta-analysis. Osteoporos Int. 2020;31(6):1049-57. https://dx.doi.org/10.1007/s00198-020-05318-y
Liang Y, Li J, Zhang Z, Jiang T, Yang Z. Extrahepatic conditions of primary biliary cholangitis: A systematic review and meta-analysis of prevalence and risk. Clin Res Hepatol Gastroenterol. 2024;48(5):102321. https://dx.doi.org/10.1016/j.clinre.2024.102321
Schönau J, Wester A, Schattenberg JM, Hagström H. Risk of fractures and postfracture mortality in 3980 people with primary biliary cholangitis: A population-based cohort study. J Intern Med. 2023;294(2):164 − 77. https://dx.doi.org/10.1111/joim.13624
Ebina K, Nagayama Y, Kashii M, Tsuboi H, Okamura G, Miyama A, et al. An investigation of the differential therapeutic effects of romosozumab on postmenopausal osteoporosis patients with or without rheumatoid arthritis complications: a case-control study. Osteoporos Int. 2024;35(5):841-9. https://dx.doi.org/10.1007/s00198-024-07019-2
Elsawy NA, Ghazala RA, Elnemr R. Cartilage and bone loss in premenopausal women with rheumatoid arthritis: Radiological and laboratory assessments. Int J Rheum Dis. 2023;26(11):2195 − 205. https://dx.doi.org/10.1111/1756-185x.14915
Sato H, Takai C, Kazama JJ, Wakamatsu A, Hasegawa E, Kobayashi D, et al. Serum hepcidin level, iron metabolism and osteoporosis in patients with rheumatoid arthritis. Sci Rep. 2020;10(1):9882. https://dx.doi.org/10.1038/s41598-020-66945-3
Elsawy NA, Mohamed RA, Ghazala RA, Abdelshafy MA, Elnemr R. Anti-carbamylated protein antibodies in premenopausal rheumatoid arthritis women: relation to disease activity and bone loss. Rheumatology (Oxford). 2021;60(3):1419-28. https://dx.doi.org/10.1093/rheumatology/keaa549
Wang L, Hua L, Hong X, Chen F, Du H. Association of serum anti-carbamylated protein antibodies with disease activity and bone loss in rheumatoid arthritis. Clin Chim Acta. 2023;546:117371. https://dx.doi.org/10.1016/j.cca.2023.117371
Chen YM, Chen HH, Huang WN, Liao TL, Chen JP, Chao WC, et al. Tocilizumab potentially prevents bone loss in patients with anticitrullinated protein antibody-positive rheumatoid arthritis. PLoS One. 2017;12(11):e0188454. https://dx.doi.org/10.1371/journal.pone.0188454
Onizuka N, Onizuka T. Disparities in Osteoporosis Prevention and Care: Understanding Gender, Racial, and Ethnic Dynamics. Curr Rev Musculoskelet Med. 2024;17(9):365 − 72. https://dx.doi.org/10.1007/s12178-024-09909-8
Skalny AV, Korobeinikova TV, Aschner M, Paoliello MMB, Lu R, Skalny AA, et al. Hair and Serum Trace Element and Mineral Levels Profiles in Women with Premenopausal and Postmenopausal Osteoporosis. Biol Trace Elem Res. 2024;202(9):3886-99. https://dx.doi.org/10.1007/s12011-023-03970-z
Huang J, Zhang Z, He P, Zhou J. Possible mechanisms underlying the regulation of postmenopausal osteoporosis by follicle-stimulating hormone. Heliyon. 2024;10(15):e35405. https://dx.doi.org/10.1016/j.heliyon.2024.e35405
Schuler MS, Rose S. Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies. Am J Epidemiol. 2017;185(1):65–73. https://dx.doi.org/10.1093/aje/kww165

No competing interests reported.

Unveiling the Etiology of Osteoporosis Onset: A Mendelian Randomization Investigation

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusion

Figures

Introduction

Materials and Methods

Data sources of exposure factors and outcomes

Selection of Genetic Instrumental Variables

Statistical Analysis

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1