Causal associations of specific immunoglobulin G N-glycosylation subtypes with osteoporosis: A two-sample Mendelian randomization

Abstract

Objectives: This study aims to examine whether genetically predicted immunoglobulin G (IgG) N-glycosylation patterns (IGPs) affect osteoporosis risk using a two-sample Mendelian randomization (MR) method.

Materials and methods: In a collaborative effort involving the Medical Research Council (MRC) Human Genetics Unit and the FinnGen consortium, we conducted genome-wide association studies (GWAS) to explore the relationship between 77 IGPs (8,090 samples) and osteoporosis (438,872 samples). Utilizing the inverse-variance weighted (IVW) method as our primary analytical tool, we delved into these complex genetic associations. To further substantiate our findings, we employed additional complementary methods such as MR-Egger, weighted median, and weighted mode. Sensitivity analyses, including MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO), MR-Egger, Cochran’s Q, and leave-one-out methods, were used to test the core MR assumptions and validate the robustness of the results. This multi-faceted approach allowed us to detect underlying causal relationships with greater confidence.

Results: The IGP4 exhibited a protective effect against osteoporosis with an odds ratio (OR) of 0.77 (95% confidence interval [CI]: 0.63-0.95, p=0.012). In contrast, IGP45 demonstrated a modest risk increase with an OR of 1.10 (95% CI: 1.01-1.19, p=0.021). Similarly, the results of the present MR study suggest that IGP56 also showed a protective trend, with an OR of 0.86 (95% CI: 0.78-0.96, p=0.006). To confirm our findings, we conducted rigorous sensitivity analyses utilizing MR-PRESSO, MR-Egger, Cochran’s Q, and leave-one-out methods. These analyses revealed no evidence of heterogeneity or horizontal pleiotropy, thereby reinforcing the robustness and reliability of our findings.

Conclusion: Our study results indicate that IgG45 contributes positively to osteoporosis, whereas IgG4 and IgG56 exhibit a negative correlation. Nonetheless, additional research is crucial to understand their mechanisms and devise broader preventive strategies for osteoporosis.

Introduction

Osteoporosis is a global health burden which affects over 200 million individuals worldwide.[1,2] Age-related bone mass loss leads to fractures in one-third of women and one-fifth of men over 50 years.[3,4] Osteoporosis constitutes a major global economic burden, with estimated annual direct medical costs reaching €37 billion in the European Union and projected to surpass $25 billion in the United States of America by 2025, largely due to the high cost of treating fractures.[5] The disease disrupts bone homeostasis through complex hormonal and cytokine interactions, although its exact mechanisms remain unclear.[6] Emerging evidence suggests that immune regulation and inflammation, including immunoglobulin G (IgG), may play a previously overlooked role in bone metabolism, potentially offering new therapeutic targets.

Immunoglobulin G N-glycosylation patterns (IGPs) have recently emerged as promising biomarkers for osteoporosis.[7] Specific IgG glycosylation alterations, including hypogalactosylation and hyperglucosaminidation, have been associated with disease progression in various ch ron ic inflammatory conditions.[8] These modifications significantly affect the immunomodulatory functions of IgGs by altering their binding affinity to Fc gamma receptors (FcγR) and complement C1q through structural changes in the Asn297-linked N-glycans.[9,10] Notably, IgG may exert protective effects against glucocorticoid-induced osteoporosis via FcγRI interactions, suggesting a potential mechanistic link between IgG glycosylation and bone metabolism regulation.[7] These findings highlight the critical role of IgG glycosylation in bone homeostasis and provide new insights into osteoporosis pathogenesis from immunological and inflammatory perspectives. Furthermore, humanized monoclonal antibodies of the IgG2 isotype and subtype have emerged as effective and safe options for preventing fractures in patients with osteoporosis.[11] These findings underline the potential therapeutic implications of targeting IGPs in the management of bone health.

However, not all changes in IGPs are beneficial. In patients with multiple myeloma, high serum IgG levels can induce osteoclast activation, leading to bone loss.[12] It suggests a complex interplay between IgG glycosylation, inflammation, and bone metabolism. Indeed, inflammatory factors have been closely linked to IgG N-glycosylation and osteoporosis.[13] Changes in IgG subclasses and Fc glycosylation are associated with inflammatory diseases and hint at possible associations between inflammatory factors and osteoporosis.[14,15] Still, the number of observational studies is limited in establishing causality due to potential confounding and reverse causation. Further research is needed to clarify the underlying mechanisms and evaluate the therapeutic potential of targeting IGPs for osteoporosis prevention and treatment.

In the present study, the Mendelian randomization (MR) methodology was used to explore the potential causal links between various exposures and health outcomes.[16] It is a powerful method for uncovering causal relationships that are difficult to isolate in traditional studies due to confounders and reverse causality.[17] Following this approach, Sun et al.[18] recently demonstrated a causal link between human IgG N-glycosylation and aging. Yet, to the best of our knowledge, no MR study has investigated the connection between IGPs and osteoporosis. We, therefore, applied a two-sample MR analysis to test for a causal link between IGPs and osteoporosis, as well as between inflammatory cytokines and osteoporosis.

Patients and Methods

Study design

This MR study was conducted at Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Department of Endocrinology, Rheumatology and Immunology, and the Information Center between November 26, 2024, and January 26, 2025 and reported following the Strengthening the Reporting of Observational Studies in Epidemiology using-MR (STROBE-MR) guidelines.[19] The analysis relied on the three core MR assumptions:[20] (i) the instrumental variables (IVs) are strongly associated with the exposure, (ii) the IVs are not associated with any confounding factors, and (iii) the IVs affect the outcome only through the exposure (Figure 1).

Figure 1. Overview of Mendelian randomization.
SNPs: Single-nucleotide polymorphisms; IgG: N-glycosylation patterns.

All data used in this Mendelian randomization study were obtained from publicly available genome-wide association study (GWAS) summary statistics, published literature, and other public databases. The study was conducted in accordance with the GWAS data access policies and ethical guidelines, and did not require additional ethical approval or written informed consent.

Data source

Summary-level data for osteoporosis were obtained from the FinnGen consortium (Release R11). The analysis included 9046 cases of osteoporosis and 429,826 controls, all of European ancestry. The osteoporosis phenotype was defined as a composite endpoint including cases diagnosed under the International Classification of Diseases, 10^th Revision (ICD-10) codes M80 (osteoporosis with pathological fracture), M81 (osteoporosis without pathological fracture), and M82 (osteoporosis in diseases classified elsewhere).

Data on 41 inflammation cytokine traits were sourced from a GWAS published in the American Journal of Human Genetics.[21] In addition, GWAS results for 77 IGPs from 8090 samples originating from the Medical Research Council (MRC) Human Genetics Unit were used, focusing on European ancestry (accessible via https://datashare.ed.ac.uk/ handle/10283/3238).[22]

All GWAS data were sourced from European ancestry to avoid confounding caused by racial factors.

Instrumental variable selection

Single-nucleotide polymorphisms (SNPs) were selected as IVs for the exposures based on the following four criteria. First, SNPs associated with each exposure at a p-value threshold of p<5×10^-6 were identified.[23] Second, to ensure independence, the SNPs were pruned for linkage disequilibrium (LD) using a strict cutoff (r²<0.001, window size=10,000 kb).[24] Third, SNPs with a minor allele frequency (MAF) below 0.01 were excluded.[25] When selected IVs were not available in the outcome GWAS data, proxy SNPs with high LD (r²>0.8) were used.[26] Finally, the F-statistic was calculated for each SNP to assess instrument strength, excluding any with an F-statistic <10 to minimize weak instrument bias.[27] The formula used was F=R² * (N-2) / (1-R² ), where R² is the variance in the exposure explained by the SNP and N is the sample size.

Mendelian randomization analysis

The inverse-variance weighted (IVW) method was used as the primary approach to estimate the causal effect of IGPs on osteoporosis. The results were reported as odds ratios (ORs) with 95% confidence intervals (CIs).[28] Three additional MR analyses were also performed to support the robustness of the results: MR-Egger, weighted median (WM), and weighted mode.[29] The MR-Egger method is particularly advantageous in accounting for pleiotropic biases by considering potential intercepts, ensuring accurate causal effect estimates.[28,30] A p value of <0.05 was considered statistically significant. All analyses were performed using the “TwoSampleMR” package in R 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria).

Sensitivity analysis

Several sensitivity analyses were performed to test the robustness of the MR results. Cochran’s Q test was used to quantify heterogeneity among IVs. A Q-test p value of >0.05 suggests no significant heterogeneity.[31] The MR-Egger regression intercept was used to test for horizontal pleiotropy; an intercept not significantly different from zero (p>0.05) indicates no evidence of pleiotropy.[32,33] The MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) method was used to identify outlier SNPs (global p<0.05), and the causal associations were reassessed after their removal to correct for pleiotropy.[33] Funnel plots were generated for visual inspection of heterogeneity. A leave-one-out analysis was performed to check if any single SNP was driving the overall causal estimate.

Results

Instrumental variable selection

In this study, 1,048 IVs related to 77 IGPs were screened (the mean F statistic was 43.14, with a minimum of 20.77 and a maximum of 1165.43) ( Supplementary Table I). A total of 452 IVs related to 41 cytokines were screened. The F statistics of all identified SNPs were over 10, suggesting no indication of weak instrument bias in our analysis. All SNPs in the summary data are shown in Supplementary Table II and Supplementary Table III

Causal association between IGPs and osteoporosis

The results of the MR-Egger regression indicated the potential presence of horizontal pleiotropy while analyzing IGP45 as the exposure (Supplementary Table IV). Sensitivity analyses, including leave-one-out plots and the MR-PRESSO test, subsequently identified SNP rs909674 as an influential outlier (Supplementary Table V). After removing this outlier SNP, the re-analysis revealed a statistically significant positive association between genetically predicted IGP45 and osteoporosis (Table I). For some other IGPs where initial IVW analyses did not show a significant association, the MR-PRESSO test also detected potential outliers (Supplementary Table V). However, after removing these identified outliers, the associations for these traits remained nonsignificant or negative (Supplementary Table VI).

After eliminating outliers, we re-analyzed the results. The IVW results showed that IGP4 (OR (95% CI): 0.77 (0.63-0.95), p=0.012), IGP45 (OR: 1.1 (1.01-1.19), p=0.021) and IGP56 (OR: 0.86 (0.78-0.96), p=0.006) were causally associated with osteoporosis (Table I). Supplementary Table VII provides a comprehensive summary, including detailed descriptions of each IGP as well as their potential candidate genes, which were primarily extracted from the source study.[22] The scatter and forest plots of positive results are shown in Figure 2 and 3. The scatter plots showed that the slope of the trend line was less or more than 0, indicating that the protective or risk of an outcome decreases as the level of exposure increases (Figure 2). Similarly, the forest plots composite line lies to the left or right of 0 and the confidence interval does not contain 0, indicating a significant negative or positive causal relationship between IGPs and osteoporosis (Figure 3). However, no causal association between other IGPs and osteoporosis was found using IVW methods, and the other three methods (MR-Egger, WM, and weighted mode) were analyzed with similar results (Supplementary Table VIII).

Figure 2. The Scatter plots between IgG N-glycosylation patterns and Osteoporosis (Positive results). The Scatter plots of (a) IGP4, (b) IGP45 and (c) IGP56 on osteoporosis.
MR: Mendelian randomization; SNP: Single-nucleotide polymorphism; IgG: N-glycosylation patterns.

Figure 3. The Forest plots between IgG N-glycosylation patterns and Osteoporosis (Positive results). The Forest plots of (a) IGP4, (b) IGP45 and (c) IGP56 on osteoporosis.
MR: Mendelian randomization; IgG: N-glycosylation patterns.

In addition, in analyzing the correlation between 41 cytokines and osteoporosis, the IVW results show that interleukin (IL)-8 (OR=0.78, 0.63-0.98, p=0.0303) and monocyte chemoattractant protein-3 (MCP3) (OR=1.22, 1.08-1.38, p=0.0017) were causally associated with osteoporosis (Supplementary Table IX).

Sensitivity analysis

Furthermore, the results of MR-Egger regression indicated that this analysis was not influenced by horizontal pleiotropy and remained robust, as depicted in Table II. Cochran’s Q test results showed significant heterogeneity in the analysis of IGP15, IGP23, IGP24, IGP28, IGP31, IGP38, and IGP77 with osteoporosis (Table II). After eliminating outliers, the funnel plots suggested that there was no observable horizontal pleiotropy for the results of this analysis ( Supplementary Figure 1). And the leave-one-out sensitivity analysis plots demonstrated that no single SNP was likely to have influenced the causal association and that our conclusions were, therefore, robust ( Supplementary Figure 2). Similarly, the sensitivity analysis of cytokines on osteoporosis was robust ( Supplementary Table X and Supplementary Table XI

Discussion

In the present study, we used random-effects IVW to provide strong evidence for a causal relationship between IGPs and osteoporosis. We provide evidence that individuals with high levels of IGP45 have a higher risk of developing osteoporosis, whereas those with high levels of IGP4 and IGP56 have a lower risk of developing osteoporosis. But this association is not present in other IGPs and osteoporosis. These findings not only reveal the potential regulatory role of IgG glycoconjugate structure in bone metabolism, but also provide new molecular perspectives for understanding the immune-metabolic interaction mechanism of osteoporosis. By using genetic variants as IVs in our study, we believe that the MR approach avoids the major confounding factors of observational studies and can offer clearer insights into the causes of osteoporosis.

Immunoglobulin G N-glycosylated patterns are specific glycosylations in IgG’s N-linked oligosaccharides, impacting IgG function, FcγR binding, immunomodulation, and inflammation.[34,35] Of note, IGP45 represents the proportion of G0FN-type glycans in the total neutral IgG glycan chain, and its core modification is characterized by the absence of galactose residues but the presence of fucose.[22] The present MR study suggests that IGP45 may be a risk factor for osteoporosis. The proportion of IGP45 is lower in chronic inflammatory and autoimmune diseases, suggesting a potential role in immune responses.[36,37] Different IGPs and their proportions alter IgG function and their binding to FcγR.[38] Hence, the different IGPs and their proportions affect inflammation signaling.[39] In addition, IGP45-related glycosylation changes IgG’s structure, interactions, and immune response nature, directly affecting IgG functions like ADCC and CDC, increasing susceptibility to infections, and indirectly influencing bone metabolism.[40,41]

However, findings from MR suggest that IGP4 and IGP56 may be protective factors against osteoporosis. The IGP4 and IGP56 are specific IGPs, and their glycosylated forms may have an important impact on IgG function and activity. IGP4 represents the proportion of mannose pentasaccharide (M5) in the total IgG glycan chain, which is a high-mannose-type glycan chain usually associated with immature B-cell-derived IgG and has low pro-inflammatory activity, while IGP56 reflects the proportion of all monogalactosylated structures in the neutral IgG glycan chain, which is suggestive of low overall glycan chain maturity.[22] On the one hand, IGP4 and IGP56 may be involved in the promotion of bone formation and the inhibition of bone resorption. IGP4 and IGP56 may have a direct role in promoting osteoblast activity, thereby increasing the synthesis and mineralization of bone matrix. This helps to maintain or increase bone mass and reduce the risk of osteoporosis. In contrast to IGP45, IGP4 and IGP56 may reduce bone resorption by inhibiting osteoclast activity or reducing the number of osteoclasts, helping to maintain bone stability and strength. On the other hand, it may be involved in regulating the endocrine system. There is a close interaction between the bone and endocrine system.[42] IGP4 and IGP56 may indirectly affect bone health by influencing the secretion or activity of hormones related to bone metabolism (e.g., estrogen, parathyroid hormone, vitamin D, etc.).[43] Furthermore, IGP4 and IGP56 may have synergistic effects with other known protective factors (e.g., appropriate physical activity, balanced diet, adequate sun exposure, etc.). Together, these protective factors act on the skeletal system to help maintain bone health and stability.

In view of the causal relationship between IGPs and osteoporosis described above, we believe that the levels of these IGPs can, on the one hand, serve as potential markers of osteoporosis and can be used for early diagnosis and risk assessment, and on the other hand, the understanding of the protective/risk mechanism of IGPs in osteoporosis can provide new targets for the development of new therapeutic approaches; In addition, personalized therapeutic regimens can be developed based on the level of IGPs in patients. In the future, further basic research is needed to explore the specific mechanisms of IGPs in the pathogenesis of osteoporosis, including how they affect inflammatory responses, osteogenesis, immunomodulation, and anti-oxidative stress. There is still a need to validate the relationship between IGPs and osteoporosis risk in larger cohorts and to explore their applicability in different populations. Intervention strategies targeting IGPs should also be developed to find new therapeutic approaches.

The N-glycosylation patterns of IgG not only affect its immune function, but also interact with inflammatory factors through a variety of mechanisms which, in turn, jointly affect bone metabolism and the development of osteoporosis. Inflammatory factors can alter the glycosylation pattern of IgG by regulating the activity or expression of glycosylation enzymes;[44] altered glycosylation affects the immunoreactivity of IgG, including its affinity for the FcγR, which in turn regulates the intensity of the inflammatory response.[13] Previous MR studies have shown that inflammatory factors may have a causal relationship with osteoporosis.[45] The current MR results disagree with prior studies on IL-8 as an osteoporosis protector. Multiple MR studies support IL-8’s protective role, suggesting it reduces the risk of bone loss. IL-8’s anti-apoptotic effect and angiogenesis promotion may be key mechanisms. The complexity and varying inflammatory factor roles of osteoporosis, in addition to external factors, contribute to diverse findings. Several studies have demonstrated that different patterns of N-glycosylation can significantly alter the function of IgG, giving it anti-inflammatory or pro-inflammatory properties.[44] To illustrate, complex glycan chains containing fucose (e.g., IGP4 and IGP56) usually exhibit antiinflammatory properties.[46] and are able to inhibit FcγR-mediated immune responses.[47] and reduce the release of inflammatory cytokines. High mannosetype glycan chains without fucose (like IGP45) tend to elicit stronger immune responses and boost inflammatory cytokines. Inflammatory factors, in turn, affect IgG glycosylation, which is altered by metabolic changes during chronic inflammation. The IL-6 and tumor necrosis factor-alpha (TNF-α) specifically contribute to osteoporosis by activating osteoclasts and inhibiting osteoblasts. [48] The IgG glycosylation modifications may affect the level of inflammation in vivo by modulating the anti-inflammatory or pro-inflammatory activity of IgG. This regulation may indirectly affect the onset and progression of osteoporosis. Overall, the roles of IgG glycosylation and inflammatory factors in osteoporosis may be combined. While inflammatory factors may directly affect bone metabolism and remodeling, IgG glycosylation may indirectly affect bone health by regulating the activity of inflammatory factors.

There are several strengths of this study. This is the first two-sample MR study to investigate the causality between IGPs and osteoporosis, which is the closest approximation to a randomizedcontrolled study and allows for random allocation based on the genotype. This study design can prevent some limitations of conventional observational studies, including reverse causation and potential confounding factors. The large sample sizes of included studies and IVs robustly associated with IGPs (F statistics ≥10) are used in our MR study. Nevertheless, certain limitations should also be considered. Although this study used stringent screening criteria for IVs, the threshold for some IVs for this screening was 10-6, which was larger than the usual 10-8 and not the conventional threshold, and this more lenient criterion may have had an impact on disease outcome. Moreover, all participants included in the study were European, which may limit the generalizability of our findings to other ethnic populations. So, more studies should be conducted to confirm the applicability of our findings to other populations. On the other hand, the specific mechanism of action of IGPs in osteoporosis has not been thoroughly investigated. Therefore, there is a lack of sufficient experimental evidence to support a direct link between the protein and osteoporosis. Finally, there is still a need to evaluate the potential of IGPs as diagnostic and prognostic biomarkers for osteoporosis.

In conclusion, our study performed MR analysis of the putative causal relationship between IGPs and osteoporosis. The results highlight the strong association between IGPs and osteoporosis. These findings emphasize the potential role of early screening and prevention of IGPs in patients with osteoporosis. Nonetheless, further well-designed studies are warranted to establish more reliable conclusions on this subject.

Citation: Zhao Y, Zhu Y, Zhang W, Wang L, Yan C, Yuan C, et al. Causal associations of specific immunoglobulin G N-glycosylation subtypes with osteoporosis: A two-sample Mendelian randomization. Jt Dis Relat Surg 2026;37(1):42-53. doi: 10.52312/jdrs.2026.2361,

Carried out the studies, participated in collecting data, and drafted the manuscript: Y.Z., Y.M.Z.; Performed the statistical analysis and participated in its design: C.Y.Y., C.Y.Y.; Participated in acquisition, analysis, or interpretation of data and draft the manuscript: W.Z., L.J.W.; Designed the study, supervised the research and revised the manuscript: L.J.W. All authors read and approved the final manuscript.

The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.

This study was supported by Medical and Health Science and Technology Project of Zhejiang Province (grant number 2025KY552)--Lijuan Wang; Zhejiang Provincial Department of Education General Project (Y202146105); Medical and Health Science and Technology Project of Zhejiang Province (grant number 2022KY068)-- Wei Zhang.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

1. Shen Y, Huang X, Wu J, Lin X, Zhou X, Zhu Z, et al. The global burden of osteoporosis, low bone mass, and its related fracture in 204 countries and territories, 1990-2019. Front Endocrinol (Lausanne) 2022;13:882241.
2. Gong L, Bai Z. Association of genetically predicted blood metabolites with osteopenia in individuals over 60 years of age: A Mendelian randomization study. Jt Dis Relat Surg 2025;36:229-39. doi: 10.52312/jdrs.2025.1991.
3. Salari N, Darvishi N, Bartina Y, Larti M, Kiaei A, Hemmati M, et al. Global prevalence of osteoporosis among the world older adults: A comprehensive systematic review and meta-analysis. J Orthop Surg Res 2021;16:669. doi: 10.1186/s13018-021-02821-8.
4. Porter JL, Varacallo MA. Osteoporosis. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2025.
5. Rashki Kemmak A, Rezapour A, Jahangiri R, Nikjoo S, Farabi H, Soleimanpour S. Economic burden of osteoporosis in the world: A systematic review. Med J Islam Repub Iran 2020;34:154. doi: 10.34171/mjiri.34.154.
6. Fischer V, Haffner-Luntzer M. Interaction between bone and immune cells: Implications for postmenopausal osteoporosis. Semin Cell Dev Biol 2022;123:14-21. doi: 10.1016/j.semcdb.2021.05.014.
7. Jiang L, Qiu W, Wang X, Duan X, Han X, Yu T, et al. Immunoglobulin G inhibits glucocorticoid-induced osteoporosis through occupation of FcγRI. iScience 2023;26:107749. doi: 10.1016/j.isci.2023.107749.
8. Schwedler C, Häupl T, Kalus U, Blanchard V, Burmester GR, Poddubnyy D, et al. Hypogalactosylation of immunoglobulin G in rheumatoid arthritis: relationship to HLA-DRB1 shared epitope, anticitrullinated protein antibodies, rheumatoid factor, and correlation with inflammatory activity. Arthritis Res Ther 2018;20:44. doi: 10.1186/s13075-018-1540-0.
9. Liu R, Oldham RJ, Teal E, Beers SA, Cragg MS. Fc-engineering for modulated effector functions-improving antibodies for cancer treatment. Antibodies (Basel) 2020;9:64. doi: 10.3390/ antib9040064.
10. Li T, DiLillo DJ, Bournazos S, Giddens JP, Ravetch JV, Wang LX. Modulating IgG effector function by Fc glycan engineering. Proc Natl Acad Sci U S A 2017;114:3485-90. doi: 10.1073/pnas.1702173114.
11. Kroupova K, Palicka V, Rosa J. Monoclonal antibodies for treatment of osteoporosis. Drugs Today (Barc) 2023;59:195- 204. doi: 10.1358/dot.2023.59.3.3453905.
12. Nurkkala-Karlsson M, Lagerquist MK, Gupta P, Ohlsson C, Mellström D, Engdahl C. Essential role of local antibody distribution in mediating bone-resorbing effects. Sci Rep 2024;14:5684. doi: 10.1038/s41598-024-56192-1.
13. Buhre JS, Becker M, Ehlers M. IgG subclass and Fc glycosylation shifts are linked to the transition from pre- to inflammatory autoimmune conditions. Front Immunol 2022;13:1006939. doi: 10.3389/ fimmu.2022.1006939.
14. Iantomasi T, Romagnoli C, Palmini G, Donati S, Falsetti I, Miglietta F, et al. Oxidative stress and inflammation in osteoporosis: Molecular mechanisms involved and the relationship with microRNAs. Int J Mol Sci 2023;24:3772. doi: 10.3390/ijms24043772.
15. Buttgereit F, Palmowski A, Bond M, Adami G, Dejaco C. Osteoporosis and fracture risk are multifactorial in patients with inflammatory rheumatic diseases. Nat Rev Rheumatol 2024;20:417-31. doi: 10.1038/s41584-024-01120-w.
16. Birney E. Mendelian randomization. Cold Spring Harb Perspect Med 2022;12:a041302. doi: 10.1101/cshperspect. a041302.
17. Guo JZ, Xiao Q, Gao S, Li XQ, Wu QJ, Gong TT. Review of Mendelian randomization studies on ovarian cancer. Front Oncol 2021;11:681396. doi: 10.3389/fonc.2021.681396.
18. Sun W, Jian X, Zhang J, Meng X, Wang H, Zheng D, et al. The causality between human Immunoglobulin G (IgG) N-Glycosylation and aging: A Mendelian randomization study. Molecules 2024;29:1281. doi: 10.3390/ molecules29061281.
19. Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian Randomisation (STROBE-MR): Explanation and elaboration. BMJ 2021;375:n2233. doi: 10.1136/bmj.n2233.
20. Martens EP, Pestman WR, de Boer A, Belitser SV, Klungel OH. Instrumental variables: Application and limitations. Epidemiology 2006;17:260-7. doi: 10.1097/01. ede.0000215160.88317.cb.
21. Ahola-Olli AV, Würtz P, Havulinna AS, Aalto K, Pitkänen N, Lehtimäki T, et al. Genome-wide association study identifies 27 loci influencing concentrations of circulating cytokines and growth factors. Am J Hum Genet 2017;100:40- 50. doi: 10.1016/j.ajhg.2016.11.007.
22. Klarić L, Tsepilov YA, Stanton CM, Mangino M, Sikka TT, Esko T, et al. Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases. Sci Adv 2020;6:eaax0301. doi: 10.1126/sciadv.aax0301.
23. Chen H, Zhang Y, Li S, Tao Y, Gao R, Xu W, et al. The association between genetically predicted systemic inflammatory regulators and polycystic ovary syndrome: A Mendelian randomization study. Front Endocrinol (Lausanne) 2021;12:731569. doi: 10.3389/fendo.2021.731569.
24. Clarke L, Zheng-Bradley X, Smith R, Kulesha E, Xiao C, Toneva I, et al. The 1000 Genomes project: Data management and community access. Nat Methods 2012;9:459-62. doi: 10.1038/nmeth.1974.
25. Kim JY, Song M, Kim MS, Natarajan P, Do R, Myung W, et al. An atlas of associations between 14 micronutrients and 22 cancer outcomes: Mendelian randomization analyses. BMC Med 2023;21:316. doi: 10.1186/s12916-023- 03018-y.
26. Wan B, Lu L, Lv C. Mendelian randomization study on the causal relationship between leukocyte telomere length and prostate cancer. PLoS One 2023;18:e0286219. doi: 10.1371/journal.pone.0286219.
27. Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res 2012;21:223-42. doi: 10.1177/0962280210394459.
28. 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;40:304-14. doi: 10.1002/ gepi.21965.
29. Minelli C, Del Greco M F, van der Plaat DA, Bowden J, Sheehan NA, Thompson J. The use of two-sample methods for Mendelian randomization analyses on single large datasets. Int J Epidemiol 2021;50:1651-9. doi: 10.1093/ije/ dyab084.
30. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 2017;32:377-89. doi: 10.1007/s10654-017- 0255-x.
31. Kulinskaya E, Dollinger MB. An accurate test for homogeneity of odds ratios based on Cochran's Q-statistic. BMC Med Res Methodol 2015;15:49. doi: 10.1186/s12874-015- 0034-x.
32. Bowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: The role of the I2 statistic. Int J Epidemiol 2016;45:1961-74. doi: 10.1093/ije/ dyw220.
33. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018;50:693-8. doi: 10.1038/ s41588-018-0099-7.
34. Trzos S, Link-Lenczowski P, Pocheć E. The role of N-glycosylation in B-cell biology and IgG activity. The aspects of autoimmunity and anti-inflammatory therapy. Front Immunol 2023;14:1188838. doi: 10.3389/ fimmu.2023.1188838.
35. Hayes JM, Cosgrave EF, Struwe WB, Wormald M, Davey GP, Jefferis R, et al. Glycosylation and Fc receptors. Curr Top Microbiol Immunol 2014;382:165-99. doi: 10.1007/978-3-319- 07911-0_8.
36. Prenc E, Pulanic D, Pucic-Bakovic M, Ugrina I, Desnica L, Milosevic M, et al. Significant associations of IgG glycan structures with chronic graft-versus-host disease manifestations: Results of the cross-sectional NIH cohort study. Front Immunol 2021;12:633214. doi: 10.3389/ fimmu.2021.633214.
37. Martin TC, Šimurina M, Ząbczyńska M, Martinic Kavur M, Rydlewska M, Pezer M, et al. Decreased immunoglobulin G core fucosylation, a player in antibody-dependent cellmediated cytotoxicity, is associated with autoimmune thyroid diseases. Mol Cell Proteomics 2020;19:774-92. doi: 10.1074/mcp.RA119.001860.
38. Lopez E, Scott NE, Wines BD, Hogarth PM, Wheatley AK, Kent SJ, et al. Low pH exposure during immunoglobulin G purification methods results in aggregates that avidly bind fcγ receptors: Implications for measuring Fc dependent antibody functions. Front Immunol 2019;10:2415. doi: 10.3389/fimmu.2019.02415.
39. Chen Z, Xu X, Song M, Lin L. Crosstalk between cytokines and IgG N-Glycosylation: Bidirectional effects and relevance to clinical innovation for inflammatory Diseases. OMICS 2024;28:608-19. doi: 10.1089/omi.2024.0176.
40. Lu X, Machiesky LA, De Mel N, Du Q, Xu W, Washabaugh M, et al. Characterization of IgG1 Fc deamidation at asparagine 325 and its impact on antibody-dependent cell-mediated cytotoxicity and fcγrıııa binding. Sci Rep 2020;10:383. doi: 10.1038/s41598-019-57184-2.
41. Wei B, Gao X, Cadang L, Izadi S, Liu P, Zhang HM, et al. Fc galactosylation follows consecutive reaction kinetics and enhances immunoglobulin G hexamerization for complement activation. MAbs 2021;13:1893427. doi: 10.1080/19420862.2021.1893427.
42. Turan S. Endocrine disrupting chemicals and bone. Best Pract Res Clin Endocrinol Metab 2021;35:101495. doi: 10.1016/j.beem.2021.101495.
43. Bandeira F, Lazaretti-Castro M, Bilezikian JP. Hormones and bone. Arq Bras Endocrinol Metabol 2010;54:85-6. doi: 10.1590/s0004-27302010000200001.
44. Kronimus Y, Dodel R, Galuska SP, Neumann S. IgG Fc N-glycosylation: Alterations in neurologic diseases and potential therapeutic target? J Autoimmun 2019;96:14-23. doi: 10.1016/j.jaut.2018.10.006.
45. Yi KJ, Kang RM, Zhang YY, Li Q. Causal relationship between circulating inflammatory factors and osteoporosis: a bidirectional Mendelian randomization study. Eur Rev Med Pharmacol Sci 2024;28:2237-49. doi: 10.26355/ eurrev_202403_35728.
46. Li W, Wu P, Jin T, Jia J, Chen B, Liu T, et al. L-fucose and fucoidan alleviate high-salt diet-promoted acute inflammation. Front Immunol 2024;15:1333848. doi: 10.3389/ fimmu.2024.1333848.
47. Li C, Chong G, Zong G, Knorr DA, Bournazos S, Aytenfisu AH, et al. Site-selective chemoenzymatic modification on the core fucose of an antibody enhances its fcγ receptor affinity and ADCC activity. J Am Chem Soc 2021;143:7828- 38. doi: 10.1021/jacs.1c03174.
48. Wang T, He C. TNF-α and IL-6: The link between immune and bone system. Curr Drug Targets 2020;21:213-27. doi: 10. 2174/1389450120666190821161259.