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Detecting causal relationship between metabolic traits and osteoporosis using multivariable Mendelian randomization

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Abstract

Summary

By adopting the extension approaches of Mendelian randomization, we successfully detected and prioritized the potential causal risk factors for BMD traits, which might provide us novel insights for treatment and intervention into bone-related complex traits and diseases.

Introduction

Osteoporosis (OP) is a common metabolic skeletal disease characterized by reduced bone mineral density (BMD). The identified SNPs for BMD can only explain approximately 10% of the variability, and very few causal factors have been identified so far.

Methods

The Mendelian randomization (MR) approach enables us to assess the potential causal effect of a risk factor on the outcome by using genetic IVs. By using extension methods of MR—multivariable MR (mvMR) and MR based on Bayesian model averaging (MR-BMA)—we intend to estimate the causal relationship between fifteen metabolic risk factors for BMD and try to prioritize the most potential causal risk factors for BMD.

Results

Our analysis identified three risk factors T2D, FG, and HCadjBMI for FN BMD; four risk factors FI, T2D, HCadjBMI, and WCadjBMI for FA BMD; and three risk factors FI, T2D, and HDL cholesterol for LS BMD, and all risk factors were causally associated with heel BMD except for triglycerides and WCadjBMI. Consistent with the mvMR results, MR-BMA confirmed those risk factors as top risk factors for each BMD trait individually.

Conclusions

By combining MR approaches, we identified the potential causal risk factors for FN, FA, LS, and heel BMD individually and we also prioritized and ranked the potential causal risk factors for BMD, which might provide us novel insights for treatment and intervention into bone-related complex traits and diseases.

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Data availability

No original, unprocessed data was used in the present study. The summary datasets used in our study were derived from the following resources available in the public domain (detailed in Supplemental Data).

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Acknowledgments

QZ as the first author performed data analysis and wrote the manuscript. JG contributed suggestions for manuscript revision and revised the manuscript. HS, LJZ, WDZ, and CQS provided advice and suggestions while we met some problems during the data analysis process. HWD conceived and initiated this project, provided advice on experimental design, oversaw the implementation of the statistical method, and revised/finalized the manuscript.

Funding

This research was partially supported by Key Science and Technology Development of Henan Province (Grant No.: 192102310191). We appreciate the support from Zhengzhou University in providing necessary support for this collaborative project. HWD was partially supported by grants from the NIH (R01-AR069055, U19-AG055373, R01-MH104680, R01-AR059781, and P20-GM109036) and Edward G. Schlieder Endowment fund at Tulane University.

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Authors and Affiliations

  1. School of Nursing and Health, Zhengzhou University, NO.101 Kexue Road, High-Tech Development Zone of States, Zhengzhou, 450001, People’s Republic of China

    Q. Zhang & C.-Q. Sun

  2. Center for Bioinformatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, 70112, USA

    Q. Zhang, J. Greenbaum, H. Shen, L.-J. Zhao & H.-W. Deng

  3. Deparment of Epidemiology and Statistics, College of Public Health, Zhengzhou University, NO.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, 450001, People’s Republic of China

    W.-D. Zhang & C.-Q. Sun

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  1. Q. Zhang
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  2. J. Greenbaum
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  3. H. Shen
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  4. L.-J. Zhao
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Correspondence to H.-W. Deng.

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Zhang, Q., Greenbaum, J., Shen, H. et al. Detecting causal relationship between metabolic traits and osteoporosis using multivariable Mendelian randomization. Osteoporos Int 32, 715–725 (2021). https://doi.org/10.1007/s00198-020-05640-5

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