REFERENCES

1. Che L, Wang Y, Sha D, et al. A biomimetic and bioactive scaffold with intelligently pulsatile teriparatide delivery for local and systemic osteoporosis regeneration. Bioact Mater. 2023;19:75-87.

2. Subarajan P, Arceo-mendoza RM, Camacho PM. Postmenopausal osteoporosis. Endocrinol Metab Clin North Am. 2024;53:497-512.

3. Maryanovich M, Takeishi S, Frenette PS. Neural regulation of bone and bone marrow. Cold Spring Harb Perspect Med. 2018;8:a031344.

4. Qin W, Bauman WA, Cardozo CP. Evolving concepts in neurogenic osteoporosis. Curr Osteoporos Rep. 2010;8:212-8.

5. Kumar S, Chandnani A, Aung NH, et al. Alzheimer’s disease and its association with bone health: a case-control study. Cureus. 2021;13:e13772.

6. Pignolo A, Mastrilli S, Davì C, et al. Vitamin D and Parkinson’s disease. Nutrients. 2022;14:1220.

7. Adams HHH, Hibar DP, Chouraki V, et al. Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat Neurosci. 2016;19:1569-82.

8. Douaud G, Menke RAL, Gass A, et al. Brain microstructure reveals early abnormalities more than two years prior to clinical progression from mild cognitive impairment to Alzheimer’s disease. J. Neurosci. 2013;33:2147-55.

9. Kanis JA. Diagnosis of osteoporosis and assessment of fracture risk. The Lancet. 2002;359:1929-36.

10. Bai W, Wang L, Ying Z, et al. Identification of PIEZO1 polymorphisms for human bone mineral density. Bone. 2020;133:115247.

11. Ma B, Li C, Pan J, et al. Causal associations of anthropometric measurements with fracture risk and bone mineral density: a mendelian randomization study. J Bone Miner Res. 2020;36:1281-7.

12. Choi KW, Chen C, Stein MB, et al. ; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Assessment of bidirectional relationships between physical activity and depression among adults: a 2-sample mendelian randomization study. JAMA Psychiatry. 2019;76:399.

13. Zhu Z, Zheng Z, Zhang F, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018;9:224.

14. Bulik-sullivan BK, Loh P, Finucane HK, et al. ; Schizophrenia Working Group of the Psychiatric Genomics Consortium. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291-5.

15. Gong W, Guo P, Li Y, et al. Role of the gut-brain axis in the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders: a genome-wide pleiotropic analysis. JAMA Psychiatry. 2023;80:360.

16. Lu H, Qiao J, Shao Z, Wang T, Huang S, Zeng P. A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics. BMC Med. 2021;19:314.

17. Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between type 2 diabetes and prostate cancer. PLoS Genet. 2020;16:e1009218.

18. Carter AR, Sanderson E, Hammerton G, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol. 2021;36:465-78.

19. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190-1.

20. Smith SM, Douaud G, Chen W, et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci. 2021;24:737-45.

21. Nitsch D, Molokhia M, Smeeth L, Destavola BL, Whittaker JC, Leon DA. Limits to causal inference based on mendelian randomization: a comparison with randomized controlled trials. Am J Epidemiol. 2006;163:397-403.

22. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018:k601.

23. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377-89.

24. Bulik-sullivan B, Finucane HK, Anttila V, et al. ; Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236-41.

25. Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.

26. Watanabe K, Taskesen E, Van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1826.

27. Weeks EM, Ulirsch JC, Cheng NY, et al. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat Genet. 2023;55:1267-76.

28. Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67:850-7.

29. Courchesne E, Chisum HJ, Townsend J, et al. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology. 2000;216:672-82.

30. Baldock PA, Sainsbury A, Allison S, et al. Hypothalamic control of bone formation: distinct actions of leptin and Y2 receptor pathways. J Bone Miner Res. 2005;20:1851-7.

31. Driessler F, Baldock PA. Hypothalamic regulation of bone. J Mol Endocrinol. 2010;45:175-81.

32. Takeda S. Osteoporosis: a neuroskeletal disease? Int J Biochem Cell Biol. 2009;41:455-9.

33. Fukumoto S, Nakamura Y, Watanabe M, et al. Risk HLA-DRB1 alleles differentially influence brain and lesion volumes in Japanese patients with multiple sclerosis. J Neurol Sci. 2020;413:116768.

34. James LM, Christova P, Lewis SM, Engdahl BE, Georgopoulos A, Georgopoulos AP. Protective effect of human leukocyte antigen (HLA) allele DRB1*13:02 on age-related brain gray matter volume reduction in healthy women. EBioMedicine. 2018;29:31-7.

Science Orthopedics
ISSN : XXXX-XXXX (Coming soon)
Navigation
Navigation