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Systematic Review
8 (
1
); 4-10
doi:
10.25259/JMSR_220_2023

Genetic influence on osteoporosis and fracture risk: Outcome of genome-wide association studies – A systematic review

Department of Orthopedic Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
Department of Nursing, King Fahd Hospital of the University, AlKhobar, Saudi Arabia
Department of Obstetrics and Gynecology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
Endocrinology, Obesity, Endocrine and Metabolism Center, King Fahad Medical City, Riyadh, Saudi Arabia
Corresponding author: Mir Sadat-Ali, Department of Orthopedic Surgery, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. drsadat@hotmail.com
Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Sadat-Ali M, Al-Turki RA, Al-Turki HA, Almohaya MS. Genetic influence on osteoporosis and fracture risk: Outcome of genome-wide association studies – A systematic review. J Musculoskelet Surg Res. 2024;8:4-10. doi: 10.25259/JMSR_220_2023

Abstract

This systematic review aimed to identify genome-wide association studies (GWASs) highlighting the genes and single-nucleotide polymorphisms linked to osteoporosis and fragility fracture risk. We searched the search engines EMBASE, MEDLINE, Scopus, Web of Science, Science Citation Index, and Cochrane database of systematic reviews between 2005 and May 2022. The articles were reviewed individually for risk of bias and found no variances in the papers designated for analysis. We analyzed 63 studies with 1,326,798 patients, which included postmenopausal and premenopausal women. Thirty-one studies used dual-energy x-ray absorptiometry (DXA) for the diagnosis. Three studies used ultrasonography, and one used peripheral quantitative computed tomography (pQCT) to diagnose osteoporosis. For the risk of fragility fractures, 15 studies with 744,123 were analyzed, which used DXA in 12, two studies of ultrasonography, and one of radiography. Three studies were reported in premenopausal women and three in children with 18,203 subjects. Our analysis showed that 150 genes, 515 loci that target bone mineral density and 15 loci that increase fracture risk in osteoporosis have been identified. Osteoporosis and fragility fractures are common in the Saudi Arabian population. The GWAS gives an understanding of the genetic basis of low bone density, osteoporosis, and fragility fractures. The GWAS data can provide new pathways to understanding the etiology of osteoporosis and a route to prevention and optimum treatment. Hence, we believe that we should conduct GWASs on osteoporosis sooner rather than later so that we can advise at-risk individuals to change their lifestyle so that they can limit complications of osteoporosis and related complications.

Keywords

Bone mineral density
Fragility fractures
Genome-wide association study
Human genome project
Osteoporosis

INTRODUCTION

Osteoporosis is a silent and far-reaching skeletal disease that affects over ~200 million people in the World. The condition is characterized by deterioration of the microarchitecture of bone, which leads to fragility fractures.[1,2] By 2050, over 212 million people will suffer from low bone mass.[3] The economic burden has increased in billions with the aging population, and the annual cost of treating fragility fractures in the United States has risen to $17 billion.[4,5] In contrast, by 2035, such treatment will cost nearly $19 billion in China.[6] Apart from a lack of estrogen in women and testosterone in men and environmental factors, 80% of osteoporosis is due to genetic influence.[2,7]

The human genome project (HGP) was undertaken to identify, map, and sequence all of the human body’s genes, but genome-wide association studies (GWASs) discovered many genes and thousands of single nucleotide polymorphisms (SNPs), which influence many diseases including osteoporosis and fragility fractures.[8-13] If GWAS is not performed properly and cannot identify the genes and SNPs that influence the diseases, this may result in statistically significant analysis with low odds ratios that may not give a convincing contribution. The inspiration to perform this analysis came from the GWAS in other parts of the world giving a strong indication of the genetic influence on bone mineral density (BMD) and fragility fractures. In this context, if people know that they carry genes and SNPs that will cause osteoporosis and fragility fractures, they could change their lifestyle, gain more BMD, and reduce the risk of osteoporosis and fragility fractures.

This review aimed to identify GWASs in the Middle East and the rest of the World highlighting the genes and SNPs that decrease the achievement of BMD and increase the risk of osteoporosis and fragility fractures.

MATERIALS AND METHODS

This is a systematic review in which we searched between 2005 and May 2022 all relevant databases such as EMBASE, Cochrane database of Systematic Reviews, MEDLINE, Science Citation Index, Scopus, and Web of Science with keywords of osteoporosis, BMD, and fragility fractures. In the context of GWASs, investigators will first identify locations of the genome that highlight a strong striking link to the traits in question, i.e., in the discovery cohort, areas or specific markers in which variation is more common than in the controls. The standard steps of conducting GWAS for any disease are to collect samples and traits, gather genotype samples, test statistically each SNP for association of the disease, tabulate the results, and simulate the data.

The criteria for inclusion of studies for analysis were articles involving patients with the presence or absence of the gene and SNPs related to osteoporosis, BMD and fragility fractures, case–control or family-based genetic association studies, diagnosis of osteoporosis, and fragility fractures using a standard classification system that was published in the English language in HGP, GWAS, target genes, and clinical translation. The criteria for exclusion were review articles and correspondence.

The authors reviewed all the articles independently and then together, and there was no discrepancy in the papers selected for the review. This analysis was done as per PRISMA guidelines.[14]

RESULTS

We analyzed 63 studies and 1,326,798 patients, which included those on postmenopausal and premenopausal patients [Figure 1]. The data analyzed of postmenopausal patients, which numbered 35 studies with 564,472 patients [Table 1]. Thirty-one studies used DXA for the diagnosis, three used ultrasonography, and one study used peripheral quantitative computed tomography (pQCT) to diagnose osteoporosis. Table 2 gives the data of analysis of fragility fractures and osteoporosis. Fifteen studies with 744,123 used DXA in 12, two studied ultrasonography, and one used radiography. Table 3 shows three studies in premenopausal women and three in children with 18,203 subjects. Most of the studies were conducted among Europeans, North Americans, Japanese, Chinese, Africans, Koreans, and East Asian ancestry.

PRISMA flow chart of the review.
Figure 1:
PRISMA flow chart of the review.
Table 1: List of published GWAS in adults on BMD, osteoporosis analyzed.
S. No. Authors Number of patients Method used for assessment
1. Kiel et al. (2007)[15] 1117 DXA
2. Xiong et al. (2009)[16] 9858 DXA
3. Liu et al. (2009)[17] 4355 DXA
4. Rivadeneira et al. (2009)[18] 19,195 DXA
5. Guo et al. (2010)[19] 10,352 DXA
6. Guo et al. (2010)[20] 2557 DXA
7. Hsu et al. (2010)[21] 7633 DXA
8. Tan et al. (2010)[22] 1628 DXA
9. Paternoster et al. (2010)[23] 3835 DXA
10 Kou et al. (2011)[24] 2279 DXA
11. Duncan et al. (2011)[25] 20,898 DXA
12. Lei et al. (2012)[26] 3355 DXA
13. Liu et al. (2012)[27] 24,763 PQCT
14. Guo et al. (2013)[28] 3913 DXA
15. Deng et al. (2013)[29] 5130 DXA
16. Zhang et al. (2014)[30] 15,871 DXA
17. Tan et al. (2015)[31] 2845 DXA
18. Mullin et al. (2016)[32] 5654 ULTRA
19. Hwang et al. (2016)[33] 7263 DXA
20. Choi et al. (2016)[34] 2286 DXA
21. Pei et al. (2016)[35] 7513 DXA
22. Pei et al. (2016)[36] 2874 DXA
23. Mullin et al. (2017)[37] 13,749 ULTRA
24. Villalobos-Comparán
et al. (2017)[38]
420 DXA
25. Kemp et al. (2017)[39] 142,487 ULTRA
26. Peng et al. (2017)[40] 53,236 DXA
27. Lu et al.(2017)[41] 2069 DXA
28. Pei et al. (2018)[42] 40,491 DXA
29. Lin et al. (2018)[43] 49,988 DXA
30. Qiu et al. (2018)[44] 5905 DXA
31. Gregson et al. (2018)[45] 30,970 DXA
32. Naito et al. (2018)[46] 173 DXA
33. Liang et al. (2018)[47] 3404 DXA
34. Styrkarsdottir et al(2019)[48] 50,231 DXA
35. Zhang et al. (2020)[49] 6175 DXA

DXA: Dual-energy X-ray absorptiometry, PQCT: Peripheral quantitative computed tomography, GWAS: Genome-wide association studies, BMD: Bone mineral density

Table 2: List of published GWAS in adults on fragility fractures analyzed.
S. No. Authors Number of patients Method used for assessment
1. Richards et al. (2008)[50] 6463 DXA
2. Styrkarsdottir et al. (2008)[51] 7925 DXA
3. Guo et al. (2010)[19] 10,352 DXA
4. Kung et al. (2010)[52] 18,098 DXA
5. Estrada et al. (2012)[53] 31,016 DXA
6. Zheng et al. (2012)[54] 2023 DXA
7. Hwang et al. (2013)[55] 1119 DXA
8. Zheng et al. (2013)[56] 8604 DXA
9. Taylor et al. (2016)[57] 10,305 DXA
10. Styrkarsdottir et al. (2016)[58] 10,389 DXA
11. Styrkarsdottir et al. (2016)[59] 2636 DXA
12. Kim (2018)[60] 59,378 ULTRA
13 Trajanoska et al. (2018)[61] 147,200 XRAY
14. Alonso et al. (2018)[62] 2181 DXA
15. Morris et al. (2019)[63] 426,824 ULTRA
744,513

DXA: Dual-energy X-ray absorptiometry, GWAS: Genome-wide association studies

Table 3: List of published GWAS in other groups analyzed.
S. No. Authors Number of patients Method used for assessment
Premenopausal group
1. Tang et al. (2009)[64] 1089 DXA
2. Koller et al. (2010)[65] 1524 DXA
3. Koller et al. (2013)[66] 4061 DXA
Pediatric group
4. Timpson et al. (2009)[67] 7470 DXA
5. Medina-Gomez
et al. (2012)[68]
2660 DXA
6. Chesi et al. (2015)[69] 1399 DXA

DXA: Dual-energy X-ray absorptiometry, GWAS: Genome-wide association studies

The studies have identified 150 genes and 515 SNPs, which are directly linked to BMD and Osteoporosis. Fifteen loci have been identified, which indicate the risk of fragility fractures.

DISCUSSION

Our review shows that GWAS has produced clear and reproducible findings in which more than 150 genes are implicated in the risk of individuals developing osteoporosis and its complications. The diagnosis of osteoporosis centers around the reading of BMD of reduction of more than 2.5 standard deviations from the normal mean of 35 years adult (T-Score), which is diagnosed as osteoporosis. Most GWASs were carried out based on the BMD, a proven risk factor for osteoporosis and fragility fractures. Phenotype refers to an individual’s visible traits and is fixed by both their genomic makeup and environmental factors. Both genetic and environmental factors influence the incidence of osteoporosis and fracture risk in a given population. The marvelous technique that GWAS performs is identifying genetic variants associated with a given phenotype, and the study estimates the risk of osteoporosis and fracture risk. At present, some gene-based tests have been developed to analyze multiple rare genetic variants associated with phenotypic traits.

For a long, it was known that genetics played a major role in the achievement of skeletal strength and the risk of osteoporosis.[70,71] This led to the study and identification of target genes, which increased the risk of osteoporosis. Before the era of GWAS, Stewart and Ralston,[72] in a review, reported at least 15 target genes that influence BMD and osteoporosis, but the studies reported by GWAS completely changed the concept of earlier detection of genetic predispositions to disease.

Guo et al.[19] reported the first osteoporosis-related GWAS in which a member of the aldehyde dehydrogenase gene (ALDH7A1) was found to cause osteoporosis in the Chinese population, which was later replicated in Caucasian people. Initially, genetic influence on osteoporosis was studied in specific genes but GWAS was able to look for the whole genome in a large group of people and identified all the genes and even small variations of SNPs.

Many studies followed this concept, which confirmed beyond doubt the genetic influence on osteoporosis and fragility fractures. Initial GWASs prospectively looked at the variants of LRP4, LRP5, and LRP6 genes in the Caucasian population and found that 2 SNPs (rs3736228, rs4988321) in the LRP5 gene greatly influence the decrease in BMD and osteoporosis while no influence was observed by the SNP of LRP4 and LRP6 gene.[73-75]

Genes and SNPs affect BMD at the femoral neck or the lumbar spine, and some of them affect both sites. The GWAS found that polymorphisms of CATSPERB (rs1298989 and rs1285635), PTH gene (rs9630182, rs2036417, and rs7125774), and IL21R gene (rs7199138, rs8061992, and rs8057551) were strongly associated with BMD at femoral neck.[20,65] The influence of CATSPERB gene polymorphisms (rs1298989 and rs1285635) causing lower BMD had similar effects in multi-ethnic groups.[65] The GWASs in the premenopausal studies have also indicated various SNPs, which negatively impact the attainment of the BMD.[64-66] Tang et al.[64] reported that SNP (rs3747532) in the CER1 gene not only decreases the BMD but also increases the risk of vertebral fractures. Furthermore, studies have shown that more candidate genes and SNPs affect BMD reaching genome-wide significance of a fixed P-value threshold of 5 × 10−8. To date, 150 genes and 515 loci have been directly linked to BMD, osteoporosis, and fragility fractures.[21,30,52,53,76,77]

Most of the GWAS studies have been carried out in European, African, American, Asian, and Chinese populations where the reported incidence of osteoporosis is between 11% and 13%.[4,5] The reported incidence of osteoporosis among the Saudi Arabian population is more than twice that of the Caucasian population.[78] The Saudi Human Genome Program (SHGP) was established in 2014 and got the patronage of the Crown Prince for the 2030 vision, but unfortunately, not a single GWAS for osteoporosis was conducted in Saudi Arabia even though by 2050, osteoporosis-related femoral fractures alone will cost 35 billion Saudi Riyals.[78,79]

The only genetic study to date on osteoporosis revealed that the genetic makeup of the Saudi population related to osteoporosis and fragility fractures is different from that of the Western population.[80] Hence, it is appropriate to robustly recommend that it is time that SHGP undertake GWASs on osteoporosis.

Our review has limitations, as any literature review is not without constraints. First, with respect to GWAS, which will not be able to identify all genetic influences, and GWAS cannot explain 100% of the heritability of all traits. Second, we have excluded studies that reported the same genes and SNPs, and lastly, reviews with concurrent animal studies. Our review has several strengths as we undertook a systematic approach to screening and analyzing the GWASs from recent literature and secondly from the data presented, which can be utilized for clinical translation.

CONCLUSION

The goal of the HGP was to decipher the chemical sequence of the complete human genetic material, which ultimately can predict human diseases before they occur. The GWASs on osteoporosis have unfolded genetic influence and identified genes and SNPs that reduce BMD cause osteoporosis and inflict fragility fractures. Genetic analysis can now identify at-risk individuals with impending osteoporosis and fragility fractures so that they can change their lifestyle by practicing weight-bearing exercise, improving nutrition, and reducing smoking.

The analysis showed that 150 genes and 515 loci that target BMD and 15 loci, which increasefracture risk in osteoporosis, have been identified. Based on this review, it can be emphasized that there is a strong genetic influence on the attainment of BMD and increased risk of fragility fractures.

RECOMMENDATION

It is strongly recommended that we conduct GWASs on osteoporosis in the Saudi Arabian population to identify the genetic risk so that we can advise at-risk individuals to change their lifestyle so that they can limit the complications of osteoporosis and related complications.

AUTHORS’ CONTRIBUTIONS

MSA conceived and designed the study, conducted review of literature, wrote the preliminary, manuscript, and complete the final manuscript. RAI performed the review of literature, completed the data analysis and manuscript review. HA helped in the data analysis, cross-checking of the references, and review of the final manuscript, and MA performed the final check of the analyzed data, review of literature, and final review of manuscript. All authors have critically reviewed and approved the final draft and are responsible for the manuscript’s content and similarity index.

ETHICAL APPROVAL

The Institutional Review Board approval is not required.

DECLARATION OF PATIENT CONSENT

Patient’s consent not required as patients identity is not disclosed or compromised.

USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY FOR MANUSCRIPT PREPARATION

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

CONFLICTS OF INTEREST

All authors declare that they do not have any conflict of interest related to the submitted work.

FINANCIAL SUPPORT AND SPONSORSHIP

This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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