Genetic profiling and individualized prognosis of fracture

The Genetic and Biological Basis of Pseudoarthrosis in Fractures: Current Understanding and Future Directions

Pseudoarthrosis-the failure of normal fracture healing-remains a significant orthopedic challenge affecting approximately 10-15% of long bone fractures, and is associated with significant pain, prolonged disability, and repeated surgical interventions. Despite extensive research into the pathophysiological mechanisms of bone healing, diagnostic approaches remain reliant on clinical findings and radiographic evaluations, with little innovation in tools to predict or diagnose non-union. The present review evaluates the current understanding of the genetic and biological basis of pseudoarthrosis and highlights future research directions. Recent studies have highlighted the potential of specific molecules and genetic markers to serve as predictors of unsuccessful fracture healing. Alterations in mesenchymal stromal cell (MSC) function, including diminished osteogenic potential and increased cellular senescence, are central to pseudoarthrosis pathogenesis. Molecular analyses reveal suppressed bone morphogenetic protein (BMP) signaling and elevated levels of its inhibitors, such as Noggin and Gremlin, which impair bone regeneration. Genetic studies have uncovered polymorphisms in BMP, matrix metalloproteinase (MMP), and Wnt signaling pathways, suggesting a genetic predisposition to non-union. Additionally, the biological differences between atrophic and hypertrophic pseudoarthrosis, including variations in vascularity and inflammatory responses, emphasize the need for targeted approaches to management. Emerging biomarkers, such as circulating microRNAs (miRNAs), cytokine profiles, blood-derived MSCs, and other markers (B7-1 and PlGF-1), have the potential to contribute to early detection of at-risk patients and personalized therapeutic approaches. Advancing our understanding of the genetic and biological underpinnings of pseudoarthrosis is essential for the development of innovative diagnostic tools and therapeutic strategies.

Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications

Osteoporosis is a common skeletal disease, affecting ~200 million people around the world. As a complex disease, osteoporosis is influenced by many factors, including diet (e.g. calcium and protein intake), physical activity, endocrine status, coexisting diseases and genetic factors. In this review, we first summarize the discovery from genome-wide association studies (GWASs) in the bone field in the last 12 years. To date, GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density (BMD), osteoporosis, and osteoporotic fractures. However, the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability, these two questions could be partially explained by the newly raised conceptual models, such as omnigenic model and natural selection. Finally, we introduce the clinical use of GWAS findings in the bone field, such as the identification of causal clinical risk factors, the development of drug targets and disease prediction. Despite the fruitful GWAS discoveries in the bone field, most of these GWAS participants were of European descent, and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.

Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients

We evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72-0.78), 0.64 (0.60-0.68), and 0.78 (0.75-0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65-0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.

Individualized fracture risk assessment: State-of-the-art and room for improvement

Fragility fracture is a serious clinical event, because it is associated with increased risk of mortality and reduced quality of life. The risk of fracture is determined by multiple risk factors, and their effects may be interactional. Over the past 10 years, a number of predictive models (e.g., FRAX, Garvan Fracture Risk Calculator, and Qfracture) have been developed for individualized assessment of fracture risk. These models use different risk profiles to estimate the probability of fracture over 5- and 10-year period. The ability of these models to discriminate between those individuals who will and will not have a fracture (i.e., area under the receiver operating characteristic curve [AUC]) is generally acceptable-to-good (AUC, 0.6 to 0.8), and is highly variable between populations. The calibration of existing models is poor, particularly in Asian populations. There is a strong need for the development and validation of new prediction models based on Asian data for Asian populations. We propose approaches to improve the accuracy of existing predictive models by incorporating new markers such as genetic factors, bone turnover markers, trabecular bone score, and time-variant factors. New and more refined models for individualized fracture risk assessment will help identify those most likely to sustain a fracture, those most likely to benefit from treatment, and encouraging them to modify their risk profile to decrease risk.

Fracture Risk Assessment: From Population to Individual

Fracture caused by osteoporosis remains a major public health burden on contemporary populations because fracture is associated with a substantial increase in the risk of mortality. Early identification of high-risk individuals for prevention is a priority in osteoporosis research. Over the past decade, few risk prediction models, including the Garvan Fracture Risk Calculator (Garvan) and FRAX^®, have been developed to provide absolute (individualized) risk of fracture. Recent validation studies suggested that the area under the receiver operating characteristic curve in fracture discrimination ranged from 0.61 to 0.83 for FRAX^® and from 0.63 to 0.88 for Garvan, with hip fractures having a better discrimination than fragility fractures as a group. Although the prognostic performance of Garvan and FRAX^® for fracture prediction is not perfect and there is room for further improvement, these predictive models can aid patients and doctors communicate about fracture risk in the medium term and to make rational decisions. However, the application of these predictive models in making decisions for an individual should take into account the individual's perception of the importance of fracture relative to other diseases.

Individualized Assessment of Fracture Risk: Contribution of "Osteogenomic Profile"

Over the past decade, several genetic variants or genes for osteoporosis have been identified through genome-wide association studies and candidate gene association studies. These genetic variants are common in the general population but have modest effect sizes, with odds ratio ranging from 1.1 to 1.5. Thus, the utility of any single variant is limited. However, theoretical and empirical studies have suggested that a profiling of multiple variants that are associated with bone phenotypes (i.e., "osteogenomic profile") can improve the accuracy of fracture prediction and classification beyond that obtained by conventional clinical risk factors. These results support the view that an osteogenomic profile, when integrated into existing models, can help clinicians and patients alike to better assess the risk fracture for an individual, and raise the possibility of personalized osteoporosis care.

Prediction of Bone Mineral Density and Fragility Fracture by Genetic Profiling

Although the susceptibility to fracture is partly determined by genetic factors, the contribution of newly discovered genetic variants to fracture prediction is still unclear. This study sought to define the predictive value of a genetic profiling for fracture prediction. Sixty-two bone mineral density (BMD)-associated single-nucleotide polymorphisms (SNPs) were genotyped in 557 men and 902 women who had participated in the Dubbo Osteoporosis Epidemiology Study. The incidence of fragility fracture was ascertained from X-ray reports between 1990 and 2015. Femoral neck BMD was measured by dual-energy X-ray absorptiometry. A weighted polygenic risk score (genetic risk score [GRS]) was created as a function of the number of risk alleles and their BMD-associated regression coefficients for each SNP. The association between GRS and fracture risk was assessed by the Cox proportional hazards model. Individuals with greater GRS had lower femoral neck BMD (p < 0.01), but the variation in GRS accounted for less than 2% of total variance in BMD. Each unit increase in GRS was associated with a hazard ratio of 1.20 (95% CI, 1.04 to 1.38) for fracture, and this association was independent of age, prior fracture, fall, and in a subset of 33 SNPs, independent of femoral neck BMD. The significant association between GRS and fracture was observed for the vertebral and wrist fractures, but not for hip fracture. The area under the receiver-operating characteristic (ROC) curve (AUC) for the model with GRS and clinical risk factors was 0.71 (95% CI, 0.68 to 0.74). With GRS, the correct reclassification of fracture versus nonfracture ranged from 12% for hip fracture to 23% for wrist fracture. A genetic profiling of BMD- associated genetic variants could improve the accuracy of fracture prediction over and above that of clinical risk factors alone, and help stratify individuals by fracture status. © 2016 American Society for Bone and Mineral Research.

Genetic risk score based on the prevalence of vertebral fracture in Japanese women with osteoporosis

A genetic risk score (GRS) was developed for predicting fracture risk based on the prevalence of vertebral fractures in 441 Japanese females with osteoporosis. A total of 979 (858 nonsynonymous and 121 silent) single-nucleotide polymorphisms (SNPs) located in 74 osteoporosis-susceptibility genes were genotyped and evaluated for their association with fracture prevalence. Four SNPs (protein kinase domain containing, cytoplasmic [PKDCC; rs4952590], CDK5-regulatory subunit-associated protein 1-like 1 [CDKAL1; rs4712556], wingless-type MMTV-integration site family member 16 [WNT16; rs2707466], and G-patch domain-containing gene 1 [GPATCH1; rs10416265]) showed a significant association (p < 0.05) with the fracture, in which the minor allele of the former two SNPs was the protective allele and that of the latter two SNPs was the risk allele. Applying a dominant-genetic model, we allotted − 1 point each to the protective-allele carriers and 1 point each to the risk-allele carriers, and GRS values were calculated as the sum of the points. The receiver-operating characteristic curves showed that GRS adequately predicted vertebral fracture. For the model predicted by the GRS with and without the effect of age, areas under the curves were 0.788 (95% confidence interval [CI]: 0.736–0.840) and 0.667 (95% CI: 0.599–0.735), respectively. Multiple logistic regression analysis revealed that the odds ratio for the association between fracture prevalence and GRS was 3.27 (95% CI: 1.36–7.87, p = 0.008) for scores of − 1 to 0 (n = 303) and 12.12 (95% CI: 4.19–35.07, p < 0.001) for scores of 1 to 2 (n = 35) relative to a score of − 2 (n = 103). The GRS based on the four SNPs could help identify at-risk individuals and enable implementation of preventive measures for vertebral fracture.

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A genetic risk score to predict fracture risk based on vertebral fracture prevalence is proposed.

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Four single-nucleotide polymorphisms showed significant association with fracture.

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This method helps identify at-risk individuals and promotes preventive measures for fractures.

Contribution of Quadriceps Weakness to Fragility Fracture: A Prospective Study

The association between muscle weakness and fracture is not well understood. This study sought to examine the contribution of muscle strength at baseline and change in muscle strength to the observed risk of fragility fracture in older people. The study involved 595 men and 1066 women aged 60+ years (median 69 years) who had been followed for a median of 11 years (range, 4 to 22 years). Quadriceps isometric muscle strength (MS) measured at baseline and biennially was adjusted for height. Femoral neck bone mineral density (FNBMD) was measured by DXA. Low-trauma fracture was ascertained from X-ray reports and interview. The relationship between baseline MS and serial MS and fracture assessed by time-invariant and time-variant Cox's regression models was expressed as hazard ratio (HR) and 95% confidence interval (CI). During the follow-up period, 282 (26%) women and 89 (15%) men sustained a fragility fracture. From age 60 years, women lost 0.28 kg/m (1.6%) of MS per year, whereas men lost 0.39 kg/m (1.5%) of MS per year. In the time-variant model, using serial MS, each 1 SD (4.7 kg/m) lower MS was associated with a 27% increase in the risk of fracture in women (HR 1.27; 95% CI, 1.11 to 1.43); and 46% increase in men (HR 1.46; 95% CI, 1.22 to 1.75). After adjusting for FNBMD, age and prior fracture, history of fall and smoking, HR per SD of lower MS was 1.13 (95% CI, 0.99 to 1.28) for women and 1.35 (95% CI, 1.18 to 1.64) for men. These data indicate that muscle weakness is an independent determinant of fracture risk in men, but not in women. This sex difference suggests that apart from mechanical load effect of muscle on bone, there are other muscle-bone interactions that need to be investigated in future studies. The accuracy of fracture risk prediction for men may be improved by incorporating muscle strength.

Association between fat-mass-and-obesity-associated (FTO) gene and hip fracture susceptibility

OBJECTIVE

Common variants in the fat-mass-and-obesity-associated (FTO) gene are related to body mass index (BMI), which is a predictor of hip fracture risk. This study sought to examine the association between variants in the FTO gene and hip fracture risk.

DESIGN AND PARTICIPANTS

This is a prospective study including 934 postmenopausal women aged 60 years and above living in Dubbo, Australia (Dubbo Osteoporosis Epidemiology Study), followed up between 1989 and 2007.

MEASUREMENTS

Six single nucleotide polymorphisms (SNPs) (rs1421085, rs1558902, rs1121980, rs17817449, rs9939609 and rs9930506) of the FTO gene were genotyped using Taqman assay. Bone mineral density at the lumbar spine and femoral neck was measured by DXA (GE-Lunar) at baseline. Incidence of hip fractures during the follow-up was ascertained by reviewing X-ray reports. We used Cox's models to estimate the association between the genetic variants and hip fracture risk. We also utilized Bayes factor to evaluate the association.

RESULTS

One hundred and two women (11%) had sustained a hip fracture. The incidence of hip fracture was greater in women homozygous for the minor allele of all SNPs. Women homozygous for the minor allele (AA) of rs1121980 had significantly higher risk of hip fracture (hazard ratio, 2.06; 95% CI 1.17-3.62) than women homozygous for the major allele (TT). The observed data favoured the hypothesis of FTO gene and fracture association over the hypothesis of nonassociation by a factor of nine.

CONCLUSION

Common variations in the FTO gene are associated with hip fracture risk in women and that FTO gene may help improve the predictive value of hip fracture risk.

Multiple gene polymorphisms can improve prediction of nonvertebral fracture in postmenopausal women

Clinical risk factors (CRFs), with or without bone mineral density (BMD), are used to determine the risk of osteoporotic fracture (OF), which has a heritable component. In this study we investigated whether genetic profiling can additionally improve the ability to predict OF. Using 1229 unrelated Korean postmenopausal women, 39 single-nucleotide polymorphisms (SNPs) in 30 human genomic loci were tested for association with osteoporosis-related traits, such as BMD, osteoporosis, vertebral fracture (VF), nonvertebral fracture (NVF), and any fracture. To estimate the effects of genetic profiling, the genetic risk score (GRS) was calculated using five prediction models: (Model I) GRSs only; (Model II) BMD only; (Model III) CRFs only; (Model IV) CRFs and BMD; and (Model V) CRFs, BMD, and GRS. A total of 21 SNPs within 19 genes associated with one or more osteoporosis-related traits and were included for GRS calculation. GRS associated with BMD before and after adjustment for CRFs (p ranging from <0.001 to 0.018). GRS associated with NVF before and after adjustment for CRFs and BMD (p ranging from 0.017 to 0.045), and with any fracture after adjustment for CRFs and femur neck BMD (p = 0.049). In terms of predicting NVF, the area under the receiver operating characteristic curve (AUC) for Model I was 0.55, which was lower than the AUCs of Models II (0.60), III (0.64), and IV (0.65). Adding GRS to Model IV (in Model V) increased the AUC to 0.67, and improved the accuracy of NVF classification by 11.5% (p = 0.014). In terms of predicting any fracture, the AUC of Model V (0.68) was similar to that of Model IV (0.68), and Model V did not significantly improve the accuracy of any fracture classification (p = 0.39). Thus, genetic profiling may enhance the accuracy of NVF predictions and help to delineate the intervention threshold.

Genetic profiling and individualized assessment of fracture risk

Osteoporosis and its consequence of fragility fracture impose a considerable demand on health-care services because fracture is associated with a series of adverse events, including re-fracture and mortality. One of the major priorities in osteoporosis care is the development of predictive models to identify individuals at high risk of fracture for early intervention and management. Existing predictive models include clinical factors and anthropometric characteristics but have not considered genetic variants in the prediction. Genome-wide association studies conducted in the past decade have identified several genetic variants relevant to fracture risk. These genetic variants are common in frequency but have very modest effect sizes. A remaining challenge is to use these genetic data to individualize fracture risk assessment on the basis of an individual's genetic risk profile. Empirical and simulation studies have shown that the usefulness of a single genetic variant for fracture risk assessment is very limited, but a profile of 50 genetic variants, each with odds ratio ranging from 1.02 to 1.15, could improve the accuracy of fracture prediction beyond that obtained by use of existing clinical risk factors. Thus, genetic profiling when integrated with existing risk assessment models could inform a more accurate prediction of fracture risk in an individual.

Susceptibility genes for osteoporotic fracture in postmenopausal Chinese women

To identify the susceptibility genes for osteoporotic fracture in postmenopausal Chinese women, a two-stage case-control association study using joint analysis was conducted in 1046 patients with nontraumatic vertebra, hip, or distal radius fractures and 2303 healthy controls. First, 113 single-nucleotide polymorphisms (SNPs) in 16 potential osteoporosis candidate genes reported in recent genomewide association studies, meta-analyses studies, large-scale association studies, and functional studies were genotyped in a small-sample-size subgroup consisting of 541 patients with osteoporotic fractures and 554 healthy controls. Variants and haplotypes in SPTBN1, TNFRSF11B, CNR2, LRP4, and ESR1 that have been identified as being associated with osteoporotic fractures were further reanalyzed in the entire case-control group. We identified one SNP in TNFRSF11B (rs3102734), three SNPs in ESR1 (rs9397448, rs2234693, and rs1643821), two SNPs in LRP4 (rs17790156 and rs898604), and four SNPs in SPTBN1 (rs2971886, rs2941583, rs2941584, and rs12475342) were associated with all of the broadly defined osteoporotic fractures. The most significant polymorphism was rs3102734, with increased risk of osteoporotic fractures (odds ratio, 1.35; 95% confidence interval [CI], 1.17-1.55, Bonferroni p = 2.6 × 10(-4) ). Furthermore, rs3102734, rs2941584, rs12475342, rs9397448, rs2234693, and rs898604 exhibited significant allelic, genotypic, and/or haplotypic associations with vertebral fractures. SNPs rs12475342, rs9397448, and rs2234693 showed significant genotypic associations with hip fractures, whereas rs3102734, rs2073617, rs1643821, rs12475342, and rs2971886 exhibited significant genotypic and/or haplotypic associations with distal radius fractures. Accordingly, we suggest that in addition to the clinical risk factors, the variants in TNFRSF11B, SPTBN1, ESR1, and LRP4 are susceptibility genetic loci for osteoporotic fracture in postmenopausal Chinese women.

Clinical review: Genome-wide association studies of skeletal phenotypes: what we have learned and where we are headed

CONTEXT

The primary goals of genome-wide association studies (GWAS) are to discover new molecular and biological pathways involved in the regulation of bone metabolism that can be leveraged for drug development. In addition, the identified genetic determinants may be used to enhance current risk factor profiles.

EVIDENCE ACQUISITION

There have been more than 40 published GWAS on skeletal phenotypes, predominantly focused on dual-energy x-ray absorptiometry-derived bone mineral density (BMD) of the hip and spine.

EVIDENCE SYNTHESIS

Sixty-six BMD loci have been replicated across all the published GWAS, confirming the highly polygenic nature of BMD variation. Only seven of the 66 previously reported genes (LRP5, SOST, ESR1, TNFRSF11B, TNFRSF11A, TNFSF11, PTH) from candidate gene association studies have been confirmed by GWAS. Among 59 novel BMD GWAS loci that have not been reported by previous candidate gene association studies, some have been shown to be involved in key biological pathways involving the skeleton, particularly Wnt signaling (AXIN1, LRP5, CTNNB1, DKK1, FOXC2, HOXC6, LRP4, MEF2C, PTHLH, RSPO3, SFRP4, TGFBR3, WLS, WNT3, WNT4, WNT5B, WNT16), bone development: ossification (CLCN7, CSF1, MEF2C, MEPE, PKDCC, PTHLH, RUNX2, SOX6, SOX9, SPP1, SP7), mesenchymal-stem-cell differentiation (FAM3C, MEF2C, RUNX2, SOX4, SOX9, SP7), osteoclast differentiation (JAG1, RUNX2), and TGF-signaling (FOXL1, SPTBN1, TGFBR3). There are still 30 BMD GWAS loci without prior molecular or biological evidence of their involvement in skeletal phenotypes. Other skeletal phenotypes that either have been or are being studied include hip geometry, bone ultrasound, quantitative computed tomography, high-resolution peripheral quantitative computed tomography, biochemical markers, and fractures such as vertebral, nonvertebral, hip, and forearm.

CONCLUSIONS

Although several challenges lie ahead as GWAS moves into the next generation, there are prospects of new discoveries in skeletal biology. This review integrates findings from previous GWAS and provides a roadmap for future directions building on current GWAS successes.

Genetics and the individualized prediction of fracture

Recent genome-wide association studies have identified many genetic variants associated with fracture risk. These genetic variants are common in the general population but have very modest effect sizes. A remaining challenge is to translate these genetic variant discoveries to better predict the risk of fracture based on an individual's genetic profile (ie, individualized risk assessment). Empirical and simulation studies have shown that 1) the utility of a single genetic variant for fracture risk assessment is very limited; but 2) a profile of 50 genetic variants, each with odds ratio ranging from 1.02 to 1.15, can improve the accuracy of fracture prediction and classification beyond that obtained by conventional clinical risk factors. These results are consistent with the view that genetic profiling, when integrated in existing risk assessment models, can inform a more accurate prediction of fracture risk in an individual.

No association between polymorphisms and haplotypes of COL1A1 and COL1A2 genes and osteoporotic fracture in postmenopausal Chinese women

AIM

To study whether genetic polymorphisms of COL1A1 and COL1A2 genes affected the onset of fracture in postmenopausal Chinese women.

METHODS

SNPs in COL1A1 and COL1A2 genes were identified via direct sequencing in 32 unrelated postmenopausal Chinese women. Ten SNPs were genotyped in 1252 postmenopausal Chinese women. The associations were examined using both single-SNP and haplotype tests using logistic regression.

RESULTS

Twenty four (4 novel) and 28 (7 novel) SNPs were identified in COL1A1 and COL1A2 gene, respectively. The distribution frequencies of 2 SNPs in COL1A1 (rs2075554 and rs2586494) and 3 SNPs in COL1A2 (rs42517, rs1801182, and rs42524) were significantly different from those documented for the European Caucasian population. No significant difference was observed between fracture and control groups with respect to allele frequency or genotype distribution in 9 selected SNPs and haplotype. No significant association was found between fragility fracture and each SNP or haplotype. The results remained the same after additional corrections for other risk factors such as weight, height, and bone mineral density.

CONCLUSION

Our results show no association between common genetic variations of COL1A1 and COL1A2 genes and fracture, suggesting the complex genetic background of osteoporotic fractures.