Limited clinical utility of a genetic risk score for the prediction of fracture risk in elderly subjects

A plasma protein-based risk score to predict hip fractures

As there are effective treatments to reduce hip fractures, identification of patients at high risk of hip fracture is important to inform efficient intervention strategies. To obtain a new tool for hip fracture prediction, we developed a protein-based risk score in the Cardiovascular Health Study using an aptamer-based proteomic platform. The proteomic risk score predicted incident hip fractures and improved hip fracture discrimination in two Trøndelag Health Study validation cohorts using the same aptamer-based platform. When transferred to an antibody-based proteomic platform in a UK Biobank validation cohort, the proteomic risk score was strongly associated with hip fractures (hazard ratio per s.d. increase, 1.64; 95% confidence interval 1.53-1.77). The proteomic risk score, but not available polygenic risk scores for fractures or bone mineral density, improved the C-index beyond the fracture risk assessment tool (FRAX), which integrates information from clinical risk factors (C-index, FRAX 0.735 versus FRAX + proteomic risk score 0.776). The developed proteomic risk score constitutes a new tool for stratifying patients according to hip fracture risk; however, its improvement in hip fracture discrimination is modest and its clinical utility beyond FRAX with information on femoral neck bone mineral density remains to be determined.

Femoral neck width genetic risk score is a novel independent risk factor for hip fractures

Femoral neck width (FNW) derived from DXA scans may provide a useful adjunct to hip fracture prediction. Therefore, we investigated whether FNW is related to hip fracture risk independently of femoral neck bone mineral density (FN-BMD), using a genetic approach. FNW was derived from points automatically placed on the proximal femur using hip DXA scans from 38 150 individuals (mean age 63.8 yr, 48.0% males) in UK Biobank (UKB). Genome-wide association study (GWAS) identified 71 independent genome-wide significant FNW SNPs, comprising genes involved in cartilage differentiation, hedgehog, skeletal development, in contrast to SNPs identified by FN-BMD GWAS which primarily comprised runx1/Wnt signaling genes (MAGMA gene set analyses). FNW and FN-BMD SNPs were used to generate genetic instruments for multivariable Mendelian randomization. Greater genetically determined FNW increased risk of all hip fractures (odds ratio [OR] 1.53; 95% CI, 1.29-1.82 per SD increase) and femoral neck fractures (OR 1.58;1.30-1.92), but not trochanteric or forearm fractures. In contrast, greater genetically determined FN-BMD decreased fracture risk at all 4 sites. FNW and FN-BMD SNPs were also used to generate genetic risk scores (GRSs), which were examined in relation to incident hip fracture in UKB (excluding the FNW GWAS population; n = 338 742, 3222 cases) using a Cox proportional hazards model. FNW GRS was associated with increased risk of all incident hip fractures (HR 1.08;1.05-1.12) and femoral neck fractures (hazard ratio [HR] 1.10;1.06-1.15), but not trochanteric fractures, whereas FN-BMD GRS was associated with reduced risk of all hip fracture types. We conclude that the underlying biology regulating FNW and FN-BMD differs, and that DXA-derived FNW is causally related to hip fractures independently of FN-BMD, adding information beyond FN-BMD for hip fracture prediction. Hence, FNW derived from DXA analyses or a FNW GRS may contribute clinically useful information beyond FN-BMD for hip fracture prediction.

Utilize polygenic risk score to enhance fracture risk estimation and improve the performance of FRAX in patients with osteoporosis

This study examined the use of polygenic risk scores (PGS) in combination with the Fracture Risk Assessment Tool (FRAX) to enhance fragility fractures risk estimation in osteoporosis patients. Analyzing data from over 57,000 participants, PGS improved fracture risk estimation, especially for individuals with intermediate to low risks, allowing personalized preventive strategies.

INTRODUCTION

Osteoporosis and fragility fractures are multifactorial, with contributions from both clinical and genetic determinants. However, whether using polygenic risk scores (PGS) may enhance the risk estimation of osteoporotic fracture in addition to Fracture Risk Assessment Tool (FRAX) remains unknown. This study investigated the collective association of PGS and FRAX with fragility fracture.

METHODS

We conducted a cohort study from the Taiwan Precision Medicine Initiative (TPMI) at Taichung Veterans General Hospital, Taiwan. Genotyping was performed to compute PGS associated with bone mineral density (BMD). Phenome-wide association studies were executed to pinpoint phenotypes correlated with the PGS. Logistic regression analysis was conducted to ascertain factors associated with osteoporotic fractures.

RESULTS

Among all 57,257 TPMI participants, 3744 (904 men and 2840 women, with a mean age of 66.7) individuals had BMD testing, with 540 (14.42%) presenting with fractures. The 3744 individuals who underwent BMD testing were categorized into four quartiles (Q1-Q4) based on PGS; 540 (14.42%) presented with fractures. Individuals with PGS-Q1 exhibited lower BMD, a higher prevalence of major fractures, and elevated FRAX-major and FRAX-hip than those with PGS-Q4. PGS was associated with major fractures after adjusting age, sex, and FRAX scores. Notably, the risk of major fractures (PGS-Q1 vs. Q4) was significantly higher in the subgroups of FRAX-major scores < 10% and 10-20%, but not in participants with a FRAX-major score ≧ 20%.

CONCLUSIONS

Our study highlights the potential of PGS to augment fracture risk estimation in conjunction with FRAX, particularly in individuals with middle to low risks. Incorporating genetic testing could empower physicians to tailor personalized preventive strategies for osteoporosis.

Genetic Prediction of Lifetime Risk of Fracture

CONTEXT

Fragility fracture is a significant public health problem because it is associated with increased mortality. We want to find out whether the risk of fracture can be predicted from the time of birth.

OBJECTIVE

To examine the association between a polygenic risk score (PRS) and lifetime fracture risk.

METHODS

This population-based prospective study involved 3515 community-dwelling individuals aged 60+ years who have been followed for up to 20 years. Femoral neck bone mineral density (BMD) was measured by dual-energy x-ray absorptiometry. A PRS was created by summing the weighted number of risk alleles for each single nucleotide polymorphism using BMD-associated coefficients. Fragility fractures were radiologically ascertained, whereas mortality was ascertained through a state registry. Residual lifetime risk of fracture (RLRF) was estimated by survival analysis.

RESULTS

The mortality-adjusted RLRF for women and men was 36% (95% CI, 34%-39%) and 21% (18%-24%), respectively. Individuals with PRS > 4.24 (median) had a greater risk (1.2-fold in women and 1.1-fold in men) than the population average risk. For hip fracture, the average RLRF was 10% (95% CI, 8%-12%) for women and ∼5% (3%-7%) for men; however, the risk was significantly increased by 1.5-fold and 1.3-fold for women and men with high PRS, respectively.

CONCLUSION

A genetic profiling of BMD-associated genetic variants is associated with the residual lifetime risk of fracture, suggesting the potential for incorporating the polygenic risk score in personalized fracture risk assessment.

BONEcheck: A digital tool for personalized bone health assessment

Objectives

Osteoporotic fracture is a significant public health burden associated with increased mortality risk and substantial healthcare costs. Accurate and early identification of high-risk individuals and mitigation of their risks is a core part of the treatment and prevention of fractures. Here we introduce a digital tool called 'BONEcheck' for personalized assessment of bone health.

Methods

The development of BONEcheck primarily utilized data from the prospective population-based Dubbo Osteoporosis Epidemiology Study and the Danish Nationwide Registry. BONEcheck has 3 modules: input data, risk estimates, and risk context. Input variables include age, gender, prior fracture, fall incidence, bone mineral density (BMD), comorbidities, and genetic variants associated with BMD.

Results

Based on the input variables, BONEcheck estimates the probability of any fragility fracture and hip fracture within 5 years, subsequent fracture risk, skeletal age, and time to reach osteoporosis. The probability of fracture is shown in both numeric and human icon array formats. The risk is also contextualized within the framework of treatment and management options on Australian guidelines, with consideration given to the potential fracture risk reduction and survival benefits. Skeletal age was estimated as the sum of chronological age and years of life lost due to a fracture or exposure to risk factors that elevate mortality risk.

Conclusions

BONEcheck is an innovative tool that empowers doctors and patients to engage in well-informed discussions and make decisions based on the patient's risk profile. Public access to BONEcheck is available via https://bonecheck.org and in Apple Store (iOS) and Google Play (Android).

Validation of a genome-wide polygenic score in improving fracture risk assessment beyond the FRAX tool in the Women's Health Initiative study

BACKGROUND

Previous study has established two polygenic scores (PGSs) related to femoral neck bone mineral density (BMD) (PGS_FNBMDldpred) and total body BMD (PGS_TBBMDldpred) that are associated with fracture risk. However, these findings have not yet been externally validated in an independent cohort.

OBJECTIVES

This study aimed to validate the predictive performance of the two established PGSs and to investigate whether adding PGSs to the Fracture Risk Assessment Tool (FRAX) improves the predictive ability of FRAX in identifying women at high risk of major osteoporotic fracture (MOF) and hip fractures (HF).

METHODS

The study used the Women's Health Initiative (WHI) cohort of 9,000 postmenopausal women of European ancestry. Cox Proportional Hazard Models were used to assess the association between each PGS and MOF/HF risk. Four models were formulated to investigate the effect of adding PGSs to the FRAX risk factors: (1) Base model: FRAX risk factors; (2) Base model + PGS_FNBMDldpred; (3) Base model + PGS_TBBMDldpred; (4) Base model + metaPGS. The reclassification ability of models with PGS was further assessed using the Net Reclassification Improvement (NRI) and the Integrated discrimination improvement (IDI).

RESULTS

The study found that the PGSs were not significantly associated with MOF or HF after adjusting for FRAX risk factors. The FRAX base model showed moderate discrimination of MOF and HF, with a C-index of 0.623 (95% CI, 0.609 to 0.641) and 0.702 (95% CI, 0.609 to 0.718), respectively. Adding PGSs to the base FRAX model did not improve the ability to discriminate MOF or HF. Reclassification analysis showed that compared to the model without PGS, the model with PGS_TBBMDldpred (1.2%, p = 0.04) and metaPGS (1.7%, p = 0.05) improve the reclassification of HF, but not MOF.

CONCLUSIONS

The findings suggested that incorporating genetic information into the FRAX tool has minimal improvement in predicting HF risk for elderly Caucasian women. These results highlight the need for further research to identify other factors that may contribute to fracture risk in elderly Caucasian women.

Ethnic disparities in fracture risk assessment using polygenic scores

Whether the PGS developed using data from European ancestry is predictive of fracture risk for minorities remains unclear. This study demonstrated that PGSs based on common BMD-related genetic variants discovered in the European ancestry cohort are predictive of fracture risk in people of Asian but not African ancestry.

PURPOSE

Large-scale genome-wide association studies (GWAS) on bone mineral density (BMD) have been conducted predominantly in European cohorts. Genetic models based on common variants associated with BMD have been evaluated using almost exclusively European data, which could potentially exacerbate health disparities due to different linkage disequilibrium among different ethnic groups.

METHODS

UK Biobank (UKB) is a large-scale population-based observational study starting in 2006 that recruited 502,617 individuals aged between 40 and 69 years with genotypic and phenotypic data available. Based on the summary statistics of two GWAS studies of femoral neck BMD and total body BMD, we derived four PGSs and assessed the association between each PGS and prevalent/incident fractures within each ethnic group separately using Multivariate logistic regressions and Cox proportional hazard models. All models were adjusted for age, sex, and the first four principal components.

RESULTS

We assessed four PGSs derived from European cohorts. Significant associations were observed between PGSs and fracture in European and Asian cohorts but not in the African cohort. Of all four PGSs, [Formula: see text] performed the best. A standard deviation decreases in [Formula: see text] were associated with an increased hazard ratio (HR) of 1.24 (1.22-1.27), 1.28 (0.83-1.99), and 1.34 (1.10-1.64) in European, African, and Asian ancestry, respectively. A low BMD-related PGS is associated with up to 2.35- and 4.31-fold increased fracture risk in European and Asian populations.

CONCLUSIONS

These results showed that PGSs based on common BMD-related genetic variants discovered in the European ancestry cohort are predictive of fracture risk in people of Asian but not African ancestry.

The clinical utility of the BMD-related comprehensive genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures

The potential of bone mineral density (BMD)-related genome-wide polygenic score (PGS) in identifying individuals with a high risk of fractures remains unclear. This study suggests that an efficient PGS enables the identification of strata with up to a 1.5-fold difference in fracture incidence. Incorporating PGS into clinical diagnosis is anticipated to increase the population-level screening benefits.

PURPOSE

This study sought to construct genome-wide polygenic scores for femoral neck and total body BMD and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures.

METHODS

Genome-wide polygenic scores were developed and validated for femoral neck and total body BMD. We externally tested the PGSs, both by themselves and in combination with available clinical risk factors, in 455,663 European ancestry individuals from the UK Biobank. The predictive accuracy of the developed genome-wide PGS was also compared with previously published restricted PGS employed in fracture risk assessment.

RESULTS

For each unit decrease in PGSs, the genome-wide PGSs were associated with up to 1.17-fold increased fracture risk. Out of four studied PGSs, [Formula: see text] (HR: 1.03; 95%CI 1.01-1.05, p = 0.001) had the weakest and the [Formula: see text] (HR: 1.17; 95%CI 1.15-1.19, p < 0.0001) had the strongest association with an incident fracture. In the reclassification analysis, compared to the FRAX base model, the models with [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] improved the reclassification of fracture by 1.2% (95% CI, 1.0 to 1.3%), 0.2% (95% CI, 0.1 to 0.3%), 1.4% (95% CI, 1.3 to 1.5%), and 2.2% (95% CI, 2.1 to 2.4%), respectively.

CONCLUSIONS

Our findings suggested that an efficient PGS estimate enables the identification of strata with up to a 1.7-fold difference in fracture incidence. Incorporating PGS information into clinical diagnosis is anticipated to increase the benefits of screening programs at the population level.

Genome-wide polygenic risk score for major osteoporotic fractures in postmenopausal women using associated single nucleotide polymorphisms

BACKGROUND

Osteoporosis is highly polygenic and heritable, with heritability ranging from 50 to 80%; most inherited susceptibility is associated with the cumulative effect of many common genetic variants. However, existing genetic risk scores (GRS) only provide a few percent predictive power for osteoporotic fracture.

METHODS

We derived and validated a novel genome-wide polygenic score (GPS) comprised of 103,155 common genetic variants to quantify this susceptibility and tested this GPS prediction ability in an independent dataset (n = 15,776).

RESULTS

Among postmenopausal women, we found a fivefold gradient in the risk of major osteoporotic fracture (MOF) (p < 0.001) and a 15.25-fold increased risk of severe osteoporosis (p < 0.001) across the GPS deciles. Compared with the remainder of the GPS distribution, the top GPS decile was associated with a 3.59-, 2.48-, 1.92-, and 1.58-fold increased risk of any fracture, MOF, hip fracture, and spine fracture, respectively. The top GPS decile also identified nearly twofold more high-risk osteoporotic patients than the top decile of conventional GRS based on 1103 conditionally independent genome-wide significant SNPs. Although the relative risk of severe osteoporosis for postmenopausal women at around 50 is relatively similar, the cumulative incident at 20-year follow-up is significantly different between the top GPS decile (13.7%) and the bottom decile (< 1%). In the subgroup analysis, the GPS transferability in non-Hispanic White is better than in other racial/ethnic groups.

CONCLUSIONS

This new method to quantify inherited susceptibility to osteoporosis and osteoporotic fracture affords new opportunities for clinical prevention and risk assessment.

Associations of bone mass and polygenic risk of osteoporosis with indicators of arterial wall condition

Background: The identification of genetic factors that are simultaneously responsible for the predisposition to the development of cardiovascular diseases (CVD) and osteoporosis (OP) is important for the prevention of both conditions.

Aim: The aim of this study is to evaluate three genetic risk scales (GRS) that previously showed an association with bone mineral density (BMD) and fracture risk, as well as to study the associations of these GRS with vascular wall pathology.

Materials and methods: 250 female outpatients (aged 45 to 69) were enrolled into a cross-sectional study. The intima-media thickness (IMT), the presence and number of atherosclerotic plaques (AP) were studied using duplex scanning. Pulse wave velocity (PWV), augmentation index (AI) were measured by applanation tonometry. Coronary vessels calcium deposits were registered by multispiral computed tomography (MSCT) using the Agatston calcium index (CI). The BMD of the spine, hip neck (HN) and proximal hip (PH) was measured using double energy x-ray absorptiometry. Bone resorption marker type-1 collagen C-terminal telopeptide (CTx) was assessed solid-phase enzyme immunoassay. The genetic study included DNA extraction from whole blood samples. Targeted sequencing was performed on the Nextseq550 sequencer (Illumina, USA). Statistical analysis was carried out using the SAS software package for Windows, version 9.0 (SAS Institute Inc., USA).

Results: The chance of detecting low bone mass increased more than 4 times at values of IMT ≥0.9 mm (OR=4.17; 95%CI [1.2–14.4], p<0.02), 2.4 times in the presence of AP in the carotid arteries (OR=2.45; 95%CI [1.12–4.88], p><0.05), by 6.7 times with an Agatstone CI ≥ 100 units (OR=6.68; 95%CI [1.56–28.7], p><0.001), 1.4 times (OR=1.43; 95%CI [0.56–3.68], p><0.438) with a PWV ≥10 m/s, 1.2 times (OR=1.2; 95%CI [0.601–2.43], p><0.60) with increased AI ≥ 27%. According to multivariate linear regression analysis (adjusted for age, duration of postmenopause, marker of bone resorption CTx), a significant association of all GRS with BMD in all parts of the skeleton was revealed. Both univariate and multivariate regression models adjusted for several covariants (age, total cholesterol, systolic blood pressure) showed a reliable association of GRS62 with the presence of plaques and GRS63 — with coronary artery CI. Conclusion: The results of the study demonstrated the association of polygenic genetic risk of GRS-based OP with BMD and vascular wall status indicators in women in the peri and postmenopausal periods.>< 0.02), 2.4 times in the presence of AP in the carotid arteries (OR=2.45; 95%CI [1.12–4.88], p< 0.05), by 6.7 times with an Agatstone CI ≥ 100 units (OR=6.68; 95%CI [1.56–28.7], p< 0.001), 1.4 times (OR=1.43; 95%CI [0.56–3.68], p< 0.438) with a PWV ≥10 m/s, 1.2 times (OR=1.2; 95%CI [0.601–2.43], p<0.60) with increased AI ≥ 27%. According to multivariate linear regression analysis (adjusted for age, duration of postmenopause, marker of bone resorption CTx), a significant association of all GRS with BMD in all parts of the skeleton was revealed. Both univariate and multivariate regression models adjusted for several covariants (age, total cholesterol, systolic blood pressure) showed a reliable association of GRS62 with the presence of plaques and GRS63 — with coronary artery CI.>< 0.60) with increased AI ≥ 27%. According to multivariate linear regression analysis (adjusted for age, duration of postmenopause, marker of bone resorption CTx), a significant association of all GRS with BMD in all parts of the skeleton was revealed. Both univariate and multivariate regression models adjusted for several covariants (age, total cholesterol, systolic blood pressure) showed a reliable association of GRS62 with the presence of plaques and GRS63 — with coronary artery CI.

Conclusion: The results of the study demonstrated the association of polygenic genetic risk of GRS-based OP with BMD and vascular wall status indicators in women in the peri and postmenopausal periods.

Multiple polygenic scores improve bone mineral density prediction in an independent sample of Caucasian women

PURPOSE OF THE STUDY

To determine if multiple Genetic Risk Scores (GRSs) improve bone mineral density (BMD) prediction over single GRS in an independent sample of Caucasian women.

STUDY DESIGN

Based on summary statistics of four genome-wide association studies related to two osteoporosis-associated traits, namely BMD and heel quantitative ultrasound derived estimated BMD (eBMD), four GRSs were derived for 1205 individuals in the Genome-Wide Scan for Female Osteoporosis Gene Study. The effect of each GRS on BMD variation was assessed using multivariable linear regression, with conventional risk factors adjusted for. Next, the eBMD-related GRS that explained the most variance in BMD was selected to be entered into a multi-score model, along with the BMD-related GRS. Elastic net regularised regression was used to develop the multiscore model, which estimated the joint effect of two GRSs (GRS_BMD and GRS_eBMD) on BMD variation, after being adjusted for conventional risk factors.

RESULTS

With the same clinical risk factors having been adjusted for, the model that included GRS_BMD performed best by explaining 32.53% of the variance in BMD; the single-score model that included GRS_eBMD explained 34.03% of BMD variance. The model that includes both GRS_BMD and GRS_ eBMD, as well as the clinical risk factors, aggregately explained 35.05% in BMD variation. Compared with the single GRS models, the multiscore model explained significantly more variance in BMD.

CONCLUSIONS

The multipolygenic score model explained a considerable amount of BMD variation. Compared with single score models, multipolygenic score model provided significant improvement in explaining BMD variation.

Genetic Diagnostics in Routine Osteological Assessment of Adult Low Bone Mass Disorders

CONTEXT

Many different inherited and acquired conditions can result in premature bone fragility/low bone mass disorders (LBMDs).

OBJECTIVE

We aimed to elucidate the impact of genetic testing on differential diagnosis of adult LBMDs and at defining clinical criteria for predicting monogenic forms.

METHODS

Four clinical centers broadly recruited a cohort of 394 unrelated adult women before menopause and men younger than 55 years with a bone mineral density (BMD) Z-score < -2.0 and/or pathological fractures. After exclusion of secondary causes or unequivocal clinical/biochemical hallmarks of monogenic LBMDs, all participants were genotyped by targeted next-generation sequencing.

RESULTS

In total, 20.8% of the participants carried rare disease-causing variants (DCVs) in genes known to cause osteogenesis imperfecta (COL1A1, COL1A2), hypophosphatasia (ALPL), and early-onset osteoporosis (LRP5, PLS3, and WNT1). In addition, we identified rare DCVs in ENPP1, LMNA, NOTCH2, and ZNF469. Three individuals had autosomal recessive, 75 autosomal dominant, and 4 X-linked disorders. A total of 9.7% of the participants harbored variants of unknown significance. A regression analysis revealed that the likelihood of detecting a DCV correlated with a positive family history of osteoporosis, peripheral fractures (> 2), and a high normal body mass index (BMI). In contrast, mutation frequencies did not correlate with age, prevalent vertebral fractures, BMD, or biochemical parameters. In individuals without monogenic disease-causing rare variants, common variants predisposing for low BMD (eg, in LRP5) were overrepresented.

CONCLUSION

The overlapping spectra of monogenic adult LBMD can be easily disentangled by genetic testing and the proposed clinical criteria can help to maximize the diagnostic yield.

Personalized fracture risk assessment: where are we at?

Introduction: Osteoporotic fracture imposes a significant health care burden globally. Personalized assessment of fracture risk can potentially guide treatment decisions. Over the past decade, a number of risk prediction models, including the Garvan Fracture Risk Calculator (Garvan) and FRAX®, have been developed and implemented in clinical practice. Areas covered: This article reviews recent development and validation results concerning the prognostic performance of the two tools. The main areas of review are the need for personalized fracture risk prediction, purposes of risk prediction, predictive performance in terms of discrimination and calibration, concordance between the Garvan and FRAX tools, genetic profiling for improving predictive performance, and treatment thresholds. In some validation studies, FRAX tended to underestimate fracture by as high as 50%. Studies have shown that the predicted risk from the Garvan tool is highly concordant with clinical decision. Expert opinion: Although there are some discrepancy in fracture risk prediction between Garvan and FRAX, both tools are valid and can aid patients and doctors communicate about risk and make informed decision. The ideal of personalized risk assessment for osteoporosis patients will be realized through the incorporation of genetic profiling into existing fracture risk assessment tools.

The Utility of Genetic Risk Score to Improve Performance of FRAX for Fracture Prediction in US Postmenopausal Women

The ability of the fracture risk assessment tool (FRAX) in discriminating fracture and non-fracture in postmenopausal women remains suboptimal. Adding a genetic profile may improve the performance of FRAX. Three genetic risk scores (GRSs) (GRS_fracture, GRS_BMD, GRS_eBMD) were calculated for each participant in the Women's Health Initiative Study (n = 23,981), based on the summary statistics of three comprehensive osteoporosis-related genome-wide association studies (GWAS). The primary outcomes were incident major osteoporotic fracture (MOF) and hip fracture (HF). The association between each GRS and fracture risk were evaluated in separate Cox Proportional Hazard models, with FRAX clinical risk factors adjusted for. The discrimination ability of each model was assessed using Area Under the Curve (AUC). The predictive improvement attributable to each GRSs was assessed using the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). GRS_BMD and GRS_eBMD were significantly associated with MOF and HF risk, independent of the base FRAX risk factors. Compare to the base FRAX model, the models with GRS_fracture, GRS_BMD, and GRS_eBMD improved the reclassification of MOF by 0.5% (95% CI, 0.2% to 0.9%, p = p < .01), 0.3% (95% CI, 0.1% to 0.6%, p = 0.01), and 2.1% (95% CI, 0.3% to 2.8%, p < .01), respectively. Similar results were also observed when using HF as an outcome. Our study suggested that the addition of genetic profiles provide limited improvements in the reclassification of FRAX for MOF and HF.

Improved prediction of fracture risk leveraging a genome-wide polygenic risk score

BACKGROUND

Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction.

METHODS

We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors.

RESULTS

A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072.

CONCLUSIONS

We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.

Assessing the clinical utility of genetic profiling in fracture risk prediction: a decision curve analysis

Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD.

INTRODUCTION

Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction.

METHODS

The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds.

RESULTS

In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models.

CONCLUSIONS

The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.

Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures separately, with GRS, bone density, and other risk factors as predictors. In model training, the synthetic minority oversampling technique was used to account for low fracture rate, and tenfold cross-validation was employed for hyperparameters optimization. In the testing, the area under curve (AUC) and accuracy were used to assess the model performance. The McNemar test was employed to examine the accuracy difference between models. The results showed that the prediction performance of gradient boosting was the best, with AUC of 0.71 and an accuracy of 0.88, and the GRS ranked as the 7th most important variable in the model. The performance of random forest and neural network were also significantly better than that of logistic regression. This study suggested that improving fracture prediction in older men can be achieved by incorporating genetic profiling and by utilizing the gradient boosting approach. This result should not be extrapolated to women or young individuals.

Post-GWAS Polygenic Risk Score: Utility and Challenges

Over the past decade, through genome‐wide association studies, more than 300 genetic variants have been identified to be associated with either BMD or fracture risk. These genetic variants are common in the general population, but they exert small to modest effects on BMD, suggesting that the utility of any single variant is limited. However, a combination of effect sizes from multiple variants in the form of the polygenic risk score (PRS) can provide a useful indicator of fracture risk beyond that obtained by conventional clinical risk factors. In this perspective, we review the progress of genetics of osteoporosis and approaches for creating PRSs, their uses, and caveats. Recent studies support the idea that the PRS, when integrated into existing fracture prediction models, can help clinicians and patients alike to better assess the fracture risk for an individual, and raise the possibility of precision risk assessment. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study

BACKGROUND

Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program.

METHODS AND FINDINGS

A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk.

CONCLUSIONS

Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.

Association between a literature-based genetic risk score and bone mineral density of African American women in Women Health Initiative Study

Genetic risk of low BMD in African American women remains unclear. Based on SNPs discovered from a predominantly Caucasian sample, genetic profile was summarized and was found to be significantly associated with BMD variation in African American women.

INTRODUCTION

Osteoporosis is largely under-recognized and undertreated in African-American women, the post-fracture morbidity and mortality rates in this racial group is rather high. Since BMD was proved to be highly heritable, based on a comprehensive genome-wide meta-analysis that reported 63 BMD-related single nucleotide polymorphisms (SNPs), we aim to unravel the overall genetic risk for decreased BMD and osteoporosis in African-American women.

METHODS

Genotype data of 842 African American women in a Women's Health Initiative cohort were analyzed. Comprehensive genotype imputation was conducted at the Sanger Imputation Server. Multi-locus genetic risk scores (GRSs) based on 62 BMD-related single-nucleotide polymorphisms (SNPs) were calculated. The association between GRS and BMD was assessed by regression analysis. Longitudinal data was further analyzed using a generalized estimating equation, which helps achieve more efficient and unbiased regression parameters by accounting for the within-subject correlation of responses on dependent variables.

RESULTS

After adjusting for age, body weight, hormone use, and previous fracture, for every unit increase of GRS.FN and GRS.LS, BMD at hip and lumbar spine decreased 0.124 g/cm² and 0.086 g/cm², respectively. Collectively, the model accounted for 34.95% of the femoral neck BMD variation and 25.79% of lumbar spine BMD variation. Notably, GRS.FN and GRS.LS accounted for 2.03% and 2.39% of the total explained variance, respectively. The proportion of BMD variation can be explained by GRSs increasing as participants aged.

CONCLUSIONS

Genetic risk score was significantly associated with lower BMD in the current study, suggesting that SNPs discovered from prior meta-analysis based on primarily Caucasian population can also explain a considerable proportion of BMD variation in African Americans.

BMD-Related Genetic Risk Scores Predict Site-Specific Fractures as Well as Trabecular and Cortical Bone Microstructure

CONTEXT

It is important to identify patients at highest risk of fractures.

OBJECTIVE

To compare the separate and combined performances of bone-related genetic risk scores (GRSs) for prediction of forearm, hip and vertebral fractures separately, as well as of trabecular and cortical bone microstructure parameters separately.

DESIGN, SETTING, AND PARTICIPANTS

Using 1103 single nucleotide polymorphisms (SNPs) independently associated with estimated bone mineral density of the heel (eBMD), we developed a weighted GRS for eBMD and determined its contribution to fracture prediction beyond 2 previously developed GRSs for femur neck BMD (49 SNPs) and lumbar spine BMD (48 SNPs). Associations between these GRSs and forearm (ncases = 1020; ncontrols = 2838), hip (ncases = 1123; ncontrols = 2630) and vertebral (ncases = 288; ncontrols = 1187) fractures were evaluated in 3 Swedish cohorts. Associations between the GRSs and trabecular and cortical bone microstructure parameters (n = 426) were evaluated in the MrOS Sweden cohort.

RESULTS

We found that eBMDGRS was the only significant independent predictor of forearm and vertebral fractures while both FN-BMDGRS and eBMDGRS were significant independent predictors of hip fractures. The eBMDGRS was the major GRS contributing to prediction of trabecular bone microstructure parameters while both FN-BMDGRS and eBMDGRS contributed information for prediction of cortical bone microstructure parameters.

CONCLUSIONS

The eBMDGRS independently predicts forearm and vertebral fractures while both FN-BMDGRS and eBMDGRS contribute independent information for prediction of hip fractures. We propose that eBMDGRS captures unique information about trabecular bone microstructure useful for prediction of forearm and vertebral fractures. These findings may facilitate personalized medicine to predict site-specific fractures as well as cortical and trabecular bone microstructure separately.

Genetic Burden Contributing to Extremely Low or High Bone Mineral Density in a Senior Male Population From the Osteoporotic Fractures in Men Study (MrOS)

Worldwide, one in five men aged over 50 years will experience osteoporosis or a clinical bone fracture, with a greater fracture-related mortality rate than women. However, the genetic etiology of osteoporosis in men is still poorly understood. We aimed to identify the genetic variants and candidate genes associated with extremely low or high BMD for a better understanding of the biology underlying low bone density that may point to potential therapeutic targets for increasing bone mass. Subjects from the Osteoporotic Fractures in Men Study (MrOS) cohort were evaluated by age and BMI-adjusted total hip BMD. Those with BMD values 3 SDs away from the mean were selected and the remaining individuals whose adjusted BMD ranked at the highest or lowest 100 were included. Men with the lowest adjusted BMD (N = 98) and highest adjusted BMD (N = 110) were chosen for exome sequencing. Controls (N = 82) were men of Northern and Western European descent from the US Utah population of the 1000 Genomes Project. Fisher's exact test was performed to compare low- or high-BMD subjects with controls for single-gene associations. Additionally, sets of candidate genes causative of heritable disorders of connective tissue, including osteogenesis imperfecta (OI) and Ehlers-Danlos syndrome (EDS), were grouped for multigene and mutation burden analyses. No single-gene associations with rare variants were found for either the low BMD group (33 genes) or high BMD group (18 genes). In the group of OI genes, we detected a significant threefold increased accumulation of rare variants in low-BMD subjects compared with controls (p = 0.009). Additionally, genes associated with EDS had a twofold increased frequency in low-BMD subjects compared with controls (p = 0.03). These findings reveal a rare variant burden in OI and EDS disease genes at low BMD, which suggests a potential gene-panel approach to screen for multivariant associations in larger cohorts. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Performance of FRAX in Predicting Fractures in US Postmenopausal Women with Varied Race and Genetic Profiles

BACKGROUND

Whether the Fracture Risk Assessment Tool (FRAX) performed differently in estimating the 10-year fracture probability in women of different genetic profiling and race remained unclear.

METHODS

The genomic data in the Women's Health Initiative (WHI) study was analyzed (n = 23,981). The genetic risk score (GRS) was calculated from 14 fracture-associated single nucleotide polymorphisms (SNPs) for each participant. FRAX without bone mineral density (BMD) was used to estimate fracture probability.

RESULTS

FRAX significantly overestimated the risk of major osteoporotic fracture (MOF) in the WHI study. The most significant overestimation was observed in women with low GRS (predicted/observed ratio (POR): 1.61, 95% CI: 1.45-1.79) specifically Asian women (POR: 3.5, 95% CI 2.48-4.81) and in African American women (POR: 2.59, 95% CI: 2.33-2.87). Compared to the low GRS group, the 10-year probability of MOF adjusted for the FRAX score was 21% and 30% higher in the median GRS group and high GRS group, respectively. Asian, African American, and Hispanic women respectively had a 78%, 76%, and 56% lower hazard than Caucasian women after the FRAX score was adjusted. The results were similar for hip fractures.

CONCLUSIONS

Our study suggested the FRAX performance varies significantly by both genetic profile and race in postmenopausal women.

A flexible and parallelizable approach to genome-wide polygenic risk scores

The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic risk scores from meta-GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in Step 1, followed by, uniquely, long-range LD in Step 2. Our algorithm is highly parallelizable since block-wise analyses in Step 1 can be distributed across a high-performance computing cluster, and flexible, since sparsity and heritability are estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models. We compared our method to two alternative approaches: LDPred and lassosum using all seven traits in the Welcome Trust Case Control Consortium as well as meta-GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, for which there are multiple heterogeneous signals in regions of both short- and long-range LD. With sufficient compute resources, our method also allows the fastest runtimes.

Statistical learning approaches in the genetic epidemiology of complex diseases

In this paper, we give an overview of methodological issues related to the use of statistical learning approaches when analyzing high-dimensional genetic data. The focus is set on regression models and machine learning algorithms taking genetic variables as input and returning a classification or a prediction for the target variable of interest; for example, the present or future disease status, or the future course of a disease. After briefly explaining the basic motivation and principle of these methods, we review different procedures that can be used to evaluate the accuracy of the obtained models and discuss common flaws that may lead to over-optimistic conclusions with respect to their prediction performance and usefulness.

Prediction of changes in bone mineral density in the elderly: contribution of "osteogenomic profile"

The contribution of genetic variants to longitudinal bone loss has not been well documented. We constructed an "osteogenomic profile" based on 62 BMD-associated genetic variants and showed that the profile was significantly associated with bone loss, independently from baseline BMD and age. The osteogenomic profile can help predict bone loss in an individual.

INTRODUCTION

The rate of longitudinal bone loss (ΔBMD) is a risk factor for fracture. The variation in ΔBMD is partly determined by genetic factors. This study sought to define the association between an osteogenomic profile and ΔBMD.

METHODS

The osteogenomic profile was created from 62 BMD-associated SNPs from genome-wide association studies (GWAS) that were genotyped in 1384 elderly men and women aged 60+ years. Weighted genetic risk scores (GRS) were constructed for each individual by summing the products of the number of risk alleles and the sex-specific regression coefficients [associated with BMD from GWAS]. ΔBMD, expressed as annual percent change-in-BMD, was determined by linear regression analysis for each individual who had had at least two femoral neck BMD measurements.

RESULTS

The mean ΔBMD was - 0.65% (SD 1.64%) for women and - 0.57% (SD 1.40%) for men, and this difference was not statistically significant (P = 0.32). In women, each unit increase in GRS was associated with 0.21% (SE 0.10) higher ΔBMD at the femoral neck (P = 0.036), and this association was independent of baseline BMD and age. In logistic regression analysis, each unit increase of GRS was associated with 41% odds (95%CI: 1.07-1.87) of rapid bone loss (ΔBMD ≤ - 1.2%/year; mean of rapid loss group = - 2.2%/year). There was no statistically significant association between ΔBMD and GRS in men.

CONCLUSIONS

We conclude that the osteogenomic profile constructed from BMD-associated genetic variants is modestly associated with long-term changes in femoral neck BMD in women, but not in men.

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.

Predictive accuracy of combined genetic and environmental risk scores

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.

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.

Gene-Hormone Therapy Interaction and Fracture Risk in Postmenopausal Women

CONTEXT

Evidence supports a protective effect of menopausal hormone therapy (HT) on bone. However, whether genetic susceptibility modifies the association of HT and fracture risk is not sufficiently explored.

OBJECTIVE

The objective was to test an interaction between genetic susceptibility and HT on fracture risk.

DESIGN

We constructed two weighted genetic risk scores (GRSs) based on 16 fracture-associated variants (Fx-GRSs) and 50 bone mineral density variants (BMD-GRSs). We used Cox regression to estimate the main effects of GRSs and their interactions with HT on fracture risk. We estimated the relative excess risk due to interaction (RERI) as a measure of additive interaction. We also used the case-only approach to test for a multiplicative interaction.

SETTING

Forty US clinical centers.

PARTICIPANTS

A total of 9922 genotyped white postmenopausal women (age, 50 to 79) from the Women's Health Initiative HT randomized trials.

MAIN OUTCOME MEASURES

Adjudicated fracture incidence.

RESULTS

Both GRSs were associated with fracture risk per 1-unit increment in GRS (hazard ratio, 1.04 [95% confidence interval, 1.02 to 1.06] for Fx-GRS and hazard ratio, 1.03 [95% confidence interval,1.02-1.04] for BMD-GRS). We found no evidence for multiplicative interaction for either of the GRS. However, we observed a substantial additive interaction, where the highest quartile of both GRSs and randomization to placebo have excess fracture risk: Fx-GRS P for RERI = 0.047, BMD-GRS P for RERI = 0.046.

CONCLUSIONS

These results suggest that HT reduces fracture risk in postmenopausal women, especially in those at highest genetic risk of fracture and low BMD.

The influence of genetic susceptibility and calcium plus vitamin D supplementation on fracture risk

Background: Fracture is a complex trait, affected by both genetic and environmental factors. A meta-analysis of genome-wide association studies (GWASs) identified multiple bone mineral density (BMD) and fracture-associated loci.Objective: We conducted a study to evaluate whether fracture genetic risk score (Fx-GRS) and bone mineral density genetic risk score (BMD-GRS) modify the association between the intake of calcium with vitamin D (CaD) and fracture risk.Design: Data from 5823 white postmenopausal women from the Women's Health Initiative CaD randomized trial were included. Participants received 1000 mg elemental Ca with 400 IU vitamin D3/d or placebo (median follow-up: 6.5 y). Total fracture was defined as first fracture of any type. We computed the Fx-GRS with 16 fracture- and BMD-associated variants, and the BMD-GRS with 50 BMD-associated variants. We used Cox regression and a case-only approach to test for multiplicative interaction. Additive interaction was assessed with the relative excess risk due to interaction (RERI). We analyzed genetic risk score as a continuous variable and a categorical variable based on quartile (quartile 1, quartiles 2-3, and quartile 4).Results: We observed no interaction between the Fx-GRS and CaD on fracture risk; however, we observed a significant multiplicative interaction between the BMD-GRS and CaD assignment (P-interaction = 0.01). In addition, there was a significant negative additive interaction between placebo assignment and higher BMD-GRS: quartiles 2-3, PRERI = 0.03; quartile 4, PRERI = 0.03. In a stratified analysis, the protective effect of CaD on fracture risk was observed in women in the lowest BMD-GRS quartile (HR: 0.60, 95% CI: 0.44, 0.81) but not in women with a higher BMD-GRS.Conclusions: We observed significant effects of CaD intake on fracture risk only in women with the lowest genetic predisposition to low BMD. Future large-scale studies with functional characterization of GWAS findings are warranted to assess the utility of genetic risk score in analysis of risks and benefits of CaD for bone.

Towards human exploration of space: the THESEUS review series on muscle and bone research priorities

Without effective countermeasures, the musculoskeletal system is altered by the microgravity environment of long-duration spaceflight, resulting in atrophy of bone and muscle tissue, as well as in deficits in the function of cartilage, tendons, and vertebral disks. While inflight countermeasures implemented on the International Space Station have evidenced reduction of bone and muscle loss on low-Earth orbit missions of several months in length, important knowledge gaps must be addressed in order to develop effective strategies for managing human musculoskeletal health on exploration class missions well beyond Earth orbit. Analog environments, such as bed rest and/or isolation environments, may be employed in conjunction with large sample sizes to understand sex differences in countermeasure effectiveness, as well as interaction of exercise with pharmacologic, nutritional, immune system, sleep and psychological countermeasures. Studies of musculoskeletal biomechanics, involving both human subject and computer simulation studies, are essential to developing strategies to avoid bone fractures or other injuries to connective tissue during exercise and extravehicular activities. Animal models may be employed to understand effects of the space environment that cannot be modeled using human analog studies. These include studies of radiation effects on bone and muscle, unraveling the effects of genetics on bone and muscle loss, and characterizing the process of fracture healing in the mechanically unloaded and immuno-compromised spaceflight environment. In addition to setting the stage for evidence-based management of musculoskeletal health in long-duration space missions, the body of knowledge acquired in the process of addressing this array of scientific problems will lend insight into the understanding of terrestrial health conditions such as age-related osteoporosis and sarcopenia.

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.

The genetics of bone mass and susceptibility to bone diseases

Osteoporosis is characterized by low bone mass and an increased risk of fracture. Genetic factors, environmental factors and gene-environment interactions all contribute to a person's lifetime risk of developing an osteoporotic fracture. This Review summarizes key advances in understanding of the genetics of bone traits and their role in osteoporosis. Candidate-gene approaches dominated this field 20 years ago, but clinical and preclinical genetic studies published in the past 5 years generally utilize more-sophisticated and better-powered genome-wide association studies (GWAS). High-throughput DNA sequencing, large genomic databases and improved methods of data analysis have greatly accelerated the gene-discovery process. Linkage analyses of single-gene traits that segregate in families with extreme phenotypes have led to the elucidation of critical pathways controlling bone mass. For example, components of the Wnt-β-catenin signalling pathway have been validated (in both GWAS and functional studies) as contributing to various bone phenotypes. These notable advances in gene discovery suggest that the next decade will witness cataloguing of the hundreds of genes that influence bone mass and osteoporosis, which in turn will provide a roadmap for the development of new drugs that target diseases of low bone mass, including osteoporosis.

Osteoporosis in men: findings from the Osteoporotic Fractures in Men Study (MrOS)

The lifespan of men is increasing and this is associated with an increased prevalence of osteoporosis in men. Osteoporosis increases the risk of bone fracture. Fractures are associated with increased disability and mortality, and public health problems. We review here the study of osteoporosis in men as obtained from a longitudinal cohort of community-based older men, the Osteoporotic Fractures in Men Study (MrOS).

Choosing the tool for osteoporosis risk prediction

PURPOSE OF REVIEW

Predicting fracture risk is a major challenge because it allows the prevention of major osteoporotic fracture in high-risk populations. With the aging of the population, this matter will become of even greater importance. In recent years, novel clinical, biochemical, and imaging tools have been developed to improve the assessment of fracture risk.

RECENT FINDINGS

The present review summarizes novel clinical strategies, Dual energy X-ray absorptiometry (DXA)-derived tools, imaging techniques, and biochemical markers that have been developed recently to improve fracture risk prediction.

SUMMARY

DXA and clinical fracture risk prediction tools are preferential markers of fracture risk. Clinical fracture risk alone might be used if DXA facilities are unavailable. The fracture risk assessment tool may be used in osteoporosis consultation in many countries. Other tools may be used soon after more studies are performed, particularly trabecular bone score, quantitative ultrasound, bone turnover markers. Specific factors for example falls, hip axis length, vertebral fracture assessment could be used in individual patients. This may significantly improve the clinical decision-making.