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

Race-specific FRAX models are evidence-based and support equitable care: a response to the ASBMR Task Force report on Clinical Algorithms for Fracture Risk

Task Force on 'Clinical Algorithms for Fracture Risk' commissioned by the American Society for Bone and Mineral Research (ASBMR) Professional Practice Committee has recommended that FRAX® models in the US do not include adjustment for race and ethnicity. This position paper finds that an agnostic model would unfairly discriminate against the Black, Asian and Hispanic communities and recommends the retention of ethnic and race-specific FRAX models for the US, preferably with updated data on fracture and death hazards. In contrast, the use of intervention thresholds based on a fixed bone mineral density unfairly discriminates against the Black, Asian and Hispanic communities in the US. This position of the Working Group on Epidemiology and Quality of Life of the International Osteoporosis Foundation (IOF) is endorsed both by the IOF and the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO).

Integration of Spinal Musculoskeletal System Parameters for Predicting OVCF in the Elderly: A Comprehensive Predictive Model

STUDY DESIGN

Systematic literature review.

OBJECTIVES

To develop a predictive model for osteoporotic vertebral compression fractures (OVCF) in the elderly, utilizing current tools that are sensitive to bone and paraspinal muscle changes.

METHODS

A retrospective analysis of data from 260 patients from October 2020 to December 2022, to form the Model population. This group was split into Training and Testing sets. The Training set aided in creating a nomogram through binary logistic regression. From January 2023 to January 2024, we prospectively collected data from 106 patients to constitute the Validation population. The model's performance was evaluated using concordance index (C-index), calibration curves, and decision curve analysis (DCA) for both internal and external validation.

RESULTS

The study included 366 patients. The Training and Testing sets were used for nomogram construction and internal validation, while the prospectively collected data was for external validation. Binary logistic regression identified nine independent OVCF risk factors: age, bone mineral density (BMD), quantitative computed tomography (QCT), vertebral bone quality (VBQ), relative functional cross-sectional area of psoas muscles (rFCSAPS), gross and functional muscle fat infiltration of multifidus and psoas muscles (GMFIES+MF and FMFIES+MF), FMFIPS, and mean muscle ratio. The nomogram showed an area under the curve (AUC) of 0.91 for the C-index, with internal and external validation AUCs of 0.90 and 0.92. Calibration curves and DCA indicated a good model fit.

CONCLUSIONS

This study identified nine factors as independent predictors of OVCF in the elderly. A nomogram including these factors was developed, proving effective for OVCF prediction.

Enhanced fracture risk prediction: a novel multi-trait genetic approach integrating polygenic scores of fracture-related traits

The novel metaPGS, integrating multiple fracture-related genetic traits, surpasses traditional polygenic scores in predicting fracture risk. Demonstrating a robust association with incident fractures, this metaPGS offers significant potential for enhancing clinical fracture risk assessment and tailoring prevention strategies.

INTRODUCTION

Current polygenic scores (PGS) have limited predictive power for fracture risk. To improve genetic prediction, we developed and evaluated a novel metaPGS combining genetic information from multiple fracture-related traits.

METHODS

We derived individual PGS from genome-wide association studies of 16 fracture-related traits and employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures. An optimal metaPGS was constructed by combining 11 significant individual PGSs selected by the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The discrimination ability of metaPGS was assessed using the concordance index. Reclassification was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

The metaPGS had a significant association with incident fractures (HR 1.21, 95% CI 1.18-1.25 per standard deviation of metaPGS), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. Models with PGS_FNBMD, PGS_TBBMD, and metaPGS had slightly higher but statistically non-significant c-index than the base model (0.640, 0.644, 0.644 vs. 0.638). However, the reclassification analysis showed that compared to the base model, the model with metaPGS improves the reclassification of fracture.

CONCLUSIONS

The metaPGS is a promising approach for stratifying fracture risk in the European population, improving fracture risk prediction by combining genetic information from multiple fracture-related traits.

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

OBJECTIVES

Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.

METHODS

Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.

RESULTS

Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.

CONCLUSIONS

ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.

PROSPERO REGISTRATION NUMBER

CRD42022346896.

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.

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.

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.

Screening for the primary prevention of fragility fractures among adults aged 40 years and older in primary care: systematic reviews of the effects and acceptability of screening and treatment, and the accuracy of risk prediction tools

BACKGROUND

To inform recommendations by the Canadian Task Force on Preventive Health Care, we reviewed evidence on the benefits, harms, and acceptability of screening and treatment, and on the accuracy of risk prediction tools for the primary prevention of fragility fractures among adults aged 40 years and older in primary care.

METHODS

For screening effectiveness, accuracy of risk prediction tools, and treatment benefits, our search methods involved integrating studies published up to 2016 from an existing systematic review. Then, to locate more recent studies and any evidence relating to acceptability and treatment harms, we searched online databases (2016 to April 4, 2022 [screening] or to June 1, 2021 [predictive accuracy]; 1995 to June 1, 2021, for acceptability; 2016 to March 2, 2020, for treatment benefits; 2015 to June 24, 2020, for treatment harms), trial registries and gray literature, and hand-searched reviews, guidelines, and the included studies. Two reviewers selected studies, extracted results, and appraised risk of bias, with disagreements resolved by consensus or a third reviewer. The overview of reviews on treatment harms relied on one reviewer, with verification of data by another reviewer to correct errors and omissions. When appropriate, study results were pooled using random effects meta-analysis; otherwise, findings were described narratively. Evidence certainty was rated according to the GRADE approach.

RESULTS

We included 4 randomized controlled trials (RCTs) and 1 controlled clinical trial (CCT) for the benefits and harms of screening, 1 RCT for comparative benefits and harms of different screening strategies, 32 validation cohort studies for the calibration of risk prediction tools (26 of these reporting on the Fracture Risk Assessment Tool without [i.e., clinical FRAX], or with the inclusion of bone mineral density (BMD) results [i.e., FRAX + BMD]), 27 RCTs for the benefits of treatment, 10 systematic reviews for the harms of treatment, and 12 studies for the acceptability of screening or initiating treatment. In females aged 65 years and older who are willing to independently complete a mailed fracture risk questionnaire (referred to as "selected population"), 2-step screening using a risk assessment tool with or without measurement of BMD probably (moderate certainty) reduces the risk of hip fractures (3 RCTs and 1 CCT, n = 43,736, absolute risk reduction [ARD] = 6.2 fewer in 1000, 95% CI 9.0-2.8 fewer, number needed to screen [NNS] = 161) and clinical fragility fractures (3 RCTs, n = 42,009, ARD = 5.9 fewer in 1000, 95% CI 10.9-0.8 fewer, NNS = 169). It probably does not reduce all-cause mortality (2 RCTs and 1 CCT, n = 26,511, ARD = no difference in 1000, 95% CI 7.1 fewer to 5.3 more) and may (low certainty) not affect health-related quality of life. Benefits for fracture outcomes were not replicated in an offer-to-screen population where the rate of response to mailed screening questionnaires was low. For females aged 68-80 years, population screening may not reduce the risk of hip fractures (1 RCT, n = 34,229, ARD = 0.3 fewer in 1000, 95% CI 4.2 fewer to 3.9 more) or clinical fragility fractures (1 RCT, n = 34,229, ARD = 1.0 fewer in 1000, 95% CI 8.0 fewer to 6.0 more) over 5 years of follow-up. The evidence for serious adverse events among all patients and for all outcomes among males and younger females (<65 years) is very uncertain. We defined overdiagnosis as the identification of high risk in individuals who, if not screened, would never have known that they were at risk and would never have experienced a fragility fracture. This was not directly reported in any of the trials. Estimates using data available in the trials suggest that among "selected" females offered screening, 12% of those meeting age-specific treatment thresholds based on clinical FRAX 10-year hip fracture risk, and 19% of those meeting thresholds based on clinical FRAX 10-year major osteoporotic fracture risk, may be overdiagnosed as being at high risk of fracture. Of those identified as being at high clinical FRAX 10-year hip fracture risk and who were referred for BMD assessment, 24% may be overdiagnosed. One RCT (n = 9268) provided evidence comparing 1-step to 2-step screening among postmenopausal females, but the evidence from this trial was very uncertain. For the calibration of risk prediction tools, evidence from three Canadian studies (n = 67,611) without serious risk of bias concerns indicates that clinical FRAX-Canada may be well calibrated for the 10-year prediction of hip fractures (observed-to-expected fracture ratio [O:E] = 1.13, 95% CI 0.74-1.72, I2 = 89.2%), and is probably well calibrated for the 10-year prediction of clinical fragility fractures (O:E = 1.10, 95% CI 1.01-1.20, I2 = 50.4%), both leading to some underestimation of the observed risk. Data from these same studies (n = 61,156) showed that FRAX-Canada with BMD may perform poorly to estimate 10-year hip fracture risk (O:E = 1.31, 95% CI 0.91-2.13, I2 = 92.7%), but is probably well calibrated for the 10-year prediction of clinical fragility fractures, with some underestimation of the observed risk (O:E 1.16, 95% CI 1.12-1.20, I2 = 0%). The Canadian Association of Radiologists and Osteoporosis Canada Risk Assessment (CAROC) tool may be well calibrated to predict a category of risk for 10-year clinical fractures (low, moderate, or high risk; 1 study, n = 34,060). The evidence for most other tools was limited, or in the case of FRAX tools calibrated for countries other than Canada, very uncertain due to serious risk of bias concerns and large inconsistency in findings across studies. Postmenopausal females in a primary prevention population defined as <50% prevalence of prior fragility fracture (median 16.9%, range 0 to 48% when reported in the trials) and at risk of fragility fracture, treatment with bisphosphonates as a class (median 2 years, range 1-6 years) probably reduces the risk of clinical fragility fractures (19 RCTs, n = 22,482, ARD = 11.1 fewer in 1000, 95% CI 15.0-6.6 fewer, [number needed to treat for an additional beneficial outcome] NNT = 90), and may reduce the risk of hip fractures (14 RCTs, n = 21,038, ARD = 2.9 fewer in 1000, 95% CI 4.6-0.9 fewer, NNT = 345) and clinical vertebral fractures (11 RCTs, n = 8921, ARD = 10.0 fewer in 1000, 95% CI 14.0-3.9 fewer, NNT = 100); it may not reduce all-cause mortality. There is low certainty evidence of little-to-no reduction in hip fractures with any individual bisphosphonate, but all provided evidence of decreased risk of clinical fragility fractures (moderate certainty for alendronate [NNT=68] and zoledronic acid [NNT=50], low certainty for risedronate [NNT=128]) among postmenopausal females. Evidence for an impact on risk of clinical vertebral fractures is very uncertain for alendronate and risedronate; zoledronic acid may reduce the risk of this outcome (4 RCTs, n = 2367, ARD = 18.7 fewer in 1000, 95% CI 25.6-6.6 fewer, NNT = 54) for postmenopausal females. Denosumab probably reduces the risk of clinical fragility fractures (6 RCTs, n = 9473, ARD = 9.1 fewer in 1000, 95% CI 12.1-5.6 fewer, NNT = 110) and clinical vertebral fractures (4 RCTs, n = 8639, ARD = 16.0 fewer in 1000, 95% CI 18.6-12.1 fewer, NNT=62), but may make little-to-no difference in the risk of hip fractures among postmenopausal females. Denosumab probably makes little-to-no difference in the risk of all-cause mortality or health-related quality of life among postmenopausal females. Evidence in males is limited to two trials (1 zoledronic acid, 1 denosumab); in this population, zoledronic acid may make little-to-no difference in the risk of hip or clinical fragility fractures, and evidence for all-cause mortality is very uncertain. The evidence for treatment with denosumab in males is very uncertain for all fracture outcomes (hip, clinical fragility, clinical vertebral) and all-cause mortality. There is moderate certainty evidence that treatment causes a small number of patients to experience a non-serious adverse event, notably non-serious gastrointestinal events (e.g., abdominal pain, reflux) with alendronate (50 RCTs, n = 22,549, ARD = 16.3 more in 1000, 95% CI 2.4-31.3 more, [number needed to treat for an additional harmful outcome] NNH = 61) but not with risedronate; influenza-like symptoms with zoledronic acid (5 RCTs, n = 10,695, ARD = 142.5 more in 1000, 95% CI 105.5-188.5 more, NNH = 7); and non-serious gastrointestinal adverse events (3 RCTs, n = 8454, ARD = 64.5 more in 1000, 95% CI 26.4-13.3 more, NNH = 16), dermatologic adverse events (3 RCTs, n = 8454, ARD = 15.6 more in 1000, 95% CI 7.6-27.0 more, NNH = 64), and infections (any severity; 4 RCTs, n = 8691, ARD = 1.8 more in 1000, 95% CI 0.1-4.0 more, NNH = 556) with denosumab. For serious adverse events overall and specific to stroke and myocardial infarction, treatment with bisphosphonates probably makes little-to-no difference; evidence for other specific serious harms was less certain or not available. There was low certainty evidence for an increased risk for the rare occurrence of atypical femoral fractures (0.06 to 0.08 more in 1000) and osteonecrosis of the jaw (0.22 more in 1000) with bisphosphonates (most evidence for alendronate). The evidence for these rare outcomes and for rebound fractures with denosumab was very uncertain. Younger (lower risk) females have high willingness to be screened. A minority of postmenopausal females at increased risk for fracture may accept treatment. Further, there is large heterogeneity in the level of risk at which patients may be accepting of initiating treatment, and treatment effects appear to be overestimated.

CONCLUSION

An offer of 2-step screening with risk assessment and BMD measurement to selected postmenopausal females with low prevalence of prior fracture probably results in a small reduction in the risk of clinical fragility fracture and hip fracture compared to no screening. These findings were most applicable to the use of clinical FRAX for risk assessment and were not replicated in the offer-to-screen population where the rate of response to mailed screening questionnaires was low. Limited direct evidence on harms of screening were available; using study data to provide estimates, there may be a moderate degree of overdiagnosis of high risk for fracture to consider. The evidence for younger females and males is very limited. The benefits of screening and treatment need to be weighed against the potential for harm; patient views on the acceptability of treatment are highly variable.

SYSTEMATIC REVIEW REGISTRATION

International Prospective Register of Systematic Reviews (PROSPERO): CRD42019123767.

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.

Risk assessment tools for osteoporosis and fractures in 2022

Osteoporosis is one of the frequently encountered non-communicable diseases in the world today. Several hundred million people have osteoporosis, with many more at risk. The clinical feature is a fragility fracture (FF), which results in major reductions in the quality and quantity of life, coupled with a huge financial burden. In recognition of the growing importance, the World Health Organisation established a working group 30 years ago tasked with providing a comprehensive report to understand and assess the risk of osteoporosis in postmenopausal women. Dual-energy X-ray absorptiometry (DXA) is the most widely endorsed technology for assessing the risk of fracture or diagnosing osteoporosis before a fracture occurs, but others are available. In clinical practice, important distinctions are essential to optimise the use of risk assessments. Traditional tools lack specificity and were designed for populations to identify groups at higher risk using a 'one-size-fits-all' approach. Much has changed, though the purpose of risk assessment tools remains the same. In 2022, many tools are available to aid the identification of those most at risk, either likely to have osteoporosis or suffer the clinical consequence. Modern technology, enhanced imaging, proteomics, machine learning, artificial intelligence, and big data science will greatly advance a more personalised risk assessment into the future. Clinicians today need to understand not only which tool is most effective and efficient for use in their practice, but also which tool to use for which patient and for what purpose. A greater understanding of the process of risk assessment, deciding who should be screened, and how to assess fracture risk and prognosis in older men and women more comprehensively will greatly reduce the burden of osteoporosis for patients, society, and healthcare systems worldwide. In this paper, we review the current status of risk assessment, screening and best practice for osteoporosis, summarise areas of uncertainty, and make some suggestions for future developments, including a more personalised approach for individuals.

Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm

BACKGRUOUND

Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data.

METHODS

This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models.

RESULTS

Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women.

CONCLUSION

DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.

Biobank data for studying the genetic architecture of osteoporosis and developing genetic risk scores

Osteoporosis is a chronic systemic disease of the skeleton, characterized by a decrease in bone mass and an impairment of bone microarchitecture, which can lead to a decrease in bone strength and an increase in the risk of minor trauma fractures. Osteoporosis is diagnosed on the basis of bone mineral density (BMD). BMD is characterized by high heritability that ranges according to various sources from 50 to 85%. As in the case of other complex traits, the most common approach to searching for genetic variants that affect BMD is a genome-wide association study. The lower effect size or frequency of a variant is, the larger the sample size is required to achieve statistically significant data on associations. Therefore, the studies involving hundreds of thousands of participants based on biobank data can identify the largest number of variants associated with BMD. In addition, biobank data are used in the development of genetic risk scores for osteoporosis that can be used both in combination with existing prognosis algorithms and independently of them. The aim of this review was to present the most significant studies of osteoporosis genetics, including those based on biobank data and genome-wide association studies, as well as studies on the genetic risk scores and the contribution of rare variants.