Individualization of osteoporosis risk

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.

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.

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.

Genetics and the individualized prediction of fracture

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

Enhancement of absolute fracture risk prognosis with genetic marker: the collagen I alpha 1 gene

An important objective of genetic research in osteoporosis is to translate genotype data into the prognosis of fracture. The present study sought to develop a prognostic model for predicting osteoporotic fracture by using information from a genetic marker and clinical risk factors. It was designed as a prospective epidemiological study which involved 894 women of Caucasian background aged 60+ years who had been followed for a median of 9 years (from 1989 and 2008, range 0.2-18 years). During the follow-up period, fragility fracture was ascertained by X-ray reports for all women. Bone mineral density (BMD) at the femoral neck was measured by dual-energy X-ray absorptiometry. Genotypes of the Sp1 binding site in the first intron of the collagen I alpha 1 (COLIA1) gene polymorphism were determined by polymerase chain reaction, digestion with BalI restriction enzyme, and agarose gel electrophoresis. The relationship between COL1A1 genotype and fracture was assessed by the Cox proportional hazards model, from which nomograms were developed for individualizing the risk of fracture. The distribution of COL1A1 genotypes was consistent with the Hardy-Weinberg equilibrium law: GG (63.8%), GT (32.6%), and TT (3.6%). During the follow-up period, there were 322 fractures, including 77 hip and 127 vertebral fractures. There was an overrepresentation of the TT genotype in the fracture group (6.2%) compared with the nonfracture group (2.3%). Compared with carriers of GT and GG, women carrying the TT genotype had increased risk of any fracture (relative risk [RR] = 1.91, 95% CI 1.21-3.00), hip fracture (RR = 3.67, 95% CI 1.69-8.00), and vertebral fracture (RR = 3.36, 95% CI 1.81-6.24). The incorporation of COL1A1 genotypes improved the risk reclassification by 2% for any fragility fracture, 4% for hip fracture, and 5% for vertebral fracture, beyond age, BMD, prior fracture, and fall. Three nomograms were constructed for predicting fracture risk in an individual woman based on age, BMD, and COLIA1 genotypes. These data suggest that the COLIA1 Sp1 polymorphism is associated with the risk of fragility fracture in Caucasian women and that the polymorphism could enhance the predictive accuracy of fracture prognosis. The nonograms presented here can be useful for individualizing the short- and intermediate-term prognosis of fracture risk and help identify high-risk individuals for intervention for appropriate management of osteoporosis.

Osteoporosis in developing countries

Osteoporosis poses a huge challenge in developing countries due to demographic transition and aging of the population coupled with limited availability of resources. The exact disease burden is difficult to quantify because of the paucity of data. Ethnicity affects bone density as well as fracture risk. Population-specific normative data for bone density are lacking in large parts of the world. Vitamin D deficiency is common even in sunny countries. The WHO has developed an algorithm for estimation of 10-year fracture risk which may be used even in the absence of bone mineral density.