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

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tianyuan Lu ◽  
Vincenzo Forgetta ◽  
Julyan Keller-Baruch ◽  
Maria Nethander ◽  
Derrick Bennett ◽  
...  

Abstract 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.

2010 ◽  
Vol 25 (5) ◽  
pp. 1002-1009 ◽  
Author(s):  
Florence A Trémollieres ◽  
Jean-Michel Pouillès ◽  
Nicolas Drewniak ◽  
Jacques Laparra ◽  
Claude A Ribot ◽  
...  

Radiology ◽  
2008 ◽  
Vol 248 (1) ◽  
pp. 179-184 ◽  
Author(s):  
Idris Guessous ◽  
Jacques Cornuz ◽  
Christiane Ruffieux ◽  
Peter Burckhardt ◽  
Marc-Antoine Krieg

2011 ◽  
Vol 26 (8) ◽  
pp. 1774-1782 ◽  
Author(s):  
Teresa A Hillier ◽  
Jane A Cauley ◽  
Joanne H Rizzo ◽  
Kathryn L Pedula ◽  
Kristine E Ensrud ◽  
...  

2021 ◽  
Author(s):  
Evangelos K Oikonomou ◽  
Alexios S Antonopoulos ◽  
David Schottlander ◽  
Mohammad Marwan ◽  
Chris Mathers ◽  
...  

Abstract Aims Coronary CT angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular Fat Attenuation Index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardised FAI mapping together with clinical risk factors and plaque metrics to provide individualised cardiovascular risk prediction. Methods and Results The study included 3912 consecutive patients undergoing CCTA as part of clinical care in the United States (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardised interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model (Δ[C-statistic] of 0.085, P = 0.01 in the US Cohort and 0.149, P < 0.001 in the European cohort) and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk. Conclusion CaRi-Heart® reliably improves cardiovascular risk prediction by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a screening tool among patients referred for CCTA. Translational Perspective Mapping of perivascular Fat Attenuation Index (FAI) on coronary computed tomography angiography (CCTA) enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardised FAI mapping together with clinical risk factors and plaque metrics to provide age-standardised reference maps and individualised cardiovascular risk prediction. This integration advances the prognostic value of CCTA and paves the way for its use as a screening tool among patients referred for CCTA.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Li-Na Liao ◽  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
...  

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


2020 ◽  
Vol 52 (12) ◽  
pp. 1303-1313
Author(s):  
Mark K. Bakker ◽  
◽  
Rick A. A. van der Spek ◽  
Wouter van Rheenen ◽  
Sandrine Morel ◽  
...  

2020 ◽  
Vol 28 (2) ◽  
pp. 230949902091727
Author(s):  
Gang Xu ◽  
Norio Yamamoto ◽  
Katsuhiro Hayashi ◽  
Akihiko Takeuchi ◽  
Shinji Miwa ◽  
...  

Introduction: The web version of Fracture Risk Assessment (FRAX) tool is widely used in many countries to predict the 10-year probability of major osteoporotic fracture (MF) and hip fracture (HF) rate. However, other FRAX tools, calculator older version (first generation), calculator new version (second generation), and application of mobile software had also been used in Japan. Purpose: The aim of this study is to investigate the consistency of results obtained from the four predicting tools for MF and HF rate in both male and female groups. Methods: The data were extracted from 2016 medical examination report of Japanese Ministry of Health of Labor and Welfare. The MF and HF rates were calculated from 40 to 90 years old under different risk factors using four FRAX tools while the consistency of predicting value was evaluated. Results: The predicted MF or HF rates were extremely similar among calculator new version, mobile software, and website version in each risk factors. On other hand, for calculator older version, the predicted MF or HF rates are a little higher than other versions. The significant difference is only present in patients aged 75 and above, and this exceeds the FRAX threshold older than 75 years old by Japanese Society for Bone and Mineral Research. Conclusions: The application of four FRAX tools generated consistent results in predicting the 10-year probability of major osteoporotic fracture and HF for clinical practice, which provides an effective evidence for clinical application.


2009 ◽  
Vol 5 (3) ◽  
pp. 325-333 ◽  
Author(s):  
Lubna Pal

Health burden related to osteoporotic fractures in an aging female population far exceeds that imposed by other chronic disorders such as cardiovascular disease and breast cancer. Bone mineral density assessment and clinical risk factors provide independent insights into fracture risk in individuals. A finite list of clinical risk factors are identified as prognostic of fracture risk, namely among aging women, including low body mass, compromised reproductive physiology (e.g., prolonged periods of amenorrhea and early menopause), parental and personal histories of fracture, and alcohol and tobacco use. Pelvic organ prolapse is a common gynecologic entity and a contributor to age-related morbidities. The purpose of this review is to communicate data identifying pelvic organ prolapse as another clinical risk factor for fracture risk in postmenopausal women and to increase the caregiver's vigilance in anticipating and instituting preventive care strategies to a population (i.e., postmenopausal women with clinically appreciable pelvic organ prolapse) that may be at an enhanced lifetime risk for skeletal fractures.


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