Users Question & Answer:
BONEcheck was developed using methods and algorithms described in published papers (see references). These papers were based on the Dubbo Osteoporosis Epidemiology Study, Study of Osteoporotic Fractures (SOF), The Osteoporotic Fractures in Men (MrOS) Study, and Vietnam Osteoporosis Study. These studies have followed more than 20000 men and women who were at least 50 years old at the study entry. In addition to having their bone mineral density measured, the participants also provided extensive data pertaining to their medical history and lifestyle factors.
The algorithm for predicting fracture risk in BONEcheck has been thoroughly checked or validated in multiple
studies in Australia, New Zealand, Poland, Canada, and the United States. The outcomes of these studies have
been consistently favourable, with performance either deemed to be excellent or very good.
The estimation of skeletal age was developed using data from the Danish Registry and has been validated in
the Study of Osteoporotic Fractures and the Osteoporotic Fractures in Men Study (MrOS). The estimation of
time to reach osteoporosis was developed using data from the Study of Osteoporotic Fractures and is being
validated in an independent study.
The validation is a continuous process, and more validation studies are or will be carried out. For more
in-depth information, please refer to the accompanying academic papers.
Following thorough analysis and evaluation of over 50 potential risk factors, we identified 5 factors that significantly impacted fracture outcomes: age, bone mineral density, body weight, the number of prior fractures occurring after the age of 50, and the number of falls within the past 12 months. We utilized these risk factors to create and subsequently assess the accuracy of the prediction model through internal validation.
BONEcheck incorporates factors that have been previously identified through numerous studies as contributing to an increased fracture risk, with each factor operating independently. While other factors, such as steroid use, smoking, alcohol consumption, exercise, and dietary calcium intake, can influence fracture risk, they may be more challenging to measure or may impact the risk indirectly via bone mineral density, which is a critical component in the calculator's calculation. It should be noted that the exclusion of a risk factor from BONEcheck does not imply that it is not significant. Maintaining a healthy diet and exercise routine, as well as abstaining from smoking and excessive alcohol consumption, are vital components in preventing fractures.
Bone mineral density of an
individual can be measured in grams per square centimetre
(g/cm2 ) or
expressed as a T-score. The T-score represents an individual's bone mineral
density compared to the average bone mineral density of a healthy person of
the same gender between the ages of 20 and 30 years (commonly referred to as
"peak bone mass"). For instance, a T-score of -2 indicates that the person's
bone mineral density is two standard deviations below the peak bone mass
level. If an individual has a T-score lower than -2.5, they are considered
to have "osteoporosis."
BONEcheck was created using data from both men and women, with 95% of the participants not receiving any anti-fracture treatment. However, for individuals whose T-scores are below -2.5 or who have had a prior fracture, anti-fracture treatment can lead to a reduction in fracture risk ranging from 35% to 50%. As such, if an individual is taking anti-fracture medication, the risk estimate generated by the model should be adjusted downwards by 35-50%.
We consider that 5-year risk is easier to manage than 10-year risk or lifetime risk.
Indeed. Several fracture risk assessment models have been created, although many of them concentrate on hip fractures or women exclusively. Our model is relevant to both men and women. Apart from BONEcheck, the FRAX tool is also accessible online.
It is likely that the risk estimate from one prediction model may differ from another, as these models are developed using distinct data sources and methods. Nonetheless, it is improbable that the variance in risk estimates between various models would be significant enough to cause clinical concern.
Certainly. In old adults, bone mineral density is known to decline as an individual ages, and excessive loss of bone mass is a contributing risk factor for fractures. As a result, the estimated risk of fractures is not static but is likely to increase with age, although for the majority of people, this increase is anticipated to be modest.
Determining that threshold is somewhat reliant on an individual's perception of their risk of fractures and should be discussed with a doctor. However, generally speaking, we consider a 5-year risk of >10% to be high, 5-10% to be moderate, and 5% to be low. Based on a 35-50% reduction in risk of fractures with anti-fracture treatments such as bisphosphonates, the cost per prevented fracture appears reasonable at a 5-year risk of 10% or a 10-year risk of 20% or more. This threshold is also employed in the prevention of cardiovascular disease (National Cholesterol Education Program) and has been recommended by panels of osteoporosis specialists and expert groups. Given the underdiagnosis and undertreatment of osteoporosis, it is hoped that this prognostic model will help increase the acceptance of treatment and decrease the burden of osteoporosis in the general population.
A fracture, particularly a hip fracture, is associated with an increased risk of mortality. Skeletal deterioration can therefore be inferred by the occurrence of a fracture. Skeletal age is a metric used to assess the deterioration of the skeleton due to a fracture or exposure to risk factors that increase the risk of fracture. Hence, if an individual aged 60 has a skeletal age of 62, it implies that the individual shares a similar risk level as a 62-year-old with 'favourable risk factors'. It also means that the individual has a reduction in life expectancy of 2 years due to a fracture or being exposed to risk factors that raise the likelihood of fracture.
Scientific studies have discovered many genes that are associated with bone mineral density. The 'Osteogenomic Profile' is essentially a polygenic risk score that amalgamates the cumulative number of genetic variants an individual carries which heighten their risk of fractures. In BONEcheck, the Osteogenomic Profile was created from 63 genetic variants that are associated with bone mineral density.
References
- 1. Nguyen ND, Pongchaiyakul C, Center JR, Eisman JA, Nguyen TV. Identification of high-risk individuals for hip fracture: a 14-year prospective study. J Bone Miner Res 2005;20(11):1921-8.
- 2. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV. Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporos Int 2007 Aug;18(8):1109-17.
- 3. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV. Development of Development of Prognostic Nomograms for Individualizing 5-year and 10-year Risks of Fracture. Osteoporos Int 2008;19:1431-44.
- 4. Nguyen ND, Ahlborg HG, Center JR, Eisman JA, Nguyen TV. Residual lifetime risk of fractures in women and men. J Bone Miner Res 2007 Jun;22(6):781-8.
- 5. Nguyen TV, Sambrook PN, Kelly PJ, Jones G, Gilbert C, Lord S, Freund J, Eisman JA. Prediction of osteoporotic fractures by postural instability and bone density. Br Med J 1993; 307:1111-1115.
- 6. Nguyen TV, Eisman JA, Kelly PJ, Sambrook PN. Risk factors for osteoporotic fractures in men. Am J Epidemiol 1996;144:255-263.
- 7. Nguyen TV, Center JR, Sambrook PN, Eisman JA. Risk factors for proximal humerus, forearm and wrist fractures in elderly men and women: The Dubbo Osteoporosis Epidemiology Study. Am J Epidemiol 2001; 153:587-595.
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- 3. Nguyen TV (2020). Toward the era of precision fracture risk assessment. J Clin Endocrinol Metab. pii: dgaa222.
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- 5. Nguyen TV. Personalised assessment of fracture risk: which tool? Aust J Gen Pract 2022;51:189-190.
- 1. Ho-Le TP, Tran TS, Bliuc D, Pham HM, Frost SA, Center JR, Eisman JA, Nguyen TV. Epidemiological transition to mortality and refracture following an initial fracture. eLife 2021;10:e61142.
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- 2. Ho-Le TP, Center JR, Eisman JA, Nguyen HT, Nguyen TV. Prediction of Bone Mineral Density and Fragility Fracture by Genetic Profiling. J Bone Miner Res 2017;32:285-293.
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- 4. Ho-Le TP, Pham HM, Center JR, Eisman JA, Nguyen HT, Nguyen TV. Prediction of changes in bone mineral density in the elderly: contribution of "osteogenomic profile". Arch Osteoporos. 2018;13(1):68.
- 5. Ho-Le TP, Tran HTT, Center JR, Eisman JA, Nguyen HT, Nguyen TV. Assessing the clinical utility of genetic profiling in fracture risk prediction: a decision curve analysis. Osteoporos Int 2021;32:271–280.
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The development of BONEcheck is described in the following preprint:
Nguyen D, et al. BONEcheck: a digital tool for personalized bone health assessment.
MedRxiv: https://www.medrxiv.org/content/10.1101/2023.05.10.23289825v1Papers that form the basis for BONEcheck and describe the derivation of the Garvan Fracture Risk Calculators:
Papers that review current fracture risk assessment tools:
Papers that describe the concept of 'Skeletal Age':
Papers that describe the concept of 'Osteogenomic Profile':
Selected papers that reported results of validation studies:
Other relevant papers:
