China
Africa
Explore chapters and articles related to this topic
Delivery of Ovarian Hormones for Bone Health
Published in Emmanuel Opara, Controlled Drug Delivery Systems, 2020
Indeed, osteoporosis is the leading cause of bone fracture with direct costs alone expected to reach $25 billion by 2025.5 BMD is widely used to determine whether a patient has osteoporosis and is prone to fracture risk. BMD is typically reported as a T-score, which normalizes BMD to the average BMD of a healthy, young adult. Per the World Health Organization, a T-score within ± standard deviation of 0 is considered normal. T-scores of −1 to −2.5 standard deviations below 0 indicate low bone mass, which is often considered osteopenia. T-scores of more than −2.5 standard deviations from 0 indicate osteoporosis and thus has an increased risk of fracture.3 While BMD has been a traditional measure for osteoporosis, it is not particularly accurate in predicting risks of bone fracture.31,91 The use of BMD in conjunction with analysis of other risk factors, such as age, smoking history, family history of hip fracture, glucocorticoid use, and arthritis, has led to a more accurate prediction of fracture risk known as the Fracture Risk Assessment Tool (FRAX) method.154 Blood testing for bone turnover markers such as the C-telopeptide of collagen may allow further predictive capacity of osteoporosis and the likelihood of fracture.137,161 Unfortunately, bone fracture remains the most likely clinical presentation of osteoporosis.154
Human studies
Published in C M Langton, C F Njeh, The Physical Measurement of Bone, 2016
Christopher F Njeh, John Shepherd, Harry K Genant
T-scores are comparisons of the patient’s BMD with a population’s peak reference values and can be represented in units of population standard deviations (SD) or as a percentage of the reference population’s peak value. The T-scoresd represents the number of SD a BMD measurement is above or below the mean peak bone mass of a young normal population matched for sex and race. In units of standard deviations, T-scoresd is defined as () T-scoresd=(BMDpatient−BMDpeak)Std.Devpeak
A Survey on Osteoporosis Detection Methods with a Focus on X-ray and DEXA Images
Published in IETE Journal of Research, 2022
S.M. Nazia Fathima, R. Tamilselvi, M. Parisa Beham
The T-score and Z-score are expressed mathematically as in Equations (3) and (4). T-score along with the patient’s age helps to estimate the remaining lifetime fracture probability which is the number of fractures expected in a patient’s remaining lifetime. The T-score classifies the patients into one of three diagnoses: normal, osteopenia, or osteoporosis. According to WHO, T-scores, with a BMD T-score ≥1 SD being normal, between −2.5 and −1.0 SD being osteopenia (or low bone mass), and ≤ −2.5 SD being osteoporotic. To have a T-score of −2.5, a patient must fall within the lowest 2% of the reference population. Figure 23 shows a pictorial representation of values of T-score and the categories of risk of condition of osteoporosis.
Swimming and peak bone mineral density: A systematic review and meta-analysis
Published in Journal of Sports Sciences, 2018
Alejandro Gomez-Bruton, Jesús Montero-Marín, Alejandro González-Agüero, Alba Gómez-Cabello, Javier García-Campayo, Luis A. Moreno, Jose A. Casajús, Germán Vicente-Rodríguez
Since considerable heterogeneity among studies was expected, the pooled effect sizes were calculated with the random effects model. In this model the included studies can be seen as a sample drawn from a population of studies that differ from each other systematically (Borenstein, Hedges, Higgins, & Rothstein, 2009). We tested the heterogeneity using the I2 statistic (Higgins, Thompson, Deeks, & Altman, 2003). This statistic describes the variance between studies as a proportion of the total variance. A value <25% indicates low heterogeneity, between 50–75% moderate heterogeneity and >75% high heterogeneity. We also calculated its associated P-value, with a non-significant result indicating absence of heterogeneity. As previously stated, several variables can influence BMD such as sex, age, ethnicity, latitude, number of hours of physical activity and calcium intake. All the included studies were developed in Caucasian, and there were therefore no differences in ethnicity. Nevertheless, not all of the included studies presented all the mentioned variables that are known to influence BMD and thus some of those variables were not entered in the meta-analyses. Thus, a meta-regression analysis, according to the method of moments, was developed to test possible influences of age, sex and latitude, calculating the slopes, their P-value, as well as the P-value associated with the explained variance (R2). In addition, the influences that study quality could have on the heterogeneity were also studied through meta-regression.
Fracture of geometric bone models. Multiscale simulation issues
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Francisco Daniel Pérez Cano, Adrián Luque Luque, Juan José Jiménez Delgado
In the literature, the low bone mass is determined as the main cause of human bone fracture through the deterioration of the bone (Klotzbuecher et al. 2010). Traditionally, the bone mineral mass or density (BMD) has been used to quantify the bone fracture risk. However, studies have shown that BMD is not the only factor contributing to bone fracture risk (Sabet et al. 2016).The problem in studying a fracture is that it is not simply the mechanical aspects that affect the bone structure, it is also affected by a combination of factors. Burr et al. (1997) divide these factors into three categories: load factors, changes in bone structure, and changes in material properties. All categories include anomalities as well as unexplained changes in properties. The unique conclusion is that fractures involve numerous events and are influenced by multiple scales of bone structure. The goal should be to obtain a complete collection of the existing theoretical models and the establishment of a relationship between the different ones, but at present there are many limitations in this respect and it is too ambitious. Sabet et al. (2016) highlight that the toughness of the bone is the other determining factor, in addition to the BMD, that allows to determine the possibility of a fracture. Moreover, the author divides the toughness of the bone into the capacity of energy absorption by the bone and the resistance of the bone to a fracture. This last measure determines the resistance that the fracture finds while it grows through the model and establishes a series of formulas that allow the calculation of the toughness using the elasticity as a reference.