A clinical diagnosis for osteoporosis is typically made when BMD is more than -2. 5 SD below the young adult mean of the population (Kanis et al. , 2009). A low BMD is a sign of sub-optimal bone mass, which can be acquired during puberty or as a result of accelerated bone loss later in life (Gafni and Baron, 2004). Low BMD readings are heavily associated with an increased prevalence of fragility fractures (Sabin et al. , 1995). Thus, low BMD has remained one of the primary risk factors for osteoporosis. Research has indicated BMD is capable of measuring 60-80% of bone strength variance (Courtney et al., 1994).
However, the accuracy of BMD is often contested. Some studies report errors between 25%-41% when using BMD as a sole measure for osteoporosis (Gluer et al. , 1995). Gluer et al. (1995) suggested BMD scores are heavily influenced by body size and composition, two factors unaccounted for during standard analysis. In addition, non-specific reference values exist for children and adolescents, as a result of this, it is unclear how usable current reference values are in younger populations (Gafni and Baron, 2004).
Despite these caveats, a large evidence base exists that supports the use of BMD as a primary identification tool for osteoporosis (Preston, 2004). Identifying genuine osteoporosis has previously been associated with relative error and difficulty (Winzenberg et al. , 2003). This is often attributed to a lack of clarity in the definition and diagnosis of osteoporosis (Preston, 2004). Some research has suggested using wider measures in conjunction with BDM, so that the disease can be more competently identified within practice (Yasuda, Kaleta, and Bromme, 2005).
A significant limitation of BMD, is that mineral content needs to be compromised before a diagnosis can be made (Winzenberg et al. , 2003). In high-risk populations such as young individuals and female athletes, methods that detect problems at an earlier stage would provide a greater window for intervention to take place (Yasuda, Kaleta, and Bromme, 2005). Blood and urinary analysis can detect certain chemical biomarkers, which can reflect the overall health of the skeletal system (Yasuda, Kaleta, and Bromme, 2005).
One study reviewing female athletes, indicated that markers of bone turnover were between 33% and 71% lower in certain athletes (Garenero et al. , 2013). Thus, such measures should be considered within the appropriate populations. However, as a result of the inequalities which exist in female sport, it seems unlikely methods with such expense will seriously considered by the relevant bodies (Heinonen et al. , 1995). Research that surrounds “the female athlete triad”, has previously identified that more efficient measures for screening osteoporosis and secondary osteop-orosis need to be established.
However, significant developments are yet to be put in place (Garenero et al. , 2013). More precise methods for measuring osteoporosis, may be capable of providing a greater reflection of the impact certain nutrients have on skeletal health. Calcium intake is one of the most widely revised topics in the prevention of osteoporosis (Prentice, 2004). Furthermore, it is a key mineral used in the formation of bone mass, which is essential throughout all stages of life (Francis, 2007).
Inadequate calcium intake is associated with secondary hyperparathyroidism, which leads to increased bone turnover and accelerated bone loss (Ilich and Kerstetter, 2000). Although it is generally agreed that sufficient calcium intake is an important part of skeletal health, universally accepted intake values are yet to be established. For example, the National Health Service (2016) in the UK recommend 700mg/d, whilst the Depa-rtment of Health and ageing (2016) in Australia and New Zealand recommend 1300mg/d. The Institute of Medicine in the US has developed more specific recommendations across the population (IOM, 2011).