186 explained by the current lack of clinically relevant thresholds that distinguish normal from abnormal values of skeletal muscle mass. Several approaches can be adopted to identify critical cut-points. A paradigmatic example potentially lending support to the operative definition of sarcopenia might be provided by the approach previously adopted to identify osteoporosis on the basis of bone mineral density. In fact, approaches that have been developed for bone and osteoporosis may serve well for skeletal muscle and sarcopenia. The clinical definition of a specific condition (which will consequently lead to the indication for treat- ment) might be based on: 1. A parallel clinical diagnosis. For osteoporosis, diagnosis can be obtained by evaluating the presence of vertebral fractures or deformities at the X-ray examination. Verte- bral fractures indicate decreased bone strength, regardless of bone mineral density. It is well established that patients with vertebral fractures present an increased risk of new events and therefore require treatment. This approach is legitimate and may well work, but may find some limi- tations when applied in primary prevention. 2. A biological assessment. Given its well-established as- sociation with fracture risk, bone mineral density may represent the key parameter on which to rely to deter- mine the presence or absence of osteoporosis. However, bone mineral density (like any other biological marker) exists as a continuous variable, does not present a clear threshold, and is parallel to gradients of risk. Although necessary to provide clinical relevance to biological markers, any categorization will lead to a loss of infor- mation and will inevitably introduce an “arbitrary” de- cision. For the definition of osteoporosis, the cut-off defining the disease was arbitrarily set by a committee which judged the -2.5 standard deviations at the T-score as an adequate match between risk and prevalence. One major problem with the bone definition that should not be repeated for sarcopenia is the inclusion of osteope- nia. Osteopenia (defined by a bone mineral density T- score ranging between -1 and 2.5) encompasses about 50 % of the female healthy population and has led to confusion and concerns among policy-makers regarding the validity of a construct that cannot really be consid- ered abnormal. An approach consistent with this model has also been adopted in the definition of other clinical conditions such as anemia [48]. 3. The risk of adverse clinical outcomes. The indication to treatment of a specific condition (e.g., osteoporosis) might be based on the evaluation of risk of events (i.e., fractures) resulting from the assessment of multiple factors (which may even not include bone mineral den- sity) [49]. This approach will not be exclusively based J Cachexia Sarcopenia Muscle (2012) 3:181–190 on the single evaluation of a (potentially inaccurate and/ or arguable) biomarker, but on a more comprehensive screening and on cost-effectiveness analyses (e.g., treat if the 10-year risk is exceeding a critical threshold). With this rationale, the FRAX [50] and QFractureScores [51] algorithms were recently developed to guide osteo- porosis treatment. In summary, the presence of sarcopenia might be deter- mined by (1) relying on a clinical diagnosis closely related to skeletal muscle decline (e.g., mobility disability) after exclusion of secondary causes, (2) a representative scientific committee identifying a critical threshold for a biological parameter directly representative of skeletal muscle health, and/or (3) developing a risk index to guide treatment. 7 Biological markers of sarcopenia Given the syndromic nature of sarcopenia, intervention strategies aimed at preventing/treating its process might need to target multiple risk factors. In this context, several biological markers have been shown to be associated with skeletal muscle mass, strength, and function, thus represent- ing potential markers for the effect of the studied interven- tions. Such a list is quite long, and each biomarker identifies a specific mechanism contributing the age-related skeletal muscle decline, although they are not specific to muscle and many are likely to turn out to be only weakly associated with clinically relevant outcomes. The most common markers are inflammatory biomarkers [e.g., C-reactive pro- tein [52, 53], interleukin-6 [52–54], and tumor necrosis factor-α [52, 54]], clinical parameters [e.g., hemoglobin [55, 56], serum albumin [57, 58], and urinary creatinine [59]], hormones [e.g., dehydroepiandrosterone sulfate [60], testosterone [61], insulin-like growth factor-1 [62], and vi- tamin D [63–65]], products of oxidative damage [e.g., ad- vanced glycation end-products [66], protein carbonyls [67, 68], and oxidized low-density lipoproteins [69]], or antiox- idants [e.g., carotenoids [70, 71] and α-tocopherol [70]]. Other promising biomarkers have been identified in the last years and may represent useful parameters to more directly explore sarcopenia because they are closely related to skeletal muscle changes. For example, plasma concen- trations of procollagen type III N-terminal peptide (P3NP) represent an interesting marker of skeletal muscle remod- eling [72, 73]. P3NP is a fragment released by the cleav- age of procollagen type III to generate collagen III (a protein produced in soft connective tissues, skin, and muscle). Preliminary studies have also suggested an in- teresting role played by biomarkers specifically linked to the neuromuscular junction in evaluating skeletal muscle modifications [74, 75].