The relationship between muscle mass and function in cancer cachexia – smoke and mirrors?

Ramage MI1, Skipworth RJE1,2

1. University of Edinburgh Department of Clinical Surgery, Royal Infirmary of Edinburgh, Edinburgh, EH16 4SA.

2. Corresponding Author.

· Email rskipworth@hotmail.com;

· Telephone +44 (0)131 536 1000

Word count: 2561

Key Words:

Body composition

Cancer

Cachexia

Muscle

AbstractPurpose of Review

Randomised clinical trials of cancer cachexia interventions are based on the premise that an increase in the muscle mass of patients is associated with consequent improvements in muscle function, and ultimately, quality of life. However, recent trials that have succeeded in demonstrating increases in lean body mass have been unable to show associated increases in patient physical function. In this review, we examine the potential causes for this lack of association between muscle mass and function in cancer cachexia, paying particular attention to those factors that may be at play when using body composition analysis techniques involving cross-sectional imaging. Moreover, we propose a new population-specific model for the relationship between muscle mass and physical function in patients with cancer cachexia.

Recent Findings

The ROMANA 1&2 trials of anamorelin (a novel ghrelin agonist) and the POWER 1&2 trials of enobosarm (selective androgen receptor modulator) were able to demonstrate improvements in patient lean body mass, but not the functional co-primary endpoints of handgrip strength and stair climb power, respectively. We report similar confirmatory findings in other studies, and describe potential reasons for these observations.

Summary

The relationship between muscle mass and muscle function is complex and unlikely to be linear. Furthermore, the relationship is influenced by the techniques used to assess nutritional endpoints (e.g. CT); the nature of the chosen physical function outcome measures; and the sex and severity of the recruited cachectic patients. Such factors need to be considered when designing intervention trials for cancer cachexia with functional endpoints.

Introduction/Scope of Review

Regulatory agencies have previously stipulated that intervention trials in cancer cachexia should use nutritional and functional co-primary endpoints (1). With this in mind, it would seem to make prima facie sense that nutritional outcomes that assess muscle size should correlate with muscle function in any case, at least in terms of explosive measures of function, such as strength and power. Weightlifters have large muscles by definition. However, the relationship between muscle mass and muscle function is more complex than would be expected at first glance. In contrast to weightlifters, marathon runners tend to be slighter in build with less pronounced musculature, and their performance is not linked with muscle mass (2). It is arguably this latter endurance-type performance that would be of most clinical benefit to elderly patients with cancer cachexia. However, at the present time, there are no agreed recommendations or guidelines for physical function (PF) or nutritional outcome measures that should be assessed in trials of intervention for cachexia.

The use of muscle mass as a nutritional outcome measure [or lean body mass (LBM) as a surrogate outcome measure] appears to be relatively reliable in clinical trials looking to assess the effect of pharmacological interventions. To this end, the effect of anamorelin, an oral ghrelin receptor antagonist (Helsinn) was studied in a series of trials named ROMANA 1, 2, and 3. In ROMANA 1 and 2, a 12-week treatment period increased LBM (assessed by DEXA) in patients with stage 3 or 4 non-small cell lung cancer, but not hand grip strength (HGS: the functional co-primary end-point) (3). When the treatment period was extended for another 12 weeks in patients with a preserved Eastern Clinical Oncology Group (ECOG) status (≤2), there was again a failure to demonstrate an improvement in HGS compared with placebo (4).

In comparison, enobosarm [a non-steroidal selective androgen receptor modulator (GTx)] was analysed in the POWER (Prevention and treatment Of muscle Wasting in patients with cancER) 1 and 2 studies. These studies found that the use of enobosarm was also associated with an increase in LBM, but not with increased function as measured by the co-primary end-point of ≥10% improvement in stair climb power (5). Several recent observational studies have been able to show associations between muscle mass and PF in a range of muscle wasting conditions, including chronic kidney disease (6), elderly age (7), hip fracture (8), inflammatory myopathies (9) and sarcopenia in the context of obesity and metabolic syndrome (10). Thus, inability of cachexia trials to demonstrate a related and synchronous increase in muscle mass and function is somewhat unexpected, and in this review, we describe the potential reasons for this observed lack of association. In particular, we will discuss both artefactual factors that relate to methods of assessment of nutritional and PF outcome measures, and physiological factors that relate to the recruited patient population. Furthermore, we propose a new non-linear model to express the relationship between muscle mass and patient function in cancer cachexia.

Reason for the Lack of Association Between Muscle Mass and Function in Cancer Cachexia

1. Factors in the assessment of body composition and nutritional outcome measures

Imaging techniques to assess body composition and reduced muscle volumes have been used successfully for the last 30-40 years, and include CT (11), MRI (12,13), ultrasound and DEXA (14). Age-related loss of muscle (termed “sarcopenia”) was originally defined diagnostically by Baumgartner et al (14) as being below 2 standard deviations of the mean appendicular muscle mass of a group of healthy individuals undergoing DEXA scanning. With the standard use of CT for clinical diagnostic purposes in cancer, CT body composition analysis has emerged widely as a new potential gold standard to identify sarcopenia in cancer patients. Moreover, CT cut points for sarcopenia are described for diagnostic purposes using the consensus definition of cancer cachexia (15). In the future, it seems likely that CT body composition analysis will be used as both an inclusion criterion and an outcome measure in RCTs for cachexia. However, the exact methodology to utilise is still unclear, and the final chosen technique will influence the observed relationship with PF.

CT Skeletal Muscle Index (SMI)

Recognising the increasing use of CT in cancer staging, Mourtzakis et al (11) utilised a cohort of patients undergoing both DEXA and CT to produce an equation converting the original DEXA measures of LBM to those of muscle cross-sectional area produced by CT at the lumbar L3 level. Such area measurements can be indexed for height to derive estimates of whole-body muscularity (skeletal muscle index: SMI), which in turn have been shown to be of prognostic value in terms of patient survival and post-operative morbidity(16,17). Noting the increasing prevalence of obesity in the general population (18), Martin et al (19) used a cohort of lung and gastro-intestinal cancer patients to advance the use of sarcopenic diagnostic cut-points by assessing the influence of patient sex and whether or not they had high or low BMI. A method of relating the original research by Baumgartner et al to the current cancer patient phenotype seen in modern practice is of paramount clinical importance. However, despite such efforts to validate and standardise L3-CT diagnosis of sarcopenia, authors continue to use different measures (e.g. psoas muscle area) and different cutpoints (20–22), with no agreed consensus. Although frustrating, such a varied approach is understandable when one considers the various populations that have been studied. The cut-points derived by Martin et al (19) were from a Canadian cohort, which from the Alberta census consists of a 70% European origin population (23). It is known that for DEXA scanning, cut-points for defining sarcopenia are different in European populations and Asian populations. Specifically, the EWGSOP (24) defines low muscle mass in European females below 5.5 kg/m2, whereas in a Korean population Kwon et al (21) found the cut-point to be 4.4 kg/m2. Similarly for CT derived variables, Fujiwara et al (25) found different cut-points in a Japanese cohort to those developed in the Canadian group. Some studies have shown that it is sarcopenic obesity that carries the worst clinical risk (26), whereas more recently, the negative prognostic impact of subcutaneous fat (27,28) has been demonstrated. Indeed, we have previously identified a high prevalence of sarcopenia in healthy controls when using cut points derived in a cancer population (29). It therefore seems that population-specific cut-points for SMI need to be derived in any trial population before assessing muscle function.

CT Skeletal Muscle Radiodensity (SMD)

An alternative way to assess muscle nutritional response to pharmacological intervention by CT would be to assess muscle quality rather than quantity, by measuring the radio-density of skeletal muscle in Hounsfield Units. The effect of low SMD on outcome has been noted in several diseases. In pancreatitis, Grinsven et al found a decrease in muscle radio-density during the course of the disease resulted in poorer survival (30). Similar findings were noted in pancreatic cancer by van Dijk et al (31) where in a prospective study involving 199 patients with head of pancreas cancer, medium or low muscle radio-density was associated with worsened survival. Poorer survival was also noted in patients with endometrial studied by Paula et al (32), and in palliative pancreatic cancer studied by Rollins et al (17). In these cohorts, the combination of low muscle mass with low SMD appeared to worsen outcome. In the latter study, previously published muscle mass and density cut-points (19) were used to stratify patients into sarcopenic/normal and myosteatotic/normal. Both sarcopenia and myosteatosis carried poorer prognosis, but in combination these factors were synergistic in their survival reduction. However, there is some preliminary evidence that SMD may correlate more closely with PF in elderly cancer patients compared with SMI (33).

The inference, in line with the observations of Martin et al (19), is that low SMD is the result of altered muscle composition at a cellular level, and that protein is replaced by intramuscular fat. However, once again, this assumption may not represent the entire picture. We have previously demonstrated a positive, but only poor, correlation between SMD and rectus abdominis muscle protein content in an upper GI cancer cohort (34), suggesting that any change in CT variables may not necessarily confer an equivalent change in muscle composition.

Similar to SMI, it seems that radio-density cut-points also require specific development for each population under study. Studies performed by differing centres have found differing radio-density cut-points apply to their cohorts (31,35), and a recent review noted similar findings (36) throughout the literature.

Increasing interest in muscle radio-density has led to concerns regarding the effect of intravenous CT contrast media on body composition variables derived from scans performed in different phases. Earlier authors did not disclose which CT scan phases were used in their manuscripts (11,19). However, work carried by van Vuht et al (37) investigated 50 liver transplant patients undergoing triple-phase CT scans and found statistically and clinically significant differences in SMD between non-contrast and contrast-enhanced scan phases, but not in muscle area. Applying the Martin et al (19) criteria to these values, resulted in 80% having low SMD in unenhanced scan phases whilst 50% and 38% had low SMD in arterial and portal venous phases respectively. The findings from Rollins et al (23) in a population of pancreatic cancer patients was that there was a statistically significant difference between contrast phases on CT, however there was a linear relationship between these phases which could be translated via simple equations to provide comparable values. In more closely defining the relationship between contrast phase, these authors have moved the literature forward to allow abdominal CT scans performed for any clinical indication to be compared. However, such phase-dependent values will obviously impact on any statistical relationship with PF outcome measures. This problem can be compounded further by other CT technique-dependent variables such as tube potential (38). Authors are now turning to complex nonlinear trimodal regression analysis techniques of entire radiodensitometric muscle distributions to compare with standard CT metrics and lower limb muscle function (39).

2. Factors in the assessment of physical function outcome measures

The identity of the best PF outcome measure to relate nutritional outcome measures, including CT, is unknown. There is a lack of PF tests that specifically analyse upper abdominal/L3 muscle activity. Equally, it is an unproven assumption that L3-CT should, in some way, correlate with either targeted assessments of isolated limb strength and power, or complex whole-body assessments of PF [e.g. Timed-Up and Go-Test (TUG)] (40). For example, we have shown previously that cancer cachexia defined using CT-derived sarcopenic cut-points can be identified in patients with normal TUG times (41).

We have previously advocated physical activity meters as devices to measure global patient function in the free-living environment (42,43). The advantage of such devices is that they offer multiple outcome measures from a single application. However, once again, for the purposes of clinical trial design, a single outcome measure is required to be pre-chosen as co-primary endpoint. HGS has been used widely in nutritional studies in this role, and has been validated in various populations (44,45). However, as described previously, it was unsuccessful in the ROMANA studies. From a clinical perspective, it might seem that measures of upper limb PF might be less meaningful or less affected by pharmacological intervention when compared with measures of lower limb PF, which in turn might dictate activities of daily living and overall exercise limitation. However, stair climb power was unchanged in the POWER studies (5).

3. Patient Factors in the Trial Population

An alternative explanation for the failure of previously trialled interventions to achieve significant improvements in functional end-points is an error in understanding of the nature of the relationship between muscle mass and function. To date, trial design has assumed a strong and linear relationship between muscle mass and function. However, it seems likely that, the relationship will alter dependent on the patient’s morbid status at the time. The prolonged immobility, reduced food intake, and muscle depletion of a patient with refractory cachexia are likely to accelerate the rate of functional decline compared with a patient with pre-cachexia. Equally, a patient with pre-cachexia/cachexia may be more responsive or demonstrate faster functional improvements after weight/muscle gain compared with a patient with refractory cachexia, who may be functionally bedbound. Therefore, rather than a linear relationship between muscle mass and physical function, we propose a more complex sigmoid relationship that pursues a delayed path on recovery following initial decline (see Figure 1). We suggest that in the early stages of disease functionality is maintained despite loss of muscle mass, and that outcome measures of functionality currently in use are insufficiently sensitive to detect the subtle changes at this stage. These pre-cachectic patients are likely to have a minimal functional effect from increasing muscle mass but would be most likely to benefit from prophylactic intervention to prevent further muscle mass loss and subsequent functional decline. Secondly, after a significant amount of muscle mass has been lost, function begins to decline at an increased rate. It is these cachectic patients who, we suggest, are likely to have the greatest maximal effect from interventions to preserve muscle mass. Thirdly, once a critical mass of muscle has been lost, function declines rapidly and likely in an irreversible fashion, resulting in refractory cachexia. Interventions at this stage to improve muscle mass and function are unlikely to be successful due to the refractory nature of the patient’s condition and a palliative approach may be more suitable at this stage. The high rates of patient attrition in previous cancer cachexia trials means that is highly likely that they have recruited significant numbers of such patients. In recruiting for future trials, it would be important to recognise these patients as a distinct sub-group in whom mass and functional improvements may be futile. Fourthly, post-intervention, we suggest that the recovery of muscle mass long precedes functional recovery. This is frequently seen in studies of patients recovering from critical illness. Finally, we suggest that the curve described by patients on the cachexia pathway can be shifted in a deleterious direction by the presence of systemic inflammation, co-morbidities, or advanced age as frequently reported in the literature and seen clinically. This hypothetical model requires evidence in the form of larger observational studies, in combination with data from current clinical trials. Furthermore, it is highly likely that if proven, such a model will require adjustment for patient age (46) and sex (47), plus transient factors such as sleep, depression and fatigue (48), as well as menopausal and habitual exercise status (49). For example, we have shown previously that lower limb muscle strength and power assessments exhibit sexual dimorphism in patients with cancer cachexia (50). However, the aim is that by carrying out model validation work, target groups for intervention trials can be identified accurately and the appropriate outcome measures more easily chosen.

Conclusion

Imaging methodology and physical function investigation have advanced greatly over the last 30-40 years. Increasing numbers of variables can thus be interrogated and related to patient outcomes, however the absence of a consensus on interpreting and reporting these variables, and the methods used to collect them, has led to confusion. The true relationship between muscle mass and function remains to be determined, and improved modelling may be required to define the role of body composition analysis in revealing this.

Key points

· There has been an unexpected inability of cachexia trials to demonstrate a related and synchronous increase in muscle mass and function.

· The best methodology for measuring body composition is still unclear, and the final chosen technique will influence any observed relationship with physical function.

· The identity of the best physical function outcome measure to relate nutritional outcome measures is unknown, and its relationship to body composition measures is unclear.

· There is likely to be an error in the understanding of the nature of the relationship between muscle mass and function, and we propose a new model.

· The true relationship between muscle mass and function remains to be determined, and improved modelling may be required to define the role of body composition analysis in revealing this.

Figure 1: Proposed relationship between muscle mass and function

Acknowledgments

We would like to acknowledge the late Professor Fearon for his guidance and tutelage in this subject over many years.

Financial support and sponsorship

MR received a grant from the Royal College of Surgeons of Edinburgh in 2015

MR’s salary was supported through the University of Edinburgh by Novartis in 2015/2016 and by ESPEN in 2016/2017

RS holds the post of NRS Clinician

Conflicts of interest

MR received a grant from the Royal College of Surgeons of Edinburgh in 2015

MR’s salary was supported through the University of Edinburgh by Novartis in 2015/2016 and by ESPEN in 2016/2017

RS holds the post of NRS Clinician

The authors declare no other conflicts of interest

References

1. Fearon K, Argiles J, Baracos V, Bernabei R, Coats A, Crawford J, et al. Request for regulatory guidance for cancer cachexia intervention trials. J Cachexia Sarcopenia Muscle. 2015 Dec;6(4):272–4.

2. Knechtle B, Rüst CA, Knechtle P, Rosemann T. Does Muscle Mass Affect Running Times in Male Long-distance Master Runners? Asian J Sports Med. 2012 Dec;3(4):247–56.

3. Temel JS, Abernethy AP, Currow DC, Friend J, Duus EM, Yan Y, et al. Anamorelin in patients with non-small-cell lung cancer and cachexia (ROMANA 1 and ROMANA 2): results from two randomised, double-blind, phase 3 trials. Lancet Oncol. 2016 Apr 1;17(4):519–31.

* This paper delivers and discusses the results of one of the most recent large cachexia studies performed, and shows increase in muscle mass without corresponding increase in function

4. Currow D, Temel JS, Abernethy A, Milanowski J, Friend J, Fearon KC. ROMANA 3: a phase 3 safety extension study of anamorelin in advanced non-small-cell lung cancer (NSCLC) patients with cachexia. Ann Oncol Off J Eur Soc Med Oncol. 2017 Aug 1;28(8):1949–56.

* This paper discusses the extended results of the Anamorelin trial, and again shows increase in muscle mass without corresponding increase in function

5. Crawford J, Johnston MA, Taylor RP, Dalton JT, Steiner MS. Enobosarm and lean body mass in patients with non-small cell lung cancer. J Clin Oncol [Internet]. 2014 [cited 2017 May 15];32:5s(suppl; abstr 9618). Available from: http://meetinglibrary.asco.org/content/128938-144

6. Highton PJ, Neale J, Wilkinson TJ, Bishop NC, Smith AC. Physical activity, immune function and inflammation in kidney patients (the PINK study): a feasibility trial protocol. BMJ Open. 2017 May;7(5):e014713.

7. Altubasi I. Physical performance of elderly adults in association with thigh tissue composition: a cross-sectional study. J Phys Ther Sci. 2017 Dec;29(12):2194–8.

8. Steihaug OM, Gjesdal CG, Bogen B, Kristoffersen MH, Lien G, Ranhoff AH. Sarcopenia in patients with hip fracture: A multicenter cross-sectional study. PLoS ONE [Internet]. 2017 Sep 13 [cited 2018 Jul 17];12(9). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5597226/

9. Cleary LC, Crofford LJ, Long D, Charnigo R, Clasey J, Beaman F, et al. CT based muscle density predicts muscle function and health-related quality of life in patients with idiopathic inflammatory myopathies. Arthritis Care Res. 2015 Jul;67(7):1031–40.

10. Rosique-Esteban N, Babio N, Díaz-López A, Romaguera D, Alfredo Martínez J, Sanchez VM, et al. Leisure-time physical activity at moderate and high intensity is associated with parameters of body composition, muscle strength and sarcopenia in aged adults with obesity and metabolic syndrome from the PREDIMED-Plus study. Clin Nutr [Internet]. 2018 Jun 6 [cited 2018 Jul 17]; Available from: http://www.sciencedirect.com/science/article/pii/S0261561418302097

11. Mourtzakis M, Prado CMM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. 2008 Oct;33(5):997–1006.

12. Mersmann F, Bohm S, Schroll A, Boeth H, Duda G, Arampatzis A. Muscle shape consistency and muscle volume prediction of thigh muscles. Scand J Med Sci Sports. 2015 Apr 1;25(2):e208–13.

13. Willis TA, Hollingsworth KG, Coombs A, Sveen M-L, Andersen S, Stojkovic T, et al. Quantitative Muscle MRI as an Assessment Tool for Monitoring Disease Progression in LGMD2I: A Multicentre Longitudinal Study. PLoS ONE [Internet]. 2013 Aug 14 [cited 2016 May 18];8(8). Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3743890/

14. Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. 1998;147(8):755–763.

15. Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol. 2011 May;12(5):489–95.

16. Parsons HA, Baracos VE, Dhillon N, Hong DS, Kurzrock R. Body Composition, Symptoms, and Survival in Advanced Cancer Patients Referred to a Phase I Service. PLOS ONE. 2012 Jan 3;7(1):e29330.

17. Rollins KE, Tewari N, Ackner A, Awwad A, Madhusudan S, Macdonald IA, et al. The impact of sarcopenia and myosteatosis on outcomes of unresectable pancreatic cancer or distal cholangiocarcinoma. Clin Nutr Edinb Scotl. 2016 Oct;35(5):1103–9.

*This paper shows the synergistic effects of low muscle mass together with low muscle radio-attenuation in pancreatic cancer sufferers.

18. Agha M, Agha R. The rising prevalence of obesity: part A: impact on public health. Int J Surg Oncol. 2017 Aug;2(7):e17.

19. Martin L, Birdsell L, MacDonald N, Reiman T, Clandinin MT, McCargar LJ, et al. Cancer Cachexia in the Age of Obesity: Skeletal Muscle Depletion Is a Powerful Prognostic Factor, Independent of Body Mass Index. J Clin Oncol. 2013 Apr 20;31(12):1539–47.

20. Rutten IJG, Ubachs J, Kruitwagen RFPM, Beets-Tan RGH, Olde Damink SWM, Van Gorp T. Psoas muscle area is not representative of total skeletal muscle area in the assessment of sarcopenia in ovarian cancer. J Cachexia Sarcopenia Muscle. 2017 Jan 1;n/a-n/a.

**This paper adds a degree of clarity in the body composition field by comparing the values obtained in whole-slice CT analysis with those obtained by psoas alone analysis and finds that they do not closely correlate.

21. Kwon H-J, Ha Y-C, Park H-M. The Reference Value of Skeletal Muscle Mass Index for Defining the Sarcopenia of Women in Korea. J Bone Metab. 2015 May;22(2):71–5.

22. Werf A, Langius J a. E, Schueren M a. E, Nurmohamed SA, Pant K a. MI, Blauwhoff-Buskermolen S, et al. Percentiles for skeletal muscle index, area and radiation attenuation based on computed tomography imaging in a healthy Caucasian population. Eur J Clin Nutr. 2018 Feb;72(2):288–96.

*This paper provides a range of data covering what could be considered to be normal values of CT body composition variables in a normal healthy population.

23. Government of Canada SC. Immigration and Ethnocultural Diversity Highlight Tables - Ethnic Origin, both sexes, age (total), Alberta, 2016 Census – 25% Sample data [Internet]. 2017 [cited 2018 Jun 18]. Available from: https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/hlt-fst/imm/Table.cfm?Lang=E&T=31&Geo=48&SO=4D

24. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis. Age Ageing. 2010 Jul;39(4):412–23.

25. Fujiwara N, Nakagawa H, Kudo Y, Tateishi R, Taguri M, Watadani T, et al. Sarcopenia, intramuscular fat deposition, and visceral adiposity independently predict the outcomes of hepatocellular carcinoma. J Hepatol. 2015 Jul 1;63(1):131–40.

26. Tan BHL, Birdsell LA, Martin L, Baracos VE, Fearon KCH. Sarcopenia in an overweight or obese patient is an adverse prognostic factor in pancreatic cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2009 Nov 15;15(22):6973–9.

27. Ebadi M, Martin L, Ghosh S, Field CJ, Lehner R, Baracos VE, et al. Subcutaneous adiposity is an independent predictor of mortality in cancer patients. Br J Cancer. 2017 Jun 27;117(1):148–55.

28. Montano-Loza AJ, Mazurak VC, Ebadi M, Meza-Junco J, Sawyer MB, Baracos VE, et al. Visceral adiposity increases risk for hepatocellular carcinoma in male patients with cirrhosis and recurrence after liver transplant. Hepatol Baltim Md. 2018 Mar;67(3):914–23.

29. Ramage MI, Staniszewska A, Schuepbach E, Roubenof R, Oniscu G, Wigmore S, et al. Low muscularity by CT definition is present in both cancer and non-cancer control populations. In: Abstracts of the 10th International Conference on Cachexia, Sarcopenia and Muscle Wasting, Rome, Italy, 8-10 December 2017 (Part 2) [Internet]. Journal of Cachexia, Sarcopenia and Muscle; 2018 [cited 2018 Mar 12]. p. 183–212. Available from: http://onlinelibrary.wiley.com/doi/10.1002/jcsm.12284/abstract

30. Grinsven J van, Vugt JLA van, Gharbharan A, Bollen TL, Besselink MG, Santvoort HC van, et al. The Association of Computed Tomography-Assessed Body Composition with Mortality in Patients with Necrotizing Pancreatitis. J Gastrointest Surg. 2017 Jun 1;21(6):1000–8.

31. van Dijk DPJ, Bakens MJAM, Coolsen MME, Rensen SS, van Dam RM, Bours MJL, et al. Low skeletal muscle radiation attenuation and visceral adiposity are associated with overall survival and surgical site infections in patients with pancreatic cancer. J Cachexia Sarcopenia Muscle. 2017 Apr 1;8(2):317–26.

**This study complements the emerging literature on muscle radio-density in cancer survival and builds upon the idea that population-specific cut-pints may need to be identified and calculated for each study performed in this area.

32. Paula NS, Rodrigues CS, Chaves GV. Comparison of the prognostic value of different skeletal muscle radiodensity parameters in endometrial cancer. Eur J Clin Nutr. 2018 Apr 25;1.

33. Williams GR, Deal AM, Muss HB, Weinberg MS, Sanoff HK, Nyrop KA, et al. Skeletal muscle measures and physical function in older adults with cancer: sarcopenia or myopenia? Oncotarget. 2017 Apr 5;8(20):33658–65.

34. Ramage MI, Johns N, Deans CDA, Ross JA, Preston T, Skipworth RJE, et al. The relationship between muscle protein content and CT-derived muscle radio-density in patients with upper GI cancer. Clin Nutr Edinb Scotl. 2016 Dec 27;

*This paper details a range of muscle protein content values obtained using modern techniques more readily available than those used to create reference values.

35. Antoun S, Lanoy E, Iacovelli R, Albiges‐Sauvin L, Loriot Y, Merad‐Taoufik M, et al. Skeletal muscle density predicts prognosis in patients with metastatic renal cell carcinoma treated with targeted therapies. Cancer. 119(18):3377–84.

36. Kazemi-Bajestani SMR, Mazurak VC, Baracos V. Computed tomography-defined muscle and fat wasting are associated with cancer clinical outcomes. Semin Cell Dev Biol. 2016 Jun;54:2–10.

37. van Vugt JLA, Coebergh van den Braak RRJ, Schippers HJW, Veen KM, Levolger S, de Bruin RWF, et al. Contrast-enhancement influences skeletal muscle density, but not skeletal muscle mass, measurements on computed tomography. Clin Nutr [Internet]. 2017 Jul 14 [cited 2018 May 28]; Available from: http://www.sciencedirect.com/science/article/pii/S0261561417302467

**This study investigates the effect of confounding factors in the field of CT body composition analysis with specific regards to CT contrast phase, adding certainty to previously-raised concerns in this area and calling for clarity in reporting.

38. Morsbach F, Zhang Y-H, Nowik P, Martin L, Lindqvist C, Svensson A, et al. Influence of tube potential on CT body composition analysis. Nutrition. 2018 Sep 1;53:9–13.

39. Edmunds K, Gíslason M, Sigurðsson S, Guðnason V, Harris T, Carraro U, et al. Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities. PLOS ONE. 2018 Mar 7;13(3):e0193241.

40. Macdonald AJ, Stephens N, Greig CA, Gray C, Ross J, Fearon KC. PP083-SUN CT DERIVED MEASURES OF MUSCLE MASS ARE ASSOCIATED WITH FUNCTIONAL ABILITY BUT NOT STRENGTH AND POWER IN PATIENTS WITH UPPER GI CANCER. Clin Nutr. 2013 Sep 1;32:S53.

41. Ramage MI, Miller J, Schuepbach E, Roubenof R, Deans DAC, Wigmore S, et al. The emerging disconnect between muscle mass and function: evidence from different patient populations. In: Abstracts of the 10th International Conference on Cachexia, Sarcopenia and Muscle Wasting, Rome, Italy, 8-10 December 2017 (Part 2) [Internet]. Journal of Cachexia, Sarcopenia and Muscle; 2018 [cited 2018 Mar 12]. p. 202. Available from: http://onlinelibrary.wiley.com/doi/10.1002/jcsm.12284/abstract

42. Skipworth RJE, Stene GB, Dahele M, Hendry PO, Small AC, Blum D, et al. Patient-focused endpoints in advanced cancer: Criterion-based validation of accelerometer-based activity monitoring. Clin Nutr. 2011 Dec;30(6):812–21.

41. Ferrioli E, Skipworth RJE, Stenseth J, Wall L, Greig CA, Fearon KCH. Abstract 142; Abstracts of the 5 th Cachexia Conference, Barcelona, Spain, December 5-8, 2009. J Cachexia Sarcopenia Muscle. 2010 Sep;1(1):43–128.

44. Lee SH, Kim SJ, Han Y, Ryu YJ, Lee JH, Chang JH. Hand grip strength and chronic obstructive pulmonary disease in Korea: an analysis in KNHANES VI. Int J Chron Obstruct Pulmon Dis. 2017 Aug 4;12:2313–21.

45. Norman K, Stobäus N, Smoliner C, Zocher D, Scheufele R, Valentini L, et al. Determinants of hand grip strength, knee extension strength and functional status in cancer patients. Clin Nutr. 2010 Oct 1;29(5):586–91.

46. Perkin OJ, McGuigan PM, Thompson D, Stokes KA. Habitual physical activity levels do not predict leg strength and power in healthy, active older adults. PLOS ONE. 2018 Jul 2;13(7):e0200089.

47. Resnick B, Hebel JR, Gruber-Baldini AL, Hicks GE, Hochberg MC, Orwig D, et al. The impact of body composition, pain and resilience on physical activity, physical function and physical performance at 2 months post hip fracture. Arch Gerontol Geriatr. 2018 May 1;76:34–40.

48. Brady AO, Straight CR. Muscle capacity and physical function in older women: What are the impacts of resistance training? J Sport Health Sci. 2014 Sep 1;3(3):179–88.

49. Straight CR, Ward-Ritacco CL, Evans EM. Association between accelerometer-measured physical activity and muscle capacity in middle-aged postmenopausal women: Menopause. 2015 Nov;22(11):1204–11.

50. Stephens NA, Gray C, MacDonald AJ, Tan BH, Gallagher IJ, Skipworth RJE, et al. Sexual dimorphism modulates the impact of cancer cachexia on lower limb muscle mass and function. Clin Nutr. 2012 Aug;31(4):499–505.