Low radiographic muscle density is associated with lower overall and disease-free survival in early-stage colorectal cancer patients

Baar, Harm van; Beijer, Sandra; Bours, Martijn J.L.; Weijenberg, Matty P.; Zutphen, Moniek van; Duijnhoven, Fränzel J.B. van; Slooter, Gerrit D.; Pruijt, J.F.M.; Dronkers, Jaap; Haringhuizen, A.; Bilgen, Ernst Jan Spillenaar; Hansson, B.; Wilt, Johannes H. W. de; Kampman, Ellen; Winkels, Renate M.

doi:10.1007/s00432-018-2736-z

Cited by 24 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our study, lower SMR was associated with more fatigue; prior research among cancer survivors have linked lower SMR to other outcomes, including worse physical function [30] and higher mortality [31][32][33][34][35][36][37][38]. This link may potentially partly be explained by increased pro-inflammatory cytokine production, as this has been reported with lower levels of SMR [39].…”

Section: Discussionsupporting

confidence: 67%

“…Since fatigue is more prevalent among late-stage cancer patients [5], the association Fig. 1 Flowchart of the inclusion process of patients in this study J Cancer Surviv (18) 200 (38) 56 (38) 145 (34) 24 (39) 153 (35) Number 34 (30) 196 (37) 50 (34) 163 (38) 17 (28) 168 ( 16 (14) 152 (29) 50 (34) 104 2421 (35) 114 (26) No 38 (33) 145 (28) 74 5087 2018 (30) 113 (26) No between low SMI and fatigue among late-stage patients might be driven by the progressive tumor instead of low SMI itself. This might explain why no association was observed at diagnosis in the present study.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Body composition and its association with fatigue in the first 2 years after colorectal cancer diagnosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Purpose Persistent fatigue among colorectal cancer (CRC) patients might be associated with unfavorable body composition, but data are sparse and inconsistent. We studied how skeletal muscle index (SMI), skeletal muscle radiodensity (SMR), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) at diagnosis are associated with fatigue up to 24 months post-diagnosis in stage I–III CRC patients. Methods SMI, SMR, VAT, and SAT were assessed among 646 CRC patients using pre-treatment computed tomography images. Fatigue at diagnosis, at 6, and 24 months post-diagnosis was assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire. The association of SMI, SMR, VAT, and SAT with fatigue (yes/no) was assessed using confounder-adjusted restricted cubic spline analyses. Results Prevalence of fatigue at diagnosis was 18%, at 6 months 25%, and at 24 months 12%. At diagnosis, a significant (p = 0.01) non-linear association of higher levels of SAT with higher prevalence of fatigue was observed. Lower levels of SMR were linearly associated with higher prevalence of fatigue at 6 months post-diagnosis (overall association p = 0.02). None of the body composition parameters were significantly associated with fatigue at 24 months. Conclusion Having more SAT was associated with more fatigue at diagnosis, while low levels of SMR were associated with more fatigue at 6 months post-diagnosis. Implications for Cancer Survivors Our results suggest that it may be interesting to investigate whether interventions that aim to increase SMR around the time of diagnosis may help to lower fatigue. However, more knowledge is needed to understand the mechanisms behind the association of SMR with fatigue.

show abstract

Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Body composition and its association with fatigue in the first 2 years after colorectal cancer diagnosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“… 1, Antoun et al (2013); 2, Martin et al (2013); 3, Akahori et al (2015); 4, Aust et al (2015); 5, Fujiwara et al (2015); 6, Malietzis et al , (2015); 7, Malietzis et al , (2016); 8, Malietzis, Johns, et al (2016); 9, Malietzis, Lee, et al (2016); 10, Boer et al (2016); 11, Cushen et al (2016); 12, Hayashi et al (2016); 13, Kumar et al (2016); 14, Pędziwiatr et al (2016); 15, Rollins et al (2016); 16, Sjøblom et al (2016); 17, Tamandl, Pedley, Hoffmann, Fox, and Murabito (2016); 18, Atlan et al (2017); 19, Bye et al (2017); 20, Chu et al (2017); 21, Daly et al (2017); 22, Daly, Ní Bhuachalla, et al (2018); 23, Kubo, Naito, Mori, Osawa, and Aruga (2017); 24, Loumaye et al (2017); 25, Okumura et al (2017a); 26, Okumura et al (2017b); 27, Rier et al (2017); 28, Rier et al (2018); 29, Shachar, Deal, Weinberg, Williams, et al (2017); 30, Shachar, Deal, Weinberg, Nyrop, et al (2017); 31, Van Rijssen et al (2017); 32, van Roekel et al (2017); 33, Williams et al (2017); 34, Williams et al (2018); 35, Choi et al (2018); 36, Deng et al (2018); 37, Ní Bhuachalla et al (2018); 38, Rodrigues and Chaves (2018); 39, Silva de Paula et al (2018); 40, Souza et al (2018); 41, Versteeg et al (2018); 42, Charette et al (2019); 43, Kiss et al (2019); 44, Zhang et al (2018); 45, Dohzono, Sasaoka, Takamatsu, Hoshino, and Nakamura (2019); 46, van Baar et al (2018); 47, Atasevenet et al (2018); 48, Martin et al (2018); 49, Stretch et al (2018); 50, van Dijk et al (2018); 51, van Vugt, Gaspersz, et al (…”

Section: Resultsmentioning

confidence: 99%

“…1, Mayer et al (1989); 2, Goodpaster, Kelley, et al (2000); 3, Hicks et al (2005a); 4, Hicks et al (2005b); 5, Komiya et al (2006); 6, Laroche and Cintas (2010); 7, Anderson et al (2013); 8, Anderson et al (2014); 9, Antoun et al (2013); 10, Miljkovic et al (2013); 11, Therkelsen et al (2013); 12, Therkelsen et al (2016); 13, Kim et al (2014); 14, Akahori et al (2015); 15, Aust et al (2015); 16, Malietzis et al (2015); 17, Malietzis, Currie, et al (2016); 18, Malietzis, Johns, et al (2016); 19, Malietzis, Lee, et al (2016); 20, Cushen et al (2016); 21, Hayashi et al (2016); 22, Kumar et al (2016); 23, Looijaard et al (2016); 24, Montano‐Loza et al (2016); 25, Rollins et al (2016); 26, Wang et al (2016); 27, Atlan et al (2017); 28, Azuma et al (2017); 29, Bye et al (2017); 30, Erlandson et al (2017); 31, Locke et al (2017); 32, Loumaye et al (2017); 33, Okumura et al (2017b); 34, Rier et al (2017); 35, Rier et al (2018); 36, Shachar, Deal, Weinberg, Williams, et al (2017); 37, Shachar, Deal, Weinberg, Nyrop, et al (2017); 38, van Roekel et al (2017); 39, Williams et al (2017); 40, Williams et al (2018); 41, Choi et al (2018); 42, Rodrigues and Chaves, (2018); 43, Silva de Paula et al (2018); 44, Souza et al (2018); 45, Versteeg et al (2018); 46, Charette et al (2019); 47, Kiss et al (2019); 48, Zhang et al (2018); 49, Coats et al (2018); 50, Vella et al (2018); 51, Dohzono et al (2019); 52, van Baar et al (2018); 53, ...…”

Section: Resultsmentioning

confidence: 99%

Assessing skeletal muscle radiodensity by computed tomography: An integrative review of the applied methodologies

Poltronieri

Paula

Chaves

2020

Clin Physio Funct Imaging

View full text Add to dashboard Cite

Low‐radiodensity skeletal muscle has been related to the degree of muscle fat infiltration and seems to be associated with worse outcomes. The aim of this study was to summarize the methodologies used to appraise skeletal muscle radiodensity by computed tomography, to describe the terms used in the literature to define muscle radiodensity and to give recommendations for its measurement standardization. An integrative bibliographic review in four databases included studies published until August 2019 in Portuguese, English or Spanish and performed in humans, adults and/or the elderly, of both sex, which investigated skeletal muscle radiodensity through computed tomography (CT) of the region between the third and fifth lumbar vertebrae and evaluated at least two muscular groups. One hundred and seventeen studies were selected. We observed a trend towards selecting all abdominal region muscle. A significant methodological variation in terms of contrast use, selection of skeletal muscle areas, radiodensity ranges delimitation and their cut‐off points, as well as the terminologies used, was also found. The methodological differences detected are probably due to the lack of more precise information about the correlation between skeletal muscle radiodensity by CT and its molecular composition, among others. Therefore, until the gaps are addressed in future studies, authors should avoid arbitrary approaches when reporting skeletal muscle radiodensity, especially when it comes to prognosis inference. Studies using both CT and direct methods of muscle composition evaluation are encouraged, to enable the definition and validation of the best approach to classify fat‐infiltrated muscle tissue, which will favour the nomenclature uniformization.

show abstract

“…Fat cells replace skeletal muscle cells in the aging process, and a lower muscle intensity is detected on CT scans (11,(32)(33)(34). Both skeletal MI and density values have been reported to be inversely associated with clinical outcomes (8,13,35). Weinberg et al rst presented a SMG, which was measured by multiplying the cross-sectional skeletal muscle area by the muscle density.…”

Section: Discussionmentioning

confidence: 99%

Predicting Outcomes of Abdominal Surgical Emergencies in the Elderly Population Using a CT Muscle Gauge

Tee

Cheng

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Frailty has been shown to be an independent negative predictor of surgical outcomes in geriatric patients. Traditional measurements of frailty are impractical in emergency settings, and computed tomography (CT)-measured skeletal muscle mass has been proposed as an alternative. However, the cutoff values of these CT metrics for frailty are still unknown, and their impact on abdominal emergencies in the elderly population is unclear.Study Design: A total of 462 young trauma patients aged 18-40 years were analyzed to establish sex-specific reference cutoff values for the CT-measured muscle index (MI) and muscle gauge (MG) values. The impacts of low MI and MG values were investigated in 1192 elderly patients (aged ≥ 65 years) undergoing abdominal surgery.Results: The sex-specific cutoff values for MI and MG were determined by adopting European Working Group on Sarcopenia in Older People (EWGSOP) guidelines. The correlation between MG and ageing was significantly stronger than that between MI and ageing. With regard to the MG, the L4 psoas muscle gauge (L4 PMG) was further investigated in an elderly cohort owing to its high predictive value and ease of use in the clinical setting. A low L4 PMG value was an independent risk factor for overall complications and mortality in elderly patients with abdominal emergencies.Conclusion: The current study was the largest study investigating the correlations between MG values and ageing in the Asian population. Frailty, as indicated by a low L4 PMG value, may help surgeons during preoperative decision making regarding geriatric patients with abdominal emergencies.

show abstract

Low radiographic muscle density is associated with lower overall and disease-free survival in early-stage colorectal cancer patients

Abstract: In early-stage CRC patients, low muscle density was significantly associated with higher overall mortality, and worse disease-free survival.

Cited by 24 publications

References 27 publications

Body composition and its association with fatigue in the first 2 years after colorectal cancer diagnosis

Body composition and its association with fatigue in the first 2 years after colorectal cancer diagnosis

Assessing skeletal muscle radiodensity by computed tomography: An integrative review of the applied methodologies

Predicting Outcomes of Abdominal Surgical Emergencies in the Elderly Population Using a CT Muscle Gauge

Contact Info

Product

Resources

About