Metabolic and Molecular Basis of Sarcopenia: Implications in the Management of Urothelial Carcinoma

Fukushima, Hiroshi; Fujii, Yasuhisa; Koga, Fumitaka

doi:10.3390/ijms20030760

Cited by 23 publications

(13 citation statements)

References 81 publications

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“…These findings were consistent with a previous study (20) and emphasized the contemporary dilemma caused by the swift increase of MetS. The associations between MetS and increased risks of urological tumors such as bladder cancer, prostate cancer had been reported widely (21)(22)(23). Similarly, the results in our study revealed that MetS was an independent risk factor for developing UTUC.…”

Section: Discussionsupporting

confidence: 93%

Metabolic Syndrome and Risk of Upper Tract Urothelial Carcinoma: A Case-Control Study From Surveillance, Epidemiology and End Results-Medicare-Linked Database

et al. 2021

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BackgroundMetabolic syndrome (MetS) and its components are associated with increased risks of several cancers. However, the relationship between MetS and upper tract urothelial carcinoma (UTUC) has never been investigated before.MethodsWe identified 3,785 UTUC cases aged over 65 years old within the Surveillance, Epidemiology and End Results-Medicare database between 2007 and 2016. For comparison, non-cancer controls (n = 189,953) were selected from the 5% random sample of individuals residing within regions of SEER registries and matched with cases through diagnosis date and pseudo-diagnosis date. MetS and its components were all defined by using ICD-9-CM codes. Multivariate logistic regression models were conducted to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Time trends for MetS and its components were reported and we also performed dose-response effect analysis to test the concomitant effect of these components. The study was presented following the STROBE reporting checklist.ResultsUTUC risk was associated with metabolic syndrome (NCEP-III: OR: 1.669, 95% CI: 1.550–1.792; IDF: OR: 1.924, 95% CI: 1.676–2.172) and its component factors: elevated waist circumference/central adiposity (OR: 1.872, 95% CI: 1.693–2.055), impaired fasting glucose (OR: 1.306, 95% CI: 1.133–1.480), high blood pressure (OR: 1.295, 95% CI: 1.239–1.353), high triglycerides (OR: 1.280, 95% CI: 1.222–1.341), and low high-density lipoprotein cholesterol (OR: 1.354, 95% CI: 1.118–1.592). Consistent associations could also be observed in the subgroup analyses by tumor stages, grades, and tumor size. Additionally, the rates of MetS increased over time in both UTUC and control cohort (NCEP-III criterion; EAPC: +18.1%, P <0.001; EAPC: +16.1%, P <0.001, respectively). A significantly gradual increase in UTUC rates could be seen as the No. of the MetS components increase (χ² = 37.239, Ptrend = 0.000).ConclusionsAmong people aged over 65, MetS and its components were significant risk factors for UTUC with consistent associations in different tumor stages, grades, and tumor size. Even if a subject who did not meet the criteria for MetS had only one of the components, he (she) still had an elevated risk for UTUC. Strategies to control the epidemic of MetS and its components might contribute to a reduction in the UTUC burden. The findings should be considered tentative until ascertained by more researches.

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Section: Discussionsupporting

confidence: 93%

Metabolic Syndrome and Risk of Upper Tract Urothelial Carcinoma: A Case-Control Study From Surveillance, Epidemiology and End Results-Medicare-Linked Database

et al. 2021

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“…In other words, skeletal muscle status affects tumorigenesis and systemic inflammation. Conversely, tumor-derived factors and pro-inflammatory cytokines can inhibit protein synthesis and muscle regeneration and trigger anorexia, protein degradation, and apoptosis of myofibers [39]. Similarly, several inflammation-induced molecular pathways can promote involuntary muscle loss, which is mediated by factors such as tumor necrosis factor (TNF)-α, NF-κB, and calcium-dependent enzymes [40,41].…”

Section: Discussionmentioning

confidence: 99%

Prognostic Impact of Sarcopenia and Skeletal Muscle Loss During Neoadjuvant Chemoradiotherapy in Esophageal Cancer

Yoon

Ahn

et al. 2020

Cancers

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Backgrounds: The relationship between sarcopenia, characterized by loss of muscle mass and strength, and survival outcomes of esophageal cancer is controversial. This study aimed to assess the effect of sarcopenia and skeletal muscle loss on overall survival (OS) and recurrence-free survival (RFS) of esophageal cancer patients. Methods: We retrospectively collected the medical records of 248 male patients diagnosed with squamous cell esophageal cancer and who underwent neoadjuvant chemoradiotherapy (NACRT) followed by surgery. We measured the cross-sectional area of the skeletal muscle at the L3 vertebra level using computed tomography images and calculated the skeletal muscle index (SMI). Sarcopenia was defined as SMI <52.4 cm2/m2, and excessive muscle loss was defined as SMI change <−10.0%/50 days during NACRT. Moreover, laboratory test results, such as albumin, prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) before and after NACRT, were collected. Results: In the univariable Cox analysis, pre- (p = 0.689) and post-radiotherapy (RT) sarcopenia (p = 0.669) were not associated with OS. However, excessive muscle loss had a significant association with OS in both the univariable and multivariable analyses (all p = 0.001). Excessive muscle loss was also related to RFS in both the univariable (p = 0.011) and multivariable (p = 0.022) Cox analysis. Patients with excessive muscle loss had significantly lower levels of post-RT albumin (p < 0.001) and PNI (p < 0.001), higher levels of post-RT NLR (p = 0.031) and PLR (p = 0.071), larger decrease in albumin (p < 0.001) and PNI (p < 0.001) after NACRT, and larger increase in NLR (p = 0.051) and PLR (p = 0.088) after NACRT than in those with non-excessive muscle loss. Conclusion: Excessive muscle loss rather than pre- and post-RT sarcopenia was a significant prognostic factor for OS and RFS, and it was also related to nutritional and inflammatory markers.

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“…The tumour itself releases molecules and pro‐inflammatory cytokines that can impair the protein synthesis and muscle regeneration mechanisms. 29 Neoadjuvant treatment can further exacerbate muscle wasting, 30 , 31 reduce muscle strength, 32 , 33 and cause adverse effects such as esophagitis, dysphagia, nausea, vomiting, and anorexia. 34 , 35 Our previous study has shown that excessive skeletal muscle loss after treatment is a significant factor for prognosis and recurrence rather than the presence of sarcopenia itself prior to treatment.…”

Section: Discussionmentioning

confidence: 99%

Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer

Yoon

Noh

et al. 2021

J cachexia sarcopenia muscle

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Background Excessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour-intensive. In this study, we built machine-learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results. Methods We randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine-learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio over 50 days. According to our previous study results, which used the maximal χ 2 method, 10.0% decrease of skeletal muscle index over 50 days was determined as the cut-off value to define the excessive skeletal muscle loss. ResultsThe five input variables were significantly different between the excessive and the non-excessive muscle loss group (all P < 0.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve = 0.808, 95% confidence interval (CI): 0.708-0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%-89.5%) and 74.5% (95% CI: 62.7%-86.3%), respectively. Conclusions Machine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT.

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Metabolic and Molecular Basis of Sarcopenia: Implications in the Management of Urothelial Carcinoma

Cited by 23 publications

References 81 publications

Metabolic Syndrome and Risk of Upper Tract Urothelial Carcinoma: A Case-Control Study From Surveillance, Epidemiology and End Results-Medicare-Linked Database

Metabolic Syndrome and Risk of Upper Tract Urothelial Carcinoma: A Case-Control Study From Surveillance, Epidemiology and End Results-Medicare-Linked Database

Prognostic Impact of Sarcopenia and Skeletal Muscle Loss During Neoadjuvant Chemoradiotherapy in Esophageal Cancer

Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer

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