Cancer & Metabolism最新文献

Background: The relationship between obesity and non-Hodgkin's lymphoma (NHL) was controversial, which may be due to the crudeness definition of obesity based on body mass index (BMI). As obesity and metabolic abnormalities often coexist, we aimed to explore whether the classification of obesity based on metabolic status can help to evaluate the real impact of obesity on the readmission of NHL.

Methods: In this retrospective cohort study, utilizing the 2018 Nationwide Readmissions Database, we identified NHL-related index hospitalizations and followed them for non-elective readmission. The patients with NHL were classified as metabolically healthy non-obese (MHNO) and obese (MHO) and metabolically unhealthy non-obese (MUNO) and obese (MUO). Readmission rates for each phenotype were calculated at 30-day intervals. Multiple COX regression was used to analyze the association of metabolic-defined obesity with 30-day, 90-day, and 180-day readmission rates in patients with NHL.

Results: There were 22,086 index hospitalizations with NHL included. In the multivariate COX regression, MUNO was associated with increased 30-day (HR = 1.113, 95% CI 1.036-1.195), 90-day (HR = 1.148, 95% CI 1.087-1.213), and 180-day readmission rates (HR = 1.132, 95% CI 1.077-1.189), and MUO was associated with increased 30-day (HR=1.219, 95% CI: 1.081-1.374), 90-day (HR = 1.228, 95% CI 1.118-1.348), and 180-day readmission rates (HR = 1.223, 95% CI 1.124-1.33), while MHO had no associations with readmission rates.

Conclusions: The presence of metabolic abnormalities with or without obesity increased the risk of non-selective readmission in patients with NHL. However, obesity alone had no associations with the risk of non-selective readmission, suggesting that interventions for metabolic abnormalities may be more important in reducing readmissions of NHL patients.

Background: Bladder cancer (BLCA) research in Koreans is still lacking, especially in focusing on the prediction of BLCA. The current study aimed to discover metabolic signatures related to BLCA onset and confirm its potential as a biomarker.

Methods: We designed two nested case-control studies using Korean Cancer Prevention Study (KCPS)-II. Only males aged 35-69 were randomly selected and divided into two sets by recruitment organizations [set 1, BLCA (n = 35) vs. control (n = 35); set 2, BLCA (n = 31) vs. control (n = 31)]. Baseline serum samples were analyzed by non-targeted metabolomics profiling, and OPLS-DA and network analysis were performed. Calculated genetic risk score (GRS) for BLCA from all KCPS participants was utilized for interpreting metabolomics data.

Results: Critical metabolic signatures shown in the BLCA group were dysregulation of lysine metabolism and tryptophan-indole metabolism. Furthermore, the prediction model consisting of metabolites (lysine, tryptophan, indole, indoleacrylic acid, and indoleacetaldehyde) reflecting these metabolic signatures showed mighty BLCA predictive power (AUC: 0.959 [0.929-0.989]). The results of metabolic differences between GRS-high and GRS-low groups in BLCA indicated that the pathogenesis of BLCA is associated with a genetic predisposition. Besides, the predictive ability for BLCA on the model using GRS and five significant metabolites was powerful (AUC: 0.990 [0.980-1.000]).

Conclusion: Metabolic signatures shown in the present research may be closely associated with BLCA pathogenesis. Metabolites involved in these could be predictive biomarkers for BLCA. It could be utilized for early diagnosis, prognostic diagnosis, and therapeutic targets for BLCA.

Background: Macrophages are one of the most prevalent subsets of immune cells within the tumor microenvironment and perform a range of functions depending on the cytokines and chemokines released by surrounding cells and tissues. Recent research has revealed that macrophages can exhibit a spectrum of phenotypes, making them highly plastic due to their ability to alter their physiology in response to environmental cues. Recent advances in examining heterogeneous macrophage populations include optical metabolic imaging, such as fluorescence lifetime imaging (FLIM), and multiphoton microscopy. However, the method of detection for these systems is reliant upon the coenzymes NAD(P)H and FAD, which can be affected by factors other than cytoplasmic metabolic changes. In this study, we seek to validate these optical measures of metabolism by comparing optical results to more standard methods of evaluating cellular metabolism, such as extracellular flux assays and the presence of metabolic intermediates.

Methods: Here, we used autofluorescence imaging of endogenous metabolic co-factors via multiphoton microscopy and FLIM in conjunction with oxygen consumption rate and extracellular acidification rate through Seahorse extracellular flux assays to detect changes in cellular metabolism in quiescent and classically activated macrophages in response to cytokine stimulation.

Results: Based on our Seahorse XFP flux analysis, M0 and M1 macrophages exhibit comparable trends in oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Autofluorescence imaging of M0 and M1 macrophages was not only able to show acute changes in the optical redox ratio from pre-differentiation (0 hours) to 72 hours post-cytokine differentiation (M0: 0.320 to 0.258 and M1: 0.316 to 0.386), mean NADH lifetime (M0: 1.272 ns to 1.379 ns and M1: 1.265 ns to 1.206 ns), and A1/A2 ratio (M0: 3.452 to ~ 4 and M1: 3.537 to 4.529) but could also detect heterogeneity within each macrophage population.

Conclusions: Overall, the findings of this study suggest that autofluorescence metabolic imaging could be a reliable technique for longitudinal tracking of immune cell metabolism during activation post-cytokine stimulation.

Background: Breast cancer (BC) is the most prevalent tumor in women. Improvements in treatment led to declined mortality, resulting in more survivors living with cancer- or therapy-induced comorbidities. In this study, we investigated the impact of neoplasia and chemotherapy on resting energy expenditure (REE) and body composition, in relation to cancer-related fatigue. Inflammatory parameters were checked as possible explanation for changes in REE.

Methods: Fifty-six women participated: 20 women with BC and 36 healthy controls. Patients were assessed at baseline (T0) and follow-up (T1) after 12 weeks of chemotherapy. Controls were measured once. REE was assessed with indirect calorimetry: body composition (body weight, fat mass, fat-free mass) by air plethysmography. The multidimensional fatigue index (MFI-20) was used to analyze fatigue. Baseline measurements of patients were compared to results of the healthy controls with the independent-samples T-test. The paired-samples T-test investigated the effects of chemotherapy from T0 to T1. A Pearson correlation analysis was conducted between REE, body composition, and fatigue and between REE, body composition, and inflammatory parameters. A linear regression analysis was fitted to estimate the contribution of the significantly correlated parameters. The measured REE at T0 and T1 was compared to the predicted REE to analyze the clinical use of the latter.

Results: At baseline, patients with BC had significantly higher REE in the absence of differences in body composition. From baseline to T1, REE and body weight did not change. In contrast, fat-free mass declined significantly with concordant increase in fat mass. Fatigue deteriorated significantly. C-reactive protein at baseline predicted the change in energy expenditure. Predicted REE significantly underestimated measured REE.

Conclusions: Women with BC have higher REE in the tumor-bearing state compared to healthy controls. Chemotherapy does not affect REE but alters body composition. Predictive equations are invalid in the BC population. Results of our study can be used to implement personalized nutritional interventions to support energy expenditure and body composition and minimize long-term comorbidities.