Background Cancer cachexia is a poorly understood metabolic consequence of cancer. During cachexia, different adipose depots demonstrate differential wasting rates. Animal models suggest adipose tissue may be a key driver of muscle wasting through fat–muscle crosstalk, but human studies in this area are lacking. We performed global gene expression profiling of visceral (VAT) and subcutaneous (SAT) adipose from weight stable and cachectic cancer patients and healthy controls. Methods Cachexia was defined as >2% weight loss plus low computed tomography‐muscularity. Biopsies of SAT and VAT were taken from patients undergoing resection for oesophago‐gastric cancer, and healthy controls ( n = 16 and 8 respectively). RNA was isolated and reverse transcribed. cDNA was hybridised to the Affymetrix Clariom S microarray and data analysed using R/Bioconductor. Differential expression of genes was assessed using empirical Bayes and moderated‐ t ‐statistic approaches. Category enrichment analysis was used with a tissue‐specific background to examine the biological context of differentially expressed genes. Selected differentially regulated genes were validated by qPCR. Enzyme‐linked immunosorbent assay (ELISA) for intelectin‐1 was performed on all VAT samples. The previously‐described cohort plus 12 additional patients from each group also had plasma I = intelectin‐1 ELISA carried out. Results In VAT vs. SAT comparisons, there were 2101, 1722, and 1659 significantly regulated genes in the cachectic, weight stable, and control groups, respectively. There were 2200 significantly regulated genes from VAT in cachectic patients compared with controls. Genes involving inflammation were enriched in cancer and control VAT vs. SAT, although different genes contributed to enrichment in each group. Energy metabolism, fat browning (e.g. uncoupling protein 1), and adipogenesis genes were down‐regulated in cancer VAT (P = 0.043, P = 5.4 × 10 −6 and P = 1 × 10 −6 respectively). The gene showing the largest difference in expression was ITLN1, the gene that encodes for intelectin‐1 (false discovery rate‐corrected P = 0.0001), a novel adipocytokine associated with weight loss in other contexts. Conclusions SAT and VAT have unique gene expression signatures in cancer and cachexia. VAT is metabolically active in cancer, and intelectin‐1 may be a target for therapeutic manipulation. VAT may play a fundamental role in cachexia, but the down‐regulation of energy metabolism genes implies a limited role for fat browning in cachectic patients, in contrast to pre‐clinical models.
Background: Stress and poor mental health are significant issues in the workplace and are a major cause of absenteeism and reduced productivity. Understanding what might contribute towards employee stress is important for managing mental health in this setting. Physical activity has been shown to be beneficial to stress but less research has addressed the potential negative impact of sedentary behaviour such as sitting. Therefore, the aim of this study was to assess the relationship between device-measured occupational desk-based sitting patterns and stress (hair cortisol levels (HCL), as a marker of chronic stress and self-reported perceived stress (PS)). Methods: Employees were recruited from four workplaces located in Central Scotland with large numbers of desk-based occupations. Seventy-seven participants provided desk-based sitting pattern data (desk-based sitting time/day and desk-based sit-to-stand transitions/day), a hair sample and self-reported perceived stress. HCL were measured using enzyme-linked immunosorbent assay and PS using the Cohen Self-Perceived Stress Scale. Linear regression models were used to test associations between desk-based sitting time/day, desk-based sit-to-stand transitions/day, HCL and PS. Results: There were no associations between any of the desk-based sitting measures and either HCL or PS. Conclusions: Desk-based sitting patterns in the workplace may not be related to stress when using HCL as a biomarker of chronic stress or PS. The relationship between sitting patterns and stress therefore requires further investigation.
Nighttime eating is often associated with a negative impact on weight management and cardiometabolic health. However, data from recent acute metabolic studies have implicated a benefit of ingesting a bedtime snack for weight management. The present study compared the impact of ingesting a milk snack containing either 10 (BS10) or 30 g (BS30) of protein with a non-energetic placebo (BS0) 30 min before bedtime on next morning metabolism, appetite and energy intake in mildly overweight males (age: 24.3 (SEM 0.8) years; BMI: 27.4 (SEM 1.1) kg/m 2 ). Next morning measurements of resting metabolic rate (RMR), appetite and energy intake were measured using indirect calorimetry, visual analogue scales and an ad libitum breakfast, respectively. Bedtime milk ingestion did not alter next morning RMR (BS0: 7822 (SEM 276) kJ/day, BS10: 7482 (SEM 262) kJ/day, BS30: 7851 (SEM 261) kJ/day, P = 0.19) or substrate utilisation as measured by respiratory exchange ratio (P = 0.64).Bedtime milk ingestion reduced hunger (P = 0.01) and increased fullness (P = 0.04) during the evening immediately after snack ingestion, but elicited no effect the next morning. Next morning breakfast (BS0: 2187 (SEM 365) kJ, BS10: 2070 (SEM 336) kJ, BS30: 2582 (SEM 384) kJ, P = 0.21) and 24 h post-trial (P = 0.95) energy intake was similar between conditions. To conclude, in mildly overweight adults, compared to a non-energetic placebo, a bedtime milk snack containing 10 or 30 g of protein does not confer changes in next morning whole-body metabolism and appetite that may favour weight management.3
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