Late isocaloric eating increases hunger, decreases energy expenditure, and modifies metabolic pathways in adults with overweight and obesity.

Late eating has been linked to obesity risk. It is unclear whether this is caused by changes in hunger and appetite, energy expenditure, or both, and whether molecular pathways in adipose tissues are involved. Therefore, we conducted a randomized, controlled, crossover trial (ClinicalTrials.gov NCT02298790) to determine the effects of late versus early eating while rigorously controlling for nutrient intake, physical activity, sleep, and light exposure. Late eating increased hunger (p < 0.0001) and altered appetite-regulating hormones, increasing waketime and 24-h ghrelin:leptin ratio (p < 0.0001 and p = 0.006, respectively). Furthermore, late eating decreased waketime energy expenditure (p = 0.002) and 24-h core body temperature (p = 0.019). Adipose tissue gene expression analyses showed that late eating altered pathways involved in lipid metabolism, e.g., p38 MAPK signaling, TGF-ß signaling, modulation of receptor tyrosine kinases, and autophagy, in a direction consistent with decreased lipolysis/increased adipogenesis. These findings show converging mechanisms by which late eating may result in positive energy balance and increased obesity risk.

SELDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of a boosting decision tree model for diagnosis of pancreatic cancer.

AIM: New technologies for the early detection of pancreatic cancer (PC) are urgently needed. The aim of the present study was to screen for the potential protein biomarkers in serum using proteomic fingerprint technology. METHODS: Magnetic beads combined with surface-enhanced laser desorption/ionization (SELDI) TOF MS were used to profile and compare the protein spectra of serum samples from 85 patients with pancreatic cancer, 50 patients with acute-on-chronic pancreatitis and 98 healthy blood donors. Proteomic patterns associated with pancreatic cancer were identified with Biomarker Patterns Software. RESULTS: A total of 37 differential m/z peaks were identified that were related to PC (P<0.01). A tree model of biomarkers was constructed with the software based on the three biomarkers (7762 Da, 8560 Da, 11654 Da), this showing excellent separation between pancreatic cancer and non-cancer., with a sensitivity of 93.3% and a specificity of 95.6%. Blind test data showed a sensitivity of 88% and a specificity of 91.4%. CONCLUSIONS: The results suggested that serum biomarkers for pancreatic cancer can be detected using SELDI-TOF-MS combined with magnetic beads. Application of combined biomarkers may provide a powerful and reliable diagnostic method for pancreatic cancer with a high sensitivity and specificity.