METHODS: Using a randomized, crossover and double-blinded design, 15 men and 15 women with metabolic syndrome consumed high-fat meals enriched with SFA, MUFA or n-6 PUFA, or a low-fat/high-sucrose (SUCR) meal. C-peptide, insulin, glucose, gastrointestinal peptides and satiety were measured up to 6 h.
RESULTS: As expected, SUCR meal induced higher C-peptide (45 %), insulin (45 %) and glucose (49 %) responses compared with high-fat meals regardless of types of fatty acids (P < 0.001). Interestingly, incremental area under the curve (AUC0-120min) for glucagon-like peptide-1 was higher after SUCR meal compared with MUFA (27 %) and n-6 PUFA meals (23 %) (P = 0.01). AUC0-120min for glucose-dependent insulinotropic polypeptide was higher after SFA meal compared with MUFA (23 %) and n-6 PUFA meals (20 %) (P = 0.004). Significant meal x time interaction (P = 0.007) was observed for ghrelin, but not cholecystokinin and satiety.
CONCLUSIONS: The amount of fat regardless of the types of fatty acids affects insulin and glycemic responses. Both the amount and types of fatty acids acutely affect the gastrointestinal peptide release in metabolic syndrome subjects, but not satiety.
METHODS: We randomized 108 overweight and obese patients with T2D (46 M/62F; age 60 ± 10 years; HbA1c 8.07 ± 1.05%; weight 101.4 ± 21.1 kg and BMI 35.2 ± 7.7 kg/m2) into three groups. Group A met with RDN to develop an individualized eating plan. Group B met with RDN and followed a structured meal plan. Group C did similar to group B and received weekly phone support by RDN.
RESULTS: After 16 weeks, all three groups had a significant reduction of their energy intake compared to baseline. HbA1c did not change from baseline in group A, but decreased significantly in groups B (- 0.66%, 95% CI -1.03 to - 0.30) and C (- 0.61%, 95% CI -1.0 to - 0.23) (p value for difference among groups over time
METHODS: This human postprandial study evaluated 3 edible fat blends with differing polyunsaturated to saturated fatty acids (P/S) ratios (POL = 0.27, AHA = 1.00, PCAN = 1.32). A cross-over design included mildly hypercholestrolemic subjects (9 men and 6 women) preconditioned on test diets fats at 31% energy for 7 days prior to the postprandial challenge on the 8th day with 50 g test fat. Plasma lipids and lipoproteins were monitored at 0, 1.5, 3.5, 5.5 and 7 hr.
RESULTS: Plasma triacylglycerol (TAG) concentrations in response to POL, AHA or PCAN meals were not significant for time x test meal interactions (P > 0.05) despite an observed trend (POL > AHA > PCAN). TAG area-under-the-curve (AUC) increased by 22.58% after POL and 7.63% after PCAN compared to AHA treatments (P > 0.05). Plasma total cholesterol (TC) response was not significant between meals (P > 0.05). Varying P/S ratios of test meals significantly altered prandial high density lipoprotein-cholesterol (HDL-C) concentrations (P AHA > PCAN). Paired comparisons was significant between POL vs PCAN (P = 0.009) but not with AHA or between AHA vs PCAN (P > 0.05). A significantly higher HDL-C AUC for POL vs AHA (P = 0.015) and PCAN (P = 0.001) was observed. HDL-C AUC increased for POL by 25.38% and 16.0% compared to PCAN and AHA respectively. Plasma low density lipoprotein-cholesterol (LDL-C) concentrations was significant (P = 0.005) between meals and significantly lowest after POL meal compared to PCAN (P = 0.004) and AHA (P > 0.05) but not between AHA vs PCAN (P > 0.05). AUC for LDL-C was not significant between diets (P > 0.05). Palmitic (C16:0), oleic (C18:1), linoleic (C18:2) and linolenic (C18:3) acids in TAGs and cholesteryl esters were significantly modulated by meal source (P
DESIGN AND METHODS: This is a cross-sectional study conducted among 184 eligible hemodialysis patients at four dialysis units in Malaysia. Three days dietary recall were used in the analysis of dietary intake and behavior. Sleep quality was assessed through Pittsburgh Sleep Quality Index.
RESULTS: More than half of the patients were poor sleepers. Among the sleep components, sleep latency affected patients the most, with the use of sleep medications was relatively low. A majority of the patients had inadequate dietary intake of energy (88%) and protein (75%). Dietary protein, potassium adjusted for body weight, and sodium intake were significantly increased in poor sleepers. Lower percentage of energy from carbohydrates; higher percentage of energy from fats; higher intakes of dietary protein, fat, phosphorus, and sodium were correlated with poorer sleep quality and its components. Skipping dinner on non-dialysis days and having supper on dialysis days were associated with poor sleep quality.
CONCLUSION: Poor sleep is prevalent among hemodialysis patients. Sleep quality of hemodialysis patients was highly associated with certain dietary factors. Periodical assessment of sleep quality and dietary intake is necessary to identify poor sleepers with inappropriate dietary intake to allow effective clinical and nutritional interventions to improve the sleep quality and nutritional status of these patients.