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Pasta Consumption and Connected Dietary Habits: Associations with Glucose Control, Adiposity Measures, and Cardiovascular Risk Factors in People with Type 2 Diabetes-TOSCA.IT Study.
Vitale, M, Masulli, M, Rivellese, AA, Bonora, E, Babini, AC, Sartore, G, Corsi, L, Buzzetti, R, Citro, G, Baldassarre, MPA, et al
Nutrients. 2019;(1)
Abstract
BACKGROUND Pasta is a refined carbohydrate with a low glycemic index. Whether pasta shares the metabolic advantages of other low glycemic index foods has not really been investigated. The aim of this study is to document, in people with type-2 diabetes, the consumption of pasta, the connected dietary habits, and the association with glucose control, measures of adiposity, and major cardiovascular risk factors. METHODS We studied 2562 participants. The dietary habits were assessed with the European Prospective Investigation into Cancer and Nutrition (EPIC) questionnaire. Sex-specific quartiles of pasta consumption were created in order to explore the study aims. RESULTS A higher pasta consumption was associated with a lower intake of proteins, total and saturated fat, cholesterol, added sugar, and fiber. Glucose control, body mass index, prevalence of obesity, and visceral obesity were not significantly different across the quartiles of pasta intake. No relation was found with LDL cholesterol and triglycerides, but there was an inverse relation with HDL-cholesterol. Systolic blood pressure increased with pasta consumption; but this relation was not confirmed after correction for confounders. CONCLUSIONS In people with type-2 diabetes, the consumption of pasta, within the limits recommended for total carbohydrates intake, is not associated with worsening of glucose control, measures of adiposity, and major cardiovascular risk factors.
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Influence of dietary fat and carbohydrates proportions on plasma lipids, glucose control and low-grade inflammation in patients with type 2 diabetes-The TOSCA.IT Study.
Vitale, M, Masulli, M, Rivellese, AA, Babini, AC, Boemi, M, Bonora, E, Buzzetti, R, Ciano, O, Cignarelli, M, Cigolini, M, et al
European journal of nutrition. 2016;(4):1645-51
Abstract
PURPOSE The optimal macronutrient composition of the diet for the management of type 2 diabetes is debated, particularly with regard to the ideal proportion of fat and carbohydrates. The aim of the study was to explore the association of different proportions of fat and carbohydrates of the diet-within the ranges recommended by different guidelines-with metabolic risk factors. METHODS We studied 1785 people with type 2 diabetes, aged 50-75, enrolled in the TOSCA.IT Study. Dietary habits were assessed using a validated food-frequency questionnaire (EPIC). Anthropometry, fasting lipids, HbA1c and C-reactive protein (CRP) were measured. RESULTS Increasing fat intake from <25 to ≥35 % is associated with a significant increase in LDL-cholesterol, triglycerides, HbA1c and CRP (p < 0.05). Increasing carbohydrates intake from <45 to ≥60 % is associated with significantly lower triglycerides, HbA1c and CRP (p < 0.05). A fiber intake ≥15 g/1000 kcal is associated with a better plasma lipids profile and lower HbA1c and CRP than lower fiber consumption. A consumption of added sugars of ≥10 % of the energy intake is associated with a more adverse plasma lipids profile and higher CRP than lower intake. CONCLUSIONS In people with type 2 diabetes, variations in the proportion of fat and carbohydrates of the diet, within the relatively narrow ranges recommended by different nutritional guidelines, significantly impact on the metabolic profile and markers of low-grade inflammation. The data support the potential for reducing the intake of fat and added sugars, preferring complex, slowly absorbable, carbohydrates.
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The combination of UCP3-55CT and PPARγ2Pro12Ala polymorphisms affects BMI and substrate oxidation in two diabetic populations.
Lapice, E, Monticelli, A, Cocozza, S, Pinelli, M, Massimino, E, Giacco, A, Rivellese, AA, Cocozza, S, Riccardi, G, Vaccaro, O
Nutrition, metabolism, and cardiovascular diseases : NMCD. 2016;(5):400-6
Abstract
BACKGROUND AND AIM To evaluate the combined contribution of UCP3-55CT and PPARγ2 Pro12Ala polymorphisms as correlates of BMI, energy expenditure (REE) and substrate oxidation in people with type 2 diabetes. METHODS AND RESULTS Two independent population with type 2 diabetes were studied: population A, n = 272; population B, n = 269. Based on both UCP3 and PPARγ2 genotypes three groups were created. Carriers of the PPARγ2 Pro12Ala in combination with the CC genotype of UCP3 (ProAla/CC, group 1); carriers of only one of these genotypes (either CC/ProPro or CT-TT/ProAla, group 2); people with neither variants (CT-TT/ProPro, group 3). In both populations BMI (kg/m(2)) was highest in group 1, intermediate in group 2 and lowest in group 3, independent of energy intake (i.e 35.3 ± 6.7 vs 33.4 ± 5.4 vs 31.8 ± 3, p < 0.02, population A; 32.4 ± 4.2 vs 31.7 ± 3.8 vs 30.1 ± 2.7; p < 0.03, population B). People with the ProAla/CC genotype (group 1) showed similar REE, but lower lipid oxidation (10.9 vs 13.9 g/kg fat free mass/day; p = 0.04) and higher carbohydrate oxidation (23.6 vs 15.6 g/kg fat free mass/day; p = 0.02) than carriers of other genotypes. CONCLUSIONS The combination of UCP3-55 CC and PPARγ2 Pro12Ala genotypes is associated with significantly higher BMI than other PPARγ2-UCP3 genotype combinations, partly due to a reduced ability in lipids oxidation. The relative importance of these mechanism(s) may be different in non diabetic people.
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Glycaemic load versus carbohydrate counting for insulin bolus calculation in patients with type 1 diabetes on insulin pump.
Bozzetto, L, Giorgini, M, Alderisio, A, Costagliola, L, Giacco, A, Riccardi, G, Rivellese, AA, Annuzzi, G
Acta diabetologica. 2015;(5):865-71
Abstract
AIMS: To evaluate feasibility and effectiveness on short-term blood glucose control of using glycaemic load counting (GLC) versus carbohydrate counting (CC) for prandial insulin dosing in patients with type 1 diabetes (T1D). METHODS Nine T1D patients on insulin pump, aged 26-58 years, HbA1c 7.7 ± 0.8 % (61 ± 8.7 mmol/mol), participated in this real-life setting study. By a crossover design, patients were randomised to calculate their pre-meal insulin dose based on the insulin/glycaemic load ratio (GLC period) or the insulin/carbohydrate ratio (CC period) for 1 week, shifting to the alternate method for the next week, when participants duplicated their first week food plan. Over either week, a blind subcutaneous continuous glucose monitoring was performed, and a 7-day food record was filled in. RESULTS Total daily insulin doses (45 ± 10 vs. 44 ± 9 I.U.; M ± SD, p = 0.386) and basal infusion (26 ± 7 vs. 26 ± 8 I.U., p = 0.516) were not different during GLC and CC periods, respectively. However, the range of insulin doses (difference between highest and lowest insulin dose) was wider during GLC, with statistical significance at dinner (8.4 ± 6.2 vs. 6.0 ± 3.9 I.U., p = 0.041). Blood glucose iAUC after lunch was lower, albeit not significantly, during GLC than CC period (0.6 ± 8.6 vs. 3.4 ± 8.2 mmol/l∙3 h, p = 0.059). Postprandial glucose variability, evaluated as the maximal amplitude after meal (highest minus lowest glucose value), was significantly lower during GLC than CC period at lunch (4.22 ± 0.28 vs. 5.47 ± 0.39 mmol/l, p = 0.002) and dinner (3.89 ± 0.33 vs. 4.89 ± 0.33, p = 0.026). CONCLUSIONS Calculating prandial insulin bolus based on glycaemic load counting is feasible in a real-life setting and may improve postprandial glucose control in people with T1D.