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Bean and rice meals reduce postprandial glycemic response in adults with type 2 diabetes: a cross-over study.

ABSTRACT: Around the world, beans and rice are commonly consumed together as a meal. With type 2 diabetes increasing, the effect of this traditional diet pattern on glycemic response has not been studied fully.We evaluated the glycemic response of bean and rice traditional meals compared to rice alone in adults with type 2 diabetes. Seventeen men and women with type 2 diabetes controlled by metformin (n?=?14) or diet/exercise (n?=?3) aged 35-70 years participated in the randomized 4?×?4 crossover trial. The white long grain rice control, pinto beans/rice, black beans/rice, red kidney beans/rice test meals, matched for 50 grams of available carbohydrate, were consumed at breakfast after a 12 hour fast. Capillary blood glucose concentrations at baseline and at 30 minute intervals up to 180 minutes postprandial were collected. MANOVA for repeated measures established glucose differences between treatments. Paired t tests identified differences between bean types and the rice control following a significant MANOVA.Postprandial net glucose values were significantly lower for the three bean/rice treatments in contrast to the rice control at 90, 120 and 150 minutes. Incremental area under the curve values were significantly lower for the pinto and black bean/rice meals compared to rice alone, but not for kidney beans.Pinto, dark red kidney and black beans with rice attenuate the glycemic response compared to rice alone. Promotion of traditional foods may provide non-pharmaceutical management of type 2 diabetes and improve dietary adherence with cultural groups.

SUBMITTER: Thompson SV

PROVIDER: S-EPMC3489574 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Bean and rice meals reduce postprandial glycemic response in adults with type 2 diabetes: a cross-over study.

Thompson Sharon V SV Winham Donna M DM Hutchins Andrea M AM

Nutrition journal 20120411

<h4>Background</h4>Around the world, beans and rice are commonly consumed together as a meal. With type 2 diabetes increasing, the effect of this traditional diet pattern on glycemic response has not been studied fully.<h4>Methods</h4>We evaluated the glycemic response of bean and rice traditional meals compared to rice alone in adults with type 2 diabetes. Seventeen men and women with type 2 diabetes controlled by metformin (n = 14) or diet/exercise (n = 3) aged 35-70 years participated in the ...[more]

PMID: 22494488

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Project description:BACKGROUND:Postprandial lipemia is a risk factor for cardiovascular disease. Dairy products differ in nutrient content and food matrix, and little is known about how different dairy products affect postprandial triglyceride (TG) concentrations. OBJECTIVE:We investigated the effect of meals with similar amounts of fat from different dairy products on postprandial TG concentrations over 6 h in healthy adults. METHODS:A randomized controlled cross-over study was performed on 47 subjects (30% men), with median (25th-75th percentile) age of 32 (25-46) y and body mass index of 23.6 (21.0-25.8) kg/m2. Meals included 1 of butter, cheese, whipped cream, or sour cream, corresponding to 45 g of fat (approximately 60 energy%). Serum concentrations of TGs (primary outcome), and total cholesterol (TC), low density lipoprotein cholesterol (LDL cholesterol), high density lipoprotein cholesterol (HDL cholesterol), insulin, glucose, non-esterified fatty acids, and plasma glucose-dependent insulinotropic polypeptide (secondary outcomes) were measured before the meal and 2, 4, and 6 h postprandially. Incremental AUC (iAUC) was calculated for the responses, and data were analyzed using a linear mixed model. RESULTS:Sour cream induced a 61% larger TG-iAUC0-6 h compared to whipped cream (P < 0.001), a 53% larger TG-iAUC0-6 h compared to butter (P < 0.001), and a 23% larger TG-iAUC0-6 h compared to cheese (P = 0.05). No differences in TG-iAUC0-6 h between the other meals were observed. Intake of sour cream induced a larger HDL cholesterol-iAUC0-6 h compared to cheese (P = 0.01). Intake of cheese induced a 124% larger insulin iAUC0-6 h compared to butter (P = 0.006). No other meal effects were observed. CONCLUSIONS:High-fat meals containing similar amount of fat from different dairy products induce different postprandial effects on serum TGs, HDL cholesterol, and insulin in healthy adults. The potential mechanisms and clinical impact of our findings remain to be further elucidated. The study was registered at www.clinicaltrials.gov as NCT02836106.

Project description:In the clinic setting both fasting levels of glucose and the area under the curve (AUC) of glucose, by determination of HbA1c levels, are used for risk assessments, in type 2 diabetes (NIDDM). However little is known about postprandial levels, and hence AUC, regarding other traditional risk factors such as insulin and blood-lipids and how this is affected by different diets.To study postprandial effects of three diets, during a single day, in NIDDM.A low-fat diet (45-56 energy-% from carbohydrates), and a low-carbohydrate diet (16-24 energy-% from carbohydrates) was compared with a Mediterranean-style diet (black coffee for breakfast and the same total-caloric intake as the other two diets for lunch with red wine, 32-35 energy-% from carbohydrates) in a randomized cross-over design. Total-caloric intake/test-day at the clinic from food was 1025-1080 kCal in men and 905-984 kCal in women. The test meals were consumed at a diabetes ward under supervision.Twenty-one participants were recruited and 19 completed the studies. The low-carbohydrate diet induced lower insulin and glucose excursions compared with the low-fat diet (p<0.0005 for both AUC). The insulin-response following the single Mediterranean-style lunch-meal was more pronounced than during the low-fat diet lunch (insulin increase-ratio of the low-fat diet: 4.35 ± 2.2, of Mediterranean-style diet: 8.12 ± 5.2, p?=?0.001) while postprandial glucose levels were similar. The increase-ratio of insulin correlated with the elevation of the incretin glucose-dependent insulinotropic-polypeptide following the Mediterranean-style diet lunch (Spearman, r?=?0.64, p?=?0.003).The large Mediterranean-style lunch-meal induced similar postprandial glucose-elevations as the low-fat meal despite almost double amount of calories due to a pronounced insulin-increase. This suggests that accumulation of caloric intake from breakfast and lunch to a single large Mediterranean style lunch-meal in NIDDM might be advantageous from a metabolic perspective.ClinicalTrials.gov NCT01522157 NCT01522157.

Project description:BackgroundThe breakfast meal often results in the largest postprandial hyperglycemic excursion in people with type 2 diabetes.ObjectiveOur purpose was to investigate whether restricting carbohydrates at breakfast would be a simple and feasible strategy to reduce daily exposure to postprandial hyperglycemia.DesignAdults with physician-diagnosed type 2 diabetes [n = 23; mean ± SD age: 59 ± 11 y; glycated hemoglobin: 6.7% ± 0.6%; body mass index (kg/m2): 31 ± 7] completed two 24-h isocaloric intervention periods in a random order. Participants consumed one of the following breakfasts: 1) a very-low-carbohydrate high-fat breakfast (LCBF; <10% of energy from carbohydrate, 85% of energy from fat, 15% of energy from protein) or 2) a breakfast with dietary guidelines-recommended nutrient profile (GLBF; 55% of energy from carbohydrate, 30% of energy from fat, 15% of energy from protein), with the same lunch and dinner provided. Continuous glucose monitoring was used to assess postprandial glucose responses over 24 h, and visual analog scales were used to assess ratings of hunger and fullness.ResultsThe LCBF significantly reduced postprandial hyperglycemia after breakfast (P < 0.01) and did not adversely affect glycemia after lunch or dinner. As such, overall postprandial hyperglycemia (24-h incremental area under the glucose curve) and glycemic variability (mean amplitude of glycemic excursions) were reduced with the LCBF (24-h incremental area under the glucose curve: -173 ± 361 mmol/L; P = 0.03; mean amplitude of glycemic excursions: -0.4 ± 0.8 mmol/L · 24 h; P = 0.03) compared with the GLBF. Premeal hunger was lower before dinner with the LCBF than with the GLBF (P-interaction = 0.03).ConclusionsA very-low-carbohydrate high-fat breakfast lowers postbreakfast glucose excursions. The effects of this simple strategy appear to be sufficient to lower overall exposure to postprandial hyperglycemia and improve glycemic variability. Longer-term interventions are warranted. This trial was registered at clinicaltrials.gov as NCT02982330.

Project description:ImportanceEmerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels.ObjectivesTo describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed.Design, setting, and participantsThis cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied.Main outcomes and measuresGlycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed.ResultsPostprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels.Conclusions and relevanceAcross the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

Project description:BACKGROUND/OBJECTIVE:There is substantial interest in dietary approaches to reducing postprandial glucose (PPG) responses, but the quantitative contribution of PPG to longer-term glycemic control (reflected in glycated hemoglobin, HbA1c) in the general population is not known. This study quantified the associations of preprandial glucose exposure, PPG exposure, and glycemic variability with HbA1c and estimated the explained variance in HbA1c in individuals with and without type 2 diabetes (T2D). SUBJECTS/METHODS:Participants in the A1c-Derived Average Glucose (ADAG) study without T2D (n = 77) or with non-insulin-treated T2D and HbA1c<6.5% (T2DHbA1c?<?6.5%, n = 63) or HbA1c???6.5% (T2DHbA1c???6.5%, n = 34) were included in this analysis. Indices of preprandial glucose, PPG, and glycemic variability were calculated from continuous glucose monitoring during four periods over 12 weeks prior to HbA1c measurement. In linear regression models, we estimated the associations of the glycemic exposures with HbA1c and calculated the proportion of variance in HbA1c explained by glycemic and non-glycemic factors (age, sex, body mass index, and ethnicity). RESULTS:The factors in the analysis explained 35% of the variance in HbA1c in non-diabetic individuals, 49% in T2DHbA1c?<?6.5%, and 78% in T2DHbA1c???6.5%. In non-diabetic individuals PPG exposure was associated with HbA1c in confounder-adjusted analyses (P?<?0.05). In the T2DHbA1c?<?6.5% group, all glycemic measures were associated with HbA1c (P?<?0.05); preprandial glucose and PPG accounted for 14 and 18%, respectively, of the explained variation. In T2DHbA1c???6.5%, these glycemic exposures accounted for more than 50% of the variation in HbA1c and with equal relative contributions. CONCLUSIONS:Among the glycemic exposures, PPG exposure was most strongly predictive of HbA1c in non-diabetic individuals, suggesting that interventions targeting lowering of the PPG response may be beneficial for long-term glycemic maintenance. In T2D, preprandial glucose and PPG exposure contributed equally to HbA1c.

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Bean and rice meals reduce postprandial glycemic response in adults with type 2 diabetes: a cross-over study.

Publications

Bean and rice meals reduce postprandial glycemic response in adults with type 2 diabetes: a cross-over study.

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