Carbohydrate-last meal pattern lowers postprandial glucose and insulin excursions in type 2 diabetes.
Quanto cambia la glicemia se gli stessi cibi si mangiano in un ordine diverso? E' lo studio da cui viene il '54%' che citiamo ovunque.
16 persone con diabete tipo 2, stesso pasto in tre giornate cambiando solo la sequenza. Con i carboidrati per ULTIMI, rispetto ai carboidrati per primi: area incrementale della glicemia nelle 3 ore inferiore del 53% (3.124,7 ± 501,2 contro 6.703,5 ± 904,6 mg/dL × 180 min; p<0,001) e picco incrementale inferiore del 54% (34,7 ± 4,1 contro 75,0 ± 6,5 mg/dL; p<0,001). Anche rispetto al mangiare tutto insieme il vantaggio resta: −44% di area e −40% di picco.
E' la fonte del numero che ripetiamo: 54% in meno di picco senza togliere nulla dal piatto. Il campione e' piccolo, e va detto — ma l'effetto e' enorme, si e' ripetuto in studi successivi e piu' lunghi, e costa solo cambiare l'ordine delle portate.
Abstract (in lingua originale)
Testo integrale (Open Access, in lingua originale)
Research design and methods
Male and female participants between 35 and 65 years of age, body mass index (BMI) 25–40 kg/m2 and metformin-treated T2DM of less than 10 years duration with HbA1c ≤8% were included in the study.
Patients taking corticosteroids, antidiabetic medication other than metformin, and patients with chronic renal or hepatic disease or history of prior bariatric surgery were excluded. The study was approved by the Weill Cornell Medical College Institutional Review Board (IRB#1502015945). All participants gave written informed consent.
We used a crossover design in which all participants consumed isocaloric meals (table 1) of the same composition on three separate days, 1 week apart, after a 12 hours overnight fast. Participants were instructed to maintain their usual level of physical activity and diet throughout the study period and in particular the day prior to each test session. All meals were prepared in the metabolic kitchen of the Clinical and Translational Science Center at Weill Cornell Medical College. Each meal was consumed in 30 min, under the following conditions that were randomly assigned using research randomizer:Carbohydrate first (CF) (ciabatta bread and orange juice) over 10 min, a 10 min rest interval, and then protein (skinless grilled chicken breast) and vegetables (lettuce, tomatoes and cucumber with Italian vinaigrette) over 10 min.Protein and vegetables first over 10 min, a 10 min rest interval, and then carbohydrate over 10 min (carbohydrate last (CL)).All meal components together as a sandwich with each half consumed with half the orange juice over 10 min and a 10 min interval in between (sandwich (S)).
Carbohydrate first (CF) (ciabatta bread and orange juice) over 10 min, a 10 min rest interval, and then protein (skinless grilled chicken breast) and vegetables (lettuce, tomatoes and cucumber with Italian vinaigrette) over 10 min.
Protein and vegetables first over 10 min, a 10 min rest interval, and then carbohydrate over 10 min (carbohydrate last (CL)).
All meal components together as a sandwich with each half consumed with half the orange juice over 10 min and a 10 min interval in between (sandwich (S)).
Participants were closely monitored to ensure that all meals were consumed in their entirety within the allotted time. Blood samples were drawn from an in-dwelling venous cannula at baseline (just before meal ingestion) and at 30 min intervals up to 180 min after the start of the meal. Glucose concentrations were assessed in whole blood using a quantitative enzymatic photometry cassette from Alere (San Diego, California, USA). The intra-assay and inter-assay coefficients of variation are ≤6.2% and≤5.0%, respectively. The plasma concentrations of insulin and glucagon were determined using quantitative immunoradiometric assay kits from Millipore (St. Charles, Missouri, USA). The intra-assay and inter-assay coefficients of variation are ≤4.4% and ≤6.0% for insulin and ≤4.8% and≤6.4% for glucagon, respectively. The measurement range is 3.125–200.0 µU/mL for insulin and 4.7–150 pmol/L for glucagon. The plasma concentration of active GLP-1 was determined using an electrochemiluminescent assay kit from Meso Scale Diagnostics (Rockville, Maryland, USA) with collection of blood samples in the BD P800 blood collection tube containing a proprietary cocktail of protease, esterase and dipeptidyl peptidase IV inhibitors which provides immediate protection of bioactive peptides from degradation in plasma. The intra-assay and inter-assay coefficients of variation are ≤11.2% and ≤13.4%, respectively, and the measurement range is 0.24–1000 pg/mL.
Demographics for participants were described as mean±SD. Glucose, insulin, iGLP-1 and glucagon concentrations (at time intervals of interest) and their respective incremental areas under the curves (iAUCs) at 180 min were described as mean±SEM for each group. Incremental glucose peaks were also described as mean±SEM. Linear mixed effects models accounting for correlation within the same participant were implemented for each outcome of interest to compare the three groups. Post-hoc analyses were performed by Tukey's method with Bonferroni adjustment. p Values were two-sided with statistical significance evaluated at the 0.05 alpha level or the Bonferroni-corrected 0.05 alpha level, where applicable. Analyses were performed in R V.3.4.0 (Vienna, Austria).
Results
The study population included 16 participants with overweight/obesity (nine female and seven male) with T2DM on a stable dose of metformin. The average (mean±SD) age and BMI were 57.7±7.6 years and 32.8±3.3 kg/m2, respectively. The average duration of diabetes among the participants was 3.8±2.4 years and the mean HbA1c was 6.5%±0.7%. One participant did not have sandwich data; however, this participant was not lost in analyses because mixed effects models handle missing values by maximum likelihood estimation and are robust to missing random data. However, sensitivity analyses by excluding this participant did not change the significance of results.
There were no differences between baseline fasting glucose concentrations in the three meal conditions. Postprandial mean glucose concentrations were significantly decreased by 20.8%, 30.2%, and 23.1% at 30, 60, and 90 min (figure 1), respectively, and the iAUC0–180 was 53.4% lower (3124.7±501.2 vs 6703.5±904.6 mg/dL×180 min, p<0.001) following the CL meal order, compared with CF, the reverse meal order (table 2). CL meal pattern showed reduced postprandial glucose levels compared with the S meal pattern: a decrease of 19.8%, 25.2%, and 15.3% at 30, 60, and 90 min, respectively, and a decrease in iAUC0–180 of 44.1% (3124.7±501.2 vs 5587.1±828.7 mg/dL×180 min, p=0.003). Following the CL meal order, glucose levels plateaued between 90 and 180 min postprandially, whereas, there were marked fluctuations in glucose concentrations in the CF meal condition, with the glucose level being significantly lower than CL at 180 min. Incremental glucose peaks were 53.8% and 40.4% lower for the CL meal order compared with CF and S, respectively (34.7±4.1 vs 75.0±6.5 mg/dL, p<0.001; 34.7±4.1 vs 58.2±5.9 mg/dL, p<0.001) and 22.4% lower for S compared with CF (58.2±5.9 mg/dL vs 75.0±6.5 mg/dL, p<0.001).
Postprandial glucose, insulin and glucagon-like peptide-1 (GLP-1) levels following carbohydrate-first (CF), carbohydrate-last (CL) and sandwich (S) meal orders. Values are mean±SEM. ¥Statistically significant differences (p<0.05) between CF and CL. ΘStatistically significant differences (p<0.05) between CF and S. ◊Statistically significant differences (p<0.05) between CL and S.
Incremental areas under the curves (iAUCs) (0–180 min) during the three visits
*Values are expressed in SI units as mean±SEM, n=16.
†Blood samples were collected immediately before the meal (t=0 min) and at 30, 60, 90, and 180 min after the start of the meal.
‡Intervals were measured in minutes from the start of the meal. The 30-minute time point was immediately collected after the meal was finished.
§Statistically significant differences (p<0.05) between CF and CL.
¶Statistically significant differences (p<0.05) between CL and S.
The CL meal resulted in lower insulin excursions; iAUC0–180 was 24.8% lower compared with CF (7354.1±897.3 vs 9769.7±1002.1 µU/mL×180 min, p=0.003). The insulin excursions were not significantly different between the CF and S meal conditions (7354.1±897.3 vs 8861.2±1050.5 µU/mL×180 min, p=0.137). The GLP-1 response to the CL meal order was greater compared with CF (3487.56±327.7 vs 2519.11±494.8 pg/mL×180 min, p=0.019) and similar to the S meal condition (3487.56±327.7 vs 3153.2±449.4 pg/mL×180 min, p=0.999). The glucagon excursions were not significantly different between the three meal conditions.
Conclusions
In this study, we demonstrated that the temporal sequence of carbohydrate ingestion during a meal has significant impact on postprandial glucose regulation. These findings confirm and extend results from our previous pilot study11; the inclusion of a third nutrient order condition, a sandwich, had intermediate effects on glucose excursions compared with CL versus CF.
Previous studies investigating the effect of premeal ingestion of whey protein have demonstrated that the glucose-lowering effect is accompanied by an insulinotropic response.6 7 In contrast, our results demonstrate that consumption of protein and vegetables first, followed by carbohydrate, reduces both postmeal glucose and insulin excursions, suggesting that the CL meal pattern requires less insulin controlling for carbohydrate amount.
Modifying the rate of nutrient absorption is a therapeutic principle of particular relevance to diabetes. A plausible explanation for the attenuated glycemic response observed with the CL meal pattern is delayed gastric emptying and consequently slower rates of carbohydrate absorption, a mechanism that would not be entirely mediated by GLP-1. The finding of lower insulin iAUC in the context of increased GLP-1 excursions contrasts with the effect of protein preloads that augment both GLP-1 and insulin secretion6 7 12 and suggests a role for vegetable fiber in moderating this response.
The effect of food order on postprandial glycemia in this study is comparable to the magnitude observed with pharmacological agents that preferentially target postprandial glycemia; acarbose and nateglinide reduce iAUCs by 31% and 64%, respectively, compared with placebo.13 14 In non-insulin treated patients, managed with diet/oral hypoglycemic agents, pramlintide was shown to lower glucose excursions by 57%.15 Limitations of our study include the small sample size and unclear generalizability to meals with different macronutrient compositions and patient populations including those with type 1 diabetes and prediabetes. Further study is needed to explore the mechanisms, including gastric emptying and rates of nutrient absorption with extended follow-up beyond 180 min. Strengths of the study include stringent study design and the use of real-world meals that suggest practical utility. In conclusion, our findings suggest that the timing of carbohydrate intake during a meal may have major effects on postprandial glucose excursions comparable in magnitude to many hypoglycemic drugs.