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      The impact of food order on postprandial glycaemic excursions in prediabetes

      Diabetes, Obesity and Metabolism
      Wiley

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          Abstract

          Data suggest that nutrient order during a meal significantly impacts postprandial glucose and insulin excursions in type 2 diabetes, while its effects in prediabetes have not been reported. Fifteen participants with prediabetes consumed the same meal on 3 days in random order: carbohydrate first, followed 10 minutes later by protein and vegetables (CF); protein and vegetables first, followed 10 minutes later by carbohydrate (PVF); or vegetables first followed by protein and carbohydrate (VF). Blood was sampled for glucose and insulin measurements at 0, 30, 60, 90, 120, 150 and 180 minutes. Incremental glucose peaks were similarly attenuated by >40% in the PVF and VF meal conditions compared with CF. The incremental area under the curve for glucose was 38.8% lower following the PVF meal order, compared with CF, and postprandial insulin excursions were significantly lower in the VF meal condition compared with CF. The CF meal pattern showed marked glycaemic variability whereas glucose levels were stable in the PVF and VF meal conditions. Food order presents a novel, simple behavioural strategy to reduce glycaemic excursions in prediabetes.

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          Most cited references7

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          Risk of cardiovascular and all-cause mortality in individuals with diabetes mellitus, impaired fasting glucose, and impaired glucose tolerance: the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab).

          Diabetes mellitus increases the risk of cardiovascular disease (CVD) and all-cause mortality. The relationship between milder elevations of blood glucose and mortality is less clear. This study investigated whether impaired fasting glucose and impaired glucose tolerance, as well as diabetes mellitus, increase the risk of all-cause and CVD mortality. In 1999 to 2000, glucose tolerance status was determined in 10,428 participants of the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab). After a median follow-up of 5.2 years, 298 deaths occurred (88 CVD deaths). Compared with those with normal glucose tolerance, the adjusted all-cause mortality hazard ratios (HRs) and 95% confidence intervals (CIs) for known diabetes mellitus and newly diagnosed diabetes mellitus were 2.3 (1.6 to 3.2) and 1.3 (0.9 to 2.0), respectively. The risk of death was also increased in those with impaired fasting glucose (HR 1.6, 95% CI 1.0 to 2.4) and impaired glucose tolerance (HR 1.5, 95% CI 1.1 to 2.0). Sixty-five percent of all those who died of CVD had known diabetes mellitus, newly diagnosed diabetes mellitus, impaired fasting glucose, or impaired glucose tolerance at baseline. Known diabetes mellitus (HR 2.6, 95% CI 1.4 to 4.7) and impaired fasting glucose (HR 2.5, 95% CI 1.2 to 5.1) were independent predictors for CVD mortality after adjustment for age, sex, and other traditional CVD risk factors, but impaired glucose tolerance was not (HR 1.2, 95% CI 0.7 to 2.2). This study emphasizes the strong association between abnormal glucose metabolism and mortality, and it suggests that this condition contributes to a large number of CVD deaths in the general population. CVD prevention may be warranted in people with all categories of abnormal glucose metabolism.
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            Epidemiological evidence of altered cardiac autonomic function in subjects with impaired glucose tolerance but not isolated impaired fasting glucose.

            Autonomic dysfunction is present in diabetes mellitus (DM), but no study is available on alteration in cardiac autonomic function (CAF) across different glycemic statuses including normal glucose tolerance (NGT), isolated impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and DM. Our objective was to examine whether CAF is altered in subjects with IGT and isolated IFG. The study was a stratified systematic cluster sampling design within the general community. A total of 1440 subjects were classified as NGT (n = 983), isolated IFG (n = 163), IGT (n = 188), and DM (n = 106). CAF was determined by 1) standard deviation of normal-to-normal (SDNN) or RR interval, power spectrum in low and high frequency (LF, 0.04-0.15 Hz; HF, 0.15-0.40 Hz), and LF/HF ratio in supine position for 5 min; 2) ratio between 30th and 15th RR interval after standing from supine position (30/15 ratio); and 3) average heart rate change during breathing of six deep breaths for 1 min (HR(DB)). Univariate analysis showed significant differences in SDNN, 30/15 ratio, HR(DB), HF power, and LF/HF ratio among subjects with NGT, isolated IFG, IGT, and DM. In multivariate analysis, none of the indices of CAF was related to isolated IFG in the reference group of NGT. IGT and DM were negatively associated with 30/15 ratio and HF power but positively associated with LF/HF ratio. In addition, DM was also related to a lower SDNN. DM and IGT subjects had an impaired CAF independent of other cardiovascular risk factors. The risk of altered CAF is not apparent in subjects with isolated IFG.
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              Food Order Has a Significant Impact on Postprandial Glucose and Insulin Levels

              Postprandial hyperglycemia is an important therapeutic target for optimizing glycemic control and for mitigating the proatherogenic vascular environment characteristic of type 2 diabetes. Existing evidence indicates that the quantity and type of carbohydrate consumed influence blood glucose levels and that the total amount of carbohydrate consumed is the primary predictor of glycemic response (1). Previous studies have shown that premeal ingestion of whey protein, as well as altering the macronutrient composition of a meal, reduces postmeal glucose levels (2–4). There are limited data, however, regarding the effect of food order on postprandial glycemia in patients with type 2 diabetes (5). In this pilot study, we sought to examine the effect of food order, using a typical Western meal, incorporating vegetables, protein, and carbohydrate, on postprandial glucose and insulin excursions in overweight/obese adults with type 2 diabetes. A total of 11 subjects (6 female, 5 male) with metformin-treated type 2 diabetes were studied using a within-subject crossover design. The average (mean ± SD) age and BMI were 54 ± 9 years and 32.9 ± 5 kg/m2, respectively. The average duration of diabetes was 4.8 ± 2.4 years and the mean HbA1c was 6.5 ± 0.7%. After a 12-h overnight fast, subjects consumed an isocaloric meal (628 kcal: 55 g protein, 68 g carbohydrate, and 16 g fat) with the same composition on 2 separate days, 1 week apart. During the first visit, the food order was carbohydrate (ciabatta bread and orange juice), followed 15 min later by protein (skinless grilled chicken breast) and vegetables (lettuce and tomato salad with low-fat Italian vinaigrette and steamed broccoli with butter); the food order was reversed a week later. Blood was sampled for glucose and insulin measurements at baseline (just before meal ingestion) and 30, 60, and 120 min after the start of the meal. The mean postmeal glucose levels were decreased by 28.6% (P = 0.001), 36.7% (P = 0.001), and 16.8% (P = 0.03) at 30, 60, and 120 min, respectively, and the incremental area under the curve (iAUC0–120) was 73% lower (2,001 ± 376.9 vs. 7,545 ± 804.4 mg/dL × 120 min, P = 0.001) when vegetables and protein were consumed first, before carbohydrate, compared with the reverse food order (Table 1). Postprandial insulin levels at 60 and 120 min and the iAUC0–120 were also significantly lower when protein and vegetables were consumed first. Table 1 Glucose and insulin levels/iAUC for various time points/intervals during the two visits Time (min) Carbohydrates first Carbohydrates last P c Change (%) Blood glucose (mg/dL) a 0 106.7 ± 5.3 107.3 ± 6.3 0.752 0.5 30 156.8 ± 8.2 112.0 ± 5.8 0.001 −28.6 60 199.4 ± 12.2 125.6 ± 6.9 0.001 −37.0 120 169.2 ± 13.8 140.8 ± 7.7 0.030 −16.8 Serum insulin (µIU/mL) a 0 18.8 ± 2.4 16.3 ± 1.4 0.154 −13.6 30 62.4 ± 8.6 42.9 ± 9.7 0.083 −31.2 60 125.4 ± 20.1 63.2 ± 11.0 0.002 −49.6 120 152.0 ± 31.7 90.9 ± 16.6 0.014 −40.2 Glucose iAUC (mg/dL × min) b 0–30 751.4 ± 71.0 90.0 ± 26.8 0.001 −88.0 0–60 3,396.8 ± 606.9 444.2 ± 103.8 0.001 −86.9 0–120 7,545.0 ± 804.4 2,001.5 ± 376.9 0.001 −73.5 Insulin iAUC (µIU/mL × min) b 0–30 657.5 ± 131.8 399.5 ± 132.6 0.102 −39.2 0–60 2,908.5 ± 432.0 1,510.5 ± 407.4 0.002 −48.1 0–120 10,097.9 ± 1,646.9 5,202.8 ± 1,061.6 0.002 −48.5 Data are means ± SEM, n = 11. a Blood samples were collected immediately before the meal (t = 0 min) and at 30, 60, and 120 min after the start of the meal. b Intervals were measured in minutes from the start of the meal. c P values were calculated using the Wilcoxon matched-pairs signed rank test. In this pilot study, we demonstrated that the temporal sequence of carbohydrate ingestion during a meal has a significant impact on postprandial glucose and insulin excursions. The magnitude of the effect of food order on glucose levels is comparable to that observed with pharmacological agents that preferentially target postprandial glucose. Moreover, the reduced insulin excursions observed in this experimental setting suggest that this meal pattern may improve insulin sensitivity. A limitation of the study is that we analyzed glucose and insulin responses up to 120 min following meal ingestion, as this study was designed to test postprandial glucose levels as practically measured by patients with type 2 diabetes. Further studies with longer follow-up to delineate the full impact, including delayed effects and the mechanisms underlying the glycemic effect of food order, are indicated. In contrast to conventional nutritional counseling in diabetes, which is largely restrictive and focuses on “how much” and “what not to eat,” this pilot study suggests that improvement in glycemia may be achieved by optimal timing of carbohydrate ingestion during a meal.
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                Author and article information

                Journal
                Diabetes, Obesity and Metabolism
                Diabetes Obes Metab
                Wiley
                14628902
                September 10 2018
                Affiliations
                [1 ]Comprehensive Weight Control Center, Division of Endocrinology, Diabetes, & Metabolism, Weill Cornell Medicine; New York New York
                [2 ]Institute of Human Nutrition, Columbia University; New York New York
                [3 ]Department of Healthcare Policy & Research; Weill Cornell Medicine; New York New York
                [4 ]Rockefeller University; New York New York
                Article
                10.1111/dom.13503
                7398578
                30101510
                5b6c9368-672c-4e75-921f-0b3672ae85d4
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

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