Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index

Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic/Latino adults with or at risk of type 2 diabetes

Digital phenotyping refers to characterizing human bio-behavior through wearables, personal devices, and digital health technologies. Digital phenotyping in populations facing a disproportionate burden of type 2 diabetes (T2D) and health disparities continues to lag compared to other populations. Here, we report our study demonstrating the application of multimodal digital phenotyping, i.e., the simultaneous use of CGM, physical activity monitors, and meal tracking in Hispanic/Latino individuals with or at risk of T2D. For 14 days, 36 Hispanic/Latino adults (28 female, 14 with non-insulin treated T2D) wore a continuous glucose monitor (CGM) and a physical activity monitor (Actigraph) while simultaneously logging meals using the MyFitnessPal app. We model meal events and daily digital biomarkers representing diet, physical activity choices, and corresponding glycemic response. We develop a digital biomarker for meal events that differentiates meal events into normal and elevated categories. We examine the contribution of daily digital biomarkers of elevated meal event count and step count on daily time-in-range 54-140 mg/dL (TIR54-140) and average glucose. After adjusting for step count, a change in elevated meal event count from zero to two decreases TIR54-140 by 4.0% (p = 0.003). An increase in 1000 steps in post-meal step count also reduces the meal event glucose response by 641 min mg/dL (p = 0.0006) and reduces the odds of an elevated meal event by 55% (p < 0.0001). The proposed meal event digital biomarkers may provide an opportunity for non-pharmacologic interventions for Hispanic/Latino adults facing a disproportionate burden of T2D.

The Effect of Meal Glycemic Index and Meal Frequency on Glycemic Control and Variability in Female Nurses Working Night Shifts: A Two-Arm Randomized Cross-Over Trial

BACKGROUND

Night shift workers are exposed to circadian disruption, which contributes to impaired glucose tolerance. Although fasting during the night shift improves glucose homeostasis, adhering to this dietary strategy may be challenging.

OBJECTIVES

This study evaluated the effect of fasting compared with the consumption of meals with different combinations of glycemic index (GI, low or high) and frequency (1 or 3 times) during the night shift on continuous glucose monitoring metrics.

METHODS

A 2-arm randomized cross-over trial was conducted on female nurses working night shifts. In each of those arms, the participants were either provided with no meal (fasted), low GI, or high-GI meal during the night shift with a meal frequency according to which arm they were randomly allocated to, either 1-MEAL or 3-MEAL. Outcome variables were glycemic control and variability (GC and GV) metrics during the night shift (21:30-7:00), in the morning after the night shift (07:00-13:00), and in the 24 h period (18:00-18:00).

RESULTS

Compared to no meal, the consumption of 1 high-GI meal increased all GV metrics not only during the night shifts but also in the morning, for instance, as observed in the coefficient of variation (β = 0.03 mmol/L; 95% CI: 0.01, 0.05), and GV percentage (β = 4.13; 95% CI: 2.07, 6.18). The consumption of 1 or 3 low GI meals did not raise GC or GV metrics except for continuous overall net glycemic action during the night shifts after consuming 3 low GI meals. When controlling for GI, night shift meal frequency did not affect any metrics in any timeframe.

CONCLUSIONS

High meal GI but not higher meal frequency during the night shift increased GC and GV in female night shift workers. Results for 1 low-GI meal during the night shift were not different from a glucose profile after no meal. This trial was registered at trialsearch.who.int as NL8715.

Comparative Compositions of Grain of Bread Wheat, Emmer and Spelt Grown with Different Levels of Nitrogen Fertilisation

Five cultivars of bread wheat and spelt and three of emmer were grown in replicate randomised field trials on two sites for two years with 100 and 200 kg nitrogen fertiliser per hectare, reflecting low input and intensive farming systems. Wholemeal flours were analysed for components that are suggested to contribute to a healthy diet. The ranges of all components overlapped between the three cereal types, reflecting the effects of both genotype and environment. Nevertheless, statistically significant differences in the contents of some components were observed. Notably, emmer and spelt had higher contents of protein, iron, zinc, magnesium, choline and glycine betaine, but also of asparagine (the precursor of acrylamide) and raffinose. By contrast, bread wheat had higher contents of the two major types of fibre, arabinoxylan (AX) and β-glucan, than emmer and a higher AX content than spelt. Although such differences in composition may be suggested to result in effects on metabolic parameters and health when studied in isolation, the final effects will depend on the quantity consumed and the composition of the overall diet.

Associations between Conventional and Emerging Indicators of Dietary Carbohydrate Quality and New-Onset Type 2 Diabetes Mellitus in Chinese Adults

Dietary glycemic index (GI), carbohydrate to fiber ratio (CF) and carbohydrate quality index (CQI) are conventional and emerging indicators for carbohydrate quality. We aimed to investigate the associations between these indicators and new-onset type 2 diabetes mellitus (T2DM) risk among Chinese adults. This prospective cohort study included 14,590 adults from the China Health and Nutrition Survey without cardiometabolic diseases at baseline. The associations between dietary GI, CF and CQI and T2DM risk were assessed using Cox proportional hazard regression analysis and dose-response relationships were explored using restricted cubic spline and threshold analysis. After a mean follow-up duration of 10 years, a total of 1053 new-onset T2DM cases occurred. There were U-shaped associations between dietary GI and CF and T2DM risk (both P-nonlinear < 0.0001), and T2DM risk was lowest when dietary GI was 72.85 (71.40, 74.05) and CF was 20.55 (17.92, 21.91), respectively (both P-log likelihood ratio < 0.0001). Inverse associations between CQI and T2DM risk specifically existed in participants < 60 y or attended middle school or above (both P-trend < 0.05). These findings indicated that moderate dietary GI and CF range and a higher dietary CQI score may be suggested for T2DM prevention in Chinese adults.

Predicting the Glycemic Index of Biscuits Using Static In Vitro Digestion Protocols

In vitro digestion methods that can accurately predict the estimated GI (eGI) values of complex carbohydrate foods, including biscuits, are worth exploring. In the current study, standard commercial biscuits with varied clinical GI values between 9~30 were digested using both the INFOGEST and single-enzyme digestion protocols. The digestion kinetic parameters were acquired through mathematical fitting by mathematical kinetics models. The results showed that compared with the INFOGEST protocol, the AUR180 deduced from digesting using either porcine pancreatin or α-amylase showed the best potential in predicting the eGI values. Accordingly, mathematical equations were established based on the relations between the AUR180 and the GI values. When digesting using porcine pancreatin, GI= 1.834 + 0.009 ×AUCR180 (R2= 0.952), and when digesting using only α-amylase, GI= 6.101 + 0.009 ×AUCR180 (R2=0.902). The AUR180 represents the area under the curve of the reducing-sugar content normalized to the total carbohydrates versus the digestion time in 180 min. The in vitro method presented enabled the rapid and accurate prediction of the eGI values of biscuits, and the validity of the formula was verified by another batch of biscuits with a known GI, and the error rate of most samples was less than 30%.

The impact of glycaemic load on cognitive performance: A meta-analysis and guiding principles for future research

The effect of breakfast glycaemic load (GL) on cognition was systematically examined. Randomised and non-randomised controlled trials were identified using PubMed, Scopus, and Cochrane Library (up to May 2022). 15 studies involving adults (aged 20 - 80 years) were included. Studies had a low risk, or some concerns, of bias. A random-effects meta-analysis model revealed no effect of GL on cognition up to 119 min post-consumption. However, after 120 min, immediate episodic memory scores were better following a low-GL compared to a high-GL (SMD = 0.16, 95% confidence interval [CI] = -0.00 to 0.32, p = 0.05, I2 = 5%). Subgroup analyses indicated that the benefit was greater in younger adults (<35 years) and those with better GT. A qualitative synthesis of 16 studies involving children and adolescents (aged 5 - 17 years) suggested that a low-GL breakfast may also benefit episodic memory and attention after 120 min. Methodological practises were identified which could explain a failure to detect benefits in some studies. Consequently, guiding principles were developed to optimise future study design.

The relative contribution of diurnal and nocturnal glucose exposures to HbA1c in type 1 diabetes males: a pooled analysis

Purpose

The exact contribution of daily glucose exposure to HbA1c in people with type 1 diabetes (T1D) remains controversial. We examined the contribution of pre- and postprandial glycaemia, nocturnal and early-morning glycaemia, and glycaemic variability to HbA1c levels in T1D. In this analysis, we used clinical data, namely age, BMI and HbA1c, as well as glycaemic metrics (24-h glycaemia, postprandial, nocturnal, early-morning glycaemia, wake-up glucose, and glycaemic variability) obtained over a four-week period of continuous glucose monitoring (CGM) wear in thirty-two males with T1D.

Methods

The trapezoid method was used estimate the incremental area under the glucose curve (iAUC) for 24-h, postprandial (3-h period following breakfast, lunch, and dinner, respectively), nocturnal (between 24:00–04:00 AM), and early-morning (2-h period 2-h prior to wake-up) glycaemia. Linear regression analysis was employed whereby CGM-derived glycaemic metrics were explanatory variables and HbA1c was the outcome.

Results

Thirty-two T1D males (mean ± SD: age 29 ± 4 years; HbA1c 7.3 ± 0.9% [56 ± 13 mmol/mol]; BMI 25.80 ± 5.01 kg/m²) were included in this analysis. In linear models adjusted for age and BMI, HbA1c was associated with 24-h mean glucose (r² = 0.735, p < 0.001), SD (r² = 0.643, p = 0.039), and dinner iAUC (r² = 0.711, p = 0.001). CGM-derived metrics and non-glycaemic factors explained 77% of the variance in HbA1c, in which postprandial glucose accounted for 32% of the variance explained. The single greatest contributor to HbA1c was dinner iAUC resulting in 0.6%-point (~7 mmol/mol) increase in HbA1c per SD increase in dinner iAUC.

Conclusions

Using comprehensive CGM profiling, we show that postprandial glucose, specifically evening-time postprandial glucose, is the single largest contributing factor to HbA1c in T1D.

Trial registration number

NCT02204839 (July 30th 2014); NCT02595658 (November 3rd 2015).

Dietary Copper and Selenium Intakes and the Risk of Type 2 Diabetes Mellitus: Findings from the China Health and Nutrition Survey

The long-term associations between dietary copper (Cu) and selenium (Se) intakes and type 2 diabetes mellitus (T2DM) risk are unclear. We aimed to examine the prospective associations between dietary Cu and Se intakes and T2DM risk in Chinese adults. A total of 14,711 adults from the China Health and Nutrition Survey (1997–2015) were included. Nutrient intakes were assessed by 3 consecutive 24 h recalls and food-weighing methods. T2DM was identified by a validated questionnaire and laboratory examination. Cox regression models were used for statistical analysis. A total of 1040 T2DM cases were diagnosed during 147,142 person-years of follow-up. In fully adjusted models, dietary Cu or Se intake was not associated with T2DM risk. Dietary Se intake significantly modified the association between dietary Cu intake and T2DM risk, and dietary Cu intake was positively associated with T2DM risk when Se intake was lower than the median (p-interaction = 0.0292). There were no significant effect modifications on the associations by age, sex, BMI, or region. Although dietary Cu or Se intake was not independently associated with T2DM risk in Chinese adults free from cardiometabolic diseases and cancer at the baseline, there was a significant interaction between dietary Cu and Se intakes on T2DM risk.

Perspective: The Glycemic Index Falls Short as a Carbohydrate Food Quality Indicator to Improve Diet Quality

This perspective examines the utility of the glycemic index (GI) as a carbohydrate quality indicator to improve Dietary Guidelines for Americans (DGA) adherence and diet quality. Achieving affordable, high-quality dietary patterns can address multiple nutrition and health priorities. Carbohydrate-containing foods make important energy, macronutrient, micronutrient, phytochemical, and bioactive contributions to dietary patterns, thus improving carbohydrate food quality may improve diet quality. Following DGA guidance helps meet nutrient needs, achieve good health, and reduce risk for diet-related non-communicable diseases in healthy people, yet adherence by Americans is low. A simple indicator that identifies high-quality carbohydrate foods and improves food choice may improve DGA adherence, but there is no consensus on a definition. The GI is a measure of the ability of the available carbohydrate in a food to increase blood glucose. The GI is well established in research literature and popular resources, and some have called for including the GI on food labels and in food-based dietary guidelines. The GI has increased understanding about physiological responses to carbohydrate-containing foods, yet its role in food-based dietary guidance and diet quality is unresolved. A one-dimensional indicator like the GI runs the risk of being interpreted to mean foods are "good" or "bad," and it does not characterize the multiple contributions of carbohydrate-containing foods to diet quality, including nutrient density, a core concept in the DGA. New ways to define and communicate carbohydrate food quality shown to help improve adherence to high-quality dietary patterns such as described in the DGA would benefit public health.

Acute Effects of Split Pea-Enriched White Pan Bread on Postprandial Glycemic and Satiety Responses in Healthy Volunteers—A Randomized Crossover Trial

Pulse consumption has been associated with reduced postprandial glucose response (PPGR) and improved satiety. The objective of this study was (i) to investigate the effects of fortifying white pan bread with split yellow pea (Pisum sativum L.) flour on PPGR and appetite-related sensations, and (ii) to determine whether Revtech heat processing of pea flour alters the postprandial effects. A randomized controlled crossover trial was performed with 24 healthy adults. Participants consumed 50 g available carbohydrate from bread containing 20% pea flour that was untreated (USYP), Revtech processed at 140 °C with no steam (RT0%), Revtech processed at 140 °C with 10% steam (RT10%), or a control bread with 100% white wheat flour (100%W). Blood samples were analyzed for glucose and plasma insulin at 0, 15, 30, 45, 60, 90, and 120 min post-meal. Appetite sensations and product acceptability were measured using visual analogue and 9-point hedonic scales. Results showed no significant difference in the postprandial glucose and insulin responses of different bread treatments. However, pea-containing variants resulted in 18% higher fullness and 16–18% lower hunger, desire to eat, and prospective food consumption ratings compared to 100% W. No differences in the aroma, flavor, color, and overall acceptability of different bread products were observed. This trial supports using pea flour as a value-added ingredient to improve the short-term appetite-related sensations of white pan bread without affecting the overall acceptability.

Use of a Continuous Glucose Monitor to Determine the Glycaemic Index of Rice-Based Mixed Meals, Their Effect on a 24 h Glucose Profile and Its Influence on Overweight and Obese Young Adults' Meal Preferences

Postprandial hyperglycaemia is associated with an increased risk of type-2 diabetes. This study aims to determine the glycaemic index (GI) of three varieties of rice-based mixed meals and their effects on glycaemic variability (GV), 24 h mean glucose levels and target ranges, and rice variety preferences among overweight and obese young adults using real-time continuous glucose monitoring (rtCGM). In a randomised controlled crossover design, 14 participants (22.8 ± 4.6 years, 32.9 ± 5.8 kg/m2) were randomly assigned to receive 3 rice-based mixed meals containing 50 g of available carbohydrates (white rice meal = WRM; brown rice meal = BRM; and parboiled basmati rice meal = PBRM) and 50 g of a glucose reference drink on alternate days. GI, GV, 24 h mean glucose levels and target ranges were measured. Rice variety preferences were compared with those of baseline data and determined at the end of the study period. Results: The analysis found that PBRM was low in GI (45.35 ± 2.06), BRM medium in GI (56.44 ± 2.34), and WRM high in GI (83.03 ± 2.19). PBRM had a significantly (p < 0.05) lower 24 h mean glucose level, higher in-target 24 h glucose level percentage and non-significantly (p > 0.05) lower GV compared to WRM. Prior to observing their postprandial glucose levels generated by rtCGM, the participants preferred WRM (64.3%) over other meals, whereas this preference changed significantly (p < 0.05) at the endpoint (PBRM, 71.4%). PBRM reduced 24 h glucose level and GV of overweight and obese young adults. The rtCGM is proven to be reliable in measuring GI, while providing robust continuous glycaemic information. This may serve as an educational tool that motivates eating behaviour changes among overweight and obese young adults.

The interaction between glycemic index, glycemic load, and the genetic variant ADIPOQ T45G (rs2241766) in the risk of colorectal cancer: a case-control study in a Korean population

PURPOSE

The glycemic index (GI), glycemic load (GL), and adiponectin level contribute to glycemic response and insulin sensitivity in the body. Studies have shown that tumor development is related to glycemic disorders; however, the results are contradictory. We aimed to investigate the association of GI and GL with colorectal cancer (CRC) risk in a Korean population and their possible interactions with the genetic variant ADIPOQ T45G.

METHODS AND RESULTS

A case-control study including 2096 participants with 695 CRC cases was conducted. The results showed that diets with high GI or GL were significantly associated with an increased risk of CRC [odds ratio (OR) = 5.44, 95% confidence interval (CI) 3.85-7.68; OR = 4.43, 95% CI 3.18-6.15, respectively; all p-trends < 0.001]. Moreover, even with a low-GI and low-GL diet, G/G genotype carriers may have 2.93-fold and 3.77-fold higher risk of rectal cancer compared to carriers of other genotypes (T/T + T/G), (OR = 2.93, 95% CI 1.01-8.59, p-interaction = 0.011 for GI; OR = 3.77, 95% CI 1.46-9.77, p-interaction = 0.025 for GL).

CONCLUSIONS

Overall, our study suggests positive associations of GI and GL with CRC risk. Moreover, the associations of GI and GL with rectal cancer risk could be modified by ADIPOQ T45G in a Korean population. Further studies with larger sample sizes are needed to confirm our findings.

Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial

BACKGROUND

Dietary modifications are crucial for managing newly diagnosed type 2 diabetes mellitus (T2DM) and preventing its health complications, but many patients fail to achieve clinical goals with diet alone. We sought to evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet.

METHODS

We enrolled 23 adults with newly diagnosed T2DM (aged 53.5 ± 8.9 years, 48% males) for a randomized crossover trial of two 2-week-long dietary interventions. Participants were blinded to their assignment to one of the two sequence groups: either PPT-MED or MED-PPT diets. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGR). We further evaluated the long-term effects of PPT diet on glycemic control and metabolic health by an additional 6-month PPT intervention (n = 16). Participants were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using a smartphone application.

RESULTS

In the crossover intervention, the PPT diet lead to significant lower levels of CGM-based measures as compared to the MED diet, including average PPGR (mean difference between diets, - 19.8 ± 16.3 mg/dl × h, p < 0.001), mean glucose (mean difference between diets, - 7.8 ± 5.5 mg/dl, p < 0.001), and daily time of glucose levels > 140 mg/dl (mean difference between diets, - 2.42 ± 1.7 h/day, p < 0.001). Blood fructosamine also decreased significantly more during PPT compared to MED intervention (mean change difference between diets, - 16.4 ± 37 μmol/dl, p < 0.0001). At the end of 6 months, the PPT intervention leads to significant improvements in multiple metabolic health parameters, among them HbA1c (mean ± SD, - 0.39 ± 0.48%, p < 0.001), fasting glucose (- 16.4 ± 24.2 mg/dl, p = 0.02) and triglycerides (- 49 ± 46 mg/dl, p < 0.001). Importantly, 61% of the participants exhibited diabetes remission, as measured by HbA1c < 6.5%. Finally, some clinical improvements were significantly associated with gut microbiome changes per person.

CONCLUSION

In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach.

TRIAL REGISTRATION

ClinicalTrials.gov number, NCT01892956.

Egg and saturated fat containing breakfasts have no acute effect on acute glycemic control in healthy adults: a randomized partial crossover trial

BACKGROUND/OBJECTIVES

High egg consumption is associated with poor glycemic control. Considering the widespread consumption of eggs, it is crucial to determine causality in this association. We tested if egg consumption acutely alters glucose disposal in the absence or presence of saturated fat, which is frequently consumed with eggs.

SUBJECTS/METHODS

In a randomized partial crossover clinical trial, 48 subjects (consuming ≥ 1 egg/week) received two of four isocaloric, macronutrient-matched breakfasts. The groups were defined based on the main ingredient of the breakfasts offered: eggs (EB); saturated fat (SB); eggs and saturated fat (ES); and control, which included a cereal based breakfast (CB). The breakfasts were offered in two testing sessions spaced seven days apart. Six blood samples (pre breakfast (fasting); 30, 60, 90, 120, and 180 minutes post breakfast) were collected to measure glucose and insulin levels. Area under the curves (AUC) were analyzed controlling for the baseline concentrations using mixed-effects models accounting for within-subject dependencies to compare these across breakfast assignments.

RESULTS

Forty-eight patients (46% males, age 25.8 ± 7.7 years, BMI 25.7 ± 4.6 kg/m²) were included. Neither EB, SB nor ES was associated with a significant difference in AUC of glucose or insulin compared to CB (p > 0.1).

CONCLUSIONS

Acutely, consumption of egg breakfast with or without accompanying saturated fat does not adversely affect glucose disposal in healthy adults. While this is reassuring for continued egg consumption, a long-term evaluation of egg intake with or without saturated fat would be the next step.

Association between dietary glycemic index and glycemic load and glioma: a case-control study

OBJECTIVE

Although glycemic index (GI) and load (GL) have been linked with several health outcomes, no information is available linking dietary GI and GL with glioma. This study aimed to investigate the relationship between dietary GI and GL and odds of glioma.

METHODS

This hospital-based case-control study was conducted between November 2009 and September 2011 in the hospital affiliated to Shahid Beheshti University of Medical Sciences. We recruited 128 newly diagnosed cases of glioma and 256 age- and sex-matched controls. All cases were pathologically diagnosed with glioma patients, with no history of any type of other pathologically confirmed cancers and chemotherapy or radiotherapy (due to cancers). Dietary GI and GL were measured by using a validated, self-administered, dish-based, semi-quantitative food-frequency questionnaire.

RESULT

A significant positive association was found between dietary GI and glioma (OR: 3.01; 95% CI: 1.75-5.17, P < 0.001); such that after considering for potential confounders, participants in the highest tertile of dietary GI had 3.51 times greater risk of glioma than those in the lowest tertile (OR: 3.51; 95% CI: 1.69-7.28, P_trend = 0.001). Furthermore, we observed a significant positive association between dietary and glioma (OR: 3.74; 95% CI: 1.97-6.11, P_trend < 0.001). This association remained significant even after further controlling for potential confounders (OR: 2.42; 95% CI: 1.02-5.69, P_trend= 0.04).

DISCUSSION

We observed a significant positive association between dietary GI and GL and risk of glioma in adults. However, prospective cohort studies are required to confirm this association.

Periconceptional Maternal Diet Characterized by High Glycemic Loading Is Associated with Offspring Behavior in NEST

Maternal periconceptional diets have known associations with proper offspring neurodevelopment. Mechanisms for such associations include improper energy/nutrient balances between mother and fetus, as well as altered offspring epigenetics during development due to maternal nutrient and inflammatory status. Using a comprehensive food frequency questionnaire and assessing offspring temperament with the Infant-Toddler Social and Emotional Assessment (n = 325, mean age = 13.9 months), we sought to test whether a maternal periconceptional diet characterized by high glycemic loading (MGL) would affect offspring temperament using adjusted ordinal regression. After limiting false discovery to 10%, offspring born to mothers in tertile 3 of glycemic loading (referent = tertile 1) were more likely to be in the next tertile of anxiety [OR (95% CI) = 4.51 (1.88-11.07)] and inhibition-related behaviors [OR (95% CI) = 3.42 (1.49-7.96)]. Male offspring were more likely to exhibit impulsive [OR (95% CI) = 5.55 (1.76-18.33)], anxiety [OR (95% CI) = 4.41 (1.33-15.30)], sleep dysregulation [OR (95% CI) = 4.14 (1.34-13.16)], empathy [6.68 (1.95-24.40)], and maladaptive behaviors [OR (95% CI) = 9.86 (2.81-37.18)], while females were more likely to exhibit increased anxiety-related behaviors [OR (95% CI) = 15.02 (3.14-84.27)]. These associations persisted when concurrently modeled with the maternal-Mediterranean dietary pattern. In a subset (n = 142), we also found MGL associated with increased mean methylation of the imprint control region of SGCE/PEG10. In conclusion, these findings highlight the importance of maternal dietary patterns on offspring neurodevelopment, offering avenues for prevention options for mothers.

Personalized Postprandial Glucose Response-Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes

OBJECTIVE

To compare the clinical effects of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet on glycemic control and metabolic health in prediabetes.

RESEARCH DESIGN AND METHODS

We randomly assigned adults with prediabetes (n = 225) to follow a MED diet or a PPT diet for a 6-month dietary intervention and additional 6-month follow-up. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses. During the intervention, all participants were connected to continuous glucose monitoring (CGM) and self-reported dietary intake using a smartphone application.

RESULTS

Among 225 participants randomized (58.7% women, mean ± SD age 50 ± 7 years, BMI 31.3 ± 5.8 kg/m², HbA_1c, 5.9 ± 0.2% [41 ± 2.4 mmol/mol], fasting plasma glucose 114 ± 12 mg/dL [6.33 ± 0.67 mmol/L]), 200 (89%) completed the 6-month intervention. A total of 177 participants also contributed 12-month follow-up data. Both interventions reduced the daily time with glucose levels >140 mg/dL (7.8 mmol/L) and HbA_1c levels, but reductions were significantly greater in PPT compared with MED. The mean 6-month change in "time above 140" was -0.3 ± 0.8 h/day and -1.3 ± 1.5 h/day for MED and PPT, respectively (95% CI between-group difference -1.29 to -0.66, P < 0.001). The mean 6-month change in HbA_1c was -0.08 ± 0.19% (-0.9 ± 2.1 mmol/mol) and -0.16 ± 0.24% (-1.7 ± 2.6 mmol/mol) for MED and PPT, respectively (95% CI between-group difference -0.14 to -0.02, P = 0.007). The significant between-group differences were maintained at 12-month follow-up. No significant differences were noted between the groups in a CGM-measured oral glucose tolerance test.

CONCLUSIONS

In this clinical trial in prediabetes, a PPT diet improved glycemic control significantly more than a MED diet as measured by daily time of glucose levels >140 mg/dL (7.8 mmol/L) and HbA_1c. These findings may have implications for dietary advice in clinical practice.

Rice Bran Makes a Healthy and Tasty Traditional Indonesian Goat Meatball, 'Bakso'

Meatballs are popular in Asia and traditionally made from beef or chicken with tapioca (≈8% wt/wt) as filler. Tapioca has a high glycaemic index (GI); therefore, rice bran was evaluated as a substitute to create a healthier meatball of acceptable quality. Substitution of tapioca with rice bran (100:0; 75:25, 50:50; 25:75; 0:100% tapioca: % rice bran) decreased the starch content (7.8 to 3.3%) and GI (56.08 to 43.85) whilst increasing the protein (10.9 to 12.8%) and fibre (8.1 to 10.3%) contents. Although consistency (995 to 776 N/mm) was affected, firmness (90.6 to 90.5 N) and shear force (300 to 312 N) were only slightly affected by the ratio of tapioca to rice bran. Sensory analysis revealed that the goat meatball with the substitution of tapioca with up to 25% rice bran was deemed acceptable by 40 Indonesian consumers.

Perspective: Does Glycemic Index Matter for Weight Loss and Obesity Prevention? Examination of the Evidence on "Fast" Compared with "Slow" Carbs

High-glycemic index (high-GI) foods (so-called fast carbs) have been hypothesized to promote fat storage and increase risk of obesity. To clarify whether dietary GI impacts body weight, we searched PubMed and the Cochrane Database of Systematic Reviews for observational studies reporting associations between BMI and dietary GI, and for meta-analyses of randomized controlled trials (RCTs) comparing low-GI and high-GI diets for weight loss. Data on 43 cohorts from 34 publications, totaling 1,940,968 adults, revealed no consistent differences in BMI when comparing the highest with the lowest dietary GI groups. In the 27 cohort studies that reported results of statistical comparisons, 70% showed that BMI was either not different between the highest and lowest dietary GI groups (12 of 27 cohorts) or that BMI was lower in the highest dietary GI group (7 of 27 cohorts). Results of 30 meta-analyses of RCTs from 8 publications demonstrated that low-GI diets were generally no better than high-GI diets for reducing body weight or body fat. One notable exception is that low-GI diets with a dietary GI at least 20 units lower than the comparison diet resulted in greater weight loss in adults with normal glucose tolerance but not in adults with impaired glucose tolerance. While carbohydrate quality, including GI, impacts many health outcomes, GI as a measure of carbohydrate quality appears to be relatively unimportant as a determinant of BMI or diet-induced weight loss. Based on results from observational cohort studies and meta-analyses of RCTs, we conclude that there is scant scientific evidence that low-GI diets are superior to high-GI diets for weight loss and obesity prevention.

Breakfast and Exercise Improve Academic and Cognitive Performance in Adolescents

This study examined the combined effects of breakfast and exercise on short-term academic and cognitive performance in adolescents. Eighty-two adolescents (64 female), aged 14–19 years, were randomized to four groups over a 4-hour morning: (i) a group who fasted and were sedentary (F-S); (ii) a group who ate breakfast but were sedentary (B-S); (iii) a group who fasted but completed a 30-min exercise bout (F-E); and (iv) a group who ate breakfast and completed a 30-min exercise bout (B-E). Individuals completed academic and cognitive tests over the morning. Adolescents in B-E significantly improved their mathematics score (B-E: 15.2% improvement on correct answers, vs. F-S: 6.7% improvement on correct answers; p = 0.014) and computation time for correct answers (B-E: 16.7% improvement, vs. F-S: 7.4% improvement; p = 0.004) over the morning compared with the F-S group. The B-E group had faster reaction times for congruent, incongruent and control trials of the Stroop Color-Word Task compared with F-S mid-morning (all p < 0.05). Morning breakfast and exercise combine to improve short-term mathematical task performance and speed in adolescents.

Global evaluation of the use of glycaemic impact measurements to food or nutrient intake

Objective:

Measures of glycaemic impact (e.g. postprandial glucose (PPG), oral glucose tolerance test (OGTT) and glycaemic index (GI)) are used by government health and regulatory agencies and public health associations around the world. The objective of this global review was to identify similarities and differences in the use of glycaemic impact measures for potential considerations for harmonisation.

Design:

A literature and internet search was conducted to identify country government agencies and health associations that provide guidance or recommendations for PPG, OGTT, GI and glycaemic load.

Results:

Based on this global review, the use of GI for food labelling (e.g. low GI) is limited and its use is voluntary. The application of OGTT as a diagnostic measurement of diabetes and gestational diabetes is widely used and in a consistent manner among the different regions of the world. Time-specific (e.g. 2 h) PPG is commonly used as a target not to exceed in individuals with diabetes and gestational diabetes. PPG is used by regulatory agencies for the substantiation of food labelling. There are differences, however, among regulatory agencies in the specific measure of PPG (i.e. PPG AUC v. peak PPG). Maximum targets for 2-h PPG for individuals with diabetes and gestational diabetes, ranging between 6 and 10 mmol/l, across countries suggest a potential consideration to harmonise PPG targets.

Conclusions:

There is general consistency in the use and/or target levels of glycaemic impact measures; however, there is a potential need to investigate harmonisation strategies on certain aspects of glycaemic impact measures.

Determining the glycaemic responses of foods: conventional and emerging approaches

A low-glycaemic diet is crucial for those with diabetes and cardiovascular diseases. Information on the glycaemic index (GI) of different ingredients can help in designing novel food products for such target groups. This is because of the intricate dependency of material source, composition, food structure and processing conditions, among other factors, on the glycaemic responses. Different approaches have been used to predict the GI of foods, and certain discrepancies exist because of factors such as inter-individual variation among human subjects. Besides other aspects, it is important to understand the mechanism of food digestion because an approach to predict GI must essentially mimic the complex processes in the human gastrointestinal tract. The focus of this work is to review the advances in various approaches for predicting the glycaemic responses to foods. This has been carried out by detailing conventional approaches, their merits and limitations, and the need to focus on emerging approaches. Given that no single approach can be generalised to all applications, the review emphasises the scope of deriving insights for improvements in methodologies. Reviewing the conventional and emerging approaches for the determination of GI in foods, this detailed work is intended to serve as a state-of-the-art resource for nutritionists who work on developing low-GI foods.

Associations between Dietary Glycemic Index and Glycemic Load Values and Cardiometabolic Risk Factors in Adults: Findings from the China Health and Nutrition Survey

Studies investigating the associations between dietary glycemic index (GI) and glycemic load (GL) values and cardiometabolic risk factors (CMRF) among Chinese populations are strikingly limited. To assess the associations between dietary GI and GL values and CMRF, including dyslipidemia, hyperglycemia, and hyperuricemia in Chinese adults, we extracted data of 7886 apparently healthy adults from the 2009 wave of the China Health and Nutrition Survey. Dietary GI and GL values were calculated using data collected from three consecutive 24 h dietary recalls. Fasting lipid, glucose, and uric acid concentrations were measured and CMRF were defined on the basis of established criteria. There were no significant associations between dietary GI values and CMRF, and analyzing the data by age, sex, body mass index (BMI), and region did not alter these results. Dietary GL values were positively associated with prevalence of hyperuricemia in all participants (Q4 compared with Q1: odds ratio (OR) = 1.46; 95% CI: 1.14, 1.87; p-trend = 0.0030) and prevalence of hypercholesterolemia in participants ≥ 60 years old (Q5 compared with Q1: OR = 1.72; 95% CI: 1.11, 2.68; p-trend < 0.0010). Higher dietary GL but not GI values were associated with increased prevalence of hyperuricemia in apparently healthy Chinese adults and hypercholesterolemia in older Chinese adults. Further studies are required to confirm the public health implication of these findings.

Lower nocturnal blood glucose response to a potato-based mixed evening meal compared to rice in individuals with type 2 diabetes

BACKGROUND & AIMS

Guidelines for reducing postprandial blood glucose concentrations include avoiding high glycemic index (GI) foods, such as white potatoes. However, GI testing is often undertaken in the morning with foods consumed in isolation by non-clinical cohorts. We investigated the impact of potato preparation and consumption as part of a mixed-evening meal on postprandial and nocturnal glycemic responses, and postprandial insulin response, in individuals with Type 2 Diabetes Mellitus (T2DM).

METHODS

In a randomized, cross-over design, 24 males and females (age 58.3 ± 9.3 y; BMI: 31.7 ± 6.8 kg/m²) with T2DM (diet or metformin controlled) completed four experimental trials after consuming a standardized breakfast (25% daily energy intake (EI)) and lunch (35% EI). Dinner (40% EI) was consumed at 1800 h being either: 1) boiled potato (BOIL); 2) roasted potato (ROAST); 3) boiled potato cooled for 24 h (COOLED); or 4) basmati rice (CONTROL). Each meal contained 50% carbohydrate, 30% fat and 20% protein. Blood samples were collected prior to, immediately post meal and at 30-min intervals for a further 120 min. A continuous glucose monitor was worn to assess nocturnal interstitial glucose concentrations.

RESULTS

No differences were detected in postprandial venous glucose area under the curve (iAUC) between CONTROL and all three potato conditions. Postprandial insulin iAUC was greater following COOLED compared to CONTROL (P = 0.003; 95% CI: 18.9-111.72 miU/mL). No significant differences between CONTROL and BOIL or ROAST were detected for postprandial insulin concentrations. All potato meals resulted in lower nocturnal glucose AUC than CONTROL (P < 0.001; 95% CI 4.15-15.67 mmol/L x h).

CONCLUSION

Compared to an isoenergetic rice meal, boiled, roasted or boiled then cooled potato-based meals were not associated with unfavourable postprandial glucose responses or nocturnal glycemic control, and can be considered suitable for individuals with T2DM when consumed as part of a mixed-evening meal.

CLINICAL TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry https://www.anzctr.org.au/, ACTRN 12618000480280.

Glycemic index of pulses and pulse-based products: a review

Pulses are a major source for plant-based proteins, with over 173 countries producing and exporting over 50 million tons annually. Pulses provide many of the essential nutrients and vitamins for a balanced and healthy diet, hence are health beneficial. Pulses have been known to lower glycemic index (GI), as they elicit lower post prandial glycemic responses, and can prevent insulin resistance, Type 2 diabetes and associated complications. This study reviews the GI values (determined by in vivo methodology) reported in 48 articles during the year 1992-2018 for various pulse type preparations consumed by humans. The GI ranges (glucose and bread as a reference respectively) for each pulse type were: broad bean (40 ± 5 to 94 ± 4, 75 to 93), chickpea (5 ± 1 to 45 ± 1, 14 ± 3 to 96 ± 21), common bean (9 ± 1 to 75 ± 8, 18 ± 2 to 99 ± 11), cowpea (6 ± 1 to 56 ± 0.2, 38 ± 19 to 66 ± 7), lentil (10 ± 3 to 66 ± 6, 37 to 87 ± 6), mung bean (11 ± 2 to 90 ± 9, 28 ± 1 to 44 ± 6), peas (9 ± 2 to 57 ± 2, 45 ± 8 to 93 ± 9), pigeon peas (7 ± 1 to 54 ± 1, 31 ± 4), and mixed pulses (35 ± 5 to 66 ± 23, 69 ± 42 to 98 ± 29). It was found that the method of preparation, processing and heat applications tended to affect the GI of pulses. In addition, removal of the hull, blending, grinding, milling and pureeing, reduced particle size, contributed to an increased surface area and exposure of starch granules to the amylolytic enzymes. This was subsequently associated with rapid digestion and absorption of pulse carbohydrates, resulting in a higher GI. High or increased heat applications to pulses were associated with extensive starch gelatinization, also leading to a higher GI. The type of reference food used (glucose or white bread) and the other nutrients present in the meal also affected the GI.

Dietary Glycemic Index and Glycemic Load Are Not Associated with the Metabolic Syndrome in Lebanese Healthy Adults: A Cross-Sectional Study

High dietary glycemic index (GI) and glycemic load (GL) were suggested to increase the risk of metabolic syndrome (MetS). This study aims to estimate dietary GI and GL in a sample of healthy Lebanese adults and examine their association with MetS and its individual abnormalities. The study uses data from a community-based survey of 501 Lebanese urban adults. Dietary intake was assessed using a food frequency questionnaire. Biochemical, anthropometric, and blood pressure measurements were obtained. Subjects with previous diagnosis of chronic disease, metabolic abnormalities, or with incomplete data or implausible energy intakes were excluded, yielding a sample of 283. Participants were grouped into quartiles of GI and GL. Multivariate logistic regression analyses were performed. Average dietary GI and GL were estimated at 59.9 ± 8 and 209.7 ± 100.3. Participants belonging to the highest GI quartile were at increased risk of having MetS (odds ratio (OR) = 2.251, 95% CI:1.120–4.525) but this association lost significance with further adjustments. Those belonging to the second quartile of GI had significantly lower odds of having hyperglycemia (OR: 0.380, 95% CI:0.174–0.833). No associations were detected between GL and MetS. The study contributes to the body of evidence discussing the relationship between GI, GL, and MetS, in a nutrition transition context.

The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients With Gestational Diabetes

The incorporation of glycemic index (GI) and glycemic load (GL) is a promising way to improve the accuracy of postprandial glycemic response (PPGR) prediction for personalized treatment of gestational diabetes (GDM). Our aim was to assess the prediction accuracy for PPGR prediction models with and without GI data in women with GDM and healthy pregnant women. The GI values were sourced from University of Sydney’s database and assigned to a food database used in the mobile app DiaCompanion. Weekly continuous glucose monitoring (CGM) data for 124 pregnant women (90 GDM and 34 control) were analyzed together with records of 1489 food intakes. Pearson correlation (R) was used to quantify the accuracy of predicted PPGRs from the model relative to those obtained from CGM. The final model for incremental area under glucose curve (iAUC120) prediction chosen by stepwise multiple linear regression had an R of 0.705 when GI/GL was included among input variables and an R of 0.700 when GI/GL was not included. In linear regression with coefficients acquired using regularization methods, which was tested on the data of new patients, R was 0.584 for both models (with and without inclusion of GI/GL). In conclusion, the incorporation of GI and GL only slightly improved the accuracy of PPGR prediction models when used in remote monitoring.

Effect of Non-Conventional Drying Methods on In Vitro Starch Digestibility Assessment of Cooked Potato Genotypes

Potatoes (Solanum tuberosum L.) are a good dietary source of carbohydrates in the form of digestible starch (DS) and resistant starch (RS). As increased RS content consumption can be associated with decreased chronic disease risk, breeding efforts have focused on identifying potato varieties with higher RS content, which requires high-throughput analysis of starch profiles. For this purpose, freeze drying of potatoes has been used but this approach leads to inaccurate RS values. The present study objective was to assess the starch content (RS, DS and total starch (TS)) of three cooked potato genotypes that were dried using freeze drying and innovative drying techniques (microwave vacuum drying, instant controlled pressure drop drying and conductive hydro-drying) relative to freshly cooked potato samples. Depending on the genotype, all drying methods showed one or more starch measures that were significantly different from freshly cooked values. The combination of ultrasound and infrared assisted conductive hydro-drying was the only method identified to be associated with accurate assessment of DS and TS content relative to fresh samples. The drying treatments were all generally associated with highly variable RS content relative to fresh controls. We conclude that freshly cooked samples must be used for selecting varieties with a high proportion of RS starch as drying of cooked potatoes leads to unreliable RS measurements.

Effect of nutrient composition in a mixed meal on the postprandial glycemic response in healthy people: a preliminary study

BACKGROUND/OBJECTIVES

The glycemic index (GI) is a measure of the postprandial glucose response (PPGR) to food items, and glycemic load (GL) is a measure of the PPGR to the diet. For those who need to maintain a healthy diet, it is beneficial to regulate appropriate levels of blood glucose. In reality, what influences the meal GI or GL depends on the macronutrient composition and the physical chemistry reactions in vivo. Thus, we investigated whether different macronutrients in a meal significantly affect the PPGR and the validity of calculated GI and GL values for mixed meals.

SUBJECTS/METHODS

12 healthy subjects (6 male, 6 female) were recruited at a campus setting, and subjects consumed a total of 6 test meals one by one, each morning between 8:00 and 8:30 am after 12 h of fasting. PPGR was measured after each consumed meal and serial finger pricks were performed at indicated times. Test meals included 1) 68 g oral glucose, 2) 210 g rice, 3) rice plus 170 g egg white (RE), 4) rice plus 200 g bean sprouts (RS), 5) rice plus 10 g oil (RO), and 6) rice plus, egg white, bean sprouts, and oil (RESO). The incremental area under the curve (iAUC) was calculated to assess the PPGR. Mixed meal GI and GL values were calculated based on the nutrients the subjects consumed in each of the test meals.

RESULTS

The iAUC for all meals containing two macronutrients (RS, RO, or RE) were not significantly different from the rice iAUC, whereas, the RESO iAUC (2,237.5 ± 264.9) was significantly lower (P < 0.05). The RESO meal's calculated GI and GL values were different from the actual GI and GL values measured from the study subjects (P < 0.05).

CONCLUSIONS

The mixed meal containing three macronutrients (RESO) decreased the PPGR in healthy individuals, leading to significantly lower actual GI and GL values than those derived by nutrient-based calculations. Thus, consuming various macronutrient containing meals is beneficial in regulating PPGR.

Comparison of Low Glycaemic Index and High Glycaemic Index Potatoes in Relation to Satiety: A Single-Blinded, Randomised Crossover Study in Humans

High glycaemic index (GI) foods have been proposed to reduce satiety and thus promote overweight and obesity. Generally, potatoes have a high GI, but they also provide many beneficial nutrients and they are a highly important food source globally. In this study, we investigated how a low GI potato affected subjective satiety as compared to a high GI potato. Twenty healthy men (aged 18–40 years; body mass index (BMI) 18–27 kg/m²) participated in this single-blinded, controlled, randomised crossover trial. On each of the two trial days, the subjects were given a 500-gram portion of either a low or high GI potato variety (Carisma^® low GI and Arizona high GI). Subjective appetite sensations were measured at baseline and at +15 min, +45 min, +75 min, +105 min, and +135 min after consumption of the test meal until an ad libitum meal was served at +150 min. No significant differences in the primary endpoint, satiety, were found between the two potato varieties (all p > 0.05). Furthermore, no significant differences were found in the secondary endpoints; hunger, fullness, and prospective food consumption, or ad libitum energy intake (all p > 0.05). In conclusion, the results of this study do not indicate that the GI of potatoes is important for satiety in normal-weight men.

Glycaemic and insulinaemic impact of oats soaked overnight in milk vs. cream of rice with and without sugar, nuts, and seeds: a randomized, controlled trial

Background/Objectives

Soaking oats overnight in milk renders them ready to eat the next morning, however, it is unknown whether oats prepared this way will retain its relatively low glycaemic and insulinaemic impact. Therefore, we compared the glycaemic, insulinaemic and subjective hunger responses elicited by oats soaked overnight in 110 g skim-milk (ONO) vs. cooked cream of rice cereal (CR), both with and without inclusions.

Subjects/Methods

The project was performed at two research centers (Toronto, Winnipeg) as two separate studies each using a randomized, cross-over design with similar methods. The glycaemic and insulinaemic responses of overnight-fasted participants without diabetes (males:females: Toronto, 24:16; Winnipeg, 20:20) were measured for 3 h after consuming CR and ONO fed alone (Toronto) or with added sugar, nuts, and seeds (CRsns and ONOsns) (Winnipeg). Participants rated subjective hunger using visual analog scales. Data were analyzed by paired t-test. The primary endpoint was 0–2 h incremental area under the curve (iAUC) for glucose.

Results

Mean glucose iAUC was 33% less, after ONO than CR (mean difference was 39 (51–27) mmol × min/l, p < 0.0001) and 24% less, after ONOsns than CRsns (mean difference was 43 (65–21) mmol × min/l, p = 0.0003). Serum-insulin iAUC was 33% less, after ONO than CR (mean difference 57 (81–40) pmol × hl, p < 0.0001) and 32% less, after ONOsns than CRsns (966 (1360–572) pmol × h/l, p < 0.0001). In both Toronto and Winnipeg, subjective hunger ratings were similar across the two treatments.

Conclusions

Oats prepared by soaking overnight in skimmed milk without and with inclusions retain their relatively low glycaemic and insulinaemic impact.

Metabolome analysis for investigating host-gut microbiota interactions

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.

Predicting mixed-meal measured glycaemic index in healthy subjects

PURPOSE

To determine the influence of meal composition on the glycaemic impact of different carbohydrate staples, and the accuracy of "adjusted calculated meal GI" compared with "measured mixed-meal GI".

METHODS

In a non-blind randomized crossover trial fasted healthy subjects consumed four dinner-type mixed meals of realistic serving size comprising a carbohydrate staple of either mashed potato, pasta, rice or a glucose drink, combined with fixed portions of boiled carrots, poached salmon and herb sauce. Blood samples collected between 0 and 180 min were analysed for glucose and insulin concentrations. Adjusted calculated meal GI values were determined against a 50 g reference glucose drink, and compared to corresponding measured mixed-meal GIs, supplemented with data from four previous mixed-meal postprandial glycaemic response studies.

RESULTS

The common carbohydrate staples, and the glucose drink, ingested as part of the salmon mixed meal induced a significantly lower post-prandial relative glycaemic response (RGR) and concurrent higher relative insulin response than the same amount of staple eaten alone. Adjusted calculated mixed-meal GI closely predicted measured mixed-meal GI in healthy subjects for 15 out of 17 mixed meals examined, showing the need to account for effects of fat and protein when predicting measured mixed-meal GI. Further, we showed the validity of using customarily consumed food amounts in mixed-meal postprandial RGR study design.

CONCLUSIONS

Adjusted calculated mixed-meal GI appears a useful model to predict measured mixed-meal GI in healthy subjects and with further development and validation could aid nutrition research and rational design of healthy meals for personalized nutrition and particular consumer groups.

Effects of Providing High-Fat versus High-Carbohydrate Meals on Daily and Postprandial Physical Activity and Glucose Patterns: a Randomised Controlled Trial

We determined the effects of altering meal timing and diet composition on temporal glucose homeostasis and physical activity measures. Eight sedentary, overweight/obese men (mean ± SD, age: 36 ± 4 years; BMI: 29.8 ± 1.8 kg/m²) completed two × 12-day (12-d) measurement periods, including a 7-d habitual period, and then 5 d of each diet (high-fat diet [HFD]: 67:15:18% fat:carbohydrate:protein versus high-carbohydrate diet [HCD]: 67:15:18% carbohydrate:fat:protein) of three meals/d at ±30 min of 0800 h, 1230 h, and 1800 h, in a randomised order with an 8-d washout. Energy intake (EI), the timing of meal consumption, blood glucose regulation (continuous glucose monitor system (CGMS)), and activity patterns (accelerometer and inclinometer) were assessed across each 12-d period. Meal provision did not alter the patterns of reduced physical activity, and increased sedentary behaviour following dinner, compared with following breakfast and lunch. The HCD increased peak (+1.6 mmol/L, p < 0.001), mean (+0.5 mmol/L, p = 0.001), and total area under the curve (+670 mmol/L/min, p = 0.001), as well as 3-h postprandial meal glucose concentrations (all p < 0.001) compared with the HFD. In overweight/obese males, the provision of meals did not alter physical activity patterns, but did affect glycaemic control. Greater emphasis on meal timing and composition is required in diet and/or behaviour intervention studies to ensure relevance to real-world behaviours.

Association between Dietary Glycemic Index and Knee Osteoarthritis: The Korean National Health and Nutrition Examination Survey 2010-2012

BACKGROUND

Obesity and metabolic abnormalities are important risk factors for knee osteoarthritis (KOA). Recent epidemiologic studies have found that a high glycemic index (GI) and glycemic load (GL) diet are associated with a higher risk for metabolic complications and cardiovascular mortality.

OBJECTIVE

We aimed to examine the association between dietary GI, dietary GL, and KOA among Korean adults.

DESIGN

This was a cross-sectional study that analyzed data obtained from the Korean National Health and Nutrition Examination Survey 2010-2012.

PARTICIPANTS/SETTING

A total of 9,203 participants (5,275 women) aged ≥50 years were included.

MAIN OUTCOME MEASURES

KOA was defined as the presence of radiographic features of Kellgren-Lawrence grade ≥2. Chronic knee pain was defined as the presence of knee pain for more than 30 days during the past 3 months. Dietary information was collected using a single 24-hour recall method.

STATISTICAL ANALYSES PERFORMED

The association between the quintiles of dietary GI and dietary GL and knee conditions was analyzed using a multinomial logistic regression analysis adjusting for age, physical activity, obesity, hypertension and diabetes, serum low-density lipoprotein, and total energy intake.

RESULTS

Among the women, the association between dietary GI and symptomatic KOA was: quintile 1: 1.00 (reference); quintile 2: 1.29 (95% CI 0.87 to 1.92); quintile 3: 1.59 (95% CI 1.11 to 2.28); quintile 4: 1.74 (95% CI 1.21 to 2.51); and quintile 5: 1.77 (95% CI 1.20 to 2.60) (P=0.001). Chronic knee pain without KOA was associated with dietary GI; however, this association was not linear across quintiles. There was no significant association between dietary GI and asymptomatic KOA. Among the men, no significant association was found between dietary GI and any knee conditions. There was no significant association between dietary GL and KOA in both men and women.

CONCLUSIONS

There was a significant positive association between dietary GI and symptomatic KOA in women.

Lower Dietary Inflammatory Index Scores Are Associated with Lower Glycemic Index Scores among College Students

The association between the Dietary Inflammatory Index (DII^®), the glycemic index (GI), and the glycemic load (GL) is not known, although it is known that carbohydrates are pro-inflammatory. We aimed to measure the association between the DII and both GI and GL among college students. In this cross-sectional study, 110 college students completed a 3-day food diary, which was used to calculate the DII, the GI, the GL, and the healthy eating index (HEI)-2010. Least square means and 95% confidence intervals of the GI, the GL, and the HEI-2010 were presented per DII tertile using generalized linear mixed models. Participants in tertile 1 of DII scores had lower GI and GL scores, but higher HEI-2010 scores than those in tertile 3. Pearson correlations showed that DII score was positively correlated with the GI score (r = 0.30, p < 0.01), but negatively correlated with the HEI-2010 (r = −0.56, p < 0.001). DII score was not correlated with GL score. Results from this study suggest that increased inflammatory potential of diet, as represented by higher DII scores, was associated with increased GI scores and lower quality of diet on the HEI-2010. Use of the DII suggests new directions for dietary approaches for preventing chronic diseases that moves beyond convention by decreasing systemic inflammation.

Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus

BACKGROUND

Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage.

OBJECTIVE

This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients' electronic record management to guide BG prediction-based personalized recommendations for patients with GDM.

METHODS

A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients' characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models.

RESULTS

The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions.

CONCLUSIONS

The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study.

Effect of prior meal macronutrient composition on postprandial glycemic responses and glycemic index and glycemic load value determinations

Background: The potential impact of prior meal composition on the postprandial glycemic response and glycemic index (GI) and glycemic load (GL) value determinations remains unclear.Objective: We determined the effect of meals that varied in macronutrient composition on the glycemic response and determination of GI and GL values of a subsequent standard test food.Design: Twenty healthy participants underwent 6 test sessions within 12 wk. The subjects received each of 3 isocaloric breakfast meals (i.e., high carbohydrate, high fat, or high protein) on separate days in a random order, which was followed by a standard set of challenges (i.e., white bread and a glucose drink) that were tested on separate days in a random order 4 h thereafter. Each challenge provided 50 g available carbohydrate. Arterialized venous blood was sampled throughout the 2-h postchallenge period. GI, GL, and insulin index (II) values were calculated with the use of the incremental area under the curve (AUCi) method, and serum lipids were determined with the use of standard assays.Results: The consumption of the high-protein breakfast before the white-bread challenge attenuated the rise in the postprandial serum glucose response (P < 0.0001) and resulted in lower glucose AUCi (P < 0.0001), GI (P = 0.0096), and GL (P = 0.0101) values than did the high-carbohydrate and high-fat breakfasts. The high-protein breakfast resulted in a lower insulin AUCi (P = 0.0146) for white bread than did the high-fat breakfast and a lower II value (P = 0.0285) than did the high-carbohydrate breakfast. The 3 breakfasts resulted in similar serum lipid responses to the white-bread challenge.Conclusions: These data indicate that the macronutrient composition of the prior meal influences the glycemic response and the determination of GI and GL values for white bread. Future studies are needed to determine whether the background food macronutrient composition influences mean dietary GI and GL values that are calculated for eating patterns, which may alter the interpretation of the associations between these values and chronic disease risk. This trial was registered at clinicaltrials.gov as NCT01023646.

Dietary glycemic index, glycemic load, and cancer risk: results from the EPIC-Italy study

Factors linked to glucose metabolism are involved in the etiology of several cancers. High glycemic index (GI) or high glycemic load (GL) diets, which chronically raise postprandial blood glucose, may increase cancer risk by affecting insulin-like growth factor. We prospectively investigated cancer risk and dietary GI/GL in the EPIC-Italy cohort. After a median 14.9 years, 5112 incident cancers and 2460 deaths were identified among 45,148 recruited adults. High GI was associated with increased risk of colon and bladder cancer. High GL was associated with: increased risk of colon cancer; increased risk of diabetes-related cancers; and decreased risk of rectal cancer. High intake of carbohydrate from high GI foods was significantly associated with increased risk of colon and diabetes-related cancers, but decreased risk of stomach cancer; whereas high intake of carbohydrates from low GI foods was associated with reduced colon cancer risk. In a Mediterranean population with high and varied carbohydrate intake, carbohydrates that strongly raise postprandial blood glucose may increase colon and bladder cancer risk, while the quantity of carbohydrate consumed may be involved in diabetes-related cancers. Further studies are needed to confirm the opposing effects of high dietary GL on risks of colon and rectal cancers.

Effect of macronutrients and fiber on postprandial glycemic responses and meal glycemic index and glycemic load value determinations

Background: The potential confounding effect of different amounts and proportions of macronutrients across eating patterns on meal or dietary glycemic index (GI) and glycemic load (GL) value determinations has remained partially unaddressed.Objective: The study aimed to determine the effects of different amounts of macronutrients and fiber on measured meal GI and GL values.Design: Four studies were conducted during which participants [n = 20-22; women: 50%; age: 50-80 y; body mass index (in kg/m²): 25-30)] received food challenges containing different amounts of the variable nutrient in a random order. Added to the standard 50 g available carbohydrate from white bread was 12.5, 25, or 50 g carbohydrate; 12.5, 25, or 50 g protein; and 5.6, 11.1, or 22.2 g fat from rice cereal, tuna, and unsalted butter, respectively, and 4.8 or 9.6 g fiber from oat cereal. Arterialized venous blood was sampled for 2 h, and measured meal GI and GL and insulin index (II) values were calculated by using the incremental area under the curve (AUC_i) method.Results: Adding carbohydrate to the standard white-bread challenge increased glucose AUC_i (P < 0.0001), measured meal GI (P = 0.0066), and mean GL (P < 0.0001). Adding protein (50 g only) decreased glucose AUC_i (P = 0.0026), measured meal GI (P = 0.0139), and meal GL (P = 0.0140). Adding fat or fiber had no significant effect on these variables. Adding carbohydrate (50 g), protein (50 g), and fat (11.1 g) increased the insulin AUC_i or II; fiber had no effect.Conclusions: These data indicate that uncertainty in the determination of meal GI and GL values is introduced when carbohydrate-containing foods are consumed concurrently with protein (equal amount of carbohydrate challenge) but not with carbohydrate-, fat-, or fiber-containing foods. Future studies are needed to evaluate whether this uncertainty also influences the prediction of average dietary GI and GL values for eating patterns. This trial was registered at clinicaltrials.gov as NCT01023646.

Siri, What Should I Eat?

Zeevi et al. report that extensive monitoring of a human cohort for variations in dietary intake, lifestyle, host phenotype, and the gut microbiome has enabled the development of a machine-learning algorithm that accurately predicts the individual glycemic response to meals, providing an important first step toward personalized nutrition.

Personalized Nutrition by Prediction of Glycemic Responses

Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.