Next Article in Journal
An Overview of Different Vitamin D Compounds in the Setting of Adiposity
Next Article in Special Issue
Anti-Growth and Anti-Metastatic Potential of Raw and Thermally Treated Eragrostis tef Extract in Human Cancer Cells
Previous Article in Journal
The Effects of Polyphenol Supplementation on BDNF, Cytokines and Cognition in Trained Male Cyclists following Acute Ozone Exposure during High-Intensity Cycling
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 16
Issue 2
10.3390/nu16020234
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Glycemic Response in Nonhuman Primates Fed Gluten-Free Rice Cakes Enriched with Soy, Pea, or Rice Protein and Its Correlation with Nutrient Composition

by
Yong Yang
1,2,†,
Qingsu Liu
3,† and
Feng Yue
1,2,*
1
State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Sanya 572025, China
2
Collaborative Innovation Center of One Health, Hainan University, Haikou 570228, China
3
Food, Water, Waste Research Group, Faculty of Engineering, The University of Nottingham, University Park Campus, Nottingham NG7 2RD, UK
*
Author to whom correspondence should be addressed.
†
These authors contribute equally to the work.
Nutrients 2024, 16(2), 234; https://doi.org/10.3390/nu16020234
Submission received: 11 December 2023 / Revised: 1 January 2024 / Accepted: 3 January 2024 / Published: 11 January 2024
(This article belongs to the Special Issue Dietary Ancient Grains: Gluten-Free Cereals and Pseudocereals and Their Role in Human Health)
Browse Figures
Versions Notes

Abstract

:
Celiac disease (CD) is a chronic disease caused by the consumption of gluten foods and is closely related to type 1 diabetes (T1D). Adherence to a gluten-free (GF) diet is the cornerstone of treating CD, and certain plant proteins added to GF foods affect blood glucose to varying degrees. The aim of this study was to analyze and compare the changes in glycemic index (GI) and incremental area under the postprandial glucose tolerance curve (IAUC) of various foods through consumption of GF foods supplemented with certain plant proteins in non-human primates. The test foods were GF rice cakes with 5%, 10%, and 15% added single plant proteins (rice protein, soy protein, and pea protein) mixed with rice flour, as well as 5%, 10%, and 15% gluten rice cakes, and rice flour alone, for a total of 13 food items, and 12 healthy cynomolgus monkeys were examined for their glucose levels in the blood after fasting and after eating each test food (50 g) for 15, 30, 45, 60, 90, and 120 min after fasting and eating each test food. Fingertip blood glucose levels were measured, and the nutrient content of each food, including protein, fat, starch, ash, and amino acids, was examined. All foods tested had a low GI (<50) when analyzed using one-way ANOVA and nonparametric tests. Postprandial IAUC was significantly lower (p < 0.05) for GF rice cakes with 15% pea protein (499.81 ± 34.46) compared to GF rice cakes with 5% pea protein (542.19 ± 38.78), 15% soy protein (572.94 ± 72.74), and 15% rice protein (530.50 ± 14.65), and GF rice cakes with 15% wheat bran protein (533.19 ± 34.89). A multiple regression analysis showed that glycine was negatively associated with IAUC in GF rice cakes with 5%, 10%, and 15% pea protein added (p = 0.0031 < 0.01). Fat was negatively correlated with IAUC in GF rice cakes supplemented with 5%, 10%, and 15% soy protein (p = 0.0024 < 0.01). In this study, GF rice cakes made with added pea protein were superior to other gluten and GF rice cakes and had a small effect on postprandial glucose.

Graphical Abstract

1. Introduction

Rapid changes in the lifestyles of people around the world have occurred due to economic globalization, urbanization, and fast-paced development in recent years [1]. These changes have significantly impacted people’s dietary habits, leading to poor health among many people due to the foods they consume. Gluten, a complex protein found in many grains [2], contains alcohol-soluble proteins that are known to be pathogenic factors for celiac disease (CD) [3] and have been implicated in other diseases such as type 1 diabetes [4], rheumatoid arthritis [5], and multiple sclerosis [6], among others [7,8,9]. Increasing evidence indicates that a considerable number of patients exhibit neurological dysfunction. The first systematic evidence of gluten-related neurological disease dates back to 1966 when electron microscopy and hematoxylin and eosin staining were used to examine neurodegenerative changes in muscle biopsies of adult patients with CD. Subsequent studies reported the first fatal case caused by neurological disease, comparing autopsy results with nine other CD cases presenting progressive central nervous system disorders. The case showed a common histopathological feature of progressive neurodegeneration in the cerebellum, deep gray matter, brainstem, and spinal cord [10]. Another study demonstrated that following a gluten-free (GF) diet stabilized not only gastrointestinal symptoms but also neurological symptoms in patients with CD [11]. This demonstrated that the long-term consumption of gluten led to neurological dysfunction. Moreover, GF food is the only way to achieve complete symptom relief among patients with CD [12].
Diabetes mellitus (DM) is a chronic, systemic metabolic disease characterized by elevated blood glucose levels [13]. Type 1 DM (T1DM) has a high comorbidity with CD [7,14]. Long-term gluten consumption also increases the risk of developing T1DM [15]. Early studies in mice suggest a role of gluten in the pathogenesis of T1DM, as it alters the composition of the gut microbiota, which may further promote the development of T1DM [16]. A lifelong GF diet reduced the incidence of autoimmune diabetes from 64% to 15% [17]. Therefore, dietary changes are crucial for achieving and maintaining metabolic control and help reduce the burden of diabetes-related complications [18].
GF foods refer to those that are completely free of gluten or do not contain gluten-containing grains [19]. Although significant progress has been made in understanding and improving GF foods over the past two decades through assessing different ingredients, additives, and technologies, finely processed GF foods may contain higher amounts of sugar and oil, increasing the risk of diabetes and obesity. The development of GF products still faces challenges. Currently, the focus is more on plant-based functional foods because their health benefits primarily come from plant proteins. Some plant proteins are typically added to GF foods to improve their structure, texture, and glycemic control [19]. Developing plant-protein-based GF foods is an important trend, especially in managing CD and diabetes. Rice is the second largest consumed cereal after wheat. It is a highly nutritious food source, rich in protein and an excellent source of carbohydrates, besides containing various vitamins and minerals such as niacin and thiamine [20]. Also, it is GF, making it a good choice for individuals with gluten sensitivity. Rice flour also plays a vital role as an ingredient in traditional and new food products [21,22,23], and frequently is the main ingredient applied in GF products both from food industry and food scientists. However, rice has a glycemic index (GI) ranging from 54 to 121 [24]. The long-term consumption of foods with a GI between 79 and 82 significantly increases the risk of developing diabetes [25]. Consuming certain different types of plant-based proteins can reduce the incremental area under the blood glucose curve (iAUC) after a meal [26], which can help control blood sugar levels. Therefore, rice is not the most appropriate food choice for patients with diabetes. Legumes such as soy and peas are an important source of plant proteins and are rich in carbohydrates, fibers, proteins, and various micronutrients [27]. Consuming legume protein is associated with a reduced incidence of diabetes [28]. Therefore, it is recommended that individuals choose low-GI GF foods rich in legume protein, which helps control blood sugar levels and alleviates related diseases.
In the development of GF foods, GI is a way to evaluate the speed and extent of the rise in blood sugar caused by the carbohydrates present in food within 2 h of consumption [29]. Compared with regular foods, most GF foods have a lower GI and can help control blood sugar [30]. The incremental area under the glucose curve (IAUC), calculated by measuring the increase in the blood sugar level after a meal relative to the fasting level, serves as an evaluation index for the blood glucose response to each test meal. GI refers to the percentage increase in the area under the blood glucose curve after consuming a target amount (usually 50 g) of available carbohydrates in a test food compared with the corresponding increase after consuming the same amount of available carbohydrates in a reference food (such as glucose). Foods are classified into high GI (>70), medium GI (55–70), and low GI (<55) based on their GI values. High-GI foods can lead to a rapid release of carbohydrates, resulting in an increase in blood glucose concentration [31]. In contrast, low-GI foods have a slower digestion and absorption rate, leading to a gradual rise in blood glucose levels [32]. This positively affects the prevention and management of diabetes, obesity, cardiovascular disease, hyperlipidemia, and hypercholesterolemia [33,34]. For patients with both CD and T1DM, maintaining good glycemic control and adhering to a strict low-GI GF diet is essential to avoid complications associated with these two diseases.
Nonhuman primate (NHP) models have been successfully established for studying CD-related issues [35]. By feeding NHPs with gluten-containing food, gluten-sensitive macaques exhibited obvious CD symptoms, including chronic diarrhea, fat malabsorption, and intestinal lesions. These clinical, histological, and serological features were reversed through intervention with a GF diet [36]. Therefore, these NHP models are of great significance for studying both fundamental and practical CD-related issues [37]. Moreover, NHP models have been used to investigate the development and pathophysiological changes of obesity and diabetes [38]. The blood glucose regulation and pathological characteristics exhibited by the cynomolgus monkeys are quite similar to the clinical features of certain human diseases. Additionally, captive macaques have a longer lifespan, and their living environment and dietary habits are relatively fixed and uniform, making it more feasible to study diseases through dietary interventions in NHPs [39]. Thus, NHP models can effectively help with learning about many diseases and facilitate long-term research on the effects of dietary interventions on animals.
This study aimed to investigate the effect of GF rice cakes made with different ratios of plant-based protein and rice flour, and whole-grain rice cakes, on the postprandial blood glucose response in nonhuman primates. Thirteen test foods were prepared, including GF rice cakes mixed with 5%, 10%, or 15% of a single plant-based protein (rice protein, soy protein, and pea protein) and whole-grain rice cakes mixed with 5%, 10%, or 15% wheat protein as well as plain rice flour. Twelve healthy cynomolgus macaques were tested for fasting and postprandial fingertip blood glucose levels 15, 30, 45, 60, 90, and 120 min after consuming each test food (50 g). The GI and IAUC were analyzed for each test food, and the nutritional components, including protein, fat, starch, ash, and amino acids, were determined. It provides valuable insights into the selection and long-term consumption of GF foods with added plant-based protein for blood glucose control. This is particularly important for those with gluten intolerance and diabetes, and those who regularly consume gluten-containing foods and are at risk of developing neurodegenerative diseases.

2. Materials and Methods

2.1. Experimental Animals

Before starting the experiment, all animals were screened for health and had normal blood and biochemical parameters that met the experimental criteria. Table 1 summarizes the physical signs of 12 healthy male cynomolgus monkeys aged 12–16 years. These 12 cynomolgus monkeys were naïve and had never been involved in any pharmacological tests or studies before the experiment. During the study period, the animals were kept in stainless steel monkey cages in the nonhuman primate facility of Thinxon Biomedical Co., Ltd. (Nanning, China), which obtained a laboratory animal use license accredited by Guangxi province. The animals were fed normally during the experimental period without trial feeding, twice a day, and supplemented with fresh fruit once a day. All animals were kept at room temperature of 22–28 °C for 12 h with light and dark cycles (7:00 a.m. to 7:00 p.m.). The relative humidity was 30–75%, and water was consumed ad libitum. All experiments were performed during the daytime. In addition, animals underwent a thorough examination before the experiments and were confirmed to be free of other diseases such as tuberculosis. The study protocol was approved by the Institutional Animal Care and Use Committee (IACUC). The approval number is SSLl-21002, and the approval time is 22 August 2021.
The animals were screened as follows: (1) body mass index (BMI) < 35 kg/m2; (2) body weight and other biochemical parameters such as fasting glucose within normal limits; (3) no history of a neurological or psychiatric diagnosis; (4) no history of drug/alcohol abuse; (5) not currently taking any psychoactive drugs, nutrient supplements affecting glucose tolerance within 3 months of participation in any other study, and oral contraceptives, acetylsalicylic acid, steroids, protease inhibitors, and so forth; (6) screening of monkeys not infected with chronic diarrhea through fecal testing.

2.2. Experimental Material

The materials required for the experiment are presented in Table 2.

2.3. Experimental Methods

2.3.1. Test Food Preparation Method

SPI, PPI, RPI, and WPI were mixed into rice flour (where PI stands for single protein and is replaced sequentially according to the corresponding protein) in proportion to each other (meal formulation as in Table 3), and the batter was formed by pouring water into the powdered mixture. The moisture content of the raw batter reached 65%. The batter (about 25 g) was transferred to aluminum molds and steamed for 20 min. The rice cakes were then demolded, cooled at room temperature for 5 min, and stored in an airtight container. The samples were freeze-dried, and the contents of proteins, amino acids, and so forth were determined. A certain amount of sugar-free sweetener (not more than 0.5 g/100 g in solids) was added appropriately according to the national food addition standards, considering the taste. For LAGG, the dosage was 1%. That is, 0.35 g of LAGG was added to 35 g of rice flour.

2.3.2. Reference Food Preparation Method

An appropriate amount of food-grade anhydrous dextrose (50 g) was dissolved in 250 mL of purified water. After nasal gavage within 2 min, blood glucose values were measured using a glucometer with a pinprick of fingertip blood at 0, 15, 30, 45, 60, 90, and 120 min.

2.4. Feeding Cycle

Taking WPI as an example, the food-feeding cycles are presented in Table 4. Both food feeding and data collection were conducted on days 1, 5, 9, and 13 of the experiment. Day 0 animals were fasted for ≥16 h. Twelve healthy experimental animals were kept on the same diet for four feeding cycles, and the animals in each cycle were fed the rice cake with 15%, 10%, and 5% PI, with the following feeding cycles: (1) Thai fragrant rice + wheat protein; (2) Thai fragrant rice + soy protein; (3) Thai fragrant rice + pea protein; and (4) Thai fragrant rice + rice protein. PI protein was replaced with SPI, PPI, and RPI sequentially according to the experimental advancement.

2.5. Experimental Operation Method

The experimental animals maintained regular rest and a normal diet. High-dietary-fiber and high-sugar foods were avoided at dinner the day before the measurement, fasting (≥16 h) was started, and strenuous exercise was avoided in the early morning of the measurement day. Two weeks before the start of the experiment, all experimental animals were acclimatized in a monkey chair to avoid severe stress reactions in the experimental process. The experimental animals were rested in the monkey chair for 10 min after 5 min. The glucose levels were recorded twice from the fingertips of the animals under the fasting condition. Then, the animals were allowed to start eating, and the eating time was strictly controlled, starting from the first bite of eating time. The eating was completed within 5–10 min. The blood was collected 15, 30, 45, 60, 90, and 120 min after the meal and measured using the glucose meter; if necessary, the blood collection time could be extended (e.g., 180 min). The consistency and accuracy of blood collection time points were ensured. Consistent blood collection sites should be used during the measurement cycle, and feces should be collected on the day of the experiment and frozen at −80 °C.

2.6. Statistical Analysis

The data were statistically analyzed and plotted using GraphPad Prism 9.0.0 (GraphPad Software, Inc., San Diego, CA, USA), IBM SPSS Statistics 27.0.1 (SPSS, Inc., Chicago, IL, USA), and Origin 2021b (Origin Lab Corporation, Northampton, MA, USA) software. Data are presented as the mean ± standard error (S) unless otherwise noted. The quantitative data were presented as the mean ± standard error. The within-group and between-group comparisons were analyzed using independent-sample t tests and nonparametric tests. For all data analyses, * p < 0.05 and ** p < 0.01 were considered to indicate statistical significance. BMI was calculated as follows: BMI = weight/height2 (where height is the length from the crown of the head to the buttocks).

3. Results

3.1. Postprandial Glycemic Response and GI of Reference and Test Foods Consumed by Healthy Experimental Animals

The foods were tested once, and the glucose reference beverages (50 g of glucose) were each tested twice.
Figure 1A shows the changes in the mean blood glucose levels in healthy cynomolgus monkeys (n = 12) following two infusions of a 200 g/L glucose solution. Figure 1B–F indicate the change in mean postprandial blood glucose concentration in healthy cynomolgus monkeys (n = 12) after ingestion of the test food, which showed a peak at 45 min and a near return to baseline within 2 h (Figure 1). Among all gluten-containing and GF rice cakes made with different concentrations of added vegetable protein and rice flour, GI values ranged from 29 to 47 (Figure 2 and Table 5). They were all at the low-GI (low GI < 50) level. GF rice cakes made from 15% pea protein and rice flour had a lower GI value (29.16 ± 7.96), and GF rice cakes made from 15% soy protein and rice flour had a lower high GI value (44.35 ± 17.68). The GI of rice cakes with gluten-containing and GF proteins under the condition of adding the same proportion of vegetable protein is illustrated in Figure 3. The GI of rice cakes with gluten-containing and GF protein under the condition of adding the same vegetable protein is depicted in Figure 4. In rice cakes with 15% wheat protein and 15% GF vegetable protein (Figure 3A), the GI of GF rice cakes with 15% pea protein was significantly lower than the GI of the GF rice cake with 15% soy protein (p < 0.05).

3.2. IAUC 0–120 min after the Intake of 13 Gluten-Containing and GF Tested Foods in Healthy Experimental Animals

Each of the healthy experimental animals consumed the test food, and their blood glucose levels were measured at various intervals: before eating and 15, 30, 45, 60, 90, and 120 min after eating. IAUC was calculated using the trapezoidal rule for the 0 to 120 min period to determine the differences in blood glucose kinetics. Table 6 and Figure 5 illustrate the IAUC for healthy cynomolgus monkeys (n = 12) who consumed gluten-containing and GF versions of the 13 tested foods. Figure 6 shows the 0 to 120 min postprandial IAUC for gluten-containing and GF rice cakes with the same proportion of added vegetable protein. The 0 to 120 min postprandial IAUC for GF rice cakes with 15% pea protein (499.81 ± 34.46) was significantly lower than that for GF rice cakes with 15% soy protein (572.94 ± 72.74), and 15% rice protein (530.50 ± 14.65), and 15% wheat protein in gluten rice cakes (533.19 ± 34.89) (p < 0.05). Furthermore, the 0 to 120 min postprandial IAUC for GF rice cakes made with 15% pea protein (499.81 ± 34.46) was significantly lower than that for GF rice cakes made with pure rice flour (534.91 ± 22.51) (p < 0.01). Finally, the 0 to 120 min postprandial IAUC for GF rice cakes with 5% soy protein (542.19 ± 38.78) was significantly higher than that for both GF rice cakes with 5% rice protein (536.88 ± 16.07) and the one with pure rice flour (534.91 ± 22.51). The 0 to 120 min postprandial IAUC for GF rice cakes with 15% soy protein (572.94 ± 72.74) was significantly higher than that for GF rice cakes with 10% soy protein (516.69 ± 27.82) (p < 0.01). Figure 7 illustrates the 0 to 120 min postprandial IAUC of rice cakes with and without gluten protein under the condition of adding the same vegetable protein. The study found that the 0 to 120 min postprandial IAUC of GF rice cakes with 15% pea protein (499.81 ± 34.46) was significantly lower than that of GF rice cakes with 5% pea protein (542.19 ± 38.78) (p < 0.05). Additionally, the 0 to 120 min postprandial IAUC for GF rice cakes with 15% pea protein (499.81 ± 34.46) was significantly lower than that for GF rice cakes made from pure rice flour (534.91 ± 22.51) (p < 0.01). Moreover, the 0 to 120 min postprandial IAUC for GF rice cakes with 15% soy protein (572.94 ± 72.74) was significantly higher than that for GF rice cakes with 10% soy protein (516.69 ± 27.82) (p < 0.01). The 0 to 120 min postprandial IAUC for GF rice cakes with 10% rice protein (521.63 ± 12.06) was significantly lower than that for GF rice cakes with 5% rice protein (536.88 ± 16.07) (p < 0.05).

3.3. Nutritional Composition of 13 Gluten-Containing and GF Tested Foods

The nutritional composition of food is one of the factors affecting the postprandial glycemic response. Therefore, we analyzed the nutritional composition of 13 gluten-containing and GF tested foods, including protein, fat, starch, ash, and amino acids. The nutritional properties and composition of the different food types are presented in Table 7 and Table 8. We used the statistical method of a multiple regression analysis to investigate the relationship between the IAUC of blood glucose from 0 to 120 min after a meal and various types of nutritional components. The results showed that the content of lysine was negatively correlated with IAUC (p = 0.013 < 0.05) whereas the content of ash was positively correlated with IAUC (Figure 8) (p = 0.001 < 0.01) in gluten-containing rice cakes and those made from pure rice flour without gluten, which had 15% added plant-based protein. The regression equation was IAUC = 408.715 ash − 0.038 lysine + 572.675, with an F-value of 9.754 and a p value of <0.01, indicating that the fitted equation had statistical significance. The R2 value was 0.255, indicating a less-than-ideal fit, but the Variance Inflation Factor (VIF) value was 1.001 < 5, indicating no collinearity between variables. Additionally, the Durbin–Watson test value was 2.278, showing that the observations in the model were independent of each other. A highly significant negative correlation was found between glycine and IAUC in GF rice cakes and pure rice flour GF rice cakes with 5%, 10%, and 15% added pea protein (Figure 9) (p = 0.003 < 0.01). The regression equation was IAUC = −0.123 × glycine + 574.062, with an F-value of 9.719 and a p value of <0.01, indicating that the fitted equation had statistical significance. The R2 value was 0.174, indicating a less-than-ideal fit, but the VIF value was 1.000 < 5, indicating no collinearity between variables. Additionally, the Durbin–Watson test value was 2.559, showing that the observations in this model were independent of each other. A highly significant negative correlation was observed between fat content and IAUC in GF rice cakes and pure rice flour GF rice cakes with 5%, 10%, and 15% added soy protein (Figure 10) (p = 0.002 < 0.01). The regression equation was IAUC = −22.518 × fat + 568.801, with an F-value of 10.359 and a p value of <0.01, indicating that the fitted equation had statistical significance. The R2 value was 0.184, indicating a less-than-ideal fit, but the VIF value was 1.000 < 5, indicating no collinearity between variables. Additionally, the Durbin–Watson test value was 2.291, showing that the observations in this model were independent of each other.

4. Discussion

A high incidence of CD and T1DM [40] is considered a risk factor for metabolic diseases. Therefore, a critical task for these patients is to maintain good blood sugar control while adhering to a strict GF diet. Postprandial hyperglycemia is a significant concern for those diagnosed with prediabetes or diabetes, as it can lead to elevated glycated hemoglobin; such patients are advised to consume low-GI foods. The main determinants of postprandial glycemic response are the amount of carbohydrates ingested and the common constituents of the entire food (such as water, fat, protein, and fiber), processing techniques, and external factors. Some amino acids also affect postprandial glycemic response. Currently, only a few studies have examined the impact of GF diets on patients with T1DM and CD [41]. Also, only a limited number of small prospective and retrospective studies have discussed the glycemic benefits of GF diets [42]. Previous research has demonstrated a significant difference in glucose response to GF pasta between healthy participants and patients with CD, with healthy individuals exhibiting a significantly higher glucose response than patients with CD [43]. A study found that the fasting blood glucose levels increased significantly after 12 months on a GF diet [44]. In healthy individuals, the postprandial blood glucose response was higher with GF bread than with bread containing gluten [43]. Therefore, some GF foods may not be suitable for patients with abnormal glucose metabolism, and hence, further research on GF foods is needed. Although GF products are essential dietary needs for patients with CD, consumers without celiac disease need to consider that GF products are not necessarily a “healthier” food choice. Consumers should choose GF products that meet their individual needs while also paying attention to a balanced diet to avoid the negative effects of certain foods. The introduction of the GI concept provides a basis for patients with diabetes to make rational choices about carbohydrate-containing foods. A low-GI diet improves insulin sensitivity, lowers plasma triglyceride levels, reduces the risk of diabetes and heart disease, and helps treat obesity [45]. Low-GI foods stay in the digestive tract longer, have a lower absorption rate, and release glucose slowly, leading to lower postprandial blood glucose responses. The changes in postprandial blood glucose responses represent the balance between glucose entering and exiting the bloodstream. Lowering the GI of foods is a promising method, especially by adding some plant-based protein substitutes for traditional refined flour and starch materials, such as non-gluten grains and legumes, instead of using refined basic flours and starches, such as rice and corn flour, corn, potatoes, and cassava starch, because increasing the protein content can alter the digestion rate of starch, dilute the amount of available carbohydrates, and lower the GI.
Research has shown a significant correlation between the consumption of legume protein and a reduced incidence of diabetes. Legume protein can mitigate postprandial blood glucose response. Green pea legumes produce lower blood glucose peak responses in mixed diets with different types of legumes. Chickpeas, lentils, and green peas generate significantly lower postprandial blood glucose responses than pasta (~35%). Isolated green pea protein produces a lower glucose area under the curve (AUC), making it a valuable food component for improving blood sugar control [46], which is consistent with our research results. Some plant-based proteins are widely used to produce GF products. In this study, pea protein, rice protein, soy protein, and wheat protein were selected and processed into gluten-containing and GF rice cakes to examine their impact on postprandial blood glucose response in nonhuman primates. The GI values of the 10 GF rice cakes and 3 gluten-containing rice cakes ranged from 29 to 47, all with low GI (GI < 50). Rice cakes made from 15% pea protein and rice flour had a lower GI, while those made from 15% soy protein and rice flour had a higher GI. Meanwhile, the IAUC of postprandial blood glucose was the lowest for GF rice cakes made from 15% pea protein and rice flour. The IAUC of postprandial blood glucose was also highest for GF rice cakes made from 15% soy protein and rice flour. Additionally, the IAUC of GF rice cakes made with 15% pea protein was significantly lower than that of GF rice cakes made with 5% pea protein, 15% soy protein, 15% rice protein, and 15% wheat protein to produce gluten-containing rice cakes. Adding pea protein to make GF rice cakes had a minimal impact on postprandial blood glucose, which has important implications for a deeper understanding of the impact of nutrient content on blood sugar and the development of healthier food products. The impact of amino acids on blood glucose levels is mainly related to their metabolic pathways. Specifically, when branched-chain amino acids, including leucine, isoleucine, and valine, and aromatic amino acids, including phenylalanine, tyrosine, and tryptophan, are metabolized in the body, they generate ketones or biogenic amines that can stimulate insulin secretion and lower blood glucose levels [47]. Additionally, specific amino acids such as arginine, serine, and lysine, which are commonly found in proteins, can improve insulin sensitivity and promote glucose uptake through different metabolic pathways after ingestion, thereby affecting blood sugar levels [48]. Glycine can also promote insulin secretion, thereby reducing blood glucose levels and decreasing the peak value and AUC of postprandial blood glucose [49,50]. We employed a multiple regression analysis to analyze the relationship between the IAUC of blood glucose from 0 to 120 min after eating and various nutritional components. The results demonstrated that the content of lysine was negatively correlated with IAUC in gluten-containing and GF rice cakes with added 15% plant protein (p = 0.013 < 0.05). Glycine was significantly negatively correlated with IAUC in GF rice cakes with added 5%, 10%, and 15% pea protein and pure rice flour GF rice cakes (p = 0.003 < 0.01). In the future, we will further study the different effects of the intake of lysine and glycine on blood sugar levels. Apart from the types and amounts of amino acids, other nutritional components in food, such as fat, and an individual’s metabolic status can also affect postprandial blood glucose levels. An increase in fat content in food leads to a slower rise in blood glucose levels, and the peak value of postprandial blood glucose is also lowered. This suggests that the AUC of postprandial blood glucose is accordingly reduced. We found that the fat content in GF rice cakes with added 5%, 10%, and 15% soy protein and pure rice flour GF rice cakes was significantly negatively correlated with IAUC (p = 0.002 < 0.01), and an increase in fat content in food led to a significant decrease in IAUC at 2 h after the meal.
In this study, we used captive nonhuman primates, specifically the cynomolgus monkeys, as experimental animals to investigate the effects of gluten-containing and GF foods on postprandial blood glucose response. These nonhuman primates share many biological similarities with humans and exhibit similar morphology and functionality, making them a crucial animal model for such studies. Furthermore, their diet is relatively homogeneous, consisting mainly of various grains as well as supplemented nutrients such as milk, powdered milk, eggs, fish meal, bone meal, and salt. Disinfected vegetables and fruits are also added to ensure that the animals receive sufficient amounts of nutrients, such as vitamin C and minerals. We can ensure the relative stability of nutrient content in the feed through diversified feeding regimes. We also provide appropriate amounts of animal-sourced food to animals to ensure that they consume more than 50 types of nutrients, including carbohydrates, proteins, fats, vitamins, and minerals. We control the amount of food intake, especially for long-term captive monkeys, where overeating may lead to digestive problems or obesity. Nonhuman primates have a long history of application in translational medicine research, particularly in exploring the mechanisms underlying metabolic diseases and evaluating novel therapeutic approaches. Additionally, their longer lifespan facilitates the investigation and monitoring of the effects of long-term dietary patterns on animal health indices, such as gluten-containing and GF diets, high-sugar diets, high-fat diets, and so forth.

5. Conclusions

Among gluten-containing or GF foods made by mixing pea protein, rice protein, soy protein, wheat protein, and rice flour in varying ratios, GF rice cakes containing 15% pea protein exhibit a lower GI, lower postprandial 0 to 120 min IAUC, and potential for hypoglycemic effects. These findings can be applied to the development of GF foods using pea protein and rice flour as ingredients. Since GF products can be formulated with various alternative ingredients and further improved, more research is needed to determine the optimal substitutes for GF products to maintain blood glucose levels and promote health. Considering the increasing prevalence of CD, adhering to a lifelong GF diet remains the cornerstone of treating this disease. For patients with both T1DM and CD, not only a low-GI GF diet but also professional dietary counseling is necessary. Further research and development are needed in the GF product domain, especially for individuals with gluten sensitivity, diabetes, and neurodegenerative disease prevention needs.

Author Contributions

Y.Y. and Q.L. were responsible for formulating experimental protocols, designing experimental procedures, and drawing conclusions as well as manuscript editing and revision. Y.Y. collected and analyzed data. F.Y. was involved in designing and overseeing the study, reviewing the manuscript, and providing technical and funding support. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the STI2030-Major Projects (grant 2021ZD0200900 to F.Y.), National Key Research and Development Project of China (grant 2018YFA0108503 to F.Y.), National Hainan Key Research and Development Project (grant ZDYF2021SHFZ049 to F.Y.), Hainan Province Natural Science Foundation of high-level talent project (grant 322RC588 to F.Y.), Project of Collaborative Innovation Center of One Health (grant XTCX2022JKC05 to F.Y.).

Institutional Review Board Statement

The study protocol was approved by the Institutional Animal Care and Use Committee (IACUC) (Ref. No. SSLl-21002; 22 August 2021).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available within the article. The raw data are available on request from the corresponding author.

Acknowledgments

We appreciate Zhouquan Zhang in nonhuman primate laboratory of Thinxon biomedical Co., Ltd., providing the technical support and coordinating the animal experiment.

Conflicts of Interest

We declare that there is no conflict of interest regarding the publication of this paper.

References

  1. Jnawali, P.; Kumar, V.; Tanwar, B. Celiac disease: Overview and considerations for development of gluten-free foods. Food Sci. Hum. Wellness 2016, 5, 169–176. [Google Scholar] [CrossRef]
  2. Wieser, H. Chemistry of gluten proteins. Food Microbiol. 2007, 24, 115–119. [Google Scholar] [CrossRef] [PubMed]
  3. Lebwohl, B.; Ludvigsson, J.F.; Green, P. Celiac disease and non-celiac gluten sensitivity. BMJ 2015, 351, h4347. [Google Scholar] [CrossRef] [PubMed]
  4. Thomsen, H.L.; Jessen, E.B.; Passali, M.; Frederiksen, J.L. The role of gluten in multiple sclerosis: A systematic review. Mult. Scler. Relat. Disord. 2018, 27, 156–163. [Google Scholar] [CrossRef] [PubMed]
  5. Van Buiten, C.B.; Lambert, J.D.; Elias, R.J. Green Tea Polyphenols Mitigate Gliadin-Mediated Inflammation and Permeability in Vitro. Mol. Nutr. Food Res. 2018, 62, 1700879. [Google Scholar] [CrossRef] [PubMed]
  6. Gujral, N.; Suh, J.W.; Sunwoo, H.H. Effect of anti-gliadin IgY antibody on epithelial intestinal integrity and inflammatory response induced by gliadin. BMC Immunol. 2015, 16, 41. [Google Scholar] [CrossRef] [PubMed]
  7. Antvorskov, J.C.; Josefsen, K.; Engkilde, K.; Funda, D.P.; Buschard, K. Dietary gluten and the development of type 1 diabetes. Diabetologia 2014, 57, 1770–1780. [Google Scholar] [CrossRef] [PubMed]
  8. De Punder, K.; Pruimboom, L. The dietary intake of wheat and other cereal grains and their role in inflammation. Nutrients 2013, 5, 771–787. [Google Scholar] [CrossRef]
  9. Koszarny, A.; Majdan, M.; Suszek, D.; Dryglewska, M.; Tabarkiewicz, J. Autoantibodies against gliadin in rheumatoid arthritis and primary Sjögren’s syndrome patients Autoprzeciwciała przeciwko gliadynie u chorych na reumatoidalne zapalenie stawów oraz pierwotny zespół Sjögrena. Wiad Lek 2015, 68, 240–245. [Google Scholar]
  10. Internal Clinical Guidelines Team. Coeliac Disease: Recognition, Assessment and Management; National Institute for Health and Care Excellence: London, UK, 2015. [Google Scholar]
  11. Ludvigsson, J.F.; Bai, J.C.; Biagi, F.; Card, T.R.; Ciacci, C.; Ciclitira, P.J.; Green, P.H.; Hadjivassiliou, M.; Holdoway, A.; Van Heel, D.A. Diagnosis and management of adult coeliac disease: Guidelines from the British Society of Gastroenterology. Gut 2014, 63, 1210–1228. [Google Scholar] [CrossRef]
  12. American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2019. Diabetes Care 2019, 42 (Suppl. S1), S139–S147. [Google Scholar] [CrossRef] [PubMed]
  13. Schmidt, A.M. Highlighting diabetes mellitus: The epidemic continues. Arterioscler. Thromb. Vasc. Biol. 2018, 38, e1–e8. [Google Scholar] [CrossRef] [PubMed]
  14. Bhadada, S.K.; Rastogi, A.; Agarwal, A.; Kochhar, R.; Bhansali, A. Comparative study of clinical features of patients with celiac disease & those with concurrent celiac disease & type 1 diabetes mellitus. Indian J. Med. Res. 2017, 145, 334–338. [Google Scholar] [PubMed]
  15. Ludvigsson, J.F.; Ludvigsson, J.; Ekbom, A.; Montgomery, S.M. Celiac disease and risk of subsequent type 1 diabetes: A general population cohort study of children and adolescents. Diabetes Care 2006, 29, 2483–2488. [Google Scholar] [CrossRef] [PubMed]
  16. Marietta, E.V.; Gomez, A.M.; Yeoman, C.; Tilahun, A.Y.; Clark, C.R.; Luckey, D.H.; Murray, J.A.; White, B.A.; Kudva, Y.C.; Rajagopalan, G. Low Incidence of Spontaneous Type 1 Diabetes in Non-Obese Diabetic Mice Raised on Gluten-Free Diets Is Associated with Changes in the Intestinal Microbiome. PLoS ONE 2013, 8, e78687. [Google Scholar] [CrossRef] [PubMed]
  17. Funda, D.P.; Kaas, A.; Bock, T.; Tlaskalová-Hogenová, H.; Buschard, K. Gluten-free diet prevents diabetes in NOD mice. Diabetes/Metab. Res. Rev. 1999, 15, 323–327. [Google Scholar] [CrossRef]
  18. Xu, J.; Zhang, Y.; Wang, W.; Li, Y. Advanced properties of gluten-free cookies, cakes, and crackers: A review. Trends Food Sci. Technol. 2020, 103, 200–213. [Google Scholar] [CrossRef]
  19. Taylor, J.R.; Taylor, J.; Campanella, O.H.; Hamaker, B.R. Functionality of the storage proteins in gluten-free cereals and pseudocereals in dough systems. J. Cereal Sci. 2016, 67, 22–34. [Google Scholar] [CrossRef]
  20. Kale, S.; Jha, S.; Nath, P. Effects of variable steaming on chemical composition, starch characteristics, and glycemic index of basmati (Pusa Basmati 1121) rice. J. Food Process Eng. 2017, 40, e12567. [Google Scholar] [CrossRef]
  21. Hu, E.A.; Pan, A.; Malik, V.; Sun, Q. White rice consumption and risk of type 2 diabetes: Meta-analysis and systematic review. BMJ 2012, 344, e1454. [Google Scholar] [CrossRef]
  22. Matos, M.E.; Sanz, T.; Rosell, C.M. Establishing the function of proteins on the rheological and quality properties of rice based gluten free muffins. Food Hydrocoll. 2014, 35, 150–158. [Google Scholar] [CrossRef]
  23. Prasad, K.; Prakash, P.; Prasad, K. Rice Based Functional Cookies for Celiac: Studies on Its Formulation; LAP Lambert Academic Publishing: Saarbrucken, Germany, 2010; p. 128. [Google Scholar]
  24. Villareal, C.P.; Juliano, B.O.; Hizukuri, S. Varietal differences in amylopectin staling of cooked waxy milled rices. Cereal Chem. 1993, 70, 753. [Google Scholar]
  25. Association, A.D. Standards of Medical Care in Diabetes—2016: Foundations of Care and Comprehensive Medical Evaluation. Diabetes Care 2016, 39, S23–S35. [Google Scholar]
  26. Silva Ton, W.T.; das Graças de Almeida, C.; de Morais Cardoso, L.; Girondoli, Y.M.; Feliciano Pereira, P.; Viana Gomes Schitini, J.K.; Galvão Cândido, F.; Marques Arbex, P.; de Cássia Gonçalves Alfenas, R. Effect of different protein types on second meal postprandial glycaemia in normal weight and normoglycemic subjects. Nutr. Hosp. 2014, 29, 553–558. [Google Scholar] [PubMed]
  27. Sluijs, I.; Beulens, J.W.; Van Der, A.D.L.; Spijkerman, A.M.; Grobbee, D.E.; Van Der Schouw, Y.T. Dietary Intake of Total, Animal, and Vegetable Protein and Risk of Type 2 Diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-NL Study. Diabetes Care 2010, 33, 43. [Google Scholar] [CrossRef] [PubMed]
  28. Mollard, R.C.; Zykus, A.; Luhovyy, B.L.; Nunez, M.F.; Wong, C.L.; Anderson, G.H. The acute effects of a pulse-containing meal on glycaemic responses and measures of satiety and satiation within and at a later meal. Br. J. Nutr. 2012, 108, 509–517. [Google Scholar] [CrossRef] [PubMed]
  29. Del Prato, S. Loss of early insulin secretion leads to postprandial hyperglycaemia. Diabetologia 2003, 46, M2–M8. [Google Scholar] [CrossRef] [PubMed]
  30. Romao, B.; Falcomer, A.L.; Palos, G.; Cavalcante, S.; Botelho, R.B.A.; Nakano, E.Y.; Raposo, A.; Shakeel, F.; Alshehri, S.; Mahdi, W.A. Glycemic index of gluten-free bread and their main ingredients: A systematic review and meta-analysis. Foods 2021, 10, 506. [Google Scholar] [CrossRef]
  31. Ajani, R.; Oboh, G.; Adefegha, S.A.; Nwokocha, K.E.; Akindahunsi, A.A. Sensory attributes, nutritional qualities, and glycemic indices of bread blends produced from cocoa powder flavored yellow-fleshed cassava-wheat composite flours. J. Food Process. Preserv. 2020, 44, e14673. [Google Scholar] [CrossRef]
  32. Brand-Miller, J.; Hayne, S.; Petocz, P.; Colagiuri, S. Low–glycemic index diets in the management of diabetes: A meta-analysis of randomized controlled trials. Diabetes Care 2003, 26, 2261–2267. [Google Scholar] [CrossRef]
  33. Tongkaew, P.; Purong, D.; Ngoh, S.; Phongnarisorn, B.; Aydin, E. Acute Effect of Riceberry Waffle Intake on Postprandial Glycemic Response in Healthy Subjects. Foods 2021, 10, 2937. [Google Scholar] [CrossRef] [PubMed]
  34. Henry, C.J.; Quek, R.Y.C.; Kaur, B.; Shyam, S.; Singh, H.K.G. A glycaemic index compendium of non-western foods. Nutr. Diabetes 2021, 11, 2. [Google Scholar] [CrossRef] [PubMed]
  35. Mazumdar, K.; Alvarez, X.; Borda, J.T.; Dufour, J.; Martin, E.; Bethune, M.T.; Khosla, C.; Sestak, K. Visualization of transepithelial passage of the immunogenic 33-residue peptide from α-2 gliadin in gluten-sensitive macaques. PLoS ONE 2010, 5, e10228. [Google Scholar] [CrossRef] [PubMed]
  36. Sestak, K.; Conroy, L.; Aye, P.P.; Mehra, S.; Doxiadis, G.G.; Kaushal, D. Improved xenobiotic metabolism and reduced susceptibility to cancer in gluten-sensitive macaques upon introduction of a gluten-free diet. PLoS ONE 2011, 6, e18648. [Google Scholar] [CrossRef] [PubMed]
  37. Bethune, M.T.; Borda, J.T.; Ribka, E.; Liu, M.-X.; Phillippi-Falkenstein, K.; Jandacek, R.J.; Doxiadis, G.G.; Gray, G.M.; Khosla, C.; Sestak, K. A non-human primate model for gluten sensitivity. PLoS ONE 2008, 3, e1614. [Google Scholar] [CrossRef] [PubMed]
  38. Mustafa, T.; Li, Q.; Kelly, L.E.; Gibbon, A.; Ryan, I.; Roffey, K.; Simonds, S.; Cowley, M.A.; Sleeman, M.W. Food hypersensitivity-induced chronic gastrointestinal inflammation in a non-human primate model of diet-induced obesity. PLoS ONE 2019, 14, e0214621. [Google Scholar] [CrossRef] [PubMed]
  39. Butler, A.A.; Zhang, J.; Price, C.A.; Stevens, J.R.; Graham, J.L.; Stanhope, K.L.; King, S.; Krauss, R.M.; Bremer, A.A.; Havel, P.J. Low plasma adropin concentrations increase risks of weight gain and metabolic dysregulation in response to a high-sugar diet in male nonhuman primates. J. Biol. Chem. 2019, 294, 9706–9719. [Google Scholar] [CrossRef] [PubMed]
  40. Scaramuzza, A.E.; Mantegazza, C.; Bosetti, A.; Zuccotti, G.V. Type 1 diabetes and celiac disease: The effects of gluten free diet on metabolic control. World J. Diabetes 2013, 4, 130. [Google Scholar] [CrossRef]
  41. Kaur, P.; Agarwala, A.; Makharia, G.; Bhatnagar, S.; Tandon, N. Effect of gluten-free diet on metabolic control and anthropometric parameters in type 1 diabetes with subclinical celiac disease: A randomized controlled trial. Endocr. Pract. 2020, 26, 660–667. [Google Scholar] [CrossRef]
  42. Camarca, M.E.; Mozzillo, E.; Nugnes, R.; Zito, E.; Falco, M.; Fattorusso, V.; Mobilia, S.; Buono, P.; Valerio, G.; Troncone, R. Celiac disease in type 1 diabetes mellitus. Ital. J. Pediatr. 2012, 38, 10. [Google Scholar] [CrossRef]
  43. Berti, C.; Riso, P.; Monti, L.; Porrini, M. En la digestibilidad del almidón in vitro y la respuesta de la glucosa in vivo de los alimentos sin gluten y sus homólogos en gluten. Eur. J. Nutr. 2004, 43, 198–204. [Google Scholar]
  44. Tortora, R.; Capone, P.; De Stefano, G.; Imperatore, N.; Gerbino, N.; Donetto, S.; Monaco, V.; Caporaso, N.; Rispo, A. Metabolic syndrome in patients with coeliac disease on a gluten-free diet. Aliment. Pharmacol. Ther. 2015, 41, 352–359. [Google Scholar] [CrossRef] [PubMed]
  45. Riccardi, G.; Rivellese, A.A.; Giacco, R. Role of glycemic index and glycemic load in the healthy state, in prediabetes, and in diabetes. Am. J. Clin. Nutr. 2008, 87, 269S–274S. [Google Scholar] [CrossRef] [PubMed]
  46. Östman, E.M.; Liljeberg Elmståhl, H.G.; Björck, I.M. Inconsistency between glycemic and insulinemic responses to regular and fermented milk products. Am. J. Clin. Nutr. 2001, 74, 96–100. [Google Scholar] [CrossRef] [PubMed]
  47. Woo, S.-L.; Yang, J.; Hsu, M.; Yang, A.; Zhang, L.; Lee, R.-p.; Gilbuena, I.; Thames, G.; Huang, J.; Rasmussen, A. Effects of branched-chain amino acids on glucose metabolism in obese, prediabetic men and women: A randomized, crossover study. Am. J. Clin. Nutr. 2019, 109, 1569–1577. [Google Scholar] [CrossRef] [PubMed]
  48. Jobgen, W.S.; Fried, S.K.; Fu, W.J.; Meininger, C.J.; Wu, G. Regulatory role for the arginine–nitric oxide pathway in metabolism of energy substrates. J. Nutr. Biochem. 2006, 17, 571–588. [Google Scholar] [CrossRef] [PubMed]
  49. Travassos, P.B.; Vilela, V.R.; Antunes, M.M.; Bazotte, R.B. Investigation of the Acute Effects of Dry Extract of Glycine Max on Postprandial Glycemia in Rats. Braz. Arch. Biol. Technol. 2016, 59, e16150085. [Google Scholar] [CrossRef]
  50. Yan-Do, R.; MacDonald, P.E. Impaired “glycine”-mia in type 2 diabetes and potential mechanisms contributing to glucose homeostasis. Endocrinology 2017, 158, 1064–1073. [Google Scholar] [CrossRef] [PubMed]
Nutrients 16 00234 g001
Figure 1. Changes in mean blood glucose concentrations of healthy cynomolgus monkeys (n = 12) after ingesting the reference food and the test food (mean ± SD). Note: (A) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after two infusions of 500 g/L glucose solution (reference food) (mean ± SD). (B) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after consuming gluten rice cakes with three proportions (5%, 10%, and 15%) of wheat protein (mean ± SD). (C) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after consuming GF rice cakes with three proportions (5%, 10%, and 15%) of soy protein (mean ± SD). (D) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after consuming GF rice cakes with three proportions (5%, 10%, and 15%) of soy protein (mean ± SD). (E) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after consuming three proportions (5%, 10%, and 15%) of rice protein (mean ± SD). (F) Mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after four servings of GF rice cakes (control) with pure rice flour (mean ± SD).
Figure 1. Changes in mean blood glucose concentrations of healthy cynomolgus monkeys (n = 12) after ingesting the reference food and the test food (mean ± SD). Note: (A) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after two infusions of 500 g/L glucose solution (reference food) (mean ± SD). (B) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after consuming gluten rice cakes with three proportions (5%, 10%, and 15%) of wheat protein (mean ± SD). (C) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after consuming GF rice cakes with three proportions (5%, 10%, and 15%) of soy protein (mean ± SD). (D) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after consuming GF rice cakes with three proportions (5%, 10%, and 15%) of soy protein (mean ± SD). (E) Plots of mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after consuming three proportions (5%, 10%, and 15%) of rice protein (mean ± SD). (F) Mean blood glucose concentration changes in healthy cynomolgus monkeys (n = 12) after four servings of GF rice cakes (control) with pure rice flour (mean ± SD).
Nutrients 16 00234 g001
Nutrients 16 00234 g002
Figure 2. Glycemic index (GI) of 13 gluten-containing and GF tested foods (mean ± SE).
Figure 2. Glycemic index (GI) of 13 gluten-containing and GF tested foods (mean ± SE).
Nutrients 16 00234 g002
Nutrients 16 00234 g003
Figure 3. Glycemic index (GI) of gluten-containing and gluten-free rice cakes with the same proportion of vegetable protein (mean ± SE). Note: (A) Glycemic index (GI) of rice cakes with 15% wheat protein and 15% gluten-free vegetable protein added and rice cakes with plain rice flour (mean ± SEM). (B) Glycemic index (GI) of rice cakes supplemented with 10% wheat protein and 10% gluten-free vegetable protein and plain rice flour rice cakes (mean ± SEM). (C) Glycemic index (GI) of rice cakes with 5% wheat protein and 5% gluten-free vegetable protein and plain rice flour rice cakes (mean ± SEM). * p < 0.05.
Figure 3. Glycemic index (GI) of gluten-containing and gluten-free rice cakes with the same proportion of vegetable protein (mean ± SE). Note: (A) Glycemic index (GI) of rice cakes with 15% wheat protein and 15% gluten-free vegetable protein added and rice cakes with plain rice flour (mean ± SEM). (B) Glycemic index (GI) of rice cakes supplemented with 10% wheat protein and 10% gluten-free vegetable protein and plain rice flour rice cakes (mean ± SEM). (C) Glycemic index (GI) of rice cakes with 5% wheat protein and 5% gluten-free vegetable protein and plain rice flour rice cakes (mean ± SEM). * p < 0.05.
Nutrients 16 00234 g003
Nutrients 16 00234 g004
Figure 4. Glycemic index (GI) of rice cakes with and without gluten protein added to the same plant-based protein (mean ± SE). Note: (A) Glycemic index (GI) of rice cakes with 5%, 10%, and 15% pea protein and rice cakes with pure rice flour (mean ± SE). (B) Glycemic index (GI) of rice cakes with 5%, 10%, and 15% soy protein and plain rice flour (mean ± SE). (C) Glycemic index (GI) of rice cakes with 5%, 10%, and 15% rice protein and pure rice flour (mean ± SE). (D) Glycemic index (GI) of rice cakes with 5%, 10%, and 15% wheat protein and pure rice flour (mean ± SE).
Figure 4. Glycemic index (GI) of rice cakes with and without gluten protein added to the same plant-based protein (mean ± SE). Note: (A) Glycemic index (GI) of rice cakes with 5%, 10%, and 15% pea protein and rice cakes with pure rice flour (mean ± SE). (B) Glycemic index (GI) of rice cakes with 5%, 10%, and 15% soy protein and plain rice flour (mean ± SE). (C) Glycemic index (GI) of rice cakes with 5%, 10%, and 15% rice protein and pure rice flour (mean ± SE). (D) Glycemic index (GI) of rice cakes with 5%, 10%, and 15% wheat protein and pure rice flour (mean ± SE).
Nutrients 16 00234 g004
Nutrients 16 00234 g005
Figure 5. Incremental area under the 0–120 min postprandial glucose curve (IAUC) for 13 gluten-containing and gluten-free tested foods (mean ± SE).
Figure 5. Incremental area under the 0–120 min postprandial glucose curve (IAUC) for 13 gluten-containing and gluten-free tested foods (mean ± SE).
Nutrients 16 00234 g005
Nutrients 16 00234 g006
Figure 6. Incremental area under the postprandial blood glucose curve (IAUC) for gluten and rice cakes without gluten protein under the condition of adding the same proportion of vegetable protein (mean ± SE). Note: (A) Incremental area under the postprandial 0–120 min glucose curve (IAUC) for rice cakes and pure rice flour rice cakes supplemented with 15% wheat protein and 15% gluten-free vegetable protein (mean ± SE). (B) Incremental area under the postprandial 0–120 min glucose curve (IAUC) for rice cakes and pure rice flour rice cakes supplemented with 10% wheat protein and 10% gluten-free vegetable protein (mean ± SE). (C) Incremental area under the postprandial 0–120 min glucose curve (IAUC) for rice cakes supplemented with 5% wheat protein and 5% gluten-free vegetable protein and pure rice flour rice cakes (mean ± SE). * p < 0.05; ** p < 0.01.
Figure 6. Incremental area under the postprandial blood glucose curve (IAUC) for gluten and rice cakes without gluten protein under the condition of adding the same proportion of vegetable protein (mean ± SE). Note: (A) Incremental area under the postprandial 0–120 min glucose curve (IAUC) for rice cakes and pure rice flour rice cakes supplemented with 15% wheat protein and 15% gluten-free vegetable protein (mean ± SE). (B) Incremental area under the postprandial 0–120 min glucose curve (IAUC) for rice cakes and pure rice flour rice cakes supplemented with 10% wheat protein and 10% gluten-free vegetable protein (mean ± SE). (C) Incremental area under the postprandial 0–120 min glucose curve (IAUC) for rice cakes supplemented with 5% wheat protein and 5% gluten-free vegetable protein and pure rice flour rice cakes (mean ± SE). * p < 0.05; ** p < 0.01.
Nutrients 16 00234 g006
Nutrients 16 00234 g007
Figure 7. Incremental area under the postprandial 0–120 min glucose curve (IAUC) for gluten-containing and gluten-free protein rice cakes under the condition of adding the same plant-based protein (mean ± SE). Note: (A) Incremental area under the postprandial 0–120 min blood glucose curve (IAUC) for rice cakes with 5%, 10%, and 15% pea protein and pure rice flour added (mean ± SE). (B) Incremental area under the postprandial 0–120 min blood glucose curve (IAUC) for rice cakes with 5%, 10%, and 15% soy protein and pure rice flour added (mean ± SE). (C) Incremental area under the postprandial 0–120 min blood glucose curve (IAUC) for rice cakes with 5%, 10%, and 15% rice protein and pure rice flour rice cakes added (mean ± SE). (D) Incremental area under the postprandial 0–120 min blood glucose curve (IAUC) for rice cakes with 5%, 10%, and 15% wheat protein and pure rice flour rice cakes added (mean ± SE). * p < 0.05; ** p < 0.01.
Figure 7. Incremental area under the postprandial 0–120 min glucose curve (IAUC) for gluten-containing and gluten-free protein rice cakes under the condition of adding the same plant-based protein (mean ± SE). Note: (A) Incremental area under the postprandial 0–120 min blood glucose curve (IAUC) for rice cakes with 5%, 10%, and 15% pea protein and pure rice flour added (mean ± SE). (B) Incremental area under the postprandial 0–120 min blood glucose curve (IAUC) for rice cakes with 5%, 10%, and 15% soy protein and pure rice flour added (mean ± SE). (C) Incremental area under the postprandial 0–120 min blood glucose curve (IAUC) for rice cakes with 5%, 10%, and 15% rice protein and pure rice flour rice cakes added (mean ± SE). (D) Incremental area under the postprandial 0–120 min blood glucose curve (IAUC) for rice cakes with 5%, 10%, and 15% wheat protein and pure rice flour rice cakes added (mean ± SE). * p < 0.05; ** p < 0.01.
Nutrients 16 00234 g007
Nutrients 16 00234 g008
Figure 8. Relationship between the incremental area under the glycemic curve (IAUC) and the contents of various nutrients in gluten-containing and gluten-free protein rice cakes and pure rice flour rice cakes at 0 to 120 min postprandial under the condition of 15% added vegetable protein. Note: IAUC, and the content of lysine and ash.
Figure 8. Relationship between the incremental area under the glycemic curve (IAUC) and the contents of various nutrients in gluten-containing and gluten-free protein rice cakes and pure rice flour rice cakes at 0 to 120 min postprandial under the condition of 15% added vegetable protein. Note: IAUC, and the content of lysine and ash.
Nutrients 16 00234 g008
Nutrients 16 00234 g009
Figure 9. Relationship between the incremental area under the postprandial blood glucose curve (IAUC) and the content of various nutrients for 0–120 min with the addition of 5%, 10%, and 15% pea protein and pure rice flour rice cakes. Note: IAUC and the content of glycine.
Figure 9. Relationship between the incremental area under the postprandial blood glucose curve (IAUC) and the content of various nutrients for 0–120 min with the addition of 5%, 10%, and 15% pea protein and pure rice flour rice cakes. Note: IAUC and the content of glycine.
Nutrients 16 00234 g009
Nutrients 16 00234 g010
Figure 10. Relationship between the incremental area under the postprandial glucose curve (IAUC) and the contents of various nutrients for 0–120 min of rice cakes with 5%, 10%, and 15% soy protein and pure rice flour. Note: IAUC and fat content.
Figure 10. Relationship between the incremental area under the postprandial glucose curve (IAUC) and the contents of various nutrients for 0–120 min of rice cakes with 5%, 10%, and 15% soy protein and pure rice flour. Note: IAUC and fat content.
Nutrients 16 00234 g010
Table 1. Animals used in this study.
Table 1. Animals used in this study.
Animal NumberSexAgeWeight/kgSitting Height/cmBMIFBG
(Fasting Blood Glucose)		Routine Stool Test
Healthy Animal-1♂169.7153.034.62.95Normal
Healthy Animal-2♂148.2949.034.53.68Normal
Healthy Animal-3♂158.4451.032.42.93Normal
Healthy Animal-4♂166.8646.032.43.71Normal
Healthy Animal-5♂147.1447.032.33.77Normal
Healthy Animal-6♂158.3551.032.13.96Normal
Healthy Animal-7♂157.6849.032.03.30Normal
Healthy Animal-8♂137.6049.031.74.23Normal
Healthy Animal-9♂166.8747.031.13.84Normal
Healthy Animal-10♂157.0548.030.62.68Normal
Healthy Animal-11♂126.9448.030.13.53Normal
Healthy Animal-12♂167.0049.029.23.34Normal
Table 2. Materials used in this study.
Table 2. Materials used in this study.
Materials/InstrumentsManufacture Factory
Thai fragrant riceSiam Grains Co. (Bangkok, Thailand)
Wheat protein (WPI)Your Health Store (London, UK)
Rice protein (RPI)Xian Dongfeng Biotechnology Co., Ltd. (Xian, China)
Soy protein (SPI)Linyi Shan Song Biological Products Co., Ltd. (Linyi, China)
Pea protein (PPI)Roquette (China) Nutrition Food Co., Ltd. (Lianyungang, China)
Low-acyl gellan gum (Kelco, UK)Guangdong Qian Heng Sheng Wu Technology Co., Ltd. (Guangzhou, China)
Sugar-free sweetenerAnhui Shu Jun Biotechnology Co., Ltd. (Hefei, China)
Food aromatics (peach flavor)Hangzhou Bai Rui Flavor & Fragrance Co., Ltd. (Hangzhou, China)
Food-grade anhydrous glucoseXiwang food co., Ltd. (Binzhou, China)
Blood glucose test paper and blood glucose meterRoche Diagnostics in Germany (Basel, Switzerland)
Table 3. Test food formulation.
Table 3. Test food formulation.
Rice CakeRice Cake TypeRice FlourProteinWater
ControlThai fragrant rice35–65
5% PIThai fragrant rice + 5% PI30565
10% PIThai fragrant rice + 10% PI251065
15% PIThai fragrant rice + 15% PI201565
Table 4. Food feeding cycle.
Table 4. Food feeding cycle.
DayDiet Group
1Control
2–4Washout period
5Thai fragrant rice + 15% wheat protein
6–8Washout period
9Thai fragrant rice + 10% wheat protein
10–12Washout period
13Thai fragrant rice + 5% wheat protein
14–16Washout period
Table 5. Glycemic index (GI) of the 13 gluten-containing and GF tested foods (mean ± SE).
Table 5. Glycemic index (GI) of the 13 gluten-containing and GF tested foods (mean ± SE).
IDXFood TypeGI (%)Notes
1Thai fragrant rice + 15% pea protein29.16 ± 7.96Gluten-free
2Thai fragrant rice + 15% wheat protein33.27 ± 17.19Gluten
3Thai fragrant rice + 10% soy protein34.95 ± 21.62Gluten-free
4Thai fragrant rice + 10% rice protein35.28 ± 12.40Gluten-free
5Thai fragrant rice + 10% wheat protein35.60 ± 16.24Gluten
6Thai fragrant rice + 5% rice protein37.24 ± 10.55Gluten-free
7Thai fragrant rice37.46 ± 12.83Gluten-free
8Thai fragrant rice + 10% pea protein37.76 ± 15.40Gluten-free
9Thai fragrant rice + 15% rice protein37.86 ± 15.54Gluten-free
10Thai fragrant rice + 5% pea protein38.49 ± 18.63Gluten-free
11Thai fragrant rice + 5% wheat protein40.67 ± 15.36Gluten
12Thai fragrant rice + 5% soy protein42.89 ± 15.29Gluten-free
13Thai fragrant rice + 15% soy protein44.35 ± 17.68Gluten-free
Table 6. Incremental area under the blood glucose curve (IAUC) for 13 gluten-containing and gluten-free tested foods 0–120 min postprandial (mean ± SE).
Table 6. Incremental area under the blood glucose curve (IAUC) for 13 gluten-containing and gluten-free tested foods 0–120 min postprandial (mean ± SE).
IDXFood Type0- to 120-min IAUC (mmol∙min/L)Notes
1Thai fragrant rice + 15% pea protein499.81 ± 34.46Gluten-free
2Thai fragrant rice + 10% soy protein516.69 ± 27.82Gluten-free
3Thai fragrant rice + 10% pea protein519.25 ± 36.94Gluten-free
4Thai fragrant rice + 10% rice protein521.63 ± 12.06Gluten-free
5Thai fragrant rice + 15% rice protein530.50 ± 14.65Gluten-free
6Thai fragrant rice + 15% wheat protein533.19 ± 34.89Gluten
7Thai fragrant rice534.91 ± 22.51Gluten-free
8Thai fragrant rice + 5% rice protein536.88 ± 16.07Gluten-free
9Thai fragrant rice + 10% wheat protein540.00 ± 31.83Gluten
10Thai fragrant rice + 5% pea protein542.19 ± 38.78Gluten-free
11Thai fragrant rice + 5% wheat protein549.50 ± 48.80Gluten
12Thai fragrant rice + 5% soy protein557.31 ± 12.94Gluten-free
13Thai fragrant rice + 15% soy protein572.94 ± 72.74Gluten-free
Table 7. Protein, starch, fat, and ash contents of 13 gluten-containing and gluten-free tested foods.
Table 7. Protein, starch, fat, and ash contents of 13 gluten-containing and gluten-free tested foods.
Name of the Food%
Moisture ContentProteinStarchFatAsh Content
Thai fragrant rice + 15% pea protein58.467.0126.871.890.0062
Thai fragrant rice + 15% wheat protein62.157.9426.131.590.0021
Thai fragrant rice + 10% soy protein60.26.2127.272.230.056
Thai fragrant rice + 10% rice protein53.297.0732.840.080.0037
Thai fragrant rice + 10% wheat protein61.55.7325.980.160.0032
Thai fragrant rice + 5% rice protein60.796.129.020.0190.057
Thai fragrant rice60.424.3430.311.410.0071
Thai fragrant rice + 10% pea protein63.417.0321.182.390.18
Thai fragrant rice + 15% rice protein59.497.4825.880.520.051
Thai fragrant rice + 5% pea protein69.25.8128.862.230.073
Thai fragrant rice + 5% wheat protein61.155.2627.192.120.034
Thai fragrant rice + 5% soy protein54.384.3535.770.0160.0197
Thai fragrant rice + 15% soy protein57.325.4932.420.490.12
Table 8. Levels of all amino acids in the 13 gluten-containing and gluten-free tested foods.
Table 8. Levels of all amino acids in the 13 gluten-containing and gluten-free tested foods.
Name of the Foodmg/kg
Aspartic AcidGlutamic AcidSerineHistidineGlycineThreonineArginineAlanineTyrosineValineMethioninePhenylalanineIsoleucineLeucineLysineProline
Thai fragrant rice + 15% pea protein5310.47463.112009.71266.25585.761480.774959.792848.662601.354299.89655.893131.283731.556227.552004.92835.16
Thai fragrant rice + 15% wheat protein2239.211,079.031682.77933.98402.061002.812933.352020.122119.023200.9684.452551.482784.384778.491154.885148.36
Thai fragrant rice + 10% soy protein2665.794232.141207.7910.81380.64856.422973.751788.161686.062550.4674.141731.12128.943604.321286.411882.02
Thai fragrant rice + 10% rice protein4132.297413.591885.491156.76581.511329.194790.293038.972920.994412.08892.942944.693272.15901.151446.182697.84
Thai fragrant rice + 10% wheat protein2125.758874.51487.7832.72362.13888.662865.462021.441957.963003.53671.312179.512552.434361.471133.444109.98
Thai fragrant rice + 5% rice protein2523.874704.831223.83861.03328.99782.633252.52132.782030.543011.59636.691844.422262.673869.581180.441884.41
Thai fragrant rice1381.392569.24745.58550.75259.73454.532000.361298.791178.581859.16431.591049.681449.132294.621002.231452.19
Thai fragrant rice + 10% pea protein3907.925903.821622.68991.99445.541141.043970.392303.412115.373397.79573.362305.622954.874864.491673.192259.08
Thai fragrant rice + 15% rice protein5020.328704.482239.321316.82646.681567.825559.873552.883507.315138.93917.053513.53857.786980.231537.563010.23
Thai fragrant rice + 5% pea protein2807.264179.931284.74881.82334.82893.943180.021903.591709.352789.17555.321787.462288.683795.221306.291791.83
Thai fragrant rice + 5% wheat protein1697.575375.741149.51763.46302.17691.632327.971555.281482.012332.46532.941547.261929.283167.41063.212687.88
Thai fragrant rice + 5% soy protein2017.433493.021029.2800305.13690.872536.781530.511381.382175.67475.591355.361811.252904.041106.461426.73
Thai fragrant rice + 15% soy protein2892.744977.321201.52846.13338.22778.693129.621983.191707.992790.74560.721846.372396.843865.461326.542102.3
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yang, Y.; Liu, Q.; Yue, F. Glycemic Response in Nonhuman Primates Fed Gluten-Free Rice Cakes Enriched with Soy, Pea, or Rice Protein and Its Correlation with Nutrient Composition. Nutrients 2024, 16, 234. https://doi.org/10.3390/nu16020234

AMA Style

Yang Y, Liu Q, Yue F. Glycemic Response in Nonhuman Primates Fed Gluten-Free Rice Cakes Enriched with Soy, Pea, or Rice Protein and Its Correlation with Nutrient Composition. Nutrients. 2024; 16(2):234. https://doi.org/10.3390/nu16020234

Chicago/Turabian Style

Yang, Yong, Qingsu Liu, and Feng Yue. 2024. "Glycemic Response in Nonhuman Primates Fed Gluten-Free Rice Cakes Enriched with Soy, Pea, or Rice Protein and Its Correlation with Nutrient Composition" Nutrients 16, no. 2: 234. https://doi.org/10.3390/nu16020234

APA Style

Yang, Y., Liu, Q., & Yue, F. (2024). Glycemic Response in Nonhuman Primates Fed Gluten-Free Rice Cakes Enriched with Soy, Pea, or Rice Protein and Its Correlation with Nutrient Composition. Nutrients, 16(2), 234. https://doi.org/10.3390/nu16020234

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop