Impact of cooked rice products developed for persons with diabetes on the postprandial blood glucose response in healthy adults

Abstract

BACKGROUND/OBJECTIVES

An adequate postprandial glycemic response (PPGR) is crucial for glycemic control in diabetes. However, predicting glycemic responses to mixed meals is challenging, as they are influenced by various nutritional factors. Moreover, despite the increasing demand for convenience foods, the validation of their impact on the PPGR remains insufficient. Therefore, we investigated the impact of consuming cooked rice, a ready meal developed for persons with diabetes, on the PPGR.

SUBJECTS/METHODS

Twenty-seven healthy adults participated in this study, and they consumed 6 test products over a 10-h fasting or non-fasting state. The test products included one type of regular cooked rice (OTOKI Cooked Rice & Curry Sauce [Curry-R]) and 5 types of cooked rice developed for persons with diabetes (Cooked Rice & Curry Sauce [Curry-D], Spicy Sauce with Pork [Spicy pork-D], Bulgogi [Bulgogi-D], Soybean Paste Sauce [Soybean-D], and Jjajang Sauce [Jjajang-D]). Blood glucose levels were measured for 2 h after test-product consumption using a continuous glucose monitoring device. Incremental area under the curve (iAUC), Peak_max, total AUC (tAUC), and glycemic load (GL) values were calculated to determine the PPGR.

RESULTS

In the fasting state, Spicy pork-D, Bulgogi-D, and Soybean-D significantly reduced the iAUC and GL, with Spicy pork-D and Soybean-D also lowering Peak_max compared with Curry-R. In the non-fasting state, Spicy pork-D, Bulgogi-D, Soybean-D, and Jjajang-D yielded significantly lower tAUC and Peak_max values.

CONCLUSION

Cooked rice products developed for persons with diabetes potentially reduce the PPGR in healthy individuals under both fasting and non-fasting states. Nutritional adjustments, such as modifications of protein, fat, dietary fiber, and sugar content in convenience foods, can moderate the PPGR.

INTRODUCTION

Diabetes is a metabolic disorder characterized by hyperglycemia resulting from impairments in insulin production, insulin action, or both, and it requires ongoing management rather than a definitive cure [1]. According to the latest data from the International Diabetes Federation, approximately 537 million people aged 20–79 yrs were diagnosed with diabetes globally in 2021, signifying an increase of 74 million people from 2019, and this number is predicted to reach 643 million by 2030 [2]. In South Korea, 5.07 million individuals aged ≥ 30 yrs were diagnosed with diabetes in 2022, accounting for 14.8% of the total population [3]. Diabetes necessitates ongoing blood glucose management, as it cannot be fully cured, and the rise in average life expectancy has led to a corresponding extension in the period of required management. Therefore, glycemic control has become increasingly significant, and managing the postprandial glycemic response (PPGR) through dietary interventions is essential.

The glycemic index (GI) quantifies the glycemic reaction to a food containing an equivalent amount of carbohydrate in comparison to a reference food (glucose or white bread) expressed as a percentage [4]. Glycemic load (GL) is a metric that predicts the glycemic response to actual food consumption by considering both the GI and portion size [5]. The GI and GL are indicators representing the glycemic response to food [6,7]. A low-GI diet can be beneficial in reducing glycated hemoglobin (HbA1c) levels and coronary heart disease risk [8,9]. Four weeks of a low-GI diet compared with a conventional carbohydrate exchange diet reportedly improved fasting blood glucose (FBG) levels, waist circumference, and postprandial glycaemia in Asian patients with type 2 diabetes [10]. Furthermore, a high-GI or -GL diet has been associated with an elevated risk of cardiovascular disease (CVD) compared with a low-GI or -GL diet [11]. The calculated meal GI, a weighted average of the GI values of individual foods, is commonly used to estimate the glycemic response to a mixed meal. However, this merely accounts for the source and amount of available carbohydrates and does not justify the effects of non-carbohydrate components on the glycemic response [12]. Studies indicate that predicting the glycemic response to mixed meals based on the GI values of specific foods may be inaccurate [13,14]. The glycemic response is primarily determined by carbohydrate quantity; however, it is also affected by factors such as carbohydrate type, protein and fat quantity, dietary fiber content, and processing and cooking methods [4]. Protein and fat are known to reduce postprandial blood glucose levels by augmenting insulin secretion and postponing glucose digestion and absorption [15,16]. Dietary fiber has been reported to affect postprandial glycemia by delaying the absorption of macronutrients [17]; specifically, soluble fiber evidently prolongs stomach emptying and reduces postprandial glycemia when ingested with carbohydrates in people with diabetes [18]. Therefore, the glycemic response to a mixed meal potentially varies depending on the food consumed because the interactions among available carbohydrates, protein, fat, and dietary fiber result in different degrees of digestion and absorption. While research on the glycemic response to mixed meals is ongoing, it is limited when compared to studies on individual food items, especially those involving Asian groups, including Koreans. Glycemic responses are affected by not only meal composition but also race, sex, and physical characteristics [19,20,21]. Considering that the Korean diet predominantly comprises rice-based mixed meals, examining the glycemic response to such meals in Koreans is imperative.

The busy urban lifestyle and increase in single-person households have propelled the global need for convenience meals. In 2020, the global market size for home meal replacement (HMR) was $146.5 billion, reflecting a 22.4% increase since 2016. In Korea, sales reached approximately KRW 3,652.6 billion in 2020, marking a 61% increase from 2016 [22]. Although convenience foods are linked to adverse health outcomes, they have been identified as a viable option for enhancing dietary practices among individuals unable to prepare meals, such as the older population [23]. The increasing demand for convenience foods that support health management, particularly blood sugar control, highlights the need to verify their efficacy. Therefore, this study aimed to examine the postprandial blood glucose response after consuming cooked rice, a HMR developed for persons with diabetes, in the Korean population. It also sought to generate foundational data on the glycemic response to mixed meals and diverse convenience foods for glycemic regulation.

SUBJECTS AND METHODS

Study design and participants

Participants aged 19–39 yrs without diabetes or related diseases were recruited through campus bulletin board announcements. Through a pre-interview, participants who had diabetes or a family history of diabetes; had received medical treatment requiring hospitalization within the preceding 3 mon; were taking medications that affect glucose intolerance, steroids, or antipsychotics; were pregnant; had a serious illness; or had food allergies were excluded. In total, 30 individuals were finally included in the study. Three participants dropped out of the study owing to withdrawal, illness, or adverse events linked to the continuous glucose monitor (CGM). A total of 14 participants for the breakfast trial and 27 participants (including the 14 breakfast trial participants) for the lunch trial were included in the statistical analysis. A flowchart of the study is shown in Fig. 1. This study was approved by Seoul National University Institutional Review Board (IRB No. 2404/002-013).

Fig. 1. Study flowchart.

Test products

A glucose solution containing 50 g of glucose (gluorange solution 100; Mcnulty Pharmaceutical, Seoul, Korea) was used as the standard for measuring the glycemic response. The test products included one type of regular cooked rice (OTOKI Cooked Rice & Curry Sauce [Curry-R]) and 5 types of cooked rice developed for persons with diabetes (Cooked Rice & Curry Sauce [Curry-D], Spicy Sauce with Pork [Spicy pork-D], Bulgogi [Bulgogi-D], Soybean Paste Sauce [Soybean-D], and Jjajang Sauce [Jjajang-D]) (OTOKI Co., Anyang, Korea). The nutrient contents of the test products are listed in Table 1. The test products were prepared by mixing rice with sauce in a bowl and microwaving for 2 min.

Table 1. Composition of the test products.

Test product	Portion size (g)	Energy (kcal)	Carbohydrate (g)	Fiber (g)	Sugars (g)	Protein (g)	Fat (g)
OTOKI Cooked Rice & Curry Sauce (Curry-R)	320	420.0	61.0	2.0	9.0	12.0	14.0
Cooked Rice & Curry Sauce (Curry-D)	320	521.6	63.4	9.5	4.1	20.8	20.6
Cooked Rice & Spicy Sauce with Pork (Spicy pork-D)	320	531.2	67.5	9.6	4.0	21.5	19.5
Cooked Rice & Bulgogi (Bulgogi-D)	320	528.0	67.2	9.3	3.6	21.4	19.3
Cooked Rice & Soybean Paste Sauce (Soybean-D)	336	554.4	73.6	12.2	2.4	22.4	20.7
Cooked Rice & Jjajang Sauce (Jjajang-D)	320	531.2	77.1	9.2	3.6	22.1	16.5

Study schedule

Participants made 9 visits over a 2-week period. They were instructed to abstain from alcohol consumption and fast for 10 h prior to each visit. On the first day, participants underwent anthropometric assessments; moreover, blood pressure was measured, blood was collected, and CGM devices were attached to the upper arm of each participant (Freestyle Libre; Abbott Co., Green Oaks, IL, USA).

Breakfast trial participants arrived at the laboratory between 9:00 and 10:00 am after 10 h of overnight fasting. Compliance with the fasting requirement was confirmed by checking the time of their last food intake and FBG levels on the CGM device. They consumed the glucose solution or one of the 5 types of cooked rice: Curry-R, Curry-D, Spicy pork-D, Bulgogi-D, or Soybean-D. The glucose solution was consumed for 2 non-consecutive days, and each cooked rice type was consumed once throughout the study. The provided foods were consumed within 20 min under the supervision of the researchers, and water intake was limited to a single serving in a 190 mL paper cup. The participants were prohibited from leaving their seats, except for restroom use, during the 120-min postprandial period. A 15-min break was permitted thereafter, and no additional food or beverages were consumed during this interval.

Lunch trial participants consumed one of the 6 types of cooked rice daily-Curry-R, Curry-D, Spicy pork-D, Bulgogi-D, Soybean-D, or Jjajang-D- and each product was consumed once. They consumed the cooked rice within 20 min under the supervision of the researchers and were provided with half a serving of water in a 190 mL paper cup, and were seated during the 60-min postprandial period, except for restroom use. All participants subsequently returned home and were instructed to restrict all physical activity, except for walking home, and limit water intake to < 200 mL for the next hour. A guideline outlined the permitted food intake times and the instructions, and adherence was monitored through CGM data.

Glycemic response analysis

Blood glucose levels were automatically measured every 15 min using a non-invasive CGM device for 2 h following test product consumption.

The glycemic response was calculated as the area under the curve over 120 min using methods described by the International Organization for Standardization [24]. For the breakfast trial, the incremental area under the curve (iAUC) was calculated by exclusively totaling the area above baseline based on the FBG level. Peak_max was determined as the highest blood glucose level recorded every 15 min for 2 h.

GL was calculated using the GI value, and the equations for the GI and GL are as follows:

$$\mathrm{G}\mathrm{I}\mathrm{ }=\mathrm{ }\frac{\mathrm{i}\mathrm{A}\mathrm{U}\mathrm{C}\mathrm{ }\mathrm{A}\mathrm{f}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{ }\mathrm{T}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{ }\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{s}\mathrm{ }\mathrm{I}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}{\mathrm{i}\mathrm{A}\mathrm{U}\mathrm{C}\mathrm{ }\mathrm{A}\mathrm{f}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{ }\mathrm{G}\mathrm{l}\mathrm{u}\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{e}\mathrm{ }\mathrm{S}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{ }\mathrm{I}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}\mathrm{ }\times \mathrm{ }100$$

$$\mathrm{G}\mathrm{L}\mathrm{ }=\mathrm{ }\mathrm{G}\mathrm{I}\mathrm{ }\times \mathrm{ }\frac{Glycemic Carbohydrate}{100}$$

The glycemic response in the lunch trial group was determined based on the total area under the curve (tAUC), since the participants were not in a fasting state. The tAUC was calculated by summing the tAUC, with 0 as the baseline.

Anthropometric measurements

Participant body composition was measured on the first day. Height was measured using a stadiometer (BSM330; InBody Co., Ltd., Seoul, Korea). Weight, body mass index (BMI), skeletal muscle mass, and body fat were determined via bioelectrical impedance analysis (InBody 380; InBody Co., Ltd.).

Clinical characteristics

Blood sample collection and analysis as well as blood pressure measurement were performed at the Seoul National University Health Service Center on the first day. Blood samples were collected after 10 h of overnight fasting. FBG, fasting insulin, HbA1c, total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels were measured from venous blood collected in serum separator tubes (SST^TM II Advance Plus Blood Collection Tubes; BD, Franklin Lakes, NJ, USA). The homeostasis model assessment for insulin resistance (HOMA-IR) index was calculated by multiplying fasting insulin by FBG and dividing by 405. Blood pressure was measured using a hospital sphygmomanometer.

Nutrient intake and physical activity

Throughout the study, participants maintained a dietary record to document every food and beverage they consumed. Nutrient intake was determined using nutritional analysis software (Can Pro-6.0; Korean Nutrition Society, Seoul, Korea) over a 10-day period. Physical activity was evaluated using the Korean version of the International Physical Activity Questionnaire-short form and computed as metabolic equivalent of task scores.

Statistical analysis

All data are expressed as the mean ± SE of the mean. As the glycemic responses to the test products were repeatedly evaluated within the same individual, the iAUC, Peak_max, GL, and tAUC values were analyzed using the paired t-test and adjusted for repeated measures using the Bonferroni correction. Curry-R was used as the control for cooked rice developed for persons with diabetes. All statistical analyses were performed using SPSS 29.0 (IBM Corp. Armonk, NY, USA). Statistical significance was set at P < 0.05.

RESULTS

Participant baseline characteristics

The baseline characteristics of the participants are shown in Table 2. The mean age was 23.9 ± 3.0 yrs, and men accounted for 55.6% of the participants. The average BMI values of the male and female participants were 23.2 ± 1.3 and 20.2 ± 2.3 kg/m², respectively. The average body fat values were 18.2 ± 4.4% and 27.6 ± 4.0% in male and female participants, respectively. The male and female participants’ BMI and body fat values were within the normal range.

Table 2. Demographic and anthropometric characteristics of the participants.

Variables	Total (n = 27)	Male (n = 15)	Female (n = 12)
Age (yrs)	23.9 ± 3.0	23.5 ± 3.2	24.4 ± 2.7
Height (cm)	169.8 ± 8.6	176.0 ± 5.3	162.1 ± 4.7
Weight (kg)	63.6 ± 11.9	72.1 ± 7.7	53.0 ± 6.2
BMI (kg/m2)	21.9 ± 2.3	23.2 ± 1.3	20.2 ± 2.3
Body fat (%)	22.4 ± 6.3	18.2 ± 4.4	27.6 ± 4.0
Skeletal muscle (kg)	27.5 ± 7.5	33.3 ± 4.2	20.1 ± 2.4
Physical activity (METs)1)	3,935.6 ± 522.2	4,312.9 ± 683.8	3,455.4 ± 820.1

Data are presented as means ± SE of the means.

BMI, body mass index; MET, metabolic equivalent of task.

¹⁾Data were available for 25 participants (male: 14, female: 11).

The clinical characteristics of the participants are presented in Table 3. The average FBG concentration, HOMA-IR, and HbA1c values were 95.7 ± 7.1 mg/dL, 1.3 ± 0.6, and 5.4 ± 0.3%, respectively, indicating that the participants’ blood glucose-related values were within the normal range.

Table 3. Clinical characteristics of the participants.

Variables	Total (n = 27)	Male (n = 15)	Female (n = 12)
Fasting blood glucose (mg/dL)	95.7 ± 7.1	98.9 ± 5.0	91.8 ± 7.5
Fasting insulin (μU/mL)	5.7 ± 2.5	5.9 ± 2.6	5.3 ± 2.6
HOMA-IR	1.3 ± 0.6	1.4 ± 0.6	1.2 ± 0.6
HbA1c (%)	5.4 ± 0.3	5.4 ± 0.3	5.5 ± 0.2
Total cholesterol (mg/dL)	181.5 ± 34.7	170.7 ± 32.1	195.0 ± 34.2
Triglyceride (mg/dL)	84.4 ± 47.2	87.8 ± 56.9	80.1 ± 33.4
HDL-C (mg/dL)	63.6 ± 12.3	58.1 ± 10.9	70.6 ± 10.6
LDL-C (mg/dL)	101.0 ± 32.4	95.0 ± 31.2	108.4 ± 33.7
Systolic blood pressure (mmHg)	111.3 ± 10.1	117.5 ± 7.7	103.5 ± 6.9
Diastolic blood pressure (mmHg)	69.9 ± 9.3	72.2 ± 9.6	67.1 ± 8.5

Data are presented as means ± SE of the mean.

HOMA-IR, homeostasis model assessment for insulin resistance; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

Table 4 shows the participants’ nutrient intake during the study period. While energy and macronutrient intakes were higher in men, sugar intake was higher in women (45.7 ± 4.5 g) than in men (44.1 ± 2.5 g).

Table 4. Nutrient intake of the participants during the study period.

Variables	Total (n = 27)	Male (n = 15)	Female (n = 12)
Energy (kcal)	1,821.1 ± 71.0	1,980.4 ± 77.3	1,621.9 ± 104.0
Carbohydrate (g)	220.3 ± 47.2	240.2 ± 46.2	195.5 ± 36.4
Fiber (g)	17.1 ± 0.7	18.1 ± 3.8	16.0 ± 0.8
Sugars (g)	44.8 ± 2.4	44.1 ± 2.5	45.7 ± 4.5
Protein (g)	75.1 ± 3.6	83.0 ± 3.8	65.2 ± 5.5
Fat (g)	65.4 ± 2.9	70.8 ± 3.4	58.7 ± 4.3

Data are presented as means ± SE of the means.

Glycemic response to regular cooked rice and cooked rice for diabetes in the fasting state

Fig. 2 shows the average iAUC and Peak_max values of the test foods in the fasting state. The iAUC values of Spicy pork-D (144.4 ± 4.2), Bulgogi-D (153.3 ± 4.6), and Soybean-D (139.9 ± 3.4) were significantly lower than that of Curry-R (161.6 ± 8.7). Although the iAUC of Curry-D (156.6 ± 5.0) was lower than that of Curry-R, the difference was not statistically significant.

Fig. 2. Glycemic response to test products consumed by participants in a fasting state. (A) iAUC and (B) Peak_max. Data are presented as means ± SE of the mean (n = 14). Paired t-test was used to analyze statistical difference from the control, Curry-R, and adjusted for repeated measures using the Bonferroni correction.

iAUC, incremental area under the curve; Curry-R, OTOKI Cooked Rice & Curry Sauce; Curry-D, Cooked Rice & Curry Sauce; Spicy pork-D, Cooked Rice & Spicy Sauce with Pork; Bulgogi-D, Cooked Rice & Bulgogi; Soybean-D, Cooked Rice & Soybean Paste Sauce.

^*P < 0.05, ^**P < 0.01, ^***P < 0.001.

The Peak_max values of Spicy pork-D (144.4 ± 4.2 mg/dL) and Soybean-D (139.9 ± 3.4 mg/dL) were significantly lower than that of Curry-R (160.7 ± 5.0 mg/dL). Those of Curry-D (156.6 ± 5.0 mg/dL) and Bulgogi-D (153.3 ± 4.6 mg/dL) were lower than that of Curry-R, but not in a statistically significant manner.

The GL values of the test foods are shown in Fig. 3. The GL values of Spicy pork-D (38.5 ± 5.3), Bulgogi-D (39.3 ± 4.9), and Soybean-D (41.1 ± 3.5) were significantly lower than that of Curry-R (56 ± 5.9). The GL value of Curry-D (47.4 ± 4.0) was lower than that of Curry-R; nevertheless, the difference was not statistically significant.

Fig. 3. Glycemic load of the test products. Data are presented as means ± SE of the means (n = 14). Paired t-test was used to analyze statistical difference from the control, Curry-R, and adjusted for repeated measures using the Bonferroni correction.

Curry-R, OTOKI Cooked Rice & Curry Sauce; Curry-D, Cooked Rice & Curry Sauce; Spicy pork-D, Cooked Rice & Spicy Sauce with Pork; Bulgogi-D, Cooked Rice & Bulgogi; Soybean-D, Cooked Rice & Soybean Paste Sauce.

^*P < 0.05.

Glycemic response to regular cooked rice and cooked rice for diabetes in the non-fasting state

Fig. 4 shows the average tAUC and Peak_max values of the test foods in the non-fasting state. The tAUC values of Spicy pork-D (701.9 ± 13.3), Bulgogi-D (691.0 ± 13.6), Soybean-D (678.0 ± 16.1), and Jjajang-D (721.3 ± 20.8) were significantly lower than that of Curry-R (767.9 ± 17.4). Although the tAUC of Curry-D (734.6 ± 16.6) was lower than that of Curry-R, the difference was not statistically significant.

Fig. 4. Glycemic response to test foods consumed by participants in the non-fasting state. (A) tAUC and (B) Peak_max. Data are presented as means ± SE of the means (n = 27). Paired t-test was used to analyze statistical difference from the control, Curry-R, and adjusted for repeated measures using the Bonferroni correction.

tAUC, total area under the curve; Curry-R, OTOKI Cooked Rice & Curry Sauce; Curry-D, Cooked Rice & Curry Sauce; Spicy pork-D, Cooked Rice & Spicy Sauce with Pork; Bulgogi-D, Cooked Rice & Bulgogi; Soybean-D, Cooked Rice & Soybean Paste Sauce; Jjajang-D, Cooked Rice & Jjajang Sauce.

^*P < 0.05, ^**P < 0.01, ^***P < 0.001.

The Peak_max values of Spicy pork-D (128.8 ± 3.0 mg/dL), Bulgogi-D (127.0 ± 2.8 mg/dL), Soybean-D (124.7 ± 2.6 mg/dL), and Jjajang-D (128.7 ± 4.0 mg/dL) were significantly lower than that of Curry-R (137.9 ± 3.3 mg/dL). Curry-D (133.6 ± 2.6) yielded a lower Peak_max value than Curry-R, but not in a statistically significant manner.

DISCUSSION

The present study demonstrates that cooked rice developed for persons with diabetes by adjusting the protein, fat, fiber, and sugar content effectively modulates the PPGR. In the test performed in the fasting state, Spicy pork-D, Bulgogi-D, and Soybean-D yielded significantly lower iAUC and GL values than the control (Curry-R), and a significantly lower Peak_max value was observed with the consumption of Spicy pork-D and Soybean-D. In the test conducted in the non-fasting state, tAUC and Peak_max values were significantly lower in the Spicy pork-D, Bulgogi-D, Soybean-D, and Jjajang-D groups. Significant differences in the PPGR existed, despite similar amounts of total glycemic carbohydrates, confirming that the PPGR is differentially influenced by other nutritional components, besides carbohydrates [12,25].

The protein content of the cooked rice products developed for persons with diabetes ranged from 20.8 to 22.38 g per serving, nearly double the amount in the regular cooked rice (12 g). Protein is known to reduce the postprandial rise in blood glucose concentration by stimulating the secretion of insulin and incretins, such as duodenal glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1), and delaying gastric emptying [26]. In healthy adults, the measurement of postprandial glucose, GIP, GLP-1, and insulin levels after the consumption of glucose, gelatin, or their combination revealed that protein alone stimulates glucose-independent insulin and incretin release. Moreover, when ingested alongside carbohydrates, protein reportedly attenuates the rapid postprandial rise in blood glucose by postponing stomach emptying [27].

The fat content of the cooked rice products developed for persons with diabetes ranged from 16.45 to 20.7 g per serving, exceeding the 14 g fat content of the regular cooked rice. Fat recognizably reduces postprandial blood glucose concentration by delaying gastric emptying and stimulating insulin and incretin secretion [28,29,30]. When different amounts of margarine were added to white bread consumed by healthy adults, increasing margarine content resulted in lower peak rise and iAUC values for blood glucose [31]. Similarly, providing pasta with or without sunflower oil to healthy adults demonstrated that oil addition significantly lowered the PPGR [32]. A study revealed that the addition of fat and proteins to glucose beverages reduced the PPGR in adults without diabetes in dose-independent and dose-dependent manners, respectively [15]. In the present study, the higher protein and fat content of the cooked rice developed for persons with diabetes than that of the regular cooked rice product presumably helped to lower the PPGR.

Dietary fibers are not hydrolyzed by endogenous enzymes in the human small intestine; in particular, gel-forming soluble fibers reportedly reduce the glycemic response by delaying gastric emptying, slowing digestion and carbohydrate absorption, and modulating gut hormone secretion and microbiota [33,34]. When healthy adults consumed glucose beverages with or without soluble dietary fibers, beverages containing dietary fiber significantly lowered the PPGR [35]. Wu et al. [36] reported that meals with dietary fiber resulted in a significantly lower AUC for glucose compared with those without dietary fiber. The cooked rice products developed for persons with diabetes had a dietary fiber content of 9.22–12.16 g per serving, a figure approximately 4.5–6 times higher than the 1.97 g in a serving of the regular cooked rice. The cooked rice products developed for persons with diabetes contained additional indigestible maltodextrin. Resistant maltodextrin, derived from the heat treatment of corn starch, is a water-soluble fermentable functional fiber, and it is known for its multiple benefits, such as lowering blood glucose and triglycerides levels, enhancing satiety, and improving gut health [37]. A study revealed that beverages containing resistant maltodextrin significantly reduced postprandial glucose and insulin AUC values in healthy adults compared with those without resistant maltodextrin [38].

The sugar content of the cooked rice products developed for persons with diabetes ranged from 2.35 to 4.1 g per serving, approximately half of the 9 g in one serving of the regular cooked rice. Simple sugars, which can be rapidly broken down and absorbed, can elicit an accelerated rise in blood glucose concentration [39]. A study reported that when glucose, sucrose, and starch were provided either as beverages or as meals, postprandial glucose and insulin responses were less pronounced for starch than for glucose and sucrose [40]. These findings suggest that the carbohydrate form affects the PPGR, and that the increased dietary fiber and reduced sugar content of the cooked rice products developed for persons with diabetes compared with that of the regular cooked rice product potentially contributed to their effects on the glycemic response.

The combined hypoglycemic effects of protein, fat, and dietary fiber have also been reported. A study revealed that adding protein- and fat-rich toppings (cheddar cheese, eggs, and baked beans) to carbohydrate foods (potatoes, pasta, and toast) resulted in a lower GI than consuming the carbohydrate foods alone, with cheddar cheese exhibiting the lowest GI [41]. Sun et al. [14] reported that consuming white rice with chicken breast, peanut oil, and/or Bok choy—either individually or in combination—resulted in a lower PPGR than consuming white rice alone, and the lowest PPGR was observed when all 3 were consumed together. Furthermore, a study evaluating the PPGR in healthy adults who received high-carbohydrate meals alone or with added protein, fat, and/or dietary fiber indicated that mixed meals elicit a lower PPGR, with the combination of protein and dietary fiber yielding the most significant reduction [42]. While previous studies have examined the effects of adding protein, fat, and dietary fiber to carbohydrate on the PPGR, research focusing on the impact of varying quantities of these additions remains limited. Despite the higher caloric content per serving in cooked rice products developed for persons with diabetes (521.6–554.4 kcal) compared with Curry-R (420.0 kcal), the PPGR was lower. Curry-R contains protein, fat and dietary fiber; however, these components are present in higher amounts in cooked rice products developed for persons with diabetes. These findings indicate that the reduced PPGR observed with the cooked rice products developed for persons with diabetes is likely attributable to the combined effects of the elevated protein, fat, and dietary fiber contents.

The Peak_max value of Bulgogi-D did not significantly differ from that of Curry-D; nonetheless, its iAUC was significantly lower. Soybean-D demonstrated the lowest Peak_max value; however, its iAUC, though not in a statistically significant manner, exceeded that of Bulgogi-D and Spicy pork-D. This indicates that the iAUC and peak blood glucose level do not always align. Postprandial blood glucose spikes are associated with increased oxidative stress and immune cell activation, potentially elevating the risk of CVD [43]. Therefore, managing rapid postprandial glucose spikes is also an important aspect of blood glucose control, as the iAUC alone may not entirely reflect the glucose peak pattern. Regarding mixed meals, various factors influence glycemic responses, highlighting the need to comprehensively evaluate these glucose spike patterns.

In the present study, no significant differences in the iAUC, Peak_max, or GL value were observed between Curry-D and Curry-R in the fasting state. Similarly, in the non-fasting state, no significant differences in the tAUC or Peak_max value were found between Curry-D and Curry-R. Major differences between Curry-D and Curry-R are substitution of sugar with oligosaccharide and allulose, as well as higher meat and dietary fiber content in the Curry-D. Both Curry-D and Curry-R contain potato, carrot, onion, and pork as their main ingredients. These findings suggest that nutritional adjustments, such as increasing protein, lipid, and dietary fiber levels while reducing sugar content in curry ready meals, may not exert a significant impact on blood glucose regulation. Previous studies have demonstrated that predicting the GI of composite foods based on the GI values of their individual ingredients may not represent the actual GI of the composite foods. Furthermore, the blood glucose response of individual foods may differ from that of composite foods [25,44]. This implies that diverse factors, such as food preparation processes, can influence the blood glucose response of composite foods, leading to different responses depending on the specific composition. This study’s findings emphasize that the blood glucose regulatory effect of composite foods via nutritional adjustments may not be consistent across all foods. Therefore, the development of health-promoting convenience foods should prioritize conducting clinical trials on the composite food as an integrated entity rather than solely focusing on individual ingredients.

In general, glycemic response measurements are performed following overnight fasting to eliminate the influence of recent food intake and stabilize glucose levels [45]. However, in practical situations, food is usually consumed while the effects of previously consumed meals persist. Therefore, this study also aimed to determine whether cooked rice products developed for persons with diabetes exhibit comparable glycemic control effects in the non-fasting state. In the lunch trial, Spicy pork-D, Bulgogi-D, Soybean-D, and Jjajang-D yielded significantly lower tAUC and Peak_max values than Curry-R, exhibiting consistency with the results of the breakfast trial. These findings indicate that the glycemic control effects of cup meals for diabetes remain consistent regardless of fasting status, demonstrating that comparable blood glucose-lowering benefits can be achieved, even when prior PPGRs persist.

This study demonstrates that optimizing the nutritional composition of convenience meals can positively influence blood glucose regulation, providing evidence to support the competitiveness of nutrition-focused HMR in the convenience food market. As the demand for convenience meals continues to rise [22], nutritional factors, such as “helps improve health,” “provides nutrients beneficial to the body,” and “excellent nutritional value,” have been identified as key contributors to consumer trust [46]. Since the COVID-19 pandemic, consumers have increasingly prioritized not only convenience but also health, rendering health-related factors a key consideration when selecting convenience meals [47]. However, Ahn [48] reported that health-conscious consumers display low repurchase rates for convenience meals, indicating that the current products may fail to adequately satisfy their health-related needs. This study’s findings also indicate that HMR developed for persons with diabetes may have a beneficial impact on blood glucose regulation, providing evidence of their efficacy and potentially influencing the decision-making processes of health-conscious consumers.

Notwithstanding, this study has certain limitations. First, blood glucose levels are affected by various factors beyond food consumed, including physical activity, stress, hormonal fluctuations, and dehydration, leading to considerable intra-individual variability [49,50]. Although repeated measurements are recommended when assessing glycemic responses, the stability and limited usage duration of the CGM device restricted the number of tests, rendering it impossible to perform repeated measurements for each product in the current study. However, glycemic response to a glucose solution was repeatedly measured to verify intra-individual variability. Second, this study was conducted on healthy individuals, as previous research has reported no significant differences in GI values between healthy individuals and those with diabetes [45]. Hence, further research is required to determine whether the same effects are observed in individuals with impaired glycemic control, such as those with diabetes. Third, abdominal obesity of the participants cannot be determined because waist circumference was not measured. Abdominal obesity is one of the factors that can affect glycemic response. However, none of the participants seem to have insulin resistance or abnormal glycemic response based on participants’ fasting glucose levels and HOMA-IR.

In conclusion, cooked rice products developed for persons with diabetes demonstrate a significant reduction in postprandial blood glucose levels compared with regular cooked rice products in both the fasting and non-fasting states. This suggests that nutritional adjustments, such as increasing the protein, fat, and dietary fiber content and reducing the amount of sugar, potentially contribute to glycemic control in mixed meals, with the effects varying depending on the type of food. Furthermore, this study provides preliminary evidence that functional convenience foods potentially address health-related demands, indicating the need for further research. With the increasing demand for convenience foods and rising emphasis on health, this study validated the glycemic control effects of newly developed convenient meals. However, to enhance their practical applicability, further research is warranted, particularly focusing on individuals with impaired glycemic regulation and the long-term effects of these meals.