The impact of eating methods on eating rate and ...

Physiology & Behavior 139 (2015) 505–510

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The impact of eating methods on eating rate and glycemic response in healthy adults Lijuan Sun, Dinesh Viren Ranawana, Wei Jie Kevin Tan, Yu Chin Rina Quek, Christiani Jeyakumar Henry ⁎ Clinical Nutrition Research Centre, Singapore Institute for Clinical Sciences, Singapore 117609, Singapore

H I G H L I G H T S • • • •

Glycemic response of consuming rice with chopstick was lower than with spoon. The glycemic index of rice using chopsticks (GI: 68) was lower than spoon (GI: 81). Little observed differences (GI) between using fingers with spoon or with chopsticks. Different ways of consuming white rice alter glycemic index.

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Article history: Received 7 July 2014 Received in revised form 1 December 2014 Accepted 3 December 2014 Available online 4 December 2014 Keywords: White rice Glycemic response Mastication Eating rate Eating method

a b s t r a c t Singapore is an island state that is composed of three major ethnic groups, namely Chinese, Malay and Indian. Its inhabitants consume food either using chopsticks (Chinese), fingers (Malay and Indian) or spoon (Chinese, Malay and Indian). Previous work by our group showed that the degree of mastication significantly influenced the glycemic response. The degree of mastication in turn may depend on the eating method as the amount of food taken per mouthful and chewing time differs between eating methods. Eleven healthy volunteers came in on six non-consecutive days to the laboratory and evaluated three methods of eating white rice (spoon, chopsticks and fingers) once and the reference food (glucose solution) three times in a random order. Their glycemic response (GR) was measured for the subsequent 120 min. Mastication parameters were determined using surface electrode electromyography. The GR to white rice eating with chopsticks was significantly lower than spoon. The GI of eating rice with chopsticks was 68 which is significantly lower than eating with spoon (GI = 81). However there were no differences between fingers and spoon, and between fingers and chopsticks either in GR 120 min or GI. The inter-individual number of mouthful, number of chews per mouthful, chewing time per mouthful and the total time taken to consume the whole portion of rice were significantly different between spoon and chopsticks groups. Significant correlations between the number of mouthful to take the entire portion of rice and amount of rice per mouthful during mastication and the GR were observed for eating rice with spoon and chopsticks, but not for fingers. The results suggest that individual differences in number of mouthful and amount of rice per mouthful may be two of the causes for inter-individual differences in the GR between spoon and chopsticks. The present study suggests that eating rice with different feeding tools has different chewing times and amount of food taken per mouthful and then alters the GI of the rice. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Singapore is an island state that is composed of three major ethnic groups, namely Chinese, Malay and Indian. In addition to Singaporean diverse cuisine, its inhabitants consume food either using chopsticks (Chinese), fingers (Malay and Indian) or spoon (Chinese, Malay and ⁎ Corresponding author at: Clinical Nutrition Research Centre, Singapore Institute for Clinical Sciences, A*STAR; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive #07-02, MD 6 Building, Singapore 117599, Singapore. E-mail address: [email protected] (C.J. Henry).

http://dx.doi.org/10.1016/j.physbeh.2014.12.014 0031-9384/© 2014 Elsevier Inc. All rights reserved.

Indian). The major staple in Singapore and South-east Asia in particular is rice. The Asian diet is characterized as one that is high in carbohydrates, and in most regions herein rice remains the major staple. Refined grains such as white rice have been linked to type 2 diabetes and the metabolic syndrome [1–3]. The glycemic index (GI) concept was developed by Jenkins over three decades ago [4] and is useful to quantify the glycemic impact of carbohydrate foods. Understanding the GI and devising simple ways to reduce glycemic response are particularly beneficial for those with diabetes and pre-diabetes. The Asian region is showing the largest rise in diabetes and related chronic disease incidence both as a result of a large population size and changes in lifestyle and economic status. It is estimated that diabetes and impaired glucose

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tolerance incidence rates will increase by up to 60% by 2025 compared with 2007 levels [5]. The present study aims to investigate the role of eating methods of rice in glycemic response and glycemic index. The introduction into the diet of meals that contain carbohydrates in a form that is absorbed slowly in the small intestine or not at all has been advocated as a simple means of preventing or treating diabetes or obesity [6]. Mastication, the physiological function of which is the mechanical breaking down of food into small particles suitable for gastrointestinal absorption of nutrients, influences postprandial plasma glucose concentrations [7,8]. Chewing breaks food into smaller particles, enhances salivation and mixes it with salivary enzymes initiating hydrolysis of carbohydrate in the mouth and stomach [6]. These effects would be expected to increase the glycemic and insulinemic responses. It was reported that swallowing white rice without chewing reduced the blood glucose response [6]. Thus, mastication should play a crucial role in determining the postprandial plasma glucose concentrations. In our previous studies, we found that rice elicited the greatest inter-individual variations in the glycemic response compared to spaghetti and carbohydrate drinks [9]. This suggested that difference in habitual mastication of whole grains may be a contributor to its glycemic variations. One aim of this study is to obtain more knowledge about the association of mastication on glucose metabolism. Although dietary interventions are the most effective and economical methods in diabetes management and prevention [10], limited data is currently available on the mastication (bite size, the same portion with different numbers of mouthfuls) effects on glycemic of white rice consumed in Asia [11–14]. Previous work by our group showed that the degree of mastication significantly influenced the glycemic response [15]. The degree of mastication in turn may depend on the eating method as the amount of food taken per mouthful and chewing time differs between eating methods. Therefore it may be possible that the eating method may influence the GI. The eating method may have greater effects on the GI in multiethnic countries like Singapore where each ethnic group has their own peculiar way of eating. Whereas ethnic groups of Chinese descent prefer chopsticks, South Asians and Malays use their fingers. Both groups also use western utensils on occasion. Therefore, determining the impact of eating method on the GI may be more important from a Singapore perspective. The GI has become a widely used tool in the selection of foods suitable for glycemic control. However, wide variations in the GI value to any single food are a consistent observation. A clear understanding of all the factors affecting the GI and thereby contributing to its variations is imperative if it is to be effectively used as a clinical tool. The results of this study will investigate whether the eating method has any bearing on the GI of rice. The findings of this study are especially important for Singapore as it is home to a multi-ethnic population who consumed rice extensively. 2. Experimental methods 2.1. Subjects Eleven healthy participants (4 females and 7 males) were recruited for the study by means of advertisements, flyers, and personal communications and consisted entirely of staff and students of University of Singapore. Before inclusion in the study, potential participants were briefed on all aspects of the experiment and were given the opportunity to ask questions. Following the securing of consent, a health assessment was performed which included anthropometric measurements (Table 1) and a health questionnaire (giving details of food allergies/intolerance, metabolic diseases, special dietary needs, and smoking habits). Those who fulfilled all the inclusion criteria (body mass index, 18.5–24.9 kg/m2; blood pressure (BP)–systolic BP between 110 and 120 mm Hg and diastolic BP between 75 and 85 mm Hg; age 21–50 years; fasting blood glucose, 4–6 mmol/L; not on prescription medication; non-smoking; no genetic or metabolic

Table 1 Mean (±SE) baseline measurements of participants. Males

Females

Number (n) Age (y) Height (m) Weight (kg) BMI (kg/m2) Body fat content (%) Fasting blood glucose (mmol/L)

7 23.0 ± 0.3 1.8 ± 0.02 68.8 ± 3.09 21.8 ± 0.92 18.8 ± 1.7 4.6 ± 0.2

4 24.8 ± 1.5 1.6 ± 0.02 49.1 ± 2.6 19.0 ± 0.7 24.8 ± 1.7 4.6 ± 0.1

Blood pressure Systolic (mm Hg) Diastolic (mm Hg) Physical activity score

119.7 ± 2.15 68.8 ± 2.38 7.6 ± 0.2

104.3 ± 2.3 68.8 ± 1.7 7.5 ± 0.3

diseases, full set of natural teeth and the subjects were chosen on their abilities to eat with fingers, chopsticks or spoon comfortably) were enrolled in to the study. Physical activity was quantified using the questionnaire of Baecke et al. [16], and only those not partaking in competitive sports and endurance events were included. Participants were informed that the purpose of the study was to investigate the effect of different eating methods on plasma glucose. When the participants completed the study, they were given the option to withdraw their data from the study. The study was conducted at the Clinical Nutrition Research Centre (CNRC), Singapore Institute for Clinical Sciences (SICS). All participants gave written informed consent before starting, and the study was initiated after ethical approval by the Domain Specific Review Board (DSRB) of National Healthcare Group. 2.2. Study design Randomized, within-subjects, non-blind design was adopted in this study. Each participant returned for six test sessions on 6 nonconsecutive days. At three random sessions subjects tested the glucose reference and on the remaining three random days tested the white rice with different eating methods (spoon, chopsticks and fingers). While the participants tested the in vivo GR (as described in measurement of glucose response section) on all 6 days, oral processing parameter data were collected on 3 random days for white rice eating with different methods. At each session, subjects came to the CNRC laboratory between 7:30 and 8:30 am following a 12-h overnight fast. Upon arrival at the laboratory, the participants were first allowed to rest for 10 min before testing began. Following a brief rest, two baseline finger prick blood samples were taken 5 min apart to measure fasting glucose levels. During the white rice test sessions where oral processing parameters were measured, electromyogram (EMG) electrodes were attached to the participant's cheeks just before they begin to consume the white rice. The test or reference food was given to consume within 15 min. Further blood samples for glucose were taken for the subsequent 120 min (every 15 min in the first hour and every 30 min for the subsequent hours). 2.3. Test foods The subjects were given white rice and glucose. The rice and the reference food were given in portions containing 50 g of available carbohydrates. White rice amounted to 63.6 g of uncooked rice. The glucose reference drink was made using 50 g of anhydrous glucose dissolved in 250 mL of room temperature water. The white rice used was Thai Hom Mali fragrant rice (NTUC Fairprice, Singapore). All of the rice was from a single cultivated batch. Compositional information (per 100 g) was obtained from the supplier (total carbohydrate 79 g, fiber 0.4 g, protein 7.1 g, fat 0.5 g). The percentage increase in weight after cooking was 156% for rice. For cooking, the rice ratio to water was maintained at 1:8. Each white rice portion was cooked individually. The water was brought to


the boil at the maximum setting of 9. The white rice was then added into the water and allowed to come to the boil on high, after which the hob setting was lowered to 7, the rice was allowed to simmer for exactly 10 min. Even though some leaching of carbohydrates into the water may have occurred during cooking, this was not factored into the design as the cooking procedure for three rice sessions was standardized for all the participants. The losses, therefore, were considered to be constant at all the rice sessions and may not contribute to the difference observed between treatments. After cooking, the rice was immediately drained using a sieve and served to the participant. The glucose and test foods were freshly prepared in the morning on test days and served to the participants within 30 min of preparation. 2.4. Study protocol The protocol used was adopted from that described by Brouns et al. [17] and is in line with procedures recommended by the Food and Agricultural Organization (FAO)/World Health Organization (WHO) [18] for glycemic response studies. Upon arrival at the laboratory, subjects were first allowed to rest for 10 min. Blood samples in the fasting state were then obtained 5 min apart (−5 and 0 min) for blood glucose analysis. Subsequently subjects consumed the test/reference food at a comfortable pace and chewing rice at the subject's usual pace and finished it within 15 min. Water (150 mL) was served with the test and reference foods. Blood samples for glucose analysis were obtained at 15, 30, 45, 60, 90 and 120 min following the start of the meal. The participants were encouraged to keep physical activity to a minimum during testing and remain seated as far as possible. Computers, work tables, reading areas and a television were provided for their use. Snacks were provided at the end of testing. Upon completion of all six sessions, they were debriefed and compensated for their time and travel. 2.5. Blood glucose analysis Blood was obtained by finger prick using the Accu-Chek sterile, single-use lancing device (Abbott, US). Before a finger prick, subjects were encouraged to warm their hand to increase blood flow. To minimize plasma dilution, fingertips were not squeezed to extract blood but were instead gently massaged starting from the base of the hand moving towards the tips. The first 2 drops of expressed blood were discarded, and the next drop was used for testing. Blood glucose was measured using the HemoCue Glucose 201 analyzer (HemoCue Glucose 201 RT, Sweden). The HemoCue is a reliable method of blood glucose analysis [19].

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2.6. Measurement of oral processing parameters EMG was used to collect information related to mastication parameters [20]. Data on the number of mouthfuls taken to consume the entire portion of each measurement, the number of chews per mouthful, and the time taken per mouthful were measured. Electromyograms were obtained using bipolar surface electrodes from the left and right masseter muscles. The surfaces of the cheeks were cleaned with alcohol and wiped dry. The muscles were identified by palpitating the area while the participants clenched their jaws. The electrodes were moistened with distilled water and attached lengthwise along the muscles using adhesive strips. An additional earth electrode was placed on the left wrist. The electrodes were attached to a programmable data acquisition unit (DataLog model MWX8, Biometrics Ltd.,); the instrument was zeroed after attaching to the participant. Participants were not given any special instructions, they were asked to eat the test food as they normally would. The number of mouthfuls to consume the test food, the number of chews per mouthful, and the time taken for each mouthful were quantified for each set of data, and the average values for each attribute and for each test eating method for each participant were calculated.

2.7. Data processing and statistical analysis Studies of the analysis of GR and GI in humans have been based on 10 subjects, as reviewed by the FAO/WHO [18] to take into account the inter-individual variations. A sample size of 11 was therefore considered adequate for the current study. Statistical analysis was performed using Statistical Package for the Social Sciences (version 16.0; SPSS), and data and figures were processed in Microsoft Excel spreadsheet (2007). A Shapiro–Wilk test indicated that the data were normally distributed. The glycemic response (GR) data were converted to ‘the change in GR’ values. The change in GR was calculated by computing the difference between the blood glucose concentration at a time point and mean baseline blood glucose concentration (based on 2 baseline values taken 5 min apart). The total blood glucose response was expressed as the iAUC ignoring the area beneath the baseline and was calculated geometrically using the trapezoidal rule [17,21]. The change in GR that was used for all further analyses, including iAUC calculations, blood glucose response curve construction and statistics. The AUCs for specific time periods (first 30, 45, 60 min) were calculated with the same procedure, but using only the data associated with the particular time frame.

Mean change in capillary blood glucose (mmol/l)

4 glucose spoon

3 *

chopsticks

*

finger

2

1

0

0

15

30

45

60

75

90

105

120

Time (min)

Fig. 1. Temporal curves of the average blood glucose response of 11 participants consuming the same quantity (50 g of available carbohydrate) of glucose and rice. Curves constructed from data obtained for glucose, eating rice with spoon, chopsticks and finger over 120 min of in vivo determination. The mean change in the GR was calculated by subtracting from the GR at each time point, the baseline GR. Error bars are SEs. The GR peak for glucose and finger at 30 min and spoon and chopsticks at 45 min, respectively. Difference occurred between spoon and chopsticks group. P = 0.082 at 30 min, P = 0.038 at 45 min.

508


iAUC (mmol.min/l)

a

250 200

*

150 100 50 0

glucose

b

spoon

chopsticks

fingers

120

Glycemic Index

100

*

80 60 40 20 0

glucose

spoon

chopsticks

fingers

Fig. 2. Temporal blood glucose response curve, (a) incremental area under the blood glucose response curve and (b) glycemic index of rice eating with spoon, chopsticks and fingers. All values are the mean of 11 healthy subjects. The total iAUC was calculated using the trapezoid rule, ignoring the area below the baseline. Chopsticks group had the lower iAUC and GI than spoon group. Error bars are standard errors. *P b 0.05 compared with spoon group.

The GI was defined as the percentage of the plasma glucose iAUC of the study meal from that of the reference glucose solution. Variations in the interindividual number of chews per mouthful, number of mouthfuls taken to complete the entire food portion, chewing time per mouthful, and differences in the iAUC for the GR for eating rice with different eating methods were compared using one-way repeatedmeasures analysis of variance (P ≤ 0.05). Comparisons between different eating methods were done by examining contrasts within the analysis of variance with Bonferroni correction. Comparisons in GR across time were compared using two-way repeated-measures analysis of variance with two within subject factors-time and methods. The linear regression procedure was used to determine correlations with number of chews per mouthful, number of mouthful, chewing time, and amount of food per mouthful as the independent factors, and the total iAUC as the dependent factor. Alpha (α) was set at .05 for all the statistical analyses. Values are presented as mean ± standard deviation. 3. Results All participants completed the study and ingested the test meals. The anthropometric measurements of the males and females in the subject group indicated that they all fell within the acceptable normal limits

for BMI, body fat content, fasting blood glucose, and blood pressure (Table 1). The results of the study therefore apply to a normal weight young adult group that is not hyperglycemic or hypertensive. The mean total iAUCs, AUC for first 30 min, 45 min and 60 min for eating rice with chopsticks group were significantly different from spoon group. All the rice group total iAUCs were significantly different from glucose control solution. The GI of chopsticks group was significantly different from spoon group (Fig. 2, Table 2). The mean GR for spoon and chopsticks groups peaked at 45 min, while glucose and fingers groups maximized at 30 min. The mean change in GR at 45 min for chopsticks and spoon groups was significantly different. There were no significant differences between spoon and fingers, and between fingers and chopsticks groups in both iAUC and GR (Fig. 1, Table 2). The mastication rate (chews per mouthful) varied significantly for both with chopsticks and spoon groups. The chewing time per mouthful for spoon, chopsticks and fingers were 20 s, 12 s and 19 s respectively. A significant difference was found between spoon and chopsticks groups. A significant difference in the number of mouthfuls taken to consume the entire food portion was observed between chopsticks (43) and spoon (20), and between chopsticks (43) and fingers (17). We calculated the amount of rice per mouthful (the amount of cooked rice (148 g) divided the mean number of mouthful in each group) and found that chopsticks group was significantly less amount than spoon and fingers group. We also calculated the chewing rate (chews per mouthful divided chewing time) and found that the chewing rate of chopsticks group was significantly higher than the other two groups (Table 3). The total time taken to consume the portion of rice using chopsticks was 683 s which was significantly longer than spoon (418 s) and fingers (459 s). Based on the results of the total time, it means that eating with chopsticks was more slower than other eating methods. Significant correlations were observed between the numbers of mouthful to complete the entire food portion, the amount of food per bite and total iAUC for eating rice with chopsticks and spoon, but not in fingers. The mean numbers of mouthful to complete the entire food portion and the amount of food per bite significantly correlated inversely with the total iAUC for chopsticks and spoon groups. 4. Discussion To our knowledge, this is the first study to compare the difference in chewing activity in reference to eating methods in young subjects and then assess the effects of different eating methods on glucose response. The present study found that eating rice with chopsticks is associated with reduced postprandial glucose response, higher chewing rate, smaller bite size, less number of chews per mouthful and lower eating rate. Our data suggest that using chopsticks can decrease the bite size and the number of chews per mouthful and suppress postprandial plasma glucose response. This raises the possibility that choosing different eating tools (increase chewing rate, decrease number of chews per mouthful or decrease bite size) may contribute to their reduced glycemic response. In recently published international GI tables [15], high GI values have been reported for white rice. Rice provides approximately one fifth of the world's dietary energy and over one third of daily dietary energy in China, India and many Asian countries, where carbohydrate

Table 2 Mean (±SE) incremental areas under the curve and GRs to eat rice with spoon, chopsticks and fingers. Total iAUC

Total iAUC for first 30 min Total iAUC for first 45 min Total iAUC for first 60 min Mean change in GR at 30 min Mean change in GR at 45 min

(mmol·min/L) Spoon 178.8 ± 20.5 34.5 ± 4.0 Chopsticks 148.6 ± 21.0⁎ 27.8 ± 3.3⁎ Finger 153.3 ± 20.9 32.1 ± 3.1 ⁎ P b 0.05 compared with spoon group.

mmol/L 76.0 ± 7.2 62.8 ± 5.8⁎ 68.4 ± 6.5

112.4 ± 10.5 95.1 ± 9.5⁎ 98.1 ± 9.9

2.7 ± 0.3 2.3 ± 0.2 2.5 ± 0.3

2.9 ± 0.3 2.4 ± 0.3⁎ 2.3 ± 0.3


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Table 3 Mean (±SE) mastication dynamics data of different eating methods of rice by 9 participants.

Spoon Chopsticks Finger

Number of mouthful

Number of chews per mouthful

Chewing time per mouthful (s)

Chewing rate

Amount of rice per mouthful (g)

Total time consuming per portion (seconds)

19.8 ± 2.4 42.5 ± 6.5⁎ 17.4 ± 1.8⁎⁎

31.0 ± 2.5 20.4 ± 3.0⁎ 29.6 ± 4.0

19.7 ± 1.4 11.9 ± 1.6⁎ 18.7 ± 2.4

1.59 ± 0.06 1.76 ± 0.09⁎ 1.60 ± 0.08

8.1 ± 0.8 4.2 ± 0.7⁎ 9.4 ± 1.2⁎⁎

418 ± 27 683 ± 52⁎ 459 ± 33⁎⁎

⁎ P b 0.05 compared with spoon group. ⁎⁎ P b 0.05 compared with chopsticks group.

provides up to two-thirds of energy intake [22]. Thus, it is a major contributor to the overall glycemic load of most Asian diets. Several epidemiologic studies have linked higher dietary glycemic load, primarily from white rice, to an increased risk of type 2 diabetes [1–3,23]. It is no surprise that rice has been implicated in its etiology. This highlights the importance of devising ways and means of reducing the glycemic impact of rice. In societies with a tradition of rice consumption, advocacy to reduce intake will remain an untenable goal, strategies to reduce the GR of rice should therefore become a priority. It is worthwhile to examine the potential of using different eating tools to attenuate the glycemic impact of rice. The method of eating is ethnic-specific. Whereas chopsticks are predominantly used by Chinese, Japanese and Korean populations, Indians and Malays traditionally use their fingers. Both groups commonly also use spoons and forks on occasion. The study showed that eating rice with chopsticks compared to spoon and fingers decreased the GI of rice by approximately 13%. The rice tested was Thai Jasmine rice, an aromatic medium-low amylose white rice (13–18%) popularly consumed throughout the world and particularly in the United States of America, China and South-East Asia. The present study suggests that eating rice with different feeding tools had different chewing times and amount of food taken per mouthful and then alters the GI of the rice. The mean cooked weight of rice was 150 g and the mean portion weight per mouthful for each eating method was 4 (chopsticks), 8 (spoon) and 9.5 g (fingers). The mean number of mouthfuls taken to consume the portion of rice (150 g) with chopsticks (41) was significantly higher than that taken to eat with spoon and fingers (20 and 17, respectively), suggesting that the quantity consumed per mouthful was greater with the latter. The amount of rice taken per mouthful using chopsticks was almost half of spoon and fingers groups. The number of chews per mouthful using chopsticks was 30% less than spoon and fingers. Although the chewing time per mouthful using chopsticks was significantly less than the other two methods, the chewing rate for chopsticks was calculated using the number of chews per mouthful divided chewing time per mouthful was significantly higher than others. The association between speed of chewing and glucose metabolism is unclear. Further investigation of the association between the chewing rate and glycemic response is in progress. The primary purpose of mastication is the reduction of the food to particles small enough to form a cohesive, liquid-coated bolus that can be swallowed [24,25]. These oral parameters suggested that the degree of breakdown of rice using different methods may be different. There were no significant differences between spoon and fingers in the number of mouthful, number of chews and chewing time per mouthful. The mastication frequency, amount of portion per bite and the total time taken to consume the portion of rice were similar between these two eating methods. In our previous studies, we found that the degree of habitual mastication may influence the in vivo GR of individuals such as ingesting small particles produces greater glycemic [13,26], and masticating 30 times compared to 15 times increased the GI of rice by approximately 29% [14]. The results of the current study confirm these findings in different eating methods which the number of chews per mouthful less was correlated with smaller glycemic response. In our present study, the number of chews per mouthful in chopsticks group is 34% less than spoon group and the iAUC and GI are 16% smaller compared to spoon group which is consistent with our previous study [14]. The

chewing rate of chopsticks group was 10% higher than spoon group. Combined number of chews and chewing rate of different eating methods may be explained, in part, the particle size of different eating methods of groups may be different, the particle size of chopsticks group was larger which reduced the glycemic response compared to smaller particle size. Recently studies have found that eating fast by self-assessed questionnaire was associated with a higher risk of diabetes in middle-aged Japanese males [27], and slow eating was associated with decreased odds for diabetes in males [28]. In addition, recent cross-sectional studies have shown a positive relationship between the rate of eating and obesity, and cardiovascular risk factors such as elevated blood pressure and plasma triacylglycerol [29,30]. The mechanism underlying the association between rate of eating and glucose metabolism may be explained by eating slowly which can increase the postprandial response of the anorexigenic gut peptide GLP1, which plays an important role in enhancing the glucose-stimulated insulin secretion [8,31]. In our study, we found that eating rice with chopsticks was more slower than spoon and fingers which may explain the lower glycemic response of chopsticks group. In conclusion, the study is the first to propose a possible impact of eating tools on postprandial glycemic response in young populations. Different eating methods can affect the eating rate which may in turn influence postprandial glucose response; how this contribute to glycemic control in the long term warrants further investigation. This raises the possibility that feeding frequency may have a direct influence on glycemic response, hence the potential risk of developing type 2 diabetes. Conflict of interest The authors declare no conflict of interest. Acknowledgments We warmly thank the volunteers for taking the time to participate in the postprandial study. The study was supported by the Singapore Institute for Clinical Sciences (Grant number 2012/00358). References [1] E.A. Hu, et al., White rice consumption and risk of type 2 diabetes: meta-analysis and systematic review, BMJ 344 (2012) e1454. [2] G. Radhika, et al., Refined grain consumption and the metabolic syndrome in urban Asian Indians (Chennai Urban Rural Epidemiology Study 57), Metabolism 58 (5) (2009) 675–681. [3] V. Mohan, et al., Can the diabetes/cardiovascular disease epidemic in India be explained, at least in part, by excess refined grain (rice) intake? Indian J. Med. Res. 131 (2010) 369–372. [4] D.J. Jenkins, et al., Glycemic index of foods: a physiological basis for carbohydrate exchange, Am. J. Clin. Nutr. 34 (3) (1981) 362–366. [5] J. Chan, et al., The Joint Asia Diabetes Evaluation (JADE) Program: a web-based program to translate evidence to clinical practice in Type 2 diabetes, Diabet. Med. 26 (7) (2009) 693–699. [6] N.W. Read, et al., Swallowing food without chewing; a simple way to reduce postprandial glycaemia, Br. J. Nutr. 55 (1) (1986) 43–47. [7] A.M. Pedersen, et al., Saliva and gastrointestinal functions of taste, mastication, swallowing and digestion, Oral Dis. 8 (3) (2002) 117–129. [8] K. Sonoki, et al., Effects of thirty-times chewing per bite on secretion of glucagonlike peptide-1 in healthy volunteers and type 2 diabetic patients, Endocr. J. 60 (3) (2013) 311–319.

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