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Article
Eating Fast Has a Significant Impact on Glycemic
Excursion in Healthy Women: Randomized
Controlled Cross-Over Trial
Yuuki Saito 1, Shizuo Kajiyama 2,3, Ayasa Nitta 1, Takashi Miyawaki 1, Shinya Matsumoto 1,
Neiko Ozasa 4, Shintaro Kajiyama 5, Yoshitaka Hashimoto 3, Michiaki Fukui 3
and Saeko Imai 1, *
1Department of Food and Nutrition, Faculty of Home Economics, Kyoto Women’s University, 35,
Kitahiyoshi-cho, Imakumano, Higashiyama-ku, Kyoto 605-8501, Japan; k5231047@yahoo.co.jp (Y.S.);
ayasanman.n.vabo7915@gmail.com (A.N.); takashiukb@gmail.com (T.M.); matumots@kyoto-wu.ac.jp (S.M.)
2Kajiyama Clinic, Kyoto Gojyo Clinic Build. 20-1, Higasionnmaeda-cho, Nishinanajyo, Shimogyo-ku,
Kyoto 600-8898, Japan; kajiyama-clinic@dream.ocn.ne.jp
3Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho,
Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; y-hashi@koto.kpu-m.ac.jp (Y.H.);
michiaki@koto.kpu-m.ac.jp (M.F.)
4Graduate School of Medicine, Kyoto University, 54, Kawahara-cho, Syogoin, Sakyo-ku,
Kyoto 606-8507, Japan; nei126@kuhp.kyoto-u.ac.jp
5Japanese Red Cross Kyoto Daini Hospital, 355-5, Kamanza, Marutamachi, Kamigyo-ku,
Kyoto 602-8026, Japan; kaji20091025abcd@gmail.com
*Correspondence: imais@kyoto-wu.ac.jp; Tel.: +81-75-531-7128
Received: 11 August 2020; Accepted: 9 September 2020; Published: 10 September 2020
Abstract:
Epidemiological studies have shown that self-reported fast eating increases the risk
of diabetes and obesity. Our aim was to evaluate the acute effect of fast eating on glycemic
parameters through conducting a randomized controlled cross-over study with young healthy
women. Nineteen healthy women wore a flash glucose monitoring system for 6 days. Each participant
consumed identical test meals with a different eating speed of fast eating (10 min) or slow eating
(20 min) on the 4th or the 5th day. The daily glycemic parameters were compared between the 2 days.
The mean amplitude of glycemic excursion (MAGE; fast eating 3.67
±
0.31 vs. slow eating 2.67
±
0.20 mmol/L, p<0.01), incremental glucose peak (IGP; breakfast 2.30
±
0.19 vs. 1.71
±
0.12 mmol/L,
p<0.01, lunch 4.06
±
0.33 vs. 3.13
±
0.28 mmol/L, p<0.01, dinner 3.87
±
0.38 vs. 2.27
±
0.27 mmol/L,
p<0.001), and incremental area under the curve for glucose of dinner 2 h (IAUC; 256
±
30 vs. 128
±
18 mmol/L
×
min, p<0.001) for fast eating were all significantly higher than those for slow eating.
The results suggest that fast eating is associated with higher glycemic excursion in healthy women.
Keywords:
diet; eating speed; eating fast; glycemic excursion; postprandial glucose; flash glucose
monitoring; diabetes; obesity
1. Introduction
It has been demonstrated that self-reported eating at a fast speed leads to weight gain [
1
,
2
],
increases risk of type 2 diabetes [
3
–
5
], obesity [
6
,
7
], and metabolic syndrome [
8
–
10
] in epidemiological
and cohort studies. Eating fast encourages higher energy intake by increasing food stimuli, hunger,
and the desire to eat [
11
–
13
]. However, in epidemiological and retrospective reports, eating speed was
entirely assessed by self-reported questionnaire, which might lead to report bias and eating speed
might lack objectivity, leaving the effect of eating speed on glycemic response unclear. Additionally,
Nutrients 2020,12, 2767; doi:10.3390/nu12092767 www.mdpi.com/journal/nutrients
Nutrients 2020,12, 2767 2 of 9
an interventional study of the effect of eating speed on glycemic response has not been investigated.
Therefore, the rate of eating speed should be evaluated objectively by an interventional randomized
control study.
The aim of this study was to evaluate the acute effect of different eating speeds on glycemic
parameters in young healthy women with a flash glucose monitoring system (FGM, FreeStyle Libre Pro,
Abbott Japan, Tokyo, Japan). FGM is a new technology among continuous glucose monitoring systems
that does not require regular capillary glucose sampling by finger prick (self-monitored blood glucose,
SMBG) like traditional continuous glucose monitoring (CGM). The FGM sensor stores interstitial
fluid glucose levels every 15 min for 14 days and was reported to be accurate and effectively replaced
SMBG [14–16].
2. Materials and Methods
2.1. Participants
University students were recruited from Kyoto Women’s University, Kyoto, Japan, after being
informed the study requirements. Twenty-one participants were enrolled in the study and 2 participants
were excluded because of discontinuation of the study protocol of eating speed. The study was
conducted between December 2019 and February 2020. The volunteers had no history of any metabolic
diseases. None of the volunteers were pregnant, smokers, had an eating disorder, weight loss,
or followed any other special diet in the previous 6 months, and they refrained from taking any
medications and supplements known to affect their metabolism. The purpose, design, and risks of this
study were explained to each participant and written informed consent was obtained prior to the study.
2.2. Study Design
The study was designed as a randomized controlled two-treatment cross-over within-participant
clinical study to avoid the characteristic differences of the two groups in different eating speed.
The study protocol involving human subjects was approved by the Ethics Committee of Kyoto
Women’s University (2019-8) according to the guidelines laid down in the Declaration of Helsinki and
was registered at the UMIN Clinical Trials Registry (UMIN 0000038684). All participants consumed
identical test meals for two days, which were consumed at two different eating speeds during the 6 day
study period, as shown in Figure 1. The study protocol was explained to each participant prior and
during the study; the participants were reminded to follow the protocol by phone for thorough eating
procedure control. The participants wore FGMs on the back of their left upper arm for 6 days under
the physician’s management at Kyoto Women’s University. Although the FGM can start recording the
glycemic parameters 1 hour after wearing, to obtain stable and accurate glycemic data, we did not start
the interventional study until the 4th day. On the 4th day, participants were instructed to consume
three meals either quickly (10 min) or slowly (20 min). On the 5th day, they were instructed to consume
each meal at the opposite speed. The order of the two eating speeds was determined randomly prior
to the beginning of the study. The eating protocol, the sequence of the dishes consumed, and time
required to consume them was defined in slow eating, as vegetables for 7 min, main dish for 7 min,
and rice/bread for 6 min (in total of 20 min) [
17
]. In fast eating, the dishes in the identical meal were
mixed to be consumed in a total of 10 min. On the 6th day, FGMs were removed by the participants
themselves under the physician’s instruction at Kyoto Women’s University. The data recorded in FGM
were extracted and daily glycemic parameters were compared within-participant between 2 days of
consuming identical meals at a different eating speed.
Nutrients 2020,12, 2767 3 of 9
Figure 1.
Study protocol. Participants consumed identical test meals for 2 days over the 6-day study
period with flash glucose monitors. Participants consumed test meals of breakfast at 07:00, lunch at
12:00, and dinner at 18:00 for fast eating (10 min) or slow eating (20 min) on the 4th or the 5th day in the
randomized controlled cross-over study. Red triangle—fast eating; white triangle—slow eating.
2.3. Test Meals
Table 1shows the composition and macronutrient content of the test meal. The frozen boxes
of fried fish and vegetable at lunch, and gluten-meat steak and vegetable at dinner were purchased
(Tokatsu Foods, Yokohama, Japan) and provided to the participants by the researchers. The rest of the
food was prepared by the participants according the brochure prepared by the dietitians. The frozen
food boxes for lunch and dinner were kept in the freezer until consumption. Test meals of 200 g of
boiled white rice and 90 g of white bread were measured exactly and heated by each participant before
consumption. Throughout the study period, the participants were allowed to consume, other than test
meals, only water, green tea, tea, and coffee without sugar nor milk. The participants were requested
to avoid alcohol and excessive physical activity for 2 days prior to the study and during the study
period. Each participant was instructed to follow the study protocol precisely during the study period
and the collected records of eating speed and the amounts of food were assessed for compliance of the
study protocol by the dietitians of the study group. The dietitians excluded the participants who did
not follow the protocol.
Table 1. The composition and macronutrient content of the test meals.
Meal
Energy Protein Fat Carbohydrate Fiber
Detail Content
(kcal) (g) (g) (g) (g)
Breakfast 437 18.2 12 70.1 5.8
White bread 90 g, tomato 100 g,
broccoli 60 g, milk 200 g, strawberry jam
(sugar free) 13 g
Lunch 624 25.1 11.5 104 8.1
Boiled white rice 200 g, frozen lunch box
of fried fish with vegetable, tomato 100 g,
spinach 80 g
Dinner 689 23.6 17.4 107.6 7.8
Boiled white rice 200 g, tomato 100 g,
frozen lunch box of gluten-meat steak with
vegetable, spinach 80 g with fried tofu 15 g
Total 1750 66.9 40.9 281.7 21.7
The macronutrient content of the test meals was calculated by computer software (Microsoft Excel Eiyokun for
Windows Ver.7.0, Kenpakusya, Tokyo, Japan).
2.4. Measurements
Two weeks before the study, anthropometric measurements and blood samples of participants
were collected in the morning after an overnight fast. Blood samples were examined in Rakuwakai Toji
Minami Hospital. Fasting plasma glucose concentration (FPG) was measured by amperometric methods.
Nutrients 2020,12, 2767 4 of 9
Hemoglobin A1c (HbA1c) levels were determined by high-performance liquid chromatography (HPLC).
The incremental area under the curves (IAUC) for glucose after breakfast, lunch, and dinner were
calculated from the baseline by the trapezoidal method. The parameters to evaluate glycemic variability
were the mean amplitude of glycemic excursions (MAGE) [
18
] and the standard deviation (SD) of
plasma glucose. These glycemic parameters were compared within-participant for 2 days of consuming
the identical meals with different eating speeds.
2.5. Sample Size and Statistical Analysis
A sample size of 14 participants in the current study provided 95% power to detect 5% difference in
postprandial glucose concentrations (G*Power 3.1, Heinrich-Heine-Universität Düsseldorf, Germany),
referring to our previous study of consuming test meals in different sequences in healthy women [
19
].
Twenty-one participants enrolled in the study. The primary outcome was postprandial glucose
concentration and the secondary outcomes were MAGE and IAUC for glucose. We could not confirm
normal distribution and homogeneity for all glycemic parameters by Shapiro–Wilk and Levene tests,
so we performed a paired comparison by the Wilcoxon matched-pairs signed-rank test, and p<0.05 was
considered statistically significant. The results are expressed as mean
±
standard error of the mean
(SEM) unless otherwise stated. All analyses were performed with SPSS Statistics ver. 22 software (IBM
Corp., Armonk, NY, USA). The composition and macronutrient content of the menu of the test meals
was shown in Table S1.
3. Results
The results were based on 19 women (20.8
±
0.6 years, BMI 20.6
±
1.9 kg/m
2
, HbA1c 34
±
2 mmol/mol
(5.4
±
0.2%), FPG 4.86
±
0.39 mmol/L: mean
±
SD). Figure 2demonstrates the postprandial glucose
profiles for two different eating speeds in young healthy women. The SD, MAGE, incremental glucose
peak (IGP) of breakfast, lunch, and dinner, and IAUC for glucose 2 h after dinner for fast eating
(10 min) were all significantly higher compared to those for slow eating (20 min), as described in
Table 2. Although, the mean plasma glucose concentration showed no difference between the 2 days of
fast and slow eating.
Figure 2.
The mean plasma glucose profiles of fast and slow eating in healthy women (n=19).
Each participant consumed identical meals for fast eating (10 min) or slow eating (20 min) on the 4th or
the 5th day. Red solid line—fast eating; black dotted line—slow eating.
Nutrients 2020,12, 2767 5 of 9
Table 2. Characteristics of glycemic parameters of fast or slow eating in healthy women (n=19).
Glycemic Parameters Fast Eating (10 min) Slow Eating (20 min)
Mean plasma glucose concentration (mmol/L) 4.76 ±0.11 4.79 ±0.12
SD of plasma glucose concentration (mmol/L) 1.18 ±0.10 * 0.92 ±0.06
MAGE (mmol/L) 3.67 ±0.31 ** 2.67 ±0.20
IGP after breakfast (mmol/L) 2.30 ±0.19 ** 1.71 ±0.12
IGP after lunch (mmol/L) 4.06 ±0.33 ** 3.13 ±0.28
IGP after dinner (mmol/L) 3.87 ±0.38 *** 2.27 ±0.27
IAUC for glucose of breakfast 0–120 min (mmol/L
×
min)
111 ±10 107 ±8
IAUC for glucose of lunch 0–120 min (mmol/L×min) 265 ±28 216 ±21
IAUC for glucose of dinner 0–120 min (mmol/L×min) 256 ±30 *** 128 ±18
Data are mean
±
SEM. SD—standard deviation of plasma glucose concentration; MAGE—mean amplitude of
glycemic excursion; IGP—incremental glucose peak; IAUC—incremental area under the curve. The mean plasma
glucose, SD, and MAGE were calculated from 7:00 to 7:00 in the following day. The IAUCs for glucose of each meal
were calculated by the trapezoidal method. * p<0.05, ** p<0.01, *** p<0.001.
4. Discussion
To the best of our knowledge, this is the first interventional study to investigate the association
between eating speed and glycemic parameters by FGM. The results of this study suggest the possibility
that fast eating induces higher postprandial glucose concentrations and higher daily glycemic excursions
in young healthy women. Numerous studies reported that fast eating was associated with increased
body weight and overeating [
1
,
2
,
6
,
7
,
11
–
13
,
20
], elevated blood pressure and fasting plasma glucose
concentration [
8
,
10
], increased insulin resistance [
4
], and increased the risk of impaired glucose
tolerance and type 2 diabetes [
3
,
5
]. It has also been pointed out the association between fast eating and
lipid abnormality, such as elevated plasma triglyceride and reduced plasma HDL concentration [
10
,
21
].
However, it is important to mention that the extent of eating speed in these previous reports was
assessed subjectively, such as using questionnaires answered by participants themselves, which might
yield bias in evaluating the effect of eating speed on physiological parameters. The interventional
method used in the present study is expected to evaluate objectively the effect of eating speed on
glycemic parameters.
On the other hand, several reasons may be that eating slowly may exert its beneficial effect
by enhancing diet-induced thermogenesis (DIT), increasing serum adiponectin concentration,
and suppressing endotoxin and non-esterified fatty acid, as reported previously [
22
]. It has
been reported that interleukin-1
β
and interleukin-6, which are both involved in insulin resistance,
are decreased in individuals with slow eating [
23
,
24
], indicating the link between eating speed
and glycemic parameters observed in the present study. Moreover, eating slowly may influence
gastrointestinal satiety hormones, such as ghrelin and peptide tyrosine-tyrosine (PYY) which control
appetite and influence food consumption, suggesting that modifiable eating behaviors actually regulate
the hormonal response to food [
25
]. In contrast, Shah M et al. reported that eating speed could
not be explained by the changes in meal-related hormones. In their study, eating breakfast slowly
(30 min) and quickly (10 min) did not affect postprandial gut hormone responses such as ghrelin,
glucagon-like-peptide-1 (GLP-1), PYY, nor hunger and daily food consumption [
26
]. In the present
study, IAUC for glucose of breakfast and lunch demonstrated no difference, possibly because the
secretion of the incretin hormones might not be affected by eating speed in the daytime. Additionally,
another possible reason was that the energy ratio of dinner was large (40%) compared to that of
breakfast (25%) and of lunch (35%). We employed this energy ratio to design the three test meals
according to the general meal plans of Japanese dietary habits. However, as there are controversial
reports in association with gastrointestinal hormones and eating speed, the mechanisms underlying
the association with eating speed and metabolic responses needs to be verified in further studies.
The strength of our study is that this is the first randomized controlled cross-over interventional
study to explore the association between eating speed and the glycemic responses in healthy Japanese
Nutrients 2020,12, 2767 6 of 9
women. Postprandial hyperglycemia and higher MAGE are associated with increased risk of type
2 diabetes and cardiovascular diseases in people with and without diabetes [
27
–
29
]. It is reported
that the large blood glucose fluctuation not only increases tumor necrosis factor-
α
, interleukin-6 [
30
],
and platelet aggregability [
31
], but also decreases endothelium dependent vasodilation [
32
] even before
the onset of diabetes. Therefore, decreasing postprandial glucose concentrations and MAGE may
reduce the risks of developing impaired glucose tolerance, type 2 diabetes, and cardiovascular diseases
in people without diabetes.
Some limitations to the present study should be mentioned. First, the present study is an acute
interventional study, therefore, it is unable to translate all these effects on glycemic responses to
long-term benefits. Second, the participants of this study consisted only of young healthy Japanese
women who experience a diet and lifestyle specific to Japan. Therefore, we should be cautious to apply
our results to individuals with other gender, race, genetic backgrounds, and lifestyle, and individuals
with diabetes. For the third, because how the metabolism is regulated by eating speed is not fully
understood, the role of insulin, incretin hormones, cytokines, and endogenous glucose production
on significance of eating speed is still unclear. Fourth, the eating protocol between fast eating (eating
sequentially) and slow eating (eating at once as a mixture) in this study was not exactly the same,
leaving possibility that the difference in the eating protocol contributed to the results. As the fifth
limitation, psychological conditions such as satiety may have influenced the glycemic responses,
but we did not measure the extent of satiety between the study days of fast and slow eating. It has been
reported that eating fast tended to increase the amount of food intake by suppressing satiety [
11
–
13
].
Since there was no difference in the amount of food taken between study days of fast and slow eating in
our study, how and/or whether satiety influenced the glycemic parameters observed in our study is left
unanswered. The sixth limitation was the possibility that participants might not have followed strictly
the study protocol. Although, we had thoroughly and repeatedly explained and instructed the protocol
prior to and during the study period to maintain high adherence. Therefore, further study needs to
explore the comparison between fast eating and slow eating on glycemic responses when meals were
consumed in the same manner, with the meal sequence of vegetable, main dish, and rice/bread.
The disadvantage of eating fast shown in this study brings the possibility of raising the risk of
type 2 diabetes and obesity in healthy young individuals. Eating slowly is potentially advantageous
to public health, because the modification of eating speed could be cost effective for promoting
management of body weight and glycemic control [
33
,
34
]. Dietary education on the benefit of eating
slow could be a simple way to reduce excess food intake, since eating fast leads to higher energy
intake but lower satiety [
11
–
13
]. One practical approach that can be done for promoting slow eating
behavior is to try eating slowly at lunch breaks in schools or workplaces. However, in future studies,
additional investigations are required to explain the mechanisms under these effects and the long-term
effects of eating speed on metabolic control in individuals with and without diabetes.
5. Conclusions
To summarize, we have shown that eating fast is associated with higher daily glycemic excursions
and postprandial glucose concentrations in a randomized controlled cross-over trial for the first
time. Real-world approaches are needed to better understand the negative influence of eating fast on
glycemic responses and to support approaches for slowing down eating speed in healthy individuals.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2072-6643/12/9/2767/s1,
Table S1. The composition and macronutrient content of the menu of the test meals.
Author Contributions:
Y.S. contributed to recruit participants, performed the data analysis, and contributed
to the writing of the article. S.K. (Shizuo Kajiyama) conducted the experiments, contributed to the discussion,
and reviewed the article. A.N. and T.M. performed the data analysis and contributed to the writing of the article.
S.M., N.O., S.K. (Shintaro Kajiyama), Y.H., and M.F. contributed to the discussion and reviewed the article. S.I.
designed the study, recruited participants, conducted the experiments, performed the data analysis, and wrote the
article. S.I. and S.K. (Shintaro Kajiyama) are guarantors of this work and, as such, had full access to all the data in
Nutrients 2020,12, 2767 7 of 9
the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors
have read and agreed to the published version of the manuscript.
Funding:
This study was supported by JSPS KAKENHI Grant Number 20K11569 and grants from Kyoto
Women’s University.
Acknowledgments: We thank all the investigators and volunteers for participating in this study.
Conflicts of Interest:
Y.H. reports grants from Asahi Kasei Pharma and personal fees from Mitsubishi Tanabe
Pharma Corp., Novo Nordisk Pharma Ltd., Sanofi K.K., and Daiichi Sankyo Co. Ltd. outside the submitted
work. M.F. received grants from Takeda Pharma Co. Ltd., Sanofi K.K., Kissei Phama Co. Ltd., Mitsubishi Tanabe
Pharma Corp, Astellas Pharma Inc., Nippon Boehringer Ingelheim Co. Ltd., Daiichi Sankyo Co. Ltd., MSD K.K.,
Sanwa Kagagu Kenkyusho CO., Ltd., Kowa Pharma Co. Ltd., Kyowa Kirin Co., Ltd., Sumitomo Dainippon
Pharma Co., Ltd., Novo Nordisk Pharma Ltd., Ono Pharma Co. Ltd., Eli Lilly Japan K.K., Taisho Pharma Co., Ltd.,
Tejin Pharma Ltd., Nippon Chemiphar Co., Ltd., Johnson & Johnson k.k. Medical Co., Abbott japan Co. Ltd.,
and Terumo Corp., and personal fees from Teijin Pharma Ltd., Arkray Inc., Kissei Pharma Co., Ltd., Novo Nordisk
Pharma Ltd., Mitsubishi Tanabe Pharma Corp., Sanofi K.K., Takeda Pharma Co. Ltd., Astellas Pharma Inc.,
MSD K.K., Kyowa Kirin Co. Ltd., Sumitomo Dainippon Pharma Co. Ltd., Daiichi Sankyo Co. Ltd., Kowa Pharma
Co. Ltd., Ono Pharma Co. Ltd., Sanwa Kagaku Kenkyusho Co. Ltd., Nippon Boehringer Ingelheim Co., Ltd.,
Taisho Pharma Co., Ltd., Bayer Yakuhin, Ltd., AstraZeneca K.K., Mochida Pharma Co. Ltd., Abbott japan Co. Ltd.,
Eli Lilly Japan K.K., Medtronic Japan Co. Ltd., and Nipro Corp. outside the submitted work. The sponsors were
not involved in the study design; in the collection, analysis, interpretation of data; in the writing of this manuscript;
or in the decision to submit the article for publication. The authors, their immediate families, and any research
foundations with which they are affiliated have not received any financial payments or other benefits from any
commercial entity related to the subject of this article. The authors declare that although they are affiliated with a
department that is supported financially by a pharmaceutical company, the authors received no current funding
for this study and this does not alter their adherence to all the journal policies on sharing data and materials.
The other authors have nothing to disclose.
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