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Eating Fast Has a Significant Impact on Glycemic Excursion in Healthy Women: Randomized Controlled Cross-Over Trial

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Nutrients
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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.
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nutrients
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 eect 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 dierent 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 eect 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 eect 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 eect of dierent 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 eectively replaced
SMBG [1416].
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 aect 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 dierences of the two groups in dierent 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 dierent 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 dierent 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 coee 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 dierent 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% dierence in
postprandial glucose concentrations (G*Power 3.1, Heinrich-Heine-Universität Düsseldorf, Germany),
referring to our previous study of consuming test meals in dierent 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 dierent 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 dierence 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 eect of eating speed on physiological parameters. The interventional
method used in the present study is expected to evaluate objectively the eect of eating speed on
glycemic parameters.
On the other hand, several reasons may be that eating slowly may exert its beneficial eect
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 aect 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 dierence, possibly because the
secretion of the incretin hormones might not be aected 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 eects 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 dierence 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 dierence 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 eective 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 eects and the long-term
eects 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 aliated 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 aliated 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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... Other meal-related factors impact PPGR: meal sequence and timing, 26 meal duration 27 and sequence of nutrient ingestion. 8 Targeted manipulations of these factors demonstrate their effect on PPGR. ...
... For example, a more rapid eating rate is associated with higher glucose excursions in healthy women. 27 In our study, the observed duration of the research breakfast meal, relatively short, was not associated with PPGR. ...
Article
Objective Post‐prandial glucose response (PPGR) is a risk factor for cardiovascular disease. Meal carbohydrate content is an important predictor of PPGR, but dietary interventions to mitigate PPGR are not always successful. A personalized approach, considering behaviour and habitual pattern of glucose excursions assessed by continuous glucose monitor (CGM), may be more effective. Research Design and Methods Data were collected under free‐living conditions, over 2 weeks, in older adults (age 60 ± 7, BMI 33.0 ± 6.6 kg/m ² ), with prediabetes ( n = 35) or early onset type 2 diabetes ( n = 3), together with sleep and physical activity by actigraphy. We assessed the predictive value of habitual CGM glucose excursions and fasting glucose on PPGR after a research meal (hereafter MEAL‐PPGR) and during an oral glucose tolerance test (hereafter OGTT‐PPGR). Results Mean amplitude of glucose excursions (MAGE) and fasting glucose were highly predictive of all measures of OGTT‐PPGR (AUC, peak, delta, mean glucose and glucose at 120 min; R ² between 0.616 and 0.786). Measures of insulin sensitivity and β‐cell function (Matsuda index, HOMA‐B and HOMA‐IR) strengthened the prediction of fasting glucose and MAGE ( R ² range 0.651 to 0.832). Similarly, MAGE and premeal glucose were also strong predictors of MEAL‐PPGR ( R ² range 0.546 to 0.722). Meal carbohydrates strengthened the prediction of 3 h AUC ( R ² increase from 0.723 to 0.761). Neither anthropometrics, age nor habitual sleep and physical activity added to the prediction models significantly. Conclusion These data support a CGM‐guided personalized nutrition and medicine approach to control PPGR in older individuals with prediabetes and diet and/or metformin‐treated type 2 diabetes.
... However, several previous studies indicated that eating at fast speed, herein reported "fast eating", was an unhealthy habit that contributed to multiple metabolic disorders [7][8][9], especially overweight and obesity [10,11]. Individuals who ate fast were more likely to suffer from glycemic excursion [12] and develop newly-onset diabetes [13]. Furthermore, fast eating was also associated with elevated alanine aminotransferase (ALT) according to previous studies [14]. ...
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Background With the fast pace of modern life, people have less time for meals, but few studies have examined the association between the habit of fast eating and metabolic diseases. Objective Combining the results of the current study and the prior ones, we aimed to investigate the possible relationship between fast eating and the risk of metabolic dysfunction-associated steatotic liver disease (MASLD). Methods This is a sub-analysis of a multicenter cross-sectional study of 1965 participants investigated the association between fast eating and MASLD in Chinese. Fast eating was defined as meal time less than five minutes and participants were divided into three categories based on their self-reported frequency of fast eating: ≤1 time/month, ≤1 time/week and ≥2 times/week. We further conducted a literature search for available studies published before November, 2023 as well as a meta-analysis to investigate the association between fast eating and MASLD. Results The proportion of MASLD was 59.3%, 50.5%, and 46.2% in participants with fast eating ≥2 times/week, ≤1 time/week and ≤1 time/month, respectively (P for trend <0.001). The frequency of fast eating was independently associated with risk of MASLD after multiple adjustment for sex, age, demographics, smoking and drinking status, BMI and clinical metabolic parameters (OR, 1.29; 95%CI, 1.09–1.53). Participants who ate fast frequently (≥2 times/week) had 81% higher risk of MASLD (P = 0.011). A meta-analysis of five eligible studies confirmed that frequent fast eating was associated with increased risk of MASLD (pooled OR, 1.22; 95%CI, 1.07–1.39). Conclusions Frequent fast eating was associated with an increased risk of MASLD.
... However, three of the five patients with BN had hyperglycemia occurrences at least once during the 5-day monitoring period. Previous studies have reported that postprandial hyperglycemia is associated with eating and meal sequencing [29][30][31]. In the DSM-5, the definition of binge eating is 'eating, in a discrete period (e.g., within any two-hour period) an amount of food that is definitely larger than most people would eat in a similar period under similar circumstances' [1]. ...
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Background The aim of this study was to investigate the relationships between hypoglycemia, hyperglycemia, glycemic variability (GV), and eating behavior by measuring daily glucose levels through an intermittently scanned continuous glucose monitoring (isCGM) system in outpatients classified according to eating disorder subtypes. Methods We analyzed data for 18 patients (four ANR, nine ANBP, and five BN cases). A FreeStyle Libre Pro® device was attached to the posterior aspect of the upper arm for glucose monitoring. This device conducted measurements every 15 min for five consecutive days. We estimated the mean amplitude of glycemic excursions (MAGE), hypoglycemia, and hyperglycemia. Results The mean glucose levels were 91.1 ± 2.2 mg/dL in the ANR group, 94.8 ± 7.5 mg/dL in the ANBP group, and 87.1 ± 8.0 mg/dL in the BN group (P = 0.174). The overall mean MAGE index was 52.8 ± 20.5 mg/dL. The mean MAGE values according to the subtypes were 42.2 ± 5.6 mg/dL in the ANR group, 57.4 ± 23.7 mg/dL in the ANBP group, and 53.0 ± 21.8 mg/dL in the BN group (P = 0.496). Over the course of five days, the frequency of hypoglycemia was as follows: three occurrences in the ANBP group, five occurrences in the BN group, and no occurrences in the ANR group (P = 0.016). Moreover, the occurrence of hypoglycemia was statistically significantly higher in the BN group than in the ANR group (P = 0.013). In the BN group, the frequency of hypoglycemia was highest between 2 and 6 AM, while hypoglycemia was observed throughout the day in the ANBP group. The frequency of hyperglycemia was one occurrence in the ANR group, one occurrence in the BN group, and zero occurrences in the ANBP group (P = 0.641). Conclusions Varying GV, hypoglycemia, and hyperglycemia were observed in all subtypes of eating disorders. Our findings suggest that eating behaviors such as binge eating and purging are associated with GV and hypoglycemia. We showed the importance of developing different nutritional approaches tailored to the subtype of eating disorder to prevent hypoglycemia. Additional studies are needed to explore the relationship between glucose levels and eating behaviors in patients with eating disorders.
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Introduction Contradictory claims about the efficacy of several medicinal plants to promote glycemic control in patients with type 2 diabetes mellitus (T2DM) have been explained by divergences in the administration form and by extrapolation of data obtained from healthy individuals. It is not known whether the antidiabetic effects of traditional herbal medicines are influenced by gelatin capsules. This randomized crossover trial aimed to evaluate the acute effect of a single dose of raw cinnamon consumed orally either dissolved in water as a beverage or as ordinary hard gelatin capsules on postprandial hyperglycemia (>140 mg/dL; >7.8 mmol/L) in T2DM patients elicited by a nutritionally-balanced meal providing 50 g of complex carbohydrates. Methods Fasting T2DM patients (n = 19) randomly ingested a standardized meal in five experimental sessions, one alone (Control) and the other after prior intake of 3 or 6 g of crude cinnamon in the form of hard gelatin capsules or powder dissolved in water. Blood glucose was measured at fasting and at 0.25, 0.5, 0.75, 1, 1.5 and 2 hours postprandially. After each breakfast, its palatability scores for visual appeal, smell and pleasantness of taste were assessed, as well as the taste intensity sweetness, saltiness, bitterness, sourness and creaminess. Results The intake of raw cinnamon dissolved in water, independently of the dose, decreased the meal-induced large glucose spike (peak-rise of +87 mg/dL and Δ1-hour glycemia of +79 mg/dL) and the hyperglycemic blood glucose peak. When cinnamon was taken as capsules, these anti-hyperglycemic effects were lost or significantly diminished. Raw cinnamon intake did not change time-to-peak or the 2-h post-meal glycaemia, but flattened the glycemic curve (lower iAUC) without changing the shape that is typical of T2DM patients. Conclusions This cinnamon’s antihyperglycemic action confirms its acarbose-like property to inhibit the activities of the carbohydrate-digesting enzymes α-amylases/α-glucosidases, which is in accordance with its exceptionally high content of raw insoluble fiber. The efficacy of using raw cinnamon as a diabetes treatment strategy seems to require its intake at a specific time before/concomitantly the main hyperglycemic daily meals. Trial registration: Registro Brasileiro de Ensaios Clínicos (ReBEC), number RBR-98tx28b.
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The number of people experiencing loneliness is increasing, and loneliness has been reported to negatively impact health. Social communication has been proposed as a means of coping with loneliness, and socializing can reduce stress levels and improve glycemic control, although the temporal effects have not been well studied. Stress responses vary depending on the relationship with the partner, and interacting with strangers can cause higher stress levels. This study used a 2-h oral glucose tolerance test to investigate the short-term effects of social communication on biological indicators, including postprandial blood glucose levels. Three conditions were tested: no communication, friend communication, and stranger communication. Measurements included blood sugar levels, positive and negative emotions, and cortisol as an objective stress indicator. Interacting with friends reduced negative emotions, suppressed stress, lowered cortisol levels, and suppressed postprandial hyperglycemia. Conversely, interacting with strangers increased stress, cortisol, and postprandial blood sugar levels. Overall, social communication temporally influenced postprandial blood glucose levels. Socializing with friends had a positive impact on health by suppressing postprandial hyperglycemia associated with illness.
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Objectives Diabetes is a complex condition that often requires the simultaneous employment of diverse approaches for prevention and treatment. Mindful eating may be a beneficial complementary approach. This narrative review analyzes potential mechanisms of action through which mindful eating may regulate blood glucose and thereby aid in diabetes prevention and management. Findings from this review may serve to inform both clinical practice and new research in the field. Method We conducted a narrative review focusing on the meditation-independent mechanisms by which mindful eating could improve blood glucose regulation. Specifically, we analyzed the effects of mindful eating practices on eating behavior, calorie intake, weight control, and/or glucose control. Results Evidence suggests that mindful eating can improve eating behaviors by decreasing automatic and disordered eating which, in turn, may regulate blood glucose levels. Moreover, by eating slowly, attentively, and according to hunger and satiety cues, mindful eating may help align energy intake to energy needs, thereby improving weight and glycemic management. Conclusion Key mindful eating practices that may directly or indirectly improve glycemic management include eating slowly, eating with deliberate attention to the sensory properties of food, cultivating acceptance of thoughts and feelings concerning food and eating, decentering from food-related thoughts, and relying on hunger and satiety cues to guide eating. Future research may improve our knowledge of these interventions and their application to the prevention and treatment of diabetes.
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Aims The excess deposition of intra-pancreatic fat deposition (IPFD) has been reported to be associated with type 2 diabetes, chronic pancreatitis, and pancreatic ductal adenocarcinoma. In the current study, we aimed to identify a relationship between lifestyle factors and IPFD. Materials and methods 99 patients admitted to the Osaka University Hospital who had undergone abdominal computed tomography were selected. We evaluated the mean computed tomography values of the pancreas and spleen and then calculated IPFD score. Multiple regression analyses were used to assess the associations between IPFD score and lifestyle factors. Results Fast eating speed, late-night eating, and early morning awakening were significantly associated with a high IPFD score after adjusting for age, sex, diabetes status and Body Mass Index (p=0.04, 0.01, 0.01, respectively). Conclusion The current study has elucidated the significant associations of fast eating speed, late-night eating, and early morning awakening with IPFD.
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People with fast eating habits have been reported to have an increased risk of diabetes and obesity. To explore whether the speed of eating a test meal (tomato, broccoli, fried fish, and boiled white rice) influences postprandial blood glucose, insulin, triglyceride, and free fatty acid levels, 18 young, healthy women consumed a 671 kcal breakfast at fast speed (10 min) and slow speed (20 min) with vegetables first and slow speed (20 min) with carbohydrate first on three separate days. This study was conducted using a within-participants cross-over design in which all participants consumed identical meals of three different eating speeds and food orders. Significant ameliorations of both fast and slow eating with vegetables first regimen on postprandial blood glucose and insulin levels at 30 and 60 min were observed compared with those of slow eating with carbohydrates first. In addition, the standard deviation, large amplitude of excursion, and incremental area under the curve for blood glucose and insulin in both fast and slow eating with vegetables first were all significantly lower than those of slow eating with carbohydrate first. Interestingly, there was no significant difference between fast and slow eating on postprandial blood glucose and insulin levels as long as vegetables were consumed first, although postprandial blood glucose at 30 min was significantly lower in slow eating with vegetables first than that of fast eating with the same food order. These results suggest that food order with vegetables first and carbohydrate last ameliorates postprandial blood glucose and insulin concentrations even if the meal was consumed at fast speed.
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This study aimed to examine the relationship between eating speed and hemoglobin A1c (HbA1c), considering the number of teeth, using cross-sectional health examination data from community-dwelling older individuals in Japan. We used data from the Center for Community-Based Healthcare Research and Education Study in 2019. We collected data on gender, age, body mass index, blood test results, Salt intake, bone mineral density, body fat percentage, muscle mass, basal metabolic rate, number of teeth, and lifestyle information. Eating speed was evaluated subjectively as fast, normal, or slow. Overall, 702 participants were enrolled in the study and 481 participants were analyzed. Multivariate logistic regression analysis revealed a significant association between fast eating speed and being a male (odds ratio [95% confidence interval]: 2.15 [1.02–4.53]), HbA1c (1.60 [1.17–2.19]), salt intake (1.11 [1.01–1.22]), muscle mass (1.05 [1.00–1.09]), and enough sleep (1.60 [1.03–2.50]). Fast eating may be associated with overall health and lifestyle. The characteristics of fast eaters, after taking oral information into consideration, tended to increase the risk of type 2 diabetes, renal dysfunction, and hypertension. Dental professionals should provide dietary and lifestyle guidance to fast eaters.
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Background: A greater time spent with glucose above the normal range (TAR) has been associated with poorer glycaemic control amongst pre-diabetic individuals. Individual differences in oral processing behaviours and saliva amylase activity have been shown to influence glucose responses. Objective: The current study is a preliminary exploration of the associations of oral processing behaviours, bolus characteristics, and salivary amylase activity with the variability in daily glucose excursions within a free-living setting in populations with an elevated risk of type-2 diabetes. Method: Participant oral processing behaviour was derived from video recordings while they consumed a test meal. Post-meal bolus characteristics and saliva properties were measured. Participants were fitted with a continuous glucose monitor (CGM) which monitored blood glucose fluctuation over 7 consecutive free-living days. Dietary intake was recorded through a smartphone application and physical activity was monitored using a wrist worn accelerometer. Results: Participants varied in daily time spent with glucose above the normal range (>7.8 mmol l-1) from 0% to 15%. Greater saliva uptake in the bolus was associated with a higher time spent above the normal range for glucose (β = 0.067 [95% CI = 0.015, 0.120]; p < 0.05), which remained significant after adjustment for dietary carbohydrate intake and BMI. Salivary amylase and saliva flow rate were not significantly associated with the time spent above the normal range. Conclusion: In addition to conventional dietary factors, more research is needed to understand how eating behaviours such as oro-sensory exposure, bolus surface area, and saliva uptake contribute to daily variations in postprandial glucose excursions among populations with a higher risk of developing type-2 diabetes.
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Background: Research on the relationship between metabolic syndrome (MetS), its components and eating speed is limited in China. The present study aimed to clarify the association between MetS, its components and eating speed in a Beijing adult population. Methods: This cross-sectional study included 7972 adults who were 18-65 years old and who received health check-ups at the Beijing Physical Examination Center in 2016. Logistic regression was conducted to explore the associations between MetS, its components and eating speed. Results: The prevalence of MetS in this population was 24.65% (36.02% for males and 10.18% for females). Eating speed was significantly associated with a high risk for MetS, elevated blood pressure, and central obesity for both genders. Eating speed was associated with a high risk for elevated triglycerides and with a reduction in high-density lipoprotein in males, and eating speed was associated with a high risk for elevated fasting plasma glucose in females. Compared with slow eating speed, the multivariate-adjusted odds ratios of medium eating speed and fast eating speed for MetS were 1.65 (95% confidence interval 1.32-2.07) and 2.27 (95% confidence interval 1.80-2.86) for all subjects, 1.58 (95% confidence interval 1.21-2.07) and 2.21 (95% confidence interval 1.69-2.91) for males, and 1.75 (95% confidence interval 1.15-2.68) and 2.27 (95% confidence interval 1.46-3.53) for females, respectively. Conclusions: Eating speed is positively associated with MetS and its components. Future recommendations aiming to prevent MetS and its components may focus on eating speed.
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Objective Few studies have examined the causal relationships between lifestyle habits and obesity. With a focus on eating speed in patients with type 2 diabetes, this study aimed to analyse the effects of changes in lifestyle habits on changes in obesity using panel data. Methods Patient-level panel data from 2008 to 2013 were generated using commercially available insurance claims data and health check-up data. The study subjects comprised Japanese men and women (n=59 717) enrolled in health insurance societies who had been diagnosed with type 2 diabetes during the study period. Body mass index (BMI) was measured, and obesity was defined as a BMI of 25 or more. Information on lifestyle habits were obtained from the subjects’ responses to questions asked during health check-ups. The main exposure of interest was eating speed (‘fast’, ‘normal’ and ‘slow’). Other lifestyle habits included eating dinner within 2 hours of sleeping, after-dinner snacking, skipping breakfast, alcohol consumption frequency, sleep adequacy and tobacco consumption. A generalised estimating equation model was used to examine the effects of these habits on obesity. In addition, fixed-effects models were used to assess these effects on BMI and waist circumference. Results The generalised estimating equation model showed that eating slower inhibited the development of obesity. The ORs for slow (0.58) and normal-speed eaters (0.71) indicated that these groups were less likely to be obese than fast eaters (P<0.001). Similarly, the fixed-effects models showed that eating slower reduced BMI and waist circumference. Relative to fast eaters, the coefficients of the BMI model for slow and normal-speed eaters were −0.11 and −0.07, respectively (P<0.001). Discussion Changes in eating speed can affect changes in obesity, BMI and waist circumference. Interventions aimed at reducing eating speed may be effective in preventing obesity and lowering the associated health risks.
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Aims/hypothesis: Evidence for the effectiveness of interstitial glucose monitoring in individuals with type 1 diabetes using multiple daily injection (MDI) therapy is limited. In this pre-specified subgroup analysis of the Novel Glucose-Sensing Technology and Hypoglycemia in Type 1 Diabetes: a Multicentre, Non-masked, Randomised Controlled Trial' (IMPACT), we assessed the impact of flash glucose technology on hypoglycaemia compared with capillary glucose monitoring. Methods: This multicentre, prospective, non-masked, RCT enrolled adults from 23 European diabetes centres. Individuals were eligible to participate if they had well-controlled type 1 diabetes (diagnosed for ≥5 years), HbA1c ≤ 58 mmol/mol [7.5%], were using MDI therapy and on their current insulin regimen for ≥3 months, reported self-monitoring of blood glucose on a regular basis (equivalent to ≥3 times/day) for ≥2 months and were deemed technically capable of using flash glucose technology. Individuals were excluded if they were diagnosed with hypoglycaemia unawareness, had diabetic ketoacidosis or myocardial infarction in the preceding 6 months, had a known allergy to medical-grade adhesives, used continuous glucose monitoring (CGM) within the previous 4 months or were currently using CGM or sensor-augmented pump therapy, were pregnant or planning pregnancy or were receiving steroid therapy for any disorders. Following 2 weeks of blinded (to participants and investigator) sensor wear by all participants, participants with sensor data for more than 50% of the blinded wear period (or ≥650 individual sensor results) were randomly assigned, in a 1:1 ratio by a central interactive web response system (IWRS) using the biased-coin minimisation method, to flash sensor-based glucose monitoring (intervention group) or self-monitoring of capillary blood glucose (control group). The control group had two further 14 day blinded sensor-wear periods at the 3 and 6 month time points. Participants, investigators and staff were not masked to group allocation. The primary outcome was the change in time in hypoglycaemia (<3.9 mmol/l) between baseline and 6 months in the full analysis set. Results: Between 4 September 2014 and 12 February 2015, 167 participants using MDI were enrolled. After screening and the baseline phase, participants were randomised to intervention (n = 82) and control groups (n = 81). One woman from each group was excluded owing to pregnancy; the full analysis set included 161 randomised participants. At 6 months, mean time in hypoglycaemia was reduced by 46.0%, from 3.44 h/day to 1.86 h/day in the intervention group (baseline adjusted mean change, -1.65 h/day), and from 3.73 h/day to 3.66 h/day in the control group (baseline adjusted mean change, 0.00 h/day), with a between-group difference of -1.65 (95% CI -2.21, -1.09; p < 0.0001). For participants in the intervention group, the mean ± SD daily sensor scanning frequency was 14.7 ± 10.7 (median 12.3) and the mean number of self-monitored blood glucose tests performed per day reduced from 5.5 ± 2.0 (median 5.4) at baseline to 0.5 ± 1.0 (median 0.1). The baseline frequency of self-monitored blood glucose tests by control participants was maintained (from 5.6 ± 1.9 [median 5.2] to 5.5 ± 2.6 [median 5.1] per day). Treatment satisfaction and perception of hypo/hyperglycaemia were improved compared with control. No device-related hypoglycaemia or safety-related issues were reported. Nine serious adverse events were reported for eight participants (four in each group), none related to the device. Eight adverse events for six of the participants in the intervention group were also reported, which were related to sensor insertion/wear; four of these participants withdrew because of the adverse event. Conclusions/interpretation: Use of flash glucose technology in type 1 diabetes controlled with MDI therapy significantly reduced time in hypoglycaemia without deterioration of HbA1c, and improved treatment satisfaction. Trial registration: ClinicalTrials.gov NCT02232698 FUNDING: Abbott Diabetes Care, Witney, UK.
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IntroductionPublished evaluations of sensor glucose monitoring use in insulin treated type 2 diabetes are limited. The aim of this study was to assess the impact of flash glucose-sensing technology as a replacement for self-monitoring of blood glucose (SMBG) over a 12-month period in participants with type 2 diabetes who were on intensive insulin therapy. Methods An open-label, randomized, controlled study in adults with type 2 diabetes on intensive insulin therapy from 26 European diabetes centers aimed at assessing flash glucose sensing technology was conducted. Participants (N = 224) were randomized (1:2 respectively) to a control group (n = 75) that used SMBG (FreeStyle Lite™) or to an intervention group (n = 149) which used sensor glucose data (FreeStyle Libre™ Flash Glucose Monitoring System) for self-management over 6 months. All intervention group participants who completed the 6-month treatment phase continued into an additional 6-month open-access phase. ResultsA total of 139 intervention participants completed the 6-month treatment phase and continued into the open-access phase. At 12 months (end of open-access period), time in hypoglycemia [sensor glucose <3.9 mmol/L (70 mg/dL)] was reduced by 50% compared to baseline [−0.70 ± 1.85/24 h (mean ± standard deviation); p = 0.0002]. Nocturnal hypoglycemia [2300 to 0600 hours, <3.9 mmol/L (70 mg/dL)] was reduced by 52%; p = 0.0002. There was no change in time in range [sensor glucose 3.9–10.0 mmol/L (70–180 mg/dL)]. SMBG testing fell from a mean of 3.9 (median 3.9) times/day at baseline to 0.2 (0.0), with an average frequency of sensor scanning of 7.1 (5.7) times/day at 12 months, and mean sensor utilization was 83.6 ± 13.8% (median 88.3%) during the open-access phase. During this 6-month extension period no device-related serious adverse events were reported. Nine participants reported 16 instances of device-related adverse events (e.g. infection, allergy) and 28 participants (20.1%) experienced 134 occurrences of anticipated skin symptoms/sensor-insertion events expected with device use (e.g. erythema, itching and rash). Conclusion The use of flash glucose-sensing technology for glycemic management in individuals with type 2 diabetes treated by intensive insulin therapy over 12 months was associated with a sustained reduction in hypoglycemia and safely and effectively replaced SMBG. Trial RegistrationClinicalTrials.gov identifier, NCT02082184.
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Metabolic syndrome has received increased global attention over the past few years. Eating behaviors, particularly eating speed, have long been of interest as factors that contribute to the development of obesity and diabetes. The aim of this study was to assess the relationship between eating speed and incidence of metabolic syndrome among middle-aged and elderly Japanese people. A total of 8941 community residents from Soka City in Saitama Prefecture, aged from 40 to 75 years and without a diagnosis of metabolic syndrome, participated in the baseline survey in 2008 and were followed until 2011. Anthropometric measurements and lifestyle factors were measured at baseline and follow-up. The association between eating speed and incidence of metabolic syndrome was evaluated using Cox proportional hazards models adjusted for potential confounding variables. During the 3-year follow-up, 647 people were diagnosed with metabolic syndrome (25.0 cases/1000 person-years). The incidence rates of metabolic syndrome among non-fast-eating and fast-eating participants were 2.3% and 3.1%, respectively. The multivariate-adjusted hazard ratio for incidence of metabolic syndrome in the fast-eating group compared to the not-fast-eating group was 1.30 (95% confidence interval [CI], 1.05-1.60) after adjustment for the potential confounding factors. Eating speed was significantly correlated with waist circumference and high-density lipoprotein cholesterol (HDL-C) components of metabolic risk factors. Hazard ratios in the fast-eating group compared with the reference group were 1.35 (95% CI, 1.10-1.66) for waist circumference and 1.37 (95% CI, 1.12-1.67) for HDL-C. Eating speed was associated with the incidence of metabolic syndrome. Eating slowly is therefore suggested to be an important lifestyle factor for preventing metabolic syndrome among the Japanese.
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Objective Meal duration may influence cardiometabolic health. The aim of this study was to explore postprandial effects of meal duration on human metabolism and appetite.DesignPostprandial comparisons following a standard meal eaten slowly over 40-minutes (‘D40’) and the same meal eaten quickly over 10-minutes (‘D10’) on a different day. Each participant therefore acted as their own control, thereby limiting confounding factors.PatientsObese pre-menopausal Caucasian women (n=10) with confirmed normoglycaemia were recruited from an Obesity clinic at UHCW, Coventry UK. Subjects underwent whole-body calorimetry (8-hours) on two separate days.MeasurementsFollowing standard lunch (D40 vs D10), 4-hour postprandial analysis included thermic effect of food (TEF) and bloods taken at pre-defined times (including baseline fasting). Analytes included lipid-profile, adiponectin, insulin, glucose, ghrelin, leptin, endotoxin, gut and pancreatic hormones. Appetite was measured using visual-analogue scales and ad libitum food intake at subsequent meal. Paired-sample t-tests (including area under the curve [AUC]) were used to compare D40 and D10 trials.ResultsPostprandial TEF (over 240-minutes) was significantly greater for D40 than D10 (mean [SEM]: 80.9Kcal [3.8] versus 29.9Kcal [3.4]; 10.6% versus 3.9% respectively, P=0.006; AUC 71.7Kcal.hr versus 22.4Kcal.hr respectively, P=0.02). Postprandial plasma NEFA was significantly lower and adiponectin levels were significantly higher for D40 than D10 (AUC [SEM]: NEFA 627μmol.hr/l [56] vs 769μmol.hr/l [60] respectively, P=0.02; adiponectin 33.4μg.hr/ml [3.9] vs 27.3μg.hr/ml [3.8] respectively, P=0.04). Other postprandial analytes and appetite measures were equivalent.Conclusions In obese women, eating slowly associates with enhanced TEF, elevated serum adiponectin and suppressed NEFA.This article is protected by copyright. All rights reserved.
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Background: Tight control of blood glucose in type 1 diabetes delays onset of macrovascular and microvascular diabetic complications; however, glucose levels need to be closely monitored to prevent hypoglycaemia. We aimed to assess whether a factory-calibrated, sensor-based, flash glucose-monitoring system compared with self-monitored glucose testing reduced exposure to hypoglycaemia in patients with type 1 diabetes. Method: In this multicentre, prospective, non-masked, randomised controlled trial, we enrolled adult patients with well controlled type 1 diabetes (HbA1c ≤58 mmol/mol [7·5%]) from 23 European diabetes centres. After 2 weeks of all participants wearing the blinded sensor, those with readings for at least 50% of the period were randomly assigned (1:1) to flash sensor-based glucose monitoring (intervention group) or to self-monitoring of blood glucose with capillary strips (control group). Randomisation was done centrally using the biased-coin minimisation method dependent on study centre and type of insulin administration. Participants, investigators, and study staff were not masked to group allocation. The primary outcome was change in time in hypoglycaemia (<3·9 mmol/L [70 mg/dL]) between baseline and 6 months in the full analysis set (all participants randomised; excluding those who had a positive pregnancy test during the study). This trial was registered with ClinicalTrials.gov, number NCT02232698. Findings: Between Sept 4, 2014, and Feb 12, 2015, we enrolled 328 participants. After the screening and baseline phase, 120 participants were randomly assigned to the intervention group and 121 to the control group, with outcomes being evaluated in 119 and 120, respectively. Mean time in hypoglycaemia changed from 3·38 h/day at baseline to 2·03 h/day at 6 months (baseline adjusted mean change -1·39) in the intervention group, and from 3·44 h/day to 3·27 h/day in the control group (-0·14); with the between-group difference of -1·24 (SE 0·239; p<0·0001), equating to a 38% reduction in time in hypoglycaemia in the intervention group. No device-related hypoglycaemia or safety issues were reported. 13 adverse events were reported by ten participants related to the sensor-four of allergy events (one severe, three moderate); one itching (mild); one rash (mild); four insertion-site symptom (severe); two erythema (one severe, one mild); and one oedema (moderate). There were ten serious adverse events (five in each group) reported by nine participants; none were related to the device. Interpretation: Novel flash glucose testing reduced the time adults with well controlled type 1 diabetes spent in hypoglycaemia. Future studies are needed to assess the effectiveness of this technology in patients with less well controlled diabetes and in younger age groups. Funding: Abbott Diabetes Care.
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Objective: Various eating behaviors have been linked with body weight management. However, combined effects of major eating behaviors are not fully understood. This study aimed to clarify the association of the combination of eating quickly (EQ), late evening meals (LEM), and skipping breakfast (SB) with being overweight. Method: A cross-sectional study with standardized questions for EQ, LEM, and SB was conducted. Stratified random sampling of 5% of residents aged 20 to 80years was surveyed in a city in northeast Japan in 2011, and 4249 (84.9%) residents were analyzed. Association of combinations of eating behaviors on being overweight (BMI (kg/m(2)≥25.0)) was estimated by using logistic analysis, and odds ratio (OR) and 95% confidential interval were calculated after adjustment for potential covariates. Results: LEM, SB, or a combination of LEM and SB was not significantly associated with being overweight. However, the combination of EQ or only EQ was significantly associated with being overweight. As the number of eating behavior practices increased, there was a linear increase in OR for being overweight. The OR of all three combined eating behaviors was higher than that of any combined two behaviors or of each behavior. Discussion: This study result supports the evidence that EQ increases the risk of being overweight whether by itself or in combinations with LEM and/or SB. However, only LEM or only SB did not increase the risk of being overweight.
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Background: The effect of eating speed at a meal on appetite gut hormone responses and future food consumption is not clear. This study examined the effect of eating speed at breakfast on postprandial gut hormone responses, subjective appetite, and daily food consumption. Methods: Twenty-five participants [68% men; age, 25.9 (8.1) years; body mass index, 25.0 (3.2) kg/m] were recruited. Each participant consumed the same breakfast at a slow (30 minutes) and fast (10 minutes) speed, on 2 separate days, in a randomized crossover design. Blood samples were collected in the fasting state and 3 hours postprandially during each eating condition. Appetite was assessed over the same period using visual analog scales. Blood concentrations of orexigenic hormone, ghrelin, and anorexigenic hormones, glucagon-like peptide-1 (GLP-1) and peptide YY (PYY), were determined. Daily food intake was measured, by food recall, after the slow and fast breakfast. Results: Mixed-model repeated-measures analysis showed no eating condition or eating condition by time interaction effect on ghrelin, GLP-1, PYY, hunger, or fullness. Significant eating speed by time interaction effect on desire to eat was found (P=0.007). Desire to eat was lower at 60 minutes (P=0.007) after breakfast began during the slow versus fast eating condition. Eating speed at breakfast did not affect daily energy and macronutrient intake. Conclusions: Eating speed at breakfast did not affect postprandial ghrelin, GLP-1, PYY, hunger, and fullness values or daily energy and macronutrient intake. Desire to eat was lower at 60 minutes in the slow versus fast eating condition, but this result could not be explained by the changes in meal-related hormones measured in the study.