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Objective: Sedentary lifestyle increases the risk of type 2 diabetes. The aim of this study was to investigate the impact of different levels of energy turnover (ET; low, medium, and high level of physical activity and the corresponding energy intake) on glucose metabolism at zero energy balance, caloric restriction, and overfeeding. Methods: Sixteen healthy individuals (13 men, 3 women, 25.1 ± 3.9 years, BMI 24.0 ± 3.2 kg/m2) participated in a randomized crossover intervention under metabolic ward conditions. Subjects passed 3 × 3 intervention days. Three levels of physical activity (PAL: low 1.3, medium 1.6, and high 1.8 achieved by walking at 4 km/h for 0, 3 × 55, or 3 × 110 min) were compared under three levels of energy balance (zero energy balance (EB): 100% of energy requirement (Ereq); caloric restriction (CR): 75% Ereq, and overfeeding (OF): 125% Ereq). Continuous interstitial glucose monitoring, C-peptide excretion, and HOMA-IR, as well as postprandial glucose and insulin were measured. Results: Daylong glycemia and insulin secretion did not increase with higher ET at all conditions of energy balance (EB, CR, and OF), despite a correspondingly higher CHO intake (Δ low vs. high ET: +86 to 135 g of CHO/d). At CR, daylong glycemia (p = 0.02) and insulin secretion (p = 0.04) were even reduced with high compared with low ET. HOMA-IR was impaired with OF and improved with CR, whereas ET had no effect on fasting insulin sensitivity. A higher ET led to lower postprandial glucose and insulin levels under conditions of CR and OF. Conclusion: Low-intensity physical activity can significantly improve postprandial glycemic response of healthy individuals, independent of energy balance.
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Büsing et al. Nutrition and Diabetes (2019) 9:22
https://doi.org/10.1038/s41387-019-0089-6 Nutrition & Diabetes
ARTICLE Open Access
Impact of energy turnover on the
regulation of glucose homeostasis in
healthy subjects
Franziska Büsing
1
, Franziska Anna Hägele
1
,AlessaNas
2
,MarioHasler
3
, Manfred James Müller
1
and Anja Bosy-Westphal
1
Abstract
Objective: Sedentary lifestyle increases the risk of type 2 diabetes. The aim of this study was to investigate the impact
of different levels of energy turnover (ET; low, medium, and high level of physical activity and the corresponding
energy intake) on glucose metabolism at zero energy balance, caloric restriction, and overfeeding.
Methods: Sixteen healthy individuals (13 men, 3 women, 25.1 ± 3.9 years, BMI 24.0 ± 3.2 kg/m
2
) participated in a
randomized crossover intervention under metabolic ward conditions. Subjects passed 3 × 3 intervention days. Three
levels of physical activity (PAL: low 1.3, medium 1.6, and high 1.8 achieved by walking at 4 km/h for 0, 3 × 55, or 3 ×
110 min) were compared under three levels of energy balance (zero energy balance (EB): 100% of energy requirement
(Ereq); caloric restriction (CR): 75% Ereq, and overfeeding (OF): 125% Ereq). Continuous interstitial glucose monitoring,
C-peptide excretion, and HOMAIR, as well as postprandial glucose and insulin were measured.
Results: Daylong glycemia and insulin secretion did not increase with higher ET at all conditions of energy balance
(EB, CR, and OF), despite a correspondingly higher CHO intake (Δlow vs. high ET: +86 to 135 g of CHO/d). At CR,
daylong glycemia (p=0.02) and insulin secretion (p=0.04) were even reduced with high compared with low ET.
HOMAIR was impaired with OF and improved with CR, whereas ET had no effect on fasting insulin sensitivity. A
higher ET led to lower postprandial glucose and insulin levels under conditions of CR and OF.
Conclusion: Low-intensity physical activity can signicantly improve postprandial glycemic response of healthy
individuals, independent of energy balance.
Introduction
Higher postprandial glycemia even below the diabetic
threshold has been shown to be a risk factor for cardio-
vascular disease
1,2
. In addition, a higher glycemic load was
positively associated with the risk of type 2 diabetes in a
meta-analysis of prospective cohort studies
3
. Exercise
could compensate the negative effects of a high glycemic
load (GL) western diet, because it mitigates postprandial
glycemia by non-insulin-mediated glucose uptake
(NIMGU)
46
. Numerous studies with high-intensity
exercise showed lower postprandial glucose levels or/
and an improved insulin sensitivity in healthy partici-
pants
7
, as well as in patients with diabetes
8
. However,
recommendation of high-intensity exercise for primary
prevention has limitations with respect to injury in high-
risk groups like untrained obese and elderly subjects.
Nygaard et al. investigated the effect of low-intensity
physical activity on regulation of glycemia in healthy
subjects. The authors found a 31.2% decrease of post-
prandial glucose by a slow 40-min postmeal walk
Corrected: Correction
© The Author(s) 2019
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permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Correspondence: Anja Bosy-Westphal (abosyw@nutrition.uni-kiel.de)
1
Institute of Human Nutrition and Food Science, Christian-Albrechts University
of Kiel, Kiel, Germany
2
Institute of Nutritional Medicine, University of Hohenheim, Stuttgart, Germany
Full list of author information is available at the end of the article.
Trial Registration: ClinicalTrials.gov as NCT03361566
Nutrition and Diabetes
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compared with control in healthy women aged >50 years
9
.
Manohar et al. conrm this result in 12 healthy control
subjects and found a signicant reduction of the post-
prandial glucose AUC of 53.1% by walking at a speed of
1.9 km/h
10
.
The timing of physical activity may also impact post-
prandial glycemia, because of diurnal variations in energy
metabolism and insulin secretion
11
. In patients with type
2 diabetes, most
1216
but not all
17
studies showed that
exercise or moderate walking after meals had a greater
benet on postprandial glycemia compared with premeal
exercise, whereas premeal exercise had a greater impact
on improvement of fat oxidation
18
.
A major drawback of all previous studies is the condi-
tion of uncontrolled energy intake with physical activity.
Because physical activity leads to an increase in energy
expenditure and increases energy intake, only partially to
the corresponding enhanced energy requirement (Ereq)
19
,
a negative energy balance (EB) is likely created that
impacts glucose metabolism. Zero EB, where energy
intake corresponds to energy expenditure, is therefore
necessary to investigate the effect of physical activity on
regulation of glycemia without the confounder of negative
EB. A condition of xed EB at a varying physical activity
level is dened as energy turnover (ET) or energy ux. A
low ET, i.e., a low energy intake with a low energy
expenditure, resembles an inactive lifestyle, whereas a
high ET can be achieved by an increase in physical activity
at a correspondingly higher energy intake. A high
ETmight compensate for the negative impact of short-
term overfeeding (OF) on glucose metabolism. Sedentary
behavior is known to facilitate a positive EBe, e.g., over the
weekend, on holidays, at periods of celebration, or during
vacations
20,21
. There is evidence that one day of sitting
without adjusting for the lower energy expenditure
already leads to a reduced insulin action (39%) similar to
the changes reported after longer periods of bed rest or a
large reduction in ambulation
22
.
The aim of the study was to investigate the impact of
low, medium, and high ET obtained by different durations
of low-intensity physical activity (performed after the
meals) on the regulation of basal and circadian glucose
metabolism at zero EB. The impact of these three levels of
ET on glucose metabolism was also examined under
randomized conditions of controlled OF and caloric
restriction (CR).
Methods
Sixteen healthy subjects were recruited via announce-
ments on social networks and at the campuses of the
University of Stuttgart and Hohenheim. Inclusion criteria
were age between 20 and 40 years and a normal physical
activity in daily routine. Exclusion criteria for enrollment
included regular intake of supplements, chronic disease,
smoking, claustrophobia, and special diets or any food
intolerances. Subjects with chronic diseases or regular
intake of medication on a daily basis, except birth control
pills (one case) were excluded from participation. The
study was carried out at the Institute of Nutritional
Medicine, University of Hohenheim, Stuttgart, Germany,
from December 2016 to March 2018.
Study design
The randomized controlled crossover intervention
comprised three 1-week interventions: zero EB, CR 25
Ereq%, and OF +25 Ereq%. Each week was performed
under three different levels of ET: one inactive day (PAL
1.3 =low ET), one day of normal physical activity (PAL
1.6 =medium ET) and one day of high physical activity
(PAL 1.8 =high ET; Fig. 1). The three intervention days
per week were separated by a washout day. A subject who
completed the entire study thus went through nine dif-
Fig. 1 Schematic study protocol. *randomized order
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Nutrition and Diabetes
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ferent intervention days and additional 3 days, where the
Ereq for low, medium, and high ET was measured with ad
libitum energy intake (baseline week). Each of the nine
intervention days consisted of a 36-h stay in the metabolic
chamber. Participants entered the metabolic chamber in
the evening before each test day. The measurement period
started at 6 a.m. in the morning and ended 24 h later. The
sleeping period was constant and always from 10:30 p.m.
to 6 a.m. Throughout the whole study period, participants
wore an actiwatch (Actiwatch 2, Koninklijke Philips N.V.,
Amsterdam, The Netherlands), to monitor sleeping peri-
ods (duration, start/end, and sleep quality). EB was con-
ducted as the second intervention week, whereas CR and
OF were performed in randomized order as the rst or
third week of study. All intervention days (low, med, and
high) were kept in randomized order, which was obtained
by block randomization.
The study was registered at clinicaltrials.gov as
NCT03361566 and the study protocol was approved by
the ethics committee of the Medical Council of Baden-
Württemberg, Germany. All participants provided
informed written consent before participation.
Twenty-four-hour energy expenditure
Resting Ereq of all study participants was measured
before the baseline week by a hood calorimetry system.
Resting Ereq was multiplied by a physical activity level
estimated from prescribed physical activity during the
baseline week in the metabolic chamber. In order to
provide a sufcient amount of food during baseline week
~20% of extra energy was provided and all leftovers were
weight back to assess food consumption. Energy expen-
diture at different levels of activity (baseline week) was
measured in a room calorimeter and was used to deter-
mine individual Ereq (for details see ref.
23
). Total energy
expenditure (TEE) was determined at a constant ow rate
of 120 l/min fresh air by continuously measuring rates of
oxygen and carbon dioxide concentrations using the
Promethion integrated whole-room indirect calorimeter
system (Sable Systems International, Las Vegas, USA) and
the Weir equation
24
. Using energy intake and TEE data,
individual EB for different ET levels was calculated as ΔEB
[%] =(EI/TEE × 100) 100. The inuence of ET on
macronutrient oxidation and macronutrient balances is
subject of a separate publication (Nas A et al.,
submitted 2019).
Body composition
Height was measured using a stadiometer (seca 274,
seca GmbH & Co.KG, Hamburg, Germany). Body weight
was measured in the morning before breakfast at the
beginning and end of each EB condition using a calibrated
electronic scale (seca mBCA 515, seca GmbH & Co.KG),
in light clothes, without shoes and after voiding. Before
starting the study, body composition of the subjects was
analyzed using Air Displacement Plethysmography by the
BodPod
TM
Body Composition System (COSMED USA,
Inc., Concord, CA, USA) as described elsewhere
25
.
Standardization of the diet
To ensure equal baseline conditions, each intervention
was preceded by a 3-day entrance phase with controlled
macronutrient composition of the diet. On intervention
days, a strict daily structure with three meals was
achieved. Breakfast was served at 7 a.m., lunch at 1 p.m.,
and dinner at 7 p.m.
The study diet consisted of a lot of energy dense, low
ber convenient food with a high content in saturated
fatty acids (e.g., ready-to-serve pizza) and included sweets.
The macronutrient composition of 35% fat, 50% carbo-
hydrates, and 15% protein represents a common diet in
Germany and was kept constant throughout the whole
study period. A constant macronutrient composition was
achieved by weighing each food item and by evaluation of
the menus with the software Prodi®6 (Scientic Publish-
ing Company, Stuttgart). Meal composition on interven-
tion and washout days as well as during the entrance
phase was kept constant. During the baseline week for
assessment of Ereq at different levels of ET, as well as in
the entrance phase under free living conditions and dur-
ing the washout days, participants received all food in
abundance and leftovers were weighed back to assess
energy and macronutrient intake. On all intervention days
during EB, CR, and OF, subjects were instructed to eat
their entire meal within 30 min without any leftovers. To
ensure a controlled dietary intervention, all food con-
sumed throughout the whole study was provided by the
institute. For the duration of the entire study, participants
were asked to abstain from consumption of alcohol and
any additional food or snacks except mineral water, herbal
and fruit tea that were allowed ad libitum. GL of a food
was calculated by multiplying GI of each food
26
by the
amount of CHO in grams provided by the food and
dividing the total by 100
27
.
Physical activity
Physical activity was recorded using a triaxial activity
monitor, activPAL(Paltechnologies Ltd., Glasgow, UK).
The device was continuously worn on the middle of the
thigh xed with a waterproof patch. ActivPALProfes-
sional v7.2.32 software was used for data analysis.
The subjects had to pass three different levels of phy-
sical activity depending on the ET. All physical activity
levels were achieved by walking with a constant speed of
4 km/h on a treadmill (Kettler Track 9, Kettler GmbH,
Ense-Parsit, Germany) for different durations. On the day
with low ET (PAL 1.3), subjects were sedentary by sitting
or lying throughout the day. On the medium ET day with
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normal activity, subjects walked on the treadmill three
times a day 10 min after nishing meals (PAL =1.6) for
55 min. On the high ET day, walking units were increased
to 110 min each (PAL =1.8). Before the start of the study,
it was tested which walking time and walking speed were
appropriate to reach the given PALs. Subjects were told to
refrain from exercise during the entrance phase and on
the washout days and only follow their usual everyday
activities. Due to technical problems with the device, there
is no valid data of physical activity of one subject for the
whole intervention and two participants had missing data
for one EB condition each.
Assessment of glucose metabolism
In the morning of the intervention days and the fol-
lowing mornings, fasting blood samples were taken.
Fasting glucose concentration was determined via hex-
okinase method (OSR6121, Beckman Coulter, Brea, CA,
USA). Fasting serum insulin (Elecsys®Insulin
06923321990, Roche Cobas e801) and 24-h urinary C-
peptide excretion were measured by luminescence
immunoassay (Elecsys®C-Peptide 06923330990, Roche
Cobas e801). Homeostatic model assessmentinsulin
resistance (HOMAIR =fasting glucose (mg/dl) × fasting
insulin (μU/ml)/405
28
) was used to calculate fasting
insulin sensitivity. Daylong glycemia at different levels of
ET, was assessed by continuous interstitial glucose mea-
surement (CGM, Dexcom G4 Platinum, Nintamed,
Mainz, Germany) for at least 24 h during all nine inter-
vention days. A small sensor was placed at the back of the
upper arm in the subcutaneous tissue. Sensor readings
were recorded in intervals of 5 min. The CGM-device was
calibrated three times a day before meals against fasting
capillary blood samples. Incremental AUC was calculated
(iAUC) for 18 h (6:00 a.m.12:00 p.m.) using trapezoidal
rule
29
, whereby iAUC includes only the positive AUCe.
Glucose variability was described by mean amplitude of
glycemic excursions (MAGE-index) and calculated using
a published macro
30
. Daylong insulin secretion was
assessed by 24-h urinary C-peptide excretion.
Results of the continuous glucose monitoring were
adjusted for the difference between fasting serum glucose
and fasting CGM
glucose
(Glucose
CGM
Glucose
venous
)to
calculate postprandial iAUC of glucose. No differences in
the results were observed when using the uncorrected
glucose values. Plasma samples for the measurement of
postprandial insulin were collected 30, 60, and 120 min
after each meal in BD
TM
P800 tubes (Becton Dickinson
Inc., Franklin Lakes, USA) and measured using a Bio-Plex
Pro
TM
human Diabetes 3-Plex Kit (Bio-Rad, Hercules,
USA). Data analysis was performed with Bio-Plex Man-
ager
TM
Software 6.1. iAUC was calculated from postmeal
insulin values over 2 h.
One participant was excluded for the analysis of daylong
insulin secretion (n=15) and HOMAIR of two subjects
was missing for low ET at the condition of EB (n=14).
Statistical analyses
Data are expressed as mean ± SD. The statistical soft-
ware R (2017) was used to evaluate the data. Data eva-
luation started with the denition of an appropriate
statistical mixed model
31,32
. The data were assumed to be
normally distributed and to be heteroscedastic with
respect to different levels of EB and ET. These assump-
tions are based on a graphical residual analysis. The
statistical model included EB (zero EB, CR, and OF) and
ET (low, medium, and high), PrePost (before interven-
tion, after intervention), as well as their interaction terms
as xed factors. The ID and EB, nested in ID, were
regarded as random factors. Also, the correlations of
values between intervention days as well as the correla-
tions between pre and post measurements were taken
into account (auto-correlation). Based on this model, a
Pseudo R² was calculated
33
and an analysis of variance
(ANOVA) was conducted, followed by multiple contrast
tests (e.g., see refs.
34,35
) in order to compare the several
levels of the inuence factors. pvalues < 0.05 were con-
sidered as statistically signicant. Box-and-whisker plots
were used to display the distribution of iAUC
CGM
,C-
peptide-excretion and HOMAIR. Based on results of a
previous study of our group
36
, a total sample size of n=8
was required to assess these differences in daylong gly-
cemia at a α-level of 0.05 and a power of 95% (hypo-
thesized effect size =1.4).
Results
Three women and thirteen men aged 25.1 ± 3.9 years
with a BMI 24.0 ± 3.2 kg/m
2
and a FMI 5.3 ± 3.2 kg/m²
were included in this study. According to WHO cri-
teria, ten subjects were normal weight, ve overweight
and one obese. Regarding body composition, four sub-
jects had a FMI above the age and sex adjusted 95th
percentile
37
.
Mean resting energy expenditure of the study partici-
pants at baseline was 1788 ± 216 kcal/d. Body weight
remained unchanged during all conditions of EB (ΔCR:
0.0 ± 0.3 kg; ΔEB: 0.1 ± 1.1 kg; ΔOF: 0.3 ± 0.3 kg; all p>
0.05). Daily step count and PAL were different by design
between the three levels of ET in all conditions of EB (all
p< 0.05; Table 1) but did not differ at the same ET
between the three EB conditions (all p> 0.05; except low
ET: CR vs. OF, p< 0.05). Step count was 482, 17706, and
34587 during low, medium, and high ET interventions
(mean of all three EBs). Since step count of low ET
without any treadmill activity was 436545 steps/d, step
counts of medium and high ET minus these steps is
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assumed to be reached during treadmill challenge. EB was
3.4%, 0.0%, and 1.6% during zero EB, 20.5%, 19.5%,
and 21.0% during CR, and 27.6%, 25.4%, and 24.2%
during OF for low, medium, and high ET, respectively (EB
vs. CR vs. OF, all p< 0.001).
As intended by study design, total caloric intake, as well
as absolute carbohydrate-, fat-, and protein intake
increased within each energy balance with higher ET (all
p< 0.001) and differed between conditions of EB (all p<
0.001, Table 1). Macronutrient composition was 49.4 ±
0.5% CHO, 34.9 ± 0.7% fat, and 14.8 ± 0.1% protein and
did not differ throughout the different phases of the study.
Total daily GL ranged between 123.8 and 285.2 g/d
(Table 1). Daily GL was distributed with 27.4% at break-
fast, 28.8% at lunch, and 43.8% at dinner.
Effect on daylong and basal glucose metabolism
Changes in daylong glycemia and insulin secretion are
shown in Fig. 2. During CR, daylong glycemia and insulin
secretion were higher with low compared with high ET (p
< 0.05, Fig. 2). Despite a higher intake of carbohydrates
with increasing level of ET (Δlow vs. high ET: +86 to
135 g of CHO/d; Table 1), there was no increase in day-
long glycemia and insulin secretion with higher ET during
EB and OF. During CR, glucose variability (MAGE-Index)
was higher with low ET compared with medium and high
ET (low ET: 3.1 ± 1.0, medium ET: 2.3 ± 0.5, high ET:
1.9 ± 0.4; both p< 0.05). Fasting insulin sensitivity was
improved with CR and impaired with OF (all p< 0.01)
irrespective of ET (Fig. 3).
Effect on postprandial glucose metabolism
Changes in EB did not impact postprandial glucose and
insulin responses (EB vs. CR and EB vs. OF; all p> 0.05;
except postmeal insulin at medium ET CR vs. EB, p<
0.05). A higher ET led to lower cumulative postprandial
glucose and insulin levels (mean of postprandial iAUCs of
breakfast, lunch, and dinner) under conditions of CR and
OF (low vs. high ET for insulin: 28.9 and 44.5% both p
< 0.001, and for glucose 34.2% (p< 0.001) and 29.7%
(p=0.066)). Changes in postprandial glucose and insulin
response are shown in Fig. 4, separated for breakfast,
lunch and dinner. After breakfast, postmeal glucose (p<
0.05) and insulin (p< 0.01) levels were lower with high
compared with low ET during CR only, during OF only
postprandial insulin response was lower with high com-
pared with low ET (p< 0.05). After lunch, ET had no
effect on postprandial glucose levels whereas postprandial
insulin levels decreased with higher ET during all condi-
tions of EB (all p< 0.05). After dinner, the response in
postprandial glucose decreased with increasing ET under
all conditions of EB (all p< 0.01) whereas a decrease in
postprandial insulin levels with higher ET was only
observed during OF (p< 0.001).
Table 1 Physical activity and daily energy-, macronutrient- and glycemic load intake during caloric restriction, zero energy balance and overfeeding with low,
medium and high energy turnover (ET)
Caloric restriction Energy balance Overfeeding
low ET med ET high ET low ET med ET high ET low ET med ET high ET
Physical acticity
PAL 1.26 ± 0.06 1.52 ± 0.05 1.74 ± 0.07 1.30 ± 0.03 1.57 ± 0.04 1.75 ± 0.05 1.34 ± 0.05 1.55 ± 0.04 1.71 ± 0.05
Steps [counts/d] 545 ± 902 17 618 ± 557 34 527 ± 1 847 464 ± 912 17 710 ± 219 34 414 ± 1 539 436 ± 847 17 790 ± 382 34 821 ± 1 568
Diet
Energy intake [kcal/d] 1 792 ± 144 2 156 ± 78 2 494 ± 90 2 390 ± 97 2 863 ± 72 3 325 ±177 2 986 ± 99 3 570 ± 183 4 146 ± 320
CHO intake [g/d] 219.4 ± 20.8 255.1 ± 37.3 308.2 ± 12.0 292.7 ± 13.9 350.1 ± 6.0 406.5 ± 20.3 366.2 ± 11.8 437.2 ± 22.1 506.7 ± 37.9
Fat intake [g/d] 70.5 ± 4.9 84.7 ± 2.6 97.7 ± 5.0 93.7 ± 3.3 112.8 ± 3.9 131.0 ± 7.5 117.3 ± 3.9 140.8 ± 7.0 163.8 ± 12.5
Protein intake [g/d] 66.1 ± 4.0 79.4 ± 3.3 92.0 ± 5.6 88.4 ± 0.0 106.1 ± 5.6 120.3 ± 9.3 109.8 ± 3.0 131.0 ± 8.6 153.3 ± 13.0
Glycemic load [g/d] 124.0 ± 8.9 149.2 ± 4.7 173.8 ± 7.0 166.0 ± 6.5 197.8 ± 5.7 228.9 ± 14.1 206.4 ± 7.0 247.4 ± 13.0 285.9 ± 24.0
Values are means ± SDs; n=16; linear mixed model with multiple contrast tests, results of physical activity were compared at the same level of ET between all energy balance conditions (all p> 0.05) and within all energy
balances conditions at three levels of ET (all signicantly different at p< 0.001); results of dietary intake were compared at the same level of ET between all energy balance conditions (all signicantly different at p< 0.001)
and within all energy balances conditions at three levels of ET (all signicantly different at p< 0.001)
ET energy turnover, CHO carbohydrate
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Discussion
The main nding of the study was that higher ET
improved postprandial glucose and insulin responses at all
levels of EB (Fig. 4). The improvement of postprandial
insulin levels was most pronounced after lunch and for
glucose after dinner. Postprandial insulin sensitivity was
therefore enhanced by low intensity physical activity
especially after lunch and dinner. The higher GL at dinner
might have contributed to a higher postprandial glycemia.
It is therefore possible that physical activity-induced
postprandial improvements in glycemia were most pro-
nounced after dinner. The lack of a signicant change in
basal glycemia and HOMA suggests, that ET primarily
improves glucose uptake in skeletal muscle. In addition,
only healthy normoglycemic subjects were investigated
which makes an intervention effect on basal glucose and
insulin levels unlikely (Fig. 3).
Timing and frequency of physical activity
Physical activity in our study was always performed in a
postmeal situation. Colberg et al. described, that an
increase in daily activity of low intensity improves blood
glucose management and lowers postmeal hyperglycemia,
especially when physical activity was performed directly
after meals
38
. Because peak glucose values usually appear
within 90 min postprandially
39
, the optimal timing for
physical activity has been assumed to be 30 min after meal
onset
40
. The benet of a postmeal compared with a pre-
meal bout of physical activity on glycemia might be due to
the synergistic effect between insulin and NIMGU by
muscular contraction
41
. Elevated blood glucose levels 2 h
after lunch have been shown to increase the risk for
cardiovascular events by 50% and the risk for mortality by
89% in patients with type 2 diabetes
42
. Because the exer-
cise effect is insulin-independent, it works well in patients
with diabetes, too
4345
. Therefore postmeal exercise may
be an effective way to improve glucose control in patients
with type 2 diabetes
46
. Borror et al. recommended that
individuals with type 2 diabetes should focus on increas-
ing energy expenditure after the largest meal of the day.
However, 15 min of postmeal walking (3 times per day)
was more effective in lowering 3-h postdinner glucose
levels than a 45-min walk in the morning or afternoon
compared with control day in subjects at risk for impaired
glucose tolerance (p< 0.01)
16
.
In the present study, postmeal glucose and insulin levels
decreased most after lunch and dinner (Fig. 4). This effect
could be due to a cumulative effect of walking intervals
over the intervention day or it might be explained by the
circadian difference insulin sensitivity. Healthy non-
diabetic individuals have a higher insulin sensitivity in the
*
CR
low med high
EB
low med high
OF
low med high
C-peptide excretion [μg/d]
CR
low med high
EB
low med high
OF
low med high
iAUCCGM [ mg/dl x 18h]
*
B
A
Fig. 2 Comparison between the different levels of ET (low, medium, and high) at different energy balances. a Eighteen-hour iAUCs
CGM
[mg/
dl], n=16; bC-peptide excretions [µg/d], n=15; values are means ± SDs; linear mixed model with multiple contrast tests, *p< 0.05 for comparison of
ETs; CR caloric restriction, EB energy balance, OF overfeeding, med medium, ET energy turnover
ΔHOMAIR
(post- minus pre-intervention day)
CR
low med high
EB
low med high
OF
low med high
Fig. 3 Comparison of differences in HOMAIR (post- minus pre-
intervention day) between ETs (low, med, and high) within
different levels of energy balance (CR, EB, and OF). Values are
means ± SDs; linear mixed model with multiple contrast tests; fasting
insulin sensitivity was improved with CR and impaired with OF (all p<
0.01); n=1416; ET energy turnover, med medium, CR caloric
restriction, EB energy balance, OF overfeeding
Büsing et al. Nutrition and Diabetes (2019) 9:22 Page 6 of 10
Nutrition and Diabetes
Content courtesy of Springer Nature, terms of use apply. Rights reserved
morning and show a diurnal decrease in insulin sensitiv-
ity, which is in part the result of better β-cell respon-
siveness
11
. When exercise was performed after lunch,
there was a minimal impact on glycemia over the fol-
lowing 24 h
47
. By contrast, exercise performed in the
evening has been shown to reduce glycemia during
exercise and the overnight period in patients with type 2
diabetes
13
. However, the glucose- and insulin-lowering
effect of exercise after a meal does not seem to persist at
the following meal suggesting that these improvements
are short-lived
12
.
In summary, timing and frequency of physical activity
seem to play a signicant role for improvement of glucose
metabolism. With respect to feasibility and preventive
recommendations it is important that breaking up pro-
longed sitting with short bouts of walking or simply
standing already has positive effects on glycemia
4850
. The
implementation of several smaller walking units is easier
breakfastlunchdinner
CR
low med high
EB
low med high
OF
low med high
CR
low med high
EB
low med high
OF
low med high
CR
low med high
EB
low med high
OF
low med high
CR
low med high
EB
low med high
OF
low med high
CR
low med high
EB
low med high
OF
low med high
CR
low med high
EB
low med high
OF
low med high
2 hours postprandial iAUCGlucose 2 hours postprandial iAUCInsulin
DA
B
C
E
F
Fig. 4 Comparison of 2 h postprandial glucose (ac)and insulin (df)iAUCs between the different levels of ET (low, medium, and high) and
energy balance (CR, EB, OF) separated by breakfast, lunch, and dinner. Values are means ± SDs; linear mixed model with multiple contrast tests,
*p< 0.05, **p< 0.01, and ***p< 0.001 for comparison of ETs; n=16; ET energy turnover, med medium, CR caloric restriction, EB energy balance, OF
overfeeding
Büsing et al. Nutrition and Diabetes (2019) 9:22 Page 7 of 10
Nutrition and Diabetes
Content courtesy of Springer Nature, terms of use apply. Rights reserved
to realize in everyday life, especially in risk groups like
older persons.
Intensity and type of physical activity
In our study, a positive effect on postprandial glycemia
was achieved even by low intensity walking on the
treadmill at 4 km/h. Glucose uptake is enhanced with
higher intensity of physical activity because of increased
glucose delivery, transport, and metabolism
51
. Further
studies are needed to clarify a doseresponse relationship
for light intensity activity thresholds
52
. The type of exer-
cise involves different muscle groups and masses and may
therefore differently affect the extend of the glucose
lowering effect. In line with this presumption, climbing
stairs led to a more rapid decrease in postprandial blood
glucose levels compared with cycling
53
. In patients with
type 2 diabetes
54
as well as in healthy volunteers
9,10,55,56
,
slow postmeal walking was able to lower postprandial
glucose and insulin levels compared with sedentary con-
trols. We found reduced postprandial insulin levels at
high compared with low ET during CR and OF (Fig. 4df)
although the intake of CHO was considerable higher (e.g.,
during OF at low ET 366 ± 52 g vs. 507 ± 85 g at high ET).
This is likely due to improved NIMGU by physical
activity.
Physical activity and energy balance
So far, all studies that investigated the impact of low
intensity physical activity on postprandial glycemia did
not consider EB. Hence, the observed postmeal glycemia-
lowering effect of physical activity could be also explained
by a higher energy expenditure and a resulting caloric
decit. This negative energy balance alone could explain
the improvement in glycemia. This assumption is sup-
ported by the results of Larsen et al. who observed the
same effect on glycemia by 45 min of exercise after
breakfast or an equal energy decit achieved by a lower
energy intake at breakfast
12
. Both conditions led to a
similar decrease in postprandial glycemia after breakfast.
However, in the present study CR (between 19.5 and
21.0% EB) did not improve postmeal levels of glucose or
insulin compared with zero EB. Similarly, OF (between
+24.2 and +27.6% EB) did not worsen postprandial glu-
cose metabolism (Fig. 4). On the other hand, a positive EB
can result from inactivity
57
and thus might explain the
deterioration in insulin sensitivity
22
.
Postprandial glucose (p=0.066) and insulin levels (p<
0.001) were also improved by a high ET compared with a
low ET during OF (Fig. 4). The positive effect of physical
activity on postprandial glucose metabolism was therefore
independent of EB. Similar to our ndings, 25% OF
combined with a single bout of exercise (~60 min of
ergometer or treadmill at 60% of VO
2
peak) also led to a
20% decrease in postprandial insulin AUC (p< 0.05),
compared with OF alone
58
. Impaired glucose metabolism
by OF is well established
59
and may not have been
detectable in our study because of the short duration and
relatively low energy surplus.
Strength and limitations of the study
A strength of our study was the randomized crossover
study design that strictly controlled for possible con-
founding factors, such as EB, diet, exercise, and sleeping
behavior. The use of healthy participants may limit the
generalizability of the results to people with obesity or
type 2 diabetes. Studies that did not control EB already
provided some evidence that postprandial exercise is also
effective to improve glucose control in patients with type
2 diabetes
4346,54
.
Since our study only covers a 24-h intervention, the
results are not transferable to long-term effects of physical
activity. However, short-term changes in eating and
motion behavior, are very common and realistic in
everyday life
20,21
. In addition, the quite long walking
duration on the treadmill during high ET (330 min) could
appear unrealistic, but the stay in a metabolic chamber is
an articial setting that prevents other spontaneous phy-
sical activity of daily life.
Conclusion
Low intensity postprandial physical activity is effective
to lower postmeal glucose and insulin levels in healthy
adults independent of EB. Since higher postprandial gly-
cemia even below the diabetic threshold has been shown
to be a risk factor for cardiovascular disease
1,2
, walking
after the meals is an advisable preventive strategy.
Acknowledgements
This study was funded by budgetary resources of the University of Hohenheim.
We acknowledge nancial support by Land Schleswig-Holstein within the
funding program Open Access Publikationsfonds.
Authors' contributions
Writing of the paper: FB and ABW. Data acquisition: FB, FAH and AN. Data
analysis: FB. MH provided statistical support. Discussion of data: FB, MJM and
ABW. Proofreading of the paper: FB, FAH, AN, MJM and ABW. Study
design: ABW.
Author details
1
Institute of Human Nutrition and Food Science, Christian-Albrechts University
of Kiel, Kiel, Germany.
2
Institute of Nutritional Medicine, University of
Hohenheim, Stuttgart, Germany.
3
Applied Statistics, Faculty of Agricultural and
Nutritional Sciences, Christian-Albrechts University of Kiel, Kiel, Germany
Conict of interest
The authors declare that they have no conict of interest.
Publishers note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional afliations.
Büsing et al. Nutrition and Diabetes (2019) 9:22 Page 8 of 10
Nutrition and Diabetes
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Received: 25 March 2019 Revised: 9 July 2019 Accepted: 15 July 2019
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1.
2.
3.
4.
5.
6.
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... Furthermore, normalization of energy turnover for energy intake is required to investigate whether energy turnover and the rate of change in energy turnover (timing, frequency, and intensity of energy turnover) can regulate energy and macronutrient balance and thus affect metabolic health (6)(7)(8). This is implemented in the concept of energy flux, which can be described as the level of energy balance, i.e., the rate of energy conversion from absorption to expenditure or storage (9). ...
... Although the regulation of energy balance is based on transient and short-lived fluctuations, the bulk of studies performed long-term overfeeding experiments that are difficult to interpret because accumulation in fat mass and ectopic fat lead to a decrease in insulin sensitivity. A few studies investigated the effect of only 1-day overfeeding on metabolic regulation (e.g., 6,7,64,[82][83][84][85]. These studies reveal a significant impairment in insulin sensitivity, an increase in 24-hour energy expenditure, and a decrease in fat oxidation with overfeeding. ...
Full-text available
Article
Energy turnover, defined as the average daily total metabolic rate, can be normalized for basal metabolic rate in order to compare physical activity level between individuals, whereas normalization of energy turnover for energy intake (energy flux) allows investigation of its impact on regulation of energy partitioning independent of energy balance. Appetite sensations better correspond to energy requirements at a high compared with a low energy turnover. Adaptation of energy intake to habitual energy turnover may, however, contribute to the risk of weight gain associated with accelerated growth, pregnancy, detraining in athletes, or after weight loss in people with obesity. The dose–response relationship between energy turnover and energy intake as well as the metabolic effects of energy turnover varies with the habitual level of physical activity and the etiology of energy turnover (e.g., cold-induced thermogenesis, growth, or lactation; aerobic vs. anaerobic exercise). Whether a high energy turnover due to physical activity or exercise may compensate for adverse effects of overfeeding or an unhealthy diet needs to be further investigated using the concept of energy flux. In summary, the beneficial effects of a high energy turnover on regulation of energy and macronutrient balance facilitate the prevention and treatment of obesity and associated metabolic risk.
... In the 2007 study, however, subjects stayed overnight so that they only had negligible levels of activity between getting up and having breakfast. As reported by Büsing et al. and Fencher et al., low-intensity physical activity can improve postprandial glycemic response of individuals without diabetes (31,32). ...
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Background Continuous glucose monitoring (CGM) systems have initially been developed for diabetes patients but are also increasingly used by healthy people in order to monitor individual eating behaviors and the glucose responses to different foods, e.g. to support weight loss. The aim of the study was to assess the suitability of this technology to detect effects of meal sequences and nutritional content of meals on postprandial glycemic responses. In addition, the effect of meal sequences on the subsequent eating behavior was evaluated. Subjects/Methods On two consecutive days, 36 participants without diabetes received standardized test meals (TM) for breakfast and lunch, as well as a free-choice dinner. Both TM contained equal amounts of carbohydrates with different absorption characteristics and differing fat and protein content. Participants consumed TM “fast” for breakfast and “slow” for lunch on one day, and in reverse order on the other day. Dinner was selected from a buffet; meal content and amount were free-choice. Participants rated their feeling of satiety directly before dinner intake. Glucose profiles were assessed with a CGM device. Results CGM was able to distinguish postprandial glucose responses according to the nutritional content of the TM. When TM were consumed for lunch, median glucose increase was higher than when consumed for breakfast (TM “fast”: 72.7 mg/dL vs. 56.5 mg/dL; TM “slow”: 38.3 mg/dL; vs. 22.1 mg/dL). Satiety before dinner was lower and energy intake for dinner was higher after TM “fast” for lunch than after TM “slow” for lunch (5 058.3 ± 1 787.8 kJ vs. 4 429.8 ± 1 205.4 kJ). Conclusions Data collected in this evaluation with the use of CGM firstly supports its use under everyday life conditions in people without diabetes and secondly could contribute to identify beneficial dietary patterns that may be considered in the management and prevention of metabolic disorders.
... Most of the existing studies focused on investigating the differential expression or up-/down-regulation of a gene (Xin et al., 2020). But it's important to investigate the transcription regulation of healthy subjects (Busing et al., 2019;Rodriguez-Nunez et al., 2019). The metrics mqTrans is the first method to quantitatively measure the transcription regulation relationship. ...
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Differential expressions of genes are widely evaluated for the diagnosis and prognosis correlations with diseases. But limited studies investigate how transcriptional regulations are quantitatively altered in diseases. This study proposes a novel model-based quantitative measurement of transcriptional regulatory relationships between mRNA genes and Transcription Factor (TF) genes (mqTrans features). This study didn't consider the regulatory relationships between TF genes, so the mRNA genes were the protein-coding genes excluding the TF genes. The models are trained in the control samples in a lung cancer dataset and evaluated in two independent datasets and the hold-out testing samples from the third dataset. Twenty-nine mRNA genes are detected with transcriptional regulations quantitatively altered in lung cancers. The transcriptional modification technologies like RNA interference (RNAi) may be utilized to restore the altered transcriptional regulations in lung cancers.
... We acknowledge that we cannot extend our findings to free-living conditions where energy turnover may be higher due to higher physical activity and higher food intake, which might ultimately be more beneficial with regard to weight gain; for example, higher eucaloric energy turnover (~3200 kcal/day) has been linked to better appetite control (39), improved postprandial glucose and insulin regulation (40), and improved fat balance (41). However, we propose that thrifty individuals who maintain a low energy turnover also in free-living conditions might be at greater risk for weight gain than spendthrift individuals. ...
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Background The increased use of continuous glucose monitoring (CGM) and automated insulin delivery systems raises the question about therapeutic targets for glucose profiles in people with diabetes. This study aimed to assess averaged pre- and postprandial glucose profiles in people without diabetes to provide guidance for normal glucose patterns in clinical practice. For that, number and timing of meal intake were predefined. Material and Methods To assess glucose traces in 36 participants without diabetes (mean age = 23.7 ± 5.7 years), CGM was performed for up to 14 days, starting with a run-in phase (first 3 days, excluded from analysis) followed by 4 days with fixed meal times at 8:00 am, 1:00 pm, and 6:00 pm and the remaining 7 days spent under everyday life conditions. Data from two simultaneously worn CGM sensors were averaged and adjusted to capillary plasma-equivalent glucose values. Glucose data were evaluated through descriptive statistics. Results Median glucose concentration on days with fixed meal times and under everyday life conditions was 95.0 mg/dL (91.6-99.1 mg/dL, interquartile range) and 98.1 mg/dL (93.7-100.8 mg/dL), respectively. On days with fixed meal times, mean premeal glucose was 92.8 ± 9.4 mg/dL, and mean peak postmeal glucose was 143.3 ± 23.5 mg/dL. Conclusions By defining the time of meal intake, a clear pattern of distinct postprandial glucose excursions in participants without diabetes could be demonstrated and analyzed. The presented glucose profiles might be helpful as an estimate for adequate clinical targets in people with diabetes.
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Abstract Background Exercise-induced weight loss is often less than expected and highly variable in men and women. Behavioural compensation for the exercise-induced energy deficit could be through energy intake (EI), non-exercise physical activity (NEPA) or sedentary behaviour (SB). We investigated this issue in women. Methods Twenty-four overweight [body mass index (BMI) M = 27.9 kg/m2, SD = 2.7] women [age M = 33.1 years, SD = 11.7] completed 12-weeks of supervised exercise (5×500kcal per week) in a non-randomised pre-post intervention study. Body mass (BM), waist circumference (WC), body composition, resting metabolic rate (RMR), total daily EI, individual meals, appetite sensations and appetite-related peptides were measured at baseline (week 0) and post-intervention (week 12). Free-living physical activity (PA) and SB were measured (SenseWear) at baseline, week 1 and 10 of the exercise intervention, and at post-intervention (week 13). Results Following the 12-week exercise intervention BM [p = .04], BMI [p = .035], WC [p < .001] and fat mass [p = .003] were significantly reduced, and fat-free mass (FFM) significantly increased [p = .003]. Total [p = .028], ad libitum [p = .03] and snack box EI [p = .048] were significantly increased and this was accompanied by an increase in hunger [p = .01] and a decrease in fullness [p = .03] before meals. The peptides did not explain changes in appetite [p > .05]. There was no compensatory reduction in NEPA [p > .05] and no increase in SB, rather there was a decrease in SB during the exercise intervention [p = .03]. Conclusions Twelve-weeks of supervised aerobic exercise resulted in a significant reduction in FM and an increase in FFM. Exercise increased hunger and EI which only partially compensated for the increase in energy expenditure. There was no evidence for a compensatory reduction in NEPA or an increase in SB. Dietary intervention, as an adjunct to exercise, may offset the compensatory increase in EI and result in a greater reduction in BM.
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Sugar-containing beverages like orange juice can be a risk factor for obesity and type 2 diabetes although the underlying mechanisms are less clear. We aimed to investigate if intake of orange juice with or in-between meals differently affects energy balance or metabolic risk. Twenty-six healthy adults (24.7 ± 3.2 y; BMI 23.2 ± 3.2 kg/m2) participated in a 4-week cross-over intervention and consumed orange juice (20% of energy requirement) either together with 3 meals/d (WM) or in-between 3 meals/d (BM) at ad libitum energy intake. Basal and postprandial insulin sensitivity (primary outcome), daylong glycaemia, glucose variability and insulin secretion were assessed. Body fat mass was measured by air-displacement plethysmography. After BM-intervention, fat mass increased (+1.0 ± 1.8 kg; p < 0.05) and postprandial insulin sensitivity tended to decrease (ΔMatsudaISI: -0.89 ± 2.3; p = 0.06). By contrast, after WM-intervention fat mass and gamma-glutamyl transferase (GGT) decreased (-0.30 ± 0.65 kg; -2.50 ± 3.94; both p < 0.05), whereas glucose variability was higher (ΔMAGE: +0.45 ± 0.59, p < 0.05). Daylong glycaemia, insulin secretion, changes in basal insulin sensitivity, and triglycerides did not differ between WM- and BM-interventions (all p > 0.05). In young healthy adults, a conventional 3-meal structure with orange juice consumed together with meals had a favorable impact on energy balance, whereas juice consumption in-between meals may contribute to a gain in body fat and adverse metabolic effects.
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Background: Regulation of postprandial hyperglycemia is a major concern for individuals with type 2 diabetes. Exercise can reduce postprandial hyperglycemia by increasing contraction-mediated glucose uptake. However, there is no consensus with which to develop guidelines for optimal postprandial exercise timing and prescription. Objective: The current systematic review was conducted to consolidate the literature surrounding the effects of postprandial exercise on glucose regulation in individuals with type 2 diabetes. Methods: Electronic databases were searched on 17 February 2017. Inclusion criteria were: (1) trial was a randomized crossover trial; (2) subjects were diagnosed with type 2 diabetes; (3) a standardized meal was given; (4) exercise was initiated within three hours of the meal; (5) subjects were not treated with insulin. Results: Twelve studies met the inclusion criteria, involving 135 participants (108 males, 20 females, seven unknown). The included studies varied greatly in their timing, duration, intensity, modality, and glucose measures. Postprandial aerobic exercise (11 studies) decreased short-term glucose area under the curve by 3.4-26.6% and 24-h prevalence of hyperglycemia by 11.9-65%. Resistance exercise (two studies) decreased the short-term glucose area under the curve by 30% and 24-h prevalence of hyperglycemia by 35%. Conclusion: Postprandial exercise may be an effective way to improve glucose control in individuals with type 2 diabetes. The most consistent benefits were seen in long-duration (≥ 45 min), moderate-intensity aerobic exercise. Resistance training also appears to be an effective modality. We recommend that individuals with type 2 diabetes focus on increasing energy expenditure after the largest meal of the day. More research is needed in this area to confirm the results of this systematic review and to provide clinicians with specific exercise recommendations.
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Objective Stair climbing–descending exercise (ST-EX) is a convenient method to increase exercise intensity. We compared the acute effect of ST-EX on lowering postprandial hyperglycemia with that of constant bicycle exercise (BI-EX) performed at the same heart rate (HR). Research design and methods Seven people with type 2 diabetes and seven with impaired glucose tolerance volunteered for this study. The step rate for ST-EX and work rate for BI-EX were individually determined to correspond to high-moderate to low-vigorous intensity (HR ~130 beats per minute). For the ST-EX trial, the subjects performed 16 repetitions of walking down one flight of stairs followed by climbing up to the starting point (~8 min in duration) 90 min after consuming a test meal. For the BI-EX trial, the subjects performed a constant pedaling exercise for the same duration at the same time after the meal. Results The reduction in blood glucose (BG) level between 90 and 105 min after a meal was significantly greater for ST-EX (–4.0±0.7mmol/L) than for BI-EX (–2.7±0.9mmol/L). The net reduction in BG between 90 and 105 min was also significantly greater for ST-EX (–3.2±0.7mmol/L) than for BI-EX (–2.0±0.6mmol/L). Serum insulin levels did not differ between the groups. Oxygen consumption for ST-EX was higher than that for BI-EX, but the blood lactate level and respiratory exchange ratio (RER) for ST-EX were lower than those for BI-EX. Conclusions Compared with BI-EX performed at the same HR, ST-EX more rapidly decreased postprandial BG level with lower blood lactate and RER responses. A short bout of ST-EX may be clinically useful to acutely ameliorate BG levels after meals.
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Objective: Structured obesity treatment programs at primary care level are becoming increasingly important. However, evidence from current treatment approaches in the long term is lacking. In view of this fact we evaluated a standardized, meal replacement-based weight loss program (myLINE?; AENGUS, Graz, Austria) according to the currently applicable guidelines. Methods: Data of overweight and obese individuals (n = 70) who participated at least 36 months in the program were analyzed. Data were collected at baseline (T0) as well as after 1, 3, 6, 12, 24, and 36 (T1-T36) months. Body composition was measured by conventional anthropometry and bioelectrical impedance analysis. Results: Compared to T0, a maximum weight, BMI, fat mass, absolute body cell mass (BCM) reduction and an increase of relative BCM could be seen at T6. Subsequently, the findings reveal a significant reduction of body weight and body fat and a satisfying development of body cell mass during the observation period of 36 months. Conclusion: The evaluated program complies with national and international guidelines for the therapy of obesity in adults and is efficient and meaningful for a long-term therapeutic use in primary care..
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Purpose and methods This review presents established knowledge on the effects of physical activity (PA) on whole-body insulin sensitivity (SI) and summarises the findings of recent (2013–2016) studies. Discussion and conclusions Recent studies provide further evidence to support the notion that regular PA reduces the risk of insulin resistance, metabolic syndrome and type 2 diabetes, and SI improves when individuals comply with exercise and/or PA guidelines. Many studies indicate a dose response, with higher energy expenditures and higher exercise intensities, including high intensity interval training (HIIT), producing greater benefits on whole-body SI, although these findings are not unanimous. Aerobic exercise interventions can improve SI without an associated increase in cardiorespiratory fitness as measured by maximal or peak oxygen consumption. Both aerobic and resistance exercise can induce improvements in glycaemic regulation, with some suggestions that exercise regimens including both may be more efficacious than either exercise mode alone. Some studies report exercise-induced benefits to SI that are independent of habitual diet and weight loss, while others indicate an association with fat reduction, hence the debate over the relative importance of PA and weight loss continues. During exercise, muscle contraction stimulated improvements in SI are associated with increases in AMPK activity, which deactivates TCB1D1, promoting GLUT4 translocation to the cell membrane and thereby increasing glucose uptake. Postexercise, increases in Akt deactivate TCB1D4 and thereby increase GLUT4 translocation to the cell membrane. The reduction in intramuscular saturated fatty acids and concomitant reductions in ceramides, but not diacylglycerols, provide a potential link between intramuscular lipid content and SI. Increased skeletal muscle capillarisation provides another independent adaptation through which SI is improved, as does enhanced β cell activity. Recent studies are combining exercise interventions with dietary and feeding manipulations to investigate the potential for augmenting the exercise-induced improvements in SI and glycaemic control.
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Obesity is undoubtedly caused by a chronic positive energy balance. However, the early metabolic and hormonal responses to overeating are poorly described. This study determined glycaemic control and selected gut hormone responses to nutrient intake before and after 7 d of high-fat overfeeding. Nine healthy individuals (five males, four females) performed a mixed meal tolerance test (MTT) before and after consuming a high-fat (65 %), high-energy (+50 %) diet for 7 d. Measurements of plasma glucose, NEFA, acylated ghrelin, glucagon-like peptide-1 (GLP-1), gastric inhibitory polypeptide (GIP) and serum insulin were taken before (fasting) and at 30-min intervals throughout the 180-min MTT (postprandial). Body mass increased by 0·79 ( sem 0·14) kg after high-fat overfeeding ( P <0·0001), and BMI increased by 0·27 ( sem 0·05) kg/m ² ( P =0·002). High-fat overfeeding also resulted in an 11·6 % increase in postprandial glucose AUC ( P =0·007) and a 25·9 % increase in postprandial insulin AUC ( P =0·005). Acylated ghrelin, GLP-1 and GIP responses to the MTT were all unaffected by the high-fat, high-energy diet. These findings demonstrate that even brief periods of overeating are sufficient to disrupt glycaemic control. However, as the postprandial orexigenic (ghrelin) and anorexigenic/insulintropic (GLP-1 and GIP) hormone responses were unaffected by the diet intervention, it appears that these hormones are resistant to short-term changes in energy balance, and that they do not play a role in the rapid reduction in glycaemic control.
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We reinvestigated the prevailing concept that muscle contractions only elicit increased muscle glucose uptake in the presence of a so-called “permissive” concentration of insulin (Berger et al., Biochem. J. 146: 231–238, 1975; Vranic and Berger, Diabetes 28: 147–163, 1979). Hindquarters from rats in severe ketoacidosis were perfused with a perfusate containing insulin antiserum. After 60 min perfusion, electrical stimulation increased glucose uptake of the contracting muscles fivefold. Also, subsequent contractions increased glucose uptake in hindquarters from nondiabetic rats perfused for 1.5 h in the presence of antiserum. 3-O-methylglucose uptake was increased markedly by contractions in fast-twitch red and white fibers that were severely glycogen depleted but not in slow-twitch red fibers that were not glycogen depleted. In hindquarters from ketoacidotic rats perfused exactly as by Berger et al., 3-O-methylglucose uptake increased during contractions and glucose uptake was negative at rest and zero during contractions. An increase in muscle transport and uptake of glucose during contractions does not require the presence of insulin. Furthermore, glucose transport in contracting muscle may only increase if glycogen is depleted.
Article
Background: Meal skipping has become an increasing trend of the modern lifestyle that may lead to obesity and type 2 diabetes.Objective: We investigated whether the timing of meal skipping impacts these risks by affecting circadian regulation of energy balance, glucose metabolism, and postprandial inflammatory responses.Design: In a randomized controlled crossover trial, 17 participants [body mass index (in kg/m(2)): 23.7 ± 4.6] underwent 3 isocaloric 24-h interventions (55%, 30%, and 15% carbohydrate, fat, and protein, respectively): a breakfast skipping day (BSD) and a dinner skipping day (DSD) separated by a conventional 3-meal-structure day (control). Energy and macronutrient balance was measured in a respiration chamber. Postprandial glucose, insulin, and inflammatory responses in leukocytes as well as 24-h glycemia and insulin secretion were analyzed.Results: When compared with the 3-meal control, 24-h energy expenditure was higher on both skipping days (BSD: +41 kcal/d; DSD: +91 kcal/d; both P < 0.01), whereas fat oxidation increased on the BSD only (+16 g/d; P < 0.001). Spontaneous physical activity, 24-h glycemia, and 24-h insulin secretion did not differ between intervention days. The postprandial homeostasis model assessment index (+54%) and glucose concentrations after lunch (+46%) were, however, higher on the BSD than on the DSD (both P < 0.05). Concomitantly, a longer fasting period with breakfast skipping also increased the inflammatory potential of peripheral blood cells after lunch.Conclusions: Compared with 3 meals/d, meal skipping increased energy expenditure. In contrast, higher postprandial insulin concentrations and increased fat oxidation with breakfast skipping suggest the development of metabolic inflexibility in response to prolonged fasting that may in the long term lead to low-grade inflammation and impaired glucose homeostasis. This trial was registered at clinicaltrials.gov as NCT02635139.