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Hypercaloric Diets With Increased Meal Frequency, but Not Meal Size, Increase Intrahepatic Triglycerides: A Randomized Controlled Trial

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Unlabelled: American children consume up to 27% of calories from high-fat and high-sugar snacks. Both sugar and fat consumption have been implicated as a cause of hepatic steatosis and obesity but the effect of meal pattern is largely understudied. We hypothesized that a high meal frequency, compared to consuming large meals, is detrimental in the accumulation of intrahepatic and abdominal fat. To test this hypothesis, we randomized 36 lean, healthy men to a 40% hypercaloric diet for 6 weeks or a eucaloric control diet and measured intrahepatic triglyceride content (IHTG) using proton magnetic resonance spectroscopy ((1) H-MRS), abdominal fat using magnetic resonance imaging (MRI), and insulin sensitivity using a hyperinsulinemic euglycemic clamp with a glucose isotope tracer before and after the diet intervention. The caloric surplus consisted of fat and sugar (high-fat-high-sugar; HFHS) or sugar only (high-sugar; HS) and was consumed together with, or between, the three main meals, thereby increasing meal size or meal frequency. All hypercaloric diets similarly increased body mass index (BMI). Increasing meal frequency significantly increased IHTG (HFHS mean relative increase of 45%; P = 0.016 and HS mean relative increase of 110%; P = 0.047), whereas increasing meal size did not (2-way analysis of variance [ANOVA] size versus frequency P = 0.03). Abdominal fat increased in the HFHS-frequency group (+63.3 ± 42.8 mL; P = 0.004) and tended to increase in the HS-frequency group (+46.5 ± 50.7 mL; P = 0.08). Hepatic insulin sensitivity tended to decrease in the HFHS-frequency group while peripheral insulin sensitivity was not affected. Conclusion: A hypercaloric diet with high meal frequency increased IHTG and abdominal fat independent of caloric content and body weight gain, whereas increasing meal size did not. This study suggests that snacking, a common feature in the Western diet, independently contributes to hepatic steatosis and obesity. ( Trial registration: www.clinicaltrials.gov; nr.NCT01297738.)
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Hypercaloric Diets With Increased Meal Frequency,
but Not Meal Size, Increase Intrahepatic
Triglycerides: A Randomized Controlled Trial
Karin E. Koopman,
1
Matthan W.A. Caan,
2
Aart J. Nederveen,
2
Anouk Pels,
1
Mariette T. Ackermans,
3
Eric Fliers,
1
Susanne E. la Fleur,
1*
and Mireille J. Serlie
1*
American children consume up to 27% of calories from high-fat and high-sugar snacks.
Both sugar and fat consumption have been implicated as a cause of hepatic steatosis
and obesity but the effect of meal pattern is largely understudied. We hypothesized that
a high meal frequency, compared to consuming large meals, is detrimental in the accu-
mulation of intrahepatic and abdominal fat. To test this hypothesis, we randomized 36
lean, healthy men to a 40% hypercaloric diet for 6 weeks or a eucaloric control diet
and measured intrahepatic triglyceride content (IHTG) using proton magnetic resonance
spectroscopy (
1
H-MRS), abdominal fat using magnetic resonance imaging (MRI), and
insulin sensitivity using a hyperinsulinemic euglycemic clamp with a glucose isotope
tracer before and after the diet intervention. The caloric surplus consisted of fat and
sugar (high-fat-high-sugar; HFHS) or sugar only (high-sugar; HS) and was consumed
together with, or between, the three main meals, thereby increasing meal size or meal
frequency. All hypercaloric diets similarly increased body mass index (BMI). Increasing
meal frequency significantly increased IHTG (HFHS mean relative increase of 45%;
P50.016 and HS mean relative increase of 110%; P50.047), whereas increasing meal
size did not (2-way analysis of variance [ANOVA] size versus frequency P50.03).
Abdominal fat increased in the HFHS-frequency group (163.3 642.8 mL; P50.004)
and tended to increase in the HS-frequency group (146.5 650.7 mL; P50.08).
Hepatic insulin sensitivity tended to decrease in the HFHS-frequency group while
peripheral insulin sensitivity was not affected. Conclusion: A hypercaloric diet with high
meal frequency increased IHTG and abdominal fat independent of caloric content and
body weight gain, whereas increasing meal size did not. This study suggests that snack-
ing, a common feature in the Western diet, independently contributes to hepatic steato-
sis and obesity. (Trial registration: www.clinicaltrials.gov; nr.NCT01297738.)
(HEPATOLOGY 2014;00:000-000)
Obesity is a worldwide health problem and asso-
ciated with hepatic steatosis and intra-abdomi-
nal fat accumulation. Although obesity and
hepatic steatosis often coincide, hepatic steatosis can be
present in lean subjects and is not present in all obese
humans,
1
suggesting that factors besides obesity con-
tribute to fat accumulation in the liver. An obvious
candidate to be involved is the diet. Caloric content
2
and individual macronutrients are associated with
hepatic steatosis. Short-term high-fat diets increase
intrahepatic triglyceride content (IHTG) in lean and
obese humans
3,4
and induce robust hepatic steatosis in
rodents
5
and dietary glucose and fructose stimulate
de novo lipogenesis (DNL)
6
and increase IHTG, even
Abbreviations: 1H-MRS, proton magnetic resonance spectroscopy; HFHS, high-fat-high-sugar; IHTG, intrahepatic triglyceride content; MRI, magnetic resonance
imaging; REE, resting energy expenditure.
From the
1
Department of Endocrinology & Metabolism, Academic Medical Centre Amsterdam, Netherlands;
2
Department of Radiology, Academic Medical
Centre Amsterdam, Netherlands;
3
Department of Clinical Chemistry, Laboratory of Endocrinology, Academic Medical Centre Amsterdam, Netherlands.
Received January 13, 2014; accepted March 23, 2014.
The study was funded by a Ph.D. fellowship grant awarded by the AMC Executive Board and a grant from the Netherlands Organization for Scientific Research
(ZonMw VIDI 917.96.331).
*These authors contributed equally to this work.
1
in lean subjects.
7,8
Moreover, cross-sectional studies
have identified the consumption of sugar-sweetened
soft drinks as a dietary factor predicting hepatic steato-
sis.
9
Recent human studies, however, showed that over-
feeding resulted in accumulation of IHTG without a
differential effect of fructose, glucose, or fat.
10,11
This
suggests that macronutrient composition is not the only
determining dietary factor in IHTG accumulation. A
factor less often considered is the frequency and timing
of food intake. This is remarkable, since up to 27% of
U.S. childrens daily calories come from snacks
12
and
also in obese women excessive caloric intake mainly
comes from snacks between meals.
13
Interestingly,
when provided with the choice to consume saturated
fat and liquid sugar separate from their balanced chow
pellets, rats increase their meal frequency, show persis-
tent hyperphagia, and become obese.
14
Whether snack-
ing specifically affects IHTG is unknown.
Hepatic steatosis increases the risk for nonalcoholic
steatohepatitis (NASH), fibrosis, and cirrhosis and is
associated with insulin resistance.
15,16
How hepatic ste-
atosis interferes with insulin sensitivity in humans is
only in part elucidated. We recently showed in patients
with familial hypobetalipoproteinemia, which is charac-
terized by massive IHTG accumulation, that hepatic
steatosis per se is not associated with insulin resist-
ance.
17
Interference of lipid metabolites with insulin
signaling is a general concept in obesity-associated insu-
lin resistance, and macronutrients themselves are able
to modulate glucose production and insulin sensitivity
independent of obesity.
18,19
Rats snacking fat and sugar
develop insulin resistance within 1 week,
20
and female
adolescents who reported consumption of frequent
snacks throughout the day have a higher homeostatic
model assessment of insulin resistance (HOMA-IR)
compared to nonsnacking controls.
21
Randomized con-
trolled studies on the effect of increasing meal fre-
quency or meal size with different macronutrient
combinations on insulin sensitivity and IHTG are cur-
rently unavailable and was the aim of this study. We
hypothesized that increasing meal frequency, represent-
ing a snacking eating pattern, negatively affects IHTG
and insulin sensitivity.
Materials and Methods
Study Participants
We recruited 37 Caucasian, lean men (age 22 [19-
27] years, body mass index [BMI] 22.5 [19.5-24.5]
kg/m
2
) by way of local advertisements. Participants
were healthy, had no family history of type 2 diabetes
(T2DM) and a normal oral glucose tolerance test.
22
Other exclusion criteria were use of medication, sub-
stance abuse (nicotine or drugs, alcohol >2 units/day),
history of eating or psychiatric disorders, exercise >3
hours/week, and an unhealthy ad libitum diet. A
healthy diet contained balanced macronutrient compo-
sition following the Dutch guidelines.
23
Self-reported
body weight was stable in the 6 months before study
participation, thereby excluding a hypocaloric or
hypercaloric state. The study protocol conformed to
the ethical guidelines of the 1975 Declaration of Hel-
sinki and was approved by the Medical Ethics Com-
mittee of the AMC Amsterdam. Written informed
consent was obtained from all study participants before
the start of study participation.
Study Design
A schematic overview of the study design is presented
in Fig. 1. After inclusion subjects started the 1-week
run-in phase: they reported their ad libitum intake on an
online diet journal (eetmeter.voedingscentrum.nl). Body
weight before and after this week had to be similar; the
consumed amount of calories was then considered
adequate for weight maintenance, i.e., eucaloric. Sub-
jects were then randomized into one of four hypercaloric
diet groups (n 58/group) or a control group (n 55).
The control group underwent all measurements but
continued the weight maintaining ad libitum diet. The
diet was followed for 6 consecutive weeks. Subjects vis-
ited the research unit weekly for measurement of body
weight and resting energy expenditure (REE) and diet
monitoring; subjects daily reported their ad libitum
intake online. When ad libitum caloric intake was lower
than caloric need (1.4 3REE), subjects were instructed
to increase their ad libitum intake. After the interven-
tion, subjects were monitored until they returned to
Address reprint requests to: Dr. Mireille J Serlie, AMC, Dept. of Endocrinology & Metabolism, room F5-167, Meibergdreef 9, 1105 AZ Amsterdam, the Neth-
erlands. E-mail: m.j.serlie@amc.uva.nl; fax: 131(0)20-6917682.
Copyright V
C2014 The Authors. HEPATOLOGY published by Wiley on behalf of the American Association for the Study of Liver Diseases. This is an open access article
under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original
work is properly cited, the use is noncommercial and no modifications or adaptations are made.
View this article online at wileyonlinelibrary.com.
DOI 10.1002/hep.27149
Potential conflict of interest: Dr. Serlie received grants from Mediq Tefa.
2 KOOPMAN ET AL. HEPATOLOGY, Month 2014
their baseline body weight. The baseline characteristics,
study design, and changes in body weight have previ-
ously been reported.
24
Hypercaloric Diets
All diets consisted of a 40% caloric surplus on top
of the ad libitum weight-maintaining diet (calculated
as 1.4 3REE). The hypercaloric diet groups were:
1. HFHS-size group: high-fat-high-sugar (HFHS) diet
using Nutridrink Compact three times a day, con-
sumed together with the three daily main meals.
2. HFHS-frequency group: HFHS diet using Nutri-
drink Compact three times a day, consumed 2-3
hours after each meal.
3. HS-size group: high-sugar (HS) diet using com-
mercially available sucrose-sweetened beverages
three times a day, consumed together with the
three daily main meals.
4. HS-frequency group: HS diet using commercially
available sucrose-sweetened beverages three times a
day, consumed 2-3 hours after each meal.
Nutridrink Compact (Nutricia Advanced Medical
Nutrition; Zoetermeer, the Netherlands) is a liquid
meal with nutritive value of 240 kcal/100 mL; 16
energy% protein (mainly casein), 49 energy% carbohy-
drates (mainly maltose and polysaccharides) and 35
energy% fat (mainly unsaturated fat). As HS liquid,
subjects consumed commercially available sucrose-
sweetened (550% glucose/50% fructose) soft drinks.
The soft drinks contained no fat or protein. Partici-
pants chose their beverage from a list of soft drinks
with comparable nutritive value. Soft drinks contained
43.3 (range 36-49) kcal/100 mL and 10.3 (range 9-
12) g/100 mL of sucrose. Participants consumed on
average 1,000 mL 3 times per day.
Measurements and Calculations
REE. REE was measured using indirect calorime-
try. VO2 and VCO2 were measured in the supine
position for 20 minutes using a ventilated hood system
(Vmax Encore 29; SensorMedics, Anaheim, CA). REE
was calculated as described previously.
25
The abbrevi-
ated Weir equation was used to calculate 24-hour
energy expenditure.
IHTG Measurement Using Magnetic Resonance
Spectroscopy (MRS).
1
H-MRS measurements were
performed on a clinical 3.0T Philips Intera scanner
(Philips Healthcare, Best, The Netherlands). After per-
forming T1-weighted coronal and axial localizer images
of the abdomen, a voxel of 20 320 320 mm was
positioned in the right hepatic lobe, avoiding inclusion
of the diaphragm and edges of the liver, as well as vas-
cular and biliary structures. Voxel size and acquisition
times were standardized for all subjects. Spectra were
acquired using first-order iterative shimming, a PRESS
sequence with relaxation time/echo time (TR/TE)
2,000/35 ms and 64 signal acquisitions during free
breathing.
26 1
H-MRS data were processed using
jMRUI software. The water nonsuppressed spectra
were used to quantify the lipid signal resonances. Rela-
tive fat content was expressed as a ratio of the fat peak
area over the cumulative water and fat peak areas (1.3
ppm / (1.3 ppm 14.65 ppm)). Calculated peak areas
of water and fat were corrected for T2 relaxation.
Percentage IHTG was calculated as previously
described.
26
Fig. 1. Study design.
HEPATOLOGY, Vol. 00, No. 00, 2014 KOOPMAN ET AL. 3
Abdominal Fat Quantification Using MRI. We
performed abdominal fat measurements in abdominal
MRIs acquired on a clinical 3.0T Philips Intera scan-
ner (Philips Healthcare) at baseline and after the diet
period. The abdominal MRIs were bias field-cor-
rected
27
and then automatically segmented with in-
house developed software, written in MatLab (Math-
Works, Natick, MA). In short, subcutaneous fat was
segmented using snakes, after which visceral fat was
segmented by intensity thresholding. The data were
then manually corrected by one well-trained researcher
blinded for the randomization with ITK-SNAP 2.2
software. We analyzed abdominal fat in 10 consecutive
slices at the level of lumbar vertebrae L3/L4, which
has been shown to be representative for total abdomi-
nal fat.
28
Two-Step Hyperinsulinemic Euglycemic Clamp.
Insulin sensitivity was measured with a two-step hyper-
insulinemic euglycemic clamp after an overnight fast
in supine position as described previously.
29
Additional
details are given in the Supporting Material.
Laboratory Analyses. Plasma glucose concentra-
tions were measured with a glucose oxidase method
(EKF Diagnostics, Barleben/Magedeburg, Germany).
Insulin and cortisol were determined on an IMMU-
LITE 2000 system (Siemens Healthcare Diagnostics,
Breda, The Netherlands). Free fatty acids (FFA) were
measured by an enzymatic method (Nefac; Wako
Chemicals, Richmond, VA). Leptin and glucagon were
determined by radioimmunoassay (Millipore, Billerica,
MA). [6,6-
2
H
2
] glucose enrichment was measured
with gas chromatography-mass spectrometry. Addi-
tional details are provided in the Supporting Material.
Sample Size. We based our sample size calculations
on a previous study in which we showed that a hyper-
caloric diet increased HOMA-IR (measure for insulin
sensitivity) by 0.46 60.17.
30
HOMA-IR has been
shown previously to be associated with clamp-derived
insulin sensitivity measures.
31
We calculated the sample
size with a significance level a50.05, power 580% and
effect size 50.46. To determine significant differences in
insulin sensitivity we needed seven subjects per group.
Randomization Process. Subjects were randomly
allocated to one of the four hypercaloric diet groups or
the control group. Randomization was not blinded.
We performed simple, nonstratified randomization by
drawing lots.
Calculations and Statistics. Endogenous glucose
production (EGP) and peripheral glucose uptake (rate
of disappearance, Rd) were calculated using modified
versions of the Steele equations for the nonsteady-state
and expressed as micromoles/kilograms/minute as
described previously.
32,33
We calculated caloric intake/
day as the mean of the complete diet period of
6 weeks. When normality tests showed normal distri-
bution, data before and after the diet within the
groups were compared using a paired Student ttest.
Otherwise, the Wilcoxon matched pairs test was used.
Between-group differences were analyzed using a two-
way analysis of variance (ANOVA) with a post-hoc
Bonferroni for multiple comparisons.
Table 1. Baseline Characteristics of Study Participants
Controls HFHS-S HFHS-F HS-S HS-F P
N 58878
Age (y) 23.0 63.1 22.6 62.9 21.5 61.9 22.0 62.5 21.9 62.8 0.84
BMI (kg/m
2
) 22.6 62.3 22.3 61.0 22.5 61.5 21.7 61.1 22.6 61.8 0.90
Weight (kg) 76.6 67.7 78.0 65.6 81.3 68.1 77.4 67.9 81.0 68.8 0.70
Waist circumference (cm) 79.9 65.4 80.7 63.1 83.3 63.3 79.0 64.9 82.4 63.2 0.25
Leptin (ng/ml) 2.8 61.7 3.0 61.5 3.3 61.4 1.9 60.2 3.7 61.4 0.14
Resting energy expenditure (kCal/kg) 23.7 61.8 23.9 62.6 24.1 62.0 22.4 62.5 24.5 62.6 0.12
Caloric intake (kCal/day) 2490 6262 2566 6317 2616 6323 2353 6503 2456 6292 0.64
Carbohydrate intake (% of total kCal) 51 684564456545644065 0.02
Fat intake (% of total kCal) 29 643165356532663965 0.006
Protein intake (% of total kCal) 15 631562166216621662 0.48
Glucose (mmol/L) 4.8 60.4 4.7 60.3 4.7 60.2 4.7 60.1 4.9 60.2 0.61
Insulin (pmol/L) 45.0 613.4 40.1 68.6 55.0 621.5 37.0 611.4 57.7 628.7 0.21
HOMA-IR 1.4 60.4 1.2 60.3 1.7 60.6 1.1 60.3 1.7 60.9 0.19
Plasma TG (mmol/L) 0.73 60.29 0.70 60.49 0.56 60.20 0.60 60.21 0.70 60.37 0.90
Free fatty acids (mmol/L) 0.60 60.27 0.33 60.09 0.54 60.23 0.48 60.20 0.68 60.28 0.09
Hepatic TAG Content (%) 1.34 60.54 0.86 60.34 0.98 60.91 0.80 60.49 1.49 60.95 0.13
Data are presented as mean 6SD. Glucose, insulin, triglycerides (TG) and free fatty acids were determined in the fasting state.
4 KOOPMAN ET AL. HEPATOLOGY, Month 2014
Results
Recruitment and Baseline Characteristics. After
37 subjects completed the study protocol, we excluded
two subjects from the analyses because of uncertain
diet compliance. We furthermore excluded one subject
because of excessive alcohol consumption during the
last hypercaloric intervention week. Two subjects were
replaced by newly recruited participants. Baseline char-
acteristics of the subjects are presented in Table 1.
Subjects were lean and had normal insulin sensitivity
and IHTG. Control subjects consumed more carbohy-
drate and less fat at baseline compared to the four
hypercaloric groups, but intake was similar between
the four hypercaloric groups (Table 1).
Control Subjects. In the control group, BMI
remained stable between the T 50 weeks and T 56
weeks measurements (22.3 62.1 versus 22.2 62.2 kg/m
2
;
P50.37).Caloricintakeandintakeofspecicmacronu-
trientswerestableduringtheobservationalperiod(data
not shown). IHTG (1.34 60.54 versus 1.15 60.26%;
Table 2. Food Intake per Randomization Group During the Hypercaloric Diet
Post-hoc
HFHS-S HFHS-F HS-S HS-F PBetween HFHS Between HS
Total caloric intake (kCal) 3747 6137 3987 6218 3474 6694 3614 6381 0.11 1.00 1.00
Ad libitum caloric intake (kCal) 2640 6141 2886 6171 2565 6469 26336216 0.13 0.49 1.00
Caloric surplus (kCal) 1106 6132 1101 6141 909 6239 982 6262 0.18 1.00 1.00
Total carbohydrate intake (g) 437 633 444 638 507 6118 511 681 0.10 1.00 1.00
Ad libitum carbohydrate intake (g) 300 633 308 631 278 668 263 627 0.16 1.00 1.00
Excess carbohydrate intake (g) 137 616 136 617 229 660 248 666 <0.001 1.00 1.00
Total fat intake (g) 143 616 155 66 102 619 115 611 <0.001 0.64 0.42
Ad libitum fat intake (g) 100 617 112 64 102 619 115 611 0.11 0.61 0.46
Excess fat intake (g) 43 6543660 0<0.001 1.00 1.00
Total protein intake (g) 142 613 151 65 106 624 108 66<0.001 1.00 1.00
Ad libitum protein intake (g) 98 611 106 67 106 624 108 66 0.48 1.00 1.00
Excess protein intake (g) 44 6544660 0<0.001 1.00 1.00
Relative carbohydrate intake (% of total kCal)* 47 64466258635664<0.001 0.45 0.35
Relative fat intake (% of total kCal)* 35 63366127642963<0.001 0.43 0.26
Relative protein intake (% of total kCal)* 15 61156112621262<0.001 0.74 0.74
Data are presented as [mean 6SD] food intake per day over the complete 6-week diet period.
*Relative intake data are approximations due to used assumptions on converting grams to calories: 1g carb 54 kCal; 1g fat 59 kCal; 1g protein 54 kCal.
Fig. 2. (A) Ad libitum caloric intake and surplus caloric intake during the diet interventions. Data are presented as mean and SEM, average of
the 6-week diet period. (a) ANOVA of total caloric intake: P50.11, F 52.24. (B) Baseline BMI and BMI gain after the hypercaloric diets. Data
are presented as mean and SEM group averages. (b) ANOVA BMI gain: P50.42, F 50.97; (c) ANOVA BMI after the diet: P50.81, F 50.32.
HEPATOLOGY, Vol. 00, No. 00, 2014 KOOPMAN ET AL. 5
P50.50), abdominal fat (0.57 60.25 versus 0.56 60.11
liter; P50.92), insulin-mediated suppression of EGP
(75.0 67.1 versus 73.0 614.7%; P50.81), and periph-
eral rate of disappearance of glucose (64.868.7 versus
68.3 65.1 lmol/kg/min; P51.00) did not change after
the observational period. Control subjects were included
to show reproducibility of the measurements only and are
therefore not further analyzed.
Caloric Intake. Food intake and macronutrient
composition during the hypercaloric interventions are
presented in Table 2 and Fig. 2A. In summary, ad libi-
tum nutrient intake was similar between the four diet
groups. There was no difference in carbohydrate and
fat intake between both HS groups or between both
HFHS groups. There were no side effects or adverse
events reported by subjects on any of the four diets.
BMI and REE. Subjects gained 2.5 61.7 kg
within the 6 weeks. All hypercaloric diet interventions
resulted in an increase in BMI (Table 3) with no dif-
ferences between the diet groups (Fig. 2B). REE did
not change in any of the diet groups (Table 3).
IHTG. IHTG significantly increased in the
HFHS-frequency (0.98 60.91% versus 1.38 61.26%
[mean relative increase 45%]; P50.018) and the HS-
frequency (1.49 60.95% versus 3.10 62.16% [mean
relative increase 110%]; P50.043) groups (Fig. 3).
The increase in IHTG tended to be higher in the HS-
frequency group (P50.07). In the two groups with
increased meal size, IHTG did not change (HFHS-size
0.85 60.32% versus 1.05 60.57%, P50.208; HS-
size 0.80 60.45% versus 0.93 61.04%, P50.917)
(Fig. 3). Two-way ANOVA analysis of the four hyper-
caloric diet groups showed an overall effect of size ver-
sus frequency (P50.03, F 55.435) but not of HFHS
versus HS (P50.13, F 52.418).
Abdominal Fat. Total abdominal fat significantly
increased in the HFHS-frequency group and tended to
increase in the HS-frequency group. In the HFHS-size
and HF-size group abdominal fat did not change
(Table 3). The increase in abdominal fat was not
different between the two frequency groups
(P50.50). The increase in total abdominal fat was
mainly caused by an increase in subcutaneous fat in
Table 3. Intervention Data
HFHS-S HFHS-F HS-S HS-F
Baseline End diet PBaseline End diet PBaseline End diet PBaseline End diet P
BMI (kg/m
2
) 22.2 61.0 22.8 61.1 0.016 22.5 61.5 23.4 61.3 0.001 21.9 61.1 22.7 61.1 <0.001 22.6 61.8 23.1 62.2 0.070
REE (kCal/day) 1898 6187 1946 6115 0.442 1948 6157 1985 6123 0.273 1794 6167 1892 6245 0.175 1925 6140 1904 6145 0.774
Fasting glucose (mmol/L) 4.8 60.3 4.7 60.2 0.935 4.7 60.2 4.8 60.2 0.644 4.760.1 4.6 60.4 0.516 4.9 60.2 5.0 60.2 0.685
Fasting insulin (pmol/L) 41 6848615 0.398 55 622 60 620 0.207 36 611 48 613 0.028 55 628 53 615 0.889
Fasting cortisol (nmol/L) 346 6195 281 681 0.575 247 681 281 6115 0.575 186 646 196 673 0.612 347 6150 274 6164 0.161
Fasting glucagon (ng/L) 68.6 620.8 58.6 613.8 0.401 74.1 616.0 78.0 617.6 0.496 67.2 610.3 78.6 620.0 0.248 70.8 618.6 62.9 616.1 0.779
Fasting leptin (ng/ml) 2.9 61.5 3.7 61.9 0.030 3.3 61.4 5.0 62.6 0.028 1.9 60.2 2.7 60.9 0.028 3.5 61.4 4.9 62.4 0.075
Fasting triglyceride
(mmol/L)
0.69 60.45 0.78 60.35 0.647 0.56 60.21 0.84 60.32 0.012 0.66 60.24 0.83 60.40 0.176 0.68 60.35 0.85 60.38 0.233
Fasting FFA (mmol/L) 0.35 60.11 0.37 60.10 0.574 0.54 60.23 0.41 60.11 0.208 0.48 60.18 0.31 60.17 0.108 0.65 60.28 0.45 60.26 0.093
Basal EGP (lmol/kg.min) 12.1 60.8 12.1 61.7 0.899 11.7 61.0 11.9 61.1 0.610 11.8 61.2 11.8 61.0 0.964 11.9 61.1 12.2 61.4 0.341
Suppression of EGP (%) 72.7 65.9 78.9 65.7 0.166 84.6 610.9 75.2 67.2 0.083 80.2 67.8 75.367.3 0.248 71.9 615.3 73.3 66.7 0.527
Rd (lmol/kg.min) 67.367.2 65.7 68.1 1.000 65.3 611.8 65.1 69.6 0.779 62.4 610.9 59.7 69.1 0.310 57.3 65.9 54.9 67.7 0.263
Step 1 Plasma Insulin
(pmol/L)
128 625 144 646 0.122 160 640 169 638 0.342 146 632 142 635 0.629 134 635 150 643 0.238
Step 2 Plasma Insulin
(pmol/L)
424 679 445 6102 0.349 522 6103 489 673 0.262 468 691 445 6107 0.359 445 681 492 695 0.184
Step 1 FFA suppression
(%)
90.2 67.8 90.2 65.8 0.889 92.1 66.3 87.5 66.7 0.036 95.0 62.8 89.5 68.1 0.128 92.8 63.7 91.6 65.7 0.735
Step 2 FFA suppression
(%)
97.8 63.2 95.7 64.3 0.465 96.1 64.0 96.1 63.4 0.463 98.3 63.1 98.7 63.6 0.655 98.9 62.1 98.2 63.1 0.686
Intra-abdominal adipose
tissue (liter)
0.45 60.09 0.45 60.06 0.907 0.53 60.20 0.59 60.19 0.004 0.39 60.14 0.44 60.10 0.303 0.50 60.14 0.55 60.16 0.051
Subcutaneous adipose
tissue (liter)
0.25 60.06 0.23 60.06 0.393 0.29 60.14 0.33 60.14 0.007 0.19 60.07 0.22 60.06 0.151 0.26 60.08 0.29 60.09 0.020
Visceral adipose tissue
(liter)
0.20 60.06 0.21 60.05 0.177 0.24 60.08 0.26 60.08 0.074 0.22 60.12 0.21 60.05 0.565 0.24 60.08 0.27 60.08 0.175
Fig. 3. Change in IHTG (%) by the different hypercaloric interven-
tions *P<0.05. Data are presented as mean and SEM.
6 KOOPMAN ET AL. HEPATOLOGY, Month 2014
both frequency groups (Table 3). Fat in the visceral
compartment tended to increase in the HFHS-
frequency group and was unchanged in all other
groups (Table 3).
Glucose Metabolism. Fasting glucose and EGP did
not change upon the diet interventions. Fasting insulin
levels slightly but significantly increased in the HS-S
group only (Table 3). Hepatic insulin sensitivity
expressed as percent insulin-mediated suppression of
baseline EGP tended to decrease in the HFHS-
frequency group (Table 3) but not in the other groups.
Peripheral insulin sensitivity did not change in any of
the hypercaloric diet groups (Table 3). In the HFHS-
frequency group insulin-mediated suppression of FFA
significantly decreased (Table 3).
Glucoregulatory Hormones, Leptin, and Plasma
Lipids. Plasma leptin concentrations increased in all
diet intervention groups (Table 3). Glucoregulatory
hormones did not change. Fasting plasma concentra-
tions of triglycerides (TG) increased upon the HFHS-
frequency diet only.
Overall Effects of Increasing Meal Size Versus
Meal Frequency. In Table 4 the differences between
pooled data from the meal size (HS-S and HFHS-S)
and meal frequency (HS-F and HFHS-F) hypercaloric
diet groups are shown. While BMI significantly
increases in both groups, only increasing meal fre-
quency significantly increases IHTG and abdominal
(subcutaneous and visceral) fat and reduces insulin-
mediated suppression of circulating fatty acids.
Discussion
We show that a 6-week hypercaloric snacking diet
increases IHTG and abdominal fat in lean men while
increasing meal size does not. Moreover, we show that
this was irrespective of the macronutrients in the diet,
as both snacking sugar and snacking fat and sugar
resulted in IHTG and abdominal fat accumulation.
However, the increase in IHTG tended to be higher in
the HS-frequency group, indicating that the frequent
snacking of sugar leads to the most profound accumu-
lation of IHTG. Although frequent consumption of
snacks has been linked to obesity,
13,14
we are the first
to provide evidence that overeating by consuming fre-
quent meals, and not large meals, contributes to fat
accumulation in liver independent of body weight
gain. It has been shown that consumption of excessive
carbohydrates above caloric need substantially increases
fractional DNL
34
and glycogen synthesis.
11
The trend
we demonstrated for a higher increase in IHTG in
the HS-frequency group compared to the HFHS-
frequency group (which consumed fewer carbohy-
drates) is in line with this hypothesis, although subjects
in the size groups also consumed excessive carbohy-
drates but spread over three meals. Snacking of mono-
and polysaccharides seems to exert the same effect on
IHTG, as our HS diets contained monosaccharides,
whereas our HFHS diets contained polysaccharides.
The underlying molecular mechanisms remain to be
elucidated. Continuous delivery of nutrients through
the portal vein might yield a different metabolic
response compared to a pattern of fasting and feeding
cycles. Our data suggest that a continuous flow of
nutrients to hepatocytes stimulates DNL either
through induction of carbohydrate responsive tran-
scription factors like CHREBP or insulin-mediated
induction of SREBP1c, PPARc, or LXR.
35,36
Besides
the nutrients, an increased flux of portal FFA might
stimulate DNL
37
since it has been reported that the
plasma FFA pool accounts for 60% of IHTG in
humans with nonalcoholic fatty liver disease
(NAFLD).
6
Lipolysis rates from abdominal adipose tis-
sue were not directly measured in our study, but
insulin-mediated suppression of plasma FFA, a marker
of insulin sensitivity of adipose tissue, was reduced in
the HFHS-, but not HS-, frequency group. A
Table 4. Increased Meal Size vs. Increased Meal Frequency
Increased Meal Size Increased Meal Frequency
Baseline After diet PBaseline After diet P
BMI (kg/m
2
) 22.05 60.98 22.75 61.04 <0.001 22.5 61.5 23.261.6 <0.001
Basal EGP (lmol/kg.min) 11.9 61.0 11.961.3 0.94 11.8 61.0 12.1 61.3 0.29
Suppression of EGP (%) 76.5 67.7 77.1 66.6 0.84 78.3 614.0 74.7 66.6 0.28
Rd (lmol/kg.min) 64.2 69.0 62.7 68.9 0.54 61.369.9 60.0 69.9 0.45
Step 1 FFA suppression (%) 92.4 66.3 89.9 66.7 0.31 92.565.0 89.6 66.4 0.04
Step 2 FFA suppression (%) 97.9 63.1 97.2 64.1 0.51 97.563.4 97.2 63.3 0.68
Intra-abdominal adipose tissue (L) 0.421 60.112 0.444 60.075 0.35 0.51560.167 0.581 60.171 <0.001
Visceral adipose tissue (L) 0.196 60.068 0.215 60.041 0.18 0.23960.073 0.266 60.077 0.02
Subcutaneous adipose tissue (L) 0.225 60.069 0.228 60.056 0.83 0.276 60.111 0.315 60.115 <0.001
Intrahepatic triglyceride content (%) 0.83 60.38 1.00 60.77 0.35 1.22 60.93 2.18 61.90 0.01
HEPATOLOGY, Vol. 00, No. 00, 2014 KOOPMAN ET AL. 7
reduction in b-oxidation is another possible mecha-
nism since in obesity-related hepatic steatosis both
increased DNL and decreased b-oxidation have been
shown in rodents.
38
The mechanisms of excessive stor-
age of liver TGs might be different when subjects are
exposed to high-sugar versus high-fat-high-sugar diets.
Increased IHTG and abdominal fat are risk factors for
insulin resistance and T2DM and our data imply that
a long-term hypercaloric snacking diet increases the
risk for perturbed glucose metabolism independently
of obesity. A reduction in hepatic insulin sensitivity
was observed in the HFHS-frequency group compared
to the HFHS-size group, suggesting that fat and sugar
when consumed in excess and as between-meal snacks
independently affect hepatic glucose metabolism.
However, the effect was relatively modest. Although
some studies show an association between dietary
sugar consumption and insulin resistance and the
prevalence of diabetes,
10,19,39
it is difficult to discern
whether this is a direct effect of carbohydrates over-
consumption or secondary to the induction of obe-
sity. Moreover, the eating pattern was not always
monitored in those studies. We did not observe
changes in peripheral insulin sensitivity in any of the
diet groups studied. We previously showed that a
period of 4-7 weeks of a hypercaloric diet signifi-
cantly decreased insulin sensitivity.
30
However, in that
study subjects were older, had a higher baseline BMI,
and gained more body weight.
Limitations. The study was conducted under free-
living conditions with a risk of noncompliance. How-
ever, weekly visits and intensive phone and email con-
tact with the participants ensured good compliance
with the diets, confirmed by a steady weekly increase
in body weight. Furthermore, this study was con-
ducted in healthy, young, Caucasian, male volunteers.
Therefore, the results might be different in older sub-
jects, female subjects, and subjects from different eth-
nicities. Therefore, the results of this study cannot be
extrapolated to the general population. Because our
intervention was a short-term diet, results might be
different during long-term exposure to hypercaloric
diets. We did not include a high-fat-only group and
therefore the specific effect of a high-fat high-fre-
quency diet remains unknown. The total increase in
IHTG in our subjects was relatively modest and might
be different in other populations.
Finally, the lack of an effect of the short-term hyper-
caloric diet intervention on insulin sensitivity might
become apparent in other populations, since we
showed previously that short-term hypercaloric diets in
a somewhat older population affected whole body
insulin sensitivity.
30
Clinical Relevance. Reports estimate that Ameri-
can children consume up to 27% of calories from
snacks
12
and snacking is common in obese women.
13
Our findings are therefore an actual reflection of eating
habits in today’s society and might in part be an expla-
nation for the increased number of children and adults
with hepatic steatosis and T2DM.
40,41
Our data indi-
cate that attention should be paid to diet patterns
besides caloric intake in general in the treatment of
subjects with hepatic steatosis and abdominal obesity.
In obese subjects who presumably consume a hyper-
caloric diet, snacking should be strongly discouraged.
In addition, one might hypothesize that consuming
fewer meals might be beneficial in reducing hepatic
and abdominal fat accumulation.
In conclusion, hypercaloric diets with increased
meal frequency, representing snacking, increase IHTG
and abdominal fat in lean men, whereas similar diets
with increased meal size do not. This suggests that
food intake pattern independent of caloric excess and
weight gain contributes to the occurrence of hepatic
steatosis and abdominal obesity. Besides, hypercaloric
snacking of fat and sugar tended to reduce hepatic
insulin sensitivity. Therefore, reducing snacking behav-
ior and encouraging consuming 3 meals per day might
have favorable metabolic consequences in the long
term and might reduce the prevalence of NAFLD.
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in the online version of this article at the publisher’s
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HEPATOLOGY, Vol. 00, No. 00, 2014 KOOPMAN ET AL. 9

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Nonalcoholic fatty liver disease (NAFLD) is a metabolic disorder whose current rapidly expanding prevalence is causing it to develop into a major global health concern. NAFLD is closely linked to the modern, unhealthy lifestyle. The Western diet, characterized by excessive energy intake, frequent consumption of red meat, processed meat and foods, soft drinks, and sugar-sweetened beverages (SSBs), irregular meal distribution throughout the day, and unhealthy ways of cooking, predisposes to development of NAFLD. Low levels of physical activity and prolonged sedentary time are additional lifestyle risk factors for NAFLD. Given the present lack of effective pharmacological treatment, lifestyle modifications are regarded as the cornerstone of NAFLD management. Reducing daily calorie intake together with following the Mediterranean diet (MD) is an increasingly accepted approach. Furthermore, increasing the level of physical activity and limiting sedentary behavior are additional measures proposed to improve the outcomes of the disease. Apart from being affected by lifestyle, NAFLD may also affect patients’ quality of life (QoL), mostly in the domain of physical function. In this regard, while the early and more benign form of the disease, i.e., simple hepatic steatosis, may not affect QoL, there is evidence, though conflicting, of the impact of nonalcoholic steatohepatitis (NASH) on this index, with, however, most studies showing that QoL is consistently affected in advanced disease, i.e., hepatic fibrosis, cirrhosis, and hepatocellular carcinoma. Considering all the above, appropriate management of lifestyle is likely to attenuate the severity of the disease and improve the QoL of NAFLD patients.
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Energy intake is the product of portion size (PS)-the energy content of an ingestive event-and ingestive frequency (IF)-the number of ingestive events per unit time. An uncompensated alteration in either PS or IF would result in a change in energy intake and body weight if maintained over time. The objective of this meta-analysis was to assess the independent effects of PS and IF on energy intake and body weight among healthy adults in randomized controlled trials (RCTs). A total of 9708 articles were identified in PubMed, Web of Science, Cochrane, and CINAHL databases. The articles were divided among 10 researchers; each article was screened for eligibility by 2-3 independent reviewers. Exclusion criteria included: populations <19 y and >65 y, unhealthy populations (i.e. participants with an acute or chronic disease), assessments <24 h and <4 wk in duration for trials investigating energy intake or body weight, respectively. Controlled feeding trials (i.e. fixed energy intake) that manipulated IF and PS in the same study intervention (IF/PS) were evaluated separately and for the body weight outcome only. Twenty-two studies (IF = 4, PS = 14, IF/PS = 4) met the inclusion criteria. There was an insufficient number of studies to assess the effect of IF, PS, or IF/PS on body weight. There was heterogeneity in the effect sizes among all comparisons (I2 ≥75%). Consuming larger portion sizes was associated with higher daily energy intake [295 kcal (202, 388), n = 24; weighted mean differences (WMD) (95% CI), n = comparisons], and increased frequency of ingestive events was associated with higher energy intake [203 kcal (76, 330), n = 10]. Results from RCTs support that larger PS and greater IF are both associated with higher energy consumption. However, there is insufficient information to determine chronic effects on body weight. This protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO) as CRD42018104757.
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Little is known about dietary habits and their relationships with liver disease in nonalcoholic fatty liver disease (NAFLD) patients, particularly in the absence of obesity, diabetes or hyperlipidemia. OBJECTIVE: To assess the association between soft drink consumption and the presence of fatty liver in NAFLD patients who do not have classic risk factors. METHODS: Three hundred ten patients with NAFLD diagnosed by ultrasound were assessed for 36 months in a cross-sectional manner. Thirty-one patients (10%) who had NAFLD without classic risk factors were compared with 30 healthy controls. Physical activity was assessed during the preceding week and year, and every six months for 36 months. Data on daily dietary intake of food and soft drink, and the source of added sugar were collected during two seven-day periods, at the beginning of the study, and within two weeks after the metabolic tests by using a validated food questionnaire given by a trained dietician. Insulin resistance and lipid peroxidation were assessed by homeostasis model assessment-insulin resistance index (HOMA-IRI) and malondialdehyde (MDA) levels, respectively. RESULTS: Eighty per cent of patients (25 of 31) consumed an excessive amount of soft drink beverages (more than 50 g/day of added sugar) for 36 months, compared with 20% in healthy controls (P
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It is evident that there is a relationship between the brain's serotonin system and obesity. Although it is clear that drugs affecting the serotonin system regulate appetite and food intake, it is unclear whether changes in the serotonin system are cause or consequence of obesity. To determine whether obesogenic eating habits result in reduced serotonin transporter (SERT)-binding in the human hypothalamic region, we included 25 lean, male subjects who followed a 6-week-hypercaloric diet, which were high-fat-high-sugar (HFHS) or high-sugar (HS) with increased meal size or -frequency (=snacking pattern). We measured SERT-binding in the hypothalamic region with SPECT. All hypercaloric diets significantly increased body weight by 3-3.5%. Although there were no differences in total calories consumed between the diets, only a hypercaloric HFHS-snacking diet decreased SERT-binding significantly by 30%. We here show for the first time in humans that snacking may change the serotonergic system increasing the risk to develop obesity.
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Objectives: Rats subjected to a free-choice high-fat high-sugar (fcHFHS) diet persistently overeat, exhibit increased food-motivated behavior and become overtly obese. Conversely, several studies using a non-choice (nc) high-energy diet showed only an initial increase in food intake with unaltered or reduced food-motivated behavior. This raises the question of the importance of choice in the persistence of hyperphagia in rats on a fcHFHS diet. Subjects: Meal patterns, food intake and body weight gain were studied in male Wistar rats on free-choice diets with fat and/or sugar and in rats on nc diets with fat and sugar (custom made with ingredients similar to the fcHFHS diet). Results: Rats on a ncHFHS diet initially overconsumed, but reduced intake thereafter, whereas rats on a fcHFHS diet remained hyperphagic. Because half of the sugar intake in the fcHFHS group occurred during the inactive period, we next determined whether sugar intake during the light phase was a necessary requirement for hyperphagia, by restricting access to liquid sugar to either the light or dark period with unlimited access to fat and chow. Results showed that hyperphagia occurred irrespective of the timing of sugar intake. Meal pattern analysis revealed consumption of larger but fewer meals in the ncHFHS group, as well as the fcHF group. Interestingly, meal number was increased in all rats drinking liquid sugar (whether on a fcHFHS or a fcHS diet), whereas a compensatory decrease in meal size was only observed in the fcHS group, but not the fcHFHS group. Conclusion: We hereby show the importance of choice in the observation of fcHFHS diet-induced hyperphagia, which results in increases in meal number due to sugar drinking without any compensatory decrease in meal size. We thus provide a novel dietary model in rats that mimics important features of human overconsumption that have been ignored in rodent models of obesity.
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Aims/hypothesis Consumption of sugar-sweetened beverages has been shown, largely in American populations, to increase type 2 diabetes incidence. We aimed to evaluate the association of consumption of sweet beverages (juices and nectars, sugar-sweetened soft drinks and artificially sweetened soft drinks) with type 2 diabetes incidence in European adults. Methods We established a case–cohort study including 12,403 incident type 2 diabetes cases and a stratified subcohort of 16,154 participants selected from eight European cohorts participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. After exclusions, the final sample size included 11,684 incident cases and a subcohort of 15,374 participants. Cox proportional hazards regression models (modified for the case–cohort design) and random-effects meta-analyses were used to estimate the association between sweet beverage consumption (obtained from validated dietary questionnaires) and type 2 diabetes incidence. Results In adjusted models, one 336 g (12 oz) daily increment in sugar-sweetened and artificially sweetened soft drink consumption was associated with HRs for type 2 diabetes of 1.22 (95% CI 1.09, 1.38) and 1.52 (95% CI 1.26, 1.83), respectively. After further adjustment for energy intake and BMI, the association of sugar-sweetened soft drinks with type 2 diabetes persisted (HR 1.18, 95% CI 1.06, 1.32), but the association of artificially sweetened soft drinks became statistically not significant (HR 1.11, 95% CI 0.95, 1.31). Juice and nectar consumption was not associated with type 2 diabetes incidence. Conclusions/interpretation This study corroborates the association between increased incidence of type 2 diabetes and high consumption of sugar-sweetened soft drinks in European adults.
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SHR/NDmcr-cp (cp/cp) rats (SHR/NDcp) are an animal model of metabolic syndrome. A previous study of ours revealed drastic increases in the mass of palmitic (16:0), oleic (18:1n-9), palmitoleic (16:1n-7), cis-vaccenic (18:1n-7) and 5,8,11-eicosatrienoic acids in the liver of SHR/NDcp. However, detailed information on the class of lipid accumulated and the mechanism responsible for the overproduction of the accumulated lipid in the liver was not obtained. This study aimed to characterize the class of lipid accumulated and to explore the mechanism underlying the lipid accumulation in the liver of SHR/NDcp, in comparison with SHR/NDmcr-cp (+/+) (lean hypertensive littermates of SHR/NDcp) and Wistar Kyoto rats. In the liver of SHR/NDcp, de novo synthesis of fatty acids (16:0, 18:1n-9 and 16:1n-7) and triacylglycerol (TAG) synthesis were up-regulated and fatty acid β-oxidation was down-regulated. These perturbations of lipid metabolism caused fat accumulation in hepatocytes and accumulation of TAG, which were enriched with 16:0, 18:1n-9 and 16:1n-7, in the liver of SHR/NDcp. On the other hand, no changes were found in hepatic contents of diacylglycerol and unesterified fatty acid (FFA); among FFA, there were no differences in the hepatic concentrations of unesterified 16:0 and stearic acid between SHR/NDcp and two other groups of rats. Moreover, little change was brought about in the expression of genes responsive to endoplasmic reticulum stress in the liver of SHR/NDcp. These results may reinforce the pathophysiological role of stearoyl-CoA desaturase 1 and fatty acid elongase 6 in the liver of SHR/NDcp.
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& Aims: Diets high in fructose have been proposed to contribute to non-alcoholic fatty liver disease (NAFLD). We compared the effects of high-fructose and matched glucose intake on hepatic triacylglycerol (TAG) concentration and other liver parameters. In a double-blind study, we randomly assigned 32 healthy but centrally overweight men to groups that received either a high-fructose or high-glucose diet (25% energy). These diets were provided during an initial isocaloric period of 2 weeks, followed by a 6-week washout period and then again during a hypercaloric 2 week period. The primary outcome measure was hepatic level of TAG, with additional assessments of TAG levels in serum and soleus muscle, hepatic levels of ATP, and systemic and hepatic insulin resistance. During the isocaloric period of the study, both groups had stable body weights and concentrations of TAG in liver, serum, and soleus muscle. The high-fructose diet produced an increase of 22±52 μmol/L in serum level of uric acid, whereas the high-glucose diet led to a reduction of 23±25 μmol/L (P<.01). The high-fructose diet also produced an increase of 0.8±0.9 in the homeostasis model assessment of insulin resistance, whereas the high-glucose diet produced an increase of only 0.1±0.7 (P=.03). During the hypercaloric period, participants in the high-fructose and high-glucose groups had similar increases in weight (1.0±1.4 kg vs 0.6±1.0 kg; P=.29) and absolute concentration of TAG in liver (1.70±2.6% vs 2.05±2.9%; P=.73) and serum (0.36±0.75 mmol/L vs 0.33±0.38 mmol/L; P=.91), and similar results in biochemical assays of liver function. Body weight changes were associated with changes in liver biochemistry and concentration of TAGs. In the isocaloric period, overweight men on neither a high-fructose nor a high-glucose diet developed any significant changes in hepatic concentration of TAGs or serum levels of liver enzymes. However, in the hypercaloric period both high-fructose and high-glucose diets produced significant increases in these parameters without any significant difference between the 2 groups. This indicates an energy-mediated, rather than specific macronutrient-mediated effect. Clinical trials.gov no: NCT01050140.
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Objectives: To review the magnitude, characteristics, and public health importance of type 2 diabetes in North American youth. Results: Among 15- to 19-year-old North American Indians, prevalence of type 2 diabetes per 1000 was 50.9 for Pima Indians, 4.5 for all US American Indians, and 2.3 for Canadian Cree and Ojibway Indians in Manitoba. From 1967-1976 to 1987-1996, prevalence increased 6-fold for Pima Indian adolescents. Among African Americans and whites aged 10 to 19 years in Ohio, type 2 diabetes accounted for 33% of all cases of diabetes. Youth with type 2 diabetes were generally 10 to 19 years old, were obese and had a family history of type 2 diabetes, had acanthosis nigricans, belonged to minority populations, and were more likely to be girls than boys. At follow-up, glucose control was often poor, and diabetic complications could occur early. Conclusions: Type 2 diabetes is an important problem among American Indian and First Nation youth. Other populations have not been well studied, but cases are now occurring in all population groups, especially in ethnic minorities. Type 2 diabetes among youth is an emerging public health problem, for which there is a great potential to improve primary and secondary prevention.