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Enhanced food intake regulatory responses after a glucose drink in hyperinsulinemic men


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To determine the effect of hyperinsulinemia on food intake and plasma concentrations of glucose and food intake regulatory hormones in men after a glucose drink. Cross-sectional clinical intervention study of the effect of a glucose drink on food intake regulation. Thirty-three normoinsulinemic (NI) (body mass index (BMI)=25.3+/-0.6; age=41.4+/-2.4) and 32 hyperinsulinemic (HI) men (BMI=29.5+/-0.6; age=43.4+/-2.6). Food intake was measured from a pizza meal 1 h after subjects consumed either a noncaloric sweetened drink or a glucose-containing drink (75 g/300 ml) in random order on two occasions. On another occasion, blood samples were taken every 30 min for 2 h after the glucose drink. Fasting insulin in the HI and NI men was 65+/-3 (mean+/-s.e.m.) and 26+/-1.5 pmol/l, respectively. Food intake at the pizza meal was reduced by the glucose drink (P<0.01), but more so in HI (-9.7+/-4.1 %) than NI men (-5.4+/-3.4 %) (P=0.06). The increase in plasma insulin and cholecystokinin (CCK) after the glucose drink was greater and the plasma concentrations of leptin were higher, and ghrelin and adiponectin were lower in HI men than in NI men (P<0.05). These results support epidemiological data suggesting that hyperinsulinemia, at least in the early stages, may provide resistance to weight gain, possibly through physiological mechanisms of food intake control.
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Enhanced food intake regulatory responses after
a glucose drink in hyperinsulinemic men
R Abou Samra, TMS Wolever and GH Anderson
Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
Objective: To determine the effect of hyperinsulinemia on food intake and plasma concentrations of glucose and food intake
regulatory hormones in men after a glucose drink.
Design: Cross-sectional clinical intervention study of the effect of a glucose drink on food intake regulation.
Subjects: Thirty-three normoinsulinemic (NI) (body mass index (BMI) ¼25.370.6; age ¼41.472.4) and 32 hyperinsulinemic
(HI) men (BMI ¼29.570.6; age ¼43.472.6).
Measurements: Food intake was measured from a pizza meal 1 h after subjects consumed either a noncaloric sweetened drink
or a glucose-containing drink (75g/300 ml) in random order on two occasions. On another occasion, blood samples were taken
every 30 min for 2 h after the glucose drink.
Results: Fasting insulin in the HI and NI men was 6573 (mean7s.e.m.) and 2671.5 pmol/l, respectively. Food intake at the
pizza meal was reduced by the glucose drink (Po0.01), but more so in HI (9.774.1 %) than NI men (5.473.4 %) (P¼0.06).
The increase in plasma insulin and cholecystokinin (CCK) after the glucose drink was greater and the plasma concentrations
of leptin were higher, and ghrelin and adiponectin were lower in HI men than in NI men (Po0.05).
Conclusion: These results support epidemiological data suggesting that hyperinsulinemia, at least in the early stages, may
provide resistance to weight gain, possibly through physiological mechanisms of food intake control.
International Journal of Obesity (2007) 31, 1222–1231; doi:10.1038/sj.ijo.0803565; published online 27 February 2007
Keywords: insulin; preload; appetite; men; appetite regulatory hormones
Overfeeding and weight gain cause hyperinsulinemia.
Elevated fasting plasma insulin has been associated with
increased weight gain in Pima Indian children,
that hyperinsulinemia promotes increased energy intake.
However, longitudinal studies of adults from several ethnic
groups report that hyperinsulinemic (HI) or insulin-
resistance subjects gain less weight than insulin-sensitive
implying better intake control relative to
expenditure. Recently, it has been proposed that ‘the
development of insulin resistance/hyperinsulinemia pro-
motes the occurrence of the metabolic syndrome but also
favors over time the re-equilibration of energy intake and
An association between elevated fasting insulin and
equilibrium between energy intake and expenditure would
be expected from the role of insulin in the brain as a central
regulator of food intake.
During hyperinsulinemia and
with stable glycemia, insulin concentrations are increased
in brain areas involved in suppression of food intake and the
regulation of energy homeostasis.
Centrally, insulin reduces
the gene expression of neuropeptide Y (NPY),
a neuropep-
tide with orexigenic effects, and stimulates proopiomelano-
cortin (POMC), a precursor molecule with anorexigenic
In addition, insulin stimulates leptin mRNA
and increases leptin concentrations even in
physiological amounts.
Furthermore, hyperinsulinemia in
men with normal glucose tolerance has been associated with
reduced fasting ghrelin,
an orexigenic hormone.
Because insulin resistance and type 2 diabetes are preceded
by hyperinsulinemia and obesity is associated with hyper-
insulinemia, it may be that hyperinsulinemia creates a
protective–adaptive response in food intake mechanisms,
thereby contributing to weight stability. This hypothesis
has not been explored. In normoinsulinemic (NI) indivi-
duals, appetite and food intake are inversely related to
blood concentrations of glucose,
Received 4 September 2006; revised 20 December 2006; accepted 8 January
2007; published online 27 February 2007
Correspondence: Professor GH Anderson, Department of Nutritional Sciences,
Faculty of Medicine, University of Toronto, Fitzgerald Building, 150 College
Street, Toronto, Ontario, Canada M5S 3E2.
Presented at the Experimental Biology Annual Meeting, April 2004,
Washington, DC, USA (FASEB J 18; A1109, Abs. 727.3)
International Journal of Obesity (2007) 31, 1222– 1231
2007 Nature Publishing Group All rights reserved 0307-0565/07
gastrointestinal hormones including cholecystokinin
immediately following carbohydrate consumption.
However, the effect of hyperinsulinemia in the presence
of normal glycemia on short-term food intake and satiety
hormones after carbohydrate consumption has not been
reported. Therefore, we examined data derived from a study
originally designed to determine the effect of insoluble
cereal fiber consumption on plasma glucose and insulin
responses in HI and NI men.
Materials and methods
Seventy-eight healthy men aged 18–75 years, with a body
mass index (BMI) of 20–35 kg/m
were recruited, as described
Sixty-five men were eligible. Subjects were
screened with a fasting blood sample for glucose, insulin,
lipids, and had their weight, height and waist circumference
measured. They were excluded if they had diabetes (fasting
glucose X7.0 mmol/l), impaired liver enzyme (aspartate
aminotransferase (AST) 42 times upper limit of normal),
impaired renal function (serum creatinine 41.2 times upper
limit of normal), serum triacylglycerol 410 mmol/l (inter-
feres with measurement of glucose/insulin), were taking
diuretics, b-blockers or weight reducing agents, had a
major gastrointestinal disease (e.g. ulcerative colitis), moti-
lity disorder or malabsorption, liver or kidney disease or
have had a major medical or surgical event within the last
6 months. The subjects who ate o600 kcal of test pizza after
the control preload were excluded from the data analysis
on the assumption that they did not like pizza and there-
fore restricted their intake. Unfortunately, in this study,
subjects were not asked whether they liked pizza. An intake
of 600 kcal is below the normal range (mean72 s.d.) of pizza
consumption found in previous studies when subjects were
asked if they liked pizza. Sample size calculation to identify a
150 kcal response to the treatment was based on an intake
average of 1000 kcal.
Subjects were classified into two groups: 32 HI and 33 NI.
Subjects were classified as HI if their fasting plasma insulin
was X41 pmol/l and as NI if their fasting plasma insulin was
p40 pmol/l on more than two occasions (B67th percentile
for nondiabetic subjects in our laboratory).
An elevated
fasting plasma insulin concentration has been strongly
correlated with insulin resistance measured by the HI clamp
All procedures were reviewed and approved
by the Human Subjects Review Committee, Ethics Review
Office of the University of Toronto, and all subjects gave
informed consent.
Study design
The study design was split-plot with ‘crossed’ preload
(control and glucose drink) and ‘nested’ insulin status (NI
and HI) effects (i.e. each subject changed preload but
remained within the given insulin classification). The study
was composed of two phases. The objective of phase 1 of this
study was to measure mealtime energy intake after a glucose
preload in HI and NI men. Subjects were given either a
glucose or control treatment in random order. The glucose
treatment contained 75 g of glucose (82.5 g of glucose
monohydrate source) dissolved in 300 ml of cold spring
water. The control treatment was matched for sweetness
to the glucose treatment with 250 mg of sucralose, a non-
caloric sweetener (McNeil Speciality Products Company,
New Brunswick, NJ, USA). Sucralose was chosen because it
does not affect carbohydrate metabolism, blood glucose
or insulin secretion.
All test beverages were prepared 1 h
before consumption, stored in a refrigerator, and was served
chilled. Lemon concentrate (Equality; The Great Atlantic
and Pacific Company of Canada LTD, Toronto, Canada) was
added in to match for palatability.
The objective of phase 2 of this study was to measure
the response of plasma glucose, insulin, CCK, leptin and
adiponectin to a glucose preload in HI and NI men. The
glucose preload consisted of 75 g of glucose (82.5 g of glucose
monohydrate) dissolved in 300 ml of cold spring water.
Phase 1: food intake in response to glucose drink. Subjects
chose a time between 0700 and 1000 to participate in the
sessions, and they were asked to arrive at the same time for
the subsequent session. There was a 1-week washout period
between the two sessions. Subjects arrived at the study room
in the Department of Nutritional Sciences at the University
of Toronto for each session after an overnight fast (10–14 h).
Water was allowed up to 1 h before the start of each session.
On arrival, subjects completed baseline visual analogue scale
(VAS) questionnaires measuring their motivation to eat, and
then they were asked to proceed to a taste panel room where
they consumed either the glucose treatment (82.5 g of
glucose monohydrate, 5 ml lemon juice and water up to
300 ml) or the control treatment (250 mg of sucralose, 5 ml
of lemon and water up to 300 ml) within 5 min. The two
treatments were given in a random order. The subjects then
returned to a study room and they completed VAS questions
at 15, 30, 45 and 60 min after the preloads. Each page of
the questionnaire was folded out of view after each rating.
The subjects remained seated throughout the study period.
At 60 min after the glucose or control treatment, subjects
returned to the taste panel room and were given 1.5 l of
bottled spring water (Crystal Springs; Aquaterre Corp,
St-Laurent, Canada) and an ad libitum pizza meal, and were
instructed to eat until comfortably full. Before the sessions,
the subjects ranked the pizza according to their preference.
The participants were served two pizzas of their first choice
and one each of their second and third choices per tray.
Each pizza was cut into four quarters. The subjects were told
that additional identical hot trays would be presented in
6–7 min. Four varieties of small round (5-inch diameter)
Hyperinsulinemia, food intake and satiety hormones
RA Samra et al
International Journal of Obesity
pizzas (Deluxe, Pepperoni, Three Cheese and Deli Lovers;
McCain Foods Ltd, Florenceville, Canada) purchased from
local retailers were available. The amount of cooked pizza
and water consumed was determined by the difference
between the weight of the pizza and water before and after
the subjects ate. An advantage of using these pizzas was the
lack of an outer crust, which results in a pizza with uniform
energy content and eliminates the possibility that the
subject will eat the energy-denser filling and leave the
outside crust of the pizza.
Each variety of pizza was weighed separately and the
energy consumed was calculated by converting the net
weight to kcal by use of information provided by the
manufacturer (McCain). Water consumption was deter-
mined by weighing before and after the test meal. On
termination of the test meal, the subjects rated the palat-
ability of the test meal and completed the post-meal
motivation-to-eat questionnaire. In addition, all subjects
completed an Eating Habits Questionnaire to determine
their eating restraint (ER) score.
The motivation-to-eat VAS questionnaire, used to assess
appetite, was composed of four standardized questionnaires
or scales: (1) How strong is your desire to eat? (‘very weak’ to
‘very strong’), (2) How hungry do you feel? (‘not hungry at
all’ to ‘as hungry as I’ve ever felt’), (3) How full do you feel?
(‘not full at all’ to ‘very full’), and (4) How much food do you
think you could eat? (‘nothing at all’ to ‘a large amount’).
Each VAS consisted of a 100-mm line anchored at the
beginning and end by opposing statements.
The subjects
marked an ‘X’ on the line to indicate their feelings at that
given moment. Scores were determined by measuring the
distance (in mm) from the left starting point of the line to
the intersection of the ‘X’. The palatability of the test meal
was measured with a VAS. The question ‘how pleasant have
you found the food?’ could be answered by marking on a
100-mm line anchored at the beginning and end by the
statements ‘not at all pleasant’ and ‘very pleasant.’
Phase 2: satiety hormone response to a glucose drink.Ona
separate day, both HI and NI subjects arrived between 0730
and 1000 at St Michael’s Hospital where they consumed the
75 g glucose drink within 10 min after a 10–14 h overnight
fast. Venous blood was obtained at fasting and 30, 60, 90
and 120 min after they started drinking the glucose solution
for determining glucose, insulin, CCK, ghrelin, leptin and
Biochemical analyses
Blood samples were analyzed for plasma glucose (Cobas
Integra 800; Roche Diagnostics GmbH, Mannheim,
Germany), insulin (Insulin ECLIA; Roche Diagnostics
GmbH, Mannheim, Germany) and CCK (EURIA-CCK, Euro-
Diagnostica AB, Malmo, Sweden) at times 0, 30 60, 90 and
120 min after the glucose drink, for ghrelin (Human Ghrelin
RIA, Phoenix Pharmaceuticals Inc., Belmont CA, USA) at 0,
30, 60 and 120 min, and for leptin (Human ELISA Leptin,
Linco Research Inc., St Charles, MO, USA) and adiponectin
(Human Adiponectin RIA, Linco Research Inc., St Charles,
MO, USA) at 0, 60 and 120 min and for fasting free fatty
acids (FFAs; NEFA C, ACS-ACOD method; WAKO Chemicals
USA, Richmond, VA, USA). Fasting total cholesterol (C),
high-density lipoprotein (HDL) and triacylglycerols (TG)
were measured enzymatically using a Vitros Analyzer 950
(Johnson & Johnson Clinical Diagnostics, Rochester, NY, USA).
Low-density lipoprotein cholesterol (LDL-C) was calculated
for samples with TGo4.5 mmol/l as LDL ¼C–(HDL þTG/
2.2). Apo B was analyzed by nephelometry.
Statistical analysis
Subjects’ characteristics in HI and NI groups were compared
using Student’s t-test. Food intake and average appetite
were analyzed by two-way analysis of variance (ANOVA)
with preload (control and glucose drink), group (NI and HI),
preload group interaction and subject identification (ID)
number as the random variable. Tukey’s post hoc test was
performed to compare treatments when treatment effects
were statistically significant. Plasma glucose, insulin, CCK,
ghrelin, leptin and adiponectin response was analyzed by
repeated measures analysis of covariance using Proc Mixed to
test the effect of time and group. Both BMI and respective
fasting concentrations of the blood parameters were
included in the model as covariates to control for differences
in these variables between subjects. Preliminary exploration
of the data showed that the relationship between blood
parameters and time is best described as curvilinear. To
model this we added the factor time
(time time) to our
model. This approach to modeling nonlinear relationships
is often called polynomial or curvilinear regression.
interaction of group and time was included in the model to
assess whether the time of the blood variable peak was
different between the groups, and the interaction of time
and group was included to test differences in the shape of the
curves for NI and HI subjects. Subject was included in the
model as a factor nested within group because subjects were
not randomly assigned to the groups.
Correlation analysis was conducted to evaluate the rela-
tion among dependent measures with the use of Pearson
partial coefficients controlling for subjects.
Net incremental area under the curves (AUC) for plasma
glucose insulin, CCK, leptin and adiponectin response and
net incremental area above the curve for plasma ghrelin
response was calculated by applying the trapezoid rule.
The net AUC included all incremental area below the curve,
including the area below fasting concentration. As net AUC is
calculated by applying the trapezoid rule to both positive and
negative blood glucose increments, the effect is to subtract the
area below the fasting concentration from that above.
The magnitude of response at 60 min of each hormone was
calculated by dividing the concentration of the hormone at
60 min with its concentration at 0 min (baseline).
Hyperinsulinemia, food intake and satiety hormones
RA Samra et al
International Journal of Obesity
Average appetite score, a measure of the motivation-to-eat,
was calculated at each time of measurement for each test
beverage by the formula:
Appetite score ¼fdesire to eat þhunger þð100
fullnessÞþprospective consumptiong=4
which reflected the four questions on the motivation-to-eat
Statistical analysis was conducted using SPSS software
(version 11.5; SPSS Inc., Chicago, IL, USA) and SAS software
(version 9; Cary, NC, USA). All values are presented as
means7standard error of the mean (s.e.) and Po0.05
indicates statistical significance.
Subject characteristics
HI subjects had a higher BMI, weight, waist circumference,
fasting plasma triacylglycerols, LDL, total cholesterol/HDL
ratio, glucose, insulin and leptin and a significantly lower
plasma HDL and ghrelin than the NI subjects (Po0.05).
However, age, height, ER score, fasting plasma total choles-
terol, FFAs, CCK and adiponectin were not different between
the two groups (Table 1).
Food intake
Analysis of food intake was adjusted for BMI to control for
the difference in BMI between the HI and NI groups. After
adjustment, food intake was reduced by the glucose preload
(Po0.01) and by hyperinsulinemia (P¼0.06), with no
interaction between the effects of these main factors. Mean
food intake reduction in response to the glucose drink was
greatest in the HI men (Table 2).
Average appetite
Average appetite scores, expressed as the difference from
baseline and adjusted for BMI, did not differ between the two
groups. However, appetite changed over time (P¼0.05). Post
hoc analysis showed that at 15 min, the reduction in appetite
score was greater after both the control and glucose
treatments in NI than in HI men. Average appetite score
was unchanged after the glucose treatment in the HI group,
whereas the score was lower in the NI group at 15 min
(Po0.05) (Figure 1). This means that the NI group had less
motivation to eat at 15 min compared to the HI group.
Average appetite did not differ between groups at the other
times, and there was no statistically significant difference
between average appetite scores AUC after control and
glucose treatments in HI and NI groups.
Of the individual motivation-to-eat scores, only ‘desire to
eat’ showed a treatment effect at 15 min (Po0.05). The
increase in desire to eat was higher in the HI group than in
the NI group after the glucose drink (Po0.05), consistent
with the overall appetite score.
No differences in subjective ratings of palatability for the
pizza test meal were found between the HI and NI groups.
Plasma glucose, insulin, CCK, ghrelin, leptin and adiponectin
To control for the effect of the differences between the two
groups in BMI and fasting concentrations of the measures
(Table 1), these variables were included as covariates in the
analysis of the effect of group and time on treatment
responses. In addition, because the data for insulin, CCK
and leptin were not normally distributed, the analysis
was conducted on the log-transformed data (Table 3).
BMI was a significant predictor of the plasma CCK, ghrelin
and leptin response to a glucose load. Similarly, fasting
concentrations of insulin, glucose, ghrelin leptin and
adiponectin affected their respective later response to a
glucose load.
Plasma glucose increased with time (Po0.001) and was
higher in the HI group than the NI (P¼0.02) (Table 3). But
AUC of plasma glucose was not significantly different
between the HI and NI groups, and there was no significant
group time and time
interactions, indicating that the
Table 1 Baseline characteristics of NI and HI subjects
NI HI P-value
Age (years) 41.472.4 43.472.6 0.6
Height (m) 1.7770.01 1.7470.01 0.07
BMI (kg/m
) 25.370.6 29.570.6 o0.01
Waist circumference (cm) 9072.1 10372.1 o0.01
Weight (kg) 78.672.1 89.972.7 o0.01
ER Score 10.971.01 11.270.95 0.8
Cholesterol (mmol/l) 4.870.2 5.170.15 0.2
HDL (mmol/l) 1.2470.1 0.9970.04 o0.01
Triacylglycerol (mmol/l) 1.5170.1 2.1870.2 0.03
Free fatty acids (meq/ml) 0.970.1 0.770.1 0.07
LDL (mmol/l) 3.370.2 3.770.1 0.05
Ratio (total cholesterol/HDL) 4.1870.2 5.3570.2 o0.01
Glucose (mmol/l) 5.0870.2 5.4070.1 0.03
Insulin (pmol/l) 28.372.2 72.776o0.01
CCK (pmol/l) 1.2370.1 1.070.05 0.15
Ghrelin (pg/ml) 871762 688748 0.02
Leptin (mg/l) 3.1970.18 6.3870.9 o0.01
Adiponectin (ng/ml) 18.171.2 16.271.1 0.2
Values are mean7s.e. Abbreviations: NI, normoinsulinemic (n¼33); HI,
hyperinsulinemic (n¼32); BMI, body mass index; ER, eating restraint; HDL,
high-density lipoprotein; LDL, low-density lipoprotein.
Table 2 Food intake after glucose and control preloads in HI and NI subjects
Control Glucose Control Glucose
FI (kcal) 997750*
Values are means7s.e. Abbreviations: FI, food intake; NI, normoinsulinemic
group (n¼33); HI, hyperinsulinemic group (n¼32). *Different alphabets
represent means that are significantly different at Po0.05 (Tukey’s test).
Overall ANOVA: treatment effect, Po0.01.
group effect, P¼0.06; group-by-
treatment interaction, P¼0.44.
Hyperinsulinemia, food intake and satiety hormones
RA Samra et al
International Journal of Obesity
plasma glucose response and shape of the curve were similar
in HI and NI groups (Table 3).
Plasma insulin concentration increased with time, and was
higher in the HI group as also shown by the higher AUC
compared to the NI group (Po0.0001) (Table 3, Figure 2).
Both HI and NI groups showed a similar insulin response and
curve shape over time (Figure 2).
Plasma CCK over time and the AUC for plasma CCK were
higher in the HI group than the NI group (P¼0.01)
(Figure 2), but did not change over time (Table 3).
Plasma ghrelin decreased with time and was lower in the
HI group (P¼0.01; Table 3, Figure 2). There was no time or
group interaction; suggesting that the HI and NI
groups showed a similar pattern of change and curve shapes
with time (Table 3), which was confirmed by the similar net
AUC for the two groups (Figure 2).
Plasma leptin was higher in the HI group than the NI
group (Po0.001), but did not change in response to the
glucose preload (Figure 3; Table 3). On the other hand,
plasma adiponectin was lower in the HI group (P¼0.01), and
did not change after the glucose preload (Figure 3; Table 3).
The magnitude of response to the glucose load up to
60 min showed plasma insulin to increase 10-fold in the HI
group and 11-fold in the NI group (Table 4). This was
followed by plasma CCK that increased by 2.1-fold in the HI
group and was unchanged in the NI group. Plasma glucose
increased by 1.6- and 1.7-fold in the NI and HI groups,
respectively. Plasma ghrelin decreased by 0.8-fold in both HI
and NI groups. Plasma adiponectin and leptin did not
change from fasting to 60 min after the glucose load (Table 4).
Correlations between food intake and other dependent variables
Significant associations between food intake and dependent
measures after the glucose preload were few and are
0 15 30 45 60
bNI Control
NI Glucose
HI Control
HI Glucose
Time (min)
Average appetite change
from baseline (mm)
Net iAUC
HI glucose
HI control
NI glucose
NI control
Figure 1 Change in average appetite scores and net incremental AUC, measured by visual analogue scales, over 1 h in normoinsulinemic (NI) (n¼33) subjects
after a control preload (), in NI subjects after a glucose preload (m), in hyperinsulinemic (HI) (n¼32) subjects after a control preload (&), and HI subjects after a
glucose preload (n). The two preloads were equalized for sweetness with the addition of the noncaloric sweetener sucralose, and all treatments were provided as
isovolumetric (300 mL) beverages. An insulin status effect was observed at 15 min (Po0.05).
Treatments with different letters are significantly different, Po0.05
(Tukey’s adjustment).
Table 3 Plasma glucose, insulin, CCK, ghrelin, leptin and adiponectin
response to 75 g glucose
Parameter Effect F-value P-value
Glucose Group (HI vs NI) 5.6 0.02
Time 86.6 o0.0001
Time group 0.5 0.5
(curvilinear regression)
12.4 o0.001
Group 1.7 0.2
Log insulin
Group (HI vs NI) 47.7 o0.0001
Time 330 o0.0001
Time Group 2.1 0.1
(curvilinear regression) 129.6 o0.0001
group 0.0 1.0
Group (HI vs. NI) 6.5 0.01
Time 2.5 0.1
Time group 1.2 0.3
(curvilinear regression) 0.5 0.5
Group 0.1 0.7
Ghrelin Group (HI vs NI) 7.7 0.01
Time 0.3 0.6
Time group 1.05 0.3
(curvilinear regression) 7.1 o0.001
group 1.6 0.2
Log leptin
Group (HI vs NI) 129.5 o0.0001
Time 0.4 0.5
Time group 0.6 0.4
Adiponectin Group (HI vs NI) 7.1 0.01
Time 0.5 0.5
Time group 1.7 0.2
Repeated measures analysis of covariance using Proc Mixed to test the effect
of time and group. BMI and fasting concentrations of the blood parameters
were included in the model as covariates.
Curvilinear regression was used to
model nonlinear relationships.
Log transformed plasma insulin and CCK was
used since original data and residuals were non-normal.
Leptin was used as
original data and residuals were non-normal. Abbreviations: NI, normoinsu-
linemic group (n¼33); HI, hyperinsulinemic group (n¼32); BMI, body mass
index; CCK, cholecystokinin.
Hyperinsulinemia, food intake and satiety hormones
RA Samra et al
International Journal of Obesity
described below. Food intake correlated positively with
ghrelin at 60 min (r¼0.4, Po0.01) in the pooled data.
However, this association was found within the NI group
(r¼0.64, Po0.001) and not the HI group.
No statistically significant correlations were found
between food intake and insulin, CCK, leptin and adipo-
nectin responses in either the pooled data or within
each group.
Correlations between plasma insulin and other dependent
For all subjects, fasting plasma insulin correlated positively
with BMI, waist circumference, total cholesterol/HDL ratio
and leptin at 0, 60 and 120 min and negatively with fasting
HDL and ghrelin (Table 5). In the NI group, fasting plasma
insulin correlated positively with BMI, waist circumference,
triacylglycerol, total cholesterol/HDL ratio and leptin at
0 30 60 90 120
Time (min)
Plasma Glucose (mmol/l)
0 30 60 90 120
Time (min)
Plasma Insulin (pmol/l)
0 30 60 90 120
Time (min)
Plasma CCK (pmol/l)
030 60 90 120
Time (min)
Plasma Ghrelin (pg/ml)
400 iAUC
Net iAUC
Net iAUC
Figure 2 Mean (7s.e.) responses of plasma glucose, insulin, CCK and ghrelin and net incremental area under the curve (AUC) in 32 hyperinsulinemic (HI) subjects
(closed circles) and 33 normoinsulinemic (NI) subjects (open circles) over 2 h. Glucose preload was consumed at 0 min. There was a group effect for glucose
(Po0.05), insulin (Po0.0001), CCK (P¼0.01) and ghrelin (P¼0.01), and a time effect for glucose and insulin (Po0.0001) with no significant group time
interaction. Means or bars with different letters are significantly different, Po0.05. AUC units are mmol min/l for glucose, pmol min/l for insulin, pmolmin/l for CCK
and pg min/ml for ghrelin.
0 30 60 90 120
Time (min)
Plasma Leptin (µg/l)
0 30 60 90 120
Time (min)
Plas ma Adiponectin (ng/ml)
Figure 3 Mean (7s.e.) response of plasma leptin and adiponectin in 32 hyperinsulinemic (HI) subjects (closed circles) and 33 normoinsulinemic (NI) subjects
(open circles) over 2 h. Glucose preload was consumed at 0 min. There was a significant group effect for leptin (Po0.0001) and adiponectin (P¼0.01) with no
significant time effect or group time interaction.
Hyperinsulinemia, food intake and satiety hormones
RA Samra et al
International Journal of Obesity
60 min. In the HI group, fasting insulin correlated positively
with waist circumference and plasma leptin at 0, 60 and
120 min (Table 5).
Insulin AUC correlated positively with CCK AUC, BMI and
fasting leptin and negatively with fasting adiponectin in
the pooled sample (Table 5). In the NI group, insulin AUC
correlated positively with CCK AUC (Table 5). In the HI
group, insulin AUC correlated negatively with fasting
adiponectin (Table 5).
No significant associations were found among the hor-
mones CCK, ghrelin, leptin and adiponectin in either of the
groups or in the pooled data.
Correlations between BMI and plasma lipids
Among the measured plasma lipids, BMI was significantly
correlated with fasting plasma HDL (r¼0.4, Po0.0001),
plasma LDL (r¼0.26, Po0.05) and ratio of total cholesterol/
HDL (r¼0.38, Po0.001) in all subjects.
This study provides physiologic support for the hypothesis
that hyperinsulinemia provides a protective–adaptive re-
sponse in food intake regulatory mechanisms leading to
resistance to weight gain.
Hyperinsulinemia modifies and
integrates the effect of carbohydrate on the short-term
responses of several satiety hormones in a direction that
would be predicted to contribute to resistance to weight gain
through improved intake control.
A statistically significant reduction in food intake aver-
aging 90 kcal after the glucose load was seen only in the
HI men (Table 2). The lack of a statistically significant
response in the NI men is in contrast to the more robust
response of 150 kcal or more in young men consuming
meal intakes of 1000 kcal.
In the present study,
the subject participation was from the general population
of Toronto recruited through newspaper ads and of a
wider age and ethnic range than the young subjects
primarily from the university environment used in previous
Nevertheless, the suppression of food
intake post-glucose preload was statistically significant for
the HI men. Possibly, if the glucose load had been given
on a body weight basis, the difference between the two
groups would be greater because the HI group on average had
a higher BMI.
Lower food intake in men consuming a glucose load
compared with an energy-free control has been associated
with an increase in blood glucose in normal
and type 2
diabetic subjects.
However, the results of the present study
suggest that insulin, not glucose response, is a more likely
mechanistic explanation. The greater postprandial reduction
in mealtime food intake of the HI men occurred in
association with a similar blood glucose response to that of
the NI men (Figure 2), but is consistent with the role of
insulin in both long- and short-term regulation of food
The primary role for insulin in intake regulation is
supported by several lines of evidence. Chronic hyperinsu-
linemia alone contributes to insulin’s long-term regulatory
role through increased insulin concentration in the hy-
In rats and baboons, increasing insulin in the
brain by infusion into either the lateral or third ventricle
of the hypothalamus results in dose-dependent decreases
in food intake.
In the short term, in NI individuals,
high plasma insulin in response to carbohydrate foods
is also associated with reduced food intake at a later
Furthermore, the greater importance of insulin
compared to glucose in short-term intake regulation is
illustrated by the observation that decreased food intake
occurs in euglycemic HI rats
and baboons
peripheral insulin infusions. In humans, type 2 diabetic
patients treated with sulfonylureas, an insulin secretagogue,
have higher fasting plasma insulin and lower body weight as
compared to treatment with proglitazone, an insulin-
sensitizing agent.
Table 4 Plasma glucose, insulin, CCK, ghrelin, leptin and adiponectin
magnitude of change at 60 min in HI and NI subjects
Glucose 1.6070.1 1.7070.1
Insulin 11.270.9 9.7070.9
CCK 1.0670.2 2.1070.6
Ghrelin 0.8070.07 0.8070.03
Leptin 0.9770.02 0.9070.02
Adiponectin 1.0170.06 0.9770.04
Values are means7s.e. Abbreviations: NI, normoinsulinemic group (n¼33);
HI, hyperinsulinemic group (n¼32); CCK, cholecystokinin.
Table 5 Correlations between plasma insulin and dependent measures
Fasting insulin
BMI NS 0.33 (0.07) 0.32 (o0.01)
Waist circumference 0.45 (0.01) 0.33 (0.07) 0.39 (o0.01)
Triacylglycerol NS 0.47 (o0.01) NS
HDL NS 0.41 (o0.05) 0.39 (o0.001)
Ratio (total cholest/HDL) NS 0.37 (o0.05) 0.28 (o0.05)
Fasting leptin 0.53 (o0.01) NS 0.61 (o0.01)
Leptin 60 min 0.62 (o0.01) 0.42 (o0.05) 0.66 (o0.01)
Leptin 120 min 0.65 (o0.01) NS 0.68 (o0.01)
Fasting ghrelin NS NS 0.36 (o0.01)
Insulin AUC
CCK AUC NS 0.4 (o0.05) 0.31 (o0.05)
Fasting adiponectin 0.44 (o0.05) NS 0.28 (o0.05)
Fasting leptin NS NS 0.32 (o0.05)
Values are correlation coefficient rwith P-value in parentheses. Pearson’s
correlation was calculated per subject. Abbreviations: NI, normoinsulinemic
group (n¼33); HI, hyperinsulinemic group (n¼32); NS, not significant
correlation; BMI, body mass index; HDL, high-density lipoprotein; CCK,
cholecystokinin; AUC, area under the curves.
Hyperinsulinemia, food intake and satiety hormones
RA Samra et al
International Journal of Obesity
In addition to the objective of describing the relationship
between HI and food intake after a glucose preload, other
hormones involved in food intake regulation were measured
for two reasons. First, the physiological regulation of food
intake is based on a complex array of responses,
and their
hierarchy in determining satiety in either HI or NI indivi-
duals remains to be determined. Second, few studies report
concurrent responses in more than one or two hormones.
In general, both fasting and post-glucose response in the
satiety hormones were consistent with a physiological
explanation for the greater reduction in food intake in the
HI men. Similar to the literature, the HI men had lower
fasting ghrelin
and adiponectin,
and increased leptin.
Compared with the NI men, the postprandial response to the
glucose load was higher for insulin, CCK, leptin, and was
lower for ghrelin in the HI men (Table 3). All these responses
would be expected to favor reduced food intake 1 h later.
Evidence for a primary role for insulin in directing satiety
hormone responses, as previously suggested,
is provided by
the present study. This role is demonstrated in two ways. First,
the magnitude of response in plasma insulin after a glucose
load was highest (fivefold) compared to the other satiety
hormones (Table 4), and second, by the statistically significant
associations of fasting blood concentrations of insulin with
fasting concentration of ghrelin and leptin, and of the AUC
for insulin and CCK after the glucose drink (Table 5). In
contrast to their association with insulin, there were no
associations found among CCK, ghrelin and leptin either at
fasting or postprandially. Other reports also suggest that
insulin directs satiety hormone responses, but there is limited
data in humans. Insulin infusions to the hypothalamus of rats
result in increased CCK sensitivity,
and insulin given
intravenously results in higher plasma leptin in humans
and lower plasma ghrelin in rats
and humans.
A carbohydrate-mediated increase in plasma CCK concen-
and a decrease in plasma ghrelin
have been
reported in NI individuals. Plasma leptin did not respond to
the glucose load consistent with the evidence that systemic
leptin concentration is not affected by short-term food
The inverse correlation between insulin and
adiponectin is consistent with hyperinsulinemia as a
determinant of hypoadiponectinemia in obesity and type 2
The lack of response in plasma adiponectin
to the glucose load supports the view that it is not involved
in short-term food intake regulation, but is primarily an
adipostat regulating energy balance.
The relatively few statistically significant correlations
between hormonal and glucose responses with food intake
and appetite scores are probably because the blood samples
obtained for metabolic measurements were obtained on a
different day from the feeding studies. This was done because
of uncertainty of the effect of an indwelling catheter and
frequent blood sampling on appetite and food intake. In a
recent study, we found that blood sampling with intra-
venous indwelling catheter resulted in 10% lower food
intake at a test meal compared to when blood samples were
obtained by finger prick (T Akhavan, unpublished data).
However, others have found that when feeding and meta-
bolic studies were done on the same day, subjective appetite
was associated with plasma glucose, insulin and ghrelin
In NI men, a decrease in both appetite score and food intake
after carbohydrate consumption is often found.
In this
study, the HI men decreased food intake but did not express
the same decrease in appetite score as the NI men 15min after
the glucose preload. The failure to decrease appetite may be a
consequence of hyperinsulinemia although this is not clear,
because hyperinsulinemia induced by intravenous infusions
under euglycemic conditions has been reported to result in
either increased hunger
or no effect on sensations of
in healthy subjects. However, appetite ratings
were not different between groups at 60 min, when subjects
consumed the ad libitum meal and food intake was different.
This is not surprising because the subjective measure of
appetite is often found not to be predictive of food intake. For
example, differences in ad libitum food intake between
treatments have been observed previously with no reported
effect of treatments on appetite.
Unfortunately, the HI and NI subjects differed in many
baseline characteristics including BMI and fasting plasma
lipids and cholesterol in the present study (Table 1) as this
study was originally designed to answer a different question.
Therefore, to remove the influence of BMI, a covariate analysis
was run with only BMI included in the model for the
following two reasons. First, BMI and plasma lipids and
cholesterol are correlated and cannot be included together in
the model owing to collinearity. Second, BMI is associated
with food intake,
but there is no evidence relating fasting
plasma lipids and cholesterol to short-term food intake.
To establish that HI is a protective adaptive response
contributing to weight stability, future studies would need to
compare HI and NI individuals at similar BMI and baseline
characteristics. Also concurrent measurement of food intake
and hormone responses would be useful. However, the
present study provides support for the proposal that the
goal of achieving weight loss and improved metabolic
control through energy restriction will lead to changes in
appetite control that may be difficult in long-term main-
tenance of reduced body weight.
In summary, hyperinsulinemia was found to improve food
intake compensation in association with elevated fasting
concentrations of insulin and leptin, and with higher
postprandial insulin, glucose, CCK, leptin and lower ghrelin
The authors thank Dr Azad Azar for analyzing the blood
samples at Mt Sinai Hospital, Toronto, Canada. We also
thank Dianne Woodend, Jelena Popuvac and Francesca
Hyperinsulinemia, food intake and satiety hormones
RA Samra et al
International Journal of Obesity
Smirnakis for their help with subject recruitment and data
collection. This study was supported by a Canadian Institute
for Health Research (CIHR) University-Industry grant to TMS
Wolever and GH Anderson. General Mills Incorporated,
Minneapolis was the industry sponsor. General Mills In-
corporated provided an unrestricted award to GH Anderson
and TMS Wolever.
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Hyperinsulinemia, food intake and satiety hormones
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International Journal of Obesity
... Insulin is involved in the regulation of food intake, both in the short and the long term. Insulin release is stimulated by whey protein ingestion that affects the glycemic response and is closely associated with short-term satiety and decreased food intake [7]. Insulin response has been shown to have a closer correlation with short-term satiety and food intake than do gut hormones. ...
... Increasing evidence has shown that the effect of whey proteins on satiety and food intake is mediated by the effect of satiety-inducing hormone release [7,42]. ...
... Cholecystokinin is known as a satiety hormone [7], in rats, CCK and its receptor subtype A are involved in the suppression of food intake induced by proteins [43,44]. In humans, proteins and fats in the diet are the main stimulators of CCK secretion [45], and digestion of proteins is necessary for the release of CCK [46]. ...
... There is support for the hypothesis that hyperinsulinemia may be the "price to pay for body weight stability," as proposed by Tremblay et al. (135). Hyperinsulinemic men compensated better than normo-insulinemic men at a test meal after a glucose preload (136). After the glucose drink, plasma insulin, cholecystokinin, and leptin were higher, and ghrelin and adiponectin were lower in hyperinsulinemic men than they were in normo-insulinemic men (136). ...
... Hyperinsulinemic men compensated better than normo-insulinemic men at a test meal after a glucose preload (136). After the glucose drink, plasma insulin, cholecystokinin, and leptin were higher, and ghrelin and adiponectin were lower in hyperinsulinemic men than they were in normo-insulinemic men (136). It remains unknown how body fat and associated hormones in the presence or absence of insulin resistance affects food intake in children. ...
Full-text available
Breakfast is purported to confer a number of benefits on diet quality, health, appetite regulation, and cognitive performance. However, new evidence has challenged the long-held belief that breakfast is the most important meal of the day. This review aims to provide a comprehensive discussion of the key methodological challenges and considerations in studies assessing the effect of breakfast on cognitive performance and appetite control, along with recommendations for future research. This review focuses on the myriad challenges involved in studying children and adolescents specifically. Key methodological challenges and considerations include study design and location, sampling and sample section, choice of objective cognitive tests, choice of objective and subjective appetite measures, merits of providing a fixed breakfast compared with ad libitum, assessment and definition of habitual breakfast consumption, transparency of treatment condition, difficulty of isolating the direct effects of breakfast consumption, untangling acute and chronic effects, and influence of confounding variables. These methodological challenges have hampered a clear substantiation of the potential positive effects of breakfast on cognition and appetite control and contributed to the debate questioning the notion that breakfast is the most important meal of the day.
... [28] The possible mechanisms for this putative benefit in maintaining glycaemic control have been attributed to increased insulinemic response, less glycaemic fluctuations, more secretion of gastric inhibitory polypeptide (GIP) and glucagonlike peptide-1 (GLP-1) triggered by milk proteins, suppression of ghrelin secretion, as well as the satiating effect of αlactalbumin. [29][30][31] Hidayat et al., (2019) reported that milk proteins stimulate postprandial insulin response and reduce the postprandial rise in blood glucose levels. [32] They proposed that the bioactive peptides in milk cause the release of GLP-1 and GIP, which reduces gastric emptying and stimulates insulin secretion. ...
Background & Aim: Milk has been studied extensively for its potential benefits, and some possible harmful effects on health. However, there is a paucity of Indian data in this context. Therefore, this study examined the association between milk consumption and anthropometric measurements, body composition and biochemical markers of glycaemic control in young adults (16-25 years) in Mumbai city. Material & Methods: 1313 young adults who had participated in a previous study between 2017 to 2019, were approached, among whom 563 agreed to participate. Anthropometric measurements, blood pressure, body composition and biochemical markers of glycaemic control had been done during the original screening. These data were used to examine the association with milk consumption. Information about their milk consumption practices was obtained in 2020 and 2021 through personal interviews of participants. Results: 28 participants did not consume milk and were excluded from the statistical analysis. Daily mean milk consumption was 314 ± 239 mL. Daily milk consumption was divided into quartiles. Although, anthropometric measurements and glycaemic markers did not differ significantly between the four quartiles of milk consumption, participants in Q1 and Q2 tended to have a higher per cent body fat, HC, and fasting and stimulated insulin. Also, mean muscle mass in Q4 was significantly higher than in the lower three quartiles. Conclusion: Results show some trends that are in line with existing literature supporting the beneficial role of milk in health. Larger epidemiological studies on Indians are warranted to confirm these trends. Keywords: milk, diabetes, obesity, body fat, milk products, adolescents.
... Whey protein appears to be substantially effective in weight control (Pal et al. 2010;Sousa et al. 2012) by increasing satiety and decreasing appetite (Sousa et al. 2012). In addition, whey protein can also suppress gastric (Pal et al. 2010) and promote glucagon-like peptide 1 (GLP-1) (Brubaker and Anini 2003;Hall et al. 2003) and glucose-dependent insulinotropic polypeptide (GIP) secretion (Samra et al. 2007), accompanied by the inhibition of ghrelin secretion (Bowen et al. 2006), and subsequently contribute synergistically to the weight control. ...
Aging is one of the key contributors to a broad spectrum of chronic diseases. Reactive oxygen species (ROS) increase oxidative stress in cells and thus induces inflammatory cascades. The antioxidant defense systems are declined during aging. Antioxidant controls the oxidative radical process by suppressing the formation of free radicals and interrupting the propagation and initiation of free radicals through several mechanisms. Considering the crucial roles of oxidative stress in age-related diseases, the manipulation of ROS levels would represent a useful option to delay age-related diseases and attenuate associated symptoms. Numerous compounds with antioxidant activity have demonstrated their potential to alleviate age-related diseases; however, mixed results are yielded. Therefore, this chapter discussed the potential of dietary antioxidants against age-related diseases. We also explored on how dietary choices dampen or exacerbate the inflammation and metabolic disorders. Collectively, this information may shed light on the discovery for potential intervention, and thus promoting healthy longevity.
... Whey protein appears to be substantially effective in weight control (Pal et al. 2010;Sousa et al. 2012) by increasing satiety and decreasing appetite (Sousa et al. 2012). In addition, whey protein can also suppress gastric (Pal et al. 2010) and promote glucagon-like peptide 1 (GLP-1) (Brubaker and Anini 2003;Hall et al. 2003) and glucose-dependent insulinotropic polypeptide (GIP) secretion (Samra et al. 2007), accompanied by the inhibition of ghrelin secretion (Bowen et al. 2006), and subsequently contribute synergistically to the weight control. ...
The average life expectancy has increased worldwide in recent decades. This has presented new challenges as old age brings the onset of diseases such as cancer, neurodegenerative disorders, cardiovascular disease, type 2 diabetes, arthritis, osteoporosis, stroke, and Alzheimer’s disease. Studies and research have shown the potential preventive and therapeutic roles of antioxidants in aging and age-related diseases by inhibiting the formation or disrupting the propagation of free radicals and thus increasing healthy longevity, enhancing immune function, and decreasing oxidative stress. This has made an antioxidant rich diet of increasing importance in battling the detrimental effects of the aging process. “The Role of Antioxidants in Longevity and Age-Related Diseases” is the book that compiles research on antioxidants and their biological mechanisms that mediate age-related diseases. This book covers the major issues linked to antioxidants, aging, and age-related diseases, including changes in organ systems over the lifespan, age-related oxidative stress-induced redox imbalance, inflammaging, implications of inflammation in aging and age-related diseases, and the important role of antioxidant-rich foods in their prevention and treatment of various age-related diseases. For researchers seeking a comprehensive single source on antioxidants and their roles in aging and age-related diseases, this novel text provides an up-to-date overview.
... The concept of compensatory adaptation favoring reduced energy intake in hyperinsulinemia has been observed. Like the offspring from the high vitamin mothers, men with elevated insulin and body mass index reduced food intake more after a glucose preload than normal insulinemic controls [242]. Thus the development of insulin resistance in the high vitamin offspring may have been a factor in their stronger response to the glucose preload (Table 4.3), the normalization of GLP-1 and reduction of ghrelin at 28 wk post-weaning ( The additions of 8 of the 12 required vitamins in the high vitamin diet were on average many times lower than that reported to cause adverse effects on the fetus, although it must be recognized that the amounts have not been fully quantified [220,234]. ...
... insulinotropic polypeptide (GIP) (35), the concomitant suppression of ghrelin secretion (36), and the potent satiating effects of a-lactoalbumin (37) synergistically contribute to weight control. ...
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Milk is a widely consumed beverage that is essential to the diet of several millions of people worldwide because it provides important macro- and micronutrients. Milk is recognized as being useful during childhood and adolescence because of its composition; however, its relatively high saturated fat proportion raises issues of potential detrimental effects, namely on the cardiovascular system. This review evaluates the most recent literature on dairy and human health, framed within epidemiologic, experimental, and biochemical evidence. As an example, the effects of milk (notably skimmed milk) on body weight appear to be well documented, and the conclusions of the vast majority of published studies indicate that dairy consumption does not increase cardiovascular risk or the incidence of some cancers. Even though the available evidence is not conclusive, some studies suggest that milk and its derivatives might actually be beneficial to some population segments. Although future studies will help elucidate the role of milk and dairy products in human health, their use within a balanced diet should be considered in the absence of clear contraindications.
Since more than 1.9 billion adults worldwide are overweight, of which 600 million are obese,¹ it is of vital importance to identify treatment strategies to help overweight and obese patients to lose weight and to improve long-term health. From a nutrition perspective, research has focussed on increasing the satiating power of the diet so that individuals feel full with fewer calories. A potentially effective class of functional foods, modulating appetite and food intake in such ways, is dietary fibre. However, although emerging evidence highlights the positive effects of dietary fibre on appetite and body weight, the methodological approaches are not always consistent and give rise to many uncertainties.
This chapter discusses the role of milk and dairy products, and their ingredients in obesity and the regulation of food intake and components of metabolic syndrome. In addition to protein (whey and casein), fat (saturated, mono- and poly-unsaturated fatty acids) and carbohydrate (lactose), milk contains biologically active substances such as immunoglobulins, enzymes, antimicrobial peptides, oligosaccharides, hormones, cytokines and growth factors. Each of these may affect food intake and metabolic regulation through a large number of physiologic mechanisms. Thus, their actions may explain the positive health associations between more frequent dairy consumption, a healthier body weight, and decreased risk of developing the metabolic syndrome.
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To investigate the short-term effects of insulin on feeding, 14 fasting, young adults received 150-min euglycemic intravenous infusions of control (C), low-dose (LD, 0.8, and high-dose (HD, 1.6 insulin and ate freely from a buffet meal during the last 30 min. Steady-state preprandial plasma insulin concentrations were 5.9 +/- 0.7 (C), 47 +/- 2 (LD), and 95 +/- 6 (HD) microU/ml and increased 56-59 microU/ml during the meal. No effect of treatment type of hunger or fullness ratings, duration of eating, or the weight, energy content (1,053 +/- 95 kcal, C; 1,045 +/- 101 kcal, LD; 1,066 +/- 107 kcal, HD; P = 0.9), and composition of food eaten was observed. On a fourth study day, 12 of the subjects received an intravenous infusion of glucose only (Glc) that was identical to the glucose infusion on their HD insulin day. Mean venous glucose concentration was 9.3 +/- 0.5 mmol [P < 0.001 vs. C (5.3 +/- 0.1), LD (5.2 +/- 0.2), and HD (5.2 +/- 0.2)], and plasma insulin increased to 45 +/- 2.3 microU/ml at the start and 242 +/- 36 microU/ml at the end of the meal. Energy intake during the meal was (approximately 15%) reduced (1,072 +/- 97 kcal, C; 1,086 +/- 102 kcal, LD; 1,088 +/- 105 kcal, HD; 919 +/- 115 kcal, Glc; P < 0.05 Glc vs. C, LD, and HD). Plasma insulin normally increases to approximately 100 microU/ml after a mixed meal in lean subjects. Therefore, in the absence of altered blood glucose concentrations, physiological concentrations of insulin are unlikely to play a role in meal termination and the short-term control of appetite.
Insulin acts in the brain to suppress feeding, whereas neuropeptide Y (NPY) has the opposite effect. Since fasting lowers plasma insulin levels and increases hypothalamic synthesis of NPY, we proposed that insulin may inhibit hypothalamic NPY gene expression. To test this hypothesis, we used RIA and in situ hybridization histochemistry to determine if centrally administered insulin could reduce levels of both NPY and its messenger RNA (mRNA) in discreet hypothalamic regions during fasting. Three groups of Long-Evans rats were entered into a 72-h study protocol. One group was fed ad libitum during this period, while the others were fasted. Fed rats received intracerebroventricular (icv) injections of saline vehicle at 12-h intervals, whereas fasted groups received icv vehicle alone or with insulin (4 mU/12 h). In vehicle-only treated rats, fasting significantly increased expression of preproNPY mRNA in the arcuate nucleus to 179 +/- 20% of fed controls. Administration of icv insulin during fasting abolished this increase (99 +/- 14% of fed controls; P less than 0.05 vs. fasted, vehicle-treated rats). Central insulin administration during fasting also reduced immunoreactive NPY concentrations in samples punched from the paraventricular nucleus (PVN) (875 +/- 122 pg/punch) to levels below vehicle-only treated rats (1396 +/- 435 pg/punch; P less than 0.05), similar to free-feeding control values (814 +/- 170 pg/punch). By comparison, neither fasting nor central insulin administration altered NPY levels in four other hypothalamic regions (supraoptic, ventromedial, dorsomedial, and arcuate nuclei). Continuous icv insulin infusion at a lower dose (2 mU/day) produced a similar result during a shorter period (48 h) of food deprivation in Wistar rats. In this study, central insulin infusion also inhibited the fasting-related increase in arcuate preproNPY mRNA levels and did not affect plasma glucose or insulin levels. This suggests that insulin acts locally to inhibit hypothalamic NPY mRNA expression. We conclude that the increase of levels of NPY in the PVN and preproNPY mRNA in the arcuate nucleus during fasting are inhibited by icv insulin. Fasting, therefore, increases NPY biosynthesis along an arcuate nucleus-PVN pathway in the hypothalamus via a mechanism dependent on low insulin levels.
Hyperglycemia may influence satiety. One mechanism by which glucose could influence food intake is hyperinsulinemia. Therefore, we investigated the short-term effects of acute hyperglycemia and euglycemic hyperinsulinemia on satiety. Six healthy volunteers (aged 20 to 26 years) were studied for 240 minutes on three separate occasions in random order during (1) intravenous (i.v.) saline (control), (2) acute hyperglycemic hyperinsulinemia (HG) with plasma glucose at 15 mmol/L, and (3) euglycemic hyperinsulinemia (HI) with plasma insulin at 80 mU/L and glucose at 4 to 5 mmol/L. Subjective criteria for appetite like the wish to eat, prospective feeding intentions ("How much food do you think you can eat?"), and feelings of hunger and fullness were scored on a 100-mm visual analog scale (VAS) at 30-minute intervals. Appetite was also measured every 60 minutes with the use of a food selection list (FSL). Appetite (prospective feeding intentions, feelings of hunger, and the wish to eat) gradually increased over basal levels during control conditions and HI. In contrast, prospective feeding intentions and feelings of hunger gradually decreased during HG and were significantly (P < .05) reduced versus basal and control levels during the last hour of the experiment. The wish to eat followed the same pattern. Feelings of fullness did not significantly change in all three experiments. Total food selection was not significantly decreased during HG, but the preference for fat-rich or carbohydrate-rich items tended to be reduced. The study suggests that in humans hyperglycemia induces satiety. This effect seems not to be mediated by insulin, since HI had no effect on appetite. However, a potentiating effect of endogenous insulin on the satiating effect of high blood glucose levels cannot be excluded.
Body adiposity is normally maintained within rigid limits1-3. Although it is not clear that this regulation fits a strict negative feedback pattern, animals do maintain a relatively constant body adiposity4. It has been postulated that this regulation is mediated by some signal which informs centres controlling food intake, probably located in the brain, as to the present state of adiposity5,6. The identity of the signal is unknown, but the direct correlation between body adiposity and basal insulin levels in the plasma7-9, suggests insulin as a possible candidate. This hormone is present in the cerebrospinal fluid (CSF) of many species10-13, and is a slow integral over time of the level within the plasma14. Thus, the level of insulin in the CSF is relatively resistant to short-term plasma fluctuations of insulin. Obese humans have higher levels of CSF insulin than lean controls and the CSF insulin level of both obese and lean humans is reduced proportionately after a prolonged fast15. We have therefore postulated16 that the feedback system responding to body adiposity uses the concentration of insulin in the CSF as a major signal. Additional support for such a role is found in recent reports that insulin receptors are present in several regions of the brain and spinal cord17-20. We now present additional evidence for our hypothesis by showing that in baboons the infusion of exogenous insulin into the CSF elicits a reliable and predictable decrease in food intake and body weight.
I. Introduction EARLY studies of brain glucose metabolism established the axiom that insulin is not required for utilization of glucose by the central nervous system (CNS) (1). A corollary to this concept was the belief that circulating insulin is incapable of crossing the bloodbrain barrier (BBB) and is therefore without effects in the brain. While the first of these tenets remains unchallenged, the second has been subjected to detailed scrutiny for over a decade, following the identification of both insulin (2) and its receptor (3) in the adult mammalian brain. Early reports of relatively high concentrations of insulin in brain extracts raised the possibility that insulin is synthesized and released locally in the CNS, as had been established for several other peptide hormones (2). Recent investigations, however, indicate that “brain insulin” is derived largely from the circulation (4), and a growing body of evidence suggests that its delivery into the neuropil may be facilitated by a specialized BBB tr...