Sustained Self-Regulation of Energy Intake: Initial Hunger Improves Insulin Sensitivity

Abstract

Background. Excessive energy intake has been implicated in diabetes, hypertension, coronary artery disease, and obesity. Dietary restraint has been unsuccessful as a method for the self-regulation of eating. Recognition of initial hunger (IH) is easily learned, can be validated by associated blood glucose (BG) concentration, and may improve insulin sensitivity. Objective. To investigate whether the initial hunger meal pattern (IHMP) is associated with improved insulin sensitivity over a 5-month period. Methods. Subjects were trained to recognize and validate sensations of IH, then adjust food intake so that initial hunger was present pre-meal at each meal time (IHMP). The purpose was to provide meal-by-meal subjective feedback for self-regulation of food intake. In a randomised trial, we measured blood glucose and calculated insulin sensitivity in 89 trained adults and 31 not-trained controls, before training in the IHMP and 5 months after training.
Results. In trained subjects, significant decreases were found in insulin sensitivity index, insulin and BG peaks, glycated haemoglobin, mean pre-meal BG, standard deviation of diary BG (BG as recorded by subjects' 7-day diary), energy intake, BMI, and body weight when compared to control subjects. Conclusion. The IHMP improved insulin sensitivity and other cardiovascular risk factors over a 5-month period.

Full text

Hindawi Publishing Corporation
Journal of Nutrition and Metabolism
Volume 2010, Article ID 286952, 7pages
doi:10.1155/2010/286952
Research Article
Sustained Self-Regulation of Energy Intake:
Initial Hunger Improves Insulin Sensitivity
Mario Ciampolini,1David Lovell-Smith,2Riccardo Bianchi,3
Boudewijn de Pont,4Massimiliano Sifone,5Martine van Weeren,4
Willem de Hahn,4Lorenzo Borselli,1and Angelo Pietrobelli6
1Unit of Preventive Gastroenterology, Department of Paediatrics, Universit`
a di Firenze, 50132 Florence, Italy
2Department of General Practice and Primary Health Care, University of Auckland, Auckland, New Zealand
3Department of Physiology and Pharmacology, Robert F. Furchgott Center for Neural and Behavioral Sciences,
State University of New York Downstate Medical Center, Brooklyn, NY, USA
4AMC, 1100 DD Amsterdam, The Netherlands
5Department of Statistics, Universit`
a di Firenze, Florence, Italy
6Paediatric Unit, Universit`
adiVerona,Verona,Italy
Correspondence should be addressed to Mario Ciampolini, mlciampolini@fastwebnet.it
Received 4 January 2010; Revised 28 April 2010; Accepted 10 May 2010
Academic Editor: Peter Clifton
Copyright © 2010 Mario Ciampolini et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Background. Excessive energy intake has been implicated in diabetes, hypertension, coronary artery disease, and obesity. Dietary
restraint has been unsuccessful as a method for the self-regulation of eating. Recognition of initial hunger (IH) is easily learned,
can be validated by associated blood glucose (BG) concentration, and may improve insulin sensitivity. Objective.Toinvestigate
whether the initial hunger meal pattern (IHMP) is associated with improved insulin sensitivity over a 5-month period. Methods.
Subjects were trained to recognize and validate sensations of IH, then adjust food intake so that initial hunger was present
pre-meal at each meal time (IHMP). The purpose was to provide meal-by-meal subjective feedback for self-regulation of food
intake. In a randomised trial, we measured blood glucose and calculated insulin sensitivity in 89 trained adults and 31 not-trained
controls, before training in the IHMP and 5 months after training. Results. In trained subjects, significant decreases were found
in insulin sensitivity index, insulin and BG peaks, glycated haemoglobin, mean pre-meal BG, standard deviation of diary BG (BG
as recorded by subjects’ 7-day diary), energy intake, BMI, and body weight when compared to control subjects. Conclusion.The
IHMP improved insulin sensitivity and other cardiovascular risk factors over a 5-month period.
1. Introduction
In industrialised countries, most people regulate their energy
expenditure poorly. Individual energy expenditure may differ
up to 20-fold between resting conditions and high physical
activity, but such differences have until now been weakly
correlated to energy intake at subsequent meals [1]. Frequent
episodes of positive energy balance can lead to insulin
resistance, overweight, obesity, diabetes, and heart disease
[1,2]. Dietary regimes that attempt to restrain eating have
been only marginally successful [3,4] and the feasibility of
self-regulation of energy intake regimes has been questioned
[5]. A key reason for this lack of success may be that most
dietary methods rely on weekly or monthly measurements of
weight. These measurements provide no immediate feedback
to dieters, who usually ingest food at least three times daily.
The body’s own physiological signaling system is hunger.
Blood glucose concentration (BG) is a reliable index of
energy availability to body cells [6–8]. It seems reasonable
to assume that BG slowly declines in the absence of food
intake during the day until hunger emerges to trigger eating
behaviour [9,10]. Previous studies suggested that waiting for
hunger before eating is associated with a significant decrease
in energy intake [11–15].

2JournalofNutritionandMetabolism
Subjects can be trained to predict when BG is low by
attending to their subjective experience of hunger [16].
Thus low blood glucose (LBG) can be regarded as a
biochemical marker for hunger. The first intimations of
hunger we term Initial Hunger (IH), to differentiate it from
the uncomfortable symptoms that occur when hunger is
prolonged. IH is not a reflex conditioned by external events
such as time or social circumstances [17]. For example, IH
is not conditioned by meal times since it arises unexpectedly
(outside meal times) if energy content of the previous meal
was not planned to cover the intermeal interval [16]. The
Initial Hunger Meal Pattern (IHMP) is a pattern of eating
such that IH is present before most meals. We reasoned that
the IHMP should predict closely regulated BG concentration
with associated improvements in metabolic biomarkers.
In this study, we tested the hypothesis that the IHMP is
associated with improvements in metabolic biomarkers, in
particular insulin sensitivity.
2. Methods
2.1. Participants
2.1.1. Eligibility Criteria. The Paediatric Gastroenterology
Unit of Florence University recruited 143 subjects to this
study from 1996 to 2000. This unit diagnoses and treats celiac
disease in children and adults. Aged 18 to 60 years, subjects
suffered from symptoms of functional bowel disorders such
as dyspepsia, abdominal pain, and diarrhoea (Figure 1)
[18,19]. They showed no morphological, physical, or
biochemical signs of organic disease [11,18,19]. Subjects
with impaired glucose tolerance (fasting plasma-glucose
>115 mg/dL (6.4 mmol/L)), and noninsulin dependent dia-
betes mellitus (NIDDM), celiac, liver, heart, brain, thyroid,
and kidney diseases were excluded from this study (Figure 1).
Written informed consent was obtained from all subjects.
The local Hospital Ethics Committee approved the study in
compliance with the Helsinki Declaration.
2.1.2. Setting. The trained group continued their regular
work or recreational activities under tutorial assistance for
seven weeks and maintained the IHMP for a further three
months independently (Figure 1).
2.2. The Intervention. Subjects were trained in the IHMP,
first by identifying IH, which was guided by consistency in
subjective sensations and the association of these sensations
with BG measurement. During training, subjects measured
capillary blood by portable glucometer (Glucocard Memory;
Menarini Diagnostics; Florence, Italy) in the 15min before
a meal. Accuracy of measurements by the glucometer was
validated against periodic measurements by hospital autoan-
alyzer. Seven-day home diaries reported BG measurements
and presence or absence of IH before the three main
meal times. Also recorded in the diary were energy and
vegetable intake, hours in bed, and hours spent during
physical and outdoor activities (weekly mean and SD).
Subjects were advised that BG measurements after taking
small quantities of food (even a few grams), after changes in
ambient temperature, after physical activity such as walking
or cycling, and when under psychological stress would be
misleading since we had previously found that BG and IH
do not correlate well under these conditions [16].
Subjects reported IH as gastric pangs, sensations of
emptiness and hollowness, and mental or physical weakness
[16]. IH was cultivated pre-meal by adjusting composition,
portion size, or timing of food intake. After a few days of trial
and error, and sometimes irregular meal times, subjects were
able to arrange their food intake so that IH appeared before
the usual three meal times per day with an average error of
half-an-hour in 80% of instances [15,16,20–23]. Training
ended after the first 7 weeks, to be resumed only at the end
of the investigation. Thus, after the first 7 weeks, subjects
relied upon the identified subjective sensation (IH) alone, as
the signal to begin a meal. Control subjects (N=31) were
given the same information on food energy content and were
recommended vegetable intake and physical activity per day
as were the trained subjects (weeks 0–7, Figure 1).
120 subjects who completed the study were assessed for
blood parameters at baseline (before training), after the first
7 weeks of training, and at the end of the investigation after
a further three months (total duration of the investigation:
5 months). During the glucose tolerance test, after a 12-
hour overnight fast, all subjects were given a 75 g-oral glucose
load. Venous blood samples were taken immediately before
glucose was administered, and 30, 60, 90, 120, and 180 min
thereafter to determine plasma glucose and serum insulin.
Serum insulin was measured with the IMx insulin assay
(AbbottLab.Diagn.Div.USA)[24]. From the glucose
tolerance test (GTT), we calculated the area under the
curve (AUC), the index of whole-body insulin sensitivity
(10,000/square root of [fasting glucose ×fasting insulin]
×[mean glucose ×mean insulin during GTT]) [25], and
the insulinogenic index of beta cell function (ratio of the
increment of plasma insulin to that of plasma glucose 30 min
after glucose loading) [26].
2.3. Outcomes
2.3.1. Primary Endpoint. The primary endpoint was the
change in insulin sensitivity [25] from baseline at 5 months
in trained subjects compared to controls.
2.3.2. Secondary Endpoints. Analyses were also performed
on beta cell function [26], BG AUC, GTT measurements of
BG and insulin concentrations, and mean pre-meal BG and
HbA1c values [27] as well as energy intake, BMI, body weight
and arm and leg skinfold thickness.
2.4. Sample Size. Previous work in similar patients found
that the insulin sensitivity index in the intervention group
was greater by 3 than that in the control group, with a
standard deviation (SD) of 3.0 [23]. Based on these figures,
our sample size calculations suggested that we needed a
minimum of 14 subjects in each comparison group to detect

JournalofNutritionandMetabolism 3
31 control subjects
completed protocol
final 7-day diary after
5 months
5 withdrew
final 7-day diary
after 2 months
Blood sampling and GTT
promotion of vegetable intake
and physical activity weeks 0
–
7
36 randomized to control
group home completion
of diary-1 week to baseline
18 withdrew
final 7-day diary
after 2 months
89 trained subjects
completed protocol
final 7-day diary
after 5 months
Blood sampling and GTT
training in IHMP
weeks 0
–
7
107 randomized to training
group home completion of
diary-1 week to baseline
Recruitment-2 week
initial visit
143 subjects randomized
Figure 1: Consort flow chart and investigation design. Randomized controlled 5-month clinical investigation to study the metabolic effects of
the IHMP.
a similar difference in group means, with a power of 80% and
a 1 sided alpha of 0.05.
2.5. Randomization. A list was divided into blocks of 1
to 4 places, and the blocks were randomly assigned using
Armitage even and odds random numbers on a 3 : 1 ratio
to either training or control groups. A dietician kept the list
and subsequently assigned each recruited subject to the first
empty list place. Control or training destination was revealed
after the first visit (Figure 1).
2.6. Statistical Methods. Valuesareexpressedasmeans±
SD, except in Figure 2, where the Standard Error is shown.
Logistic regression analysis was used to investigate the
association of training with BG mean, Hb1c, insulin and
BG AUCs, intakes and anthropometric measures (trained
versus untrained control groups) for significance of multiple
results [28]. The significance of difference and correlation
was set at P<.05 in these analyses. Yates test and two-tailed
Student’s t-test on paired or unpaired samples according to
data requirements were used to analyse the significance of
difference and two-tailed Student’s t-test for correlation. The
significance was set at P<.05 for single measurements
and at P<.025 for the GTT insulin and BG AUCs
[29]. Custom-made software was used to tabulate data for
statistical analyses. Microsoft Excel (Microsoft Corp., USA)
and SAS 8 (SAS Institute Inc., Cary, NC, USA) were used for
data presentation and for statistical analyses.
A training effect and correlations between the two
body size parameters (weight and BMI), the two energy-
balance parameters (arm and skinfold thickness), the four
metabolic indexes (mean BG and HbA1c values, and BG
and insulin AUCs), and three intake factors (energy, fruit,
and vegetable) were longitudinally investigated (i.e., on
post minus predifferences) by simple, linear correlation and
regression analyses in all of the 120 subjects completing the
study (Figure 1). Results were validated by chi square test-
collinearity diagnostics-residual analysis.
3. Results
Figure 1 shows the flow chart of participants through each
phase of the study. Data were eventually collected from 120
subjects who completed the study (60 females and 60 males,
89 trained subjects and 31 control subjects).
3.1. Losses and Exclusions
3.1.1. Protocol Deviations. In this study the protocol was
to follow the IHMP. We do not have data on the extent
to which IH was present pre-meal for each meal, that is,
we do not know how closely each subject adhered to the
IHMP. Achieving the IHMP appeared to be difficult for 12
subjects who had high pretraining mean BG concentrations
(e.g., around 100 mg/dL) or participated in heavy manual
labour, especially in cold conditions. Although some subjects
may not have been faithful to the IHMP for all meals, we
have included all those who completed the study in the final
analysis, since it was our intention to treat them [30,31].
3.1.2. Dropouts. Twenty-three subjects (18 trained and 5
control) did not complete the study (dropouts). All were
contacted by telephone. Their given reasons were that they
“required no further training” or had “busy schedules.”
To ascertain whether these biases could have affected the
generalisability of the study’s conclusions, we performed a
sensitivity analysis using baseline and 7-week data from all
23 dropouts. The 18 trained dropouts significantly decreased
mean BG (from 83.3±5.9mg/dL to78.9±5.4mg/dL; P=
.005), energy intake (from 1651 ±451 to 1124 ±401; P=
.0001), BMI (from 23.7±3.4to22.9±3.2; P=.04), and arm
skinfold thickness (from 20.5±8.5to18.5±8.8; P=.03).
The 5 control dropout subjects showed no change in these
assessments.
3.2. Baseline Demographics. Since no significant gender
difference in baseline mean BG concentrations was observed

4JournalofNutritionandMetabolism
1801501209060300
Time (min)
Control, baseline
Control, after 5 mo
Trained, baseline
Trained, after 5 mo
70
80
90
100
110
120
130
140
Blood glucose (mg/dL)
∗
∗
∗∗
∗
(a)
1801501209060300
Time (min)
Control, baseline
Control, after 5 mo
Trained, baseline
Trained, after 5 mo
0
10
20
30
40
50
60
70
80
Insulin (mU)
∗∗
∗
(b)
Figure 2: Blood glucose and plasma insulin concentrations during GTT in control and trained subjects at the beginning and at the end of the
study. Blood glucose (a) and insulin (b) mean levels in control (black circles) and trained (red squares) subjects at baseline (open symbols)
and after 5 months (closed symbols). Vertical bars are standard errors. Asterisks indicate significant decrease of blood glucose (a) and insulin
(b) in the trained subjects after training compared to their respective baseline values (P<.01). In contrast, no decrease between baseline
values and those at the end of the study was observed in control subjects. The insulin decrease in trained subjects at 60 and 90min also
differed significantly from the control group (P<.01 and <.05, resp.).
in the control group (females: 82.3±8.0mg/dL; N=14;
and males: 87.5±7.6mg/dL; N=17; Student’s t-test for
unpaired data: P=.075) and in the training group (females:
84.3±8.7mg/dL; N=46; and males: 87.5±10.6mg/dL;
N=43; P=.115), the measurements from both genders
were pooled in each group (Figure 1). Baseline BG means of
thecontrolsubjects(85.2±8.1mg/dL;N=31) did not differ
from those of the training subjects (85.9±9.7mg/dL;N=89;
P=.733).
Baseline values of mean age, school education years,
body weight, BMI, arm and leg skinfold thickness, and
blood values did not significantly differ between control and
trained groups (Tables 1and 2).
3.3. Outcomes. Significant decreases among trained subjects
compared to controls were found in insulin sensitivity index,
insulin and BG peaks, insulin at 60 minutes and 90 minutes
during GTT, glycated haemoglobin, mean pre-meal BG, BG
diary standard deviation (SD), energy intake, BMI, body
weight, arm and leg skinfold thickness.
Indexofbetacellfunctionchangedfrom1.0±0.8to1.1±
1.1 in trained subjects and from 1.0±1.0to0.7±0.6incontrol
subjects. These changes were not significant. Insulin and BG
AUCs in the trained group significantly decreased in the
pre/postcomparison but the decreases were not significantly
different from those of the control subjects.
A significant decrease of preprandial BG mean values
achieved during training was maintained three months after
the training period ceased (baseline: 85.6±9.5mg/dL;after5
months: 79.4±6.5mg/dL;N=89; Student’s t-test for paired
data: P<.0001) (Tab le 2). In contrast, mean preprandial BG
in control subjects did not change from baseline (baseline:
85.2±8.1 mg/dL; after 5 months: 85.3±7.6mg/dL;N=31;
P=.935) and the longitudinal difference from the trained
group was significant (P<.001; Table 2).
3.3.1. Ancillary Analyses. Theabsolutepre/postchange
(increase or decrease) in 31 control subjects was 6.0±
4.6mg/dL(13.2% ±10.1% of the baseline range in mean BG
in the 120 investigated subjects: 64.5 mg/dL to 109.9 mg/dL).
Factors that most characterized the differences between the
trained group and the control group were investigated in
all 120 subjects together by a logistic regression analysis.
Energy intake (P=.004) and HbA1c (P=.0001) were
significantly and negatively associated with the training.
Further effects associated with training were investigated by
stepwise regression analysis. The training was significantly
and negatively associated with BMI (P=.001) and
with arm and leg skinfold thickness (balance during the 5
months of investigation; P=.005 and P=.015, resp.).
Decrease in BMI by training was significantly associated
with decreases in energy intake (P=.001) and insulin

JournalofNutritionandMetabolism 5
Tab le 1: Group composition and effects of training on anthropometry.
Control Trained
Baseline After 5 mo. Baseline After 5 mo.
Number of subjects and Gender 14 F + 17M 46 F + 43M
Schooling (years)110.6±3.212.0±2.7
Age (years)129.6±8.232.6±8.5
BMI 22.2±4.522.5±3.723.0±3.822.1±3.1∗∗∗a∗∗∗b
Weight (Kg) 59.6±9.560.9±8.864.1±12.762.0±11.3∗∗∗a∗∗∗b
Arm skinfold thickness (mm) 15.2±9.814.6±7.716.0±8.013.0±6.1∗∗a∗∗∗b
Leg skinfold thickness (mm) 20.6±12.319.8±11.021.6±11.117.4±8.5∗∗a∗∗∗b
Values are expressed as means ±SD. 1Values at the beginning of the study. Asterisks indicate significance (Student’s t-test: ∗P<.05; ∗∗P<.01; ∗∗∗P<.001)
of longitudinal difference versus respective control group (a), or versus baseline values of the same group (b).
Tab le 2: Effects of training on metabolic and intake parameters.
Control Trained
Baseline After 5 mo. Baseline After 5 mo.
Mean pre-meal BG (mg/dL) 85.2±8.185.3±7.685.6±9.579.4±6.5∗∗∗a∗∗∗b
BG diary SD (mg/dL)18.4±3.09.1±3.28.4±4.46.1±2.4∗∗∗a∗∗∗b
Glycated Hb (%) 4.55 ±0.37 4.71 ±0.40 4.71 ±0.4.24.50 ±0.43∗∗∗a∗∗∗b
Insulin AUC2(mU L−13h
−1) 211 ±91 225 ±111 220 ±127 171 ±89∗∗∗b
Insulin peak (mU L−1)71±32 74 ±38 72 ±46 55 ±29∗∗a∗∗∗b
Insulin sens. (index)36.9±3.17.0±3.87.1±4.19.4±5.2∗∗a∗∗∗b
BG AUC (mg/dL) 597 ±113 576 ±116 604 ±100 555 ±88∗∗∗b
BG peak (mg/dL) 131 ±23 127 ±28 135 ±28 126 ±26 ∗∗∗a∗∗b
Energy intake (Cal/d) 1855 ±579 1649 ±599 1756 ±652 1271 ±517∗∗∗a∗∗∗b
Meals per day43.9±0.73.9±0.73.9±0.63.7±0.6∗∗b
Vegetable intake (g/d) 199 ±209 227 ±218 313 ±242 424 ±239∗∗∗b
Fruit intake (g/d) 183 ±148 163 ±153 221 ±150 307 ±259∗a∗∗b
1Diary SD refers to BG SD of 21 measurements reported by each of 7 d diary.
2AUC: area under GTT curve.
3Whole body insulin sensitivity index [25].
4Meal was an event of higher energ y intake than 20 kcal.
Values are expressed as mean ±SD. Peak values include different observations from those at 30’ during GTT. Asterisks indicate significance (Student’s t-test:
∗P<.05; ∗∗P<.01; ∗∗∗P<.001) of longitudinal difference versus respective control group (a) or versus baseline values of the same group (b).
AUC ( P=.001). Analysis of weight confirmed the BMI
findings.
3.4. Adverse Events. Trained subjects reported few negative
effects when adjusting their food intake and in accommodat-
ing irregular intermeal intervals in the first few days of trial
and error. The reported adverse effects included a slightly
depressed BG (below 60 mg/dL (3.3 mmol/l)) and weakness
or abdominal pain.
4. Discussion
4.1. Limitations of the Study. The high number of dropouts
is an important limitation of this study. However, from our
sensitivity analysis, we conclude that the dropout subjects are
unlikely to represent a significantly different population with
respect to the endpoint measures of this study and that the
absence of final data from these subjects is unlikely to have
significantly affected the overall results.
4.2. Generalisability. Our findings are from subjects who
attended a gastroenterology clinic over a 5-month period.
Further investigation will be necessary to evaluate the effect
of the IHMP in other populations and what “reminder”
training might be necessary to ensure compliance with the
IHMP over years.
4.3. Interpretation
4.3.1. Synopsis of Key Findings. A seven-week training pro-
gram to establish the IHMP led to significant decreases
in insulin sensitivity index, insulin and BG peaks, glycated
haemoglobin, mean pre-meal BG and BG diary SD. Energy
intake, BMI, and body weight also significantly decreased.

6JournalofNutritionandMetabolism
4.3.2. Possible Mechanisms and Explanations. IH may repre-
sent an important afferent arm of a physiological regulation
mechanism that provides meal-by-meal feedback on energy
need thus optimizing energy intake. The observed improved
insulin sensitivity may reflect lowered energy intake resulting
from the IHMP.
4.3.3. Comparison with Previous Findings. Before training,
mean pre-meal BG showed high intersubject variability, in
agreement with other authors’ findings. This variability has
engendered a perception that BG has no relevance to food
intake regulation [8]. The mean pre-meal BG in trained
subjects decreased significantly over 5 months, whereas
control subjects showed no change. We suggest, therefore,
that inter-subject variability arises because in many subjects
hunger (and thus LBG) is, by habit, forestalled by premature
food intake leading to sustained mild hyperglycemia. That
the absolute pre/post change (increase or decrease) in pre-
meal BG was modest in 31 control subjects (13.2% ±10.1%
of baseline range in mean BG variation of 120 investigated
subjects) supports the contention that in untrained subjects
eating occurs according to long-standing habit.
4.3.4. Clinical and Research Implications. We suggest the
IHMP offers a viable alternative to low fat and low carbo-
hydrate diets [32] that is safe, cost-effective, and likely to be
met with greater acceptance since it does not involve energy
deprivation.
The ramifications of improved insulin sensitivity extend
well beyond glucose homoeostasis [33–36]. For example,
the chronic subclinical inflammation indicated by C reactive
protein (CRP) is now seen as part of the insulin resistance
syndrome [33,35]. Our results could thus have implications
in a variety of inflammatory conditions. Trained subjects
showed a cumulative energy balance that was negative after
5 months, and the longitudinal difference was significant in
comparison with control subjects. Elsewhere, we describe the
effect of the IHMP on body weight in relation to baseline
weight and mean BG, using a larger sample size [23].
5. Conclusions
Our data suggest that (i) IH provides meal-by-meal feedback
allowing the conscious formation of a new eating pattern
(IHMP) and sustained self-regulation of energy intake, and
(ii) over a five-month period the IHMP is associated with
improvement in insulin sensitivity, LBG, HbA1c, and other
cardiovascular risk factors.
These findings, together with those of an associated study
on weight [23], suggest that the current epidemic of insulin
resistance and overweight may have its origin in noncog-
nizance of hunger. This may owe to habitual forestalling of
hunger in early life and subsequent reinforcement of this
behaviour pattern. By restoring and validating hunger, the
IHMPcouldhelpinthepreventionandtreatmentofdiabetes
and obesity and associated disorders. This could lessen the
high economic burden of health services in industrialised
societies.
List of Abbreviations
IHMP: Initial hunger meal pattern
AUC: Area under curve
BMI: Body mass index
BG: Blood glucose concentration
GTT: Oral glucose tolerance test
LBG: Low blood glucose
Diary-BG SD: Mean pre-meal blood glucose standard
deviation reported by seven day diary
CRP: C reactive protein.
Acknowledgments
The authors thank Laura Chiesi and Stefania Bini MD for
dietary analyses and Stephen Buetow, Tim Kenealy, Chris
Harshaw, Simon Thornton, Kent Berridge, James Gibbs,
Charlotte Erlanson-Albertsson, and Michael Hermanussen
for helpful insights on earlier drafts of this paper. This
research was supported by the Italian Ministry of University,
Research, Science and Technology grants for the years 1998–
2002 and ONLUS Nutrizione e Prevenzione, Firenze for years
2003–2008. The authors declare that they have no competing
interests.
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