Article

Modeling of parametric deconvolution: Results of studies of the night eating syndrome

New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA 19348, USA.
American Journal of Clinical Nutrition (Impact Factor: 6.77). 07/2008; 87(6):1672-7.
Source: PubMed

ABSTRACT

Disordered temporal eating patterns are a feature of a number of eating disorders. There is currently no standard mathematical model to quantify temporal eating patterns.
We aimed to develop a simple model by which to describe the temporal eating patterns of adult humans. For this purpose, patients with the night eating syndrome (NES) and persons without an eating disorder were assessed.
Data were obtained from 2 studies, each involving patients with NES and control subjects. Data were analyzed by means of a novel equation to describe the 24-h temporal eating patterns. The equation employed the integration over time of 3 Gaussian equations to describe the cumulative daily caloric intake.
The new model accurately described and quantified the temporal eating patterns of the subjects in the 2 studies. The analyses showed differences in the temporal eating patterns and in the amount of intake of normal-weight and overweight subjects with NES.
This novel model can be used to accurately and objectively describe and quantify temporal food intake patterns. It can also be used to establish norms for various human populations.

Full-text

Available from: Kelly C Allison
Modeling circadian rhythms of food intake by means of parametric
deconvolution: results from studies of the night eating syndrome
1–3
Raymond C Boston, Peter J Moate, Kelly C Allison, Jennifer D Lundgren, and Albert J Stunkard
ABSTRACT
Background: Disordered temporal eating patterns are a feature of a
number of eating disorders. There is currently no standard mathe-
matical model to quantify temporal eating patterns.
Objective: We aimed to develop a simple model by which to de-
scribe the temporal eating patterns of adult humans. For this purpose,
patients with the night eating syndrome (NES) and persons without
an eating disorder were assessed.
Design: Data were obtained from 2 studies, each involving patients
with NES and control subjects. Data were analyzed by means of a
novel equation to describe the 24-h temporal eating patterns. The
equation employed the integration over time of 3 Gaussian equations
to describe the cumulative daily caloric intake.
Results: The new model accurately described and quantified the
temporal eating patterns of the subjects in the 2 studies. The analyses
showed differences in the temporal eating patterns and in the amount
of intake of normal-weight and overweight subjects with NES.
Conclusions: This novel model can be used to accurately and
objectively describe and quantify temporal food intake patterns.
It can also be used to establish norms for various human
populations. Am J Clin Nutr 2008;87:1672–7.
INTRODUCTION
Our understanding of pathological eating behavior is limited
by variability in the timing and size of meals during a 24-h period.
Although there is general agreement that most people consume 3
meals during this period, little consensus exists as to what con-
stitutes a normal temporal meal pattern. There is variability even
in the definition of types of meals. For example, breakfast has
been defined as the first meal after awakening, a meal consumed
at a certain time of day, a certain type of food consumed, and
whatever a person perceives as “breakfast” (1, 2).
Most efforts at studying 24-h food intake use tabulations of the
amounts of foods consumed at various times (3, 4). This method
and the use of arbitrary definitions of what constitutes specific
meals may have inadvertently led to distortions in the interpre-
tation of eating patterns and to a failure to detect important
features of eating patterns. Such problems have afflicted studies
of sleep-wake dynamics. Recently, however, mathematical mod-
els have been developed that describe these functions in terms of
circadian rhythms (5).
We present here a mathematical model of circadian aspects of
food intake that can facilitate the study of eating patterns, both
pathological and “normal” (6). This model is independent of
subjective definitions of what constitutes breakfast, lunch, and
dinner. It is tested by comparing the eating patterns of persons
manifesting the night eating syndrome [NES (7–9)] with the
eating patterns of a control group. NES has been conceptualized
as a delay in the circadian pattern of food intake, exemplified by
evening hyperphagia and nighttime awakenings with ingestion
(9). The definition of evening hyperphagia for NES has varied
across studies, in part because of a lack of information regarding
appropriate cutoff times for “evening” or “after-dinner” eating.
This study is the first to identify objective patterns of meal intake
for persons with NES and control participants by using a new
mathematical model.
METHODS AND SUBJECTS
Methods
In this investigation we describe energy intake, and, for this
reason, we focus on calories. The methods described, however,
could just as well be applied to dry matter (in g), protein (in g), or
any other dietary constituent. The circadian pattern of food intake
describes the food intakes occurring during 3 distinct meals. Our
mechanistic model is based on the assumption that the average
rate of food intake during each of these 3 meals can be described
by a symmetric pulse (Figure 1).
With respect to the breakfast meal, for any population or stra-
tum under consideration, the average rate of eating of calories
[EB(t) (in cal/h)] can be approximated by a Gaussian equation,
which is more commonly known as a normal distribution or
bell-shaped curve (10), as shown in the following equation:
EBt) P
1
exp[P
2
(t P
3
)
2
](1)
where P
1
(cal/h) represents the maximum rate of calorie con-
sumption/h, and P
2
(h
Ҁ2
) is a parameter related to the mean
1
From the New Bolton Center, School of Veterinary Medicine, University
of Pennsylvania, Kennett Square, PA (RCB and PJM), and the Center for
Weight and Eating Disorders, Department of Psychiatry, University of Penn-
sylvania School of Medicine, Philadelphia, PA (KCA, JDL, and AJS).
2
Supported by National Institutes of Health grants no. K12HD043459 (to
KCA) and R01 DK056735 (to AJS).
3
Reprints not available. Address correspondence to RC Boston, New
Bolton Center, 382 West Street Road, Kennett Square, PA 19348. E-mail:
drrayboston@yahoo.com.
Received November 1, 2007.
Accepted for publication March 8, 2008.
1672 Am J Clin Nutr 2008;87:1672–7. Printed in USA. © 2008 American Society for Nutrition
by guest on June 7, 2013ajcn.nutrition.orgDownloaded from
Page 1
inverse of meal spread. Meal spread is defined as the length of
time (in h) during which the rate of intake exceeds one-half the
peak intake rate (Figure 1). The meal spread for breakfast (MS
B
)
is calculated by the following equation:
MS
B
2
In2/P
2
(2)
P
3
(h) is the time at which the maximum rate of caloric intake
occurs (Figure 1). Similar equations with corresponding param-
eters can be used to describe the rate of caloric intake at lunch
[EL(t)] and dinner [ED(t)], according to the following equations:
EL(t) P
11
exp[ P
12
(t P
13
)
2
](3)
and
EDt) P
21
exp[P
22
(t P
23
)
2
](4)
With the use of the results of equations 1, 3, and 4 as input rates,
the average cumulative caloric intake CI(T) up to any time [T (h)]
in a day can be estimated by deconvolution (10) as a sum of the
integrals of the following equation:
CIT)
6
T
EB(t)dt
6
T
EL(t)dt
6
T
ED(t)dt (5)
where “dt” is the integration element.
Thus, from a practical point of view, evaluation of the indi-
vidual integrals in equation 5 between 6 and 30 h provides esti-
mates of the total caloric intakes for breakfast, lunch, and dinner.
Alternatively, because EB(t) is a Gaussian curve, the total area
under the curve (AUC) from t ҃Ҁ to t ҃ (AUC
EB
)—ie, the
breakfast caloric intake— can, as was shown previously (11), be
calculated according to the following equation:
AUC
EB
P
1
/P
2
(6)
In practice, in the analysis presented here, the meal AUCs in the
time domains 6 h and after 30 h are negligible. Therefore,
equation 6 provides a good approximation of the integrand of
equation 1 from 6 –30 h. Because equations 3 and 4 also are
Gaussian equations, it follows that, by employing the appropriate
parameters from equations 3 and 4, equations similar to equation
2 can be employed to calculate the meal spread of lunch (MS
L
)
and dinner (MS
D
). Likewise, equations similar to equation 6 can
also be used to calculate the lunch (AUC
EL
) and dinner (AUC
ED
)
caloric intake.
Subjects
The data used to develop this model of food intake were de-
rived from a study carried out at the University of Pennsylvania
(12). That study presented graphs of the 24-h (0600 to 0559 the
following day) average cumulative energy intakes (kJ) of a group
of 10 persons with NES and a group of 10 control subjects.
Subjects were classified as having NES if they initially reported
that they consumed 50% of their daily intake between 2000 and
0600. The images of these graphs [see Figure 1 in Meier et al
(10)] were electronically scanned, and the data were digitally
extracted with the use of UN-SCAN-IT software (version 5.0;
Silk Scientific Corporation, Orem, UT). The cumulative energy
intake in kJ was converted to kcal, and equation 5 was then fitted
to the data for NES and control subjects by means of nonlinear
regression using WinSAAM software (version 3.0.7; Internet:
http//www.winsaam.org) (13). Data in all regressions were
weighted to the SD of the observations (14).
This modeling approach for analyzing cumulative energy in-
take data was then tested by using data previously collected from
a second study of 148 night eaters (100 female) and 68 control
subjects (51 female) (9, 15). Of the control subjects, 23 were
normal-weight and 45 were overweight or obese. Of the night
eaters, 19 were normal-weight and 129 were overweight or
obese. All subjects completed 24-h food diaries for 7 d. For each
stratum in this investigation, normal-weight and overweight or
obese NES subjects and normal-weight and overweight or obese
control subjects, average cumulative caloric intakes were calcu-
lated at hourly intervals from 0600 on day 1 to 0559 the following
day and averaged across the 7 d and across subjects. Equation 5
was then fitted to these data. We used t tests to determine whether
means for specific parameters were significantly different (P
0.05) in the different strata (16).
RESULTS
It is shown in Figure 2 (panels C and D) how 3 Gaussian
curves (the new conceptual model, here called the Boston model)
can be used to describe the average rates of caloric intake during
breakfast, lunch, and dinner for NES and control subjects from
the first study. Panels A and B in Figure 2 show how the summed
integrands of these Gaussian curves describe the average cumu-
lative caloric intake of NES and control subjects from the first
study. The same curves for the second study (9) are shown in
Figure 3.
The parameters (x SD) of the Boston model for the control
and NES subjects in the first and second studies are shown in
Table 1. The mean demographic variables and parameters of the
Boston model for the 4 strata of the second study (normal-weight
and overweight or obese control and NES subjects) are shown in
Table 2. For all strata shown in Tables 1 and 2, the Boston model
accurately described the cumulative energy intake, and, for each
stratum, the adjusted R
2
value was 0.99 and the root mean
square errors (RMSEs) were 28 calories.
In comparisons of the NES and control subjects, the magni-
tudes of the parameters of the Boston model show similar relative
trends in the first and second studies, which confirms the findings
of the first study (12). In that study, caloric intake (AUC) for the
FIGURE 1. A schematic depicting the new conceptual model (the Boston
model) of the average rate of eating during a meal, peak intake rate, time of
peak intake rate, and meal spread (ie, intake duration).
MODELING CIRCADIAN RHYTHMS OF FOOD INTAKE 1673
by guest on June 7, 2013ajcn.nutrition.orgDownloaded from
Page 2
“breakfast” meal in the NES subjects (256 35 cal) was signif-
icantly (P 0.05) less than that in the control subjects (445 8
cal). In the second study, the caloric intake AUC for the breakfast
meal in the NES subjects (169 43 cal) was significantly (P
0.05) lower than that in the control subjects (415 24 cal), which
supports the previous conclusion of limited morning intake
(7, 8).
In both the first and second studies, the mean cumulative 24-h
caloric intakes were significantly higher in NES subjects than in
control subjects, and the difference was specific by weight class
in the second study. In the second study, the average total daily
caloric intake of overweight or obese subjects was significantly
(P 0.05) greater than that of normal-weight subjects, whether
they were NES or control subjects (Table 2). This excess con-
sumption occurred primarily during the dinner meal. In NES
subjects, the time of peak rate of eating during the dinner meal
(P
23
) was significantly (P 0.05) later in normal-weight sub
-
jects (20.07 0.10 h) than in overweight or obese subjects (18.21
0.19 h). In normal-weight control subjects, on the other hand,
P
23
occurred at 17.95 0.08, which was slightly later than it
occurred (17.83 0.08 h) in the overweight or obese control
subjects.
DISCUSSION
The Boston model accounts for all features of the cumulative
intake curves, as shown in Figures 2 and 3, and in no time domain
is there any systematic departure of model predictions from the
measured data. The very high R
2
values and relatively small
RMSE values testify to the accuracy of the Boston model in
describing intake patterns. This model is robust in that the same
estimates for model parameters were obtained even when initial
estimates for parameters were dissimilar to the final estimates.
Criteria for NES are currently defined as the consumption of
25% of daily caloric intake after the evening meal, 3 night-
time awakenings with ingestion/wk, or both (8). With respect to
the data from the first study (16), the Gaussian curve for the
evening meal of the NES subjects is significantly (P 0.05)
larger in every respect—ie, the maximum rate of eating, the time
of the maximum rate of eating, and the SD of the duration of
eating—than is the corresponding curve for that meal of the
control subjects (Table 1). In fact, with respect to the evening
meal, the NES subjects had a peak rate of eating (267 calo-
ries/h) at 21.23 h and the Gaussian curve describing their
eating pattern extended from 1000 to 0600 the next day. This
pattern is embodied in the first core criterion, consumption of
25% of daily caloric intake after the evening meal (Figures 2D
and 3D).
A second feature of both the first and second studies was the
relative magnitudes of the midday meal caloric AUC in NES
and control subjects. In both studies, the lunch caloric AUC in
the NES subjects was significantly (P 0.05) less than that in
the corresponding control subjects. This finding suggests that
0
1000
2000
3000
4000
0102030
Average Cumulative Calorie Intake
Time (h)
0
1000
2000
3000
4000
0102030
Average Cumulative C alories Intake
Time (h)
0
100
200
300
0102030
Average Intake rate (cal/h)
Time (h)
0
100
200
300
0102030
Average Intake rate (cal/h)
Time (h)
A
C
B
D
FIGURE 2. Use of the new (Boston) model to describe the average cumulative caloric intake of control subjects (A) and patients with night eating
syndrome (NES) (B). F, data; —, the model predictions. The adjusted R
2
for prediction equations shown in A and B were both 0.99, and the root mean
square errors were 15 and 18 calories for A and B, respectively. C (control subjects) and D (NES subjects): the individual Gaussian curves that describe
the average rate of eating during each of 3 separate meals. Data presented in A and B were extracted from study 1, and each data point represents a mean
of 10 patients.
1674 BOSTON ET AL
by guest on June 7, 2013ajcn.nutrition.orgDownloaded from
Page 3
the morning anorexia of NES subjects extends into the lunch
period.
There is one major difference between these 2 studies, for
which we do not yet have an explanation. In the first study, the
maximum rate of caloric intake at the evening meal (P
21
) was
significantly (P 0.05) greater in the NES subjects than in
control subjects, whereas, in the second study, P
21
was signifi
-
cantly (P 0.05) greater in the control subjects than in the NES
subjects.
The question arose as to whether the greater food intake of the
overweight or obese subjects may be due to their greater weight.
If this were the case, and if the data were first scaled on an
individual patient basis by body weight, different outcomes may
be obtained. We scaled the intake patterns in this way, but when
the Boston model was fitted to the resulting mean data (results not
shown), it did not significantly alter any of the outcomes.
The Gaussian curves depicting the rate of eating associated
with each meal pulse provide a novel means of visualizing the
distinct differences in meal patterns between the control and NES
subjects. The major strengths of the Gaussian equation is that its
parameters correspond to easily discernible and comprehensible
features of a putative intake pulse (Figure 1), that initial estimates
of these parameters are relatively easy to obtain, and that the
parameters of Gaussian equations are readily estimable. We did
consider other equation forms to represent the intake pulses.
There are theoretical grounds for assuming that gamma functions
may describe eating pulses more accurately than may Gaussian
functions (11). However, our efforts with gamma functions were
unsuccessful because their use was associated with unstable pa-
rameter estimation, especially in the case of the dinner pulse.
Other candidate pulse functions that we considered but rejected
included a sum of 2 exponentials, polynomials, and periodic
functions such as sine and cosine curves. Cognizant of the need
for parsimony with respect to the number of estimable parame-
ters, we rejected a function composed of the sum of 2 exponen-
tials, because that type of function would involve 4 parameters
for each pulse. In contrast, the Gaussian equation requires the
estimation of only 3 parameters. We rejected the possibility of
using polynomials because these equations could predict nega-
tive (physiologically unfeasible) rates of eating, whereas the
Gaussian function is always positive. Although periodic func-
tions such as sine and cosine are suitable for modeling diurnal
cyclic (repeating) patterns across days (6), we rejected using
these types of equations, principally because they do not have the
requisite shape to describe the nonrepeating pulses associated
with rates of eating within a day. Moreover, the data presented
here are means within a single day, not repeating data across
days.
The parameters of the Boston model provide a means of ob-
jectively characterizing all of the features of eating patterns of
different strata of a population. Furthermore, such quantification
0
500
1000
1500
2000
2500
3000
0102030
Average Cumulative Calorie Intake
Time (h)
0
500
1000
1500
2000
2500
3000
0102030
Average Cumulative Calorie Intake
Time (h)
0
100
200
300
0102030
Average Intake rate (cal/h)
Time (h)
0
100
200
300
0102030
Average Intake rate (cal/h)
Time (h)
BA
DC
FIGURE 3. Use of the new (Boston) model to describe the average cumulative caloric intake of control subjects (A: means of 68 subjects) and subjects with
night eating syndrome (NES) (B: means of 148 patients). F, data; —, the model predictions. The adjusted R
2
for prediction equations shown in A and B were
both 0.99, and the root mean square errors were 19 and 24 calories for A and B, respectively. C (control subjects) and D (NES subjects): the individual Gaussian
curves that describe the average rate of eating during each of 3 separate meals. Data are from study 2.
MODELING CIRCADIAN RHYTHMS OF FOOD INTAKE 1675
by guest on June 7, 2013ajcn.nutrition.orgDownloaded from
Page 4
of the 3 main daily meals becomes an objective process unaf-
fected by subjective and potentially confounding definitions of
what constitute a particular meal (1, 2). For example, some ethnic
groups may consume meals at times different from those of most
white Americans.
A further strength of the Boston model is that it allows the
Gaussian curves describing the 3 main meals to overlap tempo-
rally (Figures 2D and 3D). Thus, it is not necessary to define
meals as occurring within a specified window of time, and there
can be no inadvertent distortion of the estimation of meal caloric
intakes for particular substrata of the population who may eat
their meals at nonnormative times.
Because of these attributes, the Boston model could have ap-
plications in comparing eating patterns of different racial and
ethnic groups who eat at different times, night shift workers and
day shift workers, and groups who eat at different times during
different seasons of the years, eg, summer and winter. The
Boston model also circumvents the potentially confounding
problem associated with snacking, which is common in night
eaters (3). The Boston model should be particularly useful for
analyzing measures of change in clinical trials, because it
potentially shows more information than do the traditional
measures of change (17). This model may help to assess the
effects of bariatric surgical interventions and of the differences
among them (18).
A limitation of the Boston model is that it may not be appro-
priate for the analysis of small numbers of persons, because mean
cumulative intake may be unduly influenced by a few outliers.
Despite this limitation, the Boston model provides a simple,
objective, robust approach to quantifying circadian eating pat-
terns in populations and in strata of populations.
Conclusions
Perhaps the most important contribution of this study is that it
shows how parametric deconvolution can be used to derive ex-
plicit solutions for latent putative functions to describe the rates
of input that influence the timing, magnitude, and duration of
feed intake pulses that constitute specific meals. In this work, 3
separate Gaussian functions were found to be most suitable for
describing the rate of intake during breakfast, lunch, and dinner.
Integrating these functions over time, and combing the 3 inte-
grands, results in a mathematical model that can describe the
temporal pattern of cumulative caloric intake throughout the day.
The parameters of each of the separate Gaussian equations can be
used to quantify accurately, for human populations or population
strata, mean rates of caloric intake during specific individual
meals. In conclusion, in the study reported here, parametric de-
convolution was successfully used to provide new insights into
differences in eating patterns between populations without an
eating disorder and those with NES.
The authors’ responsibilities were as follows—KCA, JDL, and AJS: con-
ducted the studies; RCB and PJM: analyzed data and wrote the manuscript;
and all authors: contributed to critical revision of the manuscript. None of the
authors had a personal or financial conflict of interest.
TABLE 1
Characteristics of eating patterns in subjects without eating disorders (control subjects) and subjects with night eating syndrome (NES)
1
Variable
Study 1 Study 2
Control subjects
(n ҃ 10)
NES subjects
(n ҃ 10)
Control subjects
(n ҃ 68)
NES subjects
(n ҃ 148)
Body weight (kg) 87.9 26.6
a
90.6 26.1
a
BMI (kg/m
2
)
28.2 4.9
a
28.5 3.9
a
31.9 8.8
a
32.4 7.9
a
Age (y) 47.1 10.7
b
57.3 12.2
b
38.3 11.3
a
43.9 12.1
b
Breakfast
P
1
(cal/h)
96.9 1.1
b
48.3 3.6
a
113.8 3.6
c
42.6 5.2
a
P
2
(h
Ҁ2
)
0.149 0.007
b
0.112 0.020
a
0.236 0.033
d
0.200 0.065
c
P
3
(h)
7.55 0.05
a
8.68 0.36
c
7.60 0.13
a
7.91 0.44
b
MS
B
(h)
4.31 0.11
c
4.98 0.38
d
3.43 0.24
a
3.72 0.65
b
AUC
EB
(cal)
445 8
d
256 35
b
415 24
c
169 43
a
Lunch
P
11
(cal/h)
285.9 6.2
d
131.5 5.3
b
278.7 12.7
c
104.5 14.9
a
P
12
(h
Ҁ2
)
0.95 0.06
c
0.28 0.03
a
0.49 0.08
b
0.79 0.42
c
P
13
(h)
11.70 0.02
b
12.05 0.08
c
11.70 0.06
b
11.22 0.17
a
MS
L
(h)
1.71 0.06
a
3.13 0.17
d
2.38 0.19
c
1.86 0.52
b
AUC
EL
(cal)
520 9
c
439 39
b
705 32
d
208 49
a
Dinner
P
21
(cal/h)
200.6 2.7
b
267.1 1.4
d
261.1 10.9
c
179.1 3.9
a
P
22
(h
Ҁ2
)
0.164 0.005
c
0.038 0.001
b
0.174 0.018
d
0.021 0.002
a
P
23
(h)
17.90 0.02
b
21.23 0.02
d
17.85 0.06
a
18.45 0.17
c
MS
D
(h)
4.11 0.07
b
8.59 0.08
c
3.99 0.21
a
11.52 0.63
d
AUC
ED
(cal)
878 5
a
2403 11
d
1109 19
b
2211 18
c
Total daily intake (cal) 1844 2
a
3098 8
d
2229 9
b
2555 27
c
1
All values are x SD. P
1
, P
11
, and P
21
(cal/h) are parameters describing the maximum rate of calorie consumption during the morning, midday, and
evening meals, respectively. P
2
, P
12
, and P
22
(h
Ҁ2
) describe the inverse of meal spread, whereas P
3
, P
13
, and P
23
(h) describe the time of the maximum rate
of calorie intake. MS
B
,MS
L
, and MS
D
(h) are indexes describing meal spread of the morning, midday, and evening meals, respectively, and AUC
EB
, AUC
EL
,
and AUC
ED
are the areas under the Gaussian functions describing the rates of eating during the morning, midday, and evening meals, respectively. Means in
a row with different superscript letters differ significantly, P 0.05 (t test).
1676 BOSTON ET AL
by guest on June 7, 2013ajcn.nutrition.orgDownloaded from
Page 5
REFERENCES
1. Siega-Riz AM, Popkin BM, Carson T. Trends in breakfast consumption
for children in the United States from 1965–1991. Am J Clin Nutr
1999;18:563–71.
2. Siega-Riz AM, Popkin BM, Carson T. Differences in food patterns at
breakfast by sociodemographic characteristics among a nationally rep-
resentative sample of adults in the USA. Prev Med 2000;30:415–24.
3. National Center for Health Statistics. Plan and operation of the third
National Health and Nutrition Examination survey, 1988 –94. Series 1:
programs and collection procedures. Vital Health Stat 1 (32) 1994;1–
407.
4. United States Department of Agriculture. 1994-96 Continuing Survey of
Food Intakes by Individuals and 1994-96 Diet and health Knowledge
Survey. Available from National Technical Information Service,
Springfield, VA (NTIS Accession No. PB98-500457), 1998 (CD-
ROM).
5. Phillips AJ, Robinson PA. A quantitative model of sleep-wake dynamics
based on the physiology of the brainstem ascending arousal system.
J Biol Rhythms 2007;22:167–79.
6. Beersma GM. Why and how do we model circadian rhythms? J Biol
Rhythms 2005;20:304 –13.
7. Stunkard AJ, Grace WJ, Wolff HG. The night eating syndrome: a pattern
of food intake among certain obese patients. Am J Med 1955;19:78 86.
8. Allison KC, Grilo CM, Masheb RM, Stunkard AJ. Binge eating disorder
and night eating syndrome: a comparative study of disordered eating. J
Consult Clin Psychol 2005;73:1107–15.
9. O’Reardon JP, Ringel BL, Dinges DF, et al. Circadian eating and sleep-
ing patterns in the night eating syndrome. Obes Res 2004;12:1789 –96.
10. Meier JJ, Veldhuis JD, Butler PC. Pulsatile insulin secretion dictates
systemic insulin delivery by regulating hepatic insulin extraction in
humans. Diabetes 2005;54:1649 –56.
11. Abramowitz M, Stegun IA. Handbook of mathematical functions with
formulas, graphs and mathematical tables. New York, NY: Dover, 1964.
12. Birketvedt GS, Florholmen SJ, Sundsfjord J, et al. Behavioral and neu-
roendocrine characteristics of the night-eating syndrome. JAMA 1999;
282:657– 63.
13. Stefanovski D, Moate PJ, Boston RC. WinSAAM: a Windows-based
compartmental modeling system. Metabolism 2003;52:1153– 66.
14. Wastney ME, Patterson BH, Linares OA, Greif PC, Boston RC. Inves-
tigating biological systems using modeling—strategies and software.
San Diego, CA: Academic Press, 1999.
15. Stunkard AJ, Allison KC, O’Reardon JP. The night eating syndrome: a
progress report. Appetite 2005;45:182– 6.
16. Sokal RR, Rohlf FJ. Introduction to biostatistics. San Francisco, CA:
WH Freeman and Company, 1973.
17. O’Reardon JP, Allison KC, Martino NS, Lundgren JD, Heo M, Stunkard
AJ. A randomized, placebo-controlled trial of sertraline in the treatment
of night eating disorder. Am J Psychiatry 2006;163:893– 8.
18. Allison KC, Wadden TA, Sarwer DB, et al. Night eating syndrome and
binge eating disorder among persons seeking bariatric surgery: preva-
lence and related features. Obesity 2006;14(suppl 2):77S– 82S.
TABLE 2
Characteristics of eating patterns in normal-weight and overweight or obese control subjects and normal-weight and overweight or obese subjects with
night eating syndrome (NES)
1
Variable
Control subjects NES subjects
Normal-weight
(n ҃ 23)
Overweight or obese
(n ҃ 45)
Normal-weight
(n ҃ 19)
Overweight or obese
(n ҃ 129)
Body weight (kg) 58.5 8.0
a
102.3 19.6
b
60.5 6.1
a
95.1 4.9
b
BMI (kg/m
2
)
21.7 1.9
a
36.9 6.2
b
22.5 1.7
a
33.9 7.5
b
Age (y) 36.5 12.2
a
39.2 10.9
a
41.9 15.5
a,b
44.2 11.6
b
Breakfast
P
1
(cal/h)
97.1 5.1
c
122.5 4.1
d
32.1 4.5
a
44.1 5.9
b
P
2
(h
Ҁ2
)
0.153 0.03
b
0.319 0.04
d
0.116 0.042
a
0.211 0.08
c
P
3
(h)
7.52 0.19
a
7.55 0.12
a
8.59 0.84
c
7.89 0.43
b
MS
B
(h)
4.29 0.43
c
2.95 0.20
a
4.88 0.94
d
3.62 0.69
b
AUC
EB
(cal)
443 28
c
384 24
b
167 53
a
170 48
a
Lunch
P
11
(cal/h)
282.0 24.0
b
280.0 11.6
b
106.7 12.1
a
106.4 18.4
a
P
12
(h
Ҁ2
)
0.91 0.22
c
0.37 0.06
a
0.58 0.25
b
0.90 0.56
c
P
13
(h)
11.69 0.07
b
11.67 0.06
b
11.73 0.13
b
11.15 0.18
a
MS
L
(h)
1.74 0.21
a
2.74 0.21
c
2.18 0.47
b
1.75 0.57
a
AUC
EL
(cal)
523 33
c
818 37
d
248 58
b
199 50
a
Dinner
P
21
(cal/h)
199.5 10.0
c
291.1 12.0
d
174.6 3.4
a
181.5 4.2
b
P
22
(h
Ҁ2
)
0.162 0.02
c
0.180 0.02
d
0.026 0.002
b
0.021 0.002
a
P
23
(h)
17.95 0.08
b
17.83 0.06
a
20.07 0.10
d
18.21 0.19
c
MS
D
(h)
4.12 0.25
b
3.92 0.21
a
10.34 0.43
c
11.57 0.68
d
AUC
ED
(cal)
877 17
a
1216 23
b
1899 40
c
2198 18
d
Total intake (cal) 1843 8
a
2418 9
c
2314 21
b
2567 30
d
1
P
1
, P
11
, and P
21
(cal/h) are parameters describing the maximum rate of calorie consumption during the morning, midday, and evening meals, respectively.
P
2
, P
12
, and P
22
(h
Ҁ2
) describe the inverse of meal spread, and P
3
, P
13
, and P
23
(h) describe the time of the maximum rate of calorie intake. MS
B
,MS
L
, and
MS
D
(h) are indexes describing the meal spread of the morning, midday, and evening meals, and AUC
EB
, AUC
EL
, and AUC
ED
are the areas under the Gaussian
functions describing the rates of eating during the morning, midday, and evening meals, respectively. Means in a row with different superscript letters differ
significantly, P 0.05 (t test).
MODELING CIRCADIAN RHYTHMS OF FOOD INTAKE 1677
by guest on June 7, 2013ajcn.nutrition.orgDownloaded from
Page 6
  • Source
    • "One may wonder if the weight gain and metabolic shifts observed in nighttime eating are influenced by a delayed sleep phase or delayed eating pattern among those with NES. Clinical studies have shown that sleep efficiency is reduced in those with NES [80], with sleep onset and offset remaining similar to control participants in both outpatient [81] and inpatient settings [80], even with increased caloric intake [82]. Thus, those with NES go to bed and rise at roughly the same time as those without NES, but NES patients awaken more often with an accompanying drive to eat. "
    [Show abstract] [Hide abstract] ABSTRACT: Animal studies of delayed eating have provided useful information regarding the potential relationship between nighttime eating and increased weight and metabolic dysregulation, which occur in the absence of increased locomotion or increased caloric intake. We first review recent studies detailing these relationships and possible mechanisms in rodents. We then examine human data showing that sleep restriction leads to increased energy intake and weight gain, followed by a review of the human phenotype of delayed eating, night eating syndrome, and its relation to weight and metabolism. Finally, we examine human experimental studies of delayed eating and discuss preliminary data that show slight weight gain, dysfunction in energy expenditure, and abnormalities in the circadian rhythms of appetitive, stress, and sleep hormones. Well-controlled, longer-term experimental studies in humans are warranted to test the effect of delayed eating without sleep restriction to clarify whether limiting or eliminating nighttime eating could lead to weight loss and significantly improve related disorders, such as diabetes and heart disease, over time.
    Full-text · Article · Mar 2014
  • Source
    • "First, the added structure to food intake during the day in behavioral weight loss may be an important factor in modifying the delayed pattern of eating that is characteristic of NES. Indeed, Boston et al have shown that individuals with NES report unscheduled, inconsistent mealtimes over the 24-hour day as compared to control participants, and this may drive night eating behaviors.28 Self-monitoring of food intake is the second likely key element of behavioral weight loss approaches in the treatment of NES. "
    [Show abstract] [Hide abstract] ABSTRACT: Night eating syndrome (NES) is a form of disordered eating associated with evening hyperphagia (overeating at night) and nocturnal ingestions (waking at night to eat). As with other forms of disordered eating, cognitive and behavioral treatment modalities may be effective in reducing NES symptoms. This review presents evidence for a variety of behavioral treatment approaches, including behavioral therapy, phototherapy, behavioral weight loss treatment, and cognitive-behavioral therapy. A more detailed overview of cognitive-behavioral therapy for NES is provided. All of these studies have been case studies or included small samples, and all but one have been uncontrolled, but the outcomes of many of these approaches are promising. Larger randomized controlled trials are warranted to advance NES treatment literature. With the inclusion of NES in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a "Feeding or Eating Disorder Not Elsewhere Classified," more sophisticated, empirically-supported, behaviorally-based treatment approaches are much needed.
    Full-text · Article · Mar 2013 · Psychology Research and Behavior Management
  • Source
    Preview · Article ·
Show more