ArticlePDF Available

The Relationship between Depression and Severe Obesity: A Case-Control Study

Open Journal of Psychiatry, 2017, 7, 276-293
ISSN Online: 2161-7333
ISSN Print: 2161-7325
10.4236/ojpsych.2017.74024 Aug. 17, 2017 276 Open Journal of Psychiatry
The Relationship between Depression and
Severe Obesity: A Case-Control Study
Marja Koski1*, Hannu Naukkarinen2,3
1Department of Psychiatry, University of Helsinki, Helsinki, Finland
2University of Helsinki, Helsinki, Finland
3Carea Hospital District, Kymenlaakso Psychiatric Hospital, Kuusankoski, Finland
This study investigated the relationship between depression and
obesity in severely obese retired individuals using the case-control method.
The subject group consisted of 112 individuals receiving a perm
nent disability pension primarily due to
obesity. The prevalence of depression
was measured with a personal psychiatric interview and the Beck Depression
Inventory (21). Male and female controls were selected separately, with five
controls for male subjects and three controls for female subjects.
The controls
were matched with the subjects according to place of residence, age, time that
pension was granted, and occupation. The statistical analyses included χ2
-tests for paired variables, a conditional logistic linear model, correl
tion coefficients and the percent distributions.
Depression was dia
nosed more often in the subject group than in the control group according to
the psychiatric interview. Based on the conditional logistic linear model, ind
viduals with severe obes
ity had a higher risk of depression than those in the
control group. The most common disturbance was chronic depression in both
groups. Additionally, there were significant findings regarding the outcome of
depression for every classification in the psychi
atric interview. According to
the Beck Depression Inventory, depression was more common in subjects
than in controls. However, slight depression was most common in the study
group. Seven percent of the subjects had masked depression. The questions on
the B
eck Depression Inventory that measure irritability, indecisiveness, body
image and ability to work were nearly significant. Regarding weight changes,
the Beck Depression Inventory questions on both weight loss (
= 0.014) and
weight gain (
= 0.017) were statistically significant. In the study group, ind
viduals with BMI over 40 kg/m2
gained the most weight; however, weight loss
was very low overall. Regarding changes in appetite, the majority of the study
group responded that they had a poorer appetite than previously. Conclusion:
How to cite this paper:
Koski, M. and Nau-
, H. (2017) The Relationship be-
tween Depression and Severe Obe
ity: A
-Control Study.
Open Journal of Ps
, 276-293.
June 20, 2017
August 14, 2017
August 17, 2017
Copyright © 201
7 by authors and
Research Publishing Inc.
This work is
licensed under the Creative
Commons Attribution International
License (CC BY
Open Access
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 277 Open Journal of Psychiatry
This research is essential and provides information on groups that have not
been previously examined. The findings from this study can be utilized to i
prove the care and understanding of individuals with severe obesity.
Severe Obesity, Depression, Beck Inventory, Body Mass Index,
Weight in Depression
1. Introduction
Obesity is a multifaceted illness on the biological basis which includes genetic
and biological components that are involved in normal body growth, eating ha-
bits, energy expenditure, and adipose tissue function [1]. The level of obesity in
Finland has been an increasing problem in Finland as well as worldwide [2].
et al.
[3] researched the pathophysiology of obesity as well as interven-
tions targeting it. They focused on not only the factors influencing the develop-
ment of obesity but also the role of hormones and intestinal peptides. They also
conducted neuroimaging studies, and their findings provide insight into the
portion of the brain involved in the development of obesity. In their studies,
brain circuits were considered to be related to obesity. Unhealthy eating has also
provided a new perspective on obesity, and information on food addiction has
recently been updated. According to Milaneschi
et al.
[4], leptin levels are higher
in men, especially men with abdominal obesity. This finding supports the fact
that there is some type of biological link between depression and obesity that al-
so leads to negative health outcomes.
According to Chen
et al.
[5], the prevalence of depression is higher in adult-
hood among women. Depression is found more often in individuals with ab-
normal body weight. In particular, younger obese women have a higher risk of
depression. Mauri
et al.
[6] found in their research on morbid obesity that mood
disorders were the most common diagnoses in obese individuals. Obesity has
been significantly associated with mood disorders but not anxiety disorders [7].
Obesity is associated with anxiety, depression and fewer feelings of well-being in
female individuals [8]. After gastric bypass, patients who had an Axis I disorder,
especially a mood or anxiety disorder exhibited poorer weight outcomes at 6
months [9].
Obesity can also be understood as an indirect form of self-destruction. Typical
forms of indirect self-destructive behavior (ISDB) include excessive smoking,
alcohol and drug abuse, excessive self-induced stress and strain, and difficulty
accepting the restrictions imposed by long-term illnesses [10]. ISDB usually lasts
for years, and the individual is not aware of his/her behavior, especially its suicidal
nature [10]. In some cases, ISDB appears to protect the individual from under-
lying depression or anxiety. Occasionally, the individual acts very impulsively
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 278 Open Journal of Psychiatry
without thinking of the long-term consequences of his/her behavior [11].
Ma and Xiao [12] examined a population-based sample of people from the
US. The degree of obesity is an independent risk factor for depression in women
in obesity class 3. The risk for depression is homogeneous within the obese pop-
ulation. According to Zimmerman [13], several symptoms are associated with
obesity, and these symptoms are the same as those of major depressive disorder
(MDD). In their study, increased appetite, increased weight and tiredness were
lower in obese patients than in the non-obese population. Faulconbridge
et al.
[14] discovered that due to fears of a mood disorder, depressive individuals are
excluded more often from weight loss trials. Similarly, depressive participants
most likely will not lose weight to a satisfactory degree. Weight loss is not asso-
ciated with incident symptoms in depression but instead can prevent these
symptoms. Fowler-Brown
et al.
[15] assumed that obesity is not associated with
risk for depression in the general population, although it is associated with an
increased risk of having depressive problems in higher social classes; therefore,
sociodemographic characteristics are important when determining the associa-
tion between depression and obesity. According to Wild
et al.
[16], the appear-
ance of symptoms in obesity and depression varies widely between BMI classes.
They believed that women with BMIs indicating the second and third classes of
obesity experienced more depression, whereas obesity seemed to be associated
with a lower risk of depression in older men. Obesity is also associated with sev-
eral chronic diseases, most of which are related to psychiatric diseases. Zhao
[17] stated that the prevalence of depression and anxiety appeared to depend
on various BMI levels regardless of the population’s disease status and other
psychosocial and lifestyle factors.
Our study investigated the relationship between depression and severe obesi-
ty. Our findings are valuable because we found that the examinees had lost their
ability to work due to serious obesity.
2. Materials and Methods
The aims of this research were to study the individuals who are living in south-
ern Finland and were receiving a permanent disability pension primarily due to
obesity. 112 individuals (81 women and 31 men) fill in claim. The controls were
selected by random sampling and matched with subjects according to place of
residence and sex. The matching process also considered age, time that pension
was granted, and occupation. The occupation of the controls was either the same
as the subjects or unknown. The control group was selected from the same area
and consisted of individuals receiving a disability pension due to a different
primary illness. One hundred and fifty-two individuals met these criteria. Nine-
teen had been granted a temporary pension and were excluded from the sample.
Participants who died or no longer received a pension were also excluded. Be-
cause men constituted a small group, many controls were selected to ensure a re-
liable analysis [1].
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 279 Open Journal of Psychiatry
This study was a case-control study. Three controls were selected for each fe-
male subject, and five controls were selected for each male subject to obtain
more reliable results. It is notable that male and female controls were selected
separately. For the interview, we aimed to include at least two controls for each
female subject and three for each male subject. Overall, the study enlisted 510
persons, including 112 subjects and 398 controls [1].
Three letters inviting individuals to participate in the study were sent to each
subject and control. The letters were discreetly worded and emphasized the con-
fidentiality of the study. Most individuals who did not participate in the study
indicated their reasons for refusal in writing. These letters are available upon
request [1].
Table 1 shows the basic characteristics of the study participants [2].
The accumulation of all materials is shown in Figure 1.
The age distribution of matched material is shown in Figure 2.
Body mass index (BMI) was calculated as weight (kg) divided by height (m2).
According to the WHO guidelines, the weight categories were defined as follows:
overweight, BMI 25 ≤ 29; obese, BMI 30 ≤ 34; severely obese, BMI 35 ≤ 40; and
morbidly obese, BMI > 40 kg/m2 [1]. Figure 3 shows the body mass index dis-
tribution in this study.
Figure 1. Study overview.
Figure 2. Age during the psychiatric examination.
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 280 Open Journal of Psychiatry
Table 1. Basic characteristics of the study participants.
Study group
Control group
Marital status
= 0.0894
Unmarried 10.7% 15.7%
Married 62.7% 59.6%
Widowed 14.7% 13.5%
Divorced 6.7% 10.7%
Common-law marriage 5.3% 0.6%
Basic education
= 0.2457
Primary school
89.3% 90.4%
Lower secondary school 6.7% 2.2%
High school - 2.2%
Other 4% 3.9%
Occupational category
n = 22 (m)
n = 53 (f)
n = 66 (m)
n = 112 (f)
= 0.901 (m)
= 0.5930 (f)
Technical, scientific,
and artistic work
m = 0%
f = 0%
total = 0%
m = 0%
f = 4.5%
total = 2.2%
Accounting and
clerical work
m = 4.5%
f = 5.7%
total = 5.1%
m = 1.5%
f = 2.7%
total = 2.1%
Commercial work
m = 4.5%
f = 17.0%
total = 10.8%
m = 4.5%
f = 10.7%
total = 7.6%
Agricultural, forestry,
and fishing
m = 0%
f = 7.5%
total = 3.7%
m = 3.0%
f = 7.1%
total = 5.1%
Transport and
communication work
m = 27.3%
f = 7.5%
total = 17.4%
m = 24.2%
f = 4.5%
total = 14.3%
Industrial work
m = 50.1%
f = 17.0%
total = 33.5%
m = 48.6%
f = 21.4%
total = 35.0%
Service work
m = 13.6%
f = 45.3%
total = 29.5%
m = 18.2%
f = 49.1%
total = 33.7%
m = 100%
f = 100%
total = 100%
m = 100%
f = 100%
total = 100%
Social classification
According to Bruun’s
social classification
= 0.050 (m)
= 0.936 (f)
I = First social class
4.2% 2.3%
II = Second social class 12.5% 17.7%
III = Third social class 50.0% 57.7%
IV = Fourth social class 33.3% 22.3%
m = male, f = female.
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 281 Open Journal of Psychiatry
Figure 3. Body mass index distribution between subjects and controls.
2.1. Interview Form
The authors of this paper interviewed all participants. The pilot study (n = 30)
was conducted in the neurological ward of Hesperia Hospital in Helsinki. The
subjects were patients at the hospital [2].
The Diagnostic and Statistical Manual of Mental Disorders (DSM-III)
commends using a multi-axial system for evaluations to ensure that certain po-
tentially valuable information for treatment planning and outcome prediction is
recorded for each individual on the five axes outlined in the DSM. The first three
axes constitute an official diagnostic evaluation. Axes I and II include all mental
disorders, and personality disorders, and specific developmental disorders are
grouped into Axis II [18].
Occupation: The standard occupational classifications of the Social Insurance
Institution (Bruun [19]) were used.
A disability pension is payable to insured individuals (due to disease, disabili-
ty, or injury) who are unable to maintain themselves through regular work or
any other type of suitable work based on their age, occupation, education and
place of residence. The determining factors are type of disease, age, length of
service, deterioration of health, and working conditions. A special team at the
Social Insurance Institution decides individuals’ pension eligibility [20].
2.2. The Beck Depression Inventory
After the interview, the participants completed the Beck Depression Inventory
(BDI), as proposed by Beck
et al.
[21], which was used to measure depression.
The inventory consisted of 21 questions, and each question had four to six re-
sponse options. An additional factor, weight gain, was added to the original BDI
by Raitasalo [22] in 1977 because the BDI addressed only weight loss.
The patients who had gained weight had difficulty answering the original
questions, which addressed only losing weight. The questions measured the
quantitative aspects of depression and the severity of depressive symptoms re-
garding the most common characteristics of patients with depression observed
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 282 Open Journal of Psychiatry
in clinical practice (M.K.).
2.3. Statistical Methods
The statistical methods used in this study included
-tests and condi-
tional logistic linear models. Because the subjects were matched, we analyzed the
data with figures and percentages and through calculations of the means for the
subjects and controls using a matched control approach. These two groups were
then compared using
-test for paired variables. Significant T-test results were
detected and analyzed further in the conditional logistic linear model. The risk
ratios (RRs) and upper and lower confidence limits were calculated for parame-
ters that remained significant in the conditional logistic linear analysis. The sta-
tistical parameters were calculated with SPSS (Statistical Package for Social
Sciences for Windows 18/Windows, Chicago, IL, USA). The conditional logistic
analyses were performed with the Glim program [23]. GLIM analysis is straight-
forward when the data are in a convenient individual-by-individual format,
which commonly corresponds to the method compilation. A major advantage of
this technique is that it is easy to use and has inherent flexibility [1] [2].
In each set, one case was included for every 0 - 5 controls. Because these ob-
servations were considered counts, a Poisson error distribution was used, and
the logarithmic function was used as the link function. The model was a special
form of the log-linear model. The linear predictor in the systematic part of the
model for each observation was a (linear) function of the observed exposure va-
riables for each individual plus a constant (set) term, which could vary from
matched set to matched set. According to the literature regarding case-control
data, this model is called a “conditional logistic regression” (which is a mislead-
ing description for data analysts who are familiar with generalized linear model
terminology). Although the group of subjects would have remained very small
with this method, the missing controls were replaced with the nearest control.
The matched control approach meant that some of the subjects who had agreed
to participate were excluded from the study during the statistical analysis be-
cause a control was not available. In some cases, specific variables were lacking,
and the number of observations available for comparisons was further reduced
[24]. Differences between groups were considered highly significant when the
probability (
) of error in rejecting the null hypothesis was
< 0.001 (***), sig-
nificant when
< 0.01 (**), and nearly significant when
< 0.05 (*) [1] [2].
The study protocol was approved by the ethical committees of Hesperia/
Aurora Hospital (Community Psychiatric Hospital in Helsinki) and Lapinlahti
Hospital (Psychiatric Clinic of Helsinki University)/Psychiatric Centrum of Hel-
sinki University. An informed consent form was signed by the patients, and the
ethical principles of the Declaration of Helsinki were followed throughout the
study [1].
2.4. Refusal
The individuals who refused to participate to this study had the same education
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 283 Open Journal of Psychiatry
level, age and sex distribution as the participating individuals. One male subject
could not be contacted after initial inclusion in the study, and one female subject
dropped out of the study before the psychological test was administered. 37 in-
dividuals refused to participate (9 males and 28 females) in the study. The mean
ages of the male and female participants who refused to participate in the study
group were 59 years (standard deviation (SD), 3.61) and 61 years (SD, 4.46)
years, respectively. A total of 31 participants had a primary school level of edu-
cation, and 34 had no vocational education. More matched controls than sub-
jects refused to participate in this study. The complete information for the par-
ticipants of this study is in the Table 1 [2].
3. Results
Depression was divided into four subclasses based on the DSM III. Table 2 illu-
strates the outcomes regarding depression. Chronic depression (dysthymia dis-
order) accounted for 28% of subjects and 14% of the controls. Additionally, 21
women and 6 men were diagnosed with mood disorders. Based on the condi-
tional logistic linear model, individuals with severe obesity had a higher risk of
depression than persons in the control group.
Table 2 shows the RRs, namely, chronic depression 2.1, other mood distur-
bance 6.6, and serious mood disturbance 6.0.
Table 3 illustrates the BDI classifications (0 - 4); 11% of the subjects and 11%
of the controls had severe depression. Eleven percent of the subjects and 9% of
the controls had moderate depression. In the subject group, 7% had masked de-
pression, and the corresponding figure in the control group was 5%. Using
tests, no significant differences were found between the groups or between fe-
males and males.
Based on the conditional logistic linear model, no differences were observed
between the study group and control group. No study vs. control group differ-
ences were observed when men and women were considered separately.
Table 4 shows the results of the BDI, which includes twenty-one questions.
Comparisons between the study group and the control group were assessed with
2 test (these questions were investigated separately). The differences in question
eleven (
= 0.0283), which measures irritability, were almost significant, where
60.8% of the study group vs. 47.1% of the control group did not feel more irritated
Table 2. Outcome of depression in the psychiatric interview.
Occurrence of
Study group Control group
RR 95%
conf. limit
n = 75 n = 178
Chronic depression 28% 14% 2.1 1.3 - 3.5
Other mood disturbance 7% 2% 6.6 2.6 - 16.5
Serious mood disturbance 4% 1% 6.0 1.4 - 14.3
None 61% 83% - -
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 284 Open Journal of Psychiatry
Table 3. Outcome of depression in the beck depression inventory.
Beck Depression
Study group
Study group
n = 75
n = 75
= 0.960
= 0.853 (m)
= 0.778 (f)
Masked depression 7% m = 5%
f = 8% 5% m = 8%
f = 4%
No depression 49% m = 50%
f = 49% 54% m = 59%
f = 50%
Slight depression 22% m = 23%
f = 23% 21% m = 15%
f = 25%
Moderate depression 11% m = 9%
f = 11% 9% m = 9%
f = 9%
Severe depression 11% m = 13%
f = 9% 11% m = 9%
f = 12%
m = male, f = female; ***
< 0.001, highly significant; **
< 0.01, significant; *
< 0.05, nearly significant.
Table 4. Results of the beck depression inventory.
All variables
Study group Control group
m f m f
1) Sadness
22 52 66 108 0.898#
2) Pessimism 22 52 65 107 0.4498
3) Past failure 22 52 65 107 0.4355
4) Loss of pleasure 22 52 65 107 0.586#
5) Guilty feelings 22 52 65 107 0.3745
6) Punishment feelings 22 52 65 107 0.9970
7) Self-dislike 22 52 65 108 0.782#
8) Self-criticalness 22 52 66 107 0.2494
9) Suicidal thoughts or wishes 22 52 65 107 0.614#
10) Crying 22 52 65 107 0.4102
11) Irritability 22 52 65 107 0.0283
12) Loss of interest 22 52 65 107 0.596#
13) Indecisiveness 22 52 65 107 0.0573
14) Body image 22 52 65 107 0.0274
15) Work inhibition 22 52 65 107 0.0394
16) Changes in sleeping pattern 22 52 65 107 0.3408
17 Fatigability 22 52 65 107 0.1326
18) Changes in appetite 22 52 65 107 0.084#
19A) Weight loss 22 52 65 107 0.014
19B) Weight gain 10 22 18 38 0.017
20) Somatic preoccupation 22 52 65 107 0.094#
21) Loss of interest in sex 22 52 65 107 0.7346
*the value of
2 test is a common result in study and control groups. #For this question, all individuals with
symptoms were combined. (f = female, m = male).
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 285 Open Journal of Psychiatry
than before. However, 36% of the control group felt irritated all the time com-
pared to 23% in the study group. Nearly significant differences were observed for
the question of indecisiveness (
= 0.0573). Question fourteen (
= 0.0274),
which involves body image, found that 52.7% of the study group did not feel that
they looked any worse than before, compared to 58.7% of the control group; ad-
ditionally, 24.3% of the study group felt that there were permanent changes to
their appearance that made them look unattractive, compared with 10.5% in the
control group. Question fifteen (
= 0.0394) showed that both the study and the
control group felt that their ability to work had deteriorated, while 14.9% of the
study group felt that they had to push themselves very hard to do anything.
Closer attention should be paid to the results of question eighteen (
= 0.084),
which showed that 24.3% of the study group felt that they had a poorer appetite.
Table 5 illustrates the weight changes reported on question 19 in the BDI.
Question 19 indicated that over the previous weeks, weight had not changed in
44.2% of all participants, including both the study and the control group. Re-
garding weight loss, a significant difference between the study group and control
group was found, at
= 0.014, while the significance was
= 0.017 for weight
gain, both of which are nearly statistically significant. In females, 23.1% of the
Table 5. Weight changes in the beck depression inventory, question 19 in Table 4.
n = 74
n = 171
n = 245
n = 52
n = 107
n = 159
n = 22
n = 64
n = 86
n = Weight
more than 6 kg
17 14 31 12 8 20 5 6 11
23.0% 8.2% 12.7% 23.1% 7.5% 12.6% 22.7% 9.4% 12.8%
n = Weight
more than 4 kg
3 8 11 3 4 7 0 4 4
4.1% 4.7% 4.5% 5.8% 3.7% 4.4% .0% 6.3% 4.7%
n = Weight
more than 2 kg
4 14 18 3 9 12 1 5 6
5.4% 8.2% 7.3% 5.8% 8.4% 7.5% 4.5% 7.8% 7.0%
n = Weight
did not
18 80 98 12 48 60 6 32 38
24.3% 46.8% 40.0% 23.1% 44.9% 37.7% 27.3% 50.0% 44.2%
n = Weight
increased more
than 2 kg
3 7 10 2 5 7 1 2 3
4.1% 4.1% 4.1% 3.8% 4.7% 4.4% 4.5% 3.1% 3.5%
n = Weight
more than 4 kg
4 21 25 3 17 20 1 4 5
5.4% 12.3% 10.2% 5.8% 15.9% 12.6% 4.5% 6.3% 5.8%
n = Weight
more than 6 kg
25 27 52 17 16 33 8 11 19
33.8% 15.8% 21.2% 32.7% 15.0% 20.8% 36.4% 17.2% 22.1%
f = female, m = male.
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 286 Open Journal of Psychiatry
study group had lost more than 6 kg of weight, and 32.7% had gained more than
6 kg of weight during the pension period. In the male study group, the results
were similar; 22.7% had lost more than 6 kg of weight and 36.4% had gained
more than 6 kg of weight (
= 0.190) in the previous weeks. Among individuals
in the study group with a BMI ≥ 40 kg/m2, 37.9% had gained more weight and
only 6.9% had lost weight.
4. Discussion
4.1. Statement of Principal Findings
Depression was diagnosed more in the subject group than in the control group
based on the psychiatric interview. In the conditional logistic linear model, indi-
viduals with severe obesity had a higher risk of depression than persons in the
control group. The most common disturbance was chronic depression in both
groups. Statistically significant findings regarding the outcome of depression
were also observed for every classification in the psychiatric interview. We found
more masked depression in the study group.
Based on the distribution of depression in the sample according to the BDI,
the subject group suffered from depression more often than the control group.
However, slight depression was the most common in the study group. Seven
percent of the subjects had masked depression.
The BDI questions measuring irritability, indecisiveness, body image and abil-
ity to work were nearly significant. Regarding weight changes, as assessed in the
Beck Depression Inventory questions, both weight loss and weight gain were sta-
tistically significant.
In the study group, individuals with BMI over 40 kg/m2 gained the most
weight. Weight loss was very low. On the question regarding change in appetite,
the majority of the study group answered that their appetite was worse than it
had been before.
4.2. Strengths and Weaknesses of the Study
The participants of this study were not obtained from a diet group, as in most
obesity research. This approach enhanced the validity of the findings. The sub-
jects in this study were concentrated on a group of individuals receiving a disa-
bility pension due to obesity. The study group was successfully matched with the
control group. The occupational and social statuses of both groups were nearly
the same. The influence of the subject’s life situation was minimized because
members of the control group had also been receiving a pension for the same
duration. All subjects were interviewed individually, which tends to improve the
reliability of the results. The interview was conducted to ensure that the inter-
viewer did not know whether the participant was a subject or control. This
double-blind approach increased the validity of the study. The fact that controls
were selected by random sampling using data from the Social Insurance Institu-
tion of Finland adds value to the findings [1] [2].
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 287 Open Journal of Psychiatry
4.3. Strength and Weakness in Relation to Other Studies and
a Discussion of Differences in the Results
Similar results regarding the relationship between obesity and depression were
found by Beydoun and Wang [25]. According to Ma and Xiao [12], obesity is an
especially important risk factor for depression in obese female individuals with
BMI class 3 obesity. Chen
et al.
[5] demonstrated that the prevalence of depres-
sion was higher in women than men. According to Murphy
et al.
[26], depres-
sion was much more severe in obese individuals than in non-obese individuals.
et al.
[27] also used the BDI to study depression and obesity. In addi-
tion, Krukowski
et al.
[28] found that the BDI effectively predicted which obese
individuals had depression. Furthermore, Castellini
et al.
[29] demonstrated that
the BDI had high prevalence rates when symptoms of depression and anxiety
were present. Researchers [30] have used the BDI to assess depression symptoms
in binge eating and obesity groups. They used both 21- and 16-item versions of
the Fourth Edition Axis I Disorders. All three measures had the potential to in-
form depression features. According to Xiang [31], obese older adults have the
potential to become depressed. Degirmenci [32] found that depression and an-
xiety levels were high among individuals who had obesity. Their results suggest
that psychiatric care may have positive effects on quality of life and self-esteem
in individuals with obesity.
Some studies have published results that differ from those in our study. Ac-
cording to Hung
et al.
[33], obesity is associated with depression and higher BMI
increases the risk of mental illnesses. The results of this study (M.K.) differed
from those of Mattlar
et al.
[34], and the author (M.K.) has different views than
those of Ma and Xiao [12]. Sarwer
et al.
[35] found that two-thirds of morbidly
obese individuals had a psychiatric diagnosis, often major depressive disorder.
We did not find the same results. Stunkard
et al.
[36] identified an association
between obesity and major depressive disorder. Our results also differ from
et al.
[37], who found that very few obese non-binge-eaters suffered from
depression. Istvan
et al.
[38] observed that depression was only weakly corre-
lated with BMI among women and not at all among men. In this study (M.K.),
depression was more common in women than in men, and the results were
therefore not fully consistent with the abovementioned opinions.
In Kaplan
et al.
s [37] study, some forms of depression, particularly mood
disorders with a seasonal pattern, were associated with weight gain (seasonal af-
fective disorder). Thus, weight change may be a marker of subtypes of depressive
disorders. Depression is more severe among obese compared with non-obese in-
dividuals. Weight gain in obese individuals is an important marker of depression
severity, as reported by Murphy
et al.
[26] Marmorstein
et al.
[39] found that
adolescence is the time when MDD becomes more prevalent and that depression
also increases in adulthood. We did not observe these findings in our study.
In their research, Ahmadi
et al.
[40] found that total body fat was remarkably
higher in women with depression than in those without. However, total body fat
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 288 Open Journal of Psychiatry
is not associated with BMI and the geriatric depression scale (GDS). According
to Roberts and Duong [41], it is more important to research body image com-
ponents in depression and obesity than BMI and depression; the findings in this
study were highly consistent with those observed by Roberts and Duong. In this
research (M.K.), the findings differ from those of Zimmerman [13] because of
the research frame used. The author of this study (M.K.) had different attitudes
than those of Faulconbridge
et al.
[14] but partly the same opinions as Wild [16].
However, the psychiatric interview revealed the same characteristics as in the
Achte [18] study. These types of studies are rare, and therefore there is thus no
further data that can be used to compare the findings.
4.4. Meaning of the Study: Possible Mechanisms and Implication
for Clinicians or Policy Makers
Obesity and depression are both diseases of civilization, and their development
and maintenance are highly influenced by dysregulations of neuroimmunologi-
cal parameters and physiological regulatory processes. Therapeutic methods
should always be consistent with current evidence from studies and should be
tailored to patients’ individualized situations [42]. According to researchers, he-
terogeneity in depression must be observed when examining the impact of de-
pression on obesity in older age. As a preventive procedure, older adults with
atypical depression should be assessed [43]. In weight loss meetings, MDD has
been shown to predict worse weight loss outcomes. In women with MDD and
obesity, worse quality diet was associated with depression but not physical activ-
ity [44]. According to Hamer
et al.
[45], metabolic health is a risk factor for the
development of depression and obesity. This finding is not global, and more
studies are needed to support these results. Brunault
et al.
[46] in their research
on a group receiving surgery highlight that depression and binge eating should
be identified before operations. Lasserre
et al.
[47] found that the atypical sub-
type of MDD is a strong predictor of obesity. The Montgomery-Asberg Depres-
sion Rating Scale (MADRS) measures the degree of depression; this measure is
recommended as a preventive method before bariatric surgery [48]. Esposito
[49] highlighted the importance of assessing the presence of internalizing
problems such as anxiety and depression in childhood obesity.
4.5. Unanswered Questions and Future Research
Although depression is known to have a significant relationship with morbid
obesity, how depression status changes after losing weight in surgical operations
remains unknown [50]. According to Chapman
et al.
[51], mental illnesses, spe-
cifically depressive disorders, are more prevalent in chronic diseases. Chronic
disease management and the treatment of depression are connected. Age and
gender differences need to be taken into consideration when examining the as-
sociations of BMI or obesity with common mental disorders [52]. Siwek
et al.
[53] found that the connection between obesity and bipolar spectrum disorders
should be further researched. According to Rossetti
et al.
[54], preclinical and
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 289 Open Journal of Psychiatry
clinical studies illustrate that eating disorders are associated with mood distur-
bances. Zelder
et al.
[55] showed that obesity is associated with depression in
premenopausal women. These 2 risk factors should be considered in preventive
methods for cardiovascular disorders.
We should investigate the connection of obesity with masked depression and
indirect suicide in the future.
5. Conclusion
In our opinion, this study provides a new and needed overview of the relation-
ship between depression and severe obesity. These findings were necessary and
provided insight into groups that had not been previously examined. The results
of this research can be utilized to improve the care and understanding of indi-
viduals with severe obesity.
[1] Koski, M. and Naukkarinen, H. (2017) Severe Obesity, Emotions and Eating Habits:
A Case-Control Study.
BMC Obesity
, 4, 2.
[2] Koski, M. and Naukkarinen, H. (2017) The Relationship between Stress and Severe
Obesity: A Case-Control Study. Biomedicine Hub.
[3] Zhang, Y., Liu, J., Yao, J., Ji, G., Qian, L., Wang, J., Zhang, G., Tian, J., Nie, Y.,
Zhang, Y.E., Gold, M.S. and Liu, Y. (2014) Obesity: Pathophysiology and Interven-
, 6, 5153-5183.
[4] Milaneschi, Y., Simonsick, E.M., Vogelzangs, N., Strotmeyer, E.S., Yaffe, K., Harris,
T.B., Tolea, M.I., Ferrucci, L. and Penninx, B.W. (2012) Leptin, Abdominal Obesity,
and Onset of Depression in Older Men and Women.
Journal of Clinical Psychiatry
73, 1205-1211.
[5] Chen, Y., Jiang, Y. and Mao, Y. (2009) Association between Obesity and Depression
in Canadians.
Journal of Women
s Health
, 18, 1687-1692.
[6] Mauri, M., Rucci, P., Calderone, A., Santini, F., Oppo, A., Romano, A., Rinaldi, S.,
Armani, A., Polini, M., Pinchera, A. and Cassano, G.B. (2008) Axis I and II Disord-
ers and Quality of Life in Bariatric Surgery Candidates.
Journal of Clinical Psychia-
, 69, 295-301.
[7] Gadalla, T.M. (2009) Association of Obesity with Mood and Anxiety Disorders in
the Adult General Population.
Chronic Diseases in Canada
, 30, 29-36.
[8] Jorm, A.F., Korten, A.E., Christensen, H., Jacomb, P.A., Rodgers, B. and Parslow,
R.A. (2003) Association of Obesity with Anxiety, Depression and Emotional
Well-Being: A Community Survey.
Australian and New Zealand Journal of Public
, 27, 434-440.
[9] Kalarchian, M.A., Marcus, M.D., Levine, M.D., Soulakova, J.N., Courcoulas, A.P.
and Wisinski, M.S. (2008) Relationship of Psychiatric Disorders to 6-Month Out-
comes after Gastric Bypass.
Surgery for Obesity and Related Diseases
, 4, 544-549.
[10] Farberow, N.L. and Williams, J. (1983) Indirect Self-Destructive Behavior and the
Psychiatria Fennica
Supplementum 21-39.
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 290 Open Journal of Psychiatry
[11] Achté, K. (1983) Types of Indirect Self-Destruction.
Psychiatrica Fennica
, Supple-
mentum, 41-44.
[12] Ma, J. and Xiao, L. (2010) Obesity and Depression in US Women: Results from the
2005-2006 National Health and Nutritional Examination Survey.
, 18, 347-
[13] Zimmerman, M., Hrabosky, J.I., Francione, C., Young, D., Chelminski, I., Dalrym-
ple, K. and Galione, J.N. (2011) Impact of Obesity on the Psychometric Properties
of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Crite-
ria for Major Depressive Disorder.
Comprehensive Psychiatry
, 52, 146-150.
[14] Faulconbridge, L.F., Wadden, T.A., Rubin, R.R., Wing, R.R., Walkup, M.P., Fabri-
catore, A.N., Coday, M., Van Dorsten, B., Mount, D.L. and Ewing, L.J. (2012)
One-Year Changes in Symptoms of Depression and Weight in Overweight/Obese
Individuals with Type 2 Diabetes in the Look AHEAD Study.
, 20, 783-793.
[15] Fowler-Brown, A.G., Ngo, L.H. and Wee, C.C. (2012) The Relationship between
Symptoms of Depression and Body Weight in Younger Adults.
, 20, 1922-
[16] Wild, B., Herzog, W., Lechner, S., Niehoff, D., Brenner, H., Muller, H., Rothen-
bacher, D., Stegmaier, C. and Raum, E. (2012) Gender Specific Temporal and
Cross-Sectional Associations between BMI-Class and Symptoms of Depression in
the Elderly.
Journal of Psychosomatic Research
, 72, 376-382.
[17] Zhao, G., Ford, E.S., Dhingra, S., Li, C., Strine, T.W. and Mokdad, A.H. (2009) De-
pression and Anxiety among US Adults: Associations with Body Mass Index.
national Journal of Obesity
, 33, 257-266.
[18] Frances, A. and Cooper, A.M. (1981) Descriptive and Dynamic Psychiatry: A Pers-
pective on DSM-III.
American Journal of Psychiatry
138, 376-378.
[19] Bruun, K. (1954) Sosiaaliluokkajako. Tilastollisia Kuukausitietoja Helsingistä No: 3,
[20] Rinne, H.J. and Huunan-Seppälä, A. (1979) Työkyvyttömyyskysymyksen Arvioin-
, 6, 181-189.
[21] Beck, A.T., Ward, C.H., Mendelson, M., Mock, J. and Erbaugh, M.D. (1961) An In-
ventory for Measuring Depression.
Archives of General Psychiatry
, 4, 53-63.
[22] Raitasalo, R. (1977) Depression and Its Connections with the Need for Psychothe-
rapy. Publication of Social Insurance Institution of Finland.
[23] Adena, M.A. and Wilson, S.R. (1982) Generalised Linear Models in Epidemiological
Research: Case-Control Studies. The Intstat Foundation, Sydney.
[24] Armitage, P. (1971) Statistical Methods in Medical Research. Blackwell Scientific
Publications, Oxford.
[25] Beydoun, M.A. and Wang, Y. (2010) Pathways Linking Socioeconomic Status to
Obesity through Depression and Lifestyle Factors among Young US Adults.
of Affective Disorders
, 123, 52-63.
[26] Murphy, J.M., Horton, N.J., Burke, J.D., Monson Jr., R.R., Laird, N.M., Lesage, A.
and Sobol, A.M. (2009) Obesity and Weight Gain in Relation to Depression: Find-
ings from the Stirling County Study.
International Journal of Obesity
, 33, 335-341.
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 291 Open Journal of Psychiatry
[27] Werrij, M.Q., Mulkens, S., Hospers, H.J. and Jansen, A. (2006) Overweight and Ob-
esity: The Significance of a Depressed Mood.
Patient Education and Counseling
, 62,
[28] Krukowski, R.A., Friedman, K.E. and Applegate, K.L. (2010) The Utility of the Beck
Depression Inventory in a Bariatric Surgery Population.
Obesity Surgery
, 20, 426-
[29] Castellini, G., Lapi, F., Ravaldi, C., Vannacci, A., Rotella, C.M., Faravelli, C. and
Ricca, V. (2008) Eating Disorder Psychopathology Does Not Predict the Overweight
Severity in Subjects Seeking Weight Loss Treatment.
Comprehensive Psychiatry
, 49,
[30] Udo, T., McKee, S.A. and Grilo, C.M. (2015) Factor Structure and Clinical Utility of
the Beck Depression Inventory in Patients with Binge Eating Disorder and Obesity.
General Hospital Psychiatry
, 37, 120-125.
[31] Xiang, X. and An, R. (2015) Obesity and Onset of Depression among U.S. Mid-
dle-Aged and Older Adults.
Journal of Psychosomatic Research
, 78, 242-248.
[32] Degirmenci, T., Kalkan-Oguzhanoglu, N., Sozeri-Varma, G., Ozdel, O. and Fenkci,
S. (2015) Psychological Symptoms in Obesity and Related Factors.
Archives of
Noropsikiatri Arsivi
, 52, 42-46.
[33] Hung, C.F., Rivera, M., Craddock, N., Owen, M.J., Gill, M., Korszun, A., Maier, W.,
Mors, O., Preisig, M., Rice, J.P., Rietschel, M., Jones, L., Middleton, L., Aitchison,
K.J., Davis, O.S., Breen, G., Lewis, C., Farmer, A. and McGuffin, P. (2014) Rela-
tionship between Obesity and the Risk of Clinically Significant Depression: Mende-
lian Randomisation Study.
British Journal of Psychiatry
, 205, 24-28.
[34] Mattlar, C.-E., Salminen, J.K., Korhonen, M., Hellsten, E.R. and Knuts, L.-R. (1988)
Personality, Morbid Obesity and Reducing. 1
st European Congress on Obesity
Stockholm, 31-56.
[35] Sarwer, D.B., Cohn, N.I., Gibbons, L.M., Magee, L., Crerand, C.E., Raper, S.E., Ro-
sato, E.F., Williams, N.N. and Wadden, T.A. (2004) Psychiatric Diagnoses and Psy-
chiatric Treatment among Bariatric Surgery Candidates.
Obesity Surgery
14, 1148-
[36] Stunkard, A.J., Faith, M.S. and Allison, K.C. (2003) Depression and Obesity.
logical Psychiatry
, 54, 330-337.
[37] Kaplan, H.I., Sadock, B.J. and Sadock, V.A. (2000) Kaplan & Sadocks Comprehen-
sive Textbook of Psychiatry. Lippincott Williams & Wilkins, Philadelpia.
[38] Istvan, J., Zavela, K. and Weidner, G. (1992) Body Weight and Psychological Dis-
tress in NHANES I.
International Journal of Obesity and Related Metabolic Disord-
16, 999-1003.
[39] Marmorstein, N.R., Iacono, W.G. and Legrand, L. (2014) Obesity and Depression in
Adolescence and Beyond: Reciprocal Risks.
International Journal of Obesity
, 38, 906-
[40] Ahmadi, S.M., Keshavarzi, S., Mostafavi, S.A. and Bagheri Lankarani, K. (2015) De-
pression and Obesity/Overweight Association in Elderly Women: A Communi-
ty-Based Case-Control Study.
Acta Medica Iranica
, 53, 686-689.
[41] Roberts, R.E. and Duong, H.T. (2015) Does Major Depression Affect Risk for Ado-
lescent Obesity?
Journal of Affective Disorders
, 186, 162-167.
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 292 Open Journal of Psychiatry
[42] Thormann, J., Chittka, T., Minkwitz, J., Kluge, M. and Himmerich, H. (2013) Obes-
ity and Depression: An Overview on the Complex Interactions of Two Diseases.
Fortschritte der Neurologie-Psychiatrie
, 81, 145-153.
[43] Chou, K.L. and Yu, K.M. (2013) Atypical Depressive Symptoms and Obesity in a
National Sample of Older Adults with Major Depressive Disorder.
Depress Anxiety
30, 574-579.
[44] Appelhans, B.M., Whited, M.C., Schneider, K.L., Ma, Y., Oleski, J.L., Merriam, P.A.,
Waring, M.E., Olendzki, B.C., Mann, D.M., Ockene, I.S. and Pagoto, S.L. (2012)
Depression Severity, Diet Quality, and Physical Activity in Women with Obesity
and Depression.
Journal of the Academy of Nutrition and Dietetics
, 112, 693-698.
[45] Hamer, M., Batty, G.D. and Kivimaki, M. (2012) Risk of Future Depression in
People Who Are Obese but Metabolically Healthy: The English Longitudinal Study
of Ageing.
Molecular Psychiatry
, 17, 940-945.
[46] Brunault, P., Frammery, J., Couet, C., Delbachian, I., Bourbao-Tournois, C., Objois,
M., Cosson, P., Reveillere, C. and Ballon, N. (2015) Predictors of Changes in Physi-
cal, Psychosocial, Sexual Quality of Life, and Comfort with Food after Obesity Sur-
gery: A 12-Month Follow-Up Study.
Quality of Life Research
, 24, 493-501.
[47] Lasserre, A.M., Glaus, J., Vandeleur, C.L., Marques-Vidal, P., Vaucher, J., Bastardot,
F., Waeber, G., Vollenweider, P. and Preisig, M. (2014) Depression with Atypical
Features and Increase in Obesity, Body Mass Index, Waist Circumference, and Fat
Mass: A Prospective, Population-Based Study.
JAMA Psychiatry
, 71, 880-888.
[48] Paiva-Medeiros, P.F., Duarte-Guerra, L.S., Santo, M.A., Lotufo-Neto, F. and Wang,
Y.P. (2015) Psychometric Properties of the Montgomery-Asberg Depression Rating
Scale in Severely Obese Patients.
Spanish Journal of Psychology
, 18, E69.
[49] Esposito, M., Gallai, B., Roccella, M., Marotta, R., Lavano, F., Lavano, S.M., Maz-
zotta, G., Bove, D., Sorrentino, M., Precenzano, F. and Carotenuto, M. (2014) An-
xiety and Depression Levels in Prepubertal Obese Children: A Case-Control Study.
Neuropsychiatric Disease and Treatment
, 10, 1897-1902.
[50] Deliopoulou, K., Konsta, A., Penna, S., Papakostas, P. and Kotzampassi, K. (2013)
The Impact of Weight Loss on Depression Status in Obese Individuals Subjected to
Intragastric Balloon Treatment.
Obesity Surgery
, 23, 669-675.
[51] Chapman, D.P., Perry, G.S. and Strine, T.W. (2005) The Vital Link between Chron-
ic Disease and Depressive Disorders.
Preventing Chronic Disease
, 2, A14.
[52] McCrea, R.L., Berger, Y.G. and King, M.B. (2012) Body Mass Index and Common
Mental Disorders: Exploring the Shape of the Association and Its Moderation by
Age, Gender and Education.
International Journal of Obesity
, 36, 414-421.
[53] Siwek, M., Dudek, D., Jaeschke, R., Dembinska-Kiec, A., Witkowski, L., Arciszews-
ka, A., Hebal, F., Matlok, M., Malczewska-Malec, M., Wnek, D., Pilecki, M., Major,
P., Epa, R. and Rybakowski, J. (2015) Bipolar Spectrum Features in Obese Individu-
Psychiatria Polska
, 49, 993-1004.
[54] Rossetti, C., Halfon, O. and Boutrel, B. (2014) Controversies about a Common Eti-
ology for Eating and Mood Disorders.
Frontiers in Psychology
, 5, 1205.
M. Koski, H. Naukkarinen
10.4236/ojpsych.2017.74024 293 Open Journal of Psychiatry
[55] Zedler, B., von Lengerke, T., Emeny, R., Heier, M., Lacruz, M.E. and Ladwig, K.H.
(2014) Obesity and Symptoms of Depression and Anxiety in Pre- and Postmeno-
pausal Women: A Comparison of Different Obesity Indicators.
Medizinische Psychologie
, 64, 128-135.
Submit or recommend next manuscript to SCIRP and we will provide best
service for you:
Accepting pre-submission inquiries through Email, Facebook, LinkedIn, Twitter, etc.
A wide selection of journals (inclusive of 9 subjects, more than 200 journals)
Providing 24-hour high-quality service
User-friendly online submission system
Fair and swift peer-review system
Efficient typesetting and proofreading procedure
Display of the result of downloads and visits, as well as the number of cited articles
Maximum dissemination of your research work
Submit your manuscript at:
Or contact
... Modulation phénotypique du tissu adipeux par l'obésité. D'aprèsOuchi et al., 2011 Huet Lison -Etude de la voie de la kynurénine dans la symptomatologie dépressive du sujet obèse Partie 4 : L'obésité est estimée autour de 10% dans la population générale(Evans et al., 2005, Simon et al., 2008, Dawes et al., 2016, Koski & Naukkarinen, 2017. De plus, il a également été suggéré que l'obésité augmente le risque de dépression de manière doseréponse, étant donné que la prévalence de ce trouble est majorée chez les patients très sévèrement obèses (IMC ≥ 40 kg/m²)(Onyike et al., 2003, Zhao et al., 2009, Moussa et al., 2019. ...
... 8. Husky MM, Mazure CM, Ruffault A, Flahault C, Kovess-Masfety V (2017): Differential Associations Between Excess Body Weight and Psychiatric Disorders in Men and Women. J Women.Koski M, Naukkarinen H (2017): The Relationship between Depression and Severe Obesity: A Case-Control Study. Open J Psychiatry. 07: 276-293.10.Lin H-Y, Huang C-K, Tai C-M, Lin H-Y, Kao Y-H, Tsai C-C, et al. (2013):Psychiatric disorders of patients seeking obesity treatment. ...
La réaction inflammatoire est un phénomène bénéfique qui permet à l’organisme de lutter contre les agressions. Celle-ci s’accompagne de modifications locales et systémiques, ainsi que de modifications comportementales regroupées sous le terme de « comportement de maladie ». Néanmoins, l’expression prolongée de ce comportement dans certaines conditions inflammatoires peut aboutir au développement de symptômes neuropsychiatriques, notamment dépressifs. Le développement de ces symptômes pourrait reposer sur les effets néfastes de l’inflammation sur le métabolisme et l’activité des neurotransmetteurs. L’induction de l'enzyme indoléamine-2,3-dioxygénase (IDO) par les facteurs inflammatoires s’accompagne de la dégradation du TRP dans la voie de la kynurénine et par conséquent d’une diminution de sa disponibilité pour la synthèse de sérotonine. En outre, l’activation de la voie de la kynurénine en condition inflammatoire conduit également à la production de métabolites neuroactifs, capables de moduler l’activité du système glutamatergique. Bien que ces mécanismes aient été particulièrement étudiés dans le cadre d’une activation exogène et soutenue du système immunitaire, comme par exemple au cours d’une immunothérapie par IFN-α, ils restent peu investigués dans des conditions d’inflammation endogène chronique à bas bruit. En raison de la production modérée, mais soutenue, de facteurs inflammatoires par le tissu adipeux, l’obésité représente l’une de ces conditions. En outre, les personnes obèses présentent une prévalence accrue de troubles neuropsychiatriques comparativement à la population générale. Nous avons émis l'hypothèse que l’inflammation systémique liée à l'adiposité contribuerait au développement de comorbidités neuropsychiatriques chez le sujet obèse, notamment au travers de l’activation de la voie de la kynurénine. L’objectif principal de cette thèse a été d’évaluer les relations entre l’inflammation systémique et la symptomatologie dépressive dans un échantillon de sujets obèses. Nous nous sommes en particulier intéressés au rôle d'IDO et de la voie de la kynurénine comme mécanisme potentiel sous-tendant ces relations. Les résultats indiquent que l'obésité est caractérisée par une prévalence accrue de symptômes neuropsychiatriques interdépendants, dont les symptômes dépressifs, anxieux, cognitifs et la fatigue, ainsi que par un état inflammatoire chronique de bas niveau qui augmente graduellement avec le degré d'adiposité. L’existence d’une relation linéaire entre les degrés d'adiposité, l'inflammation systémique et l'intensité des altérations neuropsychiatriques a également été mise en évidence. Par ailleurs, nos résultats démontrent que cette relation n’est pas modulée par les caractéristiques métaboliques de l’individu, mais par l’inflammation systémique qui représente le principal médiateur de cette association. Enfin, les résultats de cette thèse indiquent des relations significatives entre l’inflammation systémique, l’activité d’IDO et les symptômes dépressifs chez les sujets obèses. Par ailleurs, ces résultats montrent que l’activité d’IDO est plus spécifiquement associée à l’activation de la branche neurotoxique de la voie de la kynurénine, elle-même associée à la symptomatologie dépressive des individus obèses.Ce travail permet ainsi une meilleure compréhension des mécanismes impliqués dans la survenue des comorbidités neuropsychiatriques associées à l’obésité et plus largement aux conditions/pathologies à composante inflammatoire. Il pourrait ainsi contribuer au développement de stratégies préventives et/ou thérapeutiques innovantes ciblant les mécanismes qui sous-tendent ces comorbidités et compromettent la prise en charge de ces pathologies.
... Considering the higher risk of depression in patients with severe obesity (66), treatment modalities should be tailored according to the needs imposed by such an association. ...
Full-text available
Literature on depression and obesity describes the relevance of the hypothalamic pituitary adrenal axis dysfunction, sympathetic nervous system (SNS) activation, and inflammatory processes as well as the interaction of genetic and environmental factors. Recent investigation in obesity highlights the involvement of several regulation systems, particularly in white adipose tissue. The hypothalamic pituitary adrenal axis, gonadal, growth hormone, leptin, sympathetic nervous system and adrenergic, dopaminergic, and serotoninergic central pathways, all seem interconnected and involved in obesity. From another perspective, the role of psychosocial chronic stressors, determining poor mental and physical health, is well documented. Empirical data can support biologically conceivable theories describing how perceptions of the external social environment are transduced into cellular inflammation and depression. Although in neurobiological models of depression, stress responses are associated with neuroendocrine and neuro-inflammatory processes, concerning similar pathways to those described in obesity, an integrating model is still lacking. The aim of this mini-review is to offer a reflexion on the interplay between the neuroendocrine dysfunctions related to chronic stress and the nature of the shared biologic mechanisms in the pathophysiology of both clinical entities, depression and obesity. We highlight dysfunctional answers of mind body systems that are usually activated to promote regulation and adaptation. Stress response, as a mediator between different level phenomena, may undertake the role of a plausible link between psychological and biological determinants of disease. Depression and obesity are major public health issues, urging for new insights and novel interventions and this discussion points to the need of a more in-depth approach.
Full-text available
Background: Several etiological factors for obesity have been identified, whereas other factors related to obesity, such as stress, remain poorly understood. This study used psychiatric methods to examine the relationship between stress and obesity. Methods: Matched study and control groups were established, and the female and male control subjects were selected separately by random sampling. The control subjects were matched with the case subjects with respect to place of residence, sex, age, date that a pension was granted, and occupation. Psychiatric and psychological methods were assessed using a questionnaire and statistical analyses. Results: Psychiatric interviews indicated that stress was more prevalent in the study group than in the control group. Separation from parents was nearly significantly more frequently in the study group than in the control group. The questionnaire on coping mechanisms revealed that case subjects tended to resolve their problems in an active manner. Conclusions: The aim of this case-control study was to examine the relationship between stress and obesity in individuals receiving a disability pension. We identified stress factors that affect the development of obesity. We believe our study is both necessary and important, as these findings provide valuable insight into the relationship between severe obesity and stress.
Full-text available
Background: Obesity has a multifaceted etiology that involves genetic, biological and behavioral factors, body growth, eating habits, energy expenditure and the function of adipose tissue. The present study aimed to expand upon knowledge about the relationships among obesity, emotions and eating habits in severely obese individuals using a case-control method. Methods: The subject group consisted of 112 individuals (81 females and 31 males) receiving a permanent disability pension primarily for obesity. The control subjects were randomly selected from the same area and were receiving a disability pension for a different primary illness. The controls were matched with the subjects by the place of residence, sex, age, the time since the pension was granted and occupation. Psychiatric interviews were conducted on all participants. The results were analyzed using the chi-squared test (χ(2)-test) and the percent distribution. The subject and control groups were compared using the t-test for paired variables. Conditional logistic regression analysis was also conducted. Results: The emotional state of eating was significantly associated with quarrels and feelings of loneliness. The subjects suffered from night eating syndrome, which was associated with an increased risk of early retirement. Binge eating syndrome was observed more frequently in the study group. The subjects reported feeling increased hunger compared with the controls. A significant percentage of the subjects had a body mass index of ≥ 40. No differences in eating habits were observed between the groups. Conclusion: This study provides information on the relationship between emotions and eating habits in obesity, which is a rarely studied topic. We believe that our study provides a novel and necessary overview of the associations among severe obesity, emotions and eating habits.
Full-text available
Overweight/obesity and depression are common among women especially in the elderly and can lead to unfavorable outcomes. We aimed to determine the association of overweight with depression and also to find any correlation of depression with some anthropometric indices in old women. A total of 94 depressed elderly women were compared with 99 non-depressed controls. The structured diagnostic interview based on DSM-IV were performed to diagnose depression, and Geriatric Depression Scale (GDS) was completed to rate it. Anthropometric indices were measured and compared between groups. Pearson correlation coefficients were determined for linear relations between variables. Odds Ratio of obesity and overweight in depressed subjects comparing with normal participants was 1.45 (95%CI=0.63-3.32). A significant correlation was observed between BMI and GDS score (r=0.231, P-value=0.001). Total body fat (Pvalue= 0.001) and BMI (P-value=0.016) were significantly higher in depressed women than non-depressed women. Despite the significantly higher total body fat and BMI among old women with depression, only a weak correlation was seen between BMI and GDS score.
Full-text available
Summary Aim. The relationships between obesity and bipolar spectrum disorders (BSD) are unclear. Thus, the aim of our study were to approximate the prevalence of soft bipolar features in patients seeking treatment for obesity. Methods. We performed a nested case-control study (cases: 90 patients with the mean BMI=38.1±7.0 [range: 30.1–62.5]; controls: 70 healthy volunteers with the mean BMI=21.6±2.1 [range: 18.5–24.9]). The participants were screened for the BSD symptoms with the Mood Disorder Questionnaire. Results. Patients with obesity were significantly more likely to score ≥7 pts. on the MDQ 25.6% vs. 8.6%; p=0.01). In comparison to non-obese individuals, the obese patients scored significantly higher in MDQ section I and on the MDQ items referring to the ‘irritability–racing thoughts’ dimension of hypomania. The multiple logistic regression analysis revealed that obesity had been significantly related to the odds of obtaining ≥7 pts. on the MDQ section 1 (odds ratio [OR] = 2.07; 95% confidence interval [CI]: 1.17–3.63), and marginally significantly related to experiencing periods of ‘ups’ and ‘downs’(OR = 1.67; 95% CI: 1.00–2.81). Conclusions. Our study adds to previous suggestions that obesity may be significantly related to the BSD. However, the clinical implications of this finding need to be determined in further studies, performed in accordance with the paradigm of evidence based medicine (EBM).
Obesity is a chronic condition worldwide and has frequent association with major depression. The Montgomery-Åsberg Depression Rating Scale (MADRS) was applied to obese patients in order to detect briefly and systematically depressive symptoms. The objectives were: to estimate the reliability of the MADRS and to investigate the criterion validity of MADRS. The best cut-off point to detect depressive symptoms was determined in comparison with the Structured Clinical Interview for DSM-IV Axis I Diagnosis (SCID-I). The sample was recruited consecutively from the waiting list of a bariatric surgery service of the university clinic. Trained clinical psychologists applied the assessment instruments. The final sample was comprised of 374 class III obese adults (women 79.9 %, mean age 43.3 years [ SD 11.6], mean body mass index 47.0 kg/m 2 [ SD 7.1]). The mean total score of the MADRS was 7.73 ( SD 11.33) for the total sample, with a Cronbach’s alpha coefficient of .93. Women presented higher mean score than men (8.08 versus 6.33; p = .23). The best cut-off point was 13/14 in accordance with the Receiver Operating Characteristics (ROC) curve analysis, yielding a sensitivity of .81 and specificity of .85. The overall ability to discriminate depression according to area under the curve was .87. The results showed that the MADRS is a reliable and valid scale to detect depressive symptoms among patients seeking treatment in preoperative period, displaying adequate psychometric properties.
The purpose of this paper is to reexamine the association between major depression and obesity in adolescents, testing the hypothesis that body image mediates this association. This is the first paper to examine this question using DSM-IV diagnosis of depression and data from a two-wave cohort of adolescents. Participants were 4175 youths 11-17 years of age sampled from the community who were followed up a year later (n=3134). Major depression was assessed using DSM-IV diagnostic criteria. Body image was measured with perceived weight. Obesity was defined as BMI ≥95th percentile using measured height and weight. When we examined a model which included obesity, perceived weight, major depression and covariates, there was no association between major depression at baseline and obesity at follow-up. We found no independent association between major depression and body weight. The study was limited in that it is not a national sample, BMI was the only measure of adiposity, perceived weight was the only measure of body image, and there were no data on lifetime trajectories of depression, obesity, or body image. If there is an etiologic link between major depression and body weight among adolescents, it most likely operates through processes involving components of body image, since controlling for body image eliminated the association between depression and obesity. Clinically, addressing body image in depressed patients who are obese may improve outcomes. Copyright © 2015. Published by Elsevier B.V.
The difficulties inherent in obtaining consistent and adequate diagnoses for the purposes of research and therapy have been pointed out by a number of authors. Pasamanick12 in a recent article viewed the low interclinician agreement on diagnosis as an indictment of the present state of psychiatry and called for "the development of objective, measurable and verifiable criteria of classification based not on personal or parochial considerations, but on behavioral and other objectively measurable manifestations."Attempts by other investigators to subject clinical observations and judgments to objective measurement have resulted in a wide variety of psychiatric rating scales.4,15 These have been well summarized in a review article by Lorr11 on "Rating Scales and Check Lists for the Evaluation of Psychopathology." In the area of psychological testing, a variety of paper-and-pencil tests have been devised for the purpose of measuring specific