Metabolic syndrome, psychological status and quality of life in obesity: The QUOVADIS Study

Abstract

We aimed to investigate the association of the clinical variables of the metabolic syndrome (MS) and psychological parameters on health-related quality of life (HRQL) in obesity. In particular, our aim was to investigate the relative impact of physical symptoms, somatic diseases and psychological distress on both the physical and the mental domains of HRQL.
Cross-sectional study.
A cohort of 1822 obese outpatients seeking treatment in medical centers.
HRQL was measured by the standardized summary scores for physical (PCS) and mental (MCS) components of the Short Form 36 Health Survey (SF-36). Patients were grouped according to tertiles of PCS and MCS. Metabolic and psychological profiles of PCS and MCS tertiles were compared by discriminant analysis.
The profile of metabolic and psychological variables was tertile-specific in 62.4 and 68.3% of patients in the lowest and highest tertiles of PCS, respectively, while concordance was low in the mid-tertile (32.8%). Concordance was very high in the lowest (74.4%) and in the highest (75.5%) tertiles of MCS, and was fair in the mid-tertile (53.2%). The main correlates of PCS were obesity-specific and general psychological well-being, BMI, body uneasiness, binge eating, gender and psychiatric distress. Only hypertension and hyperglycemia qualified as correlates among the components of MS. The components of MS did not define MCS.
Psychological well-being is the most important correlate of HRQL in obesity, both in the physical and in the mental domains, whereas the features of MS correlate only to some extent with the physical domain of HRQL.

Full text

ORIGINAL ARTICLE
Metabolic syndrome, psychological status and
quality of life in obesity: the QUOVADIS Study
F Corica
1
, A Corsonello
2,3
, G Apolone
4
, E Mannucci
5
, M Lucchetti
2
, C Bonfiglio
1
, N Melchionda
6
,
G Marchesini
7
and the QUOVADIS Study Group
1
Department of Internal Medicine, University of Messina, Messina, Italy;
2
Italian National Research Center on Aging
(INRCA), Ancona and Cosenza, Italy;
3
Fondazione San Raffaele, Cittadella della Carita
`
, Taranto, Italy;
4
Clinical Research
Laboratory, Institute of Pharmacological Research Mario Negri, Milan, Italy;
5
Unit of Gerontology and Geriatrics,
Department of Critical Care Medicine and Surgery, University of Florence, Florence, Italy;
6
Metabolic Unit ‘Alma Mater
Studiorum’ University, Bologna, Italy and
7
Unit of Clinical Dietetics, ‘Alma Mater Studiorum’ University, Bologna, Italy
Objective: We aimed to investigate the association of the clinical variables of the metabolic syndrome (MS) and psychological
parameters on health-related quality of life (HRQL) in obesity. In particular, our aim was to investigate the relative impact
of physical symptoms, somatic diseases and psychological distress on both the physical and the mental domains of HRQL.
Design: Cross-sectional study.
Subjects: A cohort of 1822 obese outpatients seeking treatment in medical centers.
Measurements: HRQL was measured by the standardized summary scores for physical (PCS) and mental (MCS) components of
the Short Form 36 Health Survey (SF-36). Patients were grouped according to tertiles of PCS and MCS. Metabolic and
psychological profiles of PCS and MCS tertiles were compared by discriminant analysis.
Results: The profile of metabolic and psychological variables was tertile-specific in 62.4 and 68.3% of patients in the lowest and
highest tertiles of PCS, respectively, while concordance was low in the mid-tertile (32.8%). Concordance was very high in the
lowest (74.4%) and in the highest (75.5%) tertiles of MCS, and was fair in the mid-tertile (53.2%). The main correlates of PCS
were obesity-specific and general psychological well-being, BMI, body uneasiness, binge eating, gender and psychiatric distress.
Only hypertension and hyperglycemia qualified as correlates among the components of MS. The components of MS did not
define MCS.
Conclusions: Psychological well-being is the most important correlate of HRQL in obesity, both in the physical and in the mental
domains, whereas the features of MS correlate only to some extent with the physical domain of HRQL.
International Journal of Obesity (2008) 32, 185–191; doi:10.1038/sj.ijo.0803687; published online 24 July 2007
Keywords: metabolic syndrome; well-being; psychological status; health-related quality of life; observational study
Introduction
Obesity is a complex, multifactorial disease,
1
having a
significant impact on morbidity and mortality, as well as
on psychosocial well-being and quality of life.
2
These
features make the measurement of health-related quality of
life (HRQL) a key issue in obesity studies.
2
HRQL progres-
sively deteriorates across the whole spectrum of obesity
classes among obese individuals, both seeking and not
seeking medical treatment for weight loss.
3
The physical
domain of HRQL is greatly affected by physical symptoms
and somatic diseases
4,5
and pain is a leading symptom
associated with poor HRQL.
6
Unexpectedly, also psycho-
logical distress
7
and binge eating
8
contribute to poor HRQL
in obesity not only in the mental domain, but also in the
physical domains. Therefore, an extensive assessment of
both somatic and psychological variables is needed for a
comprehensive profile of HRQL in obese subjects.
The metabolic syndrome (MS) is a cluster of inter-related
risk factors for atherosclerosis and cardiovascular disease.
9,10
While acute cardiovascular events, such as myocardial
infarction and stroke, have obvious consequences on the
HRQL, the impact of MS on HRQL is less predictable and has
not been clearly defined. Selected components of MS, such
as abdominal obesity, hypertension and hyperglycemia,
Received 27 February 2007; revised 22 May 2007; accepted 25 May 2007;
published online 24 July 2007
Correspondence: Dr A Corsonello, Italian National Research Centres on Aging
(INRCA), Contrada Muoio Piccolo, Viale della Resistenza, Pal. Alfa Scala H,
I-87036 Rende (CS), Cosenza, Italy.
E-mail: andrea_corsonello@tin.it
International Journal of Obesity (2008) 32, 185–191
&
2008 Nature Publishing Group All rights reserved 0307-0565/08
$
30.00
www.nature.com/ijo

have been reported to impair HRQL in post-menopausal
Ecuadorian women,
11
while insulin resistance was associated
with poor HRQL in the physical domain, but not in the
mental domain, in a large elderly population from the
United Kingdom.
12
The QUOVADIS program is an observational, multicen-
tered study specifically aimed at measuring the burden of
obesity and its complications on HRQL.
13
The study also
collected extensive information on the individual compo-
nents of MS as well as several data on psychological distress.
14
Thus, it represents a valuable opportunity to investigate the
relationship between MS, psychological distress and HRQL in
a large population sample of obese subjects seeking medical
treatment. The aim of the present study was to verify the
relative impact of the individual components of MS and of
variables of psychological distress on both the physical and
the mental domains of HRQL in obese outpatients.
Methods
Patients
The present report uses data from the QUOVADIS study,
which has been described in detail elsewhere.
13,14
Briefly,
during the years 1999–2000, 25 Italian centers with a specific
interest in clinical obesity research consecutively enrolled
treatment-seeking obese patients (BMI430 kg/m
2
) into the
study. The enrollment period was preceded by two meetings
of the steering committee and by a general investigators’
meeting to decide selection criteria and to agree on data
collection and management of patients. All obese subjects
seeking treatment were eligible for the study, provided that
they were not on active treatment for obesity at the time of
enrollment. All patients enrolled were in the age range
between 20 and 65 years, and agreed to fill out a package of
self-administered questionnaires.
Clinical data were collected by means of a structured
questionnaire (Case Report Form) predefined at the time of
the general investigators’ meeting. Recorded data included
information on civil and educational status, personal and
family history of metabolic and cardiovascular diseases, and
previous and current pharmacological treatment. To expe-
dite handling of data, the study was totally web-based
through an extranet system provided by CINECA (Casalec-
chio di Reno, Italy), using the Advanced Multicenter
Research methodology developed by CINECA as a result of
an extensive cooperation between clinicians, statisticians
and experts in information technology. The management of
all the data was performed by standard web browsers, and
the quality level was guaranteed by upfront quality controls
(on the client side) and consistency checks (on the server
side). On enrollment, all subjects signed an informed
consent to take part in the study, which was approved by
the Ethical Committee of the coordinating center (Bologna;
reference no. Sper65/99/U; 9 July 1999) and by the Institu-
tional Review Boards of individual local centers.
Definition of the outcomes
The main outcome of the study was HRQL, as measured by
the Short Form 36 Health Survey (SF-36) questionnaire.
15,16
SF-36 is a 36-item questionnaire measuring subjective
health status in eight domains: physical functioning, role
limitations (because of physical problems), bodily pain,
general health, vitality, social functioning, role limitations
(because of emotional problems) and mental health. The
SF-36 questionnaire has been recently proved to maintain a
robust internal structure also in obese outpatients.
17
The
standardized summary scores for physical (PCS) and mental
(MCS) components were computed as previously reported,
18
and separately used as outcome measures.
Measurements
Body weight was measured in light clothing and without
shoes to the nearest 0.5 kg. Height was measured to the
nearest 0.5 cm. BMI was calculated as weight (in kilograms)
divided by squared height (in meters). Waist circumference
was measured midway between the lower rib margin and the
iliac crest. Diagnostic criteria for metabolic syndrome
were those suggested by the US National Cholesterol
Education ProgramFAdult Treatment Panel III (NCEP ATP-
III): waist circumference 4102 cm for men or 488 cm for
women; plasma triglycerides X150 mg/dl; high-density
lipoprotein (HDL) cholesterol o40 mg/dl for men and
o50 mg/dl for women; blood pressure X130/85 mm Hg;
fasting plasma glucose X110 mg/dl.
10
Subjects treated for
diabetes and hypertension were classified as positive,
independent of glucose and blood pressure. Other variables
specifically considered in the analysis were age, gender
and education.
All patients underwent a comprehensive assessment
exploring psychological well-being, including the following:
1. Obesity-Related Well-Being (ORWELL) 97:
19
an 18-item
instrument exploring obesity-specific quality of life, and
measuring intensity and subjective relevance of obesity-
related physical and psychosocial distress. All items are
computed in a single score.
2. Symptom Check List (SCL):
20
a 90-item instrument
exploring psychiatric distress in 9 areas: somatization,
obsessive-compulsive thoughts, interpersonal sensitivity,
depression, anxiety, hostility, phobic anxiety, paranoid
conceiving and psychotic behavior. For the purpose of the
present study, we used the global score (sum of the scores
of the 90 items).
3. Psychological General Well-Being (PGWB) scale:
21
a 22-
item instrument exploring subjective well-being in six
affective states (anxiety, depressed mood, positive well-
being, self-control, general health and vitality) and a
general index used in the present study.
4. Binge Eating Scale (BES):
22
a 16-item tool measuring the
severity of binge eating. It examines both behavioral
manifestations (eating large amounts of food) and
Metabolic and psychological correlate of HRQL
F Corica et al
186
International Journal of Obesity

feeling/cognition during a binge episode (loss of control,
guilt and fear of being unable to stop eating).
5. Body Uneasiness Test (BUT):
23
a 71-item instrument
specifically developed to evaluate concern for physical
appearance, body image awareness and body parts
that most severely contribute to body dissatisfaction.
For the purpose of the present analysis, we used the
scores of Part A, combined in a single global index (Global
Severity Index).
All the scores obtained in the above tests were considered
as potential correlates of HRQL and were tested in the
analysis.
Statistical analyses
PCS and MCS scores were separately considered in the
analysis. Patients were aggregated in tertiles of standardized
scores, and the relationships between age, gender, education
and MS risk factors with tertiles were initially evaluated by
the w
2
test for categorical variables and by the analysis of
variance (ANOVA) one-way test for continuous variables.
Two-tailed P-values less than 0.05 were considered statisti-
cally significant.
A discriminant analysis of metabolic and psychological
profiles of tertiles was performed to identify tertile-specific
patterns of study variables, that is, the most common
combinations of metabolic and psychological variables
within each tertile, and, then, to cross-tabulate individual
patterns vs tertile-specific patterns. The higher the concor-
dance between the actual tertile membership, resulting from
tertile aggregation, and the predicted tertile membership,
resulting from discriminant analysis, the greater the speci-
ficity of the metabolic and the psychological profiles of the
corresponding tertile. We computed Wilk’s l coefficient to
test the null hypothesis, that is, that groups did not
differ in the means of the discriminating functions.
Box’s M coefficient was computed to assess the risk of
misclassification by the discriminant function. We identified
the items that were most useful for characterizing and
discriminating tertiles on the basis of their standardized
coefficients.
24
All analyses were performed using SPSS V10.0
(SPSS Inc., Chicago, IL, USA).
Results
During the study period, 1944 obese patients were enrolled,
but complete data for the present study were available in
1822 cases. The crude relationship between tertiles of PCS
and study variables is reported in Table 1 (upper panel).
Patients in the lowest tertile had the highest prevalence of
women and the lowest educational level. Severe obesity was
more prevalent among patients in the lower tertile of PCS, as
were high blood pressure, hyperglycemia, hypertriglyceride-
mia and abdominal adiposity. Psychological and behavioral
variables were more commonly altered among patients in
the lowest PCS tertile. At variance, when tertiles of MCS were
considered, only gender, psychological and behavioral
variables were significantly associated with the outcome in
univariate analysis (Table 1, lower panel).
The results of discriminant analyses are reported in Tables
2–4. The analyses identified two discriminant functions for
both SF-36 components, and the former was highly sig-
nificant (Table 2).
A good concordance between actual and predicted tertiles
membership was observed in the lowest and the highest
tertiles of PCS (Table 3, upper panel). Concordance between
actual and predicted tertile membership was very high in the
lowest and the highest tertile of MCS, and was fair in the
mid-tertile (Table 3, lower panel). The highly significant
Box’s M coefficients indicated that the discriminant func-
tions achieved a highly reliable definition of individual
profiles of metabolic and psychological status. When
psychological and behavioral variables were removed from
the discriminant model, the classificatory capacity was
significantly reduced, and only 48.8% of cases were correctly
classified in the PCS analysis. The corresponding figure for
MCS was 39.1%.
The contribution of individual study variables to the
discriminant function 1 for PCS is reported in Table 4 (upper
panel). ORWELL, PGWB and BMI were the main determi-
nants of function 1 for the physical component, and also
BUT, BES, gender and SCL contributed to define the clinical
profile of the discriminant function. Among the individual
components of MS, only blood pressure and plasma glucose
qualified as significant correlates of PCS.
ORWELL and PGWB were the main determinants of
discriminant function 1 for MCS, with a minor contribution
of BUT, BES and gender (Table 4, lower panel), whereas the
individual components of MS did not correlate significantly
with MCS.
The results did not change significantly when we consid-
ered the contemporary presence of three or more components
of MS instead of the single individual items. Indeed, the
tertiles of PCS and MCS were correctly classified in 52.3 and
67.5% of cases, respectively. The presence of three or more
components of MS was significantly correlated with PCS
(standardized discriminant coefficient ¼ 0.306), but not with
MCS (standardized discriminant coefficient ¼ 0.159).
Discussion
Our study shows that the presence of individual components
of MS and poor HRQL parallel each other only to some
extent. Although a higher prevalence of clinical variables
constituting the MS characterized patients with a poorer
HRQL in the physical domain, only BMI, blood pressure and
fasting plasma glucose qualified as significant correlates of
PCS in discriminant analysis, and none entered the dis-
criminant analysis of MCS.
Metabolic and psychological correlate of HRQL
F Corica et al
187
International Journal of Obesity

Table 1 Sociodemographic, clinical and psychological characteristics of patients divided according to the tertiles of the PCS (upper panel) or the MCS (lower panel)
PCS tertiles P
o39.5 39.5–49.5 449.5
N ¼ 608 N ¼ 607 N ¼ 607
Age, years 45.1711.0 44.2711.1 44.9710.8 0.300
Gender, females 524 (86.2) 478 (78.7) 418 (68.9) 0.001
Education
Illiterate 12 (2.0) 10 (1.6) 9 (1.5) 0.001
Primary school 162 (26.6) 108 (17.8) 44 (7.2)
Secondary school 226 (37.2) 195 (32.1) 182 (30.0)
Commercial or vocational school 186 (30.6) 237 (39.0) 307 (50.6)
Academic degree 22 (3.6) 57 (9.4) 65 (10.7)
Body mass index, kg/m
2
30–34.9 145 (23.8) 241 (39.7) 318 (52.4) 0.001
35–39.9 152 (25.0) 182 (30.0) 180 (29.7)
40–44.9 176 (28.9) 117 (19.3) 67 (11.0)
X45 135 (22.2) 67 (11.0) 42 (6.9)
Blood pressure X130/85 mm Hg 448 (73.7) 410 (67.5) 362 (59.6) 0.001
Waist circumference 4102 cm for men or 488 cm for women 586 (96.4) 581 (95.7) 557 (91.8) 0.001
HDL cholesterol o40 mg/dl for men or o50 mg/dl for women 314 (51.6) 290 (47.8) 286 (47.1) 0.233
Triglycerides X150 mg/dl 214 (35.2) 173 (28.5) 173 (28.5) 0.014
Fasting plasma glucose X110 mg/dl 157 (25.8) 122 (20.1) 84 (13.8) 0.001
Metabolic syndrome (three or more criteria) 377 (62.0) 315 (51.9) 273 (45.0) 0.001
Obesity-related well-being 62.9729.8 48.8726.0 36.9724.1 0.001
Symptom checklist 90.0754.8 73.7750.6 67.0738 0.001
Psychological general well-being 60.9717.5 67.8718.1 77.4716.2 0.001
Binge eating scale 17.179.9 14.678.9 12.678.9 0.001
Body uneasiness test 1.871.0 1.570.9 1.370.9 0.001
MCS tertiles
o38.0 38.0–50.0 450 P
N ¼ 618 N ¼ 589 N ¼ 615
Age, years7s.d. 45.0710.7 44.7710.8 44.4711.3 0.575
Gender, females 524 (84.8) 461 (78.3) 435 (70.7) 0.001
Education
Illiterate 11 (1.8) 9 (1.5) 11 (1.8) 0.293
Primary school 116 (18.8) 90 (15.3) 108 (17.6)
Secondary school 203 (32.8) 189
(32.1)
211 (34.3)
Commercial or vocational school 245 (39.6) 240 (40.7) 245 (39.8)
Academic degree 43 (7.0) 61 (10.4) 40 (6.5)
Body mass index, kg/m
2
30–34.9 212 (34.3) 251 (42.6) 241 (39.2) 0.060
35–39.9 180 (29.1) 164 (27.8) 170 (27.6)
40–44.9 140 (22.7) 106 (18.0) 114 (18.5)
X45 86 (13.9) 68 (11.5) 90 (14.6)
Blood pressure X130/85 mm Hg 415 (67.2) 383 (65.0) 422 (68.6) 0.413
Waist circumference 4102 cm for men or 488 cm for women 593 (96.0) 550 (93.4) 581 (94.5) 0.137
HDL cholesterol o40 mg/dl for men or o50 mg/dl for women 291 (47.1) 298 (50.6) 301 (48.9) 0.475
Triglycerides X150 mg/dl 183 (29.6) 184 (31.2) 193 (31.4) 0.757
Fasting plasma glucose X110 mg/dl 131 (21.2) 101 (17.1) 131 (21.3) 0.122
Metabolic syndrome (three or more criteria) 333 (53.9) 296 (50.3) 336 (54.6) 0.268
Obesity-related well-being 68.9728.2 46.3724.1 33.0720.9 0.001
Symptom checklist 112.8755.6 61.9736.7 55.0735.3 0.001
Psychological general well-being 51.5714.3 70.2711.9 84.5711.3 0.001
Binge eating scale 19.579.8 14.478.5 10.377.4 0.001
Body uneasiness test 2.171.1 1.570.9 1.170.8 0.001
Abbreviations: HDL, high-density lipoprotein; MCS, mental component summary; PCS, physical component summary. Data are number of cases (percentage) or
mean7s.d.
Metabolic and psychological correlate of HRQL
F Corica et al
188
International Journal of Obesity

The impact of BMI on physical domain of HRQL is well
known.
4,5,17,25
Yancy et al.
4
showed that increasing BMI
mainly affected the domains of physical activity and
bodily pain, and, similarly, Doll et al.
5
reported that obesity
had a particularly negative impact on physical well-being.
BMI can significantly affect the internal structure of
the SF-36 items exploring physical activity by affecting
two main components, one related to vigorous activities
and complex movements, and another related to all other
physical activities.
17
This particular clustering of physical
abilities with increasing BMI and poor HRQL in the
physical domain may have important implications for the
management of obese patients. Indeed, specific physical
activity programs may be tailored to regain or maintain
highly demanding physical functions or to approach
wider defects of mild and moderate physical activities.
26
Furthermore, identifying patients with a greater impairment
in mild and moderate physical activities could have some
prognostic relevance, considering the importance of a
regular physical exercise in the treatment of obesity and in
weight loss maintenance.
27
Conflicting results have been reported on the association
between high blood pressure and HRQL, a few studies
reporting a low influence,
28,29
others supporting a relevant
effect on physical functioning.
30–32
Furthermore, the sole
awareness of hypertension could contribute to worsen
HRQL, and such a negative effect may be at least partially
ascribed to the prescription of antihypertensive drugs.
28
On
the contrary, the association between diabetes and poor
HRQL is well known, with macrovascular complications,
especially coronary heart disease, and non-vascular diseases
as the strongest and the most commonly found predictors.
33
More recently, it has been demonstrated that also the early
stage of the disease, that is impaired glucose tolerance, is
associated with a reduced ability to perform physical
activities.
34
Finally, diabetes has a greater impact on the
scales of SF-36 measuring physical health as against the
mental components, and the presence of coexisting hyper-
tension in subjects with diabetes resulted in a further
significant decrease of HRQL, with an additive effect.
35
The most surprising finding of our study is the highly
relevant role of measures of psychological well-being in
Table 2 Canonical discriminant functions
Function Eigenvalue % of variance Wilks’s l P
Components of metabolic syndrome vs PCS
1 0.374 98.3 0.723 o0.001
2 0.007 1.7 0.994 0.613
Components of metabolic syndrome vs MCS
1 1.241 98.7 0.439 o0.001
2 0.004 1.3 0.997 0.113
Abbreviations: MCS, mental component summary; PCS, physical component
summary.
Table 3 Cross-tabulation of actual tertiles membership vs functional tertiles
membership
Actual PCS tertiles membership Functional PCS tertiles membership
a
o39.5 39.5–49.5 449.5
o39.5 62.4 20.9 16.7
39.5–49.5 30.9 32.8 36.3
449.5 11.6 20.1 68.3
Functional MCS tertiles membership
b
o38.0 38.0–50.0 450.0
o38.0 75.5 21.9 2.6
38.0–50 20.0 53.2 26.7
450 2.8 22.7 74.4
Abbreviations: MCS, mental component summary; PCS, physical component
summary.
a
Data are in percentages. Box’s M ¼ 1878.704; Po0.001; 54.7% of
original grouped cases correctly classified.
b
Data are in percentages. Box’s
M ¼ 1820.720; Po0.001; 67.8% of original grouped cases correctly classified.
Table 4 Variables significantly contributing to discriminant functions
Function
12
Individual components of metabolic syndrome and clinical/psychological variables
vs PCS
Obesity-related well-being 0.667 F
Psychological general well-being 0.624 F
Body mass index 0.515 F
Body uneasiness test 0.385 F
Binge eating scale 0.320 F
Gender 0.281 F
Symptom checklist 0.240 F
Blood pressure 0.188 F
Plasma glucose 0.180 F
Waist circumference F 0.424
Plasma triglycerides F 0.372
Plasma HDL cholesterol F 0.211
Education F 0.199
Age F 0.108
Individual components of metabolic syndrome and clinical/psychological variables
vs MCS
Psychological general well-being 0.964 F
Obesity-related well-being 0.537 F
Body uneasiness test 0.424 F
Binge eating scale 0.376 F
Gender 0.114 F
Education F 0.622
Body mass index F 0.536
Symptom checklist F 0.505
Plasma glucose F 0.288
Waist circumference F 0.233
Blood pressure F 0.201
Plasma HDL cholesterol F 0.171
Plasma triglycerides F 0.126
Age F 0.101
Abbreviations: HDL, high-density lipoprotein; MCS, mental component
summary; PCS, physical component summary.
Metabolic and psychological correlate of HRQL
F Corica et al
189
International Journal of Obesity

explaining PCS. Indeed, ORWELL and PGWB scores were the
main correlate of PCS, and also BUT, BES and SCL
contributed to define the discriminant function. Previous
findings suggest that psychological disturbances can con-
tribute significantly to poorly perceived health status. The
presence of depressed mood and/or anxiety, which are the
most common psychological disturbances observed in
clinical samples of obese patients,
36
can increase subjective
distress induced by disease-related physical symptoms and
functional impairment.
19
It is worth noting that also binge
eating behavior, as estimated by BES, contributes to define
the clinical profile of obese patients with poor HRQL in
the physical domain. Binge eating disorder is known to worsen
HRQL measured by disease-specific questionnaires,
19,37
and it
is possible that the association we observed between binge
eating and PCS can be partly mediated by the higher BMI.
38
Our results are partly in contrast with a previous survey
in a small sample of obese patients undergoing bariatric
surgery, where mental disorders appeared to affect psycho-
social, but not physical, domains of SF-36.
39
However, the
different sample size or type of referral could also account for
this apparent discrepancy. Furthermore, the use of specific
instruments for measuring psychological distress make our
results more reliable than those obtained using only the
formal diagnosis of mental disorders in previous studies. In
general, these findings confirm how difficult it is to
disentangle the effects of obesity on physical function from
those on mental function in treatment-seeking patients,
17
reinforcing the need for a comprehensive approach.
Only gender and measures of psychological well-being
qualified as significant correlates of MCS. This association
could be related to the higher prevalence of psychological
disorders among women,
19,40,41
or to a greater cultural drive
for thinness of women in Western societies,
42
and confirms
previous reports in clinic-based samples,
2,19,41,43
among
patients with chronic disease
44
and in population studies.
45
Additionally, the lack of association between BMI and MCS
tertiles suggests that the perception of the disease, more than
its severity, could be important in determining psychological
distress in obese outpatients.
This study has limitations too. First, waist circumference
did not contribute to any discriminant function, in disagree-
ment with recent findings in post-menopausal women.
11
Possibly, the high prevalence of abdominal adiposity in
our study population did not allow a more precise evaluation
of the relationship between abdominal adiposity and
HRQL, and this issue deserves further investigation. Second,
the results were obtained in a large outpatient population of
obese individuals seeking medical treatment, and their
external validity in the general population of obese
patients and in different settings needs to be determined.
Finally, the cross-sectional design did not allow us to
investigate the directionality of the correlation between
study variables and HRQL.
In conclusion, psychological well-being is the most
important correlate of HRQL in obesity, both in the physical
and in the mental domains, whereas the features of MS
correlate only to some extent. Since HRQL is a primary
treatment outcome in chronic diseases,
46
psychiatric and
psychological support becomes mandatory in weight man-
agement programs. Only a multidisciplinary approach
addressing both mental and somatic disorders is likely to
reduce the burden of obesity in individual patients.
Acknowledgements
The QUOVADIS study is supported by an unrestricted grant
from BRACCO Imaging Spa, Milan, Italy (Grant OBES002-
QUOVADIS-99). A complete list of the QUOVADIS investi-
gators has been previously published.
14
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