Predictors for health-related quality of life in patients
accepted for bariatric surgery
Surg Obes Relat Dis 2009;5(3):329-33.
John Roger Andersen, RN., MS.c.1 4, Anny Aasprang, RN., MS.c.1, Per Bergsholm,
MD., Ph.D.2, Nils Sletteskog, MD.3, Villy Våge, MD., Ph.D.3, Gerd Karin Natvig, RN.,
Ph.D.4
1
Faculty of Health Studies, Sogn and Fjordane University College. Box 523, 6803
Førde, Norway.
2
Department of Psychiatry, Førde Central Hospital. 6807 Førde, Norway.
3
Department of Surgery, Førde Central Hospital. 6807 Førde, Norway.
4
Section of Nursing Science, Department of Public Health and Primary Health Care,
University of Bergen. Box 7804, 5200 Bergen.
Corresponding author: John Roger Andersen. Faculty of Health Studies, Sogn og
Fjordane University College. Box 523, 6803 Førde, Norway. E-mail:
john.andersen@hisf.no, Telephone: + 47-57722522, Fax: + 47-57722501.
Keywords
bariatric surgery, body mass index, depression, musculoskeletal pain, severe obesity,
SF-36, health-related quality of life.
1
Abstract
Background
The relationship between musculoskeletal pain, depression, and health-related quality of
life (HRQL) in patients with severe obesity who are accepted for bariatric surgery
should be further explored.
Method
In this cross sectional study, we measured HRQL with the generic questionnaire “ShortForm 36 Health Status Survey” (SF-36). Multiple regression analysis was used to
explore associations between the predictors (musculoskeletal pain and depression) and
the physical cumulative summary (PCS) and mental cumulative summary (MCS). Age,
gender, body mass index, and the number of comorbidities were entered as covariates.
Results
The study subjects included 28 females and 23 males with a mean age of 37.7 years and
a mean BMI of 51.9 kg/m2. PCS and MCS scores were very poor compared to the ageand gender-adjusted population norm (p<0.001). The presence of musculoskeletal pain
was associated with a score that was 10.97 points lower on the PCS (p<0.001) and 7.05
points lower on the MCS (p=0.031). The presence of depression was associated with a
score that was 20.89 points lower on the MCS (p<0.001), while no significant
association was found between depression and the PCS.
2
Conclusions
This study shows that musculoskeletal pain was strongly associated with lower scores
on the PCS and MCS, while depression was strongly associated with lower score on the
MCS.
3
Introduction
Why do many patients who are accepted for bariatric surgery have poor health-related
quality of life (HRQL) while others have moderately reduced or even normal values?
[1] We became interested in this question when assessing our patients’ HRQL for the
purpose of evaluating the effects of bariatric surgery. To shed some light on his issue
could matter for several reasons. First, data related to this issue could reveal risk factors
for poor HRQL and thereby guide clinicians in prioritizing patients for bariatric surgery.
It may also generate hypothesis for how we can help patients who experience
insufficient improvements in HRQL after surgery.
Some studies have suggested that the most important modifiable predictors of
poorer HRQL in patients accepted for bariatric surgery are arthritis/ musculoskeletal
pain [2, 3] and psychiatric disorders (depression, binge eating disorders, etc.) [2, 4, 5].
Such conditions may influence HRQL through several mechanisms [6, 7]. Higher body
mass index (BMI) seems to be most closely related to poorer physical health [2, 5]. It
also seems that cardiovascular risk factors (i.e., diabetes, dyslipidemia, and
hypertension) may not predict HRQL in this patient group [2, 3, 8], but that coronary
heart disease may have a negative influence if prevalent [3]. The number of
comorbidities a patient has also seems to predict poorer HRQL [3].Sociodemographic
characteristics such as age, gender, and ethnicity are typically reported to be predictive
to some extent; however, it is difficult to identify any clear patterns here [2, 3, 5, 8, 9].
Although several variables seem to be associated with HRQL in this patient
group, the prevalence of the two modifiable comorbidities musculoskeletal pain and
depression seem to have a particular potential to explain much of the variance in HRQL
[2, 3, 5]. To our knowledge, the predictive value of these two variables on HRQL
(adjusted for each other) has only been investigated once before in this patient group
4
[2]. We therefore tested whether these findings could be reproduced in a patient group
from a different population.
The aim of this study was to investigate whether musculoskeletal pain and
depression were associated with overall HRQL in our patients, as measured with the
generic health status measure “Short-Form 36 Health Status Survey” (SF-36), after
adjusting for relevant and available covariates (age, gender, BMI, and other
comorbidities). We hypothesized that musculoskeletal pain would be associated with
poorer physical health and that depression would be associated with poorer mental
health.
Methods
Patients and study design
The first 51 patients with severe obesity who were accepted for bilopancreatric
diversion with a duodenal switch at Førde Central Hospital were invited to participate in
the study. Our bariatric surgery program was initiated in 2001, and the inclusion criteria
included BMI ≥ 40.0 or ≥ 35.0-39.9 with obesity-related co-morbidities, age 18-60, no
alcohol or drug problems, no active psychosis, and failure to lose weight through other
methods. All patient data was assessed using a standardized form to determine the
patient’s health status. Medical history and current health status were examined both by
the patient’s primary care physician and by a physician at the hospital prior to surgery
(except musculoskeletal pain and urinary stress incontinence, which were only assessed
by the patients and the physician at the hospital). The patients completed an HRQL
questionnaire at home and brought it with them when they arrived for surgery.
5
Outcome variables
HRQL refers to the aspects of quality of life that specially relate to a person’s health,
and can be defined as self-perceived multidimensional health status [10, 11]. To
measure HRQL, we used the SF-36 (Norwegian version 1.2), which is a wellestablished generic measure of the health burden of chronic diseases [12]. The
questionnaire has demonstrated good validity and reliability [13]. SF-36 assesses eight
dimensions of physical and mental health, each ranging from 100 (optimal) to zero
(poorest). The subscales physical functioning, physical role limitations, bodily pain, and
general health reflect physical health. The subscales vitality, social functioning,
emotional role functioning, and mental health reflect mental health. The eight SF-36
subscales can be factor-analyzed and reduced to two summary scores, the physical
component summary (PCS) and the mental component summary (MCS) [14]. These are
the outcome variables in this study. To calculate the PCS and MCS, we used the oblique
method, which allows physical and mental health to be correlated [15]. A higher score
on both summary scales represents better health. Generally, 2 to <5 points is regarded as
a small difference, 5 to <8 points as a moderate, and ≥ 8 points as a large difference.
Norm data on the SF-36 was obtained from the Short Form 36 (SF-36) health survey in
Norway 1998 (n=2323) [16].
Predictors
Our predictor variables were musculoskeletal pain and depression. Musculoskeletal pain
was considered to be present if the patient answered yes to the following question: “do
you have pain in the lower back, hips, knees, legs, or ankles and regularly use
medication and/or have physiotherapy for such a condition?” Depression was
considered to be present if the patient was on prescribed treatment for this comorbidity.
6
Covariates
Information on age, gender, and BMI were collected. Anxiety, asthma, coronary heart
disease, diabetes, hyperlipidemia, hypertension, and pickwickian/sleep apnea were
considered to be present if the patient was on prescribed treatment for these
comorbidities. All patients were also screened for diabetes (fasting plasma glucose and
hemoglobin A1c), dyslipidemia (fasting total/HDL cholesterol and triglycerides), and
hypertension at the hospital. The presence of urinary stress incontinence was considered
to be present if the patient reported having this condition. All comorbidities was
assessed as not present (= 0) or present (= 1). We constructed a comorbidity-score of the
eight comorbidities that were considered to be covariates, ranging from zero to eight
points. This score was treated as a continuous variable, as has been done previously [3].
Statistics
A one-sample t-test was used to compare the SF-36 summary scores between the
patients and the norm population. The mean SF-36 summary scores of the norm
population were adjusted for age and gender to reflect the same distribution as that of
our study sample. Associations between the SF-36 summary scores and the two
predictors (musculoskeletal pain and depression) were investigated using multiple
regression analysis. Age, gender, BMI, and the comorbidity-score were entered as
covariates. Tests were performed to ensure that the underlying assumptions for the
regression analysis were not violated. Unstandarized regression coefficients (B),
standard errors, standardized regression coefficients (Beta), p-values, and adjusted R2
for the two models are reported. A two-tailed p-value of < 0.05 indicated statistical
significance. The SF-36 summary sores were calculated with the SF Health Outcomes
7
TM
Scoring Software, basic version (Quality Metric Inc. Lincoln). The remaining
analyses were performed with the statistical program SPSS for Windows, version 15.0
(SPSS Inc. Chicago).
Ethics
Informed consent was obtained from all participants. This investigation conforms to the
principles outlined in the Declaration of Helsinki. The study was approved by The
Norwegian Social Science Data Services and by the Regional Committee of Ethics in
Medicine, West-Norway.
Results
Patient characteristics
All patients who were invited agreed to participate in the study. The patients had a high
mean BMI and a high prevalence of comorbidities (Table 1). The patients’ PCS and
MCS scores were generally very low, and significantly lower than in the norm
population (p<0.001) (Table 2).
Predictors for health-related quality of life
Having musculoskeletal pain was associated with a score that was 10.97 points lower on
the PCS (p<0.001) (Table 3) and 7.05 points lower on the MCS (p=0.031) (Table 4).
The presence of depression was associated with a score that was 20.89 points lower on
the MCS (p<0.001) (Table 4), while no significant association was found between
depression and the PCS (Table 3). Of the covariates, higher BMI was associated with
poorer PCS scores (p=0.041) (Table 3). A higher comorbidity-score was associated with
a higher MCS score (p<0.010) (Table 4). Gender and age were associated with neither
8
PCS nor MCS (Table 3 and 4). The regression model explained 32.3% of the variance
in the PCS (Table 3) and 40.9 % of the variance in the MCS (table 4).
Secondary analysis
The prevalence of musculoskeletal pain was 91.7% (11 out of 12) among the patients
with a diagnosis of depression while it was 56.4% (22 out of 39) in patients without this
diagnosis. The prevalence of depression was 33.3% (11 out of 33) in patients reporting
musculoskeletal pain and 5.9% (1 out of 17) in patients who did not report this
condition (p=0.037, Fisher’s Exact test). Patients experiencing both musculoskeletal
pain and depression (22.6%, 11 out of 51) also had a higher mean comorbidity-score
(3.7, SD 1.3) than those not having both of these comorbidities (2.03, SD 0.89)
(p<0.001, independent t-test).
Discussion
This study shows that musculoskeletal pain was strongly associated with lower scores
on the PCS and MCS, while depression was strongly associated with lower score on the
MCS. Our secondary analysis also shows that almost all of the patients with depression
also had musculoskeletal pain, and that these patients had more comorbidities than other
patients.
Our data supports the hypothesis based on the study by Dixon et al. [2], and
further suggests that musculoskeletal pain and depression may have an even larger
predictive value in other populations. In the study by Dixon et al., the comorbidities
were assessed in a similar manner as in the present study. The prevalence of depression
(19%) and arthritis/joint pain (72%) were also similar. Dixon et al. found that
arthritis/joint pain was associated with poorer PCS but not poorer MCS. In our study,
9
musculoskeletal pain was also associated with poorer MCS. The reason for this
discrepancy could be that our patients had more pain or differences between the
modified Australian version of the SF-36 and the one that was used in the current study.
They also found that depression was associated with both poorer PCS and MCS. The
statistical power in their study was, however, higher than in the present one and this
might explain why we found no such association. Our finding that higher BMI was
associated with lower PCS was expected as this variable is directly related to physical
functioning [2]. Our data also indicate that the number of comorbidities a patient has
may not predict poorer PCS and MCS when adjusting for musculoskeletal pain and
depression. In fact, the morbidity-score was associated with a higher MCS in the present
study, a finding that we are unable to explain.
Several mechanisms may explain our findings. One is that obesity increases the
biomechanical load on joints, ligaments, and muscles during activity, which may trigger
pain and interfere with physical functioning [6]. Musculoskeletal pain can also be quite
bothersome and affect mental well-being. Obesity may also induce depressive
symptoms when it leads to impaired functioning in daily life, and depression may
reduce mental well-being and functioning [7, 17]. It is, therefore, possible that the
association between comorbidities such as depression and HRQL may be bidirectional
[11]. The associations between the comorbidities and HRQL may also partly represent a
tautology. Depression and musculoskeletal pain are not pure objective conditions, but
will also be related to the patient’s evaluation of own health. As such, comorbidities and
HRQL can to some degree be regarded as overlapping constructs [18].
Our finding that almost all patients with morbid obesity and depression had
musculoskeletal pain is interesting. We have not found any similar data in the literature;
however, the prevalence of such pain is generally high in patients with morbid obesity
10
[6]. In other patient groups, the prevalence of chronic pain has been reported to be very
high in patients suffering from depression [19]. An explanation for this could be that
chronic pain induces depression and that depression causes and intensifies pain (the
depression-pain syndrome). Both serotonin and norepinephrine have been shown to
diminish peripheral pain signals. This might explain how depression, which is
associated with a dysregulation of these key modulating neurotransmitters along a
shared pathway, may contribute to the presence of painful symptoms [19].
Or finding may have some clinical implications. First, the patients’ low HRQL
prior to surgery indicates that the received treatment for musculoskeletal pain
(analgesics and/or physiotherapy) and depression (antidepressants) does not seem to
give sufficient relief from these sufferings. This indicates that the underlying cause of
these comorbidities; namely obesity, must be treated. Second, since patients with both
musculoskeletal pain and depression seem to have a particularly high risk for having
poor HRQL and having a high prevalence of other comorbidities, this could be
something to consider when prioritizing patients for bariatric surgery. Finally, if
bariatric surgeries do not lead to sufficient improvements in HRQL, we hypothesize that
musculoskeletal pain and depression could be key targets for additional treatment.
Preliminary data have indicated that patients who experienced little or no change in
these two comorbidities after surgery had a smaller improvement in SF-36 scores than
those who experienced improvements in these conditions [20].
Some limitations of this study should be considered. First, due to the crosssectional design it is not possible to draw any causal conclusions. Second, our set of
predictors was not exhaustive. Third, we cannot rule out the possibility that
misclassification of comorbidities has occurred and somewhat biased the results. It has
been shown that primary care physicians often fail to accurately diagnose patients with
11
major depression [19]. However, if this is the case in the present study, it is more likely
that it would decrease the strength of the association between depression and the MCS
than increase it. On the other hand, many undiagnosed cases of depression in people
who also suffer from musculoskeletal pain would lead to overestimation of the
association between musculoskeletal pain and the summary scores (especially the
MCS). Data on musculoskeletal pain were based on self-report. However, the
correlation between musculoskeletal pain and the SF-36 bodily pain scale was strong
(r=0.61, p<0.001), supporting its validity. Forth, the sample size was rather small;
however, this was somewhat compensated for by large effect sizes. Finally, the SF-36 is
regarded as a well-suited instrument for exploring generic HRQL in morbidly obese
patients [21]; however, we realize that data on obesity-specific HRQL would also be
interesting.
Conclusions
Our data indicate that patients who are accepted for bariatric surgery and experience
musculoskeletal pain and/or depression are at particular risk for having lower overall
HRQL. However, more confirmatory research is needed, using a larger sample size and
validated instruments for musculoskeletal pain and depression. Future studies
evaluating the effect of bariatric surgery on HRQL should also attempt to identify
patients who report no or little improvement in musculoskeletal pain and depression
after adequate weight loss, and explore the effectiveness of providing these patients
with additional treatment.
12
Acknowledgements
This study was supported by a grant from Sogn and Fjordane College University,
Norway. We are grateful for the statistical assistance provided by statistician Tore
Wentzel-Larsen (Centre for Clinical Research, Western Norway Regional Health
Authority). The authors also acknowledge the patients who participated in the study.
13
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15
Table 1. Patient characteristics (n = 51)
Variables
Age, mean (SD)
37.7 (8.0)
Gender, men/women
23/28
Body mass index (kg/m2), mean (SD)
51.9 (7.5)
Anxiety, n (%)
7 (13.7)
Asthma, n (%)
11 (21.6)
Coronary heart disease, n (%)
1 (2.0)
Depression, n (%)
12 (23.5)
Hypertension, n (%)
40 (78.4)
Hyperlipidemia, n (%)
26 (51)
Diabetes, n (%)
14 (27.5)
Musculoskeletal pain, n (%)
33 (64.7)
Pickwickian/sleep apnea, n (%)
6 (11.8)
Urinary stress incontinence, n (%)
17 (33.3)
16
Table 2. SF-36 summary scores in the study sample compared to the norm
population.
Summary scores Sample n=51
Norm population a p-value
Mean (SD)
Mean
PCS
32.3 (10.2)
53.7
<0.001
MCS
37.8 (12.7)
51.3
<0.001
PCS: physical cumulative summary. MCS: mental cumulative summary.
a
The norm population mean values were adjusted for age and gender [16].
17
Table 3. Multiple linear regression analysis with the physical cumulative summary
(PCS) as the dependent variable (n=51)
Independent variables Unstandarized coeff. St. error. Standarized coeff. P-value
B
Beta
Age
-0.13
0.15
-.10
0.404
Gender
1.41
2.52
.07
0.579
Body mass index
-0.36
0.17
-.26
0.041
Comorbidity-score
0.47
1.32
.06
0.726
Musculoskeletal pain
-10.97
2.72
-.52
<0.001
Depression
-5.23
3.85
-.22
0.181
Dichotomous variables: Gender, male = 0 and female = 1; Musculoskeletal pain, not
present = 0 and present = 1; Depression, not diagnosed = 0 and diagnosed = 1.
Adjusted R2 = 32.3%.
18
Table 4. Multiple linear regression analysis with the mental cumulative summary
(MCS) as the dependent variable (n=51)
Independent variables Unstandarized coeff. St. error. Standarized coeff. P-value
B
Beta
Age
-0.17
0.18
-.11
0.354
Gender
-1.76
2.94
-.07
0.552
Body mass index
-0.35
0.20
-.20
0.088
Comorbidity-score
4.13
1.54
.40
0.010
Musculoskeletal pain
-7.05
3.17
-.27
0.031
Depression
-20.89
4.49
-.71
<0.001
Dichotomous variables: Gender, male = 0 and female = 1; Musculoskeletal pain, not
present = 0 and present = 1; Depression, not diagnosed = 0 and diagnosed = 1.
Adjusted R2 = 40.9%.
19