Original Article | Published:

Relation of health-related quality of life to metabolic syndrome, obesity, depression and comorbid illnesses

International Journal of Obesity volume 35, pages 10871094 (2011) | Download Citation

  • A Corrigendum to this article was published on 14 February 2012

Abstract

Background:

Metabolic syndrome has been associated with impaired health-related quality of life (HRQoL) in several studies. Many studies used only one HRQoL measure and failed to adjust for important confounding variables, including obesity, depression and comorbid conditions.

Objective:

To investigate the relationship between metabolic syndrome and HRQoL using multiple measures. We also sought to determine whether increasing body mass index or diabetes status further modified this relationship.

Methods:

This cross-sectional study included 390 obese participants with elevated waist circumference and at least one other criterion for metabolic syndrome. Of these 390 participants, 269 had metabolic syndrome (that is, they met 3 out of the 5 criteria specified by the NCEP (National Cholesterol Education Program)) and 121 did not. Participants were enrolled in a primary care-based weight-reduction trial. HRQoL was assessed using two generic instruments, the Medical Outcomes Study Short-Form 12 and the EuroQol-5D, as well as an obesity-specific measure, the Impact of Weight on Quality of Life. Differences in HRQoL were compared among participants with and without metabolic syndrome. Multivariable linear regression was used to determine how HRQoL varied according to metabolic syndrome status, and whether factors including weight, depression and burden of comorbid disease modified this relationship.

Results:

Metabolic syndrome was not associated with HRQoL as assessed by any of the measures. In univariable analysis, depression, disease burden and employment status were significantly associated with worse HRQoL on all instruments. In multivariable models, only depression remained significantly associated with reduced HRQoL on all measures. Increasing obesity and diabetes status did not modify the relationship between metabolic syndrome and HRQoL.

Conclusion:

In contrast to previous studies, metabolic syndrome was not associated with impaired HRQoL as assessed by multiple measures. This suggests that metabolic syndrome in itself is not associated with decreased HRQoL, but other factors such as obesity, depression and greater disease burden may significantly influence the quality of life in this population.

Introduction

The prevalence of metabolic syndrome has increased in tandem with the obesity epidemic. At present, 34% of US adults have metabolic syndrome,1 which is defined by the NCEP (National Cholesterol Education Program) as meeting any three of the five following criteria: (1) elevated waist circumference (40 inches for men; 35 inches for women); (2) high triglycerides (150 mg per 100 ml); (3) decreased high-density lipoprotein cholesterol (<40 mg per 100 ml for men; <50 mg per 100 ml for women); (4) elevated fasting glucose (100 mg per 100 ml); and (5) elevated blood pressure (130/85 mm Hg).2 Individuals with metabolic syndrome have an increased risk of developing diabetes and cardiovascular disease,3 both of which have been associated with decreased health-related quality of life (HRQoL).4, 5, 6, 7 Impaired HRQoL is particularly important in this population, as it has been associated with a number of adverse outcomes, including poor response to therapy, disease progression and mortality.8, 9, 10

Several components of the metabolic syndrome including obesity, insulin resistance and hypertension, have been associated with reduced HRQoL,11, 12, 13, 14, 15 leading some investigators to suggest that metabolic syndrome itself may also be associated with impaired quality of life (QoL). Several studies have confirmed this finding, but they have been limited by failure to adjust for obesity, burden of comorbid disease and depression.16, 17, 18, 19 Increasing body mass index (BMI), in particular, may modify the relationship between metabolic syndrome and HRQoL, such that the association is stronger in more obese individuals. This relationship has biologic plausibility, as increasing BMI has consistently been associated with worse physical functioning, a greater number of comorbid conditions and a higher prevalence of depression.13, 20, 21, 22, 23, 24, 25, 26 Thus, increasingly obese individuals with metabolic syndrome may be required to take more medications, visit medical providers more frequently and experience greater difficulty with mobility, all of which can impair QoL.

In this study, we assessed HRQoL in obese individuals with and without metabolic syndrome using two generic QoL measures, the Medical Outcomes Study Short Form-12 (SF-12) and the EuroQol 5D (EQ-5D), as well as one obesity-specific measure, the Impact of Weight on Quality of Life (IWQoL-Lite). We hypothesized that metabolic syndrome would be associated with decreased QoL in participants with higher BMI (40 kg m−2), but this relationship would not be seen in patients with lower BMI. We also examined whether diabetes status modified the relationship between metabolic syndrome and QoL.

Materials and methods

Participants

A total of 390 obese individuals were recruited from 6 primary care practices within the University of Pennsylvania Health System to participate in the POWER (Practice-Based Opportunities for Weight Reduction) trial, a 2-year primary care-based weight-reduction trial. Eligible participants were aged 21 years, had a BMI of 30–50 kg m−2, an elevated waist circumference and at least one other criterion for the metabolic syndrome. Participants were considered to have metabolic syndrome if they met at least three of the five criteria defined by the NCEP.2 (Participants with known diabetes or hypertension were considered to have met the glucose and blood pressure criteria for metabolic syndrome, respectively.) Exclusion criteria included uncontrolled blood pressure, recent cardiovascular events, weight change 5% over the preceding 6 months, active participation in a weight loss program, previous or planned use of bariatric surgery, serious comorbid conditions (such as severe mental illness, end-stage renal disease), use of medications known to cause significant (5%) long-term changes in weight or pregnancy. The study was approved by the Institutional Review Board at the University of Pennsylvania, and all participants provided written informed consent. The questionnaires used in this analysis were collected at the randomization visit before the participants received any intervention.

Outcome measures

Medical outcomes study, short form 12 (SF-2 version 1)

The SF-12 is a 12-item condensed version of the SF-36.27 Both instruments are validated measures of HRQoL.28 The SF-12 includes eight subscales—four subscales are used to derive a summary score of physical health (physical component summary, PCS-12) and four subscales are used to derive a summary score of mental health (mental component summary, MCS-12).27 Both PCS-12 and MCS-12 scores were designed to have a mean score of 50 and a s.d. of 10 in a representative sample of the US population.29 Higher scores on the SF-12 are indicative of better functioning. Scores >50 represent above-average health status. Both summary scores are highly correlated with those derived from the SF-36 in an obese population30 and were considered as two separate outcomes.

EuroQol 5D

The EQ-5D is a generic health status instrument that describes five dimensions: mobility, self-care, usual activities, depression/anxiety and pain/discomfort. Each of the five dimensions is based on a single question with three possible responses (1=no problems, 2=some problems and 3=extreme problems). Scores from the five dimensions are combined into a single ‘utility’ score. The EQ-5D utility scores range from a full health score of 1 (in which respondents report no problems on any dimension) to the lowest score of −0.59 (when respondents report that they are at the bottom level of each dimension).31 The EQ-5D has been found to be sensitive to the effects of obesity on HRQoL, even after controlling for comorbiditites, age and sex.32

IWQoL-Lite version

The IWQoL-Lite is a validated 31-item, self-report obesity-specific measure of QoL.33 It provides a total score and scores on five domains: physical function, self-esteem, sexual function, public distress and work. Scores are transformed on a 0–100 scale, with higher scores indicating a better QoL.34 The IWQoL-Lite has been found to be a reliable and valid instrument for assessing weight-related QoL in obese persons with type 2 diabetes.35

Depression

Symptoms of depression were assessed using the Patient Health Questionnaire (PHQ-8), a validated eight-item depression scale.36 The PHQ-8 includes eight of the nine criteria for depression according to the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, fourth edition), but does not contain a question about suicidal ideation contained in the PHQ-9.37 The PHQ-8 asks the number of days over the past 2 weeks that the respondent has experienced a particular depressive symptom. Scores for each item are summed to produce a total score ranging from 0 to 24 points. Scores of 0–4 indicate no depressive symptoms, 5–9 represent mild depressive symptoms and scores of 10 are indicative of greater symptoms of depression.37 The PHQ-8 and PHQ-9 scores are highly correlated and have nearly identical operating characteristics.37, 38

Disease burden

Disease burden was assessed using the Functional Comorbidity Index (FCI). The FCI was developed for use in the general population with physical function as an outcome. The FCI includes 18 diagnoses that are commonly treated in outpatient settings, many of which are more common in obese individuals. It is scored by assigning one point to each condition present.39 Thus, the range is 0–18, with a higher score indicating a greater burden of comorbid disease. As obesity and diabetes are included in the FCI, these diagnoses were not included in the total FCI score to avoid double counting. In addition to the FCI, disease burden was also estimated by summing the number of medications for each participant. The participants’ self-reported medical histories and medications were confirmed by a study physician (MLV or AGT), who reviewed the medical records for each individual enrolled in the study.

Lifestyle habits

Lifestyle habits, including smoking and alcohol intake, were also assessed by questionnaires specifically developed for the POWER study.

Weight and cardiovascular disease risk factors:

BMI was calculated from height and weight, which were obtained at the baseline study visit. Participants were measured in light clothing without shoes on a calibrated scale to the nearest 0.1 kg (Tanita BWB 800, Tanita Corp., Tokyo, Japan). Standing height was measured to the nearest 0.1 cm using a wall-mounted stadiometer (Seca 202, Seca Corp., Hamburg, Germany). All measurements were performed by trained research staff and were repeated twice, with the average measurement used to calculate BMI. Participants were classified according to the BMI categories adopted by the National Institutes of Health40 and the World Health Organization:41 class I (BMI 30–34.9 kg m−2), class II (BMI 35–39.9 kg m−2) and class III (BMI 40 kg m−2). Waist circumference was measured in duplicate to the nearest 0.1 cm by placing an inelastic tape measure (Gulick II, model 67 020, Lafayette Instrument Co., Lafayette, IN, USA) around the abdomen horizontally at the midpoint between the highest point of the iliac crest and the lowest part of the costal margin in the mid-axillary line. The procedure was repeated until two consecutive measurements within 0.5 cm were obtained. Blood pressure was measured using an automated sphygmomanometer (Omron HEM-907-XL, Omron Healthcare Inc., Bannockburn, IL, USA) in triplicate after the participant had been sitting quietly for 5 min, with blood pressure recorded as the mean of the three measurements. Serum glucose and lipids were measured after an overnight fast, and conventional assays were used to measure total triglycerides and high-density lipoprotein-cholesterol levels.

Statistical analysis

Summary statistics for all variables, both continuous and categorical, were examined for range and to assess plausibility of values. All data were assessed for normality before analysis. Differences in weight and in other characteristics between participants with and without metabolic syndrome were compared using t-tests for continuous variables and χ2 tests for categorical variables.

Univariable regression, ANOVA (analysis of variance) and χ2 tests were used to estimate the strength of association between HRQoL and baseline characteristics. Separate models were created for each of the following four outcome variables: PCS-12, MCS-12, EQ-5D and IWQoL. We then examined the potential for effect modification by stratifying the association of metabolic syndrome and HRQoL according to: (1) obesity class (30–34.9, 35–39.9 and 40–50 kg m−2) and (2) the presence of diabetes.

On the basis of the strength of association in the univariable model (independent variables were entered if they had a P-value <0.2), a multivariable linear regression model was fit to estimate how HRQoL varied according to the presence or absence of metabolic syndrome. As the a priori hypothesis specified that metabolic syndrome was associated with HRQoL, we included this condition in the multivariable model for each outcome, regardless of its univariable association.

Secondary analyses were performed to determine whether a statistically significant interaction was present between metabolic syndrome and BMI as a continuous variable, as well as metabolic syndrome and diabetes. The association between metabolic syndrome and HRQoL was also examined in a stratified analysis by BMI and by the presence or absence of diabetes. We also evaluated whether the number of metabolic syndrome criteria met by the participants (that is, three versus four versus five criteria) affected the relationship between metabolic syndrome and HRQoL. Finally, we looked at the association between individual components of metabolic syndrome (excluding elevated waist circumference, as all participants in the trial met this criteria) and HRQoL. All analyses were conducted using Stata, Version 10.1 for Windows (Stata Corporation, College Station, TX, USA). A P-value of <0.05 was considered significant for all analyses.

Results

Participants

A total of 269 (68%) of participants met 3 criteria for metabolic syndrome, with a mean (s.d.) of 3.7 (0.7) criteria. All 390 (100%) participants met the criterion for waist circumference. Of the remaining components of metabolic syndrome, 132 (33.8%) participants had fasting glucose 100 mg per 100 ml, 184 (47.2%) had low high-density lipoprotein cholesterol, 242 (62.1%) had elevated triglycerides and 300 (76.9%) had increased blood pressure. The 269 participants with metabolic syndrome were older (P=0.028) and had higher weight (P=0.001) and BMI (P=0.040) than did the 121 participants without the syndrome (Table 1). The former participants also took significantly more medications (P<0.001), although their FCI score did not differ significantly from that of participants without metabolic syndrome. As expected, participants with metabolic syndrome had significantly higher fasting blood glucose, higher triglycerides, higher blood pressure and lower high-density lipoprotein cholesterol than did those without the condition (P0.001 for all comparisons). Participants with metabolic syndrome had lower low-density lipoprotein cholesterol levels than did those without the condition, although the difference did not reach statistical significance.

Table 1: Baseline characteristics of participants with and without metabolic syndromea

Health-related quality of life

No differences in HRQoL were observed between those with and without metabolic syndrome using the generic measures (PCS-12, MCS-12 and EQ-5D) or the obesity-specific measure (IWQoL-Lite), as shown in Table 2. The mean (s.d.) score on PCS-12 was 43.3 (9.7) in participants with metabolic syndrome, compared with 44.2 (9.6) in those without the condition. The MCS-12 scores for the two groups were 49.3 (10.0) and 49.4 (9.7), respectively. Participants with metabolic syndrome had a mean EQ-5D score of 0.820 (0.140), compared with 0.839 (0.141) in those without the condition (P=0.223).

Table 2: Baseline scores on the health-related quality of life and depression measures for participants with and without metabolic syndromea

The mean IWQoL-Lite total score in participants with metabolic syndrome was 67.2 (22.0), compared with 69.3 (21.9) in those without. None of the five IWQoL-Lite subscales was associated with metabolic syndrome.

Depression

Depression scores did not differ significantly between groups, but were consistent with mild symptoms of depression.

Unadjusted (univariable) analyses for HRQoL measures

In the unadjusted analyses, metabolic syndrome was not associated with HRQoL on any of the four measures (Table 3). Depression, disease burden (as assessed by the FCI) and employment status were significantly associated with worse HRQoL on all four measures. BMI was significantly associated with worse QoL on PCS-12 and the IWQoL-Lite, but was not associated with MCS-12 or EQ-5D. Higher PHQ-8 scores and higher BMI were significantly associated with worse QoL on all five subscales of IWQoL-Lite. Educational level, marital status, alcohol use and medication count were not significantly associated with any of the QoL measures.

Table 3: Univariable associations (P-values) of sociodemographic and clinical variables with health-related quality of lifea

Adjusted (multivariable) analyses for HRQoL measures

In adjusted models, only the depression score remained significantly associated with reduced HRQoL on all four outcome measures (Tables 4,5,6,7). Disease burden remained significantly associated with HRQoL on PCS-12, MCS-12 and EQ-5D, but not with the IWQoL-Lite, after controlling for confounding factors. Similarly, BMI remained significantly associated with HRQoL on PCS-12 and IWQoL. Each of the four outcome measures also detected unique factors that were significantly associated with HRQoL.

Table 4: Multivariable associations with the PCS-12
Table 5: Multivariable associations with MCS-12
Table 6: Multivariable associations with EQ-5D
Table 7: Multivariable associations with IWQoL-Lite

Secondary analyses

There was no evidence of statistical interaction between metabolic syndrome and continuous BMI or between metabolic syndrome and diabetes in the adjusted models. In the analysis stratified by BMI, there were no significant associations between metabolic syndrome and HRQoL on any of the four QoL measures. The relationship between increasing number of metabolic syndrome components and QoL was also examined. There was no association between metabolic syndrome and HRQoL on any of the QoL measures when participants with four or five criteria (n=152) were compared with those who met three or fewer components (n=238). Specific components of the metabolic syndrome were also not significantly associated with HRQoL.

Discussion

Surprisingly, metabolic syndrome was not associated with impaired QoL, despite a robust analysis that included two generic measures and an obesity-specific instrument (four outcomes in total). Participants in both groups reported slightly lower than average health status on PCS-12 and relatively average health status on MCS-12. (A summary score of 50 on both the physical and mental components of the SF-12 is indicative of average HRQoL.29) In contrast, both groups reported relatively high QoL on the second generic measure, EQ-5D. Although specific cutoffs for QoL have not been reported for IWQoL-Lite,42 the scores for participants with and without metabolic syndrome were intermediate between those reported in similar populations of obese individuals (range 54.6–77.6).35, 42, 43, 44 Mean IWQoL-Lite scores of 91.8 were reported in the nonobese general population.42

Secondary analyses showed no evidence of an interaction between metabolic syndrome and obesity class or between metabolic syndrome and diabetes. This is notable given that participants with metabolic syndrome were more likely to be female, older, of lower socioeconomic status and to have a higher BMI and greater medication use, compared with those without metabolic syndrome. Participants with metabolic syndrome were also more likely to have hypertension and hyperlipidemia, although low-density lipoprotein-cholesterol levels were lower in this group. (The latter finding is likely explained by the fact that more participants in the metabolic syndrome group were taking lipid-lowering medications, compared with those without the condition.) Finally, the number of criteria for metabolic syndrome did not influence the null relationship between metabolic syndrome and HRQoL. As all study participants had to have at least two criteria for metabolic syndrome to be eligible for enrollment, individuals with and without metabolic syndrome had similar baseline characteristics from a clinical perspective. Thus, it was more difficult to detect small differences between groups, and the association between metabolic syndrome and HRQoL was likely attenuated.

Consistent with previous studies, we found that BMI was associated with lower scores on PCS-12 and IWQoL-Lite, but not on mental health measures.13, 20, 45 As the impact of obesity on physical functioning is well established,11, 12, 13 the lack of association between HRQoL and metabolic syndrome in more obese subgroups was unexpected. A threshold BMI may exist for which obesity modifies the relationship between metabolic syndrome and HRQoL. Study participants were required to have a BMI between 30 and 50 kg m−2. Significant differences in QoL may have been detected if individuals with extreme obesity (BMI 50 kg m−2) and metabolic syndrome had been included.

Mean scores on PCS-12, MCS-12 and IWQoL-Lite, for participants with and without metabolic syndrome, were remarkably similar to scores reported in previous studies that evaluated the impact of obesity on QoL using the same instruments.22, 30, 35 IWQoL-Lite scores in this study were also very similar to those reported in a study that used this measure to compare weight-related QoL in 1197 obese participants with and without diabetes.35

Findings from this study raise questions about a conceptual model relating obesity, the number of comorbid conditions, depression and QoL. Depression was associated with decreased QoL across measures, whereas obesity and number of comorbidities were not. One possible explanation is that impaired QoL is an intermediary that relates these conditions. In one study, for example, increasing BMI was associated with greater reports of physical pain (a component of QOL) which, in turn, were associated with greater symptoms of depression.46 BMI alone was not associated with increased depression scores. Impaired QoL may also act as a link between comorbidity and depression.47, 48, 49 This framework would explain why we found depression to be associated with QoL on all four measures, while the associations between comorbid conditions, obesity and HRQoL were more inconsistent. However, the causality and time course of these relationships remain largely unknown. Therefore, prospective longitudinal studies, which include estimates of these factors and QoL, are required to elucidate their interrelations.

This investigation had several strengths. We performed a robust analysis that included important factors that may affect QoL, including depression and estimates of disease burden. Previous studies have failed to adjust for many of these confounding variables.16, 17, 18, 19, 20 Ford et al. reported that metabolic syndrome was significantly associated with reduced QoL in a population of 1859 US adults. However, only age, sex, ethnicity, educational status and smoking status were considered as covariates in their regression model.16 Similarly, Miettola et al.17 examined the relationship between metabolic syndrome and HRQoL in a Finnish population, but only adjusted for age, gender, marital status, education, employment status, smoking and physical activity. The FCI in this study indicated that participants had few comorbidities and were in relatively good health. Thus, a volunteerism effect may have influenced our findings, in which individuals who elected to participate in the POWER trial may have had better HRQoL than those who did not volunteer.

An additional strength of this study was the use of several measures of HRQoL. Although the SF-36 (and the SF-12) and EQ-5D have been widely used in weight loss studies, few studies have also included a disease-specific measure.50 Obesity-specific instruments may complement generic measures in capturing more subtle differences in health among participants with milder obesity and reductions in aspects of mental or physical health unique to obesity.20, 46 Although metabolic syndrome was not associated with decreased QoL on IWQoL-Lite, this measure did detect differences in HRQoL that were not observed using generic instruments.

This study also had several limitations. The particular version of the SF-12 that was used in this study only allowed calculation of summary scales but did not allow calculation of individual subscales. Thus, differences between groups may have been present in the subscales that we were unable to detect. As previously noted, there were few clinically significant differences between participants with and without metabolic syndrome, making it difficult to detect small differences between groups.

In summary, metabolic syndrome was not associated with impaired HRQoL using two generic measures and an obesity-specific instrument. Diabetes and increasing obesity did not modify this relationship. These findings suggest that metabolic syndrome in itself is not associated with a decreased QoL, but other factors such as obesity, depression and greater disease burden may have significant effects on QoL in this population. Larger studies that use multiple measures of QoL and include the important covariates described previously are required to confirm these findings.

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Acknowledgements

We thank Christopher Vinnard, MD, MPH, MSCE for his editorial assistance. This study was supported by grants from the National Heart, Lung and Blood Institute (U01HL087072-04) and from the National Institute of Diabetes and Digestive and Kidney Diseases (5K24DK065018-07).

Author information

Affiliations

  1. Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

    • M L Vetter
  2. Department of Psychiatry, Center for Weight and Eating Disorders, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

    • M L Vetter
    • , T A Wadden
    • , J Lavenberg
    • , R H Moore
    • , S Volger
    • , J L Perez
    •  & D B Sarwer
  3. Department of Epidemiology and Biostatistics, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

    • R H Moore
  4. Division of General Internal Medicine, Department of Medicine, University of Colorado Denver, Denver, CO, USA

    • A G Tsai

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Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to M L Vetter.

About this article

Publication history

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DOI

https://doi.org/10.1038/ijo.2010.230

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