PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Int J Obes (Lond). Author manuscript; available in PMC 2012 February 1.
Published in final edited form as:
PMCID: PMC3085045
NIHMSID: NIHMS264311

Relation of Health-Related Quality of Life to Metabolic Syndrome, Obesity, Depression, and Comorbid Illnesses

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 (BMI) 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, 269 had metabolic syndrome [i.e., met 3 out of 5 criteria specified by the National Cholesterol Education Program (NCEP)] 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 (SF-12) and the EuroQol-5D (EQ-5D), as well as an obesity-specific measure, the Impact of Weight on Quality of Life (IWQoL-Lite). 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 the 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 quality of life in this population.

Keywords: metabolic syndrome, obesity, health-related quality of life

INTRODUCTION

The prevalence of metabolic syndrome has increased in tandem with the obesity epidemic. Currently, 34% of U.S. adults have metabolic syndrome (1), which is defined by the National Cholesterol Education Program (NCEP) 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/dl); 3) decreased high-density lipoprotein (HDL) cholesterol (<40 mg/dl for men; <50 mg/dl for women); 4) elevated fasting glucose (≥100 mg/dl); and 5) elevated blood pressure (≥130/85 mm Hg) (2). Individuals with metabolic syndrome have an increased risk of developing diabetes and cardiovascular disease (CVD) (3), both of which have been associated with decreased health-related quality of life (HRQoL) (47). 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 (810).

Several components of the metabolic syndrome including obesity, insulin resistance, and hypertension, have been associated with reduced HRQoL (1115), leading some investigators to suggest that metabolic syndrome itself may also be associated with impaired quality of life. Several studies have confirmed this finding, but they have been limited by failure to adjust for obesity, burden of co-morbid disease, and depression (1619). 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 co-morbid conditions, and a higher prevalence of depression (13,2026). 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 quality of life.

In the present study, we assessed HRQoL in obese individuals with and without metabolic syndrome using two generic quality of life 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 quality of life in participants with higher BMI (≥ 40 kg/m2), 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 quality of life.

METHODS AND PROCEDURES

Participants

Three-hundred and ninety obese individuals were recruited from six primary care practices within the University of Pennsylvania Health System to participate in the Practice-Based Opportunities for Weight Reduction (POWER) trial, a 2-year primary care-based weight reduction trial. Eligible participants were aged 21 years and older, had a BMI of 30–50 kg/m2, 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, prior or planned use of bariatric surgery, serious co-morbid conditions (e.g., 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 participants received any intervention.

Outcome Measures

Medical Outcomes Study, Short Form 12 (SF-12 Version 1)

The SF-12 is a 12-item condensed version of the SF-36 (27). Both instruments are validated measures of health-related quality of life (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). The PCS-12 and MCS-12 scores were designed to have a mean score of 50 and a standard deviation of 10 in a representative sample of the US population (29). Higher scores on the SF-12 are indicative of better functioning. Scores greater than 50 represent above average health status. Both summary scores are highly correlated with those derived from the SF-36 in an obese population (30) and were considered as two separate outcomes.

EuroQol 5D (EQ-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).

Impact of Weight on Quality of Life-Lite Version (IWQoL-Lite)

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

Depression

Symptoms of depression were assessed with 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 Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV), 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. The scores for each item are summed to produce a total score ranging from 0 to 24 points. Scores of 0 to 4 indicate no depressive symptoms, 5 to 9 represent mild depressive symptoms, and 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 (3738).

Disease Burden

Disease burden was assessed using the Functional Co-morbidity 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 co-morbid disease. As obesity and diabetes are included in the FCI, these diagnoses were not included in the total FCI score in order 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 CVD 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 Health (40) and the World Health Organization (41): class I (BMI, 30–34.9 kg/m2), class II (BMI, 35–39.9 kg/m2); and class III (BMI ≥ 40 kg/m2). Waist circumference was measured in duplicate to the nearest 0.1 cm by placing an inelastic tape measure (Gulick II, model 67020, Lafayette Instrument Co, Lafayette, IN) 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 with an automated sphygmomanometer (Omron HEM-907-XL,Omron Healthcare Inc, Bannockburn, IL) in triplicate after the participant had been sitting quietly for 5 minutes, with the blood pressure recorded as the mean of the three measurements. Serum glucose and lipids were measured following an overnight fast, and conventional assays were used to measure total triglycerides and HDL 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 prior to analysis. Differences in weight and in other characteristics between participants with and without metabolic syndrome were compared using t-tests for continuous variables and chi-square tests for categorical variables.

Univariable regression, ANOVA, and chi square 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 kg/m2, 35–39.9 kg/m2, and 40–50 kg/m2), and 2) the presence of diabetes.

Based on the strength of association in the univariable model (independent variables were entered if they had a p value of less than 0.2), a multivariable linear regression model was fit to estimate how HRQoL varied according to the presence or absence of metabolic syndrome. Because 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 presence or absence of diabetes. We also evaluated whether the number of metabolic syndrome criteria met by the participants (i.e., three versus four versus five criteria) affected the relationship between metabolic syndrome and HRQoL. Lastly, 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). A p value of < 0.05 was considered significant for all analyses.

RESULTS

Participants

A total of 269 (68%) of participants met three or more criteria for metabolic syndrome, with a mean (SD) 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/dl, 184 (47.2%) had low HDL 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) compared with 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 HDL-cholesterol than those without the condition (p ≤ 0.001 for all comparisons). Participants with metabolic syndrome had lower low-density-lipoprotein (LDL) cholesterol levels compared to those without the condition, although the difference did not reach statistical significance.

Table 1
Baseline characteristics of participants with and without metabolic syndrome1

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 (SD) score on the PCS-12 was 43.3 (9.7) in participants with metabolic syndrome, compared to 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 score of 0.820 (0.140), compared to 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 syndrome1

The mean IWQoL-Lite total score in participants with metabolic syndrome was 67.2 (22.0), compared to 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. Body mass index (BMI) was significantly associated with worse quality of life on the PCS-12 and the IWQoL-Lite, but was not associated with the MCS-12 or the EQ-5D. Higher PHQ-8 scores and higher BMI were significantly associated with worse quality of life on all five subscales of the IWQoL-Lite. Educational level, marital status, alcohol use, and medication count were not significantly associated with any of the quality of life measures.

Table 3
Univariable associations (p values) of sociodemographic and clinical variables with health-related quality of life1

Adjusted (Multivariable) Analyses for HRQoL Measures

In the adjusted models, only the depression score remained significantly associated with reduced HRQoL on all four outcome measures (Tables 4, ,5,5, ,6,6, and and7).7). Disease burden remained significantly associated with HRQoL on the PCS-12, MCS-12, and EQ-5D, but not the IWQoL-Lite, after controlling for confounding factors. Similarly, BMI remained significantly associated with HRQoL on the 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-121
Table 5
Multivariable associations with the MCS-121
Table 6
Multivariable associations with EQ-5D1
Table 7
Multivariable associations with IWQoL-Lite1

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 quality of life measures. The relationship between increasing number of metabolic syndrome components and quality of life was also examined. There was no association between metabolic syndrome and HRQoL on any of the quality of life measures when participants with four or five criteria (n=152) were compared to 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 quality of life, 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 the PCS-12 and relatively average health status on the 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 quality of life on the second generic measure, the EQ-5D. Although specific cutoffs for quality of life have not been reported for the IWQoL-Lite (42), the scores for participants with and without metabolic syndrome were intermediate between those reported in similar populations of obese individuals (range of 54.6 to 77.6) (35, 4244). 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 to those without metabolic syndrome. Participants with metabolic syndrome were also more likely to have hypertension and hyperlipidemia, although LDL-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 to those without the condition.) Finally, the number of criteria for metabolic syndrome did not influence the null relationship between metabolic syndrome and HRQoL. Because 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 the PCS-12 and IWQoL-Lite, but not on the mental health measures (13,20,45). As the impact of obesity on physical functioning is well established (1113), 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 kg/m2 and 50 kg/m2. Significant differences in quality of life may have been detected if individuals with extreme obesity (BMI ≥ 50 kg/m2) and metabolic syndrome had been included.

Mean scores on the PCS-12, MCS-12, and the 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 quality of life using the same instruments (22,30,35). IWQoL-Lite scores in the present study were also very similar to those reported in a study that used this measure to compare weight-related quality of life 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 quality of life. Depression was associated with decreased quality of life across measures, while obesity and number of comorbidities were not. One possible explanation is that impaired quality of life is an intermediary which 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 quality of life may also act as a link between comorbidity and depression (4749). This framework would explain why we found depression to be associated with quality of life 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 quality of life, are needed to elucidate their interrelations.

The present investigation had several strengths. We performed a robust analysis that included important factors that may affect quality of life, including depression and estimates of disease burden. Previous studies have failed to adjust for many of these confounding variables (1620). Ford and colleagues reported that metabolic syndrome was significantly associated with reduced quality of life in a population of 1,859 U.S. adults. However, only age, sex, ethnicity, educational status and smoking status were considered as covariates in their regression model (16). Similarly, Miettola and colleagues 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 (17). The FCI in the present 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 the present study was the use of several measures of HRQoL. Although the SF-36 (and the SF-12) and the 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 quality of life on the IWQoL-Lite, this measure did detect differences in HRQoL that were not observed using the generic instruments.

This study also had several limitations. The particular version of the SF-12 that was used in this study only allowed the calculation of the summary scales but did not allow calculation of the individual subscales. Thus, differences between groups may have been present in the subscales that we were not able 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 quality of life, but other factors such as obesity, depression, and greater disease burden may have significant effects on quality of life in this population. Larger studies that utilize multiple measures of quality of life and include the important covariates described previously are needed to confirm these findings.

Acknowledgements

We thank Christopher Vinnard, MD, MPH, MSCE for his editorial assistance.

Grant Support: This study was supported by grants from the National Heart, Lung and Blood Institute (U01HL087072-04) and the National Institute of Diabetes and Digestive and Kidney Diseases (5K24DK065018-07).

Footnotes

Conflict of Interest: The authors have no conflicts of interest to disclose.

REFERENCES

1. Ervin RB. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race, and ethnicity and body mass index: United States, 2003–2006. National Health Statistics Reports. 2009;13:1–8. [PubMed]
2. Expert Panel on Detection and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on the detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) JAMA. 2001;285:2486–2497. [PubMed]
3. Ford ES. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care. 2005;28:1769–1778. [PubMed]
4. UK Prospective Diabetes Study Group. Quality of life in type 2 diabetic patients is affected by complications but not by intensive policies to improve blood glucose or blood pressure control (UKPDS 37) Diabetes Care. 1999;22:1125–1136. [PubMed]
5. Coffey JT, Brandle M, Zhou H, Marriott D, Burke R, Tabaei BP, et al. Valuing health-related quality of life in diabetes. Diabetes Care. 2002;25:2238–2243. [PubMed]
6. Boini S, Briancon S, Guillemin F, Galan P, Hercberg S. Occurrence of coronary artery disease has an adverse impact on health-related quality of life. Int J Cardiol. 2006;113:215–222. [PubMed]
7. Unsar S, Sut N, Durna Z. Health-related quality of life in patients with coronary artery disease. J Cardiovasc Nurs. 2007;22:501–507. [PubMed]
8. Jacobson AM, de Groot M, Samson JA. The evaluation of two measures of quality of life in patients with type 1 or type 2 diabetes. Diabetes Care. 1994;17:267–274. [PubMed]
9. Kleefstra N, Landman GW, Houweling ST, Ubink-Veltmaat LJ, Logtenberg SJ, Meyboom-de Jong B, et al. Prediction of mortality in type 2 diabestes from health-related quality of life (ZODIAC-4) Diabetes Care. 2008;31:932–933. [PubMed]
10. Schenkeveld L, Pedersen SS, van Nierop JW, Lenzen MJ, de Jaegere PP, Serruys PW, et al. Health-related quality of life and long-term mortality in patients treated with percutaneous coronary intervention. Am Heart J. 2010;159:471–476. [PubMed]
11. Soltoft F, Hammer M, Kragh N. The association of body mass index and health-related quality of life in the general population: data from the 2003 Health Survey of England. Qual Life Res. 2009;18:1293–1299. [PMC free article] [PubMed]
12. Mond JM, Baune Bt. Overweight, medical comorbidity and health-related quality of life in a community sample of women and men. Obesity. 2009;17:1627–1634. [PubMed]
13. Yancy WS, Jr, Olsen MK, Westman EC, Bosworth HB, Edelman D. Relationship between obesity and health-related quality of life in men. Obes Res. 2002;10:1057–1064. [PubMed]
14. Schlotz W, Ambery P, Syddall HE, Crozier SR, Sayyer AA, Cooper C, et al. Specific association of insulin resistance with impaired quality of life in the Hertforshire Cohort Study. Qual Life Res. 2007;16:429–436. [PubMed]
15. Mena-Martin FJ, Martin-Escudero JC, Simal-Blanco F, Carretero Ares JL, Aruzua-Mouronte D, Herreros-Fernandez V. Health-related quality of life of subjects with known and unknown hypertension: results from the population-based Hortega study. J Hypertens. 2003;21:1283–1289. [PubMed]
16. Ford ES, Li C. Metabolic syndrome and health-related quality of life among U.S. adults. Ann Epidemiol. 2008;18:165–171. [PubMed]
17. Miettola J, Kinskanene LK, Viinamäki H, Sintonen H, Kumpusalo E. Metabolic syndrome is associated with impaired health-related quality of life: Lapinlahti study. Qual Life Res. 2008;17:1055–1062. [PubMed]
18. Frisman GH, Kristensen M. Psychosocial status and health related quality of life in relation to the metabolic syndrome in a Swedish middle-aged population. Eur J Cardiovasc Nurs. 2009;8:207–215. [PubMed]
19. Han JH, Park HS, Shin CI, Chang M, Yun KE, Cho SH, et al. Metabolic syndrome and quality of life (QOL) using generalized and obesity-specific QOL scales. Int J Clin Pract. 2009;63:735–741. [PubMed]
20. Katz DA, McHorney CA, Atkinson RL. Impact of obesity on health-related quality of life in patients with chronic illness. J Gen Intern Med. 2000;15:789–796. [PMC free article] [PubMed]
21. Ford ES, Moriarty DG, Zack MM, Mokdad AH, Chapman DP. Self-reported body mass index and health-related quality of life: findings from the Behavioral Risk Factor Surveillance System. Obes Res. 2001;9:21–31. [PubMed]
22. Jia H, Lubetkin EI. The impact of obesity on health-related quality of life in the general US population. J Public Health. 2005;27:156–164. [PubMed]
23. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity. JAMA. 1999;282:1523–1529. [PubMed]
24. Roberts RE, Deleger S, Strawbridge WJ, Kaplan GA. Prospective association between obesity and depression: evidence from Alameda County Study. Int J Obesity. 2003;27:514–521. [PubMed]
25. Carpenter KM, Hasin DS, Allison DB, Faith MS. Relationships between obesity and DSM-IV major depressive disorder and suicide attempts: results from a general population study. Am J Public Helath. 2000;90:251–257. [PubMed]
26. Simon GE, Von Korff M, Saunders K, Miglioretti DL, Crane DK, van Belle G, et al. Association between obesity and psychiatric disorders in the US adult population. Arch Gen Psychiatry. 2006;63:824–830. [PMC free article] [PubMed]
27. Ware JE, Jr, Kosinski M, Keller SD. A 12-item Short-Form health survey. Construction of scales and preliminary tests of reliability and validity. Medical Care. 1996;34:220–233. [PubMed]
28. Gandek B, Ware JE, Aaronson NK, Apolone G, Bjorner JB, Brazier JE, et al. Cross-validation of item selection and scoring for the SF-12 Health Survey in nine countries: results from the IQOLA Project. J Clin Epidemiol. 1998;51:1171–1178. [PubMed]
29. The SF-Community. The SF-12®: an even shorter health survey. [Accessed on May 13, 2010]; www.sf-36.org/tools/sf12.shtml.
30. Wee CC, Davis RB, Hamel MB. Comparing the SF-12 and SF-36 health status questionnaires in patients with and without obesity. Health Qual Life Outcomes. 2008;6:11. [PMC free article] [PubMed]
31. Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997;35:1095–1108. [PubMed]
32. Sach TH, Barton GR, Doherty M, Muir KR, Jenkinson C, Avery AJ. The relationship between body mass index and health-related quality of life: comparing the EQ-5D, EuroQol VAS, and SF-6D. Int J Obesity. 2007;31:189–196. [PubMed]
33. Kolotkin RL, Crosby RD, Kosloski KD, Rhys Williams G. Development of a brief measure to assess quality of life in obesity. Obes Res. 2001;9:102–111. [PubMed]
34. Kolotkin RL, Norquist JM, Crosby RD, Suryawanshi S, Teixeira P, Heymsfield SB, et al. One-year health-related quality of life outcomes in weight loss trial participants: comparison of three measures. Health Qual Life Outcomes. 2009;7:53. [PMC free article] [PubMed]
35. Kolotkin RL, Crosby RD, Williams GR. Assessing weight-related quality of life in obese individuals with type 2 diabetes. Diabetes Res Clin Pract. 2003;61:125–132. [PubMed]
36. Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114:163–173. [PubMed]
37. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–613. [PMC free article] [PubMed]
38. Pressler SJ, Subramanian U, Perkins SM, Gradus-Pizlo I, Kareken D, Kim J, et al. Measuring depressive symptoms in heart failure: validity and reliability of the Patient Health Questionnaire-8. Am J Crit Care. 2010 April 8; [Epub ahead of print] [PubMed]
39. Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as an outcome. J Clin Epidemiol. 2005;58:595–602. [PubMed]
40. NHLBI Obesity Education Initiative. Bethesda, MD: U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, National Heart, Lung, and Blood Institute; 1998. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. NIH Publication No. 98-4083.
41. World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser. 1995. pp. 1–452. [PubMed]
42. Forhan M, Vrkljan B, MacDermid J. A systematic review of the quality of psychometric evidence supporting the use of an obesity-specific quality of life measure in persons who have class III obesity. Obes Rev. 2010;11:222–228. [PubMed]
43. Heshka S, Anderson JW, Atkinson RL, Greenway FL, Hill JO, Phinney SD, et al. Weight loss with self-help compared with a structured commercial program. JAMA. 2003;298:1982–1988. [PubMed]
44. Kolotkin RL, Norquist JM, Crosby RD, Suryawanshi S, Teixeira PJ, Heymsfield SB, et al. One-year health-related quality of life outcomes in weight loss trial participants. Health Qual Life Outcomes. 2009;7:53. [PMC free article] [PubMed]
45. Tsai AG, Wadden TA, Sarwer DB, Berkowitz RI, Womble LG, Hesson LA, et al. Metabolic syndrome and health-related quality of life in obese individuals seeking weight reduction. Obesity. 2008;16:59–63. [PubMed]
46. Fabricatore AN, Wadden TA, Sarwer DB, Faith MS. Health-related quality of life and symptoms of depression in extremely obese persons seeking bariatric surgery. Obes Surg. 2005;15:304–309. [PubMed]
47. Rothrock NE, Hays RD, Spritzer K, Yount ST, Riley W, Cella D. Relative to the general US population, chronic disease are associated with poorer health-related quality of life as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS) J Clin Epidemiol. 2010 Aug 3; [Epub ahead of print] [PMC free article] [PubMed]
48. Gallegos-Carillo K, García-Peña C, Mudgal J, Romero X, Durán-Arenas L, Salmerón J. Role of depressive symptoms and comorbid chronic disease on health-related quality of life among community-dwelling older adults. J Psychosom Res. 2009;66:127–135. 50. Fortin M, Lapointe L. [PubMed]
49. Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. 2004;2:51. [PMC free article] [PubMed]
50. Mathias S, Williams C, Colwell H, Cisternas MG, Pasta DJ, Stolshek BS, et al. Assessing health-related quality of life and health state preference in persons with obesity: a validation study. Qual Life Res. 1997;6:311–322. [PubMed]