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Objective: Research has shown that risk factors for cardiovascular disease often cluster together, most notably overweight/obesity, diabetes, hyperlipidemia, and hypertension. The impact of cardiometabolic risk factor clusters on health-related quality of life (HRQL) is not well understood. The purpose of this study was to examine and quantify the impact of cardiometabolic risk factor clusters on HRQL as measured by the SF (Short Form)-12 Mental Component Scale (MCS-12), SF-12 Physical Component Scale (PCS-12), EQ-5D index (a generic quality of life index), and Visual Analogue Scale.
Research Methods and Procedures: The Medical Expenditure Panel Survey is a nationally representative survey of the U.S. population. From 2000 to 2002, detailed information on sociodemographic characteristics and health conditions were collected for 36,697 adults with complete responses. Controlling for comorbidity and sociodemographic characteristics, this study estimated the marginal impact of cardiometabolic risk factor clusters on MCS-12, PCS-12, EQ-5D index, and Visual Analogue Scale scores. Cardiometabolic risk factor clusters were defined as the presence of BMI ≥25 kg/m2 and at least two of the following: diabetes, hyperlipidemia, and hypertension. Using BMI ≥30 kg/m2 as the cut-off was also examined.
Results: The marginal impact of cardiometabolic risk factor clusters was highly statistically significant across all four HRQL measures and seemed to be clinically significant for all but the MCS-12. The PCS-12 showed a greater decrease in HRQL associated with physical function compared with mental function-related domains of the MCS-12.
Discussion: Common cardiometabolic risk factor clusters such as overweight/obesity, diabetes, hypertension, and hyperlipidemia have a significant and negative impact on HRQL in the United States.
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- Research Methods and Procedures
Cardiovascular disease is the leading cause of death and morbidity in the United States, causing an estimated 40% of all deaths (1). In addition, more than one fourth of the population (70 million individuals) currently lives with cardiovascular disease (1). Cardiovascular disease has a profound deleterious impact on health-related quality of life (HRQL)1 (2, 3). It is largely preventable, and much research in the last century has focused on identifying key risk factors. As they were identified, it became apparent that risk factors for cardiovascular disease often cluster in the same individual, most notably overweight/obesity, diabetes, hyperlipidemia, and hypertension (4).
Although there is ample evidence of the deleterious impact of cardiovascular disease on HRQL, little is known about the impact of cardiometabolic risk factor clusters on HRQL. Wee et al. (5) found that diabetes and hypertension, as well as diabetes with other single chronic conditions, had a deleterious, additive impact on HRQL scores as measured by the short form (SF)-36 and the SF-6 days in a diverse Asian population in Singapore. Chambers et al. (6) examined the individual and combined impact of hypertension, hyperlipidemia, obesity, and smoking status on SF-36 scores in a small sample of adults from Southwestern Ohio. They found that SF-36 scores decreased as the number of cardiovascular risk factors increased. Oldridge et al. (7) showed that diabetes, hypertension, and obesity together had a deleterious impact on HRQL as measured by mobility difficulty, activities of daily living, and self-perceived health status (7).
The appraisal of HRQL is of paramount importance in the evaluation of disease and treatment (8). Measures such as the SF-12 provide an assessment of the generic health status of individuals by examining broad domains of HRQL such as physical and mental function (9). In addition, preference-based measures of HRQL, such as the EQ-5D index (10), facilitate an assessment of the value of a variety of health interventions through cost-effectiveness analysis and the calculation of quality-adjusted life years (11, 12). Rating scale methods, such as the visual analogue scale (VAS), provide important information on the subjective perception of health status (13). The purpose of this study was to examine and quantify the impact of cardiometabolic risk factor clusters on HRQL and preference-based HRQL as measured by the SF-12, EQ-5D index, and VAS in a nationally representative sample of U.S. adults.
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Men, individuals >50 years of age, blacks, individuals with lower levels of educational attainment and income, and Hispanics had a higher prevalence of cardiometabolic risk factor clusters (Table 1). The incidence of cardiometabolic risk factor clusters, BMI ≥25 kg/m2, diabetes, hypertension, and hyperlipidemia in the nationally representative adult MEPS sample with valid BMI was 6.29%, 58.95%, 6.29%, 20.18%, and 8.62%, respectively. Individuals with cardiometabolic risk factor clusters were older, had greater comorbidity burden, and had lower HRQL than those without cardiometabolic risk factor clusters and the U.S. average; these relationships were worse using BMI ≥30 kg/m2 as the cut-off than BMI ≥25 kg/m2 (Table 2).
Table 1. Prevalence of CMRFCs by selected characteristics (adult MEPS 2000 to 2002 with valid BMI)
|Population||Prevalence of CMRFC (BMI ≥ 25 kg/m2)*||Prevalence of CMRFC (BMI ≥ 30 kg/m2)*||Prevalence of BMI ≥ 25 kg/m2||Prevalence of diabetes||Prevalence of hypertension||Prevalence of hyperlipidemia|
|General MEPS (total N = 43,221)||6.29%||3.26%||58.95%||6.29%||20.18%||8.62%|
|Sex|| || || || || || |
|Age categories (years)|| || || || || || |
| 18 to 19||0.00%||0.00%||31.36%||0.66%||0.44%||0.06%|
| 20 to 29||0.25%||0.19%||48.17%||0.87%||2.21%||0.36%|
| 30 to 39||1.00%||0.72%||59.92%||1.96%||6.49%||2.26%|
| 40 to 49||4.11%||2.52%||64.97%||4.15%||15.61%||6.71%|
| 50 to 59||9.81%||5.72%||65.96%||9.39%||29.77%||13.28%|
| 60 to 69||17.14%||8.72%||67.41%||14.83%||43.74%||22.26%|
| 70 to 79||18.61%||7.90%||60.85%||17.30%||51.89%||22.95%|
|Race|| || || || || || |
| Native American||6.26%||3.39%||70.00%||8.66%||21.63%||5.08%|
|Education|| || || || || || |
| No degree||8.08%||4.30%||59.44%||9.40%||24.96%||7.89%|
| High school degree||6.46%||3.43%||60.96%||6.10%||20.59%||8.68%|
| Other degree||5.13%||3.04%||60.51%||4.92%||18.35%||8.59%|
| Bachelor's degree||4.58%||2.09%||53.86%||4.68%||14.99%||8.25%|
| MA or Ph.D.||5.23%||2.24%||52.88%||4.05%||18.13%||10.88%|
|Income|| || || || || || |
| Near poor||9.73%||5.48%||61.31%||11.06%||26.33%||8.02%|
| Low income||7.84%||3.88%||60.43%||8.84%||23.81%||8.16%|
| Middle income||5.98%||3.06%||60.12%||6.09%||19.51%||8.23%|
| High income||5.62%||2.84%||57.31%||4.84%||18.22%||9.52%|
|Ethnicity|| || || || || || |
Table 2. Unadjusted mean age, EQ-5D, SF-12, VAS, and NCC with and without CMRFCs compared with national average (adult MEPS 2000 to 2002 with valid BMI)
| ||Without CMRFC (BMI ≥ 25 kg/m2)*||With CMRFC (BMI ≥ 25 kg/m2)*||With CMRFC (BMI ≥ 30 kg/m2)*||U.S. average|
When interpreting results, it is helpful to consider the minimum clinically important difference in conjunction with statistical significance and effect size (27). While the EQ-5D index minimal clinically important difference (MCID) has not been established, previous research has estimated an MCID of 0.033 to 0.074. The former is based on an effect size of 0.2 in MEPS (11); the latter is based on changes using the SF-36 as an anchor in 11 different patient populations (28). For comparison purposes, previous research has found the MCID of other use instruments to be 0.03 to 0.041 for the SF-6D (28, 29) and 0.03 for the Health Utilities Index (30). The SF-36 MCID has been estimated to be ∼3 to 5 points (31).
After controlling for comorbidity burden, age, sex, race, ethnicity, education, smoking status, and income, the marginal impact of cardiometabolic risk factor clusters was highly statistically significant across all four HRQL measures and seemed to be clinically significant for all but the MCS-12 (Table 3). The PCS-12 showed a greater decrease in HRQL associated with physical function–related domains of the SF-12 compared with mental function–related domains measured by the MCS-12. Using BMI ≥30 kg/m2 as the cut-off resulted in greater negative magnitude of the estimates compared with BMI ≥25 kg/m2 for all four instruments. As expected, older age, lower income and education, and greater comorbidity burden were associated with lower HRQL across all four instruments (Tables 4 and 5). Hispanic ethnicity did not have a statistically significant impact on HRQL. Black, Native American, and other race were not statistically significant for most instruments and were small in magnitude where statistically significant. Male sex was not statistically significant for most instruments, and where statistically significant, the magnitude was very small.
Table 3. Summary results* of multivariate analysis of four separate regressions, PCS-12, MCS-12, EQ-5D, and VAS, on CMRFCs, controlling for sociodemographic characteristics and comorbidity burden
|CMRFC (BMI ≥ 25 kg/m2)†||−3.48||0.26||−0.866||0.26||−0.038||0.00||−6.10||0.42|
|CMRFC (BMI ≥ 30 kg/m2)†||−4.87||0.33||−1.03||0.37||−0.054||0.00||−8.55||0.41|
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This study shows that cardiometabolic risk factor clusters seem to have a significant deleterious impact on HRQL in a nationally representative sample of U.S. adults, after controlling for comorbidity and sociodemographic characteristics. In addition, cardiometabolic risk factor clusters seem to have a more deleterious impact when BMI ≥30 kg/m2 is used as the cut-off compared with BMI ≥25 kg/m2.
There have been previous examinations of the individual impact of cardiovascular risk factors on HRQL. Sullivan et al. (11) estimated the impact of chronic conditions on EQ-5D index scores in the nationally representative MEPS sample and found that hypertension, hyperlipidemia, and diabetes, among many other chronic conditions, had a significant deleterious impact on EQ-5D scores in the United States. They also found that comorbidity had both a statistically and clinically significant impact on EQ-5D scores but did not assess specific cardiometabolic risk factor clusters. When assessing the impact of individual chronic conditions, other research has also shown the negative impact of diabetes (32), obesity (33), and hypertension (34) on EQ-5D index scores (33). In addition, the individual impact of obesity, hypertension, and diabetes have been shown to negatively affect HRQL as measured by the SF-12 and VAS in the nationally representative MEPS sample (33). Using MEPS, Jia and Lubetkin (33) found overweight, Class I, and Class II obesity to have the following negative magnitudes on PCS-12 (−0.73, −1.86, −4.0); EQ-5D (−0.013, −0.033, −0.073), and VAS (−0.52, −3.23, −4.84) scores, respectively. The estimates of Jia and Lubetkin for Class II obesity (BMI ≥ 35 kg/m2) are similar (PCS-12; VAS) or larger (EQ-5D) than the estimates of cardiometabolic risk factor clusters (CMRFCs) using BMI ≥25 kg/m2 found in this research but smaller than the estimates of CMRFC using BMI ≥30 kg/m2 as the criterion. However, the estimates may not be comparable because Jia and Lubetkin did not adequately control for chronic comorbidities, resulting in a likely increase in the magnitude of the estimates. There is a clear link between chronic disease and obesity, and not controlling for comorbidities may confound the magnitude of the estimates for obesity. In contrast, this research controlled for all chronic comorbidities. Nonetheless, it is interesting to note that the magnitude of the estimates for Class II obesity reported in Jia and Lubetkin is comparable with our estimates of CMRFC using the criteria of BMI ≥25 kg/m2.
Hypertension has also been shown to negatively impact HRQL as measured by the SF-36 and the time trade-off measure (35). Examinations of the impact of hyperlipidemia have had mixed results, however. Schlenk et al. (36) found no difference in SF-36 scores between normal adults and those with hyperlipidemia. Lalonde et al. (37) examined the impact of hypertension and/or hyperlipidemia on HRQL as measured by preference-based and non–preference-based generic HRQL instruments. They found that the existence of hypertension and/or hyperlipidemia was associated with lower General Health Perception on the SF-36 but not associated with lower HRQL on the SF-36 PCS or MCS or preference-based HRQL as measured by the time trade-off or standard gamble techniques. In a separate study, Lalonde et al. (38) found a negative association between SF-36 scores and hypertension but a positive association with hyperlipidemia. Sullivan et al. (11) found a statistically significant and negative, but very small magnitude (−0.0047), effect of hyperlipidemia on EQ-5D scores after controlling for other chronic conditions and sociodemographic characteristics in a nationally representative sample. In summary, the individual impact of hyperlipidemia on HRQL is unclear and may be confounded by comorbidity and other sociodemographic factors. However, previous research has consistently shown the deleterious individual impact of hypertension, diabetes, and overweight/obesity on HRQL.
Although there are many studies examining the individual impact of separate cardiometabolic risk factors on HRQL, there are very few published studies examining the combined impact of cardiometabolic risk factors on HRQL, and no studies to the authors’ knowledge of the impact of the most common cardiometabolic risk factor clusters (diabetes, overweight/obesity, hyperlipidemia, and hypertension) on generic and preference-based HRQL as conducted in this research. The use of three different instruments provides important information on the impact of cardiometabolic risk factor clusters on HRQL. The SF-12 is a non–preference-based measure of generic quality of life and provides a broad picture of subjects’ generic HRQL. In contrast, the EQ-5D index is a preference-based measure of generic HRQL and provides a preference-based score that can be used to calculate quality-adjusted life years for cost-effectiveness analyses of treatment or prevention of cardiometabolic risk factor clusters. In addition, the VAS provides an important assessment of self-perceived health status and a subjective measure of preferences for current health status. This research quantifies the marginal impact of cardiometabolic risk factor clusters on each of these instruments after controlling for comorbidity and sociodemographic characteristics.
The finding that cardiometabolic risk factors have a relatively larger impact on physical function–related HRQL is consistent with other studies (39). Although the magnitude was small, it is interesting to note that the impact of cardiometabolic risk factor clusters on mental function–related HRQL was highly statistically significant in this analysis.
While it is clear that diabetes, hypertension, hyperlipidemia, and overweight/obesity are prone to cluster and result in an elevated risk of cardiovascular disease, there is some controversy about whether these cardiometabolic risk factor clusters reflect an underlying syndrome thought to be related to insulin resistance (commonly referred to as the metabolic syndrome) or simply that they tend to cluster (4). This analysis examines the impact of these cardiometabolic risk factor clusters on HRQL, irrespective of the underlying pathophysiology.
This analysis is not without limitations. Although higher than those estimated by Oldridge et al. (7), the diabetes, overweight/obesity, hypertension, and hyperlipidemia prevalence estimates presented here are consistent with other survey-based national-level estimates in the United States (40, 41, 42, 43, 44). However, it is likely that the prevalence rates in this study are underestimates of national prevalence. First, similar to the National Health Interview Survey and the Behavioral Risk Factor Surveillance System, MEPS is based on self-report. There is evidence that self-reported conditions may be under-reported in general (45) and that blacks, whites, and Hispanics differ in reporting of disease labeling and levels of illness and disability (46, 47, 48). Second, previous studies have shown that overweight respondents tend to underestimate their weight and overestimate their height (49, 50). Third, unlike the National Health and Nutrition Examination Survey, MEPS does not contain information on undiagnosed diabetes, hypertension, or hyperlipidemia, which contributes to the likelihood that the estimates from this analysis are biased downward. Recent estimates suggest that ∼35% of individuals with diabetes have not been diagnosed (51). As a result, it is likely that the national prevalence of cardiometabolic risk factor clusters estimated in this analysis is an underestimate. Hence, the impact of cardiometabolic risk factor clusters on HRQL is likely underestimated to a similar degree. In addition, for individuals who cannot complete the self-administered questionnaire, MEPS allows the use of proxy respondents (most commonly the wife, daughter, or mother of the individual). Proxy respondents may rate health differently than the individuals themselves, and this may introduce a source of bias. Approximately 12% of all MEPS responses to the self-administered questionnaire were completed by proxies, whereas ∼13% of responses for individuals with CMRFC were completed by proxies. This slightly higher percentage is not surprising given the greater age and morbidity level associated with CMRFC: individuals with CMRFC are more likely to have health limitations that would preclude them from filling in the self-administered questionnaire than the general population. Nonetheless, this may be a limitation to our research. Previous studies have found Hispanics to have a higher prevalence of diabetes, hyperlipidemia, and overweight/obesity (52, 53, 54, 55, 56). These results are inconsistent with these findings. There is evidence that blacks, whites, and Hispanics differ in self-reporting of disease labeling and levels of illness and disability (46, 47, 48). This study provides further evidence of this phenomenon. Other sources that may contain more specific diagnosis and laboratory data (such as fasting glucose levels) do not contain the rich array of HRQL data available in MEPS. Another potential limitation is the necessary exclusion of individuals without complete data in the multivariate analyses. Despite the limitations, this study provides important evidence of the impact of cardiometabolic risk factor clusters on HRQL in a nationally representative population and shows that common cardiometabolic risk factor clusters such as overweight/obesity, diabetes, hypertension, and hyperlipidemia have a significant and negative impact on HRQL in the United States.