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 > or = 25 kg/m(2) and at least two of the following: diabetes, hyperlipidemia, and hypertension. Using BMI > or = 30 kg/m(2) 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.
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 > or = 25 kg/m(2) and at least two of the following: diabetes, hyperlipidemia, and hypertension. Using BMI > or = 30 kg/m(2) 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.
Authors: Cheryl P Lynch; Kathleen M McTigue; James E Bost; Lesley F Tinker; Mara Vitolins; Lucile Adams-Campbell; Gloria E Sarto; Jennifer Hays-Grudo; Joann E Manson; Lewis H Kuller Journal: J Womens Health (Larchmt) Date: 2010-08 Impact factor: 2.681
Authors: Jessica R L Warsch; Tatjana Rundek; Myunghee C Paik; Mitchell S V Elkind; Ralph L Sacco; Clinton B Wright Journal: J Am Geriatr Soc Date: 2013-03-25 Impact factor: 5.562