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Table of Contents
Vol. 61, No. 4, 2012
Issue release date: December 2012
Section title: Original Paper
Ann Nutr Metab 2012;61:275–280
(DOI:10.1159/000341494)

Health-Related Quality of Life in Adults with Metabolic Syndrome: The Korea National Health and Nutrition Examination Survey, 2007–2008

Lee Y.-J.a · Woo S.Y.b · Ahn J.H.b · Cho S.a · Kim S.R.a
aDepartment of Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, and bBiostatistics Team, Samsung Biomedical Research Institute, Seoul, Republic of Korea
email Corresponding Author

Abstract

Background/Aims: An association between metabolic syndrome and impaired health-related quality of life (HRQoL) is still controversial. We investigated the association between metabolic syndrome in itself and HRQoL in the Korean adult population. Methods: The study is a cross-sectional analysis of 8,941 adults ≥19 years of age who participated in the 2007 and 2008 Korean National Health and Nutrition Examination Survey. EuroQoL five-dimension (EQ-5D), the EQ-5D index and the EQ visual analogue scale (EQ VAS) were used to assess HRQoL. Results: The prevalence of metabolic syndrome was 26.2%. Compared to the participants without metabolic syndrome, those with metabolic syndrome were older and comprised a higher proportion of men. Moreover, participants with metabolic syndrome were more likely to have a lower education level, to be current smokers, to have activity limitation and to have more frequent metabolic abnormalities and comorbidities. Metabolic syndrome was associated with HRQoL based on EQ-5D and EQ VAS in simple regression analysis. However, metabolic syndrome was not significantly associated with HRQoL after adjusting for age, sex, smoking status, income, education level, marital status, obesity, diabetes mellitus, stroke, history of heart disease and chronic kidney disease for EQ-5D, and in addition history of depression for EQ VAS. Conclusion: Metabolic syndrome in itself was not associated with impaired HRQoL after adjusting for confounding variables such as socio-demographic factors, medical comorbidities and obesity.

© 2012 S. Karger AG, Basel


  

Key Words

  • Metabolic syndrome
  • Quality of life
  • Obesity
  • Comorbidity

 Introduction

Metabolic syndrome is a combination of abnormalities including abdominal obesity, high blood pressure (BP), dyslipidemia and impaired glucose metabolism [1]. People with metabolic syndrome have an increased risk of developing diabetes and cardiovascular disease [2]. In addition, metabolic syndrome has been associated with other adverse outcomes such as chronic kidney disease (CKD), fatty liver disease and Alzheimer disease [3,4,5]. Several components of metabolic syndrome and metabolic syndrome-related adverse events such as diabetes and cardiovascular disease have been associated with decreased health-related quality of life (HRQoL) [6,7,8]. Impaired HRQoL has been associated with increased mortality and disease progression [9,10]. Although several studies have reported that metabolic syndrome is associated with impaired HRQoL [11,12,13,14], another study suggests that metabolic syndrome itself is not associated with impaired HRQoL [15]. Therefore, the impact of metabolic syndrome on HRQoL is less predictable and has not been clearly defined.

Obesity has been associated with the increase of various comorbidities, depression, worse physical functioning and a decreased HRQoL [8,16,17]. Thus, obesity may modify the relationship between metabolic syndrome and HRQoL. In Korea the prevalence of metabolic syndrome has been increased by significant socioeconomic and demographic changes and it has become an important public health problem. Here we assessed whether metabolic syndrome is associated with HRQoL regardless of medical comorbidities and obesity in the Korean adult population. We hypothesized that metabolic syndrome in itself would be associated with impaired HRQoL.

 Patients and Methods


 Study Population

The data were derived from the 2007 and 2008 Korean National Health and Nutrition Examination Survey (KNHANES), a population-based cross-sectional survey that was conducted with a stratified multistage sampling design according to age, gender and geographic area to select a representative sample of the civilian, noninstitutionalized Korean population. We used data from the health interview survey and medical examination. The health interview survey was completed by 8,941 adults aged 19 years and older. The response rates were 93.2%. KNHANES was approved by the Korea Center for Disease Control and Prevention (KCDC) Institutional Review Board, and all participants signed a written informed consent form.

 Data Collection

Trained interviewers interviewed participants face-to-face using a structured questionnaire. Participants were asked whether they had ever been told by a physician that they had hypertension, diabetes mellitus, stroke, heart disease, osteoporosis, osteoarthritis or depression. Participants were also asked whether they had a history of backache and took any medication for hypertension, diabetes mellitus or hyperlipidemia. Socio-demographic data were also collected, including age, sex, marital status, education, income, smoking status, alcohol consumption and activity limitation.

 Anthropometric Measurement, Biochemical Parameters and Health-Related Quality of Life

Waist circumference was measured at the midpoint between the lowest rib and the iliac crest at minimal respiration. BP was measured three times at 30-second intervals and we used the mean of the three measured values for systolic and diastolic BP. Fasting venous blood from participants was sampled for measurements of plasma glucose, triglycerides and high-density lipoprotein cholesterol (HDL-C) by an ADVIA 1650 Chemistry Analyzer (Siemens, Deerfield, Ill., USA). Estimated glomerular filtration rate (eGFR) was calculated with the abbreviated modification of diet in renal disease equation, corrected to a body surface area of 1.73 m2, using the following equation for men:

eGFR (ml/min/1.73 m2) = 186 × serum creatinine (mg/dl)–1.154 × age–0.203.

For women, the result derived with the same formula was multiplied by 0.742. Body mass index (BMI) was calculated as body weight (kg) divided by the square meter of height.

The EuroQoL (EQ) instrument was used for evaluating HRQoL. EQ consists of the EQ five-dimension (EQ-5D) descriptive system and the EQ visual analogue scale (EQ VAS). The EQ-5D descriptive system is made up of the following five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Participants are asked to select one of three levels (no problem, some problems and extreme problems) representing the most appropriate health state in each of the five dimensions. The EQ-5D is converted into a single summary index in which 1 indicates no problem in any of the five dimensions and zero indicates death. The EQ VAS records the participant’s self-rated health on a vertical, visual analogue scale where a value of 0 means the ‘worst imaginable health state’ and a value of 100 means the ‘best imaginable health state’. The Korean version of the EQ-5D had already been made and evaluated for its validity and reliability [18].

 Definition

Obesity was defined as a BMI ≥25. According to the National Cholesterol Education Program Adult Treatment Panel Guideline III [19] and central obesity criteria for Korean [20], participants who had three or more of the following criteria were considered to have the metabolic syndrome: (1) abdominal obesity (waist circumference ≥90 cm in men or ≥85 cm in women); (2) high triglycerides (≥150 mg/dl) or drug treatment for elevated triglycerides; (3) low HDL-C (<40 mg/dl in men or <50 mg/dl in women) or drug treatment for reduced HDL-C; (4) elevated fasting glucose (≥100 mg/dl) or drug treatment for elevated glucose, and (5) high BP (systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg) or drug treatment for hypertension. CKD was defined as an eGFR of <60 ml/min/1.73 m2 or proteinuria by a dipstick urine analysis score of +1 or more.

 Statistical Analysis

Data analyses were performed using the SAS software for survey analysis. The baseline data were presented as mean ± standard error and percentage. Rao-Scott modified χ2 test for complex sampling design using PROC SURVEYFREQ in SAS was used to compare the frequency of comorbidities and categorical variables in participants with and without metabolic syndrome. Regression analysis for complex sampling design using PROC SURVEYREG in SAS was used to compare the mean value of continuous variables. We used linear regression for complex sampling design using PROC SURVEYLOGISTIC in SAS to evaluate the effect of metabolic syndrome on EQ-5D index and EQ VAS, adjusting for sex, age, smoking status, income, educational level, marital status, obesity, diabetes mellitus, stroke, history of heart disease and CKD for EQ-5D, and in addition history of depression for EQ VAS. p values were corrected by Bonferroni’s method due to multiple testing. We also used logistic regression analysis for complex sampling design implemented in PROC SURVEYLOGISTIC in SAS to evaluate the effect of metabolic syndrome on dimensions of EQ-5D after adjusting for sex, age, smoking status, income, educational level, marital status, obesity, diabetes mellitus, stroke, history of heart disease and CKD. p values and 95% CIs for ORs were corrected by Bonferroni’s method.

 Results

The overall study sample was comprised of 6,107 nonobese participants and 2,834 obese participants. The prevalence of metabolic syndrome was 26.2%. Of the nonobese and obese participants, 13.8 and 52.9% met criteria for metabolic syndrome, respectively. The socio-demographic characteristics of participants with and without metabolic syndrome are shown in table 1. Compared to the participants without metabolic syndrome, those with metabolic syndrome were older (52.8 ± 0.40 and 40.8 ± 0.30, respectively) and comprised a higher proportion of men (56.0 and 47.7%, respectively). As expected, participants with metabolic syndrome had higher BMI, serum glucose levels, BP and triglyceride levels, and lower HDL-C levels than those without metabolic syndrome. In addition, participants with metabolic syndrome were more likely to have a lower level of education, to be current smokers and to have activity limitation. Compared to participants without metabolic syndrome, those with metabolic syndrome had more frequent comorbid disorders, including stroke, angina or myocardial infarction, osteoporosis, osteoarthritis, lumbar pain and CKD. However, the prevalence of depression did not differ between participants with and without metabolic syndrome (12.68 and 11.01%, respectively, p = 0.055).

TAB01
Table 1. Baseline characteristics of nonobese participants with and without metabolic syndrome among KNHANES 2007–2008 participants (n = 8,941)

The mean value of the EQ-5D index in participants with and without metabolic syndrome was 0.89 and 0.93, respectively, which was statistically significant (p < 0.0001). The mean value of EQ VAS was 71.0 and 75.1, respectively, in participants with and without metabolic syndrome, which was also statistically significant (p < 0.0001). On simple linear regression, metabolic syndrome was significantly associated with the EQ-5D index (table 2). However, after adjusting for socio-demographic factors and medical comorbidities, including age, sex, smoking status, income, education level, marital status, obesity, diabetes mellitus, stroke, history of heart disease and CKD, metabolic syndrome and its components, with the exception of high triglycerides, were not significantly associated with the EQ-5D index (table 3). When some/extreme problems in dimensions of EQ-5D were used as cut-off points for determining impaired HRQoL, metabolic syndrome and its components were also not associated with EQ-5D dimensional problems in multiple logistic regression analysis (data not shown). Metabolic syndrome was significantly associated with EQ VAS in simple regression analysis (table 2). In a multiple linear regression analysis, metabolic syndrome and its components, with the exception of abdominal obesity and low HDL-C, were not significantly associated with EQ VAS after adjusting for age, sex, smoking status, income, education level, marital status, history of depression, obesity, diabetes mellitus, stroke, history of heart disease and CKD.

TAB02
Table 2. Simple linear regression analysis to evaluate the associations between metabolic syndrome and HRQoL based on the EQ-5D index and EQ VAS

TAB03
Table 3. Multiple linear regression analysis to evaluate the associations between metabolic syndrome and HRQoL based on the EQ-5D index and EQ VAS

 Discussion

The purpose of the present study was to determine the association between metabolic syndrome in itself in Korean adults and impaired HRQoL. We found that metabolic syndrome was not significantly associated with HRQoL based on EQ-5D and EQ VAS after adjustments were made for confounding variables in this population. Metabolic syndrome was associated with definitive impairment of HRQoL based on EQ VAS after adjustments were made for only socio-demographic factors and obesity, even when p values were corrected by Bonferroni’s method due to multiple testing (data not shown; p = 0.004). However, interestingly, this relationship was not significant when adjustments were additionally made for medical comorbidities. This finding suggests that medical comorbidities that are increased in metabolic syndrome contribute to an increased risk of impaired HRQoL.

Previous reports have shown a significant association between metabolic syndrome and HRQoL [11,12,13,14]. However, in all of these reports, only socio-demographic factors such as age, sex, income, marital status, education, occupational status or smoking status were considered as confounding factors. It has been shown that insulin resistance, which plays a pivotal role in pathophysiology of the metabolic syndrome, is associated with impaired HRQoL [21]. Some reports have shown that hypertension may also be associated with decreased HRQoL and that dyslipidemia may be associated with a decline in physical functioning [22,23]. Obesity, which is a major risk factor for metabolic syndrome, is associated with insulin resistance, often leading to type 2 diabetes mellitus [24,25]. Obesity increases the risk for various comorbidities including hypertension, dyslipidemia, cardiovascular disease, stroke and CKD, which are also associated with worse HRQoL [26,27,28,29,30,31]. Obesity also increases the incidence of osteoarthritis, which may have adverse effects on physical functioning [32,33]. Obese subjects may often be disadvantaged in education, employment and health care because of their size, and experience frequent depression, all of which can impair HRQoL [8,16,17,34]. Therefore, obesity could directly or indirectly have an effect on a variety of clinical implications related to metabolic syndrome. In the case of HRQoL, obesity may lead to the stronger association between metabolic syndrome and HRQoL. Therefore, we considered additional medical comorbidities and obesity as confounding variables to exclude the impact that these factors have on metabolic syndrome and HRQoL. As a result, in this study, metabolic syndrome was not significantly associated with HRQoL, which is in contrast with previous studies.

In Asian populations, the risk for metabolic abnormalities including diabetes, hypertension and dyslipidemia tend to increase even at a low BMI [35]. According to the recommended classifications for BMI adopted by the National Institute of Health and World Health Organization, obesity is BMI ≥30 [36]. However, the definition of obesity varies by race. In Asians, a greater percentage of body fat is reached at a lower BMI than in other races. Therefore, for Asians, obesity is often defined as a BMI ≥25 [35]. In the present study it is reasonable to define obesity as BMI ≥25.

Of the five components that define metabolic syndrome, abdominal obesity and low HDL-C was associated with HRQoL based on EQ VAS. There are some reports that have shown the association of abdominal obesity with impaired HRQoL [37]. It seems that abdominal obesity affects impaired HRQoL differently compared to obesity based on BMI. Little seems to be known about the relationship between dyslipidemia and impaired HRQoL. A study has reported that dyslipidemia was associated with lower scores in the physical and psychological domains [38].

The limitation of this study is that the causal relationship between metabolic syndrome and HRQoL was not assessed owing to this being a cross-sectional study. However, the strengths of the study are that a large number of participants and representative population data were used to evaluate the association between metabolic syndrome and HRQoL.

In summary, metabolic syndrome was not associated with the impairment of HRQoL based on EQ-5D and EQ VAS after adjusting for confounding variables such as socio-demographic factors, medical comorbidities and obesity. The prevention and management of medical comorbidities may be important for improvement of HRQoL in adults with metabolic syndrome.

 Disclosure Statement

There are no conflicts of interest.


References

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  2. 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.
  3. Marchesini G, Brizi M, Bianchi G, Tomassetti S, Bugianesi E, Lenzi M, McCullough AJ, Natale S, Forlani G, Melchionda N: Nonalcoholic fatty liver disease: a feature of the metabolic syndrome. Diabetes 2001;50:1844–1850.
  4. Razay G, Vreugdenhil A, Wilcock G: The metabolic syndrome and Alzheimer disease. Arch Neurol 2007;64:93–96.
  5. Kurella M, Lo JC, Chertow GM: Metabolic syndrome and the risk for chronic kidney disease among nondiabetic adults. J Am Soc Nephrol 2005;16:2134–2140.
  6. Coffey JT, Brandle M, Zhou H, Marriott D, Burke R, Tabaei BP, Engelgau MM, Kaplan RM, Herman WH: Valuing health-related quality of life in diabetes. Diabetes Care 2002;25:2238–2243.
  7. 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: a longitudinal controlled study. Int J Cardiol 2006;113:215–222.
  8. Han TS, Tijhuis MA, Lean ME, Seidell JC: Quality of life in relation to overweight and body fat distribution. Am J Public Health 1998;88:1814–1820.
  9. Kleefstra N, Landman GW, Houweling ST, Ubink-Veltmaat LJ, Logtenberg SJ, Meyboom-de Jong B, Coyne JC, Groenier KH, Bilo HJ: Prediction of mortality in type 2 diabetes from health-related quality of life (ZODIAC-4). Diabetes Care 2008;31:932–933.
  10. Schenkeveld L, Pedersen SS, van Nierop JW, Lenzen MJ, de Jaegere PP, Serruys PW, van Domburg RT: Health-related quality of life and long-term mortality in patients treated with percutaneous coronary intervention. Am Heart J 2010;159:471–476.
  11. Miettola J, Niskanen LK, Viinamaki H, Sintonen H, Kumpusalo E: Metabolic syndrome is associated with impaired health-related quality of life: Lapinlahti 2005 study. Qual Life Res 2008;17:1055–1062.
  12. Ford ES, Li C: Metabolic syndrome and health-related quality of life among US adults. Ann Epidemiol 2008;18:165–171.
  13. Park SS, Yoon YS, Oh SW: Health-related quality of life in metabolic syndrome: the Korea National Health and Nutrition Examination Survey 2005. Diabetes Res Clin Pract 2011;91:381–388.
  14. Han JH, Park HS, Shin CI, Chang HM, Yun KE, Cho SH, Choi EY, Lee SY, Kim JH, Sung HN, Kim JH, Choi SI, Yoon YS, Lee ES, Song HR, Bae SC: Metabolic syndrome and quality of life (QOL) using generalised and obesity-specific QOL scales. Int J Clin Pract 2009;63:735–741.
  15. Vetter ML, Wadden TA, Lavenberg J, Moore RH, Volger S, Perez JL, Sarwer DB, Tsai AG: Relation of health-related quality of life to metabolic syndrome, obesity, depression and comorbid illnesses. Int J Obes (Lond) 2011;35:1087–1094.
  16. 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.
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  18. Kim MH, Cho YS, Uhm WS, Kim S, Bae SC: Cross-cultural adaptation and validation of the Korean version of the EQ-5D in patients with rheumatic diseases. Qual Life Res 2005;14:1401–1406.
  19. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SC Jr, Spertus JA, Costa F: Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement – executive summary. Cardiol Rev 2005;13:322–327.

    External Resources

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  31. Cerniauskaite M, Quintas R, Koutsogeorgou E, Meucci P, Sattin D, Leonardi M, Raggi A: Quality-of-life and disability in patients with stroke. Am J Phys Med Rehabil 2012;91:S39–47.
  32. Dixon JB: The effect of obesity on health outcomes. Mol Cell Endocrinol 2010;316:104–108.
  33. Hart DJ, Spector TD: The relationship of obesity, fat distribution and osteoarthritis in women in the general population: the Chingford Study. J Rheumatol 1993;20:331–335.
  34. Gortmaker SL, Must A, Perrin JM, Sobol AM, Dietz WH: Social and economic consequences of overweight in adolescence and young adulthood. N Engl J Med 1993;329:1008–1012.
  35. WHO Expert Consultation: Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–163.
  36. National Institutes of Health: Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults – the evidence report. Obes Res 1998;6(suppl 2):51S–209S.
  37. Rosmond R, Bjorntorp P: Quality of life, overweight, and body fat distribution in middle-aged men. Behav Med 2000;26:90–94.
  38. Martinelli LM, Mizutani BM, Mutti A, D’Elia MP, Coltro RS, Matsubara BB: Quality of life and its association with cardiovascular risk factors in a community health care program population. Clinics (Sao Paulo) 2008;63:783–788.

  

Author Contacts

Sung Rok Kim, MD, PhD
Department of Medicine, Samsung Changwon Hospital
Sungkyunkwan University School of Medicine, 50 Hapseong 2-dong
MasanHoiwon-Gu, Changwon 630-723 (Republic of Korea)
Tel. +82 55 290 6332, E-Mail sungrok2.kim@samsung.com

  

Article Information

Received: December 2, 2011
Accepted after revision: June 26, 2012
Published online: December 3, 2012
Number of Print Pages : 6
Number of Figures : 0, Number of Tables : 3, Number of References : 38

  

Publication Details

Annals of Nutrition and Metabolism

Vol. 61, No. 4, Year 2012 (Cover Date: December 2012)

Journal Editor: Koletzko B. (Munich)
ISSN: 0250-6807 (Print), eISSN: 1421-9697 (Online)

For additional information: http://www.karger.com/ANM


Copyright / Drug Dosage / Disclaimer

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

Abstract

Background/Aims: An association between metabolic syndrome and impaired health-related quality of life (HRQoL) is still controversial. We investigated the association between metabolic syndrome in itself and HRQoL in the Korean adult population. Methods: The study is a cross-sectional analysis of 8,941 adults ≥19 years of age who participated in the 2007 and 2008 Korean National Health and Nutrition Examination Survey. EuroQoL five-dimension (EQ-5D), the EQ-5D index and the EQ visual analogue scale (EQ VAS) were used to assess HRQoL. Results: The prevalence of metabolic syndrome was 26.2%. Compared to the participants without metabolic syndrome, those with metabolic syndrome were older and comprised a higher proportion of men. Moreover, participants with metabolic syndrome were more likely to have a lower education level, to be current smokers, to have activity limitation and to have more frequent metabolic abnormalities and comorbidities. Metabolic syndrome was associated with HRQoL based on EQ-5D and EQ VAS in simple regression analysis. However, metabolic syndrome was not significantly associated with HRQoL after adjusting for age, sex, smoking status, income, education level, marital status, obesity, diabetes mellitus, stroke, history of heart disease and chronic kidney disease for EQ-5D, and in addition history of depression for EQ VAS. Conclusion: Metabolic syndrome in itself was not associated with impaired HRQoL after adjusting for confounding variables such as socio-demographic factors, medical comorbidities and obesity.

© 2012 S. Karger AG, Basel


  

Author Contacts

Sung Rok Kim, MD, PhD
Department of Medicine, Samsung Changwon Hospital
Sungkyunkwan University School of Medicine, 50 Hapseong 2-dong
MasanHoiwon-Gu, Changwon 630-723 (Republic of Korea)
Tel. +82 55 290 6332, E-Mail sungrok2.kim@samsung.com

  

Article Information

Received: December 2, 2011
Accepted after revision: June 26, 2012
Published online: December 3, 2012
Number of Print Pages : 6
Number of Figures : 0, Number of Tables : 3, Number of References : 38

  

Publication Details

Annals of Nutrition and Metabolism

Vol. 61, No. 4, Year 2012 (Cover Date: December 2012)

Journal Editor: Koletzko B. (Munich)
ISSN: 0250-6807 (Print), eISSN: 1421-9697 (Online)

For additional information: http://www.karger.com/ANM


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: 12/2/2011
Accepted: 6/23/2012
Published online: 12/3/2012
Issue release date: December 2012

Number of Print Pages: 6
Number of Figures: 0
Number of Tables: 3

ISSN: 0250-6807 (Print)
eISSN: 1421-9697 (Online)

For additional information: http://www.karger.com/ANM


Copyright / Drug Dosage

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

References

  1. Balkau B, Valensi P, Eschwege E, Slama G: A review of the metabolic syndrome. Diabetes Metab 2007;33:405–413.
  2. 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.
  3. Marchesini G, Brizi M, Bianchi G, Tomassetti S, Bugianesi E, Lenzi M, McCullough AJ, Natale S, Forlani G, Melchionda N: Nonalcoholic fatty liver disease: a feature of the metabolic syndrome. Diabetes 2001;50:1844–1850.
  4. Razay G, Vreugdenhil A, Wilcock G: The metabolic syndrome and Alzheimer disease. Arch Neurol 2007;64:93–96.
  5. Kurella M, Lo JC, Chertow GM: Metabolic syndrome and the risk for chronic kidney disease among nondiabetic adults. J Am Soc Nephrol 2005;16:2134–2140.
  6. Coffey JT, Brandle M, Zhou H, Marriott D, Burke R, Tabaei BP, Engelgau MM, Kaplan RM, Herman WH: Valuing health-related quality of life in diabetes. Diabetes Care 2002;25:2238–2243.
  7. 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: a longitudinal controlled study. Int J Cardiol 2006;113:215–222.
  8. Han TS, Tijhuis MA, Lean ME, Seidell JC: Quality of life in relation to overweight and body fat distribution. Am J Public Health 1998;88:1814–1820.
  9. Kleefstra N, Landman GW, Houweling ST, Ubink-Veltmaat LJ, Logtenberg SJ, Meyboom-de Jong B, Coyne JC, Groenier KH, Bilo HJ: Prediction of mortality in type 2 diabetes from health-related quality of life (ZODIAC-4). Diabetes Care 2008;31:932–933.
  10. Schenkeveld L, Pedersen SS, van Nierop JW, Lenzen MJ, de Jaegere PP, Serruys PW, van Domburg RT: Health-related quality of life and long-term mortality in patients treated with percutaneous coronary intervention. Am Heart J 2010;159:471–476.
  11. Miettola J, Niskanen LK, Viinamaki H, Sintonen H, Kumpusalo E: Metabolic syndrome is associated with impaired health-related quality of life: Lapinlahti 2005 study. Qual Life Res 2008;17:1055–1062.
  12. Ford ES, Li C: Metabolic syndrome and health-related quality of life among US adults. Ann Epidemiol 2008;18:165–171.
  13. Park SS, Yoon YS, Oh SW: Health-related quality of life in metabolic syndrome: the Korea National Health and Nutrition Examination Survey 2005. Diabetes Res Clin Pract 2011;91:381–388.
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