Objective Metabolic syndrome (MetS) gains more attention due to high prevalence of obesity, diabetes and hypertension among adults. Although obesity, diabetes and hypertension can certainly compromise health-related quality of life (HRQoL), the correlations of sociodemographic factors, quality of life and MetS remains unclear. This study aims to investigate the association between HRQoL and MetS in an Asian community of the sociodemographic characteristics.
Research design and methods We performed a cross-sectional study by recruiting 2588 Taiwanese patients aged ≥30 years between August 2015 and August 2017. Sociodemographic data and anthropometric variables were obtained from medical records and physical examination. Meanwhile, HRQoL was assessed by 36-Item Short-Form Health Survey questionnaires.
Results The overall prevalence of MetS was 32.8%. Multivariate analysis revealed that age ≥65 years (OR=1.987, p<0.001), body mass index (BMI) ≥24 kg/m2 (OR=7.958, p<0.001), low educational level (OR=1.429, p=0.014), bad self-perceived health status (OR=1.315, p=0.01), and betel nut usage (OR=1.457, p=0.048) were associated with the development of MetS. For patients with MetS, the physical and mental health domains of HRQoL are negatively correlated with abdominal obesity and hypertension, respectively.
Conclusions Adult MetS in Taiwan was associated with certain sociodemographic factors including older age, high BMI, low educational level, bad self-perceived health status, and betel nut use. Abdominal obesity and hypertension was correlated with HRQoL in patients with MetS.
- metabolic syndrome
- quality of life
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
Significance of this study
What is already known about this subject?
Metabolic syndrome comprises the clustering of traditional cardiovascular risk factors that are highly associated with increased risk of cardiovascular disease and may trigger physical and mental problems.
Heterogeneity across geographical regions, study population variability, and different sociodemographic factors affect the prevalence of metabolic syndrome.
Although obesity, diabetes and hypertension can certainly compromise health-related quality of life, the correlations of sociodemographic factors, quality of life and metabolic syndrome remains unclear.
The 36-Item Short-Form Health Survey (SF-36) questionnaire is widely used for assessing health-related quality of life.
What are the new findings?
This study demonstrated no correlation between metabolic syndrome and impaired health-related quality of life among Taiwanese adults aged ≥30 years, using SF-36 questionnaire.
Old age, high body mass index, low educational level, bad self-perceived health status and betel nut usage are associated with the prevalence of metabolic syndrome.
For participants with metabolic syndrome, their physical health was correlated with abdominal obesity, and their mental health was correlated with hypertension.
Significance of this study
How might these results change the focus of research or clinical practice?
A comprehensive prevention and management program of metabolic syndrome is urgently warranted for controlling the growing obesity trend and its related diseases.
Integrate public health and primary care is important to accelerating progress in preventing obesity and expanding the role of primary care in the prevention and early treatment of obesity. Additionally, further research and development are needed to expand the role of social networking services in obesity and overweight care.
The National Cholesterol Education Program’s Adult Treatment Panel III (NCEP-ATP III) defined adults who develop metabolic syndrome (MetS) as those having at least three out of the following five anomalies: abdominal obesity, high triglycerides level, low high-density lipoprotein cholesterol (HDL-C) level, hypertension and hyperglycemia.1–3 MetS is associated with cardiovascullar disease (CVD) and increasingly found in the elderly in developed countries.1 MetS has a significant impact on morbidity and mortality on CVDs, type 2 diabetes and social-psycho illnesses.1 4 5 The association between MetS and impaired health-related quality of life (HRQoL) has been reported.2 6 7
HRQoL, an individual’s overall sense of well-being, is based on subjective physical, social and psychological functioning that are self-reported. It has become an essential outcome variable for healthcare, given for those with chronic illness.8 HRQoL can be measured using 36-Item Short-Form Health Survey (SF-36), one of the leading HRQoL measurements that is broadly applied in MetS research.7 9 The SF-36 survey contains the following 36 items covering functional health status and general health (GH): eight dimensions including physical functioning (PF), role physical (RP), bodily pain (BP), GH, vitality (VT), social functioning (SF), role emotional (RE), and mental health (MH) and two domains including physical component summary (PCS) and mental component summary (MCS). The higher scores, both on eight dimensions and two domains, indicate better functioning.10 This impaired HRQoL has a negative impact on therapy response and disease control and survival in MetS patients.11 12
However, this association was only found in participants with female gender, depression, or high body mass index (BMI) after adjusting for the confounding factors such as sociodemographic variables, medical comorbidities, and obesity.7 13–15 Impaired HRQoL was associated with high BMI rather than MetS, confirmed by a study on obese participants with a BMI of over 30 kg/m2.14 In Korea, abdominal obesity and dyslipidemia were associated with impaired HRQoL.6 13 Corica et al16 also reported that obesity, hypertension, and diabetes mellitus were the main contributors to poor HRQoL. A correlation has been considered between MetS and poor HRQoL in Japan, whereas this correlation was not found in studies from Taiwan.17 18 Furthermore, whether MetS is a mere aggregation of metabolic abnormalities or a syndrome representing a clinical entity concerns, the critical investigators19 20 and the different associations of certain MetS components with HRQoL have been reported among various populations.7 21 Finally, since heterogeneous geographic area and study population variability influence the estimates of the prevalence of MetS,22–25 in analyzing the association between MetS and HRQoL, MetS-related risk factors such as sociodemographic background and medical status that could be interrelated with each other should be considered. Nowadays, the association between HRQoL and MetS or MetS components remains debatable. Different patterns of MetS components in various ethnicities could result in different effects on HRQoL of individuals.
Therefore, this study aims to investigate the association between HRQoL and MetS in an Asia community under consideration of the sociodemographic characteristics.
We conducted a cross-sectional study and enrolled residents aged over 30 years who received a health assessment program from August 2015 to August 2017 at Chang Gung Memorial Hospital (CGMH), Keelung, Taiwan. The subjects were excluded from the study if they had already been diagnosed with MetS or had one of the following medical conditions previously: major gastrointestinal disorder; autoimmune disorder; end-stage renal failure; liver cirrhosis; heart failure; diabetes mellitus; uncontrolled blood pressure; recent cardiovascular events; dementia; ongoing infection; active participation in a weight-loss program; pregnancy; and receipt of regular medications that could substantially modulate the metabolism and weight, such as steroids or megestrol acetate. We explained the research study to the participants, including the purpose, procedures, rights, and confidentiality aspects.
They completed physical examinations, laboratory tests, and questionnaires through one-on-one interviews. To assure that they had the required cognitive ability, we asked three fact-based questions including the current year, a simple addition equation, and correct day of the week after the one identified. If any of these three questions were answered incorrectly, the participants’ questionnaires were considered ineligible. From a total of 2901 participants recruited, 313 cases (28 women and 285 men) were excluded and 2588 participants (1629 women and 959 men) completed all the required study assessments, yielding a response rate of 89.2%.
Assessment of sociodemographic variables
Sociodemographic data, including age, sex, marital status, level of educational attainment, smoking habits, alcohol, betel nut usage, and any history of obesity, diabetes, hypertension, and CVD, were collected. Participants who were employed in the construction industry including building, bridge, tunnel, railway tracks and road paving were put under the occupation category ‘Laborer’. Educational attainment was classified into the following three groups: less than 9 years (junior high school), 9–12 years (senior high school), and more than 12 years (college and above). Marital status was divided into the following two classifications: currently married and currently unmarried (including single, widowed, divorced, or separated). Smoking exposure was considered affirmative if participants were current or former smokers. Alcohol consumption was considered affirmative if participants reported consuming four drinks or more per week. Habits of betel nut usage were considered affirmative if participants indicated any usage during the previous year.
Assessment of anthropometric variables
Anthropometric data, including blood pressure, weight, height, BMI, and waist circumference (WC), were recorded for each participant. Body height and weight were measured by an automatic height–weight scale to the nearest 0.1 cm and 0.1 kg, respectively. Systolic and diastolic blood pressure was measured twice, after 5 min rest, using validated and calibrated electronic sphygmomanometers. The BMI was calculated from the height and body weight of each participant (weight in kilograms divided by the square of the height in meters, kg/m2). WC was used to examine central adiposity and measured to the nearest 0.1 cm at the midpoint between the 12th rib and right anterior superior iliac spine, using an unstretched tape meter. All data were collected consistently by the two qualified researchers who had been trained by a certified International Society for the Advancement of Kinanthropometry specialists before this study, in order to collect data in a standardized way.
Diagnostic criteria for MetS
MetS was defined according to the modified NCEP-ATP III as the presence of three or more of the following conditions: (1) hypertension: systolic blood pressure ≥130 mm Hg or diastolic blood pressure ≥85 mm Hg, or the use of antihypertensive agents; (2) hyperglycemia: fasting blood glucose level ≥100 mg/dL; (3) low serum HDL-C: ≤40 mg/dL for men or ≤50 mg/dL for women; (4) hypertriglyceridemia: triglyceride (TG) level ≥150 mg/dL; and (5) abdominal obesity: WC ≥90 cm for men and ≥80 cm for women.3
Assessment of HRQoL
HRQoL was measured using the SF-36 questionnaire.26 The SF-36 scores were summarized using two widely accepted domains, PCS and MCS, based on exploratory factor analysis of the eight SF-36 subscales related to physical health (PF, RP, BP, and GH) and related to mental well-being (VT, SF, RE, and MH). The higher scores with 0–100 range indicated better health.10
Expert validation and data collection
A structured questionnaire and direct objective measures were used to collect data, including demographic data, anthropometric data, and HRQoL. We invited six experts, including two cardiologists, one endocrinologist, one family medicine physician, and two senior nursing practitioners, all of whom had practiced for over 10 years, in order to ensure the integrity, suitability, and diction of questionnaires. They conducted a content validity test, in which the content validity index was 0.90. The questionnaires were also analyzed for internal reliability using a Cronbach’s α coefficient by 10 senior nurses with more than 3 years of working experience at internal medicine wards. The Cronbach’s α coefficient was 0.85, indicating good reliability.
Under the guidance of the study nurses who were specially trained by our seven experts, each participants took approximately 30–35 min to complete and provide their medical records, including details about their current medications. Physical examinations included gathering the data of their body height, body weight, WC, and blood pressure. Blood samples were collected after overnight fasting. The biochemical data included levels of fasting glucose, glycated hemoglobin (HbA1C), TG, total cholesterol, HDL-C, low-density lipoprotein cholesterol (LDL-C), C reactive protein (CRP), and insulin resistance were measured by homeostasis model assessment-insulin resistance using an autoanalyzer (Beckman, USA) in the CGMH central laboratory in Keelung.
All data obtained were analyzed using Statistical Package for Social Sciences software, V.21.0 for Windows. Descriptive statistics were computed using demographic, physiologic/biochemical, and HRQoL data. The Kolmogorov-Smirnov test for normality was conducted, because of the huge sample size. We found that the data were normally distributed, analyzed by the t-test. To analyze the association between the prevalence rate of MetS and the variables, including demographic, physiologic/biochemical, and HRQoL data, the independent sample Student’s t-test and χ2 test were used. A logistic regression model was used to perform a multivariate analysis to assess the association between the prevalence rate of MetS and the variables, including demographic characteristics and HRQoL data. A multivariate linear regression model was fit to estimate the association between two domains of HRQoL (PCS and MCS) and sociodemographic variables in the participants with MetS.
Demographic features of MetS
Table 1 shows the different demographic characteristics among participants with and without MetS. Among the participants, the prevalence of MetS was 32.8% (850/2,588), and the average age was 55.9±12.6 years. Most participants were women (62.9%) and married (80.5%). More than half of the participants (54.1%) had graduated from senior high school. The mean BMI was 24.9 kg/m2 (95% CI 15.0 to 51.0), and there was significant BMI differences between genders with 25.6 kg/m2 (95% CI 16.8 to 39.4) in men and with 24.3 kg/m2 (95% CI 15.2 to 40.0) in women. As compared with non-MetS, a greater proportion of participants with MetS were found in the following subgroups: male gender (40.2% vs 35.5%, p=0.022), age ≥65 years (32.9% vs 18.2%, p<0.001), BMI >24 kg/m2 (85.3% vs 41.7%, p<0.001), lower educational level (81.8% vs 72.1% at below college level, p<0.001), lower self-perceived health status (55.6% vs 48.0%, p<0.001), non-self household income (45.6% vs 38.5%, p=0.002), unemployed status (21.3% vs 19.0%, p=0.014), more smoking exposure (28.0% vs 23.8%, p=0.024), and betel nut usage (9.4% vs 6.4%, p=0.007).
MetS clinical features of participants
Participants with MetS demonstrated significantly higher values of body weight (70.3±12.4 kg vs 60.0±10.2 kg), WC (89.0±8.5 cm vs 77.8±7.4 cm), BMI (27.7±3.6 kg/m2 vs 23.6±3.0 kg/m2), systolic blood pressure (139.6±16.3 mm Hg vs 125.8±17.1 mm Hg), diastolic blood pressure (82.7±10.7 mm Hg vs 75.9±10.4 mm Hg), fasting glucose levels (118.3±35.8 mg/dL vs 96.2±15.2 mg/dL), HbA1C level (6.3%±1.1% vs 5.6%±0.9%), total cholesterol level (212.5±53.9 mg/dL vs 204.8±36. 8 mg/dL), TG level (192.8±107.7 mg/dL vs 98.1±35.4 mg/dL), LDL-C level (127.7±30.9 mg/dL vs 120.0±28.6 mg/dL), CRP level (2.9±0.4 mg/dL vs 1.8±0.2 mg/dL), insulin resistance level (11.8±3.7 mU/L vs 6.6±1.5 mU/L) and lower values of HDL-C level (48.62±10. 9 mg/dL vs 60.3±13.1 mg/dL) than those without MetS (all p<0.001). We further compared HRQoL of participants between with and without MetS group (table 2).
QoL-related factors in patients with and without MetS
The participants in the MetS group reported lower scores on PF, RP, BP, GH, and PCS, but higher scores on SF, MH, and MCS. There was no score difference between the two groups in VT and RE. The multivariate analysis using logistic regression model revealed that age ≥65 years (OR=1.987), BMI ≥24 kg/m2 (OR=7.958), low educational level (OR=1.429), bad self-perceived health status (OR=1.315), and betel nut usage (OR=1.457) were correlated with the development of MetS (table 3).
However, HRQoL, including both PCS and MCS domains, failed to show a contribution to the development of MetS (table 3). This finding suggested that PCS and MCS of participants with MetS should be linked with certain risk factors such as sociodemographic variables. The multivariate analysis revealed that PCS scores of participants with MetS were negatively correlated with age ≥65 years, lower self-perceived health status, lower educational level, and abdominal obesity; MCS scores were positively correlated with male gender and age ≥65 years but negatively correlated with lower self-perceived health status, lower educational level, household income from relatives, smoking exposure, and hypertension (table 4).
We examined the relationship between MetS and HRQoL in this study and found that MetS was not significantly related with HRQoL using SF-36 questionnaire after the adjustments of confounding factors among Asia adults. Interestingly, the PCS and MCS of HRQoL in the MetS group were associated with gender, age, self-perceived health status, educational attainment, household income, smoking exposure, abdominal obesity, and hypertension. These observations suggested that sociodemographic variables and MetS components that increased the risk of MetS development are correlated with HRQoL. Accumulative evidence has shown marked association between MetS and the worsening of HRQoL, but a growing body of studies including the current one have failed to show this association (table 5).
This discrepant observation still exists even though participants with the same ethnicity and geographic distribution were studied.10 14 17 18 The following evidence can explain this discrepancy. First, impaired HRQoL may be attributed to obesity and different patterns of MetS components, not MetS itself, thus representing some degrees of cumulative contributions from the individual components. Tsai’s group studied obese participants with a BMI of 30–50 kg/m2 in a randomized weight reduction trial and found that impaired HRQoL was associated with high BMI rather than MetS since obese participants suffer more psychiatric disorders and may be disadvantaged in education, employment, and healthcare related to MetS.14 15 27 Two studies from the Korean population found that abdominal obesity and dyslipidemia were associated with impaired HRQoL after adjusting the sociodemographic variables, medical comorbidity, and obesity.6 13 In accordance with Jahangiry’s study,28 our observation that abdominal obesity and hypertension affected HRQoL in participants with MetS further supports the close association between individual MetS component and HRQoL. Second, various validated tools to quantify the influence of HRQoL were applied. HRQoL can be assessed using generic or disease-specific measurements. Generic measurements can be applied to any health problems by assessing multiple domains of functioning; in contrast, disease-specific measurements are designed to identify specific health problem-related quality of life. Disease-specific measurements tend to be more sensitive than the generic ones.29 There is no disease-specific quality of life questionnaire for MetS, so generic instruments such as SF-36 questionnaire, which has been the most frequently used questionnaire, offer the only viable option at present.7 Because those various generic measurements focus on different aspects of quality of life, the inconsistent results were expected to be observed among studies. Furthermore, some ethnicities may be more reserved in reporting physical and mental health complaints even though the study enrolled participants with same ethnicity. It is inherently inevitable to produce measurement errors, especially in the assessment of psychiatric symptoms.4 6 Lastly, appropriate treatment, convenient medical approach, lifestyle promotion intervention, and effective health-related education improved MetS control and HRQoL scores.4 7 These studies were not certain about the programs and treatments that the participants may have been exposed to previously, potentially through the local medical service or exposure to government media health promotion campaigns. Taken together, it is necessary to conduct further longitudinal studies using MetS-specific questionnaire to confirm this relationship and verify whether this relationship is linear or only a correlation factor.
The current study must be interpreted in the light of certain limitations, namely, cross-sectional studies do not allow causal relationship inferences underlying the observed associations to be drawn and reverse causation may have played a role in our results. Furthermore, the current study only allowed the calculation of summary scales (two domains: PCS and MCS), but it did not allow the calculation of individual subscales (eight dimensions). Thus, the difference between the MetS and non-MetS groups may have been present in the subscales that were not detected. We replaced eight individual subscales to two summary scales and performed multivariate analysis using logistic regression model again. We found that PF (OR=1.389, p=0.039) and MH (OR=1.412, p=0.042) were able to contribute to MetS development independently. Furthermore, it should be more informative if we could compare our results using NCEP-ATP III to the findings according to IDF (International Diabetes Federation) criteria. However, Chen and Pan30 conducted a study with 2608 adults in Taiwan, who had the completed data for five MetS defining components, and found that the IDF definition failed to identify a portion of people who had more than three MetS component disorder. Chen and Tsai’s31 study also found that NCEP-ATPIII rated greater proportions of subjects with aged 54–91 as having MetS than IDF. Our data showed 29.8% of subjects with more than three MetS component disorder and 23.0% of subjects who are aged over 65 years. Therefore, we preferred using NCEP-ATP III to define MetS in this study. Our results were obtained in the community population who were actively seeking medical counseling and health guidance, thus their external validity in the general population and different settings requires determination. Integrate public health and primary care is important to accelerating progress in preventing obesity. Further research and development are needed to expand the role of social networking services in obesity and overweight care. Finally, we were unable to include the medications that participants took as a covariable since treatment of MetS following evidence-based practice was shown to improve HRQoL in patients with MetS.9
This study showed no correlation between MetS and impaired HRQoL among Taiwanese adults aged ≥30 years, using SF-36 questionnaire. Instead, old age, high BMI, low educational level, bad self-perceived health status and betel nut usage are associated with the prevalence of MetS. For participants with MetS, their physical health was correlated with abdominal obesity, and their mental health was correlated with hypertension. Larger and longitudinal studies that use MetS-specific questionnaire, along with important covariates described previously, are warranted to confirm our observations in this study.
We would like to thank the study participants for all the devoted help in this study.
Contributors K-YY, S-CC, Y-PL and P-HC in the design of the study. S-HC analyzed the data and wrote the paper. K-YY participated in the analysis and interpretation of the data. K-YY, S-CC and Y-PL revised it for important intellectual improvement. All authors read and approved the final manuscript.
Funding This study was supported by grants (CRRPG2B0191~2B0194) from the Chung Gang Memorial Hospital, Keelung.
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval All the participants signed a consent form in accordance with the Helsinki Declaration. The study protocol was approved by a local research ethics committee at the Chang Gung Memorial Hospital in Taiwan.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement All data relevant to the study are included in the article.