The differences of metabolic syndrome in elderly subgroups: A special focus on young-old, old-old and oldest old
Introduction
Due to the National Health Insurance, the average life expectancy has been increased recently in Taiwan. At present, it is 76.9 for men and 83.3 years old for women, respectively (Department of Statistics, 2016). Therefore, the health problems of these elderly have become major issues for the health providers and government. This is not unique in Taiwan, there are many other countries and societies facing the same challenges.
Metabolic syndrome (MetS) was first defined by the World Health Organization (WHO) due to the alarming world-wide increase of the cardiovascular diseases (CVD) and diabetes (Alberti & Zimmet, 1998). The purpose for defining this syndrome was in the hope to early detect subjects under higher risks for these two diseases (Kylin, 1923, Vague, 1947). In the same time, it is generally believed that this higher incidence of MetS is related to the simultaneously increased prevalence of obesity. The accumulation of the adiposity in human bodies can cause insulin resistance which is considered to be the core of MetS (Reaven, 1988). After its publication, many organizations modified the WHO-version and published their own definitions. Due to its simplicity and practicability, the most widely used version is suggested by Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (NCEP, 2001). Many cornerstone-studies are based on this definition and these studies contribute tremendously to our understanding of the CVD and diabetes pathophysiology (Cinti et al., 2005; Lau, Dhillon, Yan, Szmitko, & Verma, 2005; Alberti et al., 2009, Trayhurn and Wood, 2004). Moreover, the importance and accuracy of MetS to predict CVD and diabetes are further consolidated.
It should be noted that the health problems vary with the increasing of age. For example, in subjects younger than 65 years old, cancer is the most important cause of death. But once over 65, CVD, originally is in the third place, replaces cancer and becomes the number one on the top of the list (Sahyoun, Hoyert, & Robinson, 2001). Other than the age, the patterns are also not the same in different genders. In US, men and women have different top 10 causes of death is a very good example. However, it should be noted that CVD has always been a more and more important cause of death when people get older.1
Even though the impact of CVD and diabetes has become more and more severe, it is surprising to note that there has been very few studies focusing on the exploring the details of MetS components in the elderly, particularly on the effects of genders. In the present study, we hypothesized that the prevalence of MetS and its components change with age and genders in the elderly. We enrolled 18916 subjects and further classified them into three groups [young-old (≧65 and <75), old-old (≧75 and <85) and oldest-old (≧85 years-old)]. In this cohort, we explored the effects of age and genders on MetS in the elderly (Cohen-Mansfield et al., 2013, Hiramatsu et al., 2012, Nagata et al., 2012).
We enrolled subjects aged above 65 years old (included) who underwent routine health checkups at the MJ Health Screening Center in Taiwan. MJ Health Screening Centers are private chain-clinics located throughout Taiwan that provide regular health examinations to their members. All study participants were anonymous, and informed consents were obtained from each individual. Data were provided by MJ Health Screening Center for research purposes only, and the institutional review board of MJ Health Screening Center approved the study protocol. We randomly selected 57,517 records from MJ Health Screening Center’s database between 1999 and 2008. We excluded 21,338 subjects who aged less than 65 years old. We excluded another 3347 subjects who visited only once during the sampling period. Subjects with a past history of hypertension, type 2 diabetes, CVD events, or were taking medications known to affect MetS components level were excluded (n = 11,562). In addition, we excluded subjects who had missing data regarding to MetS components, or other general data (n = 2354). Finally, a total of 18,916 subjects were eligible for further analysis. They were further classified as aforementioned three age group, i.e., young-old, old-old and oldest-old.
Senior nursing staff obtained subjects’ medical history, including information on any current medications, through questionnaire, and complete physical examinations were performed. Waist circumference (WC) was measured horizontally at the level of the natural waist, which was identified as the level at the hollow molding of the trunk when the trunk was laterally concave. Both systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by nursing staff using standard mercury sphygmomanometers on the right arm of each subject when seated. After the subject had fasted for 10 h, blood samples were drawn from the antecubital vein for biochemical analysis. Plasma was separated from blood within 1 h and stored at −30 °C until analysis for fasting plasma glucose (FPG) and lipid profiles. FPG was measured using a glucose oxidase method (YSI 203 glucose analyzer, Yellow Springs Instruments, Yellow Springs, USA). Total cholesterol and triglycerides (TG) were measured using a dry, multilayer analytical slide method with the Fuji Dri-Chem 3000 analyzer (Fuji Photo Film, Tokyo, Japan). Serum HDL-C concentration were analyzed using an enzymatic cholesterol assay following dextran sulfate precipitation.
We used the latest harmonized criteria of MetS in 2009 with some modification which is the decreased WC for Chinese (≧90 for men and 80 cm for women) (Alberti et al., 2009, Weng et al., 2012). The remaining four criteria were the same; i.e. SBP ≧130 mmHg or DBP ≧85 mmHg, TG ≧150 mg/dL, FPG ≧100 mg/dL, HDL ≦40 and 50 mg/dL in men and women or taking related medications. Subjects had to have at least three criteria to be diagnosed as MetS.
All statistical analyses were performed using SPSS 18.0 software (SPSS Inc., Chicago, IL). Data are presented as mean ± standard deviation. All data were tested for normal distribution with Kolmogorov–Smirnov test and for homogeneity of variances with Levene’s test. Data were log transformed before analysis if data were not normally distributed. The t-test was used to evaluate the differences between the two groups. Analysis of variance was used to compare the three groups. To evaluate the odds ratio of abnormal MetS components to have MetS, logistic regression was performed. Whether normal or abnormal MetS components were regarded as the independent variables and, in the same time, having or not having MetS as the dependent variables. When one of the MetS components was evaluated, other components were considered as confounding factors and were adjusted. Thus, the independent effects of this particular component could be accurately evaluated.
Section snippets
Results
Table 1 shows the demographic data in the three aged groups in men and women. The mean ages were around 69, 78 and 87 for each group, respectively. For men, the SBP and HDL-C increased as the age got older. On the contrary, the DBP and TG decreased. All these changes were statistically significant. However, the WC did not change. In women, the WC and SBP increased significantly from the young-old to the oldest-old groups.
Fig. 1a shows the percentages of having MetS in each group. The highest
Discussion
In present study, we investigated the changes of MetS in three aged groups, particularly in the differences between men and women. We found that the prevalence of MetS and its components were increasing with age in women, whereas the highest prevalence of MetS was in old-old groups in men. To our knowledge, this is the first study focusing on this area for Chinese population. Our findings are in line with the other major studies. For example, Wu et al. have shown that, in men, the highest
Funding
The study was supported by a grant from the Cardinal Tien Hospital (CTH-101-1-2B11 and CTH-102-1-2A12).
Author contributions
All authors contributed to the conception and design or acquisition of data or analysis and interpretation of the data, drafting the article or revising it critically for important intellectual content, and final approval of the version to be published.
Conflict of interest
None.
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