| Summary: | 碩士 === 國立臺灣大學 === 臨床醫學研究所 === 101 === Introduction
Studies on frailty attract more and more attentions in the field of geriatric medicine. Fried (2001) tried to set up the operational definition on frailty as having 3 or more of the following conditions: unintended weight loss, exhaustion, weakness,low walk speed and low activity. Current clinical researches on frailty are nearly based on this definition.
Previous studies tend to focus on epidemiological analyses, the relationships of frailty and life quality, rate of institutionalization, morbidity and mortality. On biomarkers analyses, some studies indicate frailty is associated with C-reactive protein (CRP) and interleukin-6 (IL-6). However, researches of molecular biology on frailty are relatively sparse.
Metabolic syndrome is defined as having 3 or more of the following conditions: central obesity, hypertension, hyperglycemia, hypertriglyceridemia, low level of high-density lipoprotein. It is related to multiple chronic diseases. Aging maybe plays an important role on metabolic syndrome.
Researches between frailty, metabolic syndrome and related biomarkers in Taiwanese population are relatively scarce. We try to design a research to observe the
epidemiologic distributions on frailty and metabolic syndrome and their correlations to aging biomarkers from community- dwelling old adults in the north of Taiwan.
Materials and methods
This is a cross-sectional, observational study. Old adults with ages 65 and above who lived in the community were recruited. Basic demography, health condition,
physical activities were reviewed by structured questionnaire, 20ml peripheral blood was collected. Extract DNA from white blood cell, using quantitative polymerase chain reaction (qPCR) measure the telomere length. Extract RNA, reverse to cDNA, using p16 as a probe to perform qPCR to measure p16 mRNA expression level. Serum myostatin and follistatin level were also measured by ELISA kit.
Results
165 old adults were recruited. 75 were men(45.5%), 90 were women(54.5%); age was 75.8±7.7 on average. 6 men (8.0%) and 10 women (11.1%) were frail. Prevalence of frailty was 9.7%. 17 men (22.6%) and 26 women (28.8%) had metabolic syndrome. Prevalence of metabolic syndrome was 26.1%.
Chronic diseases, hemoglobin, p16mRNA expression levels were significant different between categories of frailty numbers. White blood cell (WBC) counts was significant different between categories of numbers of metabolic syndrome ( p<0.001). Telomere length, serum level of follistatin and myostation revealed no significant difference between categories of frailty and metabolic syndrome analyses.
Numbers of frailty was positive correlated to p16mRNA expression levels (p=0.025), but negative correlated to hemoglobin (p=0.01). This findings also revealed on linear regression model analyses.
Numbers of metabolic syndrome was positive correlated to WBC counts, p16mRNA expression levels. However, on linear regression model, p16mRNA expression levels was not associated to numbers of metabolic syndrome. The significant difference also diminished on logistic regression model analyses. Only WBC counts was positively correlated to numbers of metabolic syndrome. On logistic regression model, higher levels of WBC counts increased risk to metabolic syndrome (OR=1.59, 95% confidence interval=1.14-2.22, p=0.006).
Discussion
The prevalence of frailty in this study was higher than it was in the U.S. Although this is a single center study, we used objective tools to measure grip strength and walking speed to fit Fried’s definition avoiding subject measurements in previous researches in Taiwan.
Chronic diseases including bio-psycho-social aspects between each categories were significant differences on frailty analyses. It was supported that not only physical but mental condition will contribute to the severity of frailty. Some variables were associated with numbers of frailty, but this association was not sustained on frailty versus non-fraitly analyses. The same condition was found on metabolic syndrome analyses. It was suspected that different cutpoint on frailty and metabolic syndrome definition had different results.
This study try to link objective physical activity measurement and potential aging biomarkers; however, maybe the sample sizes were limited, the results were not significant. Since the definition of frailty was focused on clinical observation including bio-psycho-social aspects; it was difficult to use a single biomarker to explain the whole picture. Comparing to frailty, metabolic syndrome was a cluster of objective measurement regarding definite low-grade inflammatory disease, the biomarker of inflammation such as WBC counts was supposed to be higher in the populations with metabolic syndrome.
Conclusion
In this study, the prevalence of frailty and metabolic syndrome was 9.7% and 26.1% respectively. Numbers of frailty was positive correlated to age, stroke histoy, cognitive impairment, p16mRNA expression levels; but negative correlated to hemoglobin. The significant correlation was not existed on frailty versus non-frailty analyses. Metabolic syndrome was positive correlated to stroke history and WBC counts.
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