The population of China is ageing. Accompanying this aging population, dementia and frailty have a growing importance. However there is little consensus on the association between dementia and frailty, in terms of how the criteria that are part of this two syndromes overlap, as both disorders are age-related and increase the risk for falls, further leading to loss of independence. To meet the above needs, the thesis describes research into different frailty diagnostic criteria, as well as its association with dementia symptoms. We examined cognitive measures that can be used for assessment of Mild Cognitive Impairment (MCI) and dementia screening (the Hopkins Verbal Learning Test, HVLT) and compared its discriminant ability with the commonly used cognitive screening tool, the Mini-Mental State Examination (MMSE) in distinguishing Cognitive Impairment (including MCI and dementia) from No Cognitive Impairment (NCI, normal controls) in two community-dwelling elderly Chinese populations and in one institutionalised elderly population in Shanghai, China. Subsequently we investigated whether physical and cognitive symptoms clustered together to form frailty phenotypes. We employed indicators that have been widely used to diagnose frailty, including physical measures (grip strength, Time-Up and Go test, 15 feet gait speed test and Berg balance test), and psychological measures (the HVLT and the MMSE) to predict cognitive impairment (CI) and frailty. Additionally, we described demographics (age, gender, education) and other potential modifiers when detecting cognitive impairment and functional disability. We then built up a model for possible frailty phenotype using various indicators. Lastly, we examined whether demographic (age, gender, education and profession), and lifestyle (smoking/alcohol history, exercise frequency, and dietary habit) could be used to predict future cognitive impairment. It was found that advanced age, lower education (no or primary level), and being vegetarian were significant risk factors for cognitive impairment. Furthermore, whereas high consumption of green vegetables is a protector against cognitive impairment, high intake of tofu was negatively related to cognitive performance among community-dwelling elderly in China. To meet the above needs, the thesis describes research into different frailty diagnostic criteria, as well as its association with dementia symptoms. We examined cognitive measures that can be used for assessment of Mild Cognitive Impairment (MCI) and dementia screening (the Hopkins Verbal Learning Test, HVLT) and compared its discriminant ability with the commonly used cognitive screening tool, the Mini-Mental State Examination (MMSE) in distinguishing Cognitive Impairment (including MCI and dementia) from No Cognitive Impairment (NCI, normal controls) in two community-dwelling elderly Chinese populations and in one institutionalised elderly population in Shanghai, China. Subsequently we employed these two cognitive measures to investigate whether they were part of the frailty syndrome among elderly from the community-based studies. We investigated whether physical and cognitive symptoms clustered together to form frailty phenotypes. We employed indicators that have been widely used to diagnose frailty, including physical measures (grip strength, Time-Up and Go test, 15 feet gait speed test and Berg balance test), and psychological measures (the HVLT and the MMSE) to predict cognitive impairment (CI). We found four distinct subtypes of elderly characterised by increasing care needs: 1. Persona elderly as defined by age >78, year of education<6 years, grip strength <11.8 KG, and a MMSE total score <25; 2. Persona Physical frailty (fitness), defined by a total score on the Timed-Up and Go (TUG) test >12.7 seconds and 15 feet gait speed >4.4 seconds; 3. Persona Cognitive impairment, defined by a MMSE total score <25, a HVLT Immediate Recall (IR) score <15, and a HVLT Delayed Recall (DR) <5; 4. Persona Physical frailty (balance,) defined by a Berg Balance test score of <53. Additionally, we described demographics (age, gender, education) and other potential modifiers when detecting cognitive impairment and functional disability. We then built up a model for possible frailty phenotype using various indicators, Frailty here was defined as: 1. Low BMI as measured by this algorithm: BMI= Weight (kg)/Height (m)2 2. Weakness (upper and lower body): grip strength in the lowest quintile, adjusted for gender; and TUG get up with assistance or unable to get up 3. Slowness (lower body): TUG score in the lowest quintile, adjusted for gender; and 15 feet gait speed in the lowest quintile, adjusted for gender; 4. Poor balance: Berg Balance test score in the lowest quintile, adjusted for gender; 5. Low physical activity: engaging in exercise less than once per week. An individual with 4 or more present frailty components out of a total of 7 was considered to be frail , whereas equal or less than 3 characteristics were hypothesized to be pre-frail . Those with no present frailty components were considered as robust. Lastly, we examined whether demographic (age, gender, education and profession), and lifestyle (smoking/alcohol history, exercise frequency, and dietary habit) could be used to predict future cognitive impairment (as defined by a HVLT IR score of ≤19). The results of our studies show that compared to the MMSE, the HVLT is superior in differentiating MCI and dementia from NCI, and is also less affected by demographic factors in detecting frailty. Furthermore, in the current study, physical, psychological, demographic and other modifiable risk factors cluster together into different phenotypes of cognitive impairment and functional disability in these cohorts. A phenotype of frailty is built up using BMI, grip strength, TUG, 15 feet gait speed, balance and exercise frequency as indicators. The most common was the elderly phenotype followed by the cognitively impaired. A novel finding of the current study is that only 4.8% (8 out 168) of the whole sample fulfilled all three categories in the current study (cognitive impairment, functional disability and frailty). Finally, advanced age, lower education (no or primary level), and being vegetarian were significant risk factors for cognitive impairment. Furthermore, whereas high consumption of green vegetables is a protector against cognitive impairment, high intake of tofu was negatively related to cognitive performance among community-dwelling elderly in China.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:631615 |
| Date | January 2014 |
| Creators | Xu, Xin |
| Publisher | Loughborough University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://dspace.lboro.ac.uk/2134/14542 |
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