Developmental Patterns of Cognitive Function and Associated Factors among the Elderly in Taiwan
Developmental Patterns of Cognitive Function and Associated Factors among the Elderly in Taiwan
OPEN Previous research has discussed the factors associated with cognitive impairment, but the patterns of its development have been little described. Our aim was to examine long-term development of cognitive function and the related factors using longitudinal follow-up data. A group-based trajectory model and multinomial logistic regression were applied to identify trajectories and the associated baseline factors, and a mixed model was used to identify the time-varying factors associated with the trajectories. Three trajectories were identified: starting low and declining (30.8%), starting high and declining (51.8%), and high-stable (17.4%). These three trajectories were apparent at the beginning of the study and did not crossover during the study period. Smoking, diabetes, depression, and instrumental activities of daily living were significant variables for differentiating the starting high and declining group from the high-stable group. Similar patterns and emotional support as a contributing variable were observed in the starting low and declining group. Physical activity, self-rated health, cardiovascular diseases, depression score, physical function, and social support were related to the trajectories over time. Impaired physical function, cardiovascular diseases, depression symptom, and poor social support in late life may be potential risk factors affecting the decline of cognitive function. Preventive strategies should be designed accordingly.
According to the World Health Organization, the world population aged 60 years and older is expected to reach
more than 2 billion in 20501, double the current number. With the aging population growing rapidly, the number
of people with dementia is increasing. As of 2015, 46.8 million elderly people were living with dementia, with
9.9 million new cases every year worldwide, and the prevalence of dementia is expected to increase to about
74.7 million cases in 2030 and 131.5 million in 20502. The high prevalence and incidence modifies survival and
increases the risk of death3. In the United States, one of three seniors dies with Alzheimer?s or another form of
dementia4. The estimated median survival times for Alzheimer?s disease and vascular dementia are 7.1 and 3.9
The growing number of people with dementia will increase the disability burden and will bring enormous
costs from long-term care. In 2015, the total global societal costs of dementia were estimated to be US$818
billion, and more than 80% costs were attributed to informal care and residential care2. Caregivers of people with
dementia may experience higher levels of stress6, psychological morbidity7, and impaired physical health8; they
even have increased risk of mortality9. In addition, no treatments have been developed to cure dementia or alter
its progressive course, and research identifying modifiable risk factors for dementia is limited. Dementia is an
important health and care issue encountered throughout the world.
Taiwan is one of the most rapidly aging societies, and the prevalence of dementia in people aged 65 years
and over is expected to increase from 1.02% of the total population in 2013 to 5.08% in 206010. Given this steep
increase, a model for determining the causes underlying cognitive dysfunction should be established to delay the
development of dementia and reduce its social costs. Many studies have investigated possible factors affecting
cognitive function11?16, such as cardiovascular disease14, obesity17, and physical activity18, among others. However,
little research has examined the development of age-related cognitive impairment in the general population.
Therefore, our aim was to explore the developmental trajectories of cognitive impairment in community-dwelling
elderly adults and to identify the factors associated with it, using a nationally representative survey followed for
Settings and participants. This study is a secondary data analysis utilizing data from the Taiwan
Longitudinal Study on Aging (TLSA), a nationally representative investigation first conducted by the Health
Promotion Administration, Ministry of Health and Welfare of Taiwan in 198919. The prospective survey used
a three-stage equal probability sampling design to understand the socioeconomic status and the physical and
emotional health of the elderly in Taiwan. Fifty-six townships were first selected from 331 non-aboriginal
areas. Next, blocks or lins were cluster sampled in each of the selected townships. Finally, individuals aged 60
or over were randomly sampled from each of the selected blocks. This process resulted in 4049 elderly subjects,
with a survey response rate of 92% in the initial sample20. Follow-up surveys were conducted in 1993, 1996,
1999, 2003, and 200719. Prior to 2007, people took part in the survey after giving verbal consent only, but in
2007, signed consent forms were collected by the Health Promotion Administration, Ministry of Health and
Welfare of Taiwan.
For the current study, we used data collected from 1993 to 2007, since the cognitive function measurement
was implemented in 1993. All the selected participants or proxies speaking on their behalf were interviewed. A
proxy could only be used if the participant (
) was severely ill or too frail to answer questions; (
) had cognitive
) had speech or hearing problems; or (
) had dementia and could not answer the factual questions in
the TLSA study. The proxies did not undergo assessment of cognitive function and are therefore excluded from
our analysis. We analyzed data for 3155 persons whose cognitive function was measured in 1993. We further
excluded subjects under 65 years of age (n = 61); those who died before the 1993 survey began (n = 1), had
incorrect information on death (n = 14), or had a stroke in 1993 (n = 212); and those with fewer than two assessments
of the Short Portable Mental Status Questionnaires (SPMSQ) score (n = 567) during 1993 to 2007. A total of 2300
elderly subjects were included in the analysis (Fig.?1). This study was approved by the ethical committee of the
National Health Research Institutions.
When we initiated the study, the TLSA was publicly available, and we could use the data directly. Since 2014,
the use of secondary data has required ethics approval. We obtained the ethics approval from the Internal Review
Board of Academic Sinica. All methods were carried out in accordance with relevant guidelines and regulations.
Assessment of cognitive function. SPMSQ is a screening test for cognitive dysfunction21. We used a
shorter form based on SPMSQ, which included only five questions. The total Cronbach?s alpha value of the five
items was 0.63 in our population, indicating that the internal consistency was acceptable. These five items were
validated by a Chinese version of the Mini-Mental State Examination22 and were used in the Asset and Health
Dynamics among the Oldest-Old Study as an instrument of cognitive function23,24. A longitudinal study in
Taiwan also used the five items as a tool to detect cognitive impairment25. The participants were asked the
questions ?What is today?s date (including month, day, and year)??; ?What is the day of the week??; ?What is your
home address (or where are you)?? and ?How old are you??. They were also asked to subtract 3 from 20 for a total
of four consecutive times. Each correct answer was given 1 point, and the range of the total score was 0 to 5, with
0 representing all five items being answered incorrectly. Individuals who completed SPMSQ at least twice were
Covariates. We used the baseline age, sex, and education level for analysis. Other variables, including health
status, health behaviors, physical function, and social supports, were obtained in 1993, 1996, 1999, 2003, and
2007, and were thus analyzed as time-varying variables.
Age was regarded as a continuous variable and was dichotomized into 65?74 and ?75 years old, as shown in
the descriptive analysis. Sex was a categorical variable coded as 1 (male) or 2 (female). Education level was defined
) illiterate, (
) uneducated but literate, (
) primary, and (
) secondary or above based on the number of years
of formal education a person completed.
Health-related variables included body mass index (BMI), self-rated health status, chronic diseases, and
depression symptom score. Self-reported height and weight data were used to calculate BMI (kg/m2), and we
furthermore divided the participants into underweight (BMI < 18.5 kg/m2), normal weight (18.5 ? BMI < 24 kg/m2),
overweight (24 ? BMI < 27 kg/m2), and obese (BMI ? 27 kg/m2) subgroups based on the criteria of the Ministry
of Health and Welfare in Taiwan26. Health status was self-rated on a 5-point rating scale, with 5 = excellent,
4 = good, 3 = average, 2 = poor, and 1 = very poor. We regrouped the health status into three subgroups: good
(5 or 4), average (
), and poor (2 or 1). Chronic diseases such as hypertension, diabetes, heart disease, and stroke
were coded as dichotomous variables based on whether the subjects reported having those diseases and whether
they were diagnosed by a physician. Depressive symptoms were measured using a shorter version of the Center
for Epidemiologic Studies Depression Scale (CES-D)27. Each item was answered on a scale of 0 (rarely or none of
the time) to 3 (most or almost all the time), and the total score ranged from 0 to 30, with higher scores indicating
greater levels of depressive symptoms.
Health behaviors included smoking, alcohol drinking, and physical activity. Smoking and drinking status was
dichotomized as yes or no from the question ?Do you smoke/drink currently??. In our study, physical activity was
recoded as a dichotomous variable based on whether the respondents participated in the following activities: (
joining organized group activities, such as folk dance, ballroom dance, and tai chi; (
) walking, jogging, hiking, or
doing other outdoor fitness activities; or (
) planting or engaging in garden work. Respondents that participated
in any one of these activities were classified as engaging in physical activity.
Physical function was assessed by measurements of activities of daily living (ADL), instrumental
activities of daily living (IADL), and mobility tasks. ADL included bathing, dressing, eating, movement in bed/
transferring, ambulating indoors, and toilet use. IADL consisted of shopping, managing money, riding a bus
or train independently, doing heavy work around the house or yard, doing light housework, and using the
telephone. Mobility tasks comprised standing continuously for 15 minutes, squatting, lifting arms over head,
holding or grasping with fingers, lifting or carrying 12kg, running or jogging 20?30 m, climbing stairs, and
walking 200?300 m. Each item was scored from 0 (no difficulty) to 3 (unable to do the activity). The total scores
ranged from 0 to 18 for ADL, 0 to 18 for IADL, and 0 to 24 for mobility tasks. The higher scores represented
Social support contained social interaction and emotional support. Social interaction was realized as two
variables, playing games (chess, cards, or mahjong) and socializing with friends, neighbors, or relatives. Emotional
support comprised respondents being cared for when ill and being listened to by friends or relatives. Each item
was scored 0 (no) or 1 (yes), and the overall social support score was calculated based on the scores for social
interaction and emotional support.
Statistical analysis. We utilized the group-based trajectory model to identify the developmental trajectories
of cognitive function based on the SPMSQ score. The group-based trajectory model combines the methods for
finite mixture models and cluster analysis with longitudinal data. The model classifies individuals into clusters with
similar trajectories according to their longitudinal data, assuming that individual differences in trajectories can be
summarized by a finite set of different polynomial functions for age or time28,29. The model allows continuous and
discrete values. The SPMSQ with a 5-point scale is an ordinal variable, but it is analyzed as a continuous variable at
the manifest level. This model was analyzed by using the PROC TRAJ macro in SAS30,31 (http://www.andrew.cmu.
edu/user/bjones/index.htm). PROC TRAJ has an option for censored normal. We applied it and set the minimum
to 0 and maximum to 5 for SPMSQ. PROC TRAJ uses the maximum likelihood method for the estimation. It uses
all the individuals in the data set, including those with missing values. However, only available data for each
individual are incoporated in the analysis. In other words, individuals with partially missing data were not removed
from the analysis. The optimal number of groups was determined by the Bayesian Information Criterion29.
Baseline characteristics were expressed as mean and standard deviation (SD) for continuous variables and
proportions for categorical variables. Chi-squared test (for categorical variables) and analysis of variance (for
continuous variables) were used to compare the differences among the trajectory groups. Multinomial logistic
regression (MLR) was applied to identify baseline variables, including demographic, physical, mental, and social
predictors of group membership. Least squares means (LSM) of SPMSQ adjusting for demographic
characteristics were calculated, and Bonferroni correction was applied in the comparisons of SPMSQ score over time among
the trajectory groups32. We finally used a mixed model to assess the longitudinal variation of physical, mental,
and social variables among the trajectory groups. All analyses were implemented using SAS statistical software
(version 9.3 for Windows; SAS Institute, Inc., Cary, NC), and the alpha level was set at 0.05.
Development patterns of cognitive impairment. Figure?2 presents three trajectory groups based on
the Bayesian Information Criterion (BIC) value as the best model (?9237.24 for the number of participants;
?9245.01 for the number of observations). Figure?2 shows both the actual trajectory (solid line) and the predicted
trajectory (dashed line). These three groups did not cross over during the follow-up time. The average posterior
probability of the three groups ranged from 0.74 (SD = 0.13) to 0.89 (SD = 0.14), indicating adequate internal
reliability on the basis of the 0.7 threshold of posterior probability recommended by Jones and Nagin31. The three
groups were labeled as (
) starting low and declining, which represented 664 subjects (30.8%) who had a lower
score that continued to decrease over time; (
) starting high and declining, which showed a declining trend from
a high score to a lower score and included 1276 of the subjects (51.8%); and (
) high-stable, which included 360
of the subjects (17.4%), who maintained a high score of 5 over time.
We used LSM to calculate the adjusted SPMSQ score, and used Bonferroni correction to compare the
differences among trajectories. After adjustment for the socio-demographic variables, the SPMSQ score varied across
time in comparison to the initial one in the starting high and declining group and the starting low and declining
group (Fig.?2). Compared with the high-stable group, the starting high and declining group and starting low and
declining group had significantly lower SPMSQ at each time point (p < 0.025).
Characteristics at baseline. Table?1 summarizes the baseline characteristics of the three trajectory
groups. The mean age of all 2300 participants was 70.95 years (range 65?93), and 44.87% of them were
female. All variables except BMI, hypertension, and heart disease were statistically significant among the
three groups (p < 0.05). Subjects in the starting low and declining group were more likely to be older, female,
and less educated; had a lower proportion taking part in physical activity, smoking, and alcohol drinking;
and had poor health and physical functioning, more depression, and less emotional support compared with
the other two groups.
Baseline variables related to the trajectories. After adjustment for age, sex, and education, MLR was
used to calculate the relationships between baseline variables and trajectory groups (Table?2). In a comparison of
the starting high and declining group and the high-stable group, smoking (odds ratio [OR], 1.35; 95% confidence
intervals [CI], 1.01 to 1.84), diabetes (OR, 1.63; 95% CI, 1.01 to 2.99), CES-D (OR, 1.04; 95% CI, 1.01 to 1.07), and
IADL (OR, 1.21; 95% CI, 1.03 to 1.42) were significantly different at baseline. The odds ratio of 1.35 for smoking
indicated that a person who smoked had 1.35 times higher odds of being in the starting high and declining group
relative to the high-stable group than a person who did not smoke. We found that the same variables were
significantly related to being in the starting low and declining group; in addition, emotional support (OR, 0.77; 95%
CI, 0.60 to 0.99) had a protective effect in the starting low and declining group compared with the high-stable
group. In other words, increasing emotional support by 1 point decreased the odds of being in the starting low
and declining group by 23%.
Time-varying variables related to the trajectories. To account for the repeated measures, we used
a mixed model to examine the association between the time-varying variables and the trajectories, adjusting
for socio-demographic variables (Figs?3 and 4). Figure?3 shows the time trend of BMI, self-rated health score,
physical function score, depression score, and social support score for each trajectory group. Figure?4 displays the
time trend in the proportion of cardiovascular diseases for the trajectories. The majority of the variables appear to
Age, y, mean ? SD
have the same variation among the three trajectory groups. Most of the variables varied across time, especially in
the two decline groups. The time and group interaction also existed for almost all variables, with the exceptions
of BMI and CES-D score. Subjects who rated their health as poorer and had impaired physical function, worse
depression symptoms, less emotional support, less physical activity, and greater likelihood of diabetes, heart
disease, or stroke were more likely to be in the starting high and declining group than the high-stable group, after
time, time-group, sex, age, and education levels were accounted for similar patterns were observed in the starting
low and declining group.
This study utilized a longitudinal follow-up of elderly subjects to identify the developmental trajectories of
cognitive impairment and found three trajectories with no crossover during the follow-up time. The trajectories begin
prior to 65 years of age. Elderly subjects who smoked, did not drink alcohol, or had depression, diabetes, or poor
physical function at baseline tended to be clustered into the starting high and declining and starting low and
declining groups. Physical functioning, depression, cardiovascular diseases, social support, and physical activity
were related to the progression of the decline in cognitive function.
We found that elderly subjects who were smokers at baseline were more likely to have a decline in cognitive
score over time33. This finding is compatible with previous observations that smoking increases the risk of
cognitive dysfunction. However, one study found that current smokers and ever smokers may be less likely to suffer
underlying cognitive impairment49. Insulin resistance may affect the coordination of nerves in brain regions
and reduce or block blood flow to the brain50. Glucose toxicity results in vascular damage and influences the
generation of neurodegenerative disorders in brain51. Hyperglycemia may cause decrements in working
memory and attention51 and may cause cognitive impairments52. Inflammation is considered to be a factor initiating
insulin resistance and the development of diabetes53. The association between inflammation and cognitive
impairment among the elderly should be studied. Stroke patients appear to have a higher risk of cognitive
dysfunction54 possibly because stroke may damage the brain and affect the function of memory, leading to
A positive relationship between social capital and cognitive function was found in our study, and this
outcome corresponds to findings reported by Green et al.56 and Hughes et al.57. Social support has been suggested
to prevent cognitive decline by regulating mood and social capacity58. Elderly individuals may derive social and
emotional support from participating in social activities. Social support not only enhances their life
satisfaction and well-being, but also avoids isolation from a social network, potentially decreasing the risk of cognitive
The present study has several limitations. First, all variables were self-reported, thus recall bias is consequently
unavoidable. Nevertheless, parts of the questionnaire have been validated, and the interviewers were well trained.
We also made spot checks to ensure that the responses were correctly collected. Secondly, cognitive function was
assessed by SPMSQ with five questions, which might restrict the results of our study. Many other instruments are
available to evaluate cognitive function in the elderly, such as Mini-Mental State Examination (MMSE). MMSE,
the most commonly used assessment for cognitive impairment, is a 30-point scale including tests of orientation,
registration, attention and calculation, recall, and language59. SPMSQ is a screening instrument for cognitive
dysfunction rather than a precise test of declines in specific cognitive functions. We only screened a population
prior to mild cognitive impairment in our study; therefore, our results on factors of cognitive impairment may not
be generalizable to people with severe cognitive impairment and should be interpreted with caution. In addition,
the reliability in our study is 0.63, indicating that the quality of the five-item instrument is acceptable. Thirdly,
a ceiling effect may exist in the high-stable group. For the scales, a percentage of 20% at ceiling was considered
a significant effect60. However, participants in high-stable group accounted for only 15.7% of the subjects, and
not all of them had perfect score. Therefore, we could ignore the ceiling effect in our results. Finally, the factors
influencing the progression of cognitive disorders are complicated. We might not have exhausted all the potential
factors, although our results were in accord with those of other researchers.
In conclusion, the development of cognitive impairment trajectories varied. Impairment in the starting low
and declining group appeared before 65 years of age. Physical function impairment, diabetes, heart disease,
stroke, depression, and poor social capital in late life may be potential risk factors that affect the decline of
cognitive function. Prevention and screening should begin early to delay deterioration. Different risk
factors were associated with the trajectories of cognitive function, and preventive strategies should be designed
We are grateful for the team members for the ?Fieldwork Operation Center of Community-based Health Survey?
who was in charge of TLSA fieldwork.
H.-Y.C. conceptualized the study, T.-Y.C. analyzed the data and drafted the manuscript. H.-Y.C. revised the
manuscript. All authors read, reviewed, and approved the manuscript.
Competing financial interests: The authors declare no competing financial interests.
How to cite this article: Chen, T.-Y. and Chang, H.-Y. Developmental Patterns of Cognitive Function and
Associated Factors among the Elderly in Taiwan. Sci. Rep. 6, 33486; doi: 10.1038/srep33486 (2016).
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