Longitudinal Relationship Between Frailty and Cognitive Impairment in Chinese Older Adults: A Prospective Study

Abstract

We aimed to examine the longitudinal association between frailty and cognitive impairment in the older Chinese population. This prospective cohort study used data from the Chinese Longitudinal Healthy Longevity Study 2011 wave. We calculated the follow-up duration as 3 years from the baseline year. Frailty was measured using the frailty index, and cognitive function was calculated by Mini-Mental State Examination Scale. Participants who were non-frailty and those with normal cognitive function were included in 2011 and followed up in 2014, respectively. Frailty was an independent risk factor for early-onset cognitive impairment. Age, hearing impairment, and a decreased ability to perform daily activities were the main risk factors for cognitive impairment, while affluent economic status was a protective factor. Cognitive impairment was not found to be an independent risk factor for frailty. We concluded that the frailty index is a significant predictor of cognitive impairment among community-dwelling older adults.

Keywords

frailty cognitive function older adults cohort study activities of daily living

What this paper adds

• Physical activity and cognitive function in older adults are interrelated. Frailty is an independent risk factor for early cognitive impairment, and the frailty index can be used to predict early-onset cognitive impairment.

Applications of study findings

• Since comorbidities are common in older adults, considering a “bio-psychological-social” approach may help prevent frailty and improve efficiency of care for older populations.

Introduction

In the older population, health indicators may be improved by effective medications and improvement of socio-economic conditions. The global population is rapidly aging; however, lifespan extension may prolong the decline in physical and cognitive functions as more frail older individuals survive their health problems yet consequently suffer (Chan et al., 2013; Christensen et al., 2013; Jean-Pierre et al., 2021; Verbrugge et al., 2017; Zeng, 2012; Zeng et al., 2017). The rapidly aging population associated with the one-child policy has created a significant challenge and posed serious burdens on the Chinese healthcare system (Zeng & Hesketh, 2016).

Frailty is characterized by a decline in various physiological reserve functions related to aging and an increased vulnerability to stressors. It can be measured in relation to accelerated biological aging using a frailty index (FI), which relates health deficit accumulation to the risk of all-cause and cause-specific mortality in older individuals (Hoogendijk et al., 2019; Mitnitski et al., 2001; Rockwood & Mitnitski, 2007; Searle et al., 2008; Song et al., 2010). The FI includes variables, such as activity of daily living (ADL) and cognitive functions, both of which can predict underlying medical conditions and mortality in older adults (Stenholm et al., 2019; Vermeiren et al., 2016). Early prevention and treatment can reduce frailty in older adults, thereby reducing the risk of disability and minimizing adverse outcomes (Rodriguez-Mañas & Fried, 2015; Stillman et al., 2021). Therefore, it is vital to design interventions based on increased awareness of the factors that pose a high risk for frailty in the aging population (Ofori-Asenso et al., 2019).

Cognitive impairment is an age-related, progressive neurological disease that has significant adverse effects on advanced brain functions, such as orientation, memory, and language. Dementia is the most serious stage of cognitive impairment and is associated with a high mortality rate, and is a heavy societal and economic burden. Mild cognitive impairment (MCI) is a state of cognitive function between normal aging and dementia (Nguyen et al., 2003). MCI has a high risk of progressing to dementia, but this process is considered reversible (Wu et al., 2021). Early identification of high-risk older people with early-onset cognitive impairment can better prevent progression to dementia (Wu et al., 2014). Clinical studies have shown that lifestyle interventions can delay the onset of dementia. Therefore, early screening and prevention of cognitive impairment have received increasing attention recently.

Both frailty and cognitive impairment are risk factors for future adverse health consequences. Recent studies have suggested that physical frailty and cognitive impairment share common etiologies rather than separate constructs (Sargent et al., 2020). However, few studies have shown the impact of the accumulation of health deficits on early-onset cognitive impairment and the influence of cognitive impairment on the progression of debilitating conditions (Brigola et al., 2020; Ma et al., 2019). Our research aimed to examine the longitudinal association between frailty and cognitive impairment in Chinese older adults for potential disease prevention and efficient care.

Methods

Study Design and Participants

The Chinese Longitudinal Healthy Longevity Survey (CLHLS), which provides information on health status and quality of life of older persons, is conducted nationwide in randomly selected counties and cities in 22 provinces in China (Sargent et al., 2020). We used data from the 2011 and 2014 CLHLS waves. All participants provided written informed consent. Further details regarding the CLHLS have been published elsewhere (Brigola et al., 2020). We excluded individuals who had missing information on cognitive function and on more than 30% of the variables used to calculate the FI. The number of participants included in the 2011 datasets followed up in 2014 was 1657. Consequently, we included 1545 participants with normal cognitive function in 2011, excluded those with cognitive impairment, and conducted a follow-up survey in 2014. We calculated the follow-up duration from the baseline year to analyze whether frailty is a risk factor for cognitive impairment. We then performed logistics regression analysis to further explore which dimension of the FI majorly contributed to cognitive impairment. On the other hand, 1477 participants in the non-frail group in 2011 were enrolled to investigate the contribution of cognitive impairment to frailty progression after baseline enrollment.

Measures

Health deficits were calculated using the FI. We selected 40 items related to functional, psychological, comorbidity, and social deficits. Binary variables were encoded as 0 or 1. The encoding of ordered and continuous variables was based on their distribution (Zeng et al., 2008; Chen et al., 2020). A score of 2 was assigned if the respondent had suffered more than one serious disease over the past 2 years (W. B. et al., 2005; Chen et al., 2020). The FI score was calculated as the sum of health deficits divided by the total number of deficits and was measured continuously between 0 and 1 (Supplementary Table S1). A score <0.25 indicated non-frailty, while a score ≥0.25 indicated frailty (Fan et al., 2020; Goggins et al., 2005).

The adapted Mini-Mental State Examination Scale was used to evaluate cognitive functions, and the scores were as follows: illiteracy >17 points, primary school education level >20 points, and middle school education for normal cognitive function >24 points (Cui et al., 2011; Folstein et al., 1975; Su et al., 2021).

The Activity of Daily Living Scale comprises two parts: the Physical Self-Maintenance Scale (PSMS) and the Instrumental Activities of Daily Living Scale (IADL) (Folstein et al., 1975; Fan et al., 2020). PSMS and IADL comprise six and eight items, respectively (Lawton & Brody, 1969). The lowest total score is 14 points, which indicates normal function; a score >14 indicates varying degrees of functional decline, with 56 as the highest, indicating dysfunction. A single item is classified as 1 being the normal and 2–4 as a functional decline. Moreover, two or more items having a score of ≥3 points or a total score of ≥22 points are considered significant functional impairment (Wiener et al., 1990).

Statistical Methods

Binary logistic regression was used to estimate the association between cognitive impairment and frailty, with age, sex, marital status, economic status, education, smoking history, alcohol consumption, diet, exercise, social activities, sleeping hours, and body mass index (BMI) as potential confounders. Results were reported with adjusted odds ratio (OR) and 95% confidence interval (CI). All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA) and IBM SPSS Statistics version 20 (SPSS Inc., Chicago, IL, USA), and graphs were plotted by R (vesion 4.0.2).

Results

The study included a total of 1657 older adults who participated in a baseline survey in 2011 and a follow-up survey in 2014. Table 1 shows the baseline participant characteristics in 2011. The participants had a median age of 79.5 ± 9.1 years (range 64–111 years); 1477 participants were non-frailty (89.14%), and 1545 had non-early-onset cognitive impairment (93.24%).

Table 1.

Participant characteristics at baseline.

	Frailty		p-value	Cognitive function		p-value
	Non-frailty	Frailty		Normal	CI
Age, n (%)
65–74	591 (40.0)	31 (17.2)	<0.001	613 (39.7)	9 (8.0)	<0.001
75–89	704 (47.7)	66 (36.7)		724 (46.9)	46 (41.1)
≥90	182 (12.3)	83 (46.1)		208 (13.5)	57 (50.9)
Sex, n (%)
Men	1037 (70.2)	80 (44.4)	<0.001	1065 (68.9)	52 (46.4)	<0.001
Women	440 (29.8)	100 (55.6)	<0.001	480 (31.1)	60 (53.6)	<0.001
Resident, n (%)
City	416 (28.2)	74(41.1)	0.001	461 (29.8)	29 (25.9)	0.494
Town	540 (36.6)	59 (32.8)		553 (35.8)	46 (41.1)
Rural	521 (35.2)	47 (26.1)		531 (34.4)	37 (33.0)
Co-residence, n (%)
With family	1223 (82.8)	145 (80.6)	<0.001	1275 (82.5)	93 (83.0)	0.754
Alone	237 (16.0)	25 (13.9)	<0.001	246 (15.9)	16 (14.0)	0.754
In an institution	17 (1.2)	9 (5.0)		23 (1.5)	3 (2.6)
Economical status, n (%)
High income	367 (24.9)	30 (16.9)	<0.001	376 (24.4)	21 (19.4)	0.003
Low income	141 (9.6)	35 (19.8)		154 (10.0)	22 (20.4)
Middle income	964 (65.5)	112 (63.3)		1011 (65.6)	65 (60.2)
Current marital status, n (%)
Single	12 (0.8)	0 (0.0)	<0.001	12 (0.8)	0 (0.0)	<0.001
Married	930 (63.1)	73 (40.8)		969 (62.8)	34 (30.6)
Divorced widowed	532 (36.1)	106(59.2)		561 (36.4)	77 (69.4)
Activities of daily life, n (%)
Normal function	868 (58.8)	9 (5.0)	<0.001	867 (56.1)	10 (8.9)	<0.001
Function decline	536 (36.3)	42 (23.3)		544 (35.2)	34 (30.4)
Dysfunction	73 (4.9)	129 (71.7)		134 (8.7)	68 (60.7)
Schooling, n (%)
Illiterate	331 (22.4)	76 (42.2)	<0.001	350 (22.7)	57 (50.9)	<0.001
Primary school	730 (49.4)	52 (28.9)		753 (48.7)	29 (25.9)
Middle school	358 (24.2)	40 (22.2)		373 (24.1)	25 (22.3)
Higher education	58 (3.9)	12 (6.7)		69 (4.5)	1 (0.9)
Smoking, n (%)
No	1046 (71.0)	153 (85.0)	0.002	1109 (71.8)	90 (80.4)	0.038
Yes	426 (28.9)	27 (15.0)		432 (28.0)	21 (18.8)
Drinking, n (%)
No	1070 (73.5)	162 (91.5)	<0.001	1139 (73.7)	93 (83.0)	0.022
Yes	386 (26.5)	15 (8.5)		384 (24.9)	17 (15.2)
Exercise, n (%)
No	595 (40.4)	123 (69.1)	<0.001	647 (41.9)	71 (63.4)	<0.001
Yes	876 (59.6)	55 (30.9)		890 (57.6)	41 (36.6)
Total	1477	180		1545	112

CI, cognitive impairment.

A logistic regression model was used to separately analyze the impact of cognitive function on frailty status based on controlled variables. The analysis showed that frail older adults were more likely to have cognitive impairment (OR 2.089, 95% CI: 1.295–3.369, p = 0.003). The trend was maintained when confounding variables were adjusted. Model 1 examined the influence of frailty on cognitive function by controlling for demographic variables. Models 2–4 examined the impact separately by controlling for living conditions, living habits, diet, and sleep duration in addition to demographic variables. Before constructing the effect of dietary structure on cognitive function in model 4, we first constructed regression models to examine the effects of eating vegetables, fresh fruit, milk, fish, beans, nuts, and salt-preserved vegetables on cognitive function. Finally, we used a multivariate model to examine the impact of each variable. Through the final multivariate model, we eliminated dietary conditions that were considered unrelated to cognitive function and only examined the impact of certain variables on frailty (Table 2). The OR for frailty was 2.039 (95% CI: 1.238–3.358, p = 0.005). Increasing age was a risk factor for cognitive impairment (OR 1.096, 95% CI: 1.073–1.120, p < 0.001), while affluent economic status (OR 0.576, 95% CI: 0.366–0.905, p = 0.017) and eating vegetables (OR 0.600, 95% CI: 0.414–0.869, p = 0.007) were protective factors. We also examined the impact of cognitive impairment on frailty by using control variables (Table 3). Cognitive impairment was not found to be a risk factor (OR 1.408, p = 0.283); however, increasing age was a risk factor for frailty. Exercise and eating vegetables were protective factors against frailty.

Table 2.

Logistic regression model analysis of the effect of frailty on cognitive function.

	Model 1		Model 2		Model 3		Model 4
	OR(95% CI)	p-value	OR(95% CI)	p-value	OR(95% CI)	p-value	OR(95% CI)	p-value
Age	1.095 (1.074, 1.116)	<0.001	1.097 (1.074, 1.120)	<0.001	1.096 (1.073, 1.120)	<0.001	1.096 (1.073, 1.120)	<0.001
Sex	1.208 (0.835, 1.748)	0.315	1.234 (0.828, 1.839)	0.301	1.216 (0.790, 1.873)	0.375	1.216 (0.786, 1.881)	0.379
Frailty	2.089 (1.295, 3.369)	0.003	2.006 (1.236, 3.256)	0.001	2.007 (1.218, 3.308)	0.006	2.039 (1.238, 3.358)	0.005
Affluent economic status			0.581 (0.376, 0.897)	0.014	0.578 (0.370, 0.903)	0.016	0.576 (0.366, 0.905)	0.017

Eating vegetables							0.600 (0.414, 0.869)	0.007

Model 1: adjusted for age and sex.

Model 2: model 1 adjusted for education, marital, and economic status.

Model 3: model 2 adjusted for smoking, drinking, exercise, and social activities.

Model 4: model 3 adjusted for eating fruit, eating vegetables, eating salt-preserved vegetables, and sleeping hours.

OR, odds ratio; CI, confidence interval.

Table 3.

Logistic regression model analysis of the effect of cognitive function on frailty.

	OR	p-value	95% CI
	OR	p-value	Upper	Lower
Age	1.105	<0.001	1.083	1.127
Sex	1.242	0.242	0.851	1.811
Affluent economic status	0.804	0.047	0.648	0.997
Exercise	0.519	<0.001	0.376	0.715
Cognitive function	1.408	0.283	0.753	2.632

ADL, activity of daily living.

Based on the preliminary analysis, we performed univariate regression analysis of self-rated health, personality characteristics, daily activities, hearing impairment, visual impairment, and chronic comorbidities. Visual impairment, hearing impairment, and ADL dysfunction were all significant risk factors for cognitive impairment. The results of the multivariate regression analysis are shown in Figure 1. Affluent economic conditions reduced the risk of cognitive impairment by 40.1% (95% CI: 0.387, 0.927, p = 0.022). Hearing impairment was also a risk factor for cognitive function (OR 1.909, 95% CI: 1.049–3.472, p = 0.034). The risks of ADL decline and dysfunction increased by 1.903 (95% CI: 1.262, 2.869, p = 0.002) and 5.78 (95% CI: 3.455, 9.668, p < 0.001), respectively.

Figure 1.

Logistic regression of frailty components on cognitive function.

Discussion

The incidence rates of cognitive impairment and frailty are increasing in an aging population. (Davies et al., 2021; Zhang et al., 2021). Our study revealed the impact of health deficit accumulation on early-onset cognitive impairment, including the influence of cognitive impairment on the progression of debilitating conditions. To date, there are few cohort studies that have shown these results.

Some studies have suggested that overlapping risk factors and shared biological pathways may be involved in both cognitive impairment and frailty (Robertson et al., 2013; Sargent et al., 2018). Cognitive impairment and frailty may also be associated with certain risk factors. Socio-demographic factors, including old age, sex, economic status, current marital status, and education, were statistically significant at the baseline in our study. Frail older adults were at a higher risk for cognitive impairment than non-frail ones. Frailty was strongly associated with cognitive impairment, consistent with its role as a significant predictor of dementia among community-dwelling older people (Kojima et al., 2016).

Frailty transitions are independent of progression in cognitive status in the earliest stages of cognitive impairment (Chong et al., 2015; Ma et al., 2019). Frailty is related to an increased risk for Alzheimer's disease and dementia (Suo et al., 2016). Future studies should be conducted to assess how frailty is associated with cognitive changes over time. Since FI indicates the accumulation of health deficits to determine the specific influencing factors of frailty on cognitive impairment, we analyzed the various dimensions of frailty. We found that limitations in daily behavioral ability had a significant effect on cognitive function. This finding is similar to the results of previous studies (Wang et al., 2021; Wu et al., 2021). Decreased ADL ability is a risk factor for cognitive function and frailty, indicating that physical and mental activities are interrelated (Wu et al., 2021). Hearing impairment was also a significant risk factor for cognitive impairment and a decreased ability to perform ADL, which can be treated using interventions and rehabilitation equipment. Frailty contributed to early-onset cognitive impairment in this cohort and may constitute a novel modifiable target for early-onset cognitive impairment (Borges et al., 2019). Limited or impaired social functioning is associated with the development of frailty, possibly due to decreased mobility, which adversely affects social functioning (Davies et al., 2021). We assessed the impact of loneliness and emotional status on cognitive function but could not draw a causal conclusion. Research on loneliness and social isolation across large time spans should provide a better understanding of their relationships.

Cognitive impairment and physical frailty interact; as such, cognitive problems and dementia are more prevalent in physically frail individuals, and those with cognitive impairment are more prone to becoming frail (Halil et al., 2015). The therapeutic efficacy of physical and cognitive interventions depends on discrete neuronal mechanisms (Wallace et al., 2019). However, our longitudinal survey did not show any effect of cognitive impairment on frailty. This might have occurred due to several reasons. FI indicates the accumulation of overall health deficits, the burden of cognitive impairment is not that obvious, and dementia is a clear risk factor for debilitating conditions. It is also possible that the follow-up period was too short for any outcome events to be observed.

Given the rapid urbanization and upward job mobility in China, many older adults are now living alone and are thus prone to healthcare problems, in addition to having specific healthcare needs (Zeng et al., 2014). Therefore, the earlier health deficits and cognitive functioning problems are detected, the sooner suitable intervention is provided. As a limitation, this study did not include the analysis of molecular and biological markers, as well as other related factors. It is not sufficient to explore the mechanism of the influencing factors. Three years of follow-up was relatively short for some outcome events to occur and subsequently determine causality. Causal relationships should be investigated with long-term follow-up, and common-sense or telemedicine interventions can be used to evaluate the effectiveness of relevant interventions in further studies (Esfandiari et al., 2021). Chinese older adults have a long and deep-rooted tradition of filial piety, and the different way of elderly care from the West may also have a distinct impact on cognitive function and frailty (Laidlaw et al., 2010).

In conclusion, frailty is an independent risk factor for early cognitive impairment, and the FI can predict early-onset cognitive impairment. The association between mobility and cognitive function should be considered when evaluating the overall health of the older population. As the comorbidity of several diseases is common in older adults, comprehensive consideration should be given to daily behavior, psychology, and social participation to support the process of healthy aging in diverse orientations. Considering a biological, psychological, and social approach may help prevent frailty and improve efficiency of care for older populations (de Haan, 2021).

Supplemental Material

Supplementary Material - Longitudinal Relationship Between Frailty and Cognitive Impairment in Chinese Older Adults: A Prospective Study

Supplemental Material for Longitudinal Relationship Between Frailty and Cognitive Impairment in Chinese Older Adults: A Prospective Study by Xinxin Zhao, Qi Chen, Liang Zheng, Longbing. Ren, Yinghong Zhai, Jue Li, and Jia He in Journal of Applied Gerontology

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: National Key R&D Program of China (No. 2017YFC0908005), and The Outstanding Clinical Discipline Project of Shanghai Pudong (Grant No. PWYgy2021-02)

Ethical approval

The Medical Ethics Committee of Tongji University, Shanghai, People’s Republic of China approved the present study (2021tjdx070).

Informed Consent

Written informed consent was obtained from all participants prior to the study.

ORCID iD

Xinxin Zhao

Supplemental Material

Supplemental material for this article is available online.

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