Abstract
Objectives
A number of studies have reported that frailty is cross-sectionally associated with cognitive decline and is also a risk for future cognitive decline or dementia; however, there have been only a few studies that focus on the association between prefrailty and cognitive dysfunction. In the current study, we investigated the association between prefrailty and cognition
Design
A cross-sectional study of the data obtained at registration in a randomized control trial.
Setting
Toyota, Japan.
Participants
Community-dwelling older subjects (male 54.6%) who had cognitive complaints.
Measurements
A battery of neuropsychological and physical assessments were performed. Prefrailty was defined as exhibiting one or two of the five Fried criteria (weight loss, exhaustion, weakness, slow gait speed and low physical activity). We performed a multiple regression analysis to investigate the associations of cognitive performance with prefrailty, adjusting for the factors that were significantly different between the robust and prefrailty groups. To assess the cognitive attributes that were significantly associated with prefrailty, logistic analysis was performed to see if one specific criterion of the five frailty criteria was associated with cognitive performance.
Results
The study subjects included 183 prefrail and 264 robust individuals. The prefrail subjects with cognitive complaints were older, less educated, more depressive, and more likely to have diabetes mellitus than the robust subjects. The prefrail subjects had lower performance in a wide-range of cognitive domains, and after adjustments for age, education, depressive mood, and diabetes mellitus, prefrailty was associated with a decline in delayed memory and processing speed. Among the components of the Fried criteria, slow gait speed and loss of activity were significantly associated with slow processing speed as assessed by the digit symbol substitution test.
Conclusion
The current results demonstrated that prefrailty was associated with worse memory and processing speed performance, but not with other cognitive domains.
Key words: Neuropsychological assessments, memory, processing speed, digit symbol substitution, diabetes mellitus, depression
Introduction
Aging is a primary risk factor for cognitive impairment. As the aged population increases worldwide due to decreases in mortality, the number of individuals with cognitive impairment is increasing. Moreover, Japan is an especially aged society, with over one-fourth of the population over 65 years old. About 4 million people suffer from dementia in Japan (1). There are no curative or disease-modifying therapeutics for dementia, and so means of prevention have been sought.
Aging is also a strong risk for frailty. Frailty is a syndrome that results from the accumulation of multiple age-related organic types of decline with an impairment of physiological reserve, thus increasing vulnerability to adverse health outcomes including falls, hospitalization, institutionalization and mortality (2), Several studies have reported that frailty is associated with cognitive dysfunction (3).
Several models for frailty have been developed, with one of the most frequently applied being that developed by Fried et al (4). According to the Fried criteria, the diagnosis of frailty requires three or more of the following characteristics: unintended weight loss, exhaustion, weakness, slow gait speed, and low physical activity (4). The state between robustness (none of the criteria is applicable) and frailty, in which one or two of the five criteria are applicable, is called prefrailty (5). Prefrailty is an intermediate state between robustness and frailty, which is reportedly associated with chronic comorbidity (6) or functional decline (7).
A number of studies have reported that frailty is crosssectionally associated with cognitive decline and is also a risk for future cognitive decline or dementia (8).
Although it may be speculated that the risk of adverse outcomes increases proportionally from the robust to the prefrail to the frail state (6), there have been only a few studies that focus on the association of prefrailty with cognitive dysfunction. One study reported that prefrailty was not associated with cognitive dysfunction (9); however, other studies demonstrated significant decline in a wide range of cognitive domains (10, 11).
Frailty or prefrailty is associated with comorbidities including diabetes mellitus (DM) and depression, which may affect cognition. These factors should also be considered when the association between prefrailty and cognition is considered (12, 13).
It has been established that frailty is associated with unfavorable outcomes (2). Prefrailty is reportedly the status to be reversible, and seems to be an optimal target for interventions (14, 15). It is required to define the characteristics including cognitive functional changes in prefrail status in more details. It also important to determine which component of frailty is associated with cognitive deficits to search the strategies for interventions.
In the current study, we assessed the cognition of older community-dwelling subjects with cognitive complaints and investigated the association between prefrailty and cognition.
Methods
This was a randomized control trial performed to assess the effects of different types of exercise (aerobic, resistance, and a combination of both types) on cognition in older subjects with a slight decline of memory function (TOyota Preventional Intervention for Cognitive decline and Sarcopenia [TOPICS]). In the present study we analyzed the baseline data collected from October to December of 2014 cross-sectionally.
The study protocol was approved by our university's Ethics Committee (Graduate School of Medicine, Nagoya University, approval no. 2014-0155-2) and registered with the University Hospital Medical Information Network (UMIN) clinical trials registry, no. UMIN000014437. Written informed consent was obtained from all participants prior to their inclusion in the study.
Participants
Participants were screened with the use of a questionnaire comprising 25 self-completed items (Kihon Checklisty (16) including three items concerning subjective cognitive decline: Q18: Do your family or friends point out your memory loss? (e.g., «You ask the same question over and over again»); Q19: Do you make a call by looking up phone numbers? and Q20: Do you find yourself not knowing today's date? Respondents who answered “yes” to Q18 or Q20 or “no” to Q19 were regarded as being at high risk of cognitive decline (17). The questionnaire was mailed to community-dwelling residents aged 65–85 years in selected urban areas of the city of Toyota, Aichi, Japan. Residents who met the criteria for being at risk of cognitive decline according to at least one of the three questionnaire items described above were recruited through letters describing the interventional study project.
The exclusion criteria were as follows: (2) meets clinical criteria for dementia according to DSM-IV (18); (3) any disability affecting the basic and instrumental activities of daily living; (4) requires support or care from the Japanese public long-term care insurance system; (5) Mini-Mental State Examination (MMSE) score ≤19; (6) severe visual impairment; (7) has been diagnosed with a neurodegenerative disease (e.g., Parkinson's disease); (8) medical contraindications to exercise; (9) has a psychiatric disease (i.e., major depressive disorder); or (10) a history of serious cardiovascular, musculoskeletal, respiratory, or cerebrovascular disease or other severe health issue.
Diabetes Mellitus (DM) was defined as follows: self-report of diagnosed DM, current use of antidiabetic medication, fasting plasma glucose ≥126 mg/dl, or glycosylated hemoglobin (HbA1c) ≥6.5%.
The total of 447 (male 54.6%) subjects were included in the study.
Cognitive functional assessments
A battery of neuropsychological tests for the comprehensive assessment including wide-range of cognitive domains was performed by a group of clinical psychologists, speech therapists, and occupational therapists, who took specific training sessions. Each score was rechecked by a single neuropsychologist who was blinded regarding the other data associated with the participants. The tests used were as follows: the Logical Memory II subtest of the Wechsler Memory Scale- Revised (WMS-R) (19, 20); the MMSE for general cognitive ability (21); the Logical Memory I subtests and the Visual Reproduction I and II subtests of the WMS-R (22) for memory; the category fluency (animal naming) test and the letter fluency test for verbal fluency; the Digit Span (forward and backward) subtest and Visual Memory Span (forward and backward) subtest of the WMS-R for working memory; the Digit Symbol subtest of the Wechsler Adult Intelligence Scale-III (23) for processing speed; and the Trail Making Test parts A and B (24). Depressive mood was assessed by the Geriatric Depression Scale-15 (GDS-15) (25).
Physical function
We measured grip strength in the dominant hand of each participant with the use of a portable grip strength dynamometer (GRIP-D; Takei Ltd., Niigata, Japan). Gait speed was assessed during normal walking, in which the participant was asked to walk at his or her preferred speed, and maximum gait speed was also measured (26).
Frailty assessment
Frailty was defined according to the Fried criteria; i.e., meeting three or more of the five factors (weight loss, exhaustion, weakness, slow gait speed and low physical activity) was considered ‘frailty,' and meeting one or two of the factors was considered ‘prefrailty' (4). The components were defined as follows:
Weight loss: weight loss > 5% in previous two or three years;
Exhaustion: responds to “In the last two weeks have you felt tired without a reason” in Kihon checklist (16);
Weakness: handgrip strength (<26 kg for men and <18 kg for women) according to consensus report of Asian Working Group for Sarcopenia (27);
Slow gait speed: gait speed (<1 m/s) at usual walking in a 5 m course;
Low physical activity: No regular exercise habits.
In the current study nine subjects with frailty were excluded, and only robust and prefrail subjects were included in the analysis.
Body composition
We used a bioelectrical impedance data acquisition system (Inbody 430; Biospace Co., Seoul, Korea) to perform the bioelectrical impedance analyses. This system uses electrical current at three frequencies (5, 50, 250 kHz) to directly measure the amounts of extracellular and intracellular water in the human body. Based on segmental body composition, each participant's appendicular skeletal muscle mass was determined and retained for further analysis. The skeletal muscle mass index (SMI) was calculated by dividing the participant's appendicular muscle mass by his or her height squared in meters (kg/m2) (28)
Blood markers
A blood sample was obtained after the participant had fasted for ≥12 h. Serum samples were analyzed for creatinine, total cholesterol, albumin, 25OH vitamin D, glucose, HbA1c, and insulin. The apolipoprotein E (apoE) genotypes were classified into e2/e2, e2/e3, e3/e3, e2/e4, e3/e4, and e4/e4 based on the immunoblot technique using isoelectric focusing. We obtained the blood samples in the morning (between 9:30 am and 12:00 noon).
Statistical analysis
Student's t-test was used for the comparison of continuous variables. The distribution of frequencies for categorical variables was analyzed with the Chi-square (χ2) test. We performed a loigstic regression analysis to investigate the associations between cognitive performance and prefrailty. Unadjusted (univariate) analysis and adjusting for the factors that were significantly different between the robust and prefrailty groups (model 1 was adjusted with only age and educational years, and model 2 performed further adjustment with other factors). To assess the cognitive attributes that were significantly associated with prefrailty, logistic analysis was performed to see if one specific criterion of the five frailty criteria was associated with cognitive performance.
Results
A comparison of backgrounds between the robust and prefrail subjects demonstrated that the prefrailty group members were older and had shorter school years and higher GDS-15 scores and higher prevalence of DM (Table 1). Among the five frailty components, loss of activity was most frequently seen (66 subjects), followed by loss of energy (exhaustion) (59 subjects), loss of body weight (48 subjects) weakness (39 subjects), and slow gait speed (7 subjects). In a comparison of the neuropsychological assessment scores between the robust and prefrailty groups, the prefrailty group had significantly lower scores in a wide range of cognitive tests (Table 2).
Table 1.
Background information of participants
| Total | Robust | Prefrailty | |
|---|---|---|---|
| Age (years) | 72. 3± 4.6 | 71.8±4.2 | 73.1±4.9 |
| Number | 447 | 264 | 183 |
| Male (%) | 244 (54.6) | 140 (53.0) | 105 (57.4) |
| Education (years) | 11.5±2.4 | 11.8±2.3 | 11±2.6 |
| DM (%) | 70 (15.7) | 33 (14.1) | 37 (20.2) |
| HT (%) | 198 (44.3) | 112 (42.4) | 86 (47.0) |
| sBP (mmHg) | 155.0±20.1 | 155.5±20.1 | 154.4±20.3 |
| dBP (mmHg) | 79.0±12.0 | 79.2±12.1 | 78.8±12.0 |
| BMI (Kg/m2) | 22.9±2.9 | 22.8±2.7 | 22.9±3.2 |
| Albumin (g/dl) | 4.4±0.3 | 4.4±0.3 | 4.4±0.3 |
| Tchol(mg/dl) | 212.6±36.8 | 212.6±35.5 | 212.6±38.8 |
| sCr(mg/dl) | 0.8±0.2 | 0.8±0.2 | 0.8±0.3 |
| Vit D (pg/ml) | 20.2±6.6 | 19.9±6.3 | 20.2±7.0 |
| HbA1c (%) | 5.9±0.6 | 5.9±0.6 | 5.9±0.6 |
| HOMA-IR | 1.5±2.7 | 1.5±3.2 | 1.5±1.7 |
| ApoE4 (%) | 90 (20.1) | 58 (22.0) | 32 (17.5) |
| SMI (Kg/m2) | 6.5±0.9 | 6.5±1.0 | 6.5±0.9 |
| Usual gait speed (m/sec) | 1.4±0.2 | 1.5±0.2 | 1.4±0.2 |
| Max gait speed (m/sec) | 1.8±0.3 | 1.8±0.3 | 1.7±0.3 |
| Grip (Kg) male female | 33.8±5.6 | 35.2±4.7 | 32.0±6.1 |
| 21.9±4.0 | 22.8±3.6 | 20.6±4.3 |
P values for Student’s T or χ2 test are shown; DM: diabetes mellitus, HT: hypertension. sBP: systolic blood pressure, dBP: diastolic blood pressure, BMI: body mass index, Tchol: total cholesterol, sCr: serum creatinine, Vit D: 25OH vitamin D, SMI: skeletal muscle mass index
Table 2.
Neuropsychological assessments
| Total | Robust | Prefrailty | P value | |
|---|---|---|---|---|
| MMSE | 26.5±2.6 | 26.4±2.7 | 25.9±2.7 | 0,08 |
| Letter fluency | 9.2±3.2 | 9.0±3.4 | 8.3±3.2 | 0,02 |
| Semantic fluency | 16.0±3.8 | 16.4±4.1 | 15.8±4.6 | 0,16 |
| DSS | 11.4±2.7 | 11.6±2.7 | 10.5±2.4 | 0,00 |
| Digit span | 11.4±2.7 | 11.3±2.8 | 10.9±2.8 | 0,19 |
| LM I | 15.6±6.1 | 15.3±6.3 | 13.7±6.4 | 0,01 |
| LM II | 11.1±6.0 | 10.8±6.2 | 8.9±6.2 | 0,00 |
| TMT A | 44.0±15.3 | 42.8±14.6 | 48.4±17.2 | 0,00 |
| TMT B | 118.5±52.5 | 120.8±57.4 | 135.9±59.5 | 0,01 |
| GDS-15 | 4.0±2.9 | 3.3±2.4 | 5.0±3.2 | 0,00 |
P values for Student’s T test are shown.
Then, logistic regression analysis (unadjusted and 2 adjusted models) was performed. We selected age, years of schooling, GDS-15, and DM for adjusting factors because these factors were significantly different between the robust and prefrailty, and had potential to affect cognitions. In model 1 only age and education were adjusted, and in model 2 all 4 factors were used for adjustment. The analysis revealed that delayed logical memory (II) and digit symbol substitution (DSS) scores were significantly associated with prefrailty independent of age, education, GDS-15, and DM (Table 3) in fully adjusted model 2. The logistic analysis with each Fried criterion as an objective variable adjusted with the same factors as the previous analysis showed that slow gait speed and loss of activity were significantly associated with DSS (Figure 1). None of the Fried criteria were significantly associated with the logical memory II scores (Figure 1).
Table 3.
Multiple regression analysis
| Education | GDS | DM | Prefrailty | ||
|---|---|---|---|---|---|
| MMSE | -0.162(0.000) | 0.180(0.001) | -0.026(0.583) | -0.111(0.016) | -0.010(0.840) |
| Letter fluency | -0.130(0.008) | 0.164(0.001) | -0.029(0.547) | -0.006(0.894) | -0.061(0.219) |
| Semantic fluency | -0.167(0.001) | 0.013(0.793) | -0.030(0.537) | -0.137(0.004) | -0.015(0.764) |
| DSS | -0.034(0.469) | 0.243(0.000) | -0.089(0.061) | -0.113(0.013) | -0.118(0.015) |
| Digit span | -0.175(0.000) | 0.171(0.000) | -0.021(0.660) | -0.108(0.020) | 0.002(0.964) |
| LM 1 | -0.103(0.035) | 0.158(0.001) | 0.008(0.874) | -0.015(0.747) | -0.089(0.074) |
| LM II | -0.130(0.008) | 0.151(0.002) | -0.030(0.534) | -0.691(0.490) | -0.102(0.041) |
| TMT A | 0.273(0.000) | -0.132(0.005) | 0.080(0.087) | 0.083(0.064) | 0.080(0.092) |
| TMT B | 0.372(0.000) | -0.112(0.013) | 0.027(0..548) | 0.142(0.001) | 0.029(0.535) |
β(p value) is given; MMSE: mini mental state examination, DSS: digit symbol substitution, LM: logical memory, TMT: trail making test
Figure 1.
Logistic analysis for each item of frailty criteria in DSS and LM2
Discussion
The current study demonstrated that in community-dwelling subjects with cognitive complaints the prefrailty group were older, less educated, more depressive, and more likely to have DM than the robust subjects. The prefrail subjects performed worse in a wide range of cognitive domains, and after adjustment for age, education, depressive mood, and DM they still showed worse results on delayed memory and processing speed assessments. Among the five Fried criteria, slow gait speed and loss of activity were significantly associated with slow processing speed as assessed by DSS.
Several studies have reported prefrailty to be associated with depressive mood (6, 29). The current study also showed that prefrailty was associated with depressive mood. DM is reportedly associated not only with frailty (13) but also prefrailty (30). The current results also demonstrated a higher prevalence of DM in prefrail than robust subjects, while hypertension was not associated with prefrailty.
In the current study the prefrailty group performed worse in a wide range of cognitive domains. Nishiguchi et al., however, showed that prefrailty was not associated with cognitive decline (9). The current study involved subjects with cognitive complaints, which may be one of the reasons for the difference in results. In another difference between the two studies, the study by Nishiguchi et al used visual memory, while the current study used logical memory mainly depending on auditory memory. A study in Ireland demonstrated cognitive decline in a wide range of cognitive domains in relatively young (63.8 ± 9.1 yo) prefrail subjects after adjusting for several potential confounding factors (11). In the current study a simple comparison between the robust and prefrailty groups showed that prefrail subjects had declines in several cognitive functional tests, however, adjustments for several potential confounding factors diminished the statistical significance in cognitive assessments other than memory and processing speed.
In the current study, prefrail subjects had impaired processing speed as measured by DSS. Several previous studies showed bidirectional associations between DSS and gait speed; DSS performance predicted lower mobility (31), and slow gait speed was associated with low DSS performance (32). Another study also demonstrated that a combination of lower DSS score and slow gait speed predicted poor prognosis (33). The current study confirmed the association of DSS and gait speed.
Loss of activity was associated with a low DSS score. A great deal of evidence has shown that physical activity is associated with cognitive function (34, 35, 36, 37, 38). The relationship may be bidirectional; less physical activity may induce slower processing speed, or a decline in cognition may induce less physical activity. A lower level of physical activity may also be associated with less social interaction, which is reportedly associated with cognitive impairment (39).
The subjects of the current study all had cognitive complaints. The combination of cognitive complaints and slow gait is called motoric cognitive risk syndrome, and it is associated with executive dysfunction (40) and has been established as a state of high risk for dementia (41). The subjects with slow gait speed in the current study exhibited motoric cognitive risk syndrome.
An International Consensus Group organized by the International Academy on Nutrition and Aging (I.A.N.A) and the International Association of Gerontology and Geriatrics (I.A.G.G) proposed a concept of cognitive frailty, which is defined by simultaneous presence of both physical frailty and cognitive impairment (42). The prefrailty group in the current study has somewhat similar characteristics to cognitive frailty (43).
A study by Rosano et al. showed that a smaller prefrontal area volume measured by MRI was associated with both slow gait speed and slow processing speed, and suggested that the prefrontal area housed shared resources for both slowness of gait and processing speed (44). The current study also demonstrated an association between slow gait speed and slow processing speed; however, brain imaging data were lacking in the current study. Future studies including brain imaging data could help elucidate the underlying mechanisms.
Because vitamin D is associated with lower muscle quantity and performance (45), the association of serum vitamin D levels and frail status has been explored extensively (46, 47). In the current study, however, we did not observe different serum vitamin D levels between the robust and prefrailty groups. It would be warranted to investigate the association between future transitions to frail status and vitamin D levels (48, 49).
Among the five Fried criteria, loss of activity was found most frequently, followed by exhaustion, body weight loss, and weakness. Slow gait speed was observed in only nine subjects in the current study. A population-based study in Switzerland involving younger (age 65-70 yo) subjects than the current study reported that weakness was found most frequently, and that weight loss, low activity, exhaustion, and slowness were seen in descending order of frequency (6). Although the frequencies of the components reported were different than those found in the current study, the rarity with which slow gait speed was observed was a finding that this previous study had in common with the current study. We must exercise caution when making this comparison, however, because the definitions of the frailty criteria were slightly different.
There are several limitations to the current study. First, the cross-sectional design of the current analysis makes it difficult to interpret the cause-effect relationship of the association between prefrailty and cognitive dysfunction. Because the current study was a baseline analysis of a randomized control study performed to investigate the effects of several types of exercise on cognition, the current study population will be further studied to see the effects of interventions. Secondly, this type of study (TOPICS), which used exercise as an intervention, required that the subjects of the current study were relatively healthy and well-motivated, and thus they may not have accurately represented the population of older communitydwelling Japanese subjects. Thirdly, the subjects involved were also limited to those who had cognitive complaints. Forth, the information regarding the medications was lacked, which potentially affects the cognitive as well as physical performance.
The strong points of the current study include the following. It involved comprehensive assessments of a wide range of neuropsychological and physical performance parameters. It also included older subjects than those in a previous study, similar to the current one, performed by Robertson et al (8).
The association of prefrailty state with cognitive dysfunction provokes the needs for the intervention for this group. Prefrailty is an intermediate status between robustness and frailty. Frailty is a dynamic process which sometimes transits between better or worse states (14), and several interventional studies have demonstrated that prefrail and frail individuals can revert to better states (50, 51, 52). It is clinically relevant whether cognitive improvement is accompanied by a transition from prefrailty to robustness. The current study was originally planned as an interventional study to improve cognition and physical function through several types of exercise. The effects of the intervention will be investigated. Future strategies for the intervention may include enhancement of social activity as well as physical activity because lower gait speed and loss of activity were the components that were associated with cognitive dysfunction in prefrailty.
Conclusion
The current study demonstrated that prefrailty was associated with lower memory and processing speed performance, but not with other cognitive domains. Among the components of the Fried criteria, slow gait speed and loss of activity were significantly associated with slow processing speed as assessed by the digit symbol substitution test.
Funding source
The TOPICS study is supported by the Center of Innovation Program (COI STREAM), the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Science and Technology Agency (JST), and Toyota Motor Corporation.
Conflict of Interest
Nothing to declare.
Author Contributions
HU developed study concept and design, analyzed the data, and wrote manuscript. TM, KU, XWC, TH collected data. HS advised study design and interpretation. MK supervised study.
Sponsor's Role
No sponsor agencies had any role in the design or conduct of the study; collection, management, analysis, or interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication
Ethical standard
The study followed the principals of the Declaration of Helsinki. The study was approved by the ethical committee of the Nagoya University Graduate School of Medicine.
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