Tipping the Balance Between Cognitive Reserve,Frailty,and Dementia in the Very Old?

Abstract

Background:

As the population ages, the concept of frailty becomes increasingly relevant and may be considered a precursor between aging and the development of dementia in later life. Similarly, the construct of cognitive reserve (CR) is an accepted model of cognitive resilience that may account for individual differences in trajectories of brain aging, mitigating the clinical expression of dementia.

Objective:

We aim to estimate the role of CR and frailty in moderating the prediction of dementia in the population aged over 80 who are attending an Italian outpatient memory clinic.

Methods:

Comprehensive Geriatric Assessment, Clinical Frailty Scale (CFS) to screen for frailty, and Cognitive Reserve Index questionnaire (CRIq) to evaluate CR, were used to assess patients systematically. We performed multivariate logistic regression to assess associations with dementia. Model performance and interaction between frailty and cognitive reserve were then evaluated.

Results:

166 patients were consecutively enrolled (mean age was 85.7 years old, females composed 68%); 25% had a diagnosis of amnestic mild cognitive impairment, and 75% had a diagnosis of dementia. Multivariate regression analysis showed that CRIq and CFS were the main clinical assessment tools associated with the presence of dementia, even after collinearity adjustment. No significant interaction of CFS*CRIq was found.

Conclusions:

To our knowledge, this is the first study to investigate the association between CR, frailty, dementia, and their related interacting terms in a real-world population of very old patients. Our findings may suggest that both CR and frailty shape an individual’s resilience throughout their lifetime. This may potentially counteract the effects of brain neuropathology, in line with the hypothesis that meaningful associations exist between CR, frailty, and cognition in later life.

Keywords

Alzheimer’s disease cognitive reserve dementia frailty older adults

INTRODUCTION

Cognitive reserve (CR) refers to the “adaptability (i.e., efficiency, capacity, flexibility) of cognitive processes, which helps to explain differential susceptibility of cognitive abilities or day-to-day function of brain aging, pathology or insult”. 1 It is a widely accepted model of cognitive resilience proposed to account for individual differences in trajectories of brain aging, cognitive decline, and dementia. CR can dynamically modulate the brain’s flexibility and shape an individual’s cognitive performance to different environmentally-guided cognitive stimulating experiences throughout life. Consequently, CR may ultimately mitigate the clinical expression of dementia 2 and might be considered a buffer against the detrimental effects of dementia-related brain pathology.

Although aging is associated with a higher risk of developing dementia, a declining trend3 –6 in recent population cohorts has been observed, and this has been attributed to a higher level of education implying a correlation with increased CR.7,8, 7,8 Consistent with this finding, the mediating role of CR between brain health and dementia has been investigated but this association has been rarely explored in older populations. Namely, very old-age women with higher levels of education were found to be less likely to develop dementia over time. 7 On the other hand, a 5-year longitudinal study has reported that patients aged 85 years or more, despite having higher CR at baseline, showed no beneficial impact on the overall risk of dementia. 9

The construct of CR is currently understood as latent, primarily measured through proxies. A recent increase in interest has emphasized the need to incorporate multiple indicators, beyond education, to encompass the multidimensional nature of CR. As a result, the Cognitive Reserve Index questionnaire (CRIq) has emerged as the gold standard, 10 including three distinct proxies of CR: education, working activity and leisure time. 11 Despite being validated in old age populations, the utilization of CRIq in exploring the intricate relationship among physical health, CR, and dementia within these vulnerable patients has been scant.12 –16

Within an aging population, frailty emerges as a potential precursor between aging and the onset of dementia in later life. Frailty is characterized as a state of heightened individual vulnerability when exposed to a physiological or psychological stressor, leading to increased susceptibility to adverse outcomes such as mortality, comorbidity, functional decline, and cognitive decline. 17 Notably, varying degrees of frailty have been noted to offer a protective effect against the burden of brain neuropathology. Consequently, a lessened burden of pathology is required for dementia symptoms to manifest when frailty is present.

Frailty has also been identified as a key factor in influencing the development and clinical course of Alzheimer’s disease (AD). 18 Recent findings indicate that frailty modulates the trajectory of AD with the progression of frailty correlating with the accumulation of AD-related biomarkers in the brain, including Aβ and hyperphosphorylated tau protein. 19 Moreover, frailty has been implicated in the development and severity of vascular brain damage, such as macroinfarcts. 20

Therefore, a frailty construct based on the deficit accumulation model of Rockwood has shown efficacy in predicting both the onset of mild cognitive impairment (MCI) and subsequent conversion to dementia, 21 regardless of the presence of APOE ɛ4 allele presence 22 and the degree of neuropathological brain burden. 23

There is limited evidence exploring the interaction between frailty and CR in the development of dementia in very old individuals. In particular, Sardella and Quattropani24,25, 24,25 have indicated a reciprocal relationship between cognitive decline and physical performance, suggesting that the declining trajectories of dementia and frailty may share a common physiopathological and clinical course. The oldest old, individuals over the age of 85 years, constitute a growing segment of the aging population highly susceptible to dementia and frailty. Hence, understanding how these constructs intersect in this highly vulnerable population may offer valuable insights into the subsequent development of dementia in later life.

It is noteworthy that in Italy approximately 22% of the population is estimated to be elderly. Genoa, in the county of Liguria, has the highest proportion of oldest old individuals (≥85 years) in the country. 26 Therefore, our Ligurian survey results could serve as an estimate of Italy’s unique demographics, potentially serving as a predictor for a forthcoming European demographic landscape. 27

Building upon this background, this study aims to assess the association and the contributory role of CR and frailty on dementia in a cohort of very old patients followed at an Italian outpatient memoryclinic.

MATERIALS AND METHODS

This cross-sectional study included 193 patients attending the outpatient geriatric memory clinic of the IRCCS Polyclinic Hospital San Martino (Genoa, Italy), from September 2022 to March 2023. The study was conducted in accordance with the ethical standards of the Helsinki Declaration of 1975. This study was approved by the IRB (CERA N 2024-54 12/06/2024) University of Genoa, Italy.

Inclusion criteria were: age 80 years and older, diagnosis of aMCI or dementia of Alzheimer’s type, and mixed dementia, respectively, according to DSM-V diagnostic criteria. 28 This selection was made to better capture the diversity of dementia subtypes more prevalent in the oldest old population. The inclusion of mixed dementia cases reflects the real-world scenario where individuals of old age often present a combination of pathologies.

Exclusion criteria were: diagnosis of major psychiatric disorders or psychosis, diagnosis of major depression, 28 diagnosis of dementia of other neurodegenerative types (e.g., dementia with Lewy bodies, vascular dementia), diagnosis of subjective memory complaints, 29 and diagnosis of incident delirium. 28

Age, sex, body mass index (BMI), smoking and drinking habits (daily versus sporadic/never) were collected. Each patient received a comprehensive geriatric assessment (CGA), 30 including ADL/IADL 31 to assess functional status; Mini-Mental State Examination (MMSE) 32 to assess cognitive status; Clinical Dementia Rating Scale score (CDR) 33 to assess dementia severity; CRIq 11 to evaluate cognitive reserve; 15-item Geriatric Depression Scale (GDS) 34 to assess mood; Gijon’s scale to assess social vulnerability; number of medications to assess polypharmacy and ACB score to assess Anti-Cholinergic Burden; 35 Cumulative Illness Rating Scale (CIRS) 36 to assess multimorbidity; Mini-Neuropsychiatric Inventory (miniNPI) 37 to assess neuropsychiatric disturbances; Hand grip (HG, using a GIMA 28791 Smedley dynamometer) to assess sarcopenia; Clinical Frailty Scale (CFS) 38 to screen for frailty status.

Statistical analysis

A descriptive analysis of patients’ clinical phenotype based on the presence of dementia or aMCI was performed using suitable hypothesis tests (Chi-squared test for categorical and Mann-Whitney test for continuous variables).

All variables with p-value <0.10 at the univariate analysis were selected to enter the multivariate logistic regression model, 39 used to assess the overall associations with dementia.

The performance of the model was assessed using the ROC curve with AUC sensitivity analysis. An additional multivariate analysis was also performed to adjust for collinearity. ADL and IADL were excluded to avoid collinearity with CFS.

To investigate the interaction between frailty, explored by CFS, and CRIq on dementia, two separate models for CRIq and frailty were performed and the interaction terms were tested. All reported analyses were run by RStudio (Version 2022.07) and a two-sided α less than 0.05 was considered statistically significant.

RESULTS

Overall, 166 old age patients (mean age of 85.7 years) were enrolled. 41 patients out of 166 (25%) had a diagnosis of aMCI (CDR score 0.5) and 125 patients out of 166 (75%) had a diagnosis of dementia (CDR score ≥1) with different stage severity: CDR 1 (32%); CDR 2 (42%); CDR 3 (22%); CDR 4 (4%). Namely, 9.5% of patients had a diagnosis of dementia due to AD and 61% had a diagnosis of mixeddementia.

Patients’ clinical phenotype is illustrated in Table 1. Patients with dementia showed an increased functional decline (ADL 4±3; IADL 1±3, p < 0.001), higher frailty status (CFS 6±1, p < 0.001), social vulnerability (Gijon 11±3.8, p = 0.017) and anticholinergic burden (ACB score 1±2, p < 0.009), compared to those patients with a diagnosis of aMCI. The median CRIq value was lower in patients with dementia in comparison to those with aMCI (92±25.5, p < 0.001), including all three subitems (education, working activity, and leisure time).

Table 1

Patients’ clinical phenotype on the basis of diagnosis of MCI or dementia

	aMCI (CDR = 0.5)		Dementia (CDR ≥ 1)		p*
	N=41		N=125
	Median (or N) Q1–Q3 (or %)		Median (or N) Q1–Q3 (or %)
Age	85	82–86	86	83–89	0.021
Female sex (num)	26	63.4%	84	67.2%	0.705
Smoking habit	4	9.8%	6	4.8%	0.265
Alcohol	17	41.5%	34	27.2%	0.118
MMSE	24.7	23.0–26.2	18.4	14.4–21.4	<0.001
ADL	6	5–6	4	3–5	<0.001
IADL	5	4–7	1	0–3	<0.001
GDS	4.5	1.5–8.0	4.0	2.0–7.2	0.663
Gijon	9	8–11	11	9–13	0.005
ACB	0	0–1	1	0–2	0.027
CFS	4	3–5	6	5–6	<0.001
CIRS severity	1.7	1.5–1.9	1.8	1.6–2.0	0.200
CIRS comorbidity	3	3–4	4	3–5	0.075
miniNPI	14	7–22	20	13–28	0.003
Polypharmacy	7	4–9	7	5–9	0.256
HG	20	16–25	17	12–22	0.040
BMI	24	21–27	24	22–27	0.984
CRIq total	104	95–122	92	93–109	<0.001
M	103	97–135	104	87–115	0.157
F	105	94–119	87	78–99	<0.001
CRIq education	103	96–116	97	93–109	0.055
M	103	96–115	102	94–114	0.963
F	103	96–116	95	93–107	0.059
CRIq work	95	85–112	85	73–96	0.001
M	97	90–124	95	81–102	0.071
F	94	80–109	79	69–95	0.009
CRIq leisure	110	94–132	93	81–112	0.001
M	109	103–130	105	89–122	0.225
F	112	92–133	89	77–106	0.002

MMSE, Mini-Mental State Examination; ADL, Activities of Daily Living; IADL, Instrumental Activities of Daily Living; GDS, Geriatric Depression Scale; ACB, Anti-Cholinergic Burden; CFS, Clinical Frailty Scale; CIRS, Cumulative Illness Rating Scale; miniNPI, Mini-Neuropsychiatric Inventory; HG, Hand Grip; BMI, Body Mass Index; CRIq, Cognitive Reserve Index questionnaire. ^*p values refer to the hypothesis test about a difference between the two populations.

The multivariate regression analysis showed that CRIq total (OR 0.952, p = 0.002) and CFS (OR 8.424, p < 0.001) (Table 2) were mostly associated with dementia (AUC = 0.927, 95% CI 0.849-0.964), even after collinearity adjustment. Figure 1 shows the performance of the multivariate logistic model.

Table 2

Logistic models with binary CDR (MCI versus dementia) as the outcome

	Univariate logistic analysis				Multivariate logistic analysis
	OR	95% CI		P	OR	95% CI		p
Age	1.148	1.028	1.282	0.014	1.023	0.861	1.216	0.793
Female sex	1.182	0.566	2.470	0.657	1.021	0.192	5.421	0.981
Smoking habit	0.466	0.125	1.742	0.257
Alcohol	0.527	0.253	1.101	0.088	0.402	0.110	1.461	0.166
ADL	0.261	0.153	0.444	<0.001	0.783	0.409	1.496	0.459
IADL	0.455	0.355	0.582	<0.001	0.713	0.469	1.084	0.113
GDS	1.031	0.926	1.147	0.580
Gijon	1.229	1.059	1.428	0.007	0.754	0.563	1.010	0.059
ACB	1.383	0.998	1.915	0.051	0.819	0.494	1.359	0.440
CFS	8.590	4.172	17.685	<0.001	8.424	2.314	30.67	<0.001
CIRS severity	2.043	0.612	6.814	0.245
CIRS comorbidity	1.191	0.962	1.474	0.109
miniNPI	1.057	1.017	1.099	0.005	1.032	0.980	1.085	0.232
Polypharmacy	1.077	0.953	1.216	0.235
HG	0.963	0.921	1.006	0.093	1.056	0.945	1.1801	0.335
BMI	1.012	0.919	1.116	0.804
CRIq total	0.97	0.953	0.987	<0.001	0.954	0.926	0.983	0.002
CRIq education	0.98	0.959	1.002	0.078
CRIq work	0.966	0.947	0.986	0.001
CRIq leisure	0.978	0.964	0.992	0.002

CRIq, Cognitive Reserve Index questionnaire; ADL, Activities of Daily Living; IADL, Instrumental Activities of Daily Living; GDS, Geriatric Depression Scale; ACB, Anti-Cholinergic Burden; CFS, Clinical Frailty Scale; CIRS, Cumulative Illness Rating Scale; miniNPI, Mini-Neuropsychiatric Inventory; HG, Hand Grip; BMI, Body Mass Index.

Fig. 1

ROC curve demonstrating the performance of the multivariate logistic model.

As for the use of interaction terms, the significance of the interaction CFS*CRIq was analyzed. However, the p-value was not significant (CFS*CRIq p = 0.279).

DISCUSSION

In the process of aging, individual differences in lifestyle choices such as nutrition, physical activity, social engagement, and overall health status may be related to different degrees of cognitive fitness in later life.

The recent study by Dhana et al. on a cohort of 724 individuals, followed over the course of 24 years as part of the Rush Memory and Aging Project, indicated that a healthier lifestyle was associated with better global cognitive functioning up until death. 40 The association between cognition and a healthier lifestyle remained the major protective factor against the development of dementia in later life, independent of AD brain pathology burden, including β-amyloid load, phosphorylated tau tangle, or other dementia-related brain pathologies.

It is thought that a shift to a healthy lifestyle in the population could theoretically prevent around 40% of dementia cases worldwide. 41 The underlying hypothesis is that, of the many mechanisms proposed, a healthy lifestyle may build a cognitive reserve maintaining improved cognitive abilities over time. Regular physical activity, for instance, has been linked to neuroplastic changes that support cognitive resilience, including the promotion of neurogenesis in the hippocampus. 42

With aging, established findings showed that the neuropathological features of AD, such as amyloid-based plaques and neurofibrillary tangles, have little correlation with the clinical manifestations of dementia, including cognitive and functional decline. These inconsistencies imply that different underlying factors may influence the connection between AD-related pathology and dementia, especially in late life. In line with this, growing evidence supports the notion that frailty has detrimental effects on later-life cognition, diminishing the likelihood of favorable cognitive trajectories even after adjustment for education and APOE genotype.18,22,23,43 , 18,22,23,43

It is believed that frailty may accelerate the onset of dementia by diminishing an individual’s physiological reserve, which otherwise might have remained asymptomatic in a non-frail individual. The work of Wallace et al. indicates that the correlation between AD pathology and dementia varies based on levels of frailty, with the strength of the pathology– dementia relationship diminishing as frailty levels rise. Frailty could potentially lower the threshold requirement for AD-related pathology to manifest clinically, or it might serve as an indication of compromised repair mechanisms that would otherwise facilitate better tolerance of AD pathology. 18

Our findings support this notion since frailty seems to act as a moderator of the association with dementia in very old individuals. In line with that, Canevelli et al. have recently shown that increasing degrees of frailty may contribute to the biological and phenotypic heterogeneity of dementia in late life by reducing the physiological reserves of the organism. 43

On the other hand, several studies have found that a higher CR is linked to reduced dementia risk, 44 although there is limited attention on very old individuals.

Devita et al. highlighted that higher CR may mask cognitive deficits despite neuroanatomical damage, indicating the need to integrate CR measures into routine clinical practice. 45 Nelson et al. conducted a meta-analysis indicating a 47% reduced risk of MCI or dementia in the presence of higher CR levels. 46 Almeida-Meza et al. investigated markers of CR and dementia incidence in a longitudinal study of aging and found that higher CR levels were associated with a 35% lower risk of dementia over 15 years. 12 Similarly, Mendoza-Holgado et al. demonstrated significant correlations between CR, as measured by the CRIq, and cognitive status in individuals with MCI. 13 In particular, higher levels of education and engagement in leisure activities were associated with better cognitive performance, highlighting the importance of socio-behavioral factors in maintaining cognitive function.

CR does not prevent the biological factors leading to dementia but modifies the brain’s response to these factors once present. Research shows that individuals engaged in complex mental activities over their lifetimes experience less hippocampal atrophy.47,48, 47,48 CR involves both neural reserve and neural compensation. Neural reserve refers to the efficiency, capacity, or flexibility of cognitive networks built over a lifetime of mental activity, 49 whereas neural compensation involves the use of alternative cognitive networks to compensate for damaged primary networks. 2 This is evident from fMRI studies showing additional activation in older adults, interpreted as compensatory. 50

All these findings underscore the importance of CR in preserving cognitive function and reducing dementia risk, emphasizing the need for early-life cognitive stimulation and engagement in leisure activities to mitigate the impact of neurodegenerative diseases.

On the other hand, frailty appears to also play a crucial role in the brain’s adaptive responses to cognitive decline and dementia progression. Research has demonstrated that in individuals with MCI, increased physical frailty is associated with alterations in functional brain connectivity. Specifically, higher frailty levels correspond with stronger connectivity between regions not typically linked, such as the right hippocampus and temporal gyrus. 51 Additionally, in women, frailty components correlate with subjective cognitive decline, preceding overt dementia, and indicating early brain changes that could influence compensatory mechanisms. 52 Finally, population-based studies indicate that physical frailty is linked to decreased cognitive performance and adverse changes in brain structure, including increased white matter hyperintensities and reduced grey matter volume, particularly in subcortical regions. 53

Therefore, frailty, a measure of functional reserve across multiple physiological systems, and CR could potentially influence compensatory approaches to cope with brain pathology. The study conducted by Zijlmans et al. investigated the interaction between CR, brain reserve, and frailty in relation to dementia and mortality risk. Using data from the Rotterdam Study, they found that all CRIq-related sub-items (i.e., education, working activity, and leisure activities) moderated the association with dementia and that higher CR (HR 0.87 per SD, 95% CI 0.76–0.99, p = 0.036) and higher brain reserve (HR 0.85 per SD, 95% CI 0.72–1.00, p = 0.048) were associated with a lower mortality risk. Additionally, a significant interaction was observed between CR and frailty, indicating that higher CR is particularly associated with lower mortality in frail individuals (HR 0.77 per SD, 95% CI 0.66–0.90, p = 0.0012). 54 These findings highlighted the importance of considering both cognitive and brain reserve, along with frailty.

In support of this, our findings originally showed that worse CR and clinical frailty were independently associated with the presence of dementia in a population attending a memory clinic, and therefore should be carefully considered when assessing dementia in older populations. However, our results did not support the notion that frailty might interact with CR in the prediction of dementia. It can therefore be hypothesized that the relatively small sample size may have contributed to the missing interaction, as well as possible selection biases. Further longitudinal research is needed to assess this intriguing interaction.

The strength of this study is the robust methodological assessment of frailty based on a continuous measurement, and that this may support a more accurate appreciation of the mutual interplay with dementia. Similarly, using an advanced method of estimating CR, a lower CR was found to increase the risk of dementia. Furthermore, the study has strengths in the inclusion of a proportion of very old patients with dementia. This met the need for real-world data, along with the integration of a CR construct within the geriatric assessment, which may implement a tailored frailty stratification of very old patients to explore the heterogeneity of dementia in later life. However, recall or reporting biases cannot be excluded.

The limitations of the study are the small sample size, the single-center recruiting setting, and the cross-sectional nature of the study design which may limit the generalization of our findings. Confirmation will be needed from further longitudinal analyses to test interaction or causality relationships that cannot be currently inferred. Longitudinal trajectory analysis in such older populations will also help gather additional information on how CR subitems may counteract frailty and dementia in laterlife.

Conclusion

Consequently, the present findings provide additional knowledge in understanding the role of CR and frailty in moderating the prediction of dementia. CR might be considered an evolving construct during an individual’s aging, in parallel with frailty, tipping the balance between physical fitness and pathological burden, and building brain resilience towards aging and neurocognitive changes.

AUTHOR CONTRIBUTIONS

Fiammetta Monacelli (Conceptualization; Writing – review & editing); Luca Tagliafico (Data curation; Formal analysis; Investigation); Alessio Nencioni (Conceptualization; Supervision); Silvia Ottaviani (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Writing – original draft); Alessio Signori (Data curation; Formal analysis; Methodology); Mariya Muzyka (Data curation; Investigation); Ennio Ottaviani (Data curation; Formal analysis; Methodology); Marta Ponzano (Data curation; Formal analysis; Methodology).

Footnotes

ACKNOWLEDGMENTS

The authors have no acknowledgments to report.

FUNDING

The authors have no funding to report.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The data supporting the findings of this study is available on request from the corresponding author. The data is not publicly available due to privacy or ethical restrictions.

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