Abstract
Background
Frailty is characterized by declining homeostatic reserves and increased vulnerability to stressors, and has been associated with cognitive decline and dementia.
Objective
To examine the relationship between frailty, Alzheimer's disease (AD) biomarkers, and cognitive performance in individuals with mild cognitive impairment (MCI).
Methods
We enrolled 183 MCI patients from the Diagnostic Outpatient Service of the Neurodegenerative Diseases Unit at IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan. Cerebrospinal fluid (CSF) biomarkers amyloid-β42, total-tau, and phosphorylated-tau181were analyzed. Frailty was assessed using a 47-item frailty index (FI), and cognitive performance was measured with the Mini-Mental State Examination (MMSE).
Results
Higher FI scores were associated with lower MMSE scores, even after adjusting for amyloid and tau biomarkers (β = -0.72, 95% CI −1.20, −0.24). Compared to robust (FI ≤ 0.11) amyloid-negative subjects, individuals with higher FI (>0.11) and CSF amyloid positivity had significantly lower MMSE scores. Patients with lower FI had twice the odds ratio (OR 2.00, 95% CI 1.02–3.90) of presenting an amyloid-positive (A+) CSF profile compared to those with higher FI; similar trends were found for the amyloid and tau-positive (A + T+) profile (OR 2.29, 95% CI 1.14–4.61).
Conclusions
Frailty modulates the phenotypic variability of AD, influencing its clinical expression in MCI. Specifically, it is associated with lower cognitive performance in MCI independently of AD biomarker status and contributes synergistically with amyloid pathology to cognitive decline. Moreover, lower frailty levels are associated with a higher likelihood of an AD-compatible CSF profile, suggesting that frailty assessment may aid in identifying patients at greater risk.
Keywords: Alzheimer's disease, cerebrospinal fluid biomarkers, cognitive decline, frailty, mild cognitive impairment
Introduction
Frailty is an age-related condition characterized by a decline in homeostatic reserves and increased vulnerability to stressors, which is predictive of poorer health outcomes. 1 It can be operationalized through different approaches, including phenotypic models based on physical characteristics and the deficit accumulation model, which postulates that frailty is proportional to the number of health deficits accumulated over the life course. 2 The latter approach was adopted in the present study, given its multisystemic nature and its suitability for capturing the biological complexity of aging-related cognitive disorders. 2 Frailty is receiving increasing attention in neurology, particularly concerning its relationship with dementia and cognitive disorders. 3 It has been consistently associated with a higher risk of dementia development, independently of chronological age and other traditional risk factors (e.g., sex, education, genetics, lifestyle, social deprivation), regardless of the adopted operationalization4,5 and seemingly independently from any reverse causality. 6 Accordingly, increasing frailty heightens the risk of unfavorable progressions along the cognitive continuum (i.e., conversion from normal cognition to mild cognitive impairment [MCI] and from MCI to dementia) 7 and is associated with worse cognitive performance and negative health outcomes in patients with neurocognitive disorders8–10 The clinical relevance of this association has been further highlighted by the formal conceptualization of ‘cognitive frailty’, defined as the co-occurrence of physical frailty and MCI in the absence of dementia.11,12 Based on this mounting body of evidence, frailty is gaining relevance in policy efforts aimed at improving dementia prevention, diagnosis, and care. 13
Nevertheless, the pathophysiological mechanisms underlying the association between frailty and cognitive disorders have been poorly explored. Only a few studies have specifically investigated the relationship between frailty and Alzheimer's disease (AD) neuropathological changes as assessed through biomarkers or post-mortem examination; among these, two studies are particularly relevant to the present work.14,15 Autopsy-based studies have demonstrated that, for a given burden of AD neuropathology (i.e., amyloid plaques and neurofibrillary tangles), individuals with higher frailty levels had a greater likelihood of having experienced AD dementia during life compared with robust participants. 14 Consistently, among individuals with the same biomarker status, such as a positive amyloid PET scan, higher frailty is associated with an increased likelihood of MCI or AD dementia. 15 These findings suggest that frailty may lower the threshold at which brain pathology becomes clinically expressed, and may independently contribute to cognitive decline through additional pathophysiological mechanisms with important implications for the validation of biomarker-based diagnostic tools in the context of the proposed transition toward a purely biological definition of AD. 16
Against this background, this cross-sectional study aimed to investigate the association between frailty, AD biomarkers, and cognitive performance in a population of patients with MCI attending a tertiary Italian memory clinic.
Methods
Population and procedures
A total of 183 patients diagnosed with MCI at the Neurodegenerative Diseases Unit of the IRCCS Ca’ Granda Foundation Ospedale Maggiore Policlinico of Milan, between September 2016 and May 2023, were included in the present retrospective study. Patients were recruited from the routine clinical activity of the Unit. Inclusion criteria were: (1) diagnosis of MCI according to Petersen criteria 17 ; (2) availability of cerebrospinal fluid (CSF) biomarker data, namely amyloid-β42 (Aβ42), total-Tau (tTau), and phosphorylated-Tau 181 (pTau-181) and (3) completion of a standardized neuropsychological assessment. Exclusion criteria included: (1) diagnosis of dementia; (2) age >85 years; (3) severe cerebrovascular disease (Fazekas score 3); and (4) contraindications to lumbar puncture. All patients underwent a comprehensive clinical and neurological examination and a neuropsychological assessment. Comprehensive diagnostic neuroimaging was conducted for all patients, including brain CT, MRI, or PET scans. Additionally, all patients underwent a lumbar puncture to analyze CSF biomarkers, namely Aβ42, tTau, and pTau-181.
Clinical assessment
Clinical diagnoses were established prospectively at the time of the patient encounter by experienced neurologists. The diagnosis of MCI was established according to the Petersen criteria,17,18 which require the presence of subjective cognitive complaints corroborated by objective deficits on neuropsychological assessment, Mini-Mental State Examination (MMSE) scores falling between 24 and 30, indicating preserved general cognitive functioning, independence in the activities of daily living, and the absence of dementia. Global cognitive performance was measured using the MMSE, 19 whereas a comprehensive battery of neuropsychological tests was used to assess specific cognitive domains and functions. The neuropsychological battery covered the following domains: episodic memory (Rey Auditory Verbal Learning Test), language, visuospatial abilities, and executive functions. Cognitive impairment in a specific domain was defined as performance ≥1.5 SD below age- and education-adjusted normative values. In accordance with Petersen's criteria, all patients were functionally independent; however, subtle difficulties in instrumental activities of daily living (IADL), such as shopping, using transportation, or managing medications, are well recognized as compatible with an MCI diagnosis and may be captured by the Frailty Index (FI) without representing a diagnostic inconsistency. Data were collected through a multidisciplinary assessment involving neurologists, neuropsychologists, and caregivers. The FI was computed retrospectively from clinical records by trained researchers using the same standardized 47-item scale throughout the entire study period (2016–2023), ensuring methodological consistency.
CFS biomarkers
CSF levels of Aβ42, tTau, and pTau-181 were measured using ELISA (Innotest, Fujirebio; cut-offs: Aβ42 < 600 pg/mL, T-tau >450 pg/mL, pTau-181 > 61 pg/mL) until January 2019, and subsequently using an automated CLEIA platform (Lumipulse G600II, Fujirebio; cut-offs: Aβ42 < 544 pg/mL, T-tau >402 pg/mL, pTau-181 > 60.5 pg/mL). Both methods have been validated in our center and demonstrate robust analytical correlation, 20 supporting the consistency of biomarker classification across the study period. Patients were classified according to the ATN system, used to describe the biological characteristics of AD: the letter A is defined by CSF amyloid positivity (A+) and the letter T by CSF p-tau positivity (T+). 21
Frailty
Frailty was operationalized based on the deficit accumulation model, which postulates that one's degree of frailty is proportional to the amount of health deficits accrued in life. 22
For each participant, a 47-item FI was created by following a standard procedure,2,23 based on the collected clinical information and blood tests. Operational definitions for key items included: ‘Falls’ as ≥1 documented fall in the past 12 months as reported by the patient or caregiver; ‘Dizziness' as clinically significant vertigo or postural dizziness documented in the medical record; and ‘Hearing impairment' as documented audiometric impairment recorded as anamnestic data. Variables strongly associated with dementia and cognitive status (e.g., memory disorders, language disorders, spatiotemporal disorientation) were not considered in the computation. Each variable was coded as 0 (i.e., absent or normal) or 1 (i.e., present or abnormal). The FI score was calculated by dividing the sum of deficits presented by the patient by the total 47 items, with a value potentially ranging from 0 (all deficits are absent) to 1 (all deficits are present). The median value of FI distribution was used to categorize participants into two groups (i.e., higher FI versus lower FI). The 47 deficits included in the FI are listed in Table 1.
Table 1.
Deficits included in the computation of the Frailty Index.
| Deficits |
|---|
| Osteoporosis |
| Arterial hypertension |
| Arthritis |
| Gastric disorders |
| Intestinal disorders |
| Chronic ischemic heart disease |
| Chronic heart failure |
| Arrhythmia |
| History of stroke |
| History of TIA |
| Diabetes |
| Renal failure |
| Thyroid disease |
| Cancer |
| Cirrhosis |
| Peripheral arteriopathy |
| Chronic obstructive pulmonary disease (COPD) |
| Dyslipidemia |
| Falls |
| Dizziness |
| Balance disorders |
| Involuntary weight loss (≥ 4.5 kg in the past 6 months) |
| Obesity (BMI ≥ 30 kg/m2) |
| Underweight (BMI < 18.5 kg/m2) |
| Focal neurological signs |
| Parkinsonism |
| Peripheral neuropathy |
| Depression |
| Anxiety |
| Sleep disorders |
| Hearing impairment |
| Urinary incontinence |
| Motor disability (inability to walk for 400 m) |
| Need for help in therapy management (IADL) |
| Need for help with shopping (IADL) |
| Need for help with money management (IADL) |
| Need for help in using the telephone (IADL) |
| Need for help in using transportation (IADL) |
| Need for help with personal hygiene (ADL) |
| Need for help with feeding (ADL) |
| Need for help with dressing (ADL) |
| Need for help with transferring (ADL) |
| Hemoglobin < 13.5 g/dL (men) or < 12 g/dL (women) |
| Creatinine < 0.6 mg/dL |
| Albumin < 3.5 g/dL |
| Folic acid < 2.7 ng/mL |
| White blood cells < 4x103/mm3 |
Statistical analysis
Participants were divided into two groups based on their frailty status, using the median value of the FI as the cut-off point. The t-test was used to compare continuous variables between the two groups, while the chi-square test was used to compare categorical variables.
We used linear regression models to test the association between FI (continuous variable, per 0.1 increase) and MMSE scores. The analyses were adjusted for age, sex, and education (model 1); further adjustments included CSF amyloid status (model 2) and CSF amyloid and tau status (model 3). To explore the association of amyloid (A) and frailty (F) with the MMSE, participants were divided into four mutually exclusive categories (i.e., A-F-, A-F+, A + F-, A + F+), based on amyloid status (i.e., positive/negative) and frailty status (i.e., higher FI/lower FI). We then used linear regression models to explore the association between AF categories and MMSE scores, using the A-F- category (i.e., amyloid negative and non-frail) as the reference group and adjusting for age, sex, and education.
Finally, to study the association between frailty and CSF biomarkers, we used logistic regression models adjusted for age, sex, and education, with frailty status (i.e., higher FI/lower FI) as the predictor and CSF amyloid positivity (A+) and CSF amyloid and tau positivity (A + T+) as outcome. A two-tailed p-value < 0.05 was considered statistically significant in all analyses. The statistical analyses were performed using Stata version 17 (StataCorp, Texas, USA); GraphPad Prism 9 was used for graphical representations.
Ethical statements
The study was conducted retrospectively on clinical data collected as part of routine diagnostic practice. No additional procedures were performed for research purposes. All patients provided informed consent as part of standard clinical care. The study was conducted in accordance with the ethical standards of the institutional committee and the Declaration of Helsinki.
Results
A total of 183 patients with MCI were included in the study (55.2% men). Participants had a mean age of 71.9 years (SD 7.6), a mean education of 12.8 years (SD 4.5), and a mean MMSE score of 26.7 (SD 1.87). The mean FI score was 0.12 (SD 0.07), while the median was 0.11 (IQR 0.09–0.15). In total, 96 patients had a FI score of ≤ 0.11, while 87 patients had a score of >0.11. The sociodemographic and clinical characteristics of study participants are summarized in Table 2. Participants with higher FI scores were significantly older (mean difference 2.6 years, p = 0.017) and had lower MMSE scores (mean difference 0.6, p = 0.047) than those with lower FI scores.
Table 2.
Characteristics of the study population according to frailty status. Data are presented as mean (SD) for continuous variables, and n (%) for categorical variables. p values are derived from the t-test for continuous variables and the chi-square test for categorical variables. A: amyloid; FI: frailty index; MMSE: Mini-Mental State Examination; T: tau.
| Total N = 183 |
FI ≤ 0.11 N = 96 |
FI > 0.11 N = 87 |
p | |
|---|---|---|---|---|
| Age (years) | 71.9 (7.6) | 70.7 (8.1) | 73.3 (6.7) | 0.017 |
| Sex (M) | 91 (55.2%) | 53 (60.2%) | 38 (49.4%) | 0.160 |
| Education (years) | 12.8 (4.5) | 13.2 (4.3) | 12.2 (4.7) | 0.170 |
| MMSE | 26.7 (1.8) | 27.0 (2.0) | 26.4 (1.6) | 0.047 |
| A+ | 93 (50.8%) | 58 (60.4%) | 35 (40.2%) | 0.006 |
| T+ | 103 (56.6%) | 56 (58.9%) | 47 (54.0%) | 0.500 |
Statistically significant differences in amyloid positivity emerged between the two frailty groups, with a higher frequency of amyloid-positive patients observed in patients with lower versus higher FI (60.4% versus 40.2%, χ2=7.441, p = 0.006) (Table 2). No significant differences were instead observed concerning sex, education, and CSF tau positivity.
Association between frailty and MMSE
In a linear regression model adjusted for age, sex, and education, a higher FI was associated with a lower MMSE score. Specifically, each 0.1 increase in the FI (i.e., additional 4–5 deficits) was associated with a reduction of 0.62 points at the MMSE. Further adjustments for CSF amyloid status (model 2) and CSF amyloid and tau status (model 3) did not attenuate the association (Table 3).
Table 3.
Association between frailty index (FI) and MMSE. β coefficients with 95% confidence intervals are derived from linear regression models. Model 1 is adjusted for age, sex, and education. Model 2 is adjusted for model 1 + CSF amyloid status. Model 3 is adjusted for model 1 + CSF amyloid and tau status.
| MMSE score | |||
|---|---|---|---|
| Model 1 β (95% CI) | Model 2 β (95% CI) | Model 3 β (95% CI) | |
| FI (0.1 increase) | −0.62 (−1.10, −0.13) | −0.68 (−1.15, −0.20) | −0.72 (−1.20, −0.24) |
Association of amyloid and frailty status with MMSE
Linear regression models, adjusted for age, sex, and education, were then used to explore the association of amyloid (A) and frailty (F) status with cognitive performance. A synergistic effect between frailty and amyloid status on cognitive impairment emerged. Indeed, considering amyloid-negative participants with lower FI (i.e., A-F-) as the reference group, individuals in the A + F + group showed the lowest MMSE scores (β−1.55, 95% CI −2.50, −0.61), followed by those in the A + F- (β −0.75, 95% CI −1.57, 0.06) and in the A-F + groups (β −0.33, 95% CI −1.19, 0.53) (Figure 1).
Figure 1.
Association of frailty status with CSF amyloid positivity (A+) and CSF amyloid and tau positivity (A + T+). Odds ratios (OR) with 95% confidence intervals are derived from logistic regression models adjusted for age, sex, and education.
Association between frailty and CSF biomarkers
Finally, we studied the association between frailty and neuropathological damage using logistic regression models with frailty status as the predictor and CSF amyloid positivity (A+) and CSF amyloid and tau positivity (A + T+) as outcomes. Patients with lower FI had twice the likelihood of being amyloid positive (A+) relative to those with higher FI (OR 2.00, 95% CI 1.02–3.90) (Table 4). Similarly, patients with lower FI were more than twice as likely to be amyloid and tau positive (A + T+) than those with higher FI (OR 2.29, 95% CI 1.14–4.61) (Table 4).
Table 4.
Association of frailty status with CSF amyloid positivity (A+) and CSF amyloid and tau positivity (A + T+). Odds ratios (OR) with 95% confidence intervals are derived from logistic regression models adjusted for age, sex, and education.
| A+ | A + T+ | |
|---|---|---|
| OR (95%CI) | OR (95%CI) | |
| FI > 0.11 (Ref.) | 1 | 1 |
| FI ≤ 0.11 | 2.00 (1.02–3.90) | 2.29 (1.14–4.61) |
Discussion
In this cross-sectional study, we investigated the interplay between frailty, AD biomarkers, and cognitive function among subjects with MCI referred to a university memory clinic. Overall, our study suggests that frailty is associated with the clinical expression of cognitive disorders, contributing to their phenotypic heterogeneity.
Three main results emerge from our analysis. First, participants with a higher FI have a lower MMSE score, which aligns with the existing evidence on the detrimental effect of frailty on cognition. Previous studies have already shown that higher frailty degrees are associated with worse cognitive performance and trajectories, among dementia-free individuals and people living with MCI and dementia.24,25 Furthermore, we showed that this unfavorable association is independent of amyloid and tau pathologies, the typical neuropathological changes underlying AD. In this regard, previous clinical-pathological studies documented how frailty is negatively associated with global cognition and dementia regardless of neuropathological burden.26,27 Although these studies were conducted in populations with more advanced cognitive impairment, our results are consistent with and extend this evidence to individuals at the MCI stage. Taken together, these findings suggest that frailty may reflect alternative pathophysiological mechanisms associated with worse cognitive performance in patients with MCI, independently of AD neuropathology, and that, in frail individuals, even a low amount of brain pathological changes can result in overt cognitive impairment. Secondly, our analysis revealed that, relative to robust, amyloid-negative subjects, frail patients with CSF amyloid positivity have significantly lower global cognitive performance as measured by MMSE scores. This suggests that frailty and amyloid deposition contribute synergistically to cognitive decline in patients with MCI. Moreover, these findings reinforce the notion of cognitive disorders as multiply determined conditions, 27 whose occurrence and phenotypic expression result from a combination of factors that are “intrinsic” to the disease and factors that are more closely related to the biological aging process and the age-related decline in general health. When considered together, the first and second results reveal a complementary picture: while frailty is associated with cognitive performance independently of AD biomarker status, the co-occurrence of higher frailty and amyloid positivity appears to confer an additive burden on cognition, suggesting that frailty and AD neuropathology may contribute to cognitive decline through distinct but convergent pathophysiological pathways.
Finally, we found that, among subjects with the same cognitive status (i.e., MCI), higher frailty degrees are associated with lower odds of testing positive for AD biomarkers. In other words, the probability of detecting in vivo the neuropathological signature of AD is higher in robust than in frail older individuals with MCI. This aligns with the broader pathophysiological rationale outlined above, whereby frailty lowers the threshold at which AD neuropathology becomes clinically manifest. More directly, a recent independent study conducted in an MCI memory clinic population specifically showed that higher frailty degrees were associated with a lower likelihood of AD biomarker positivity, 28 consistent with our findings. Notably, that study employed a different operationalization of frailty (35-item FI), suggesting that the observed association is robust across different approaches to measure frailty. These observations support the notion that “pure” AD neuropathology tends to be expressed in individuals with a relatively preserved general health status, while mixed etiologies are more commonly observed in the context of higher clinical complexity and greater accumulation of health deficits. 28 This pattern may partly reflect the role of frailty as a modifier of AD 27 Based on these considerations, frailty has received specific focus in recent policy documents on dementia risk reduction, emerging as a promising target for individual- and population-based approaches to dementia prevention. 13 Frailty assessment can also help interpret the results of biomarker testing and identify the most suitable candidates for these procedures. Indeed, measuring frailty could help recognize which patients are most likely to exhibit or not exhibit a picture compatible with a diagnosis of AD, with a better allocation of available resources and the identification of candidates for new disease-modifying therapies for AD.
Several study limitations should be recognized and discussed. First, the cross-sectional design of the study impedes inferring the causal relationship between the explored variables. The results were obtained in a single, highly specialized center and may not apply to other samples and settings. Moreover, the study population was mostly composed of relatively robust individuals without contraindications to lumbar puncture for CSF analysis, thus limiting the assessment of the impact of frailty on the clinical and biological outcome measures. Additionally, the use of the sample-specific median as a cut-off for FI dichotomization does not correspond to externally validated frailty thresholds; the two groups should therefore be interpreted as ‘lower relative frailty’ and ‘higher relative frailty’ rather than ‘non-frail’ and ‘frail’ in absolute terms. Finally, the use of the MMSE as the primary cognitive outcome represents a limitation, as it provides a measure of global cognitive performance only. Future studies should examine the association between frailty and domain-specific neuropsychological performance, including episodic memory and executive functions. Future longitudinal, multicenter studies involving frailer and older subjects are needed to expand our findings.
In conclusion, frailty contributes to cognitive decline in patients with MCI, adding to the effect of AD neuropathology and likely reflecting alternative pathophysiological mechanisms. Elucidating the role of frailty in MCI may enhance the informative capacity of candidate biomarkers and, potentially, the identification of target populations for novel therapies. Moreover, it may support a more biologically oriented care planning.
Acknowledgements
The authors used AI-assisted tools to support manuscript preparation and revision.
Footnotes
ORCID iDs: Manuela Pintus https://orcid.org/0009-0009-4292-060X
Marina Arcaro https://orcid.org/0000-0002-3821-0682
Ethical considerations: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments, or comparable ethical standards.
Consent to participate: Informed consent was obtained from all individual participants included in the study.
Consent for publication: Not applicable.
Author contribution(s): Manuela Pintus: Data curation; Formal analysis; Investigation; Methodology; Project administration; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing.
Martina Valletta: Data curation; Formal analysis; Investigation; Methodology; Supervision; Writing – review & editing.
Luca Sacchi: Investigation; Supervision; Visualization; Writing – review & editing.
Tiziana Carandini: Data curation; Investigation; Writing – review & editing.
Laura Ghezzi: Methodology; Supervision; Validation; Visualization; Writing – review & editing.
Anna Pietroboni: Data curation; Investigation; Writing – review & editing.
Chiara Fenoglio: Data curation; Investigation; Visualization; Writing – review & editing.
Maria Serpente: Data curation; Investigation; Validation; Writing – review & editing.
Marina Arcaro: Data curation; Investigation; Supervision; Visualization; Writing – review & editing.
Giuseppe Bruno: Conceptualization; Data curation; Investigation; Methodology; Supervision; Writing – review & editing.
Daniela Galimberti: Investigation; Validation; Visualization; Writing – review & editing.
Marco Canevelli: Conceptualization; Formal analysis; Investigation; Methodology; Supervision; Validation; Visualization; Writing – review & editing.
Andrea Arighi: Conceptualization; Formal analysis; Resources; Supervision; Validation; Visualization; Writing – review & editing.
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Italian Ministry of Health (Ricerca Corrente to AA and DG and grant PNRR-POC-2023-12377354 to DG). This research was also supported by Fondazione Gigi & Pupa Ferrari, Associazione Centro Dino Ferrari ETS, Fondazione BCC Milano and Repower.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.