Frailty has been conceptualized as “a medical syndrome with multiple causes and contributors that is characterized by diminished strength, endurance, and reduced physiologic function that increases an individual’s vulnerability for developing increased dependency and/or death” (1). This construct has increasingly been adopted in order to capture the biological decline of the aging individual and his/her risk profile for negative health-related outcomes (2). Moreover, it is growingly acquiring public health relevance as it may support the realignment of models of care to the changing needs of our aging populations (3).

In these last years, the relationship between frailty and cognition has triggered special interest. The contribution of cognitive skills and capacities to the individual’s vulnerability and resiliency has more consistently been considered and recognized (4). Cross-sectional analyses have repeatedly shown that frail individuals have lower cognitive performance compared with non-frail persons (5, 6). Accordingly, several longitudinal studies have documented a higher risk of incident cognitive impairment and dementia among frail subjects (5). More recently, frailty indexes (FIs) have been found to predict poorer outcomes (i.e., mortality, institutionalization, faster cognitive worsening) in populations of patients already exhibiting overt dementing conditions (7, 8). However, to our knowledge, no study has yet explored the impact of the individual frailty status on the clinical trajectories over time of subjects with milder cognitive deficits.

The aim of the present study was to measure frailty in a cohort of individuals with mild cognitive impairment (MCI) and to explore whether frailty measures may consent to predict the risk of conversion to dementia.

One hundred thirty-two subjects were consecutively diagnosed with aMCI between April 2011 and April 2012 at our Department. The retrospective analysis showed that 109 of them were followed-up for the next 5 years. Nevertheless, only 91 subjects (women 49.5%) received two or more clinical and neuropsychological evaluations per year and were, thus, finally considered for the present analyses (Figure 1; Table 2). Participants had a mean age of 72.7 (SD 7.1) years and a mean educational level of 7.7 (SD 3.6) years. MMSE values at the baseline (mean 25.4, SD 2.8) indicated a globally preserved cognitive functioning. Low levels of frailty were documented in the sample (mean FI score 10.0, SD 5.3). Accordingly, none of the subjects resulted as frail [i.e., FI score ≥ 25.0 (8)]. A statistically significant correlation between age and FI was observed (Spearman’s r = 0.31; p < 0.01) (Figure 2A).

Over a follow-up of 5 years, 58 subjects converted from MCI to probable AD dementia, whereas 33 did not exhibit a clinical worsening. At the basal evaluation, “MCI converters” were older, more severely cognitive impaired, and exhibited a higher prevalence of diabetes compared to “MCI non-converters” (Table 2). Moreover, subjects converting to dementia had significantly higher mean scores at the FI (11.6, SD 5.3 vs. 7.3, SD 4.1; p < 0.001), indicating greater levels of frailty (Table 2 and Figure 2B).

The Cox regression model, adjusted for age and sex, showed that increasing age, male sex, lower MMSE scores, and higher FI scores were all significantly associated with an increased probability of MCI conversion (Table 3). The positive association between FI and the risk of conversion was also confirmed when restricting the analyses to only those subjects exhibiting normal MMSE scores (i.e., ≥24) (HR: 1.20; 95% CI: 1.05–1.19; p < 0.001) or the highest level of cognitive performance (i.e., MMSE ≥ 27, upper quartile of the distribution) (HR: 1.20; 95% CI: 1.05–1.19; p < 0.001).

To our knowledge, this is the first study exploring the impact of the individual’s frailty status and biological decline on the risk of conversion from MCI to dementia. Overall, frailty levels, measured through a FI, resulted to be strongly associated with the risk of cognitive and functional worsening. In fact, subjects with higher FI scores exhibited a significantly increased risk of developing future AD dementia.

Nowadays, special attention is being focused on the possibility of identifying the clinical factors and laboratory findings consenting to early/timely detect those subjects at increased risk of dementia. In this scenario, MCI has increasingly been considered as the optimal phase to explore the clinical and pathophysiological modifications anticipating the onset of overt dementing syndromes (21). To date, most of studies on the conversion of MCI have been concentrated on the contribution of crude socio-demographic (e.g., sex, age, and educational level) and clinical (e.g., comorbidities, neuropsychiatric symptoms) variables, mostly exploring the predictive value of findings and measures individually referring to a specific individual’s health domain (e.g., neuropsychological functions, functional abilities, neuroimaging abnormalities, genetic traits) (22). Nevertheless, existing models of prediction of MCI progression have been shown to have several limitations, including poor discrimination and low positive predictive values (22). Accordingly, the adoption of novel approaches, more properly accounting for the clinical and biological heterogeneity of older people at risk for cognitive decline, has repeatedly been solicited (23).

In this context, the introduction of constructs more broadly reflecting the individual’s frailty status and his/her biological aging may open promising scenarios in the field. This approach may facilitate to multidimensionally capture the pathophysiological complexity of cognitive disorders and neurodegenerative conditions. Moreover, it may consent to more holistically consider the overall health status of the aging individual experiencing the onset of cognitive disturbances, thus not neglecting the multiple and variegate aspects (from sleep disorders to depression, from nutritional deficiencies to polypharmacy) potentially contributing to their occurrence and influencing their phenotypic expression (24). As a proof, in our study, beside the well-established impact of age and baseline cognitive functioning (i.e., MMSE scores), the accumulation of clinical/biological deficits (captured by the FI) significantly influenced the risk of AD dementia. Specifically, FI scores influenced the overall risk of MCI conversion more than age, a well-established risk factor for cognitive decline and dementia. It is noteworthy that the discriminative capacity of the FI was observed despite the cohort being composed exclusively by robust subjects, and was confirmed also among those participants exhibiting the best levels of cognitive performance. These findings are in line with that obtained in cohorts of patients already exhibiting dementing conditions, with frailty measures predicting cognitive outcomes and trajectories (8).

More in particular, our results confirm that the FI may provide useful information when approaching individuals with cognitive disturbances. This model is also easy-to-adopt, being potentially applicable (even retrospectively) from existing datasets and available clinical information. Its use will be even more simplified by the increasing use of electronic medical records (25). In parallel, it can be directly implemented in the clinical practice without requiring changes in the routine/standard approach, not requiring the adoption of specific tests, tools, and ad hoc questionnaires, potentially resulting in costly and time-consuming procedures (24).

The present study has some limitations worth to be mentioned. In particular, the small sample size does not consent to draw firm conclusions on the topic. The study population was composed by highly selected MCI subjects attending a university memory clinic, thus with potentially issues in terms of external validity. Moreover, we only focused on the conversion of aMCI to AD dementia, thus not considering the outcomes of different MCI subtypes and the progression toward different dementing conditions.

In conclusions, frailty may significantly increase the individual risk of conversion from a condition of MCI to overt AD dementia. The adoption of constructs comprehensively reflecting the biological decline of the aging subject may add useful estimates and information to those provided by monodimensional variables and traditional cognitive evaluations. In this context, models of frailty (such as the FI) may be easily and promisingly introduced in the neurological practice with the aim of improving both clinical and research standards.

All the data used in the analyses were exclusively retrieved from medical charts where information was recorded as part of the standard clinical routine. The patients and their caregivers (or legal guardians when necessary) provided written informed consent for allowing the utilization of the collected data for research purposes in accordance with the Declaration of Helsinki. The local Ethics Committee, “Comitato Etico Sapienza,” approved the protocol.

AT and MC conceived and designed the work, performed the literature search, and wrote the manuscript. LI, LP, and LR collected the neuropsychological data. FD, AC, and VC collected the clinical data. GB and CL participated to the critical appraisal of the available evidence on the topic.

This research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.