Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-22T06:09:47.697Z Has data issue: false hasContentIssue false

Language performance as a prognostic factor for developing Alzheimer’s clinical syndrome and mild cognitive impairment: Results from the population-based HELIAD cohort

Published online by Cambridge University Press:  21 October 2022

Vasiliki Folia*
Affiliation:
Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Greece
Ioannis Liampas
Affiliation:
Department of Neurology, School of Medicine, University Hospital of Larissa, University of Thessaly, Mezourlo Hill, Larissa, Greece
Vasileios Siokas
Affiliation:
Department of Neurology, School of Medicine, University Hospital of Larissa, University of Thessaly, Mezourlo Hill, Larissa, Greece
Susana Silva
Affiliation:
Center for Psychology, University of Porto, Porto, Portugal
Eva Ntanasi
Affiliation:
Department of Nutrition and Dietetics, Harokopio University, Athens, Greece 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
Mary Yannakoulia
Affiliation:
Department of Nutrition and Dietetics, Harokopio University, Athens, Greece
Paraskevi Sakka
Affiliation:
Athens Alzheimer’s Association, Athens, Greece
Georgios Hadjigeorgiou
Affiliation:
Department of Neurology, School of Medicine, University Hospital of Larissa, University of Thessaly, Mezourlo Hill, Larissa, Greece School of Medicine, University of Cyprus, Engomi, Nicosia, Cyprus
Nikolaos Scarmeas
Affiliation:
1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, The Gertrude H. Sergievsky Center, Department of Neurology, Columbia University, New York, USA
Efthimios Dardiotis
Affiliation:
Department of Neurology, School of Medicine, University Hospital of Larissa, University of Thessaly, Mezourlo Hill, Larissa, Greece
Mary H. Kosmidis
Affiliation:
Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Greece
*
Corresponding author: Vasiliki Folia, email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Objectives:

There is limited research on the prognostic value of language tasks regarding mild cognitive impairment (MCI) and Alzheimer’s clinical syndrome (ACS) development in the cognitively normal (CN) elderly, as well as MCI to ACS conversion.

Methods:

Participants were drawn from the population-based Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) cohort. Language performance was evaluated via verbal fluency [semantic (SVF) and phonemic (PVF)], confrontation naming [Boston Naming Test short form (BNTsf)], verbal comprehension, and repetition tasks. An additional language index was estimated using both verbal fluency tasks: SVF-PVF discrepancy. Cox proportional hazards analyses adjusted for important sociodemographic parameters (age, sex, education, main occupation, and socioeconomic status) and global cognitive status [Mini Mental State Examination score (MMSE)] were performed.

Results:

A total of 959 CN and 118 MCI older (>64 years) individuals had follow-up investigations after a mean of ∼3 years. Regarding the CN group, each standard deviation increase in the composite language score reduced the risk of ACS and MCI by 49% (8–72%) and 32% (8–50%), respectively; better SVF and BNTsf performance were also independently associated with reduced risk of ACS and MCI. On the other hand, using the smaller MCI participant set, no language measurement was related to the risk of MCI to ACS conversion.

Conclusions:

Impaired language performance is associated with elevated risk of ACS and MCI development. Better SVF and BNTsf performance are associated with reduced risk of ACS and MCI in CN individuals, independent of age, sex, education, main occupation, socioeconomic status, and MMSE scores at baseline.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © INS. Published by Cambridge University Press, 2022

Introduction

Language decline is a long-recognized feature of mild cognitive impairment (MCI) and dementia. Heterogeneous patterns of language impairment are often considered characteristic of different neurocognitive pathologies (Klimova & Kuca Reference Klimova and Kuca2016; Vuorinen et al., Reference Vuorinen, Laine and Rinne2000). At the same time, different language aspects tend to record dissimilar longitudinal trajectories with normal aging in older adults, with some of them declining, others improving, and several remaining relatively intact over time (Harada et al., Reference Harada, Natelson Love and Triebel2013; Hartshorne & Germine, Reference Hartshorne and Germine2015; Kemper et al., Reference Kemper, Marquis and Thompson2001; Marini et al., Reference Marini, Boewe, Caltagirone and Carlomagno2005; Shafto & Tyler, Reference Shafto and Tyler2014). For example, word finding during naturalistic speech, visual confrontation naming, grammatical and syntactic complexity, prepositional content, as well as discourse coherence tend to decline over time, “‘empty’” pauses appear more frequently while semantic paraphasias and vague terms become more and more common. On the other hand, the proportion of phonologically well-formed words out of all verbalizations and the ratio of nouns to verbs produced remain unaltered, whereas conceptual knowledge generally increases even during senescence. Thus, the pattern of language impairment may provide valuable assistance in discriminating pathological neurocognitive entities from each other, as well as in differentiating healthy aging from early MCI and dementia processes (Gomez & White, 2016; Klimova & Kuca, Reference Klimova and Kuca2016; Maseda et al., Reference Maseda, Lodeiro-Fernández, Lorenzo-López, Núñez-Naveira, Balo and Millán-Calenti2014; Nutter-Upham et al., Reference Nutter-Upham, Saykin, Rabin, Roth, Wishart, Pare and Flashman2008).

Apart from the discriminant quality of language performance, a small number of studies have investigated the prognostic value of language performance regarding the development of dementia or MCI. Recently, Sutin and colleagues (Reference Sutin, Stephan and Terracciano2019) investigated the predictive value of semantic verbal fluency (SVF) with respect to the risk of developing dementia and cognitive impairment – not dementia (CIND), capitalizing data from a sample of 18,189 individuals from the Health and Retirement Study (Sutin et al., Reference Sutin, Stephan and Terracciano2019). Each standard deviation (SD) increase in SVF scores was related to 60% reduced risk of incident dementia and 25% reduced risk of incident CIND in cognitively normal (CN) adults, as well as 25% reduced risk of conversion from CIND to dementia, independently of age, sex, education, race, ethnicity, and APOE risk status. Additional smaller studies have also obtained corroborating evidence suggesting a predictive quality for SVF. SVF performance has been found to predict the development of amnestic MCI (aMCI) in CN individuals (Gustavson et al., Reference Gustavson, Elman, Panizzon, Franz, Zuber, Sanderson-Cimino, Reynolds, Jacobson, Xian, Jak, Toomey, Lyons and Kremen2020) as well as the conversion from aMCI to dementia (Gallucci et al., Reference Gallucci, Di Battista, Battistella, Falcone, Bisiacchi and Di Giorgi2018), independently of episodic memory impairment, the hallmark of aMCI and Alzheimer’s disease (AD), termed as Alzheimer’s clinical syndrome (ACS) throughout the text (Jack et al., Reference Jack, Bennett, Blennow, Carrillo, Dunn, Haeberlein, Holtzman, Jagust, Jessen, Karlawish, Liu, Molinuevo, Montine, Phelps, Rankin, Rowe, Scheltens, Siemers, Snyder and Sperling2018). Of note, an abrupt (within two years) 1-SD decrease in SVF has been even revealed to predict the onset of dementia three years following the SVF drop, with a sensitivity and specificity of 92% and 62%, respectively (Wong et al., Reference Wong, Leung, Fung, Chan and Lam2013). Furthermore, measures combining SVF with phonemic verbal fluency (PVF) scores (e.g., SVF minus PVF scores) have proved to be fair prognostic indices of MCI to dementia conversion (Vaughan et al., Reference Vaughan, Coen, Kenny and Lawlor2018). On the other hand, there is limited existing evidence not indicative of a predictive quality for PVF measures alone (Holtzer et al., Reference Holtzer, Jacobs and Demetriou2020).

Linguistic components other than verbal fluency have been less extensively explored. A number of writing (e.g., misspelling and number of commas) and syntactic (e.g., dependency labels and determiners) parameters may be useful in the prediction of ACS development in CN individuals (Eyigoz et al., Reference Eyigoz, Mathur, Santamaria, Cecchi and Naylor2020). Measures of semantic degradation and namely lexical impoverishment reflected as increased production of high-frequency words during spontaneous speech appear to be fine predictors of future cognitive testing performance in individuals with normal cognition (Ostrand & Gunstad, Reference Ostrand and Gunstad2021). Finally, little and conflicting published data support a potential prognostic value for confrontation naming in the progression of cognitive impairment, while verbal comprehension and repetition have been substantially underinvestigated as potential prognostic markers of cognitive impairment (Albert et al., Reference Albert, Moss, Tanzi and Jones2001; Chodosh et al., Reference Chodosh, Reuben, Albert and Seeman2002; Jacobs et al., Reference Jacobs, Mercuri and Holtzer2021).

Despite the limited relevant research, language indices possess several qualities that could potentially render them suitable candidates for the prediction of ACS and MCI. SVF (rather than PVF) and confrontation naming deficits are quite prominent in individuals with ACS from an early stage, potentially reflecting a degradation of the semantic store (Henry et al., Reference Henry, Crawford and Phillips2004). Language impairment appears to afflict individuals with MCI as well, with language subtests being more sensitive than other neuropsychological indices (even episodic memory) in revealing the presence of cognitive deficits in the early stages of MCI (McCullough et al., Reference McCullough, Bayles and Bouldin2019). At the same time, previous research has suggested that episodic memory and category fluency tend to deteriorate sooner than other cognitive functions, even in the preclinical course of MCI-ACS (Mistridis et al., Reference Mistridis, Krumm, Monsch, Berres and Taylor2015). Moreover, relatively poor semantic fluency performance has been even found to predict incident episodic memory deficits, although episodic memory performance has not been associated with the risk of incident language deficits (Gustavson et al., Reference Gustavson, Elman, Panizzon, Franz, Zuber, Sanderson-Cimino, Reynolds, Jacobson, Xian, Jak, Toomey, Lyons and Kremen2020). Considering the above, SVF might reflect ongoing neurodegenerative processes in the preclinical course of MCI-ACS, sooner than other neuropsychological tasks.

Therefore, the present study was undertaken to investigate the prognostic value of language performance for MCI and ACS development, as well as MCI to ACS progression. Data from the prospective Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) study were used. The prognostic value of composite and individual language measures (specifically SVF, PVF, SVF-PVF discrepancy, confrontation naming, comprehension, and repetition) was explored, while adjusting for global cognitive status (to account for the confounding of concomitant cognitive dysfunctions) and important sociodemographic parameters that might affect language impairment as well as the risk of developing ACS and MCI (Karp et al., Reference Karp, Kareholt, Qiu, Bellander, Winblad and Fratiglioni2004; Letenneur et al., Reference Letenneur, Gilleron, Commenges, Helmer, Orgogozo and Dartigues1999). It was hypothesized that relative language deficits could be present from a preclinical stage preceding the development of MCI and ACS (reflecting ongoing neurodegenerative processes) and, therefore, serve as potential clinical markers of MCI or ACS development.

Methods

Study reporting adhered to the STROBE reporting recommendations (Strengthening the Reporting of Observational Studies in Epidemiology) (von Elm et al., Reference von Elm, Altman, Egger, Pocock, Gøtzsche and Vandenbroucke2014). Participants originated from the prospective HELIAD cohort. Extensive details regarding the rationale, objectives, and other key elements of the HELIAD study have been previously reported (Dardiotis et al, Reference Dardiotis, Kosmidis, Yannakoulia, Hadjigeorgiou and Scarmeas2014; Kosmidis et al., Reference Kosmidis, Vlachos, Anastasiou, Yannakoulia, Dardiotis, Hadjigeorgiou, Sakka, Ntanasi and Scarmeas2018; Liampas et al., Reference Liampas, Folia, Ntanasi, Yannakoulia, Sakka, Hadjigeorgiou, Scarmeas, Dardiotis and Kosmidis2022). In short, it is a multidisciplinary, population-based study primarily exploring the epidemiology of dementia, cognitive impairment, as well as other neuropsychological entities, among the elderly Greek population. The Institutional Ethics Review Boards of the University of Thessaly and the Kapodistrian University of Athens approved all procedures prior to the initiation of the study and was conducted in accordance with the Declaration of Helsinki. Informed consent was acquired from all participants or surrogates prior to participation.

Participant selection was carried out by random sampling, from the elderly (>64 years) registries of two Greek municipalities, Marousi (city of Athens) and Larissa (province of Thessaly). Participants underwent extensive baseline and follow-up evaluations (ongoing to date), which were intended to take place at approximately 3-year intervals. Study procedures were carried out at participants’ homes, day care centers for the elderly, municipal public health clinics, etc., according to participants’ wishes and feasibility considerations. Collaborative assessments (2–2.5-hour sessions) designated by a consortium of expert neurologists and neuropsychologists were performed at both visits. Relevant information was collected either from participants or participant carers, when necessary (first-degree relatives, etc). A maximum of two assessments (baseline and second visit) are readily available per individual, so far (3rd visits were recently initiated). A description of the evaluations pertinent to the present article is provided below.

Neuropsychological assessments

A comprehensive neuropsychological assessment was carried out (Bougea et al., Reference Bougea, Maraki, Yannakoulia, Stamelou, Xiromerisiou, Kosmidis, Ntanasi, Dardiotis, Hadjigeorgiou, Sakka, Anastasiou, Stefanis and Scarmeas2019). The Mini Mental State Examination (MMSE) was utilized as a measure of orientation and global cognitive status (Folstein et al., Reference Folstein, Folstein and McHugh1975). The cognitive domain of language was evaluated according to the parameters of verbal fluency (semantic and phonemic), confrontation naming, comprehension, and repetition. Verbal fluency was appraised as previously described in more detail (Kosmidis et al., Reference Kosmidis, Vlahou, Panagiotaki and Kiosseoglou2004). In brief, individuals were first asked to generate as many different words as possible, belonging to one semantic category (animals, fruits, or objects), whereas, in the second part, participants were asked to generate as many different words as possible, beginning with one Greek letter ([χ] (chi), [s] (sigma) or [a] (alpha)). Participants were instructed to immediately begin generating items, following the announcement of the category or letter, and each trial lasted for 60 s. Regarding word search and production, no instructions were given, to ensure that any cognitive strategies would be spontaneously employed by the examinees. However, participants were told to abstain from reporting items irrelevant to the designated category or letter and proper nouns (regarding the phonemic test), as well as repetitions and word variations. Finally, for the purposes of the current article, an additional language index calculated using the semantic and PVF scores was devised (SVF – PVF discrepancy).

Confrontation naming, as well as verbal comprehension and repetition of words and phrases, was evaluated using subtests of the Greek version of the Boston Diagnostic Aphasia Examination short form, i.e., the Boston Naming Test short form (BNTsf, 15-item test) and selected items from the Complex Ideational Material Subtest (12-item and 6-item tests for the assessments of comprehension and repetition, respectively) (Tsapkini et al., Reference Tsapkini, Vlahou and Potagas2010). Participants’ raw scores in each language measure were converted into z scores using mean and standard deviation values of the CN individuals. Subsequently, z scores from individual language tests were averaged to generate an equally weighted composite z score for the domain of language.

The diagnostic assessment of participants’ cognitive status took place during expert meetings, involving senior neurologists (E.D., G.M.H., P.S., and N.S) and neuropsychologists (M.H.K.). For a detailed description of the diagnostic procedures, please refer to Kosmidis et al. (Reference Kosmidis, Vlachos, Anastasiou, Yannakoulia, Dardiotis, Hadjigeorgiou, Sakka, Ntanasi and Scarmeas2018). In brief, particular focus was placed on identifying potential comorbidities that could affect cognitive performance through screening the participants for depression, anxiety, essential tremor, behavioral symptoms, Parkinson’s disease, dementia with Lewy bodies (DLB), as well as personal history of cerebrovascular disease accounting for the onset or deterioration of cognitive decline. The diagnoses of dementia and possible-probable ACS were based on the Diagnostic and Statistical Manual of Mental Disorders-IV-text revision criteria and the National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer Disease and Related Disorders Association criteria (American Psychiatric Association, 2000; McKhann et al., Reference McKhann, Drachman, Folstein, Katzman, Price and Stadlan1984), respectively. MCI and its subtypes were diagnosed according to the Petersen criteria (Petersen, Reference Petersen2004).

Statistical analysis and outcome measures

Two discrete primary outcomes were defined a priori: development of ACS and MCI of any type (amnestic or nonamnestic) at follow-up. Both outcomes were investigated using Cox proportional hazards regressions. For the former outcome (ACS diagnosis at follow-up), CN participants at baseline were analyzed, excluding those with any dementia diagnosis other than ACS at follow-up, in view of the competing nature of different dementia entities. For the latter outcome (MCI of any type at follow-up), CN participants at baseline without any dementia diagnosis at follow-up were analyzed (due to the lack of information regarding the transitional MCI development). A secondary analysis investigating the conversion of MCI to ACS was also planned a priori (using a smaller participants’ set, those with MCI at baseline). Individuals with any dementia diagnosis other than ACS at follow-up were excluded from the secondary analysis as well.

Time-to-second-visit was employed as the time-to-event variable. In case of not documenting the event of interest, participants were censored at second visit. Cox models were adjusted for age at baseline, sex, years of education, standardized MMSE scores, main occupation, and socioeconomic status. Inclusion of MMSE aimed toward controlling for global cognitive function, i.e., we explored the predictive ability of language function independently of the general cognitive status of the participants. First, composite language scores were analyzed using the conventional α = 0.05 threshold of significance. Sequentially, individual language raw scores (6 in total, i.e., SVF, PVF, SVF-PVF discrepancy, confrontation naming, verbal comprehension, and repetition) were inserted into separate Cox proportional hazards models. To correct for multiple comparisons (six per participant set), the significance cutoff was set at α = 0.008.

All statistical analyses were performed using the IBM SPSS Statistics Software Version 25 (Chicago, IL, USA). Age at baseline, years of education, and standardized MMSE scores (i.e., MMSE scores of illiterate participants were converted to the literate MMSE scale of 30) were treated as scale variables. Sex, main occupation (manual or mental), and socioeconomic status (low or high) were treated as dichotomous variables (Mourtzi et al., Reference Mourtzi, Yannakoulia, Ntanasi, Kosmidis, Anastasiou, Dardiotis, Hadjigeorgiou, Megalou, Sakka and Scarmeas2018). Baseline differences between groups were tested using independent sample t-test for continuous variables and Pearson’s chi-squared test for categorical data. Depression (evaluated according to the 15-item Geriatric Depression Scale with higher values reflecting greater depression levels) and functional status (appraised using the 9-item instrumental activities of daily living – extended scale with higher values reflecting better functional status) assessments were provided for descriptive purposes (Kalligerou et al., Reference Kalligerou, Fieo, Paraskevas, Zalonis, Kosmidis, Yannakoulia, Ntanasi, Dardiotis, Hadjigeorgiou, Sakka and Scarmeas2020).

Results

Baseline characteristics and missing data

The prospective HELIAD cohort consisted of 1984 participants with baseline evaluations. Among them, there were 103 individuals with dementia, 243 with MCI, and 4 with an inconclusive cognitive diagnosis (all excluded from our analysis). From the total of 1607 individuals who were CN at baseline, a subgroup of 959 individuals had available follow-up assessments at the time of the present analysis. The mean duration between the initial and follow-up assessments was 3.09 years (range: 1.16 – 7.26 years). Compared to those without follow-up assessments, those with available follow-up evaluations were younger, more often classified as mental laborers, and had higher MMSE, composite as well as individual language scores (data not shown).

Four CN participants at baseline were missing a follow-up cognitive diagnosis. Among the remaining 955 individuals, 34 developed dementia, 29 of whom developed ACS, and 5 of whom converted to other dementia entities (excluded from our analysis). The baseline characteristics of the CN sample according to the development of ACS or not at follow-up are provided in Table 1. Those who developed ACS were older at baseline, less educated, and had worse functional status and lower MMSE and language scores.

Table 1. Baseline characteristics of cognitively normal participants (CN) according to the follow-up diagnosis of Alzheimer’s clinical syndrome (ACS) or not. Those with any other dementia diagnosis at follow-up were excluded. The number of participants with available data per parameter is provided

N, total number of participants; n, number of participants with available data per variable; M/F, male/female; MMSE, Mini Mental State Examination; SVF, semantic verbal fluency; PVF, phonemic verbal fluency; BNTsf, Boston Naming Test short form; bold denotes statistical significance.

Among the 921 nondemented participants at follow-up, 761 remained CN and 160 developed MCI of any type (amnestic or nonamnestic). The baseline characteristics of the CN sample based on the development of MCI or not at follow-up are provided in Table 2. Similar to those with dementia at second visit, those with MCI were older at baseline, less educated, and achieved lower MMSE and language scores during the initial evaluation. However, in addition to the above, a greater portion of these individuals was of low socioeconomic status and had a manual occupation.

Table 2. Baseline characteristics of cognitively normal participants (CN) based on the follow-up diagnosis of mild cognitive impairment (MCI) of any type (amnestic or nonamnestic) or not. Those with dementia diagnosis at follow-up were excluded. The number of participants with available data per parameter is provided

N, total number of participants; n, number of participants with available data per variable; M/F, male/female; MMSE, Mini Mental State Examination; SVF, semantic verbal fluency; PVF, phonemic verbal fluency; BNTsf, Boston Naming Test short form; bold denotes statistical significance.

Finally, regarding those with MCI at baseline (N = 243), 118 participants had available follow-up assessments at the time of the present analysis (secondary set). Twenty-nine individuals with MCI developed dementia at second visit, 25 of whom were diagnosed with ACS. The mean duration between the initial and follow-up assessments was 2.92 years (range: 1.29 – 6.21 years). Those with dementia at second visit were more often of lower socioeconomic status and had worse functional status, while recorded lower MMSE, SVF, verbal comprehension and SVF-PVF discrepancy scores (Table 3).

Table 3. Baseline characteristics of participants with mild cognitive impairment (MCI) of any type (amnestic or nonamnestic) based on the follow-up diagnosis of Alzheimer’s clinical syndrome (ACS) or not. Those with any other dementia diagnosis at follow-up were excluded. The number of participants with available data per parameter is provided

n, total number of participants per group; N, number of participants with available data per variable; M/F, male/female; MMSE, Mini Mental State Examination; SVF, semantic verbal fluency; PVF, phonemic verbal fluency; BNTsf, Boston Naming Test short form; bold denotes statistical significance.

The prognostic value of language measurements regarding the development of dementia or mild cognitive impairment

Tables 4 and 5 summarize the prognostic value of baseline language scores regarding the risk of developing ACS and MCI of any type (amnestic or nonamnestic). With respect to CN individuals, each SD increase in the composite language score reduced the risk of incident ACS by about 49% (Figure 1).

Table 4. Adjusted cox proportional hazards regressions with follow-up diagnosis of Alzheimer’s clinical syndrome (ACS) and mild cognitive impairment (MCI) of any type (amnestic or nonamnestic) as the dichotomous outcomes. Individuals with normal cognition (CN) at baseline were analyzed

N/A, not applicable; HR, hazard ratio; CI, confidence interval; SVF, semantic verbal fluency; PVF, phonemic verbal fluency; BNTsf, Boston Naming Test short form; bold denotes statistical significance

Table 5. Adjusted cox proportional hazards regressions with follow-up diagnosis of Alzheimer’s clinical syndrome (ACS) as the dichotomous outcome. Individuals with mild cognitive impairment (MCI) of any type (amnestic or non-amnestic) at baseline were analyzed

N/A not applicable; HR, hazard ratio; CI, confidence interval; SVF, semantic verbal fluency; PVF, phonemic verbal fluency; BNTsf, Boston Naming Test short form; bold denotes statistical significance.

Figure 1. Survival curves for incident Alzheimer’s clinical syndrome (ACS) according to the baseline composite language performance of the participants. Individuals were clustered using mean composite language values and standard deviation (SD) units to form four baseline strata: ≤−1SD unit, >−1SD unit and ≤ mean, > mean and ≤ +1SD unit, >+1SD unit.

Among the individual language parameters, only two were found to have significant predictive value regarding the development of ACS at second visit. In particular, each additional response in the SVF task was associated with approximately 16% lower hazard of developing ACS. On the other hand, for each additional item positively scored in the BNTsf, participants presented about 22% lower risk of presenting with ACS at follow-up.

Finally, no significant predictors were revealed in the MCI group, i.e., no language measurement was related to the risk of conversion from MCI of any type (amnestic or amnestic) to ACS (secondary analysis).

With respect to the prognostic value of baseline language scores regarding the risk of developing MCI, each additional SD unit in the composite language score reduced the risk of incident MCI of any type by 32% (Figure 2). The predictive value of SVF performance and naming was once again significant. Specifically, each additionally reported object in the SVF task was associated with approximately 8% lower hazard of developing MCI at follow-up, while each additional positively scored item in the BNT was related to about 16% inferior risk of presenting with MCI at follow-up (Table 4).

Figure 2. Survival curves for incident mild cognitive impairment (MCI) according to the baseline composite language performance of the participants. Individuals were clustered using mean composite language values and standard deviation (SD) units to form four baseline strata: ≤ −1SD unit, >−1SD unit and ≤ mean, > mean and ≤ +1SD unit, >+1SD unit.

Discussion

The present study demonstrated that impaired language performance among CN individuals is related to an increased risk of developing ACS and MCI. Among individual neuropsychological tests, better SVF and naming performance was specifically associated with reduced risk of incident ACS and MCI, independently of age, sex, education, main occupation, socioeconomic status, and MMSE scores. On the other hand, language measures were not associated with the risk of MCI to ACS conversion. It is important to stress, however, that in view of the small MCI sample (along with the small number of total events), our analysis may have been relatively underpowered to reveal any potential associations. Finally, comprehension, repetition, and PVF in isolation, as well as SVF-PVF discrepancy scores, were not associated with the risk of developing MCI or ACS, as well as the risk of MCI to ACS progression.

Our findings are consistent with those of previous articles suggesting the prognostic quality for SVF performance (Gallucci et al., Reference Gallucci, Di Battista, Battistella, Falcone, Bisiacchi and Di Giorgi2018; Gustavson et al., Reference Gustavson, Elman, Panizzon, Franz, Zuber, Sanderson-Cimino, Reynolds, Jacobson, Xian, Jak, Toomey, Lyons and Kremen2020; Sutin et al., Reference Sutin, Stephan and Terracciano2019; Wong et al., Reference Wong, Leung, Fung, Chan and Lam2013). Similar to former studies, we adjusted for important sociodemographic parameters that could confound the relationship between language performance and ACS or MCI development (Letenneur et al., Reference Letenneur, Gilleron, Commenges, Helmer, Orgogozo and Dartigues1999; Karp et al., Reference Karp, Kareholt, Qiu, Bellander, Winblad and Fratiglioni2004; Kempler et al., Reference Kempler, Teng, Dick, Taussig and Davis1998). Unlike previous research, however, our analyses additionally accounted for the potential confounding effect of general cognitive status (as reflected by MMSE scores, which provide a general estimate of cognitive impairment), suggesting that language performance is a prognostic factor of cognitive decline, independently of global cognitive status.

BNTsf was also revealed to have a predictive value regarding the development of MCI and ACS. BNTsf is shorter than the traditional 60-item BNT; thus, it may be less taxing given the frequently observed limited attention span, which can be introduced by multiple common conditions (depression, anxiety, sleep disorders, and so on). Regarding its predictive utility, previous research has provided conflicting evidence using relatively small sample sizes (Albert et al., Reference Albert, Moss, Tanzi and Jones2001; Chen et al., Reference Chen, Ratcliff, Belle, Cauley, DeKosky and Ganguli2000; Griffith et al., Reference Griffith, Netson, Harrell, Zamrini, Brockington and Marson2006; Jacobs et al., Reference Jacobs, Sano, Dooneief, Marder, Bell and Stern1995; Tabert et al., Reference Tabert, Manly, Liu, Pelton, Rosenblum, Jacobs, Zamora, Goodkind, Bell, Stern and Devanand2006). The present study is the largest relevant longitudinal study to date, providing robust evidence (by addressing important sociodemographic confounders and global cognitive status) for the prognostic quality of BNTsf performance in ACS and MCI development.

It is well established that the neuropathological alterations of neurodegenerative disorders such as dementia (with ACS being the most extensively investigated) precede the clinical onset of the disease (DeTure & Dickson Reference DeTure and Dickson2019; Solomon et al., Reference Solomon, Mangialasche, Richard, Andrieu, Bennett, Breteler, Fratiglioni, Hooshmand, Khachaturian, Schneider, Skoog, Kivipelto and Kivipelto2014). Considering the temporal association of our findings (the mean follow-up was about 3 years), language decline (namely semantic fluency and confrontation naming impairment) might as well represent the clinical equivalent of early pathological processes of cognitive impairment in nondemented individuals. Therefore, language impairment may constitute an early clinical marker of the disease. In this context, language decline would also represent a clinical marker of MCI to ACS progression. Despite our findings, previous research has suggested that language performance (SVF in particular) is a fair prognostic factor regarding MCI to all-type dementia or ACS conversion, strengthening the hypothesis that language decline may reflect early neuropathological dementia processes. In support of this theory, SVF impairment was recently correlated to early ACS-specific neurodegenerative alterations in nondemented adults (MEMENTO Cohort Study Group, 2020).

Of note, language impairment appears to be more sensitive than other neuropsychological indices (even episodic memory) to reveal the presence of cognitive deficits in the early stages of MCI (McCullough et al., Reference McCullough, Bayles and Bouldin2019). Previous research has also suggested that episodic memory and language (namely category fluency) tend to deteriorate sooner than other cognitive functions, even in the preclinical course of MCI-ACS (Mistridis et al., Reference Mistridis, Krumm, Monsch, Berres and Taylor2015). Intriguingly, relatively poor semantic fluency performance has been found to predict incident episodic memory deficits (but not vice versa) (Gustavson et al., Reference Gustavson, Elman, Panizzon, Franz, Zuber, Sanderson-Cimino, Reynolds, Jacobson, Xian, Jak, Toomey, Lyons and Kremen2020). Considering the above, SVF might potentially reflect ongoing neurodegenerative processes in the preclinical course of MCI-ACS, sooner than other neuropsychological tasks and potentially even sooner than episodic memory. The clinical and anatomical dissociability between semantic and episodic memory are pivotal in the understanding of the true potential of these two neuropsychological measures in the early identification of ACS. Despite the traditional teachings regarding the earlier involvement of episodic memory in aMCI and ACS, ACS neuropathology has been suggested to affect the sub-hippocampal regions including the entorhinal and perirhinal cortices (which are implicated in context-free memory processing, i.e., semantic memory) earlier than the hippocampus in the preclinical course of aMCI-ACS (Didic et al., Reference Didic, Barbeau, Felician, Tramoni, Guedj, Poncet and Ceccaldi2011; Venneri et al., Reference Venneri, Jahn-Carta, de Marco, Quaranta and Marra2018). Therefore, semantic memory has been proposed to allow the earlier clinical detection of ACS-related pathological alterations, at the preclinical point of sub-hippocampal confined neurodegeneration (Didic et al., Reference Didic, Barbeau, Felician, Tramoni, Guedj, Poncet and Ceccaldi2011; Venneri et al., Reference Venneri, Jahn-Carta, de Marco, Quaranta and Marra2018).

Although SVF may be useful as an early clinical marker of dementia, it clearly lacks specificity. Verbal fluency is quite complex in terms of the number of cognitive functions needed to perform it. Consequently, it is not possible to distinguish the specific cognitive domain that is impaired when verbal fluency performance is poor. SVF tasks are specifically regarded to be sensitive to deficits in executive skills, working and semantic memory (Amunts et al., Reference Amunts, Camilleri, Eickhoff, Heim and Weis2020; Kavé and Sapir-Yogev Reference Kavé and Sapir-Yogev2020; Rende et al., Reference Rende, Ramsberger and Miyake2002). As such, SVF performance is implicated in a number of neurodegenerative diseases, including several types of dementia and Parkinson’s disease (Suhr & Jones, Reference Suhr and Jones1998). Therefore, although SVF may be an early clinical marker of ACS and MCI, the lack of specificity introduces important limitations with respect to its clinical applicability. Similarly, BNTsf, although generally considered a more specific index of verbal naming, might also reflect semantic memory skills (Brouillette et al., Reference Brouillette, Martin, Correa, Davis, Han, Johnson, Foil, Hymel and Keller2011; Hodges et al., Reference Hodges, Salmon and Butters1990). Therefore, much alike SVF, BNTsf presents the substantial limitation of low specificity. These limitations should be investigated in future research assessing the prognostic value of language measurements with respect to the development of other dementia entities (a very small number of non-ACS dementia cases were documented in our study) as well as other neurodegenerative disorders, such as Parkinson’s disease.

Strengths and limitations

The HELIAD study is a population-based prospective cohort involving a randomly selected sample from the elderly rosters of two Greek communities (both a provincial and a metropolitan community), making it a representative sample of the entire elderly Greek population. Our study included a comprehensive neuropsychological evaluation, as well as a collaborative expert-established clinical diagnosis of dementia based on standard criteria. All analyses were adjusted for important sociodemographic confounders as well as MMSE scores, to account for the potential confounding effect of globally impaired cognition.

However, our study had a number of important limitations as well. First, nonresponse bias and residual confounding cannot be ruled out. Furthermore, although the diagnosis of dementia was clinically established by a consortium of senior experts, it was not supported by imaging and biological biomarkers (potential misclassification bias) (Horgan et al., Reference Horgan, Nobili, Teunissen, Grimmer, Mitrecic, Ris, Bernini, Federico, Blackburn, Logroscino and Scarmeas2020). Moreover, apart from ACS, the remaining dementia entities were not investigated due to the small number of events per entity (as expected, considering the small prevalence of other dementia entities and the prospective design of the HELIAD study). In addition to the above, language performance was assessed on its own and was not compared to the rest of the cognitive domains in terms of prognostic quality. Finally, the moderate follow-up duration of approximately 3 years, as well as the relatively small MCI set of participants, might have underpowered several analyses.

Conclusions

Impaired language performance was related to an increased risk of incident ACS and MCI. Better SVF and BNTsf performances were specifically associated with reduced risk of incident ACS and MCI in cognitively healthy elderly individuals, independently of their age, sex, education, main occupation, socioeconomic status, and MMSE scores at baseline. However, no language measure was related to the risk of MCI to ACS conversion.

Funding statement

This work was supported by the following grants: Alzheimer’s Association (grant number: IIRG-09-133014); ESPA-EU program Excellence Grant (ARISTEIA), which is co-funded by the European Social Fund and Greek National resources (grant number: 189 10276/8/9/2011); Ministry for Health and Social Solidarity (Greece) (grant number: DY2b/oik.51657/14.4.2009). The funders had no role in the design, analysis, or writing of this article.

Conflicts of interest

None.

Footnotes

*

Denotes equal contribution as co-first authors.

**

Denotes equal contribution as co-senior authors.

References

Albert, M.S., Moss, M.B., Tanzi, R., & Jones, K. (2001). Preclinical prediction of AD using neuropsychological tests. Journal of the International Neuropsychological Society, 7, 631639. https://doi.org/10.1017/s1355617701755105 CrossRefGoogle ScholarPubMed
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed.). American Psychiatric Association Press.Google Scholar
Amunts, J., Camilleri, J. A, Eickhoff, S. B., Heim, S., & Weis, S. (2020). Executive functions predict verbal fluency scores in healthy participants. Science Repository, 10(1), 11141. https://doi.org/10.1038/s41598-020-65525-9 Google ScholarPubMed
Bougea, A., Maraki, M. I., Yannakoulia, M., Stamelou, M., Xiromerisiou, G., Kosmidis, M. H., Ntanasi, E., Dardiotis, E., Hadjigeorgiou, G. M., Sakka, P., Anastasiou, C. A., Stefanis, L., & Scarmeas, N. (2019). Higher probability of prodromal Parkinson disease is related to lower cognitive performance. Neurology, 92(19), e2261e2272. https://doi.org/10.1212/WNL.0000000000007453 CrossRefGoogle ScholarPubMed
Brouillette, R. M., Martin, C. K., Correa, J.B., Davis, A.B., Han, H., Johnson, W.D., Foil, H. C., Hymel, A., & Keller, J. N. (2011). Memory for names test provides a useful confrontational naming task for aging and continuum of dementia. Journal of Alzheimer’s Disease, 23(4), 665671. https://doi.org/10.3233/JAD-2011-101455 CrossRefGoogle ScholarPubMed
Chen, P., Ratcliff, G., Belle, S.H., Cauley, J.H., DeKosky, S.T., & Ganguli, M. (2000). Cognitive tests that best discriminate between presymptomatic AD and those who remain undemented. Neurology, 55, 18471853. https://doi.org/10.1212/wnl.55.12.1847 CrossRefGoogle Scholar
Chodosh, J., Reuben, D. B., Albert, M. S., & Seeman, T.E. (2002). Predicting cognitive impairment in high-functioning community-dwelling older persons: MacArthur studies of successful aging. Journal of the American Geriatrics Society, 50(6), 10511060. https://doi.org/10.1046/j.1532-5415.2002.50260.x CrossRefGoogle ScholarPubMed
Dardiotis, E., Kosmidis, M. H., Yannakoulia, M., Hadjigeorgiou, G. M., & Scarmeas, N. (2014). The Hellenic Longitudinal Investigation of Aging and Diet (HELIAD): Rationale, study design, and cohort description. Neuroepidemiology, 43(1), 914. https://doi.org/10.1159/000362723 CrossRefGoogle ScholarPubMed
DeTure, M. A., & Dickson, D. W. (2019). The neuropathological diagnosis of Alzheimer’s disease. Molecular Neurodegeneration, 14(1), 32. https://doi.org/10.1186/s13024-019-0333-5 CrossRefGoogle ScholarPubMed
Didic, M., Barbeau, E. J., Felician, O., Tramoni, E., Guedj, E., Poncet, M., & Ceccaldi, M. (2011). Which memory system is impaired first in Alzheimer’s disease? Journal of Alzheimer’s Disease, 27(1), 1122. https://doi.org/10.3233/JAD-2011-110557 CrossRefGoogle ScholarPubMed
Eyigoz, E., Mathur, S., Santamaria, M., Cecchi, G., & Naylor, M. (2020). Linguistic markers predict onset of Alzheimer’s disease. EClinicalMedicine, 28, 100583. https://doi.org/10.1016/j.eclinm.2020.100583 CrossRefGoogle ScholarPubMed
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189198. https://doi.org/10.1016/0022-3956(75)90026-6 CrossRefGoogle ScholarPubMed
Gallucci, M., Di Battista, M.E., Battistella, G., Falcone, C., Bisiacchi, P.S., & Di Giorgi, E. (2018). Neuropsychological tools to predict conversion from amnestic mild cognitive impairment to dementia. The TREDEM Registry. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 25(4), 550560. https://doi.org/10.1080/13825585.2017.1349869 CrossRefGoogle ScholarPubMed
Gomez, R. G., & White, D. A. (2006). Using verbal fluency to detect very mild dementia of the Alzheimer type. Archives of Clinical Neuropsychology, 21(8), 771775. https://doi.org/10.1016/j.acn.2006.06.012 CrossRefGoogle ScholarPubMed
Griffith, H. R., Netson, K. L., Harrell, L. E., Zamrini, E. Y., Brockington, J. C., & Marson, D. C. (2006). Amnestic mild cognitive impairment: Diagnostic outcomes and clinical prediction over a two-year time period. Journal of the International Neuropsychological Society, 12, 166175. https://doi.org/10.1017/S1355617706060267 CrossRefGoogle Scholar
Gustavson, D. E., Elman, J. A., Panizzon, M. S., Franz, C. E., Zuber, J., Sanderson-Cimino, M., Reynolds, C. A., Jacobson, K. C., Xian, H., Jak, A. J., Toomey, R., Lyons, M. J., & Kremen, W. S. (2020). Association of baseline semantic fluency and progression to mild cognitive impairment in middle-aged men. Neurology, 95(8), e973e983. https://doi.org/10.1212/WNL.0000000000010130 CrossRefGoogle ScholarPubMed
Harada, C. N., Natelson Love, M. C., & Triebel, K. L. (2013). Normal cognitive aging. Clinics in Geriatric Medicine, 29(4), 737752. https://doi.org/10.1016/j.cger.2013.07.002 CrossRefGoogle ScholarPubMed
Hartshorne, J. K., & Germine, L. T. (2015). When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychological Science, 26(4), 433443. https://doi.org/10.1177/0956797614567339 CrossRefGoogle ScholarPubMed
Henry, J. D., Crawford, J. R., & Phillips, L. H. (2004). Verbal fluency performance in dementia of the Alzheimer’s type: A meta-analysis. Neuropsychologia, 42(9), 12121222. https://doi.org/10.1016/j.neuropsychologia.2004.02.001 CrossRefGoogle ScholarPubMed
Hodges, J. R., Salmon, D. P., & Butters, N. (1990). Differential impairment of semantic and episodic memory in Alzheimer’s and Huntington’s diseases: A controlled prospective study. Journal of Neurology, Neurosurgery, and Psychiatry, 53(12), 10891095. https://doi.org/10.1136/jnnp.53.12.1089 CrossRefGoogle Scholar
Holtzer, R., Jacobs, S., & Demetriou, E. (2020). Intraindividual variability in verbal fluency performance is moderated by and predictive of mild cognitive impairments. Neuropsychology, 34(1), 3142. https://doi.org/10.1037/neu0000576 CrossRefGoogle ScholarPubMed
Horgan, D., Nobili, F., Teunissen, C., Grimmer, T., Mitrecic, D., Ris, L., Bernini, C., Federico, A., Blackburn, D.Logroscino, G., & Scarmeas, N. (2020). Biomarker testing: Piercing the fog of Alzheimer’s and related dementia. Biomedicine Hub, 5(3), 1940. https://doi.org/10.1159/000511233 CrossRefGoogle ScholarPubMed
Jack, C. R. Jr, Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J. L., Montine, T., Phelps, C., Rankin, K. P., Rowe, C. C., Scheltens, P., Siemers, E., Snyder, H. M., & Sperling, R.; Contributors. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s and Dementia, 14(4), 535562. https://doi.org/10.1016/j.jalz.2018.02.018 CrossRefGoogle Scholar
Jacobs, D. M., Sano, M., Dooneief, G., Marder, K., Bell, K. L., & Stern, Y. (1995). Neuropsychological detection and characterization of preclinical Alzheimer’s disease. Neurology, 45, 317324. https://doi.org/10.1212/wnl.45.5.957 Google ScholarPubMed
Jacobs, S., Mercuri, G., & Holtzer, R. (2021). Assessing within-task verbal fluency performance: The utility of individual time intervals in predicting incident mild cognitive impairment. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 28(5), 733747. https://doi.org/10.1080/13825585.2020.1817306 CrossRefGoogle ScholarPubMed
Kalligerou, F., Fieo, R., Paraskevas, G.P., Zalonis, I., Kosmidis, M. H., Yannakoulia, M., Ntanasi, E., Dardiotis, E., Hadjigeorgiou, G., Sakka, P., & Scarmeas, N. (2020). Assessing functional status using the IADL-extended scale: Results from the HELIAD study. International Psychogeriatrics, 32(9), 10451053. https://doi.org/10.1017/S1041610219001091 CrossRefGoogle ScholarPubMed
Karp, A., Kareholt, I., Qiu, C., Bellander, T., Winblad, B., & Fratiglioni, L. (2004). Relation of education and occupation-based socioeconomic status to incident Alzheimer’s disease. American Journal of Epidemiology, 159, 175183. https://doi.org/10.1093/aje/kwh018 CrossRefGoogle ScholarPubMed
Kavé, G., & Sapir-Yogev, S. (2020). Associations between memory and verbal fluency tasks. Journal of Communication Disorders, 83, 105968. https://doi.org/10.1016/j.jcomdis.2019.105968 CrossRefGoogle ScholarPubMed
Kemper, S., Marquis, J., & Thompson, M. (2001). Longitudinal change in language production: Effects of aging and dementia on grammatical complexity and propositional content. Psychol Aging, 16(4), 600614. https://doi.org/10.1037//0882-7974.16.4.600 CrossRefGoogle ScholarPubMed
Kempler, D., Teng, E. L., Dick, M., Taussig, I. M., & Davis, D. S. (1998). The effects of age, education, and ethnicity on verbal fluency. Journal of the International Neuropsychological Society, 4(6), 531538. https://doi.org/10.1017/s1355617798466013 CrossRefGoogle ScholarPubMed
Klimova, B., & Kuca, K. (2016). Speech and language impairments in dementia. Journal of Applied Biomedicine, 14(2), 97103. https://doi.org/10.1016/j.jab.2016.02.002 CrossRefGoogle Scholar
Kosmidis, M. H., Vlahou, C. H., Panagiotaki, P., & Kiosseoglou, G. (2004). The verbal fluency task in the Greek population: Normative data, and clustering and switching strategies. Journal of the International Neuropsychological Society, 10(2), 164172. https://doi.org/10.1017/S1355617704102014 CrossRefGoogle ScholarPubMed
Kosmidis, M. H., Vlachos, G. S., Anastasiou, C. A., Yannakoulia, M., Dardiotis, E., Hadjigeorgiou, G., Sakka, P., Ntanasi, E., & Scarmeas, N. (2018). Dementia prevalence in Greece: The Hellenic Longitudinal Investigation of Aging and Diet (HELIAD). Alzheimer Disease and Associated Disorders, 32(3), 232239. https://doi.org/10.1097/WAD.0000000000000249 CrossRefGoogle ScholarPubMed
Letenneur, L., Gilleron, V., Commenges, D., Helmer, C., Orgogozo, J. M., & Dartigues, J. F. (1999). Are sex and educational level independent predictors of dementia and Alzheimer’s disease? Incidence data from the Paquid project. Journal of Neurology, Neurosurgery, and Psychiatry, 66, 177183. https://doi.org/10.1136/jnnp.66.2.177 CrossRefGoogle ScholarPubMed
Liampas, I., Folia, V., Ntanasi, E., Yannakoulia, M., Sakka, P., Hadjigeorgiou, G., Scarmeas, N., Dardiotis, E., & Kosmidis, M. H. (2022). Longitudinal episodic memory trajectories in older adults with normal cognition. The Clinical Neuropsychologist, 118. https://doi.org/10.1080/13854046.2022.2059011 Google ScholarPubMed
Marini, A., Boewe, A., Caltagirone, C., & Carlomagno, S. (2005). Age-related differences in the production of textual descriptions. Journal of Psycholinguistic Research, 34(5), 439463. https://doi.org/10.1007/s10936-005-6203-z CrossRefGoogle ScholarPubMed
Maseda, A., Lodeiro-Fernández, L., Lorenzo-López, L., Núñez-Naveira, L., Balo, A., & Millán-Calenti, J. C. (2014). Verbal fluency, naming and verbal comprehension: Three aspects of language as predictors of cognitive impairment. Aging and Mental Health, 18(8), 10371045. https://doi.org/10.1080/13607863.2014.908457 CrossRefGoogle ScholarPubMed
McCullough, K. C., Bayles, K. A., & Bouldin, E. D. (2019). Language performance of individuals at risk for mild cognitive impairment. Journal of Speech, Language, and Hearing Research, 62(3), 706722. https://doi.org/10.1044/2018_JSLHR-L-18-0232 CrossRefGoogle ScholarPubMed
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E.M. (1984). Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34(7), 939–344. https://doi.org/10.1212/wnl.34.7.939 CrossRefGoogle ScholarPubMed
MEMENTO cohort Study Group. (2020). Semantic loss marks early Alzheimer’s disease-related neurodegeneration in older adults without dementia. Alzheimer’s and Dementia (Amst), 12(1), e12066. https://doi.org/10.1002/dad2.12066 Google Scholar
Mistridis, P., Krumm, S., Monsch, A. U., Berres, M., & Taylor, K. I. (2015). The 12 years preceding mild cognitive impairment due to Alzheimer’s disease: The temporal emergence of cognitive decline. Journal of Alzheimer’s Disease, 48(4), 10951107. https://doi.org/10.3233/JAD-150137 CrossRefGoogle ScholarPubMed
Mourtzi, N., Yannakoulia, M., Ntanasi, E., Kosmidis, M. H., Anastasiou, C. A., Dardiotis, E., Hadjigeorgiou, G., Megalou, M., Sakka, P. & Scarmeas, N. (2018). History of induced abortions and frailty in older Greek women: Results from the HELIAD study. European Geriatric Medicine, 9, 301310. https://doi.org/10.1007/s41999-018-0047-1 CrossRefGoogle ScholarPubMed
Nutter-Upham, K. E., Saykin, A. J., Rabin, L. A., Roth, R. M., Wishart, H. A., Pare, N., & Flashman, L. A. (2008). Verbal fluency performance in amnestic MCI and older adults with cognitive complaints. Archives of Clinical Neuropsychology, 23(3), 229241. https://doi.org/10.1016/j.acn.2008.01.005 CrossRefGoogle ScholarPubMed
Ostrand, R., & Gunstad, J. (2021). Using automatic assessment of speech production to predict current and future cognitive function in older adults. Journal of Geriatric Psychiatry and Neurology, 34(5), 357369. https://doi.org/10.1177/0891988720933358 CrossRefGoogle ScholarPubMed
Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256, 183194.CrossRefGoogle ScholarPubMed
Rende, B., Ramsberger, G., & Miyake, A. (2002). Commonalities and differences in the working memory components underlying letter and category fluency tasks: A dual-task investigation. Neuropsychology, 16(3), 309321. https://doi.org/10.1037//0894-4105.16.3.309 CrossRefGoogle ScholarPubMed
Shafto, M. A., & Tyler, L. K. (2014). Language in the aging brain: The network dynamics of cognitive decline and preservation. Science, 346(6209): 583587. https://doi.org/10.1126/science CrossRefGoogle Scholar
Solomon, A., Mangialasche, F., Richard, E., Andrieu, S., Bennett, D. A., Breteler, M., Fratiglioni, L., Hooshmand, B., Khachaturian, A. S., Schneider, L. S., Skoog, I., Kivipelto, M., & Kivipelto, M. (2014). Advances in the prevention of Alzheimer’s disease and dementia. Journal of Internal Medicine, 275(3), 229250. https://doi.org/10.12703/P7-50 CrossRefGoogle ScholarPubMed
Suhr, J. A., & Jones, R. D. (1998). Letter and semantic fluency in Alzheimer’s, Huntington’s, and Parkinson’s dementias. Archives of Clinical Neuropsychology, 13, 447454.CrossRefGoogle ScholarPubMed
Sutin, A. R., Stephan, Y., & Terracciano, A. (2019). Verbal fluency and risk of dementia. International Journal of Geriatric Psychiatry, 34(6), 863867. https://doi.org/10.1002/gps.5081 CrossRefGoogle ScholarPubMed
Tabert, M. H., Manly, J. J., Liu, X., Pelton, G. H., Rosenblum, S., Jacobs, M., Zamora, D., Goodkind, M., Bell, K., Stern, Y., & Devanand, D.P. (2006). Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment. Archives of General Psychiatry, 63, 916924. https://doi.org/10.1001/archpsyc.63.8.916 CrossRefGoogle ScholarPubMed
Tsapkini, K., Vlahou, C. H., & Potagas, C. (2010). Adaptation and validation of standardized aphasia tests in different languages: Lessons from the Boston diagnostic Aphasia examination – short form in Greek. Behavioural Neurology, 22(3–4), 111119. https://doi.org/10.3233/ben-2009-0256 CrossRefGoogle ScholarPubMed
Vaughan, R. M., Coen, R. F., Kenny, R., & Lawlor, B. A. (2018). Semantic and phonemic verbal fluency discrepancy in mild cognitive impairment: Potential predictor of progression to Alzheimer’s disease. Journal of the American Geriatrics Society, 66(4), 755759. https://doi.org/10.1111/jgs.15294 CrossRefGoogle ScholarPubMed
Venneri, A., Jahn-Carta, C., de Marco, M., Quaranta, D., & Marra, C. (2018). Diagnostic and prognostic role of semantic processing in preclinical Alzheimer’s disease. Biomarkers in Medicine, 12(6), 637651. https://doi.org/10.2217/bmm-2017-0324 CrossRefGoogle ScholarPubMed
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2014). STROBE Initiative. The strengthening the reporting of observational studies in epidemiology (STROBE) Statement: Guidelines for reporting observational studies. International Journal of Surgery, 12(12), 14951499. https://doi.org/10.1016/j.ijsu.2014.07.014 CrossRefGoogle Scholar
Vuorinen, E., Laine, M., & Rinne, J. (2000). Common pattern of language impairment in vascular dementia and in Alzheimer disease. Alzheimer Disease and Associated Disorders, 14(2), 8186. https://doi.org/10.1097/00002093-200004000-00005 CrossRefGoogle ScholarPubMed
Wong, C. H., Leung, G. T., Fung, A. W., Chan, W. C., & Lam, L. C. (2013). Cognitive predictors for five-year conversion to dementia in community-dwelling Chinese older adults. International Psychogeriatrics, 25(7), 11251134. https://doi.org/10.1017/S1041610213000161 CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Baseline characteristics of cognitively normal participants (CN) according to the follow-up diagnosis of Alzheimer’s clinical syndrome (ACS) or not. Those with any other dementia diagnosis at follow-up were excluded. The number of participants with available data per parameter is provided

Figure 1

Table 2. Baseline characteristics of cognitively normal participants (CN) based on the follow-up diagnosis of mild cognitive impairment (MCI) of any type (amnestic or nonamnestic) or not. Those with dementia diagnosis at follow-up were excluded. The number of participants with available data per parameter is provided

Figure 2

Table 3. Baseline characteristics of participants with mild cognitive impairment (MCI) of any type (amnestic or nonamnestic) based on the follow-up diagnosis of Alzheimer’s clinical syndrome (ACS) or not. Those with any other dementia diagnosis at follow-up were excluded. The number of participants with available data per parameter is provided

Figure 3

Table 4. Adjusted cox proportional hazards regressions with follow-up diagnosis of Alzheimer’s clinical syndrome (ACS) and mild cognitive impairment (MCI) of any type (amnestic or nonamnestic) as the dichotomous outcomes. Individuals with normal cognition (CN) at baseline were analyzed

Figure 4

Table 5. Adjusted cox proportional hazards regressions with follow-up diagnosis of Alzheimer’s clinical syndrome (ACS) as the dichotomous outcome. Individuals with mild cognitive impairment (MCI) of any type (amnestic or non-amnestic) at baseline were analyzed

Figure 5

Figure 1. Survival curves for incident Alzheimer’s clinical syndrome (ACS) according to the baseline composite language performance of the participants. Individuals were clustered using mean composite language values and standard deviation (SD) units to form four baseline strata: ≤−1SD unit, >−1SD unit and ≤ mean, > mean and ≤ +1SD unit, >+1SD unit.

Figure 6

Figure 2. Survival curves for incident mild cognitive impairment (MCI) according to the baseline composite language performance of the participants. Individuals were clustered using mean composite language values and standard deviation (SD) units to form four baseline strata: ≤ −1SD unit, >−1SD unit and ≤ mean, > mean and ≤ +1SD unit, >+1SD unit.