From Subjective Cognitive Decline to Alzheimer’s Disease: The Predictive Role of Neuropsychological Assessment,Personality Traits,and Cognitive Reserve. A 7-Year Follow-Up Study

Abstract

The aim of this study was to evaluate the accuracy of neuropsychological assessment in predicting conversion from subjective cognitive decline (SCD) and mild cognitive impairment (MCI) to Alzheimer’s disease (AD) and the effect of personality traits and cognitive reserve in progression from SCD to MCI. As part of a longitudinal, clinical-neuropsychological-genetic survey on SCD and MCI, 284 patients referred to our hospital between 1990 and 2017 were included. All patients underwent clinical-extensive neuropsychological evaluation and Apolipoprotein E genotyping; personality traits were assessed in a subgroup. Each patient underwent clinical-neuropsychological follow-up. Subjects with a follow-up shorter than two years were excluded. A total of 212 subjects were, after exclusions, considered: 26 out of 109 SCD subjects progressed to MCI (SCD-p), 15 converted to AD (SCD-c), and 68 remained stable (SCD-s). Of 103 MCI subjects, 39 converted to AD (MCI-c) and 64 remained stable (MCI-s). At baseline, SCD-c performed significantly worse than SCD-s in tests assessing long-term verbal memory. MCI-c showed worse performance on neuropsychological tests for short- and long-term verbal memory and for ecological evaluation of memory (RBMT). These tests provided good accuracy in distinguishing MCI-c and MCI-s. Emotional stability was significantly lower in SCD-s than in SCD-p while higher intellectual activities were associated with a lower risk of conversion to MCI. Our results suggest that memory neuropsychological tests may represent a reliable tool to estimate the risk of progression to AD. Personality and lifestyle factors could provide useful information to identify SCD subjects who may develop an objective cognitive impairment.

Keywords

Alzheimer’s disease APOE cognitive reserve dementia mild cognitive impairment neuropsychology personality traits prediction subjective cognitive decline

INTRODUCTION

Alzheimer’s disease (AD) has a slow and progressive trend, with a presymptomatic course which can last from several years to decades [1, 2]. Identification of subjects at an early stage is crucial for therapeutic intervention and possible prevention of cognitive decline. Current research is focused on identifying characteristics of the early stages of AD and several concepts have been developed to that end [3, 4].

The concept of mild cognitive impairment (MCI) evolved over the past two decades to define subjects at the transitional stage between normal aging and dementia. Evidence from cross-sectional and longitudinal studies has shown that MCI is associated with an increased risk of positive AD biomarkers and with an annual conversion rate of 5–17% to AD. Different conditions, either neurodegenerative or non-degenerative (cerebrovascular, infective, metabolic or pharmacological), may underlie MCI. Reversion to normal cognition is also possible in MCI subjects [5].

Subjective cognitive decline (SCD) was defined as a self-experienced persistent decline in cognitive capacity in comparison with the subject’s previously normal status, during which the subject has normal age-, gender-, and education-adjusted performance on standardized cognitive tests [6].

Nevertheless, studies of patients with SCD in populations aged over 75 have described neuroradiological features similar to those seen in AD patients, such as volume loss in hippocampal/parahippocampal areas [7] and evidence of amyloid deposition using PET-imaging [8]. Longitudinal biological studies showed that SCD at baseline is linked with subsequent change in hippocampal volume [9] and a recent meta-analysis suggested that older people with SCD are twice as likely to develop dementia as individuals without [10, 11].

SCD could be related to numerous conditions such as normal aging, psychiatric, neurological or medical disorders, substance use or medication [12]. The great majority of SCD subjects are ‘worried well’ and do not deteriorate more rapidly than usual [9]; disappearance of the subjective sensation of cognitive decline is also common [13].

As a result, individuals with SCD and MCI constitute a heterogeneous group. Therefore, estimating the probability that SCD and MCI are related or not to AD is a fundamental aim for people experiencing objective or subjective cognitive decline as well as for targeting dementia prevention [12].

The accuracy of cognitive tests in predicting progression from MCI to AD has been widely investigated [14, 15]. Several studies showed that low scores in neuropsychological tests evaluating verbal and visuospatial episodic memory, abstract reasoning, learning, language and executive functions [16 –20] could support the hypothesis that MCI would be due to AD. On the other hand, to our knowledge, only few longitudinal studies investigated prognostic value of neuropsychological assessment at baseline in subjects with SCD in developing MCI and AD [18 –21], often with conflicting results.

A number of papers have tackled the question of whether premorbid personality traits are linked to the risk of developing MCI [22, 23] and AD [24 –27]. Most studies agree that high conscientiousness and low neuroticism are associated with a reduced risk of incident AD [27, 28].

Finally, several studies have reported that education [29], intellectually engaging occupations [30, 31], and a cognitive [32], physical [33], and socially integrated lifestyle may protect against dementia [34]. These findings have been interpreted by means of the Cognitive Reserve hypothesis [35, 36].

The aim of the present study is to evaluate the prognostic accuracy of neuropsychological tests in predicting conversion from SCD and MCI to AD and the role of personality traits and cognitive reserve in increasing or reducing the risk of progression to objective cognitive impairment in subjects experiencing SCD.

MATERIALS AND METHODS

Participants and clinical assessment

As part of a longitudinal, clinical-neuro-psychological-genetic survey on SCD and MCI, we included 284 consecutive spontaneous subjects who self-referred to the Centre for Alzheimer’s Disease and Adult Cognitive Disorders of Careggi Hospital in Florence between March 1990 and March 2017.

All participants underwent a comprehensive family and clinical history, general and neurological examination, extensive neuropsychological investigation, and estimation of premorbid intelligence as well as assessment of depression. A positive family history was defined as one or more first-degree relatives with documented cognitive decline. Cognitive complaints were explored during the neurological interview at baseline by using a survey based on the Memory Assessment Clinics-Questionnaire [37]. We defined the presence of cognitive complaints if participants perceived decline in cognitive capacity than in the past or if they reported difficulties in carrying out at least four of the following activities: 1) remembering the name of a person just introduced to them; 2) recalling telephone numbers or zip codes used on a daily or weekly basis; 3) recalling where they put objects (such as keys) in their home or office; 4) remembering specific facts from a newspaper or magazine article just read; 5) remembering the item(s) they intend to buy when they arrive at the grocery store or pharmacy. Out of the 284 participants, 191 underwent APOE genotype analysis. In a subgroup of 60 subjects, we evaluated personality traits and leisure activities.

For this study, inclusion criteria were: 1) complaining of cognitive decline with a duration of≥6 months; 2) normal functioning on the Activities of Daily Living and the Instrumental Activities of Daily Living scales [38]; 3) unsatisfied criteria for dementia at baseline [39, 40]; 4) attainment of the clinical endpoint, i.e., conversion to MCI according to the National Institute on Aging-Alzheimer’s Association (NIA-AA) criteria [3], and conversion to AD according to the NIA-AA criteria [40] during follow up, regardless of follow-up duration; 5) a follow-up time of more than 2 years from the baseline visit for those patients who did not develop MCI or AD. Exclusion criteria were: 1) history of head injury, current neurological and/or systemic disease, symptoms of psychosis, major depression, alcoholism or other substance abuse; 2) the complete data loss of patients’ follow-up; 3) progression to dementia other than AD. Furthermore, as AD before the age of 65 is rare and is considered to have specific diagnostic features compared to dementia in the elderly [41], we also excluded subjects who were younger than 65 years at the end of the follow-up.

From the initial sample, we excluded 41 subjects who did not convert to MCI or AD with a follow-up shorter than two years. We excluded one subject who developed a brain tumor, seven subjects who were diagnosed with other forms of dementia (six vascular dementia, according to NINDS-AIREN criteria [42] and one frontotemporal dementia, according to Neary criteria [43]); and 23 subjects were excluded as they were younger than 65 years at the end of the follow-up. Therefore, in the end 212 subjects were included. We divided this sample into two groups: 109 subjects classified as SCD, according to the terminology proposed by the Subjective Cognitive Decline Initiative (SCD-I) Working Group [6] (i.e., presence of a self-experienced persistent decline in cognitive capacities with normal performance on standardized cognitive tests); 103 subjects classified as MCI according to (NIA-AA) criteria for the diagnosis of MCI [3] (i.e., evidence of lower performance in one or more cognitive domains with preserved independence of function in daily life).

All patients underwent clinical and neuropsychological follow-up every six or 12 months. Seventeen patients (15 SCD and 2 MCI) did not refer to our Memory Centre for follow-up visits for more than two years. Therefore, to obtain relevant information on their cognitive and functional status, we conducted phone interviews both with them and a relative. Specifically, we asked if they still complained of cognitive decline, if they showed a clear worsening of cognitive functions and/or a reduction in Activities of Daily Living and the Instrumental Activities of Daily Living scale scores or whether they were followed-up by other clinicians specialized in cognitive disorders, if they received a diagnosis either of MCI or AD or other neurodegenerative disorders, if they started therapy with acetyl-cholinesterase inhibitors or memantine.

On the basis of progression from SCD to MCI and AD during the follow-up, SCD subjects were classified respectively into SCD-stable (SCD-s), SCD-progressed (SCD-p), and SCD-converters (SCD-c). In the same way, MCI subjects were classified as MCI-stable (MCI-s) and MCI-converters (MCI-c).

Neuropsychological assessment

All subjects were evaluated by means of an extensive neuropsychological battery standardized on a group of 146 normal subjects and described in further detail elsewhere [44]. The battery consisted of global measurements [Mini-Mental State Examination (MMSE), Information-Memory-Concentration Test], tasks exploring verbal and spatial short-term memory (Digit Span; Corsi Tapping Test) and verbal long-term memory [Five Words and Paired Words Acquisition (FWA, PWA); Recall after 10 min (FWR10, PWR10); Recall after 24-h (FWR24, PWR24); Babcock Short Story Immediate and Delayed Recall (BS, BSR)], language (Token Test; Category Fluency Task), visuo-motor functions (Copying Drawings). Based on a previous discriminant analysis, five tests were selected from the battery (FWA, PWA, FWR10, PWR24, Information-Memory-Concentration Test) to obtain two Composite Memory Scores (CMS1 and CMS2), with positive scores indicating worse performance [44]. Visuospatial abilities were also evaluated by Rey-Osterrieth Complex Figure copy and visuospatial long-term memory was assessed by means of recall of Rey-Osterrieth Complex Figure test [45]; attention/executive function was explored by means of Dual Task [46], Phonemic Fluency Test [47], and Trail Making Test [48]. Everyday memory was assessed by means of Rivermead Behavioral Memory Test (RBMT) [49]. All raw test scores were adjusted for age, education and gender according to the correction factor reported in validation studies for the Italian population [44 –49]. In order to estimate premorbid intelligence, all subjects were given the TIB (“Test di Intelligenza Breve”) [50], an Italian version of the National Adult Reading Test [51]. The presence and severity of depressive symptoms was evaluated by means of the 22-item Hamilton Depression Rating Scale (HRSD) [52].

Personality traits and leisure activities

We used the Big Five Factors Questionnaire [53] to assess personality traits of the subjects. At baseline, participants had to fill out a questionnaire that measures the five factors of: 1) emotional stability, 2) energy, 3) conscientiousness, 4) agreeableness, and 5) openness to culture and experience. The inventory follows a widely accepted five-traits personality model [53, 54]. For the 24 items of each factor, subjects rated their level of agreement on a five-point scale ranging from strongly agree to strongly disagree. Item scores were computed for each factor to yield a summary measure of the trait with higher values representing a greater degree of the explored dimension.

At baseline, subjects were interviewed regarding participation, when they were 30–40 years old, in nine Intellectual Activities, seven Social Activities and seven Physical Activities (modified from Yarnold et al. [55]). The frequency of participation was reported for each activity on a Likert scale ranging from 0 to 5, where 0 refers to never, 1 to occasionally, 2 to monthly, 3 to once a week, 4 to several days per week and 5 to daily. We summed the scores for each activity to generate total scores for intellectual, social, physical and other activities ranging from 0 to 30.

Apolipoprotein E ɛ4 genotyping

DNA was extracted from peripheral blood samples from all subjects by use of the phenol-chloroform procedure, and the APOE gene was amplified in the polymorphic region [56]. The frequencies of the ɛ2, ɛ3, and ɛ4 alleles were estimated by gene counting. The APOE genotype was coded as APOE ɛ4- (no APOE ɛ4 alleles) and APOE ɛ4+ (presence of one or two APOE ɛ4 alleles).

Statistical analysis

Patient groups were characterized using means and standard deviations (SD). Scores at cognitive tests were reported as z-scores (z-scores were calculated as the raw score of the patient, minus the mean score of Italian general population, divided by the SD of Italian general population). We tested for the normality distribution of the data using the Kolmogorov-Smirnov test. Depending on the distribution of our data, we used t-test or non-parametric Mann-Whitney U Tests for between groups’ comparisons and Pearson’s correlation coefficient or non-parametric Spearman’s ρ (rho) to evaluate correlations between groups’ numeric measures. We used chi-square test to compare categorical data and calculated the effect size by Cohen’s d for numeric measures and Cramer’s V for categorical data. We used ROC curve analysis to evaluate diagnostic accuracy, i.e., sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and AUC (Area Under the Curve) for neuropsychological tests. We constructed Cox regression models to ascertain the effect of the variables in predicting conversion to MCI or AD. All statistical analyses were performed with SPSS software v.13 (SPSS Inc., Chicago, USA). The significance level was set at p < 0.05.

RESULTS

Demographic and clinical features

At follow up, 26 out of 109 SCD subjects (24%) had converted to MCI (SCD-p) and 15 (14%) had converted to AD (SCD-c). Mean conversion time from SCD to MCI was 6.53 (±3.11) years and from SCD to AD was 9.14 (±4.22) years. A total of 68 subjects (62%) remained stable (SCD-s) and their mean follow-up time was 7.15 (±3.88) years (range: 2.00 – 18.48 years, IQR: 5.15 years).

Of 103 MCI subjects, 39 (38%) developed AD (MCI-c) and 62 (60%) remained stable. Two MCI subjects (2%) regressed to SCD and we included them in the MCI-s group as we considered them non-converters. Mean follow up time of MCI-s was 7.51 (±4.78) years (range: 2.00 – 27.20 years, IQR: 5.21 years).

Annual conversion rate (ACR), cumulative conversion proportion (CCP), and mean conversion time for MCI and AD are summarized in Table 1.

Table 1

Annual conversion rate (ACR), cumulative conversion proportion (CCP), and mean conversion time (MCT) from SCD to MCI, from SCD to AD, and from MCI to AD. MCT is expressed as years±SD

	ACR	CCP	MCT (±SD)
SCD to MCI	3.65%	23.85%	6.53 (±3.11)
SCD to AD	1.51%	13.76%	9.14 (±4.32)
MCI to AD	11.65%	37.86%	3.25 (±2.47)

Of the 17 out patients who were included after a telephone call, three out of 16 SCD subjects developed AD (conversion time: 11.87±4.83 years) while 13 SCD subjects remained stable (follow-up time: 10.10±3.51). Of the two MCI subjects, both remained stable, with a follow-up time of 27.20 years and 10.2 years.

In the SCD group, no significant differences were found between SCD-s and SCD-p and between SCD-s and SCD-c in sex, familiarity, disease duration, schooling, MMSE, HRSD, and TIB. There was no statistically significant difference in genotype distribution of APOE ɛ4 among the three subgroups of SCD, but a trend toward significance was found between SCD-s and SCD-c (p = 0.056).

In the MCI group, MCI-c were older than MCI-s at onset of symptoms and at baseline visit. Genotype APOE ɛ4 was more frequent in MCI-c subjects than MCI-s subjects (Table 2).

Table 2

Demographic and cognitive data

Demographics	SCD						MCI
Demographics	SCD-s (68)	SCD-p (26)	SCD-c (15)	p (1)	S.E. (1)	p (2)	S.E. (2)	MCI-s (64)	MCI-c (39)	p (3)	S.E. (3)
Age at baseline (±SD)	64.45 (±6.63)	63.80 (±8.85)	66.91 (±5.75)	0.960	0.08	0.161	0.40	67.21 (±7.025)	71.97 (±5.12)	<0.001	0.77
Age at onset (±SD)	55.65 (±8.91)	60.56 (±7.41)	62.53 (±7.07)	0.813	0.60	0.281	0.86	62.89 (±7.41)	68.59 (±5.95)	<0.001	0.85
Sex (females/males)	44/24	19/7	11/4	0.440	0.44	0.764	0.07	41/22	26/13	0.870	0.03
Familiarity (percentage)	52.94%	53.85%	46.46%	0.937	0.01	0.778	0.48	54.68%	51.28%	0.737	0.02
Follow up (±SD)	7.15 (±3.88)	6.53 (±3.11)*	9.14 (±4.22)*	0.892	0.18	0.106	0.49	7.51 (±4.78)	3.25 (±2.47)*	<0.001	1.12
Disease duration (±SD)	4.26 (±3.84)	3.98 (±3.24)	4.40 (±4.08)	0.640	0.08	0.731	0.04	4.32 (±3.24)	3.38 (±2.82)	0.092	0.31
Schooling (±SD)	11.25 (±4.77)	9.54 (±4.17)	11.20 (±5.39)	0.117	0.38	0.795	0.01	8.58 (±4.46)	9.05 (±4.57)	0.615	0.10
MMSE (±SD)	28.31 (±1.83)	28.07 (±2.04)	27.23 (±2.56)	0.601	0.12	0.069	0.49	26.72 (±2.15)	25.82 (±2.21)	0.083	0.41
APOE ɛ4+ (percentage)	21.56 %	33.33%	54.54%	0.319	0.12	0.056	0.28	12.12%	61.90%	<0.001	0.52
HRSD (±SD)	26.67 (±4.19)	26.38 (±3.91)	26.33 (±3.69)	0.909	0.07	0.932	0.09	27.10 (±4.62)	26.42 (±3.97)	0.556	0.16
TIB (±SD)	111.48 (±6.24)	109.43 (±8.77)	110.58 (±6.64)	0.281	0.27	0.409	0.14	103.73 (±12.62)	107.15 (±10.751)	0.241	0.29

Values quoted in the table are mean (±SD). Age at baseline, age at onset, disease duration, follow up and schooling are expressed in years. p (1) indicates level of significance for comparison between SCD-s and SCD-p; p (2) indicates level of significance for the comparisons between SCD-s and SCD-c; p (3) indicates level of significance for the comparisons between MCI-s and MCI-c¸ (significant differences at p < 0.05, in bold characters, underlined). S.E. (1) indicates size effect for the comparisons between SCD-s and SCD-p; S.E. (2) indicates size effect for the comparisons between SCD-s and SCD-c; S.E. (3) indicates size effect for the comparisons between MCI-s and MCI-c. *In SCD-p, SCD-c, and MCI-c groups follow up indicates conversion time to MCI and to AD time. MMSE, Mini-Mental State Examination; APOE, Apolipoprotein; HRSD, Hamilton Depression Rating Scale; TIB, Test di Intelligenza Breve.

No significant differences were found between APOE ɛ4 + and APOE ɛ4- subjects with regards to sex, familiarity, disease duration, age at onset, age at baseline, schooling, MMSE, HRSD, and TIB. There were no statistically significant associations between APOE genotype and familiarity and between APOE genotype and sex. No subjects had depression according to HRSD scores.

There were no significant correlations between neuropsychological tests, personality traits and leisure activities. No differences were found in neuropsychological tests, personality traits and leisure activities between APOE ɛ4 + and APOE ɛ4–.

Neuropsychological assessment

In the SCD group, no significant differences were found at baseline for any neuropsychological tests between SCD-s and SCD-p (Table 3). SCD-c performed significantly worse than SCD-s in FWR24 test. CMS1 and CMS2 were significantly higher in SCD-c than SCD-s. No significant differences were found at baseline for any other neuropsychological tests between SCD-s and SCD-c (Table 3). Considering those neuropsychological tests that were significantly different at baseline between SCD-s and SCD-c, we used ROC-curve analysis to find the most accurate neuropsychological tests in differentiating SCD-s from SCD-c (Table 4). FW24, CMS1, and CMS2 provided only a sufficient prognostic accuracy. FW24 had low sensitivity (60.0%) and low specificity (61.8%). CMS1 and CMS2 provided a good specificity (72.1% and 73.5%, respectively), but a very low sensitivity (60.0%). All three tests provided a very low PPV but a high NPV. A Cox regression analysis was performed to evaluate the effect of FW24, CMS1, and CMS2 on the risk of dementia and to ascertain if this effect is independent from age at baseline and APOE genotype. We considered these two variables (age at baseline and APOE), although they did not show statistically significant difference between SCD-s and SCD-c, as they are generally reported to be the strongest risk factors for the development of AD in SCD subjects [57, 58]. The Cox regression model was statistically significant (χ² = 7.91, p = 0.020). Of the neuropsychological variables included in this analysis, only CMS1 had a statistically significant effect on the risk that participants would develop AD (Table 5).

Table 3

Z-score mean values for each neuropsychological test in SCD and MCI

SCD					MCI
Tests	SCD-s (68)	SCD-p (26)	SCD-c (15)	p (1)	p (2)	MCI-s (64)	MCI-c (39)	p (3)
CMS1	– 1.05 (±0.21)	– 1.02 (±0.20)	– 0.90 (±0.22)	0.446	0.020	– 0.89 (±0.23)	– 0.69 (±0.35)	0.003
CMS2	– 0.56 (±0.46)	– 0.56 (±0.46)	– 0.27 (±0.40)	0.946	0.044	– 0.26 (±0.48)	0.12 (±0.72)	0.006
FWA	0.35 (±0.96)	0.14 (±1.05)	0.21 (±0.75)	0.384	0.395	– 0.08 (±1.07)	– 0.86 (±1.46)	0.008
FW10	0.48 (±1.05)	0.59 (±0.75)	0.42 (±0.79)	0.913	0.520	0.33 (±1.03)	– 1.06 (±1.86)	<0.001
FW24	– 0.04 (±0.51)	– 0.08 (±0.50)	– 0.40 (±0.53)	0.662	0.016	– 0.32 (±0.56)	– 0.74 (±0.83)	0.009
PWA	0.55 (±1.08)	0.54 (±1.07)	0.33 (±0.81)	0.959	0.316	– 0.17 (±1.14)	– 0.74 (±1.33)	0.063
PW10	0.51 (±0.85)	0.64 (±0.65)	0.86 (±0.26)	0.897	0.293	0.02 (±1.21)	– 0.77 (±1.82)	0.007
PW24	0.15 (±1.02)	0.15 (±0.85)	0.14 (±0.76)	0.668	0.649	– 0.38 (±0.98)	– 1.02 (±1.58)	0.042
BS	0.12 (±0.74)	– 0.01 (±0.69)	0.18 (±0.83)	0.425	0.813	– 0.63 (±0.81)	– 0.97 (±0.68)	0.046
BSR	0.56 (±0.96)	0.46 (±0.72)	0.64 (±0.93)	0.441	0.934	– 0.34 (±1.03)	– 1.14 (±0.76)	<0.001
DS	– 0.71 (±1.23)	– 1.02 (±1.22)	– 0.98 (±1.30)	0.287	0.451	– 1.39 (±0.97)	– 1.05 (±1.46)	0.309
RBMT	0.22 (±0.71)	0.11 (±0.77)	– 0.09 (±0.83)	0.626	0.237	– 0.44 (±1.12)	– 1.87 (±1.61)	<0.001
TMTa	– 0.48 (±0.80)	– 0.74 (±0.58)	– 0.39 (±0.49)	0.258	0.373	0.13 (±1.21)	– 0.08 (±0.88)	0.509
TMTb	0.76 (±0.49)	0.81 (±0.47)	0.59 (±0.41)	0.682	0.304	0.28 (±0.80)	– 0.06 (±0.93)	0.122
TMTb-a	0.77 (±0.48)	0.74 (±0.53)	0.57 (±0.42)	0.714	0.108	0.33 (±0.85)	– 1.26 (±1.03)	0.075
DT	– 0.18 (±1.43)	– 0.08 (±1.42)	0.55 (±1.32)	0.794	0.104	1.09 (±1.09)	1.20 (±0.20)	0.426
TT	0.42 (±0.49)	0.50 (±1.80)	0.29 (±0.61)	0.323	0.489	– 0.19 (±0.74)	– 0.23 (±0.71)	0.670
PFT	0.23 (±0.80)	0.34 (±0.63)	– 0.11 (±0.99)	0.565	0.207	– 0.30 (±0.73)	– 0.29 (±0.76)	0.927
CFT	0.83 (±0.40)	0.84 (±0.29)	0.94 (±0.00)	0.734	0.236	0.59 (±0.69)	0.52 (±0.75)	0.602
CD	1.58 (±0.53)	1.50 (±0.47)	1.35 (±0.62)	0.217	0.111	1.39 (±0.70)	1.53 (±0.56)	0.372
CT	– 1.28 (±1.34)	– 1.11 (±1.46)	– 0.88 (±1.09)	0.631	0.155	– 1.28 (±0.93)	– 1.04 (±1.18)	0.503
RFC	0.55 (±0.41)	0.18 (±0.87)	0.18 (±0.78)	0.095	0.143	– 0.16 (±1.29)	– 0.04 (±1.25)	0.968
RFR	0.42 (±0.87)	0.56 (±0.68)	0.46 (±0.78)	0.551	0.911	– 0.31 (±0.73)	– 0.57 (±0.86)	0.090

Negative values indicate worse performances than age-matched normal population. For CMS1, CMS2, TMT-a, TMT-b, TMT b-a, instead, the higher the score, the worse the performance. p (1) indicates level of significance for comparison between SCD-s and SCD-p; p (2) indicates level of significance for the comparisons between SCD-s and SCD-c; p (3) indicates level of significance for the comparisons between MCI-s and MCI-c (significant differences at p < 0.05, in bold characters, underlined). SCD, subjective cognitive decline; MCI, mild cognitive impairment; CMS, Composite Memory Scores; FWA, Five Words Acquisition; PWA, Paired Words Acquisition; BS and BSR, Babcock Short Story Immediate and Delayed Recall; DS, Digit Span; RBMT, Rivermead Behavioral Memory Test; TMT, Trail Making Test; DT, Dual Task; TT, Token Test; PFT, Phonemic Fluency Test; CFT, Category Fluency Task; CD, Copying Drawings; CT, Corsi Tapping Test; RFC, Rey-Osterrieth Complex Figure copy; RFR, Rey-Osterrieth Complex Figure test.

Table 4

Area under the curve (AUC), cut-off values, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of neuropsychological tests. Cut-off values are expressed as z-scores

	AUC	Cut-off	Sensitivity (±C.I)	Specificity (±C.I)	PPV (±C.I)	NPV (±C.I)
SCD-s versus SCD-c
CMS1	0.679	– 0.93	60.0% (±24.79)	72.1% (±10.6)	30,61% (±16,66)	88,81% (±8,44)
CMS2	0.671	– 0.32	60.0% (±24.79)	73.5% (±10.49)	33,31% (±17,77)	89,28% (±8,10)
FW24	0.683	0.02	60.0% (±24.79)	61.8% (±11.55)	25,73% (±14,49)	87,51% (±9,35)
MCI-s versus MCI-c
CMS2	0.794	– 0.03	72.7% (±13.98)	73.2% (±10.85)	62,31% (±14,08)	81,48% (±10,04)
RBMT	0.788	– 0.93	72.7% (±13.98)	80.5% (±9.71)	69,44% (±14,13)	82,87% (±9,37)
BSR	0.785	– 0.74	77.3% (±13.15)	65.9% (±11.61)	58,01% (±13,42)	82,65% (±10,39)
CMS1	0.782	– 0.74	72.7% (±13.98)	77.8% (±10.18)	66,62% (±14,17)	82,38% (±9,60)
BS	0.777	– 0.86	72.7% (±13.98)	70.7% (±11.15)	60,19% (±13,98)	80,95% (±10,29)
FW10	0.761	– 0.05	68.2% (±14.62)	78.0% (±10.15)	65,39% (±14,62)	80,10% (±9,91)
FW24	0.739	– 0.58	63.6% (±15.19)	65.9% (±11.61)	53,20% (±14,32)	74,82% (±11,33)
FWA	0.738	– 0.15	68.2% (±14.62)	61.0% (±11.95)	51,59% (±13,64)	75,89% (±11,69)
PW10	0.710	0.11	68.2% (±14.62)	65.9% (±11.61)	54,93% (±14,01)	77,28% (±11,12)
PW24	0.669	– 0.58	63.6% (±15.19)	68.3% (±11.40)	55,01% (±14,52)	75,49% (±11,08)

SCD, subjective cognitive decline; MCI, mild cognitive impairment; CMS, Composite Memory Scores; FWA, Five Words Acquisition; PWA, Paired Words Acquisition; BS and BSR, Babcock Short Story Immediate and Delayed Recall; RBMT, Rivermead Behavioral Memory Test.

Table 5

Cox regression analysis

	B	p	HR	95% CI
	B	p	HR	lower	upper
SCD-s versus SCD-c
CMS1	3.415	0.043	30.427	1.111	833.617
MCI-s versus MCI-c
CMS2	1.469	0.021	4.346	1.247	15.145
BSR	1.161	0.039	3.194	0.104	0.941
SCD-s versus SCD-p
Emotional stability	0.086	.013	1.089	1.019	1.165
Intellectual activities	– 0.357	0.007	0.700	0.540	0.907

Regression coefficients (B), p-value (p), hazard ratios (HR), and 95% confidence intervals (95% CI) for neuropsychological tests, personality traits and leisure activities included in the regression models are reported.

In the MCI group, MCI-c had worse performance at baseline compared to MCI-s on all neuropsychological tests assessing long term verbal memory (FWA, FWR10, FWR24, PWR10, PWR24, BS, BSR) and on RBMT. CMS1 and CMS2 was higher in MCI-c than in MCI-s. In the MCI group, CMS1, CMS2, RBMT, BSR, BS, and FW10 offered an acceptable accuracy (Table 4). BSR offered the best sensitivity (77.3%) but poor specificity (65.9%). CMS1 and FW10 provided a very good specificity (78.0%), at the cost of a low sensitivity (68.2%). CMS2 and BS had a more balanced diagnostic accuracy with acceptable sensitivity (72.7%) and specificity (73.2%, 70.7%, respectively). RBMT provided the best specificity (80.5%), with a good sensitivity (72.7%). The other tests showed lower AUC values with poorer sensitivity and specificity. Including these tests in a Cox regression analysis, only CMS2 and BSR were included in the final model corrected for APOE and age at baseline (χ² = 27.093, p < 0.001) (Table 5).

Personality traits

For the analysis on personality traits and leisure activities, 26 SCD-s and 10 SCD-p were included. Emotional stability was significantly lower in SCD-s than in SCD-p (Table 6). A Cox regression analysis was performed to evaluate the effect of emotional stability on the risk of progression from SCD to MCI and to ascertain if this effect is independent from age at baseline and APOE genotype. The Cox regression model was statistically significant (χ² = 16.877, p = 0.010). Emotional Stability had a statistically significant effect on the risk that participants would progress to MCI (Table 5). In particular, risk of progression seems to be higher in subjects with a higher Emotional Stability score (hazard ratio = 1.089).

Table 6

Raw score mean values for each personality trait and leisure activities in SCD-s and SCD-p

	SCD-s (26)	SCD-p (10)	p
Personality Traits
Energy	42.00 (±8.78)	45.50 (±8.48)	0.301
Friendship	49.69 (±10.99)	45.40 (±10.45)	0.295
Consciousness	46.00 (±11.36)	51.10 (±11.14)	0.234
Emotional Stability	46.00 (±11.36)	54.60 (±11.14)	0.027
Openness	43.04 (±9.89)	44.40 (±12.45)	0.733
Leisure Activities
Intellectual	16.13 (±4.33)	14.16 (±7.40)	0.327
Social	7.38 (±3.41)	9.10 (±5.51)	0.263
Physical	4.52 (±3.58)	6.22 (±6.05)	0.304

p indicates level of significance for the comparisons between groups (significant differences at p < 0.05, in bold characters, underlined).

Leisure activities

No differences were found at baseline between SCD-s and SCD-p in the three leisure activities scores (Table 6). Including them in a Cox Regression analysis corrected for schooling and TIB (considered as cognitive reserve indexes), intellectual activities resulted as protective factor, reducing the risk of conversion from SCD to MCI of about 30% (χ² = 12.122, p = 0.007) (Table 5).

DISCUSSION

We aimed to evaluate the role of neuropsychological tests conducted at baseline in identifying SCD and MCI subjects who will developed AD. Furthermore, we investigated the effect of personality traits and cognitive reserve variables in increasing or reducing the risk of progression to MCI in SCD subjects. To our knowledge, this is one of the first studies to have evaluated all these different features on the same sample.

Among 109 SCD subjects, 26 progressed to MCI (SCD-p) and 15 converted to AD (SCD-c). In our sample, CCP from SCD to MCI and from SCD to AD is comparable to the CCP reported in a recent meta-analysis by Mitchell et al. [10]. ACR from SCD to MCI and from SCD to AD are quite low with respect to ACR reported in the same meta-analysis, but similar to ACRs reported in studies which analyzed samples of subjects with an average age at baseline comparable with average age at baseline in our sample [59, 60]. Of 103 MCI subjects, 39 developed AD (MCI-c) with an ACR and a CCP close to the data reported in the literature [59].

Several prospective studies suggested that cognitive deficits are detectable up to 12 years before the clinical diagnosis of AD dementia [60 –67]. In our sample, SCD-c subjects obtained worse scores on verbal long-term memory tasks on average seven years before the diagnosis of AD, in line with other studies on SCD [19, 68]. On the contrary, no significant differences were found at baseline between SCD-s and SCD-c for tests exploring other cognitive domains. In the SCD group, neuropsychological tests provided only a sufficient accuracy in distinguishing converters and non-converters. In particular, as the PPVs were extremely low, neuropsychological tests did not provide useful information about SCD subjects who obtained scores below the cut-off values reported in Table 4. However, the high NPVs may suggest that SCD subjects who obtained scores above the cut-off values have a very low probability of progression to AD.

In line with the international literature on the topic, in our sample MCI-c subjects performed worse than MCI-s subjects on tests about verbal memory [69, 70] and ecological evaluation of memory [71]. In particular RBMT, an ecologically-valid memory test battery, appeared as the most accurate neuropsychological test in predicting conversion from MCI to AD. MCI subjects whose score at RBMT was lower than the cut-off value reported in Table 4 had a probability of progressing to AD of 70%, while MCI subjects who resulted negative for the same test had an 80% probability of remaining stable. This observation is confirmed by other previous studies [70, 71] and could be worthy of further investigation as it could represent a simple and cost-effective tool for initial outpatient evaluations for MCI.

Our analysis has demonstrated that the effect of CMS on the risk of progression to dementia is not influenced by other well-known risk factors, such as age at baseline and APOE, both in SCD and MCI subjects. Several studies used composite cognitive score to track decline better than the single most sensitive tests in SCD [68] and in MCI [72 –74]. Our and other groups’ data seem to confirm that composite cognitive scores could provide a more tailored and complete neuropsychological characterization than single tests in addition to a higher diagnostic accuracy.

We would like to point out that the cut-off values of neuropsychological tests we identified as distinguishing stable subjects from converters (Table 4) are different from cut-off values adopted in clinical practice to define pathological scores at neuropsychological tests (i.e., 2 standard deviations under the general population mean). In agreement with other authors [75, 76], we suppose that cut-off values adopted in clinical practice are suitable for the diagnosis of a full-blown dementia but are not sufficiently sensitive to detect those SCD and MCI patients who will convert to AD. Several studies adopted different cut-off values (– 1 SD, – 1.5 SD, – 1.96 SD) to estimate the real neuropsychological test reliability in predicting conversion from MCI to AD [77 –79]. For this reason, we suggest that more sensitive and more specific neuropsychological cut-off values should be established and applied in the evaluation of SCD and MCI patients with the goal of earlier differentiation of converters from non-converters, rather than healthy from demented subjects.

In a subgroup, we compared personality traits between SCD-s and SCD-p subjects. We are aware of only three previous studies examining all domains of the Big Five Factors Questionnaire [80 –82]. As several studies showed that among middle-aged and older adults, personality traits are associated with cognitive functioning when measured concurrently [83, 84], as a preliminary point we excluded correlations between personality traits and scores in neuropsychological tests. In our sample high emotional stability represented a risk factor for progression from SCD to MCI. This finding is partially in contrast with literature data. In fact, low emotional stability has been reported as a risk factor for clinical AD and memory deficit [27, 28] and has been associated with a faster rate of cognitive decline [84, 89]. However, these studies were conducted on a sample of aged people and not limited to SCD. Individuals with low emotional stability respond worse to stressors and are more likely to interpret minor frustrations as hopelessly difficult, by definition, and tend to report more complaints about their memory [90]. Thus, we suggest that in SCD subjects with low emotional stability, self-reported cognitive disorders could more likely represent only a subjective complaint than a manifestation of an underlying neurodegenerative disease. Further research is clearly needed to confirm this hypothesis.

The last aspect we investigated is the role of cognitive reserve in SCD. The Cognitive Reserve model refers to the capacity to cope with greater amounts of cerebral damage in brighter individuals [39, 40]. In AD patients, Cognitive Reserve theory predicts that the clinical manifestation of advancing AD pathology would be delayed in patients with higher exposure to cognitive, social and physical activities [85]. Furthermore, emerging evidence showed an inverse association between Aβ deposition and lifelong cognitive activities in cognitively normal subjects [86, 87] suggesting that lifestyle factors such as cognitive and physical activity may also have direct neuroprotective effects.

As Cognitive Reserve is a hypothetical construct, direct measurements of reserve are not available [88]. Therefore, surrogate or proxy measurements are used to approach reserve. Education [89] and engagement in leisure and cognitive activities [90] are considered as standard proxies of cognitive reserve. Performance-based measurements (such as vocabulary or reading tests) have been used as they are thought to show little change with age and remain relatively preserved in the early stages of dementia [91, 92].

We considered three categories of leisure activities, age of schooling as education index, and TIB as premorbid intelligence index. We found that Intellectual Activities could act as a protective factor, reducing risk of progression from SCD to MCI by 30%. Our results are supported by several previous studies showing that participation in cognitive or leisure activities [93, 94] delayed memory decline.

The present study has some limitations. First of all, the small size of the sample and in particular in the subgroup considered for the personality traits and leisure activities analysis. The absence of a control group represents a further limitation, as does the use of telephone interview to obtain information on a small group of subjects. Finally, as it is a single-center study, there may be biases with regard to assessment and diagnosis procedures.

However, this study has some remarkable strengths. The first is the very long, average follow-up time. In fact, follow-up time in the SCD-s group is comparable to time of conversion in SCD-c, and MCI-s is even much longer than conversion time in MCI-c. This information allows us to minimize the possible underestimation of conversion to AD and the risk of classifying as stable subjects carrying an AD pathology who will convert later in the follow-up. The second strength of this study is the inclusion of variables such as personality and cognitive reserve which complement the clinical and neuropsychological assessments.

In conclusion, our findings support the evidence that slight differences in neuropsychological measurements between converters and non-converters to AD are detectable many years before diagnosis of dementia both in SCD and MCI. We also demonstrate that emotional stability and intellectual activities influence the risk of progression from subjective to objective cognitive decline. These results suggest that memory neuropsychological tests may represent a reliable tool, which are easy to administer in outpatient evaluation of MCI, to estimate the risk of progression to AD. In SCD neuropsychological tests do not seem to be sensitive enough to discriminate between converters and non-converters to AD. However, personality and lifestyle factors could provide useful information to identify, at an earlier stage, SCD subjects who may develop an objective cognitive impairment and who should merit further investigations, such as biomarkers analysis.

Future studies on larger samples, also combining neuropsychological and biological data, are needed to further delineate the significance of these findings.

Footnotes

ACKNOWLEDGMENTS

This research was funded by Ministero della Salute and Regione Toscana (grants n° GR-2010-2316359 - Longitudinal clinical-neuropsychological study of subjective memory complaints).

Authors’ disclosures available online ().

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