Executive and Language Subjective Cognitive Decline Complaints Discriminate Preclinical Alzheimer’s Disease from Normal Aging

Abstract

Background:

There is a need to specify the profile of subjective cognitive decline in preclinical Alzheimer’s disease (preAD).

Objectives:

To explore specific items of the Subjective Cognitive Decline Questionnaire (SCD-Q) that discriminate preAD from normal aging.

Methods:

68 cognitively normal older adults were classified as controls (n = 52) or preAD (n = 16) according to amyloid-β (Aβ) levels. An exploratory factor analysis and item analysis of the SCD-Q were performed. Informant reports of the SCD-Q were used to corroborate the findings of self-reports. One-year neuropsychological follow-up was available.

Results:

Four SCD-Q factors were extracted: EM-factor (episodic memory), A-factor (attention), O-factor (organization), and L-factor (language). PreAD reported a significantly higher decline in L-factor (F(1) = 6.49; p = 0.014) and A-factor (F(1) = 4.04; p = 0.049) compared to controls, and showed a higher frequency of perceived decline in SCD-Q items related with language and executive tasks (Sig-items.) Significant discriminative powers for Aβ-positivity were found for L-factor (AUC = 0.75; p = 0.003) and A-factor (AUC = 0.74; p = 0.004). Informants in the preAD group confirmed significantly higher scores in L-factor and Sig-items. A significant time×group interaction was found in the Semantic Fluency and Stroop tests, with the preAD group showing a decrease in performance at one-year.

Conclusions:

Our results suggest that SCD-Q items related with language and executive decline may help in prediction algorithms to detect preAD. Validation in an independent population is needed.

Keywords

Alzheimer’s disease amyloid-β biomarkers preclinical Alzheimer’s disease subjective cognitive decline

INTRODUCTION

Preclinical Alzheimer’s disease (preAD) is the earliest stage of AD in which there is evidence of amyloid-β (Aβ) burden in the absence of objective cognitive impairment [1, 2]. There is great interest in characterizing preAD, as it provides a critical opportunity for trialing disease modifying-therapies [3]. Although biomarkers are necessary for defining preAD, they may be insufficient for triggering clinical dementia [4, 5]. For example, studies have shown that around 25–30% of cognitively healthy older adults show AD neuropathology at postmortem [6, 7]. Hence, biomarkers are only supportive features in the diagnostic framework [8] and defining the symptomatic phenotype of preAD may be useful for determining the risk of future dementia. Although subjects with preAD do not meet the criteria for mild cognitive impairment, it is possible that they are already experiencing subtle cognitive decline [3]. Detecting subtle cognitive symptoms cross-sectionally is challenging, as the validity of standard neuropsychological tests for detecting cognitive dysfunction decreases with reduced levels of impairment [9 –11]. In this scenario, subjective cognitive decline (SCD) measures, defined as a subjectively experienced worsening of cognitive abilities, may help detecting cognitive decline in preAD as they offer a longitudinal overview of cognitive functioning in real life tasks that are more demanding than laboratory tests [10].

There has been an increasing interest in the study of SCD in the context of preAD. Although studies’ results are inconclusive, mainly due to the high heterogeneity in the conceptualization and measurement of SCD [9, 12], most findings support SCD as one of the earliest symptomatic manifestations of AD [12, 13]. Studies of cognitively normal older adults have shown an association between SCD and the presence of AD biomarkers [13, 14], structural and functional brain changes in areas typically affected in AD [15 –18], and higher risk of future dementia [19, 20].

Other studies, however, have found a high correlation of SCD with personality traits and sub-syndromal symptoms of depression and anxiety [21 –25]. Also, SCD is not uncommon among older adults, with an estimated prevalence of 25–50% [11 , 26]. It is well-established that episodic memory, processing speed, working memory, fluid intelligence, and high level executive functions decline with age, regardless of amyloid status [27 –30]. Therefore, SCD in older adults may be simply reflecting the normal cognitive decline associated with age.

The multifactorial nature of SCD challenges its clinical utility. Nevertheless, it could be possible that cognitive concerns in preAD differ from the concerns related with normal aging, personality traits, and psychological symptoms. By developing a collation of evidence from multiple observations, we could potentially produce a set of overlapping phenomenological features of SCD in preAD that uniquely signifies this condition [31]. For this reason, the Subjective Cognitive Decline Initiative (SCD-I) has encouraged researchers to identify specific SCD items by assessing their relationship to ADbiomarkers [32].

To our knowledge, only one recent study [33] has analyzed the association between specific SCD items and Aβ deposition in a cognitively healthy sample. The authors of this study performed an item-analysis of the Cognitive Difficulties Scale [34], comparing the patterns of responses between 19 Aβ-positive and 49 Aβ-negative subjects. In their results, they found that the Aβ-positive group reported significantly higher difficulties in memory and attentional tasks.

The purpose of the present study was to further explore specific characteristics of SCD in preAD. An exploratory factor analysis and an item-analysis of the Subjective Cognitive Decline Questionnaire (SCD-Q) [35, 36] were performed, comparing the declines reported in a preAD group (i.e., Aβ-positive) with those reported in a Control group (i.e., Aβ-negative). Due to the explorative nature of the present study, no prior hypothesis was established.

METHODS

Subjects

76 cognitively healthy older adults were recruited through convenience sampling at Hospital Clinic’s memory clinic in Barcelona, Spain. The local ethics committee approved the study and subjects gave written informed consent. The inclusion criteria for recruitment were: 1) Mini-Mental State Examination (MMSE) ≥25 [37], 2) age ≥50, and c) consent to participate. The following exclusion criteria were applied: 1) obtaining abnormal scores (≤1.5 SD from normative mean) in any of the neuropsychological tests, 2) having any neurological or psychiatric major disease, 3) the presence or history of a serious or unstable medical condition that could affect cognition, and 4) the presence of suspected non-amyloid pathologies (SNAPS) defined as abnormal tau or phosphorylated tau (ptau) levels and normal Aβ levels [38]. One subject was excluded because of an abnormal score in a neuropsychological test, and another subject was excluded because of a recent history of traumatic brain injury. After the cerebrospinal fluid (CSF) analyses, six additional subjects were identified as SNAPS and thus excluded from the study.

The final sample consisted of 68 participants. Participants were first classified as: 1) Non-SCD (n = 37): subjects who answered NO to the SCD-Q question 3 (i.e., In the last two years, has your cognition or memory declined?); 2) Population-SCD (n = 6): subjects who answered YES to SCD-Q question 3 but have not sought medical assistance for their complaints; and 3) Clinical-SCD (n = 25): subjects who answered YES to the SCD-Q question 3 and have sought medical assistance for their complaints. SCD-Q question 3 was chosen to classify SCD following the SCD research criteria published by Jessen et al. [10]. No significant differences were found between groups in age, years of education, or AD biomarkers (Table 1). Although the prevalence of preAD was higher in the SCD groups compared to the Non-SCD group, the difference did not reach statistical significance. For this reason, all subjects were considered as a single sample of cognitively healthy olderadults.

Table 1

SCD groups demographics and biological data

	Non-SCD (n = 37)	P-SCD (n = 6)	C-SCD (n = 25)	F	p
Age	64.49±6.89	59.33±5.82	65.72±7.02	2.09	0.131
Years of Education	10.41±4.01	11.50±2.66	12.84±5.20	2.28	0.111
Gender (Female)	62.2%	100%	68.0%	3.38^a	0.185
ApoE ɛ4 (%)	14.3%	0%	12.0%	0.98^a	0.612
+Aβ (%)	18.9%	33.3%	28.0%	1.04^a	0.596
CSF Aβ	764.16±248.12	674.08±177.74	747.49±322.75	0.27	0.761
CSF tau	231.58±84.02	205.84±46.42	248.95±86.69	0.76	0.473
CSF ptau	52.13±15.24	47.34±12.29	53.86±14.93	0.47	0.627

Data are presented as means±standard deviation. Means were compared using ANCOVA with Bonferroni adjustment. Non-SCD, non subjective cognitive decline group; P-SCD, population subjective cognitive decline group; C-SCD, clinical subjective cognitive decline group; +ApoE ɛ4, Apolipoprotein allele ɛ4 carriers; +Aβ, abnormal levels of CSF amyloid-β isoform 42 (≤550 pg/ml); CSF Aβ_, cerebrospinal fluid levels of amyloid-β isoform 42; CSF tau, cerebrospinal levels of total protein tau; CSF ptau, cerebrospinal fluid levels of phosphorylated protein tau. ^aPearson Chi-Square.

Participants were subsequently divided into a Control group (CTR, n = 52) and a preAD group (n = 16) according to the absence or presence of abnormal levels of Aβ (i.e., ≤550 pg/ml), respectively. This classification is consistent with the guidelines proposed by the National Institute on Aging and the Alzheimer’s Association (NIA-AA) for defining preAD for research purposes [39]. Cognitive longitudinal follow-up was available for 57 participants: 43 CTR and 14 preAD. Follow-up mean time was of 12.18 (SD 1.85) months, with no significant differences between-groups (t(55) = 1.42;p = 0.16).

Neuropsychological battery

All subjects were assessed with a comprehensive neuropsychological battery administered by a trained neuropsychologist. It included the following tests: MMSE, Memory Alteration Test (M@T) [40], Boston Naming Test (BNT) [41], Comprehension of Commands BDAE [42], VOSP Incomplete letters and Numbers’ location [43], Trail Making Test part A (TMT-A) [44] and phonetic fluencies (FAS) [45]. This battery was administered for ensuring participants were cognitively healthy. Additionally, the following tests were administered to compare cognitive performance between groups: the Free and Cued Selective Reminding Test (FCSRT-IR) [46], the Semantic fluency test (animals) [42] and the Stroop Test [47]. The Hospital Anxiety and Depression Scale (HADS) [48] was used for quantifying depressive and anxiety symptoms. All tests were administered in one-hour sessions on two consecutive days. Subjects did not receive feedback on their scores; however, they were advised that they would be notified if any significant cognitive impairmentwas observed.

Subjective Cognitive Decline Questionnaire (SCD-Q)

The SCD-Q [35] is a validated questionnaire that follows the SCD-I framework for research of SCD in preAD [10]. It assesses perceived subjective cognitive decline by asking subjects whether their present performance in daily tasks is now worse than two years ago. Because subtle and progressive cognitive decline occurs as part of normal aging processes, reporting decline within a short timeframe may have a higher predictive value for preAD [10, 49]. It assesses the experience of perceived decline rather than difficulty, due to its higher specificity and sensitivity to AD [10]. It has two parallel forms, one is for the subject to quantify their own self-perceived decline (MyCog, after “My Cognition”), and the other is for the informant report of decline (TheirCog, after “Their Cognition”). Including an informant version is in line with recent findings suggesting that informant reports of cognitive decline increase the likelihood of preAD in subjects with SCD [10 , 51].

The questionnaire begins with three metacognitive questions: 1) Do you perceive memory or cognitive difficulties?; 2) Would you ask a doctor about these difficulties?; and 3) In the last two years, has your cognition or memory declined? These questions are followed by 24 items assessing perceived decline in instrumental activities of daily living that include memory, language, and executive tasks. Subjects must answer if they believe to be performing these activities worse than roughly two years ago, in a Yes/No format. The total score is computed from the 24 items (i.e., YES = 1, and NO = 0), with higher scores indicating greater perceived cognitive decline. For more details, see Rami et al. [35].

All participants in the study answered the SCD-Q. Informant reports were also available for 42 CTR and 15 preAD subjects. Questionnaires were given at the end of the first neuropsychological session, self-administered at home, and returned back on the next session.

Determination of CSF biomarkers

All subjects underwent a lumbar puncture between 9 a.m. and 12 p.m. and a 10-mL sample of CSF was collected. Samples were then centrifuged and stored in polypropylene tubes at –80°C within the first hour after extraction. CSF levels of Aβ, total tau (tau), and phosphorylated tau at threonine-181 (ptau) were measured by enzyme-linked immunosorbent assay kits (Innogenetics, Ghent, Belgium). In accordance with Hospital Clinic’s laboratory criteria, the following cutoff points for abnormality were considered for each CSF biomarker: 1) Aβ ≤550 pg/ml; 2) tau, ≥400 pg/ml for subjects between 50–70 years old and ≥450 pg/ml for subjects older than 70 years; and 3) ptau, ≥75 pg/ml. The study participants were blind to CSF results.

Apolipoprotein E (ApoE) analysis

The presence of the allele ɛ4 in the Apolipoprotein E genotype (ApoE ɛ4) has widely been described as a risk factor for late-onset AD [52]. Genomic DNA was extracted from peripheral blood of probands using the QIAamp DNA blood minikit (Qiagen AG, Basel, Switzerland). ApoE genotyping was performed by polymerase chain reaction amplification and HhaI restriction enzyme digestion. Results were not disclosed to participants.

Statistical analyses

Statistical analyses were performed using SPSS package for MAC (V.20). All statistical analyses considered p < 0.05 for significance. Demographical, AD biomarkers, and HADS scores were compared between the preAD and CTR groups using t-Student for independent samples and Chi-Squared analyses. Cross-sectional scores in the FCSRT-IR, Semantic Fluency, and Stroop tests were compared between-groups using Analysis of Covariance (ANCOVA) after adjusting for age and years of education. Bonferroni confidence interval adjustments were included in all ANCOVA analyses of the study, avoiding multiple comparisons biases.

An exploratory factor analysis (EFA) was done for exploring a preAD-SCD profile that differed significantly from normal aging SCD. Validating a factor analysis of the SCD-Q for the general population was beyond the scope of our Study. Only factors with at least two items with a load of ≥0.4, an eigenvalue >1, and a variance >5%, were considered. Resulting factors were rotated using direct oblique method, allowing them to be intercorrelated. To avoid redundancies, items that loaded in two or more factors were excluded. The total score of each factor was compared between-groups using ANCOVA. An item-analysis of the SCD-Q was also included using Chi-square analysis and comparing the frequencies of YES/NO answers in each item between groups. Items that showed a significantly higher frequency of YES in preAD were labelled as Sig-items for the sake of expediency.

The discriminative power of the SCD-Q for detecting preAD in the sample was assessed using ANCOVA, comparing MyCog score, TheirCog score, and factors scores between the preAD and CTR groups. The following covariates were considered in all analyses of self-reports (MyCog): age, gender, anxiety score, presence of ApoE ɛ4, and SCD group. Receiver operating characteristics curves (ROC) of total SCD-Q scores and factors scores were also included. Optimal cutoffs were identified using Youden’s Statistical Index (J), and the positive predictive (PPV) and negative predictive (NPV) values of these cutoffs were calculated using Chi-Squared analyses. Finally, Partial Pearsoncorrelations controlling for age were run to explore the association between SCD-Q scores and levels of CSF tau, CSF ptau, and CSF Aβ/ptau. Longitudinal cognitive follow-up was available for a subsample of subjects (n = 57). t-Student tests for independent samples and Chi-Squared analyses were performed to compare the demographic variables of the preAD and CTR groups at follow-up. Longitudinal changes in FCSRT-IR, Semantic Fluency, and Stroop Test scores were compared between-groups using ANCOVA repeated measures, adjusting for age and years of education. Additionally, t-Student for paired samples was performed to study the statistical significance of within-groups differences.

RESULTS

Characteristics of the sample

The final sample included 68 cognitively healthy older adults, with a mean age of 64.5 (SD 6.9) years and a mean of 11.4 (SD 4.5) years of education. Females represented 67.6% of the sample. The total prevalence for Aβ-positivity was 23.5%. No significant differences in age, years of education, or gender distribution, were found between preAD and CTR, although the preAD group was older, had lower educational level, and included a higher number of female subjects compared to the CTR (Table 2). Regarding AD biomarkers, only Aβ levels differed significantly between groups (t(53,9) = 10.99; p < 0.0001). ApoE ɛ4 frequency was higher in preAD compared to the CTR group; however, the difference between them did not reach statistical significance (see Table 2).

Table 2

Demographical, biological, and cognitive data of CTR and preAD

Parameters	CN (n = 52)	preAD (n = 16)	t	p
Demographics
Age	63.87±6.68	66.50±7.75	–1.33	0.188
Years of Education	11.96±4.63	9.56±3.54	1.90	0.061
Gender (Female)	63.5%	81.2%	1.77^a	0.184
Biological data
ApoE ɛ4 (%)	8.0%	25.0%	3.29^a	0.070
CSF Aβ	852.45±217.53	415.09±103.78	10.99	<0.0001^**
CSF tau	233.08±67.26	244.19±122.05	–0.35	0.732
CSF ptau	52.98±11.14	50.27±23.46	0.45	0.661
Cognitive data
FCSRT-fr	28.17±0.68	29.62±1.25	1.01^b	0.319
FCSRT-tr	44.60±0.42	44.85±0.77	0.08^b	0.782
FCSRT-dfr	10.73±0.32	10.93±0.58	0.09^b	0.769
FCSRT-dtr	15.21±0.18	14.82±0.33	1.06^b	0.306
Sem.Fluency	21.90±0.61	22.63±1.13	0.31^b	0.580
Stroop-W	97.69±1.91	96.34±3.66	0.10^b	0.751
Stroop-C	64.03±1.52	57.91±2.91	3.38^b	0.071
Stroop-CW	37.94±1.31	34.15±2.50	1.75^b	0.190
Psychological data
HADS-A	5.51±2.98	7.31±3.63	–2.01	0.049^*
HADS-D	3.37±2.79	3.81±3.67	–0.51	0.612

Data are presented as means±standard deviation. In Cognitive data, data represents the estimated marginal means±standard error after adjusting for age and education. ApoE ɛ4, Apolipoprotein allele ɛ4 carriers; CSF Aβ_, cerebrospinal fluid levels of amyloid-β isoform 42; CSF tau, cerebrospinal levels of total protein tau; CSF ptau, cerebrospinal fluid levels of phosphorylated protein tau; FCSRT-fr, Free and Cued Selective Reminding Test free recall subtest (max.48); FCSRT-tr, Free and Cued Selective Reminding Test total recall subtest (max.48); FCSRT-dfr, Free and Cued Selective Reminding Test delayed free recall subtest (max.16); FCSRT-dtr, Free and Cued Selective Reminding Test delayed total recall subtest (max.16); Sem.Fluency, Semantic Fluency test (animals); Stroop-W: Stroop word subtest; Stroop-C, Stroop color subtest; Stroop CW, Stroop color-word subtest; HADS-A, Hospital Anxiety and Depression, anxiety subscale; HADS-D, Hospital Anxiety and Depression, depression subscale. ^aPearson Chi-Square; ^bANCOVA adjusting for age and education; *p < 0.05; **p < 0.01.

No significant differences were found in the FCSRT-IR, Semantic Fluency, and Stroop tests’ cross-sectional scores (Table 2). The preAD showed a significantly higher anxiety score in the HADS compared to the CTR (t(65) = –2.01; p = 0.049), although both groups’ means were below the cutoff value for clinical anxiety (i.e., score ≥8) [53]. There were no significant between-group differences in the HADS depression score, with both groups scoring below the cutoff value for clinical depression (i.e.,score ≥8) [53].

SCD-Q factor analysis and item analysis

A Kaiser-Meyer-Olkin (KMO) of 0.824, and a significant Bartlett’s Test (χ² = 880.43; p < 0.0001), confirmed sampling adequacy for performing an exploratory factor analysis (EFA). Parallel analysis recommended the extraction of four factors accounting for 58.4% of the total variance (Supplementary Figure 1). The first factor (eigenvalue = 9.07; % variance = 37.81) included SCD-Q items 3, 7, 9, 10, and 11. As these items are related to episodic memory tasks the factor was labelled EM-factor. The second factor (eigenvalue = 2.03; % variance = 8.45) included items 1, 2, 4, 17, 18, and 24, related to attentional decline (A-factor). The third factor (eigenvalue = 1.53; % variance = 6.37) included items 8 and 19, which describe decline in organizational skills (O-factor). Finally, the fourth factor (eigenvalue = 1.39; % variance = 5.78) included items 15 y 16, which are related to language decline (L-factor).

Item-analysis showed a significantly higher frequency of self-reported decline in the preAD group compared to CTR in the following items: 15 (i.e., finding words to use in a conversation) (χ²(1) = 8.48; p = 0.004), 16 (i.e., understanding a conversation) (χ²(1) = 4.57; p = 0.033), 18 (i.e., concentrating) (χ²(1) = 6.31; p = 0.012), 20 (i.e., using electronic devices) (χ²(1) = 8.21; p = 0.004), 22 (i.e., starting a conversation) (χ²(1) = 4.90; p = 0.027), and 24 (i.e., multi-tasking) (χ²(1) = 10.26; p = 0.001). The sum of these items will be referred to as Sig-items (i.e., for significant items), for the sake of expediency. The preAD mean score of Sig-items was 2.99 (SD0.32) compared to 1.46 (SD 0.17) in the CTR (F(1) = 16.54; p < 0.0001). Item-analysis revealed no other significant differences between-groups (Table 3).

Table 3

MyCog scores and item response between-groups

MyCog	CN (n = 52)	preAD (n = 16)	F	p
Total	7.45±0.76	9.63±1.41	1.72	0.195
EM-factor	1.82±0.24	2.13±0.45	0.35	0.559
A-factor	1.88±0.25	3.00±0.47	4.04	0.049^*
O-factor	0.42±0.09	0.23±0.16	0.96	0.330
L-factor	0.73±0.08	1.20±0.16	6.49	0.014^*
Sig-items	1.46±0.17	2.99±0.32	16.54	<0.0001^**
Items (YES%)
1 (learning phone numbers)	33.8%	56.2%	2.02^a	0.129
2 (finding personal possessions)	34.6%	56.2%	2.39^a	0.122
3 (describing films’ plots)	36.5%	50.0%	0.93^a	0.336
4 (remembering doctor’s appointments)	22.3%	43.8%	2.73^a	0.061
5 (following a book’s plot)	30.8%	12.5%	2.09^a	0.147
6 (recalling details of recent family event)	17.3%	25.0%	0.47^a	0.494
7 (remembering result of recent sporting event)	27.5%	33.3%	0.19^a	0.658
8 (remembering sums of money)	26.9%	25.0%	0.02^a	0.879
9 (remembering details of conversations)	29.4%	37.5%	0.37^a	0.543
10 (remembering without using strategies)	53.8%	56.2%	0.03^a	0.866
11 (remembering details of recent news)	25.0%	37.5%	0.95^a	0.330
12 (remembering famous people’s names)	44.2%	68.8%	2.94^a	0.086
13 (remembering names of recently met people)	44.2%	37.5%	0.23^a	0.634
14 (remembering street and city names)	23.5%	25.0%	0.01^a	0.904
15 (finding the word)	46.2%	87.5%	8.48^a	0.004^**
16 (understanding conversations)	17.6%	43.8%	4.57^a	0.033^*
17 (remembering names of recently visited places)	34.6%	56.2%	2.39^a	0.122
18 (concentrate)	23.1%	56.2%	6.31^a	0.012^*
19 (planning out-of-routine events)	11.5%	0%	2.03^a	0.155
20 (using electronic devices)	11.5%	43.8%	8.21^a	0.004^**
21 (starting new things)	30.8%	43.8%	0.92^a	0.337
22 (starting conversations)	5.8%	25.0%	4.90^a	0.027^*
23 (doing mental arithmetic)	30.8%	56.2%	3.42^a	0.065
24 (multi-tasking)	25.0%	68.8%	10.26^a	0.001^**

Data are presented as estimated marginal means±standard error, after adjusting for SCD, age, ApoE ɛ4, gender, and anxiety levels. In the case of item analyses, data represents the % of YES answers for each item. YES answers were given when subjects believed they performed worse than roughly two years ago in the task. Total, total MyCog scores (0–24); EM-factor, episodic memory factor’s score (0–5); A-factor, attention factor’s score (0–6); O-factor, organization factor’s score (0–2); L-factor, language factor’s score (0–2); Sig-items, MyCog sum of items that showed a significantly higher frequency of reported decline in preAD (i.e., 15, 16, 18, 20, 22, 24) (0–6). ^aPearson Chi-Square; *p < 0.05; **p < 0.01.

SCD-Q self-reports between-groups: MyCog

Total MyCog scores did not differ significantly between groups (Table 3). The preAD group mean score was 9.64 (SD1.4) compared to 7.45 (SD0.8) in the CTR group. Both groups’ scores were above the cutoff ≥7 suggested by Rami et al. [35] for discriminating SCD in cognitively healthy older adults. EM-factor and O-factor showed no significant differences between-groups (Table 3) whereas in the preAD group significantly higher scores were found for L-factor F(1) = 6.49; p = 0.014) and A-factor (F(1) = 4.04; p = 0.049).

The ROC curves yielded significant results for L-factor (AUC = 0.75; p = 0.003) and A-factor (AUC = 0.74; p = 0.004). The following optimal cutoffs and their respective PPV and NPV values were found for L-factor (cutoff = 1, PPV = 93.8% NPV = 49%) and A-factor (cutoff = 2, PPV = 68.8% NPV = 63.5%). Regarding the association of each variable with AD biomarkers other than Aβ, CSF Aβ/ptau ratio was significantly associated with Sig-items (r = –0.40; p = 0.001), A-factor (r = –0.31; p = 0.012) and L-factor (r = –0.31; p = 0.012). No significant correlations with CSF tau or CSF ptau were found (p > 0.05) (Table 4).

Table 4

MyCog correlation with CSF AD biomarkers

	CSF Aβ/ptau	CSF tau	CSF ptau
Total	–0.276^*	0.148	0.133
EM-factor	–0.206	0.231	0.229
A-factor	–0.308^*	0.197	0.161
O-factor	0.017	–0.012	0.022
L-factor	–0.307^*	0.073	–0.053
Sig-items	–0.404^**	0.140	0.035

Data represents coefficients of correlations (r) for Pearon Partial Correlation, after adjusting for age. Total, total MyCog scores; EM-factor, episodic memory factor’s score; A-factor, attention factor’s score; O-factor, organization factor’s score; L-factor, language factor’s score; Sig-items, MyCog sum of items that showed a significantly higher frequency of reported decline in preAD (i.e., 15, 16, 18, 20, 22, 24). *p < 0.05; **p < 0.01.

SCD-Q informant reports: TheirCog

The preAD group had a higher TheirCog total score (7.41 SD1.5) compared to the CTR group (4.14 SD0.8), showing a trend towards significance (F(1) = 3.83; p = 0.055). L-factor score was significantly higher in preAD (F(1) = 9.64; p = 0.003) (Table 5). Sig-items score differed significantly between groups (F(1) = 11.58; p = 0.001), with an estimated marginal mean of 2.17 (SD0.34) in the preAD compared to 0.82 (SD0.21) in the CTR. ROC analyses for discriminating abnormal levels of Aβ showed a significant curve for Sig-items (AUC = 0.74; p = 0.008), with a cutoff of 2 showing a 66.7% PPV and 81% NPV (Table 5).

Table 5

TheirCog scores and item response between-groups

TheirCog	CN (n = 42)	preAD (n = 15)	F	p
Total	4.23±0.85	7.42±1.44	3.59	0.064
EM-factor	0.89±0.22	1.49±0.40	1.64	0.206
A-factor	1.23±0.27	1.94±0.44	1.87	0.178
O-factor	0.26±0.09	0.47±0.15	1.37	0.247
L-factor	0.29±0.11	0.95±0.18	0.64	0.003^**
Sig-items	1.03±0.24	2.65±0.41	11.18	0.002^**
SCD-Q Items (YES%)
1 (learning phone numbers)	19.0%	20.0%	0.01^a	0.936
2 (finding personal possessions)	31.7%	40.0%	0.34^a	0.562
3 (describing films’ plots)	19.0%	13.3%	0.25^a	0.617
4 (remembering doctor’s appointments)	22.0%	33.3%	0.76^a	0.384
5 (following a book’s plot)	14.3%	20.0%	0.27^a	0.602
6 (recalling details of recent family event)	16.7%	26.7%	0.71^a	0.400
7 (remembering result of recent sporting event)	14.6%	30.8%	1.70^a	0.192
8 (remembering sums of money)	16.7%	20.0%	0.09^a	0.771
9 (remembering details of conversations)	28.6%	40.0%	0.67^a	0.414
10 (remembering without using strategies)	22.0%	53.3%	5.12^a	0.024^*
11 (remembering details of recent news)	14.3%	13.3%	0.01^a	0.927
12 (remembering famous people’s names)	26.2%	26.7%	0.01^a	0.971
13 (remembering names of recently met people)	19.5%	40.0%	2.46^a	0.117
14 (remembering street and city names)	14.6%	6.7%	0.64^a	0.425
15 (finding the word)	16.7%	46.7%	5.37^a	0.021^*
16 (understanding conversations)	14.3%	46.7%	6.58^a	0.010^*
17 (remembering names of recently visited places)	14.6%	26.7%	1.08^a	0.298
18 (concentrate)	11.9%	40.0%	5.60^a	0.018^*
19 (planning out-of-routine events)	12.2%	26.7%	1.71^a	0.192
20 (using electronic devices)	7.1%	33.3%	6.28^a	0.012^*
21 (starting new things)	23.8%	60.0%	6.51^a	0.011^*
22 (starting conversations)	2.4%	13.3%	2.57^a	0.109
23 (doing mental arithmetic)	14.3%	33.3%	2.58^a	0.109
24 (multi-tasking)	29.3%	40.0%	0.58^a	0.446

Data are presented as estimated marginal means±standard error, after adjusting for SCD, age, ApoE ɛ4, gender, and anxiety levels. In the case of item analyses, data represents the % of YES answers for each item. YES answers were given when subjects believed they performed worse than roughly two years ago in the task. Total, total TheirCog scores (0–24); EM-factor, episodic memory factor’s score (0–5); A-factor, attention factor’s score (0–6); O-factor, organization factor’s score (0–2); L-factor, language factor’s score (0–2); Sig-items, sum of items that showed a significantly higher frequency of self-reported decline in preAD (i.e., 15, 16, 18, 20, 22, 24) (0–6). ^aPearson Chi-Square; *p < 0.05; **p < 0.01.

Item-analysis confirmed a significantly higher frequency of decline reported in preAD informants on Sig-items 15 (i.e., finding words to use in a conversation) (χ²(1) = 5.37; p = 0.021), 16 (i.e., understanding a conversation) (χ²(1) = 6.58; p = 0.010), 18 (i.e., concentrating) (χ²(1) = 5.60; p = 0.018), and 20 (i.e., using electronic devices) (χ²(1) = 6.28; p = 0.012). Additionally, preAD informants reported significantly higher decline in items 10 (i.e., remembering without use of strategies) (χ²(1) = 5.12; p = 0.02) and 21 (i.e., starting new things) (χ²(1) = 6.51; p = 0.011).

Cognitive longitudinal follow-up

There were no significant differences in age, years of education, gender distribution, CSF tau or CSF ptau levels between the preAD and CTR groups at follow-up (Table 6). There was a significant time×group interaction in the Semantic Fluency test (F(1) = 5.72; p = 0.020) and the Word subtest of the Stroop Test (F(1) = 5.74; p = 0.021), with the preAD group showing a decrease in performance at one-year compared to an increase in CTR (Fig. 1). Within-group analyses in the CTR group revealed a significant improvement at one-year follow-up in the FCSRT-IR free recall (t(42) = –3.87; p < 0.001), total recall (t(42) = –2.92; p = 0.006), and delayed free recall (t(23) = –2.30; p = 0.031) subtests scores, while the preAD showed no significant changes(Table 6).

Table 6

Follow-up: demographical, biological, and cognitive data

Parameters	CN (n = 43)	preAD (n = 14)	t	p
Demographics
Age	65.70±6.18	68.36±7.86	–1.31	0.197
Years of Education	11.93±4.84	9.29±3.45	1.88	0.064
Gender (% Female)	65.1%	78.6%	0.89^a	0.347
Biological data
ApoE ɛ4 (% positive)	4.8%	28.6%	6.22^a	0.013^*
CSF Aβ₄₂	854.15±210.64	424.76±102.52	10.17	<0.0001^**
CSF tau	233.95±68.03	261.22±120.89	–0.80	0.434
CSF ptau	53.31±11.25	52.88±23.91	0.65	0.949
Longitudinal change
FCSRT-fr	2.14±3.63^**	1.21±4.42	0.96^b	0.331
FCSRT-tr	1.00±2.25^**	–0.07±2.95	2.17^b	0.147
FCSRT-dfr	0.093±2.04^**	0.01±2.91	0.87^b	0.356
FCSRT-dtr	–0.06±0.79	0.14±1.29	0.41^b	0.525
Sem.fluency	0.88±3.90	–2.07±5.54	5.72^b	0.020^*
Stroop-W	2.45±9.33	–4.92±9.08	5.74^b	0.021^*
Stroop-C	0.01±7.40	1.58±11.58	0.73^b	0.397
Stroop-CW	1.53±8.13	–0.66±4.25	1.49^b	0.228

Data are presented as means±standard deviation. In Longitudinal change, data represents the difference in mean score at one-year follow-up in each neuropsychological test (i.e., one-year score –baseline score) ±standard deviation. ApoE ɛ4, Apolipoprotein allele ɛ4 carriers; CSF Aβ_, cerebrospinal fluid levels of Amyloid-beta isoform 42; CSF tau, cerebrospinal levels of total protein tau; CSF ptau, cerebrospinal fluid levels of phosphorylated protein tau; FCSRT-fr, Free and Cued Selective Reminding Test free recall subtest (max.48); FCSRT-tr, Free and Cued Selective Reminding Test total recall subtest (max.48); FCSRT-dfr, Free and Cued Selective Reminding Test delayed free recall subtest (max.16); FCSRT-dtr, Free and Cued Selective Reminding Test delayed total recall subtest (max.16); Sem.Fluency, Semantic Fluency test (animals); Stroop-W, Stroop word subtest; Stroop-C, Stroop color subtest; Stroop CW, Stroop color-word subtest. ^aPearson Chi-Square; ^bANCOVA of repeated measures for interaction time×group; *p < 0.05; **p < 0.01.

Fig.1

Longitudinal changes in Neuropsychological tests’ scores. Figure 1 shows the longitudinal slopes for changes in Neuropsychological tests at 1-year follow-up. Tests’ scores are presented as estimated marginal means after adjusting for age and years of education. Error bars in the line chart represent the 95% confidence interval of the estimated marginal means. Significant Time×Group interaction was found for Semantic Fluency test and Stroop Word subtest. FCSRT, free and cued selective reminding test; Semantic fluency, animals in one minute; Stroop Word, Stroop word subtest; CTR, Control group (n43); preAD, Preclinical Alzheimer’s disease group (n = 14).

DISCUSSION

The present study is one of the first to assess SCD in a preAD sample using a validated questionnaire that follows the SCD-I framework for research of SCD in preAD. A thorough analysis of the SCD-Q was performed in order to explore a specific profile of SCD in preAD that could be discriminated from normal aging cognitive complaints. Results obtained showed significantly higher scores in the SCD-Q L-factor (i.e., language decline), A-factor (i.e., attentional decline), and in specific language and executive decline items (Sig-items). PreAD informants confirmed a higher L-factor and Sig-items score. Finally, a significant interaction time×group was found for the longitudinal changes in the Semantic Fluency and Stroop tests scores, showing a decrease in performance in the preAD group compared to an improvement in CTR.

There was no significant difference between groups in the total MyCog scores, which is consistent with our previous report of an absence of correlation between AD biomarkers and self-reports [36]. This finding may be associated with the high prevalence of SCD among older adults [11 , 26], which suggests an unspecific nature of cognitive complaints. SCD can be related to a number of different factors such as normal aging processes, personality traits, psychiatric conditions, medical disorders, and medications [10, 14]. However, it could be possible that cognitive complaints in preAD differ qualitatively from the concerns found in normal aging.

When comparing SCD-Q factors scores between groups, we found no differences for the EM-factor (i.e., episodic memory decline). This was an unexpected result given that neuroimaging studies have consistently reported medial temporal lobe (MTL) affection in early AD [54 –57], specifically in the hippocampus and parahippocampal regions [58, 59]. Also, numerous studies have shown that episodic memory is one of the first cognitive functions to decline in preAD [60, 61]. However, episodic memory also declines with age [29, 62], and MTL volume reduction has been described to be part of the normal aging process [63 –66]. In our sample, 67% of CTR subjects had at least one positive answer in SCD-Q items enquiring about memory complaints, similar to the frequency reported in other studies [23, 26]. Given that memory complaints are highly present in normal aging, it becomes challenging to discriminate the subtle memory decline of preAD.

On the other hand, we found significantly higher complaints in executive tasks in the preAD group. Studies have reported decreased gray matter volume in the frontal lobe in cognitively normal subjects with abnormal levels of amyloid-β [4 , 67–69], and amyloid deposition has been reported to start in neocortical regions that include the prefrontal cortex [70]. Studies have also found executive decline in preAD samples [71 –73], suggesting that clinical effects from early amyloid pathophysiology may precede those from hippocampal atrophy [71]. Another hypothesis is that declines in executive functions and episodic memory appear simultaneously in preAD, but executive complaints appear to be more specific to AD pathophysiology given the cofounding effect of normal aging in memory complaints. Although frontal lobe volume reduction has also been reported in normal aging [63, 65], studies suggest that the decline is linear as opposed to a non-linear decline in the MTL [62 , 66]. It may be possible that executive decline occurs more gradually, becoming less noticeable than memory changes inaging.

The SCD-Q executive items with higher frequencies of complain in preAD were related to concentration difficulties, difficulties using electronic devices, difficulties starting a conversation, and multi-tasking difficulties. The presence of concentration difficulties expressed by preAD subjects is consistent with studies suggesting the presence of attentional control deficits in preAD [88, 89]. Even further, some authors have suggested that early memory deficits seen in AD may be due to attentional difficulties [90]. At the same time, attentional control deficits may be related to multi-tasking difficulties expressed by preAD subjects. For example, Huff et al. found that switch errors were a sensitive measure of preclinical cognitive deficits associated with accumulating amyloid pathology in cognitively healthy adults [91]. Difficulties using electronic devices were reported by almost half of preAD subjects in our sample, while it was a relatively infrequent complaint in the control group (11%). The usage of electronic devices may be particularly demanding for older adults, recruiting an extensive network of brain regions supporting diverse cognitive functions. In this regard, prospective neuropsychological studies indicate that demanding tasks in which memory, executive, and language functions are all required are an excellent indicator of future progression to AD [92]. Finally, difficulties starting a conversation were very infrequent in the CTR group while present in 25% of preAD subjects. Therefore, its presence could be considered as a high predictor of preAD. Speech production is a demanding and complex task that requires higher-order abilities such as planning and word retrieval. Previous studies have identified subtle changes in this communicative ability in the predementia stages [93, 94].

We also found significantly higher language complaints in the preAD group. This is consistent with studies suggesting that spoken language could be a sensitive measurement for predicting future AD development [74]. For example, a study found that cognitively healthy PSEN1-mutation carriers (i.e., familial preAD) produced significantly fewer semantic categories and used significantly more simple verbs than non-carriers in the Cookie Theft Picture Card task [75]. We also found a significant decline in the Semantic Fluency test score at one-year in the preAD group. This result is in line with studies describing verbal fluency deficits in preAD [76, 77] and with the neuroanatomical hypothesis that semantic processing relies on temporolimbic structures that are preferentially affected in early AD [78, 79].

The SCD-Q language items with higher frequencies of complain in preAD were related to difficulties finding words in a conversation and difficulties understanding a conversation. Word-finding difficulties have been previously reported in cognitively normal adults with a higher genetic risk for AD (i.e., ApoE ɛ4) [80, 81]. Word-finding difficulties may be related to disrupted comprehension of word meanings and/or deficits in the retrieval of the lexical representation of phonological words [82]. Previous studies suggest that difficulties accessing lexical word forms contribute to word finding difficulties in normal aging, while a breakdown of semantic knowledge may be unique to AD and its prodrome [81 , 84]. The added semantic decline with the already deficient lexical retrieval may explain the enhanced word-finding difficulties in preAD. Regarding the comprehension difficulties expressed by preAD subjects, some authors have found that oral speech comprehension is one of the first language abilities to be affected in amnestic mild cognitive impairment [85 –87].

In summary, our results showed that executive and language complaints discriminated preAD from a CTR sample, giving insights into specific complaints that may be more indicative of subjacent AD pathophysiology. Our findings were supported by informants and by a longitudinal decline in language and executive neuropsychological tests in the preAD group. Informant reports of SCD-Q are relevant in the prediction of preAD, as they appear to be more closely linked to AD biomarkers and subsequent cognitive decline than self-reported SCD [50, 51].

The main limitation of the present study is the relatively small sample size. Validations in independent populations are needed to confirm our observations. Also, the relatively short longitudinal follow-up of the present study precluded the detection of significant decline within-subjects. Neuropsychological data presented in this article should be considered as secondary results given the limited follow-up. Our group continues following this sample and future publications of longer follow-up periods will be pursued. The SCD-Q was not administered at follow-up given that subjects had received indirect feedback on their neuropsychological performance (i.e., that they would be notified if any significant cognitive impairment was detected). Future studies with longitudinal measures of SCD could assess the clinical significance of SCD changes over time. At last, the EFA performed in this study was carried out to explore the presence of an SCD profile in our preAD sample. Validating a factorial analysis of the SCD-Q was beyond the scope of the study, and the components extracted must not be extrapolated to the general population. Although four factors with an eigenvalue >1 and a variance >5% were extracted, the EM factor explained most of the variance of the model (38% of 58%). We interpret this as a high load of episodic memory complaints, and a relatively small representation of executive and language complaints, in the SCD-Q.

In conclusion, our results support the value of SCD for detecting subtle cognitive decline that is challenging to by means of standard neuropsychological tests, and highlight the relevance of language and executive complaints for discriminating preAD-SCD from complaints found in normal aging. Future studies are needed to better define the characteristics of SCD in preAD.

Footnotes

ACKNOWLEDGMENTS

This study was supported by the Spanish Ministry of Science. Dr. Lorena Rami is the recipient of a Miguel Servet grant from the Spanish Ministry of Science (CP2/00023) as senior investigator. This study was funded by research grants from the following grants: Dr. Lorena Rami (FIS PI11/01071), Fondo europeo de desarrollo regional, una manera de hacer Europa.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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