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Original Research Article

Dement Geriatr Cogn Disord 2009;28:179–186 DOI: 10.1159/000235797

Differentiation between Mild Cognitive Impairment and Alzheimer’sDisease Using the FMLL Mini-Battery

Pedro Abizanda Beatriz López-Ramos Luis Romero

Pedro Manuel Sánchez-Jurado Matilde León Elena Martín-Sebastiá

Esther López-Jiménez

Geriatrics Department, Complejo Hospitalario Universitario of Albacete, Albacete , Spain

AD was 0.879 (95% CI: 0.832–0.927; p ! 0.001). Specificity for MCI diagnosis was 0.9 when FMLL scores were above 59% and 1 when scores were above 76%. Conclusion: The FMLL mini-examination is a useful tool to differentiate between MCI and AD in patients seen in a memory clinic.

Copyright © 2009 S. Karger AG, Basel

Introduction

Mild cognitive impairment (MCI) is a common diag-nosis in memory clinics [1] . One of the main problems encountered by clinic specialists is the difficulty to dif-ferentiate between this condition and Alzheimer’s dis-ease (AD). This differentiation is extremely relevant be-cause it determines patient treatments, decision-making, and provision of health and social resources [1, 2] .

Currently, both conditions are diagnosed by clinical evidence, supported by the medical history, physical and neurological examinations, functional assessments, lab-oratory workup, neuroimaging studies, and neuropsy-chological assessments.

Ongoing efforts are being made to develop short cog-nitive tests that help detect or diagnose the cognitive im-

Key Words

Mild cognitive impairment � Alzheimer’s disease � Differential diagnosis � Neuropsychological assessment � Validation � Specificity

Abstract

Aim: To construct and validate a mini-battery to discriminate between Alzheimer’s disease (AD) and mild cognitive im-pairment (MCI) in patients seen at a hospital memory clinic. Methods: In a cohort of 310 subjects (137 with MCI and 173 with AD), the area under the receiver operating curve (AUC) was used to select the neuropsychologic diagnostic test bat-tery subtests with the best overall performance, namely, the Mini-Mental State Examination (MMSE, 0.715), Logical Mem-ory II (LMII, 0.721), Verbal Fluency Test (0.747), and Lawton index (0.742). A mini-battery test was constructed with the following formulation: FMLL = [(Fluency Test/17 + MMSE/30 + LMII/32 + Lawton/8)/4] ! 100. Another cohort of 87 sub-jects with MCI and 100 with AD was used to validate the mini-battery and to calculate the psychometric properties. Results: The concurrent validity with Reisberg’s Global De-terioration Scale was r = 0.792 (p ! 0.001). Cronbach’s alpha internal consistency was 0.6358. The AUC to diagnose MCI or

Accepted: June 26, 2009 Published online: August 28, 2009

Pedro Abizanda Geriatrics Department, Complejo Hospitalario Universitario of Albacete C/Seminario 4ES–02006 Albacete (Spain) Tel. +34 967 597 651, Fax +34 967 597 635, E-Mail pabizanda@sescam.jccm.es

© 2009 S. Karger AG, Basel1420–8008/09/0282–0179$26.00/0

Accessible online at:www.karger.com/dem

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pairment continuum from normality, through subjective memory complaints and MCI, to AD [3–7] . Nevertheless, detailed cognitive assessments of the patient and exten-sive interviews with the informant are not practical for most specialized consultations because of the time in-volved. Ideally, the tools used to study cognitive process-es should be brief, capable of being filled out in just a few minutes, and sensitive to impairment [8] . A combination of tests may be a potential strategy to improve diagnostic accuracy in cognitive impairment.

It has been reported that different neuropsychological screening tests such as the Mini-Mental State Examina-tion (MMSE) or the Clock Drawing Test do not discrim-inate well between MCI and AD [9] , whereas other re-cently published tests such as the Montreal CognitiveAssessment [10] or the DemTect [11] have an elevated sen-sitivity with variable specificity to detect MCI and to identify MCI-diagnosed subjects who are at high risk of developing dementia [12, 13] . The Memory Alteration Test T@M, a new Spanish screening test, has shown good sensitivity and specificity to discriminate amnestic-MCI from AD patients [14] . Other tests such as the Rey’s Audi-tory Verbal Learning Test (RAVLT) [15] , the AB Cogni-tive Screen [8] , or the Cambridge Cognitive Examination (CAMCOG) [16] have shown their usefulness to discrim-inate between MCI and AD. However, only a few short tests have been proven to discriminate reliably between these 2 conditions.

The purpose of our study was to develop and validate a mini-battery composed of simple, easy-to-administer subtests for regular use in memory clinics that would help differentiate between patients with MCI and AD at in-hospital memory consultations.

Materials and Methods

Design, Subjects, and Scope of Study Our study was a 2-stage observational study to validate a scale

consisting of an initial phase to construct the mini-battery and a second phase to validate it. All subjects were men and women aged 6 65 years referred from the primary care center of Albacete to the Memory Unit of the Geriatrics Department at the Comple-jo Hospitalario Universitario of Albacete for the study of cogni-tive impairment.

The study cohort included 310 subjects referred consecutively from primary care during 2005, and the validation cohort was composed of 187 subjects referred during 2006.

Initial Diagnosis of MCI and AD In the construction cohort, the patients were initially assessed

at the memory clinic where a complete medical history was taken, including educational level, job history, family history, cardiovas-

cular risk factors, concomitant diseases, and current treatments. The first assessment was performed using the Barthel index (BI) and Lawton index (LI) for functional status, Folstein’s MMSE for cognitive status, and the Yesavage Geriatric Depression Scale (GDS) for affective status. All patients were given a physical ex-amination and a detailed neurological examination. A complete laboratory workup (complete blood count, biochemistry, thyroid hormones, vitamin B 12 , and urine) and a neuropsychological as-sessment were requested, and a neuroimaging study was per-formed by computed tomography (CT) or magnetic resonance imaging (MRI).

White matter ischemic lesions were analyzed by evaluating the age-related white matter changes (ARWMC) using the Wah-lund scale [17] . Patients were divided into 4 groups according to the anatomic location where the lesions were most severe: no le-sions (0), focal lesions (1), confluent lesions (2), or diffuse white matter involvement (3).

All patients received neuropsychological assessment from the same expert neuropsychologist. The academic basis for the selec-tion of the test battery followed the recommendations of experts [18] adapted to Spanish elderly populations [19] . We used the fol-lowing tests: • Verbal memory: Wechsler Memory Scale (WMS-III) subtests

Logical Memory I and II (LMI and LMII; range 0–50) [20] . • Visual memory: WMS-III subtests Visual Reproduction I and

II (range 0–104) [20] . • Language: Boston Naming Test (30 items; range 0–30) [21] ,

F-A-S Test from the Multilingual Aphasia Examination [22] and the Category Fluency Test (FT; animals and fruits) [23] .

• Attention and executive functions: Color Trail Making Test A (TMT A; time in seconds) [24] , Digit Span Forward and Back-ward [20] , Stroop Color Test [25] , Cancellation Test (yes/no) [26] , Digit Symbol Test [20] , Card Sorting Test [27] , and Tem-poral Judgment from the Behavioral Assessment of the Dys-executive Syndrome (range 0–4) [27] .

• Praxis: Luria-Nebraska Neuropsychological Battery (range 0–4) and Luria postural sequence test (range 0–2) [28] .

• Psychological and behavioral symptoms taken from the Neu-ropsychiatric Inventory (NPI) [29] : the presence or absence of symptoms was determined and the number of symptoms that each patient presented was calculated. Once all data had been collected, a consensus meeting was

held in which 2 geriatricians, experts in dementia, participated in diagnosing patients as MCI or AD. The Petersen criteria were used to diagnose MCI with the respective subtypes [30] , and the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS/ADRDA) [31] criteria were used to diag-nose AD. The severity of cognitive impairment was subsequently tested by Reisberg’s GDS [32] .

FMLL Mini-Battery Construction To construct the FMLL mini-battery (acronym for FT, MMSE,

LMII and LI), the overall performance of each test in distinguish-ing between MCI and AD was determined by calculating the re-spective receiver operating curves (ROC) with 95% confidence intervals (CI). The tests that provided greater diagnostic perfor-mances were identified and multiple combinations were tried un-til a set of tests that improved the area under the curve (AUC) was identified.

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Dement Geriatr Cogn Disord 2009;28:179–186 181

The test combination that improved the AUC in the construc-tion cohort was the set that included the FT, MMSE, LMII, and LI. The addition of other tests did not improve the AUC, and the subtraction of any of the 4 included yielded worse results. In order to give the same importance to each test comprising the FMLL, each test score obtained by each study subject was divided by the highest score obtained on the same test by any subject in the sam-ple. The weighted scores of the 4 tests were then added, the sum was divided by 4, and the quotient was multiplied by 100 to obtain the final weighted percentage of the highest score possible from our study sample. The resulting FMLL method was as follows:

FMLL = [(FT/17 + MMSE/30 + LMII/32 + LI/8)/4] ! 100

As a result, the FMLL scores ranged between 0 (minimum) and 100 (maximum), and took approximately 15 min to adminis-ter. Because an accurate diagnosis is the primary goal of specialist consultations, the specificity was considered key when establish-ing the best cut-off values.

Validation Cohort In the validation cohort, patients were diagnosed using the

Petersen criteria for MCI and the NINCDS/ADRDA criteria for AD; in addition, Reisberg’s GDS was determined. All patients were then given the FMLL mini-battery by a trained neuropsy-chologist, blinded to the diagnosis, to establish its validity.

Statistical Analysis To choose the most accurate tests for the diagnosis of MCI in

the construction cohort, we calculated the respective AUC of the ROC with 95% CI for all tests. The tests providing the best diag-nostic performances were identified and multiple combinations were tried until a set of tests that improved the AUC was identi-fied to construct the FMLL. Because an accurate diagnosis is the primary goal of specialist consultations, the specificity of FMLL was considered key when establishing the best cut-off values. As a result, the best cut-off point for the mini-battery to achieve a specificity of 1 and 0.9 in the diagnosis of MCI versus AD was determined.

In the validation cohort, concurrent validity of the mini-bat-tery was determined by Spearman rank correlation with Reis-berg’s GDS. We used factor analysis to determine the construct validity and the principal components of the battery. The FT, MMSE, LMII, and LI were included and analyzed with the prin-cipal component method based on correlation analysis. The suit-ability was proven with Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin measure of sampling adequacy test. The percentage of the variance explained by principal factors was determined and factor structural matrix was analyzed to determine the saturation level of the variables in each component. We tested that all vari-ables mainly saturated in 1 factor to prove the suitability of the solution. Rotation analysis was done with Varimax method. The internal consistency was determined by Cronbach’s alpha. The ROC was constructed to distinguish between MCI and AD, cal-culating the AUC and respective 95% CI; the sensitivity and spec-ificity was also determined for the best possible cut-off. As in the construction cohort, the cut-off point was determined to achieve a specificity of 1 and 0.9. SPSS 11.0 was used for the statistical analysis.

Results

The baseline characteristics of the construction and validation cohorts are shown in table 1 .

Construction Cohort Table 2 describes the AUC and 95% CI for each of the

tests used in the construction cohort to differentiate be-tween MCI and AD. Figure 1 a shows the ROC curve that relates the FMLL mini-battery scores in diagnosing MCI versus AD in the construction cohort. The AUC was 0.839 (95% CI: 0.796–0.882; p ! 0.001). A 50% cut-off gave a sensitivity of 0.75 and a specificity of 0.75. To diagnose MCI, specificity was 0.9 for FMLL scores above 59% and 1 for scores above 75%.

Validation Cohort In the validation cohort, the Spearman rank correla-

tion between the FMLL mini-battery and Reisberg’s GDS was r = 0.792 (p ! 0.001), indicating a good concurrent validity. Factor analysis based on correlation identified 2 factors. The first one was designated cognitive because it included the 3 cognitive tests, and the other one was des-ignated functional because it included the LI. These 4 variables explained 68% of the variance (p ! 0.001) in the diagnosis. In addition, the FMLL mini-battery had a Cronbach’s alpha internal consistency of 0.6358. The area under the ROC curve of the FMLL mini-battery in the differential diagnosis between MCI and AD in the valida-tion cohort was 0.879 (95% CI: 0.832–0.927; p ! 0.001) ( fig. 1 b). The best cut-off was 53%, with a sensitivity of 0.78 and a specificity of 0.82. A specificity of 0.9 was ob-tained for MCI diagnosis when the FMLL score was above 59% and a specificity of 1 when the score was above 76%.

Discussion

The differential diagnosis between MCI and AD is a common, important problem in memory clinics. Tests or test combinations are needed to help identify which pa-tients belong to each group. The main conclusion of our study is that the FMLL mini-battery has a moderately good result in the differential diagnosis between MCI and AD with an AUC = 0.879 [33] .

When comparing our results with previously de-scribed tests, the MMSE, the most widely used test in routine clinical practice, has shown limited capacity to correctly distinguish between MCI and AD patients. A

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Table 1. Baseline characteristics of the construction and validation cohorts

Construction cohort Validation cohort

MCI (n = 137) AD (n = 173) MCI (n = 87) AD (n = 100)

Age, years 75.4 (4.5)*** 77.6 (4.6)*** 75.3 (5.1)*** 78.1 (4.4)***Sex

MaleFemale

33.6%66.4%

36.4%63.6%

24.1%75.9%

27.0%73.0%

Years of educationIlliteratePrimary studies not completedPrimary studies completedSecondary studiesUniversity studies

14.6%65.7%16.1%

3.6%0.0%

17.9%54.9%19.1%

3.5%4.6%

4.6%69.0%19.5%

3.4%3.4%

2.0%64.0%25.0%

4.0%5.0%

Barthel index 96.2 (5.2)*** 92.7 (10.8)*** 96.3 (7.0) 94.5 (6.9)Lawton index 5.5 (2.0)*** 3.5 (2.1)*** 6.0 (1.7)*** 4.0 (2.1)***MMSE 22.3 (4.0)*** 18.8 (4.6)*** 23.1 (3.2)*** 19.1 (3.8)***MCI subtypes

Amnestic single-domainAmnestic multi-domainNon-amnestic single-domainNon-amnestic multi-domain

26 (19.0)85 (62.0)16 (11.7)10 (7.3)

8 (9.2)57 (65.5)12 (13.8)10 (11.5)

Reisberg’s GDS (3/4/5) 137/0/0 0/108/65 87/0/0 0/46/54Yesavage GDS 5.4 (3.0) 5.6 (3.6) 5.9 (3.1) 5.9 (2.9)ARWMC

0123

54.1%20.7%20.0%

5.2%

53.5%15.1%20.9%10.5%

41.7%25.0%17.9%15.5%

40.2%15.5%30.9%13.4%

Logical Memory I 13.4 (6.5)*** 8.7 (5.8)*** 15.8 (7.6)*** 9.3 (6.6)***Logical Memory II 10.1 (7.4)*** 4.5 (5.4)*** 12.4 (9.2)*** 3.9 (5.5)***Visual Reproduction I 35.1 (17.0)*** 25.0 (15.2)*** 53.4 (18.6)*** 34.7 (18.5)***Visual Reproduction II 16.6 (15.2)*** 7.0 (10.0)*** 36.7 (22.5)*** 14.7 (16.9)***Boston Naming Test 27.7 (2.3) 27.2 (2.8) 29.1 (1.2)* 28.7 (1.4)*Verbal Fluency Test 10.1 (2.4)*** 7.7 (2.7)*** 10.6 (2.1)*** 8.7 (2.5)***F-A-S Test 4.2 (3.2) 3.6 (3.4) – –TMT A 130 (62)** 155 (71)** 144 (112)*** 255 (250)***Stroop Color Test –0.1 (7.3)* –2.7 (7.6)* –1.9 (8.3)** –5.2 (7.2)**Digit Symbol Test 9.5 (6.3)*** 6.5 (6.5)*** – –Cancellation Test

YesNo

76.7%***23.3%

45.7%***54.3%

– –

Card Sorting Test 1.2 (1.0)** 0.9 (0.9)** – –Digit Span Forward 4.2 (0.8) 4.1 (0.8) 4.1 (0.8) 4.1 (1.0)Digit Span Backward 2.7 (0.9) 2.5 (1.0) 2.9 (0.8)* 2.7 (0.7)*Temporal Judgment 2.0 (1.0)** 1.6 (1.1)** 1.9 (0.8)*** 1.2 (0.8)***Praxis 3.7 (0.6)*** 3.2 (0.9)*** 3.7 (0.5)*** 3.2 (0.9)***Postural sequence 1.4 (0.5)*** 1.1 (0.6)*** 1.4 (0.6)*** 1.0 (0.6)***NPI 2.1 (1.7)*** 3.2 (2.2)*** 1.9 (1.5)*** 3.2 (2.3)***FMLL 60.0 (12.2)*** 42.2 (12.6)*** 63.3 (11.2)*** 44.3 (11.2)***

* p < 0.05; ** p < 0.01; *** p < 0.001.

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recent meta-analysis found a sensitivity of 89.2% with a specificity of 45.1%. The authors concluded that the MMSE alone is not adequate to diagnose MCI, although they do recognize that no test currently achieves this goal well [34] .

As a result, traditional tests that help differentiate be-tween MCI and AD have been reevaluated, and new tests have recently been developed. The AB Cognitive Screen adequately distinguishes between MCI or controls and dementia with greater accuracy than the MMSE, the best being the memory recall and verbal fluency subtests [8] . In a Brazilian cohort, the CAMCOG yielded an AUC of 0.91, with a sensitivity of 81% and a specificity of 88% in differentiating between MCI and AD, but this instru-ment takes a long time to carry out. CAMCOG scores were better than MMSE scores, the clock drawing test, or the verbal fluency test [16] . Normative data have also been published on the TMT A and B. Part B had a sensitivity of 52.6% and a specificity of 57% to differentiate between MCI and AD when only the time required to do the test was used, and 43.9% and 67.0% when the response time and errors were both used [35] . The Montreal Cognitive Assessment is a brief test that has demonstrated good sensitivity to detect MCI with fair specificity (50%) [10, 12] , but is not suitable for discriminating between MCI and AD at memory clinics. The DemTect, a short instru-

Table 2. AUC for the various tests administered in the construc-tion cohort to differentiate between Alzheimer’s disease and mild cognitive impairment

n AUC 95% CI p

Barthel index 310 0.605 0.543–0.668 <0.01Lawton index 310 0.742 0.687–0.796 <0.001MMSE 310 0.715 0.659–0.772 <0.001Yesavage GDS 279 0.501 NSARWMC 307 0.521 NSLogical Memory I 310 0.705 0.646–0.764 <0.001Logical Memory II 310 0.721 0.663–0.779 <0.001Visual Reproduction I 299 0.669 0.607–0.730 <0.001Visual Reproduction II 298 0.686 0.625–0.748 <0.001Boston Naming Test 306 0.555 NSVerbal Fluency Test 310 0.747 0.693–0.801 <0.001F-A-S Test 283 0.562 NSTMT A 253 0.611 0.542–0.680 <0.01Stroop Color Test 235 0.604 0.532–0.677 <0.01Digit Symbol Test 292 0.656 0.549–0.719 <0.001Cancellation Test 297 0.655 0.592–0.717 <0.001Card Sorting Test 291 0.603 0.538–0.668 <0.01Digits Forward 310 0.532 NSDigits Backwards 310 0.545 NSTemporal Judgment 297 0.602 0.538–0.666 <0.01Praxis 304 0.676 0.615–0.736 <0.001Postural sequence 297 0.641 0.577–0.704 <0.001NPI 309 0.650 0.589–0.711 <0.001FMLL 310 0.839 0.796–0.882 <0.001

FMLL: construction cohort

1 – specificity

1.00.80.50.30

Sen

siti

vity

1.0

0.8

0.5

0.3

0

a

FMLL: validation cohort

1 – specificity

1.00.80.50.30

Sen

siti

vity

1.0

0.8

0.5

0.3

0

b

Fig. 1. ROCs for the differential diagnosis of MCI and AD using the FMLL mini-battery in the construction and validation cohorts.

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ment based on 5 tasks, demonstrated both a good sensi-tivity (84.6%) and specificity (85.7%) in a small sample of patients, and is considered a screening test by the authors [13] .

Recently a new Spanish test that evaluates orientation and memory, the M@T, has been validated in a commu-nity sample of subjects 1 60 years, with good sensitivity (92%) and specificity (98%) to discriminate between MCI and AD patients using a cut-off point of 28 [14] . Although it seems a promising instrument, only amnestic-MCIpatients were included, and its psychometric properties have not been assessed in memory clinics.

The strength of the FMLL compared to other instru-ments is that it is composed of subtests commonly used worldwide and evaluates both memory and verbal flu-ency as well as all MMSE domains and functions. More-over, in our cohorts (representative of the usual clinical practice at memory clinics), the FMLL discriminates MCI from AD independently of MCI subtype.

Memory deficit in AD is mainly due to information encoding and storage problems, rather than information retrieval problems. MCI subjects have also shown epi-sodic and semantic memory impairment [36] and higher retrieval impairment as time progresses than subjects without memory deficit, with this being the main cogni-tive problem [37] . The WMS-III memory subtest LMII identifies mnemic deficits, distinguishing between stor-age and retrieval problems. Thus, its inclusion in the FMLL mini-battery is fully justified. As previously men-tioned, correct function of mnemic processing also de-pends on the integrity of semantic skills, which means the use of the verbal category FT would also be justified [38, 39] . Other additional problems of MCI patients are the inability to inhibit responses and the cognitive inflex-ibility. In our sample, we used the Cancellation and Card Sorting Tests to analyze these areas, but global diagnostic performance was not good enough to be included in the FMLL mini-battery. However, any future diagnostic tools to be validated for use in diagnosing these patients will need to include other subtests that assess these cognitive capacities [40] .

Maintaining activities of daily living (ADL) forms part of the diagnostic criteria for MCI. Nevertheless, re-cent studies have shown that complex ADL may be al-tered in MCI patients, particularly if these activities re-quire intact memory and reasoning [41] . It has also been reported that MCI subjects have greater difficulty and are slower to perform key ADL than subjects without cogni-tive impairment [42, 43] . ADL performance is minimally influenced by age but not by educational level, whereas

cognitive tests are influenced by both. Hence, functional autonomy assessment has been proposed as a better tool for dementia screening than current mental tests, par-ticularly in populations with a lower level of education [44] . Therefore, screening or diagnostic tools for cogni-tive impairment should include functional items in their construct, as occurred with our mini-battery.

A combination of tests has been reported as a potential strategy to improve diagnostic accuracy in cognitive im-pairment. The combination of the MMSE and the Cogni-tive Capacity Screening Examination improved the sen-sitivity in identifying MCI, while also maintaining spec-ificity [45] . In our study, we demonstrated that the FMLL test combination offered a better overall performance in differentiating MCI from AD.

Recent publications have suggested that white matter ischemic lesions are associated with a high risk of pro-gression from normal cognition to MCI [46] . In our co-horts, ischemic lesions of periventricular white matter did not help differentiate between MCI and AD subjects, nor can we ensure that they serve as markers of cognitive impairment progression since the study was not designed for that purpose. Nevertheless, cognitive tests were better able to predict which subjects would progress from MCI to AD than volumetric neuroimaging measurements and, therefore, neuropsychological assessment continues to be the reference standard to differentiate these 2 dis-eases [47] .

Our sample subjects were elderly and had a low edu-cational level, hence, the FMLL cut-off points found in our cohorts may not be applicable to other populations. However, it is reasonable to assume that when the same mini-battery tests are used, different cut-off values may be calculated according to age and educational level in other areas. A tool with better psychometric properties than the FMLL for distinguishing between MCI and AD could not be designed with the tests used in the neuro-psychological assessment of the construction cohort, but substituting or adding other tests not used in our sample might provide better results.

The mean MMSE score was low in both of our cohorts with MCI. Most studies in MCI patients have reported mean MMSE scores around 26 or 27, much higher than ours [48, 49] . It could be argued that some of our MCI patients may be very mild AD patients. However, in Span-ish populations with low literacy levels, similar results have been described. del Ser Quijano et al. [50] studied 527 home-dwelling elderly and found a mean MMSE score of 25.3 in subjects –1 SD and 20.5 in –2 SD, and Bu-fill et al. [51] reported that 21.3% of subjects aged 1 80

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years with MMSE 24 met MCI criteria. In nondement-ed low-literacy Spanish subjects between 76 and 80 years of age, the 50th percentile of category fluency was 13 words [52] , and the –1 SD was 10.3 [50] . The diagnostic methodology supported by a consensus meeting of geri-atricians with extensive experience in cognitive decline in the elderly was rigorous, and the follow-up of subjects at our clinic confirmed the diagnosis. These low scores among Spanish elderly patients could be attributed to low literacy, old age or sociodemographic characteristics, and have highlighted the importance of normalization of data from English-language tests when applied to Spanish populations to prevent false-positives [53] .

We did not validate our instrument for differentiation between the MCI patients and controls. This issue could

be of interest for patient follow-up, but the main challenge in cognitive decline is to distinguish between MCI and AD patients because of the therapeutic implications. Fu-ture research could focus on validation of our instrument or comparison with those already available.

The development of new therapeutic agents for mild stages of AD will require accurate diagnosis of subjects to be enrolled in clinical studies and, therefore, MCI and AD subjects must be clearly distinguished. This distinc-tion is difficult even for trained specialists when the pa-tient shows signs common to both conditions. The FMLL mini-battery may prove to be a useful tool in this field, although additional studies are needed to perfect it.

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