Advertisement

Neurological Sciences

, Volume 39, Issue 7, pp 1203–1210 | Cite as

Biomarkers study in atypical dementia: proof of a diagnostic work-up

  • Gemma Lombardi
  • Cristina Polito
  • Valentina Berti
  • Camilla Ferrari
  • Giulia Lucidi
  • Silvia Bagnoli
  • Irene Piaceri
  • Benedetta Nacmias
  • Alberto Pupi
  • Sandro Sorbi
Original Article

Abstract

Background

An early differentiation between Alzheimer’s Disease (AD) and other dementias is crucial for an adequate patients’ management, albeit it may result difficult for the occurrence of “atypical presentations.” Current diagnostic criteria recognize the importance of biomarkers for AD diagnosis, but still an optimal diagnostic work-up isn’t available.

Objective

Evaluate the utility and reproducibility of biomarkers and propose an “optimal” diagnostic work-up in atypical dementia.

Methods

(1) a retrospective selection of “atypical dementia cases”; (2) a repetition of diagnostic assessment by two neurologists following two different diagnostic work-ups, each consisting of multiple steps; (3) a comparison between diagnostic accuracy and confidence reached at each step by both neurologists and evaluation of the inter-rater agreement.

Results

In AD, regardless of the undertaken diagnostic work-up, a significant gain in accuracy was reached by both neurologists after the second step, whereas in frontotemporal dementia (FTD), adding subsequent steps was not always sufficient to increase significantly the baseline accuracy. A relevant increment in diagnostic confidence was detectable after studying pathophysiological markers in AD, and after assessing brain metabolism in FTD. The inter-rater agreement was higher at the second step for the AD group when the pathophysiological markers were available and for the FTD group when the results of FDG-PET were accessible.

Conclusions

In atypical cases of dementia, biomarkers significantly raise diagnostic accuracy, confidence, and agreement. This study introduces a proof of diagnostic work-up that combines imaging and CSF biomarkers and suggests distinct ways to proceed on the basis of a greater diagnostic likelihood.

Keywords

Biomarker Diagnostic work-up Alzheimer’s Disease Frontotemporal Dementia Cerebrospinal fluid PET imaging 

Introduction

Alzheimer’s Disease (AD) and other dementias are nowadays diagnosed based on clinical criteria [1], nevertheless accuracy and diagnostic confidence obtained using a basic clinical evaluation can be inadequate [2], due to the existence of atypical presentations of the disease [3]. Assuming that biomarkers variations reflect the pathophysiological processes underlying AD, the use of in vivo biomarkers may contribute to the diagnosis in the dementia spectrum, supporting the identification of AD and the differentiation from other dementias. An early classification of the dementia subtype is pivotal in order to improve management of patients. Both the IWG-2 research diagnostic criteria [4] and the NIA-AA criteria [1] recognize the importance of imaging and cerebrospinal fluid (CSF) markers (Aß, t-Tau, p-Tau) in detecting AD cases. Each biomarker is able to capture distinctive aspects of the underlying pathology and each biomarker has characteristic advantages and limitations regarding diagnostic and prognostic value [5], practical availability and costs [6]. Most clinicians agree that a combination of biomarkers positive for amyloid pathology and neuronal injury are strongly indicative of AD pathology [7]; however, contradictory results between biomarkers have been described, also within the same category of markers [8, 9]. Moreover, new clinical concepts “biomarker-based” are still under definition [10]. Sufficient evidence of analytical validity is available for all biomarkers, but their clinical validity and utility are sofar incomplete. For this purpose, biomarkers performance in detecting early disease is under evaluation and defining a diagnostic algorithm is a priority in current clinical research [6]. Some authors proposed a “multimodal imaging approach” that provides to perform in sequence PET imaging with different tracers in order to stratify the AD risk or to identify AD cases [11, 12, 13]. Alternatively, the CSF Consortium emphasized the role of CSF biomarkers in the dementia diagnosis and proposed a diagnostic model where the lumbar puncture (LP) is considered the first exam to be performed (after brain MRI/CT) in atypical cases. Only if the results of CSF biomarkers are “intermediate,” it is suggested to proceed with further investigation [14].

In order to understand the utility of biomarkers, we investigated whether the diagnostic accuracy and the diagnostic confidence change on the basis of the undertaken diagnostic work-up. Subsequent goals of the study were highlighting which step is the most effective in increasing the baseline values in diagnostic accuracy and confidence and to evaluate the reproducibility of the results (estimated as a measure of inter-rater agreement). Based on the study’s results, an optimal diagnostic work-up in atypical dementia was proposed.

Methods

Study design

We designed a single-center retrospective observational study including a cohort of 45 patients affected by cognitive decline characterized by an “atypical presentation.” All consecutive patients admitted to our clinic in the period 2014–2015 were enrolled if they met all of the following criteria: uncertain nosographical diagnosis after a baseline clinical evaluation (diagnostic confidence 50–70%), combined pathophysiological and topographical biomarkers availability (according to biomarkers’ definition of Dubois B [4]), at least 1-year clinical follow-up. All subjects gave written informed consent prior to data collection and permission to use their clinical data. A selection of cases was performed by an expert neurologist (Neurologist 0, N0) that disposed the “final diagnosis” of each patient, reached after a complete diagnostic work-up and confirmed at the end of the follow-up period. Patients with high vascular burden, with prevailing extrapyramidal signs or carriers of pathogenic mutations were excluded. Diagnostic assessment was replicated by two other raters (Neurologist 1, N1 and Neurologist 2, N2) skilled in dementia diagnosis, both of them blinded to the mutual diagnosis and to the “final diagnosis.” Evaluations were performed twice in situations characterized by different biomarkers availability, defined as 3 diagnostic steps: “CLINICAL” (comprehensive of standardized brain CT/MRI and neuropsychological assessment), “PATHOLOGY” (including amyloid-PET and/or CSF biomarkers as pathophysiological markers), “FDG-PET” (including brain metabolism as topographical marker). CSF Aß1–42 and amyloid-PET were considered approximately interchangeable as a measure of amyloid burden and if both were available, both results were showed to the raters, together with other CSF biomarkers. As the first diagnostic step was indisputably “CLINICAL,” in accordance with the good clinical practice, the potential alternative diagnostic work-ups were two: one that provided “PATHOLOGY” as a second step, the other that provided “FDG-PET” as a second step. After every diagnostic step, the neurologists made a diagnosis, giving a value of diagnostic confidence ranging from 1 to 100. The accuracy at each step was calculated following the statistical definition and was expressed in a percentage of correct diagnosis. For each neurologist, the accuracy and the diagnostic confidence of the initial diagnosis have been compared with those achieved at each diagnostic step, in the two different diagnostic work-ups. Raters were considered in agreement when both their results and the “final diagnosis” were in accordance. The inter-rater agreement was expressed as Cohen’s K. On the basis of the study’s results, an optimal diagnostic work-up was proposed. The study design has been represented in Fig. 1.
Fig. 1

Study design represented in 3 phases (1–2–3). On the basis of the study’s results, a diagnostic work-up in atypical dementia was proposed

Patients and clinical procedures

All the enrolled subjects had performed a complete neuropsychological assessment, a structural imaging (conventional 1.5 T brain MRI or CT scan) and a brain [18F]FDG-PET scan. The neuropsychological evaluation was performed with standard tests [15, 16, 17, 18, 19], some reported in Table 1. Moreover, all the patients had at least one biomarker of amyloid burden available: 60% CSF Aβ1–42, 22% amyloid-PET scan and 18% had both CSF Aβ1–42 and amyloid-PET. The [18F]FDG-PET scan and amyloid-PET were carried out following the standard national and international guidelines. The PET data were analyzed by two experienced readers in a consensus reading based on visual assessment. The CSF samples have been collected at 8 am by LP, immediately centrifuged and stored at − 80 °C until performing the analysis. t-Tau, p-Tau, and Aß1–42 were measured with ELISA kits (commercial enzyme-linked immunosorbent assay). Cutoff for normal values was for total-Tau < 300 pg/ml, for p-Tau < 60 pg/ml, and for Aß1–42 > 650 pg/ml. The “final diagnosis,” information blinded for Neurologist 1 and 2, included 32 AD, defined according to the NIA-AA criteria [1], 10 FTD according to Gorno-Tempini criteria [20] and Rascovsky criteria [21], 3 “unclassified cognitive decline” (UCD).
Table 1

Descriptive statistics, main results (mean ± SD): for MMSE raw values were shown; for other tests, data have been corrected for age and education. Quantitative variables between AD and FTD were compared using the independent sample Mann-Whitney U test; categorical variables using Fisher’s exact test. Mini Mental Examination Test (MMSE) [15], Frontal Assessment Battery (FABit) [16], Rey Auditory Verbal Learning Test immediate recall (RAVL-IR) and delayed recall (RAVL-DR) [17], Attentive Matrices [18], Phonemic and Semantic Fluency [17, 19]

 

AD (n = 32)

FTD (n = 10)

p value (AD-FTD)

UCD (n = 3)

Cutoff

Age at onset

66.47 ± 9.93

67.4 ± 8.47

> 0.05

59.33 ± 11.85

 

Education

11.03 ± 5.81

7 ± 3.23

> 0.05

10 ± 6.81

 

M/F

19/13

5/5

> 0.05

0/3

 

MMSE

21.66 ± 4.32

22.60 ± 2.37

> 0.05

23 ± 3.46

< 24

FABit

12.66 ± 3.67

12.50 ± 3.42

> 0.05

15.17 ± 2.63

< 13.5

RAVL-IR

24.65 ± 9.87

21.43 ± 10.26

> 0.05

29.47 ± 7.52

< 28.53

RAVL-DR

2.96 ± 3.15

0.51 ± 1.45

0.03*

3.93 ± 1.08

< 4.69

Attentive Matrices

36.88 ± 13.31

34.31 ± 8.53

> 0.05

40.58 ± 4.65

< 31

Phonemic Fluency

26.16 ± 10.22

23.89 ± 9.77

> 0.05

24.55 ± 8.13

< 17.35

Semantic Fluency

28.05 ± 9.25

25.11 ± 13.18

> 0.05

35 ± 12.73

< 24

CSF Aß 1–42

484.33 ± 131.50

968.60 ± 326.21

0.0002*

/

> 650

CSF t-Tau

591.05 ± 296.21

383.8 ± 194.17

> 0.05

/

< 300

CSF p-TAu

85.12 ± 42.31

57.89 ± 31.18

> 0.05

/

< 60

CSF pTAu/Aß1–42

0.20 ± 0.14

0.07 ± 0.05

0.0023*

/

 

*Significance level p < 0.05

Statistical analysis

Statistical analysis was made using MedCalc software and graphics using Excel. Quantitative variables between the main groups (AD and FTD) were compared using the independent sample Mann-Whitney U test; categorical variables were tested using Fisher’s exact test. Step by step multiple comparisons in accuracy and diagnostic confidence were estimated for each neurologist utilizing respectively Cochran’s Q test and Friedman test. A p value < 0.05 was considered to be significant. The inter-rater agreement was expressed as Cohen’s K; values between 0.41 and 0.6 were considered indicative of moderate agreement, whereas values between 0.61 and 0.80 indicative of good agreement.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Results

Descriptive statistics

Groups of patients were comparable for age, years of education, and for most of neuropsychological test results; only the Rey Auditory Learning Test in delayed recall score (RAVL-DR) [17] was significantly worse in the FTD group (p = 0.03). About CSF biomarkers, the mean level of Aß1–42 was significantly reduced in the AD compared to the FTD (p = 0.0002); the mean values of t-Tau were increased in both AD and FTD; p-Tau was increased in the AD and a higher ratio p-Tau/Aß1–42 in AD vs FTD was detected (p = 0.0023) (Table 1).

Accuracy

In the AD group following the sequence “CLINICAL-PATHOLOGY-FDG-PET,” accuracy reached the best value from step 2 for Neurologist 1 (66.67% in step 1, 91.11% in steps 2 and 3); for Neurologist 2, the accuracy increased gradually reaching the best value in step 3 (60.00% in step 1, 84.44% in step 2, 91.11% in step 3). The multiple comparison test revealed for two neurologists similar results: accuracy increased significantly between step 1 and step 2 (p ≤ 0.001) but not between step 2 and step 3 (Fig. 2a). Within “PATHOLOGY step,” no difference in accuracy was found between AD cases evaluated only with amyloid-PET (n = 8) compared to cases evaluated only through CSF (n = 18). In the situation “CLINICAL-FDG-PET-PATHOLOGY,” for Neurologist 1, the accuracy increased between steps (66.67% in step 1, 91.11% in step 2, 93.33% in step 3) as for Neurologist 2 (60.00% in step 1, 82.22% in step 2, 91.11% in step 3). The multiple comparison test revealed a significant gain in accuracy again between step 1 and 2 (p ≤ 0.001), but not between steps 2 and 3 for both neurologists (Fig. 2b). Considering the FTD group in the sequence “CLINICAL-PATHOLOGY-FDG-PET,” the accuracy increased between steps for both neurologists (Neurologist 1: 80.00% in step 1, 91.11% in step 2, 93.33% in step 3; Neurologist 2: 75.56% in step 1, 88.89% in step 2, 93.33% in step 3). The multiple comparison test revealed differences between neurologists: according to Neurologist 2, the third step was useful in order to significantly increase baseline accuracy (p = 0.031); according to Neurologist 1, any step was pointless in this purpose (Fig. 3a). In the sequence “CLINICAL-FDG-PET-PATHOLOGY,” according to Neurologist 1, a higher accuracy was reached starting from step 2; according to Neurologist 2, the accuracy increased gradually in the course of the diagnostic work-up (Neurologist 1: 80.00%, in step 1, 93.33% in step 2, 93.33% in step 3; Neurologist 2: 75.56% in step 1, 86.67% in step 2, 93.33% in step 3). Again, according to Neurologist 2, the third step was useful in order to significantly increase baseline accuracy (p = 0.038); according to Neurologist 1, any step was pointless in this purpose (Fig. 3b).
Fig. 2

Accuracy reached by neurologists in AD group, in different diagnostic work-ups: a CLINICAL-PATHOLOGY-FDG-PET. b CLINICAL-FDG-PET-PATHOLOGY. Multiple comparison between steps for Neurologist 1 (N1) and Neurologist 2 (N2)

Fig. 3

Accuracy reached by neurologists in FTD group, in different diagnostic work-ups: a CLINICAL-PATHOLOGY-FDG-PET. b CLINICAL-FDG-PET-PATHOLOGY. Multiple comparison between steps for Neurologist 1 (N1) and Neurologist 2 (N2)

Diagnostic confidence

In the AD group, diagnostic confidence values achieved by Neurologist 1 and Neurologist 2 following the sequence “CLINICAL-PATHOLOGY-FDG-PET” were respectively 60 and 64% (step 1), 72 and 73% (step 2), 80 and 79% (step 3); following the sequence “CLINICAL-FDG-PET-PATHOLOGY,” diagnostic confidence values were respectively 60 and 64% (step 1), 69 and 69% (step 2), 79 and 79% (step 3). The gain in confidence was significant for both neurologists after the “step PATHOLOGHY” in the two sequences but the confidence reached a satisfactory value (around 80%) only after three diagnostic steps. In the FTD group, diagnostic confidence values reached by Neurologist 1 and Neurologist 2 following the sequence “CLINICAL-PATHOLOGY-FDG-PET” were respectively: 64 and 66% (step 1), 71 and 70% (step 2), 81 and 81% (step 3); following the sequence “CLINICAL-FDG-PET-PATHOLOGY,” diagnostic confidence values were respectively 64 and 66% (step 1), 76 and 81% (step 2), 80 and 81% (step 3). The highest diagnostic confidence was obtained after “FDG-PET step” (around 80%) for both neurologists, irrespective to the applied work-up. The gain in diagnostic confidence compared to baseline value was significant for both neurologists in the sequence “CLINICAL-PATHOLOGY-FDG-PET,” after “FDG-PET step.”

Agreement between neurologists

After “CLINICAL step,” the strength of agreement was < 0.6 in the AD group and < 0.5 in the FTD group. Cohen’s K was higher for the AD when the second step was “PATHOLOGY” (0.69) and for the FTD when the second step was “FDG-PET” (0.63) and it increased a little with the subsequent step (0.73 vs 0.69 e 0.64 vs 0.63).

Discussion

An early differentiation between Alzheimer’s Disease (AD) and other dementias is crucial for a proper management of patients but it may result difficult for “atypical presentations” of the disease. To understand the utility of biomarkers in atypical dementia, we investigated whether diagnostic accuracy and diagnostic confidence modify on the basis of the undertaken diagnostic work-up. Eventually, an optimal diagnostic work-up in atypical dementia was proposed.

Descriptive statistics

No significant differences in neuropsychological tests were found between the two main groups (AD and FTD), except for results regarding RAVL-DR, that was worse in the FTD group. This finding contradicts the strong evidence about more pronounced memory impairment in the AD compared to the FTD and reflects the cognitive complexity of patients characterized by an “atypical presentation.” Patients enrolled in the study did not show a specific neuropsychological profile and this justifies the use of biomarkers to clarify the underlying pathology. CSF biomarker profile instead reflects the expected differences [22].

Accuracy

In the AD group, the accuracy increased with the number of diagnostic steps. Both neurologists in the two sequences reached a significant increment in accuracy, compared to baseline value, when a second step was performed, regardless of the order of execution. Also for the FTD group, the accuracy increased adding steps to the diagnostic work-up. In this group, adding subsequent steps was not always sufficient to increase significantly the baseline accuracy. Neither of the work-ups resulted better to the other in increasing the baseline accuracy. Differences between Neurologists 1 and 2 in baseline accuracy may be justified by the difficulty of diagnosis in atypical dementia; this observation is supported by the reduction in discrepancy during the diagnostic work-up completion.

Diagnostic confidence

According to both neurologists, in the AD group, the diagnostic confidence increased after “PATHOLOGY step,” as expected due to the intrinsic characteristics of this biomarker. The [18F]FDG-PET was useful for both neurologists to increase the diagnostic confidence in the FTD, probably due to the ability in identifying specific metabolic patterns, which may confirm the subtype of pathology (behavioral variant, primary non fluent aphasia, semantic dementia).

Agreement between neurologists

A higher agreement in second step occurred for the AD group performing “PATHOLOGY,” for the FTD group performing “FDG-PET.” After the third step, the strength of agreement between neurologists (Cohen’s K) was around 0.7, a value that corresponded to a good, but not perfect, agreement. Again, these data reflect the difficulty in diagnosis of atypical cases and the possibility of discordant or inconsistent biomarkers results. Anyway, the gain in the inter-rater agreement during the work-up hinted the reproducibility of the method and suggested, with other results, some elements to devise a possible diagnostic work-up in atypical dementia: when AD is a possible diagnosis, it is better to perform “PATHOLOGY” as a second step. Indeed in the AD, the accuracy increased regardless of the diagnostic work-up whereas the diagnostic confidence and the inter-rater agreement were better at the second step following this sequence. If FTD is suspected, it is better to perform “FDG-PET” as a second step: even if baseline accuracy did not increase significantly with this step, FDG-PET provided better agreement and diagnostic confidence following this sequence.

Proof of diagnostic work-up in atypical dementia

The work-up proposed (Fig. 4) takes into account clinical considerations coming from literature together with the study results and argues to combine imaging markers together with CSF biomarkers in a single diagnostic algorithm. Since the utility to confirm or discard the suspected pathology, the second step is the most important to be defined. If the AD is a possible diagnosis (low confidence level, 50–70%), it is recommended to perform the step “PATHOLOGY” as a second step, preferably in the form of CSF biomarkers. Indeed, compared to amyloid-PET, LP allows to detect all forms of Aß, probably at an earlier stage of the disease [23] and the exam is capable to capture simultaneously the two degenerative processes of AD pathology, amyloidosis and tauopathy paths, in a single procedure. Even if the procedure is invasive, today the LP is considered well tolerated and accepted with a low complication rate when performed in patients without contraindications [24]. Subsequently, if the suspected pathology is AD, but still with an inadequate confidence level (< 80%), it is advisable to perform an amyloid-PET in order to confirm the diagnosis. In other conditions, when AD is not the principal suspected diagnosis (diagnostic confidence< 50%) and in particular when FTD is a possible diagnosis, an FDG-PET is indicated as a second step in order to confirm the diagnosis. Extending the algorithm to other dementias, in the occurrence of extrapyramidal signs, when Parkinson Disease Dementia (PDD) or Lewy Body Dementia (LBD) are suspected, DaTSCAN SPECT is to be considered the most useful second step, because of the ability to distinguish between LBD and AD with high specificity [25, 26]. Conversely, the amyloid-PET has poor capability to detect differences between AD, LBD, and PDD [27]. When the prominent diagnostic hypothesis is an atypical parkinsonism (AP) different from LBD, FDG-PET is recommended as a second step because of its ability to discriminate among atypical APs [28, 29]. A general recommendation is to reserve the third diagnostic step for cases that remain unresolved. In situations characterized by incongruence between topographical markers (if negative) and pathophysiological markers (if positive), according to Dubois definition of AD pathology, a diagnosis of atypical AD is to be supported. In the occurrence of contradictory results within amyloid dysfunction markers, the pathologic finding must be enhanced. Indeed, a single positive amyloid marker can be relevant in the evaluation of selected cases, even if generally combining CSF biomarkers and amyloid-PET is not useful to increase the diagnostic accuracy in AD [30].
Fig. 4

A proof of diagnostic work-up in atypical dementia (AD Alzheimer’s Disease, FTD Frontotemporal Dementia, AP Atypical Parkinsonism different from LBD, LBD Lewy Body Dementia, PDD Parkinson Disease Dementia, NPSY neuropsychological tests). The recommended second step changes on the basis of the suspected pathology: (2a) PATHOLOGY (CSF or amyloid-PET for AD), (2b) FDG-PET for FTD and AP, (2c) DaTSCAN SPECT for LBD and PDD

A limitation of the present study is the lack of pathological confirmation of the “final diagnosis,” assumed as a gold standard. Moreover, pathophysiological markers (CSF biomarkers and amyloid-PET) were aggregated into a single category, according to the definition of Dubois [4]. This assumption could be questionable as these exams can also give discordant results about the amyloid burden. A basic assumption in the proposed work-up is that AD diagnosis depends on the occurrence of pathophysiological biomarkers, according to amyloid hypothesis unlike evidence coming from literature, in which AD-like cognitive decline occurs also in amyloid negative subjects [10, 31].

In conclusion, an optimal diagnostic work-up for atypical dementia is not yet available.

According to our results, biomarkers availability in atypical cases of dementia significantly raises diagnostic accuracy, diagnostic confidence and diagnostic agreement. However, the increment in FTD accuracy appears less certain as it was significant only for Neurologist 2.

A work-up combining imaging and CSF biomarkers in a single diagnostic algorithm is proposed. The algorithm suggests that distinct ways should be followed on the basis of the most likely diagnostic hypothesis and a third diagnostic step should be reserved for unresolved cases. The growing inter-rater agreement between neurologists supports the reproducibility of the method. The new method will be verified by application to a larger cohort, including also patients affected by other pathologies.

Notes

Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    McKhann GM et al (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:263–269CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Knopman DS et al (2001) Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of neurology. Neurology 56:1143–1153CrossRefPubMedGoogle Scholar
  3. 3.
    Li P, Zhou YY, Lu D, Wang Y, Zhang HH (2016) Correlated patterns of neuropsychological and behavioral symptoms in frontal variant of Alzheimer disease and behavioral variant frontotemporal dementia: a comparative case study. Neurol Sci 37:797–803CrossRefPubMedGoogle Scholar
  4. 4.
    Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, DeKosky ST, Gauthier S, Selkoe D, Bateman R, Cappa S, Crutch S, Engelborghs S, Frisoni GB, Fox NC, Galasko D, Habert MO, Jicha GA, Nordberg A, Pasquier F, Rabinovici G, Robert P, Rowe C, Salloway S, Sarazin M, Epelbaum S, de Souza LC, Vellas B, Visser PJ, Schneider L, Stern Y, Scheltens P, Cummings JL (2014) Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol 13:614–629CrossRefPubMedGoogle Scholar
  5. 5.
    Berti V, Polito C, Lombardi G, Ferrari C, Sorbi S, Pupi A (2016) Rethinking on the concept of biomarkers in preclinical Alzheimer’s disease. Neurol Sci 37:663–672CrossRefPubMedGoogle Scholar
  6. 6.
    Frisoni GB, Boccardi M, Barkhof F, Blennow K, Cappa S, Chiotis K, Démonet JF, Garibotto V, Giannakopoulos P, Gietl A, Hansson O, Herholz K, Jack CR Jr, Nobili F, Nordberg A, Snyder HM, ten Kate M, Varrone A, Albanese E, Becker S, Bossuyt P, Carrillo MC, Cerami C, Dubois B, Gallo V, Giacobini E, Gold G, Hurst S, Lönneborg A, Lovblad KO, Mattsson N, Molinuevo JL, Monsch AU, Mosimann U, Padovani A, Picco A, Porteri C, Ratib O, Saint-Aubert L, Scerri C, Scheltens P, Schott JM, Sonni I, Teipel S, Vineis P, Visser PJ, Yasui Y, Winblad B (2017) Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. Lancet Neurol 16:661–676CrossRefPubMedGoogle Scholar
  7. 7.
    Bocchetta M, Galluzzi S, Kehoe PG, Aguera E, Bernabei R, Bullock R, Ceccaldi M, Dartigues JF, de Mendonça A, Didic M, Eriksdotter M, Félician O, Frölich L, Gertz HJ, Hallikainen M, Hasselbalch SG, Hausner L, Heuser I, Jessen F, Jones RW, Kurz A, Lawlor B, Lleo A, Martinez-Lage P, Mecocci P, Mehrabian S, Monsch A, Nobili F, Nordberg A, Rikkert MO, Orgogozo JM, Pasquier F, Peters O, Salmon E, Sánchez-Castellano C, Santana I, Sarazin M, Traykov L, Tsolaki M, Visser PJ, Wallin ÅK, Wilcock G, Wilkinson D, Wolf H, Yener G, Zekry D, Frisoni GB (2015) The use of biomarkers for the etiologic diagnosis of MCI in Europe: an EADC survey. Alzheimers Dement 11:195–206CrossRefPubMedGoogle Scholar
  8. 8.
    Blennow K, Mattsson N, Schöll M, Hansson O, Zetterberg H (2015) Amyloid biomarkers in Alzheimer’s disease. Trends Pharmacol Sci 36:297–309CrossRefPubMedGoogle Scholar
  9. 9.
    Weise D, Tiepolt S, Awissus C, Hoffmann KT, Lobsien D, Kaiser T, Barthel H, Sabri O, Gertz HJ (2015) Critical comparison of different biomarkers for Alzheimer’s disease in a clinical setting. J Alzheimers Dis 48:425–432CrossRefPubMedGoogle Scholar
  10. 10.
    Dani M, Brooks DJ, Edison P (2017) Suspected non Alzheimer’s pathology—is it non-Alzheimer’s or non-amyloid? Ageing Res Rev 36:20–31CrossRefPubMedGoogle Scholar
  11. 11.
    Mosconi L, Berti V, Glodzik L, Pupi A, De Santi S, de Leon MJ (2010) Pre-clinical detection of Alzheimer’s disease using FDG-PET, with or without amyloid imaging. J Alzheimers Dis 20:843–854CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Drzezga A (2010) Amyloid-plaque imaging in early and differential diagnosis of dementia. Ann Nucl Med 24:55–66CrossRefPubMedGoogle Scholar
  13. 13.
    Guerra UP, Nobili FM, Padovani A, Perani D, Pupi A, Sorbi S, Trabucchi M (2015) Recommendations from the Italian interdisciplinary working group (AIMN, AIP, SINDEM) for the utilization of amyloid imaging in clinical practice. Neurol Sci 36:1075–1081CrossRefPubMedGoogle Scholar
  14. 14.
    Molinuevo JL, Blennow K, Dubois B, Engelborghs S, Lewczuk P, Perret-Liaudet A, Teunissen CE, Parnetti L (2014) The clinical use of cerebrospinal fluid biomarker testing for Alzheimer’s disease diagnosis: a consensus paper from the Alzheimer’s biomarkers standardization initiative. Alzheimers Dement 10:808–817CrossRefPubMedGoogle Scholar
  15. 15.
    Measso G, Cavarzeran F, Zappalà G, Lebowitz BD, Crook TH, Pirozzolo FJ, Amaducci LA, Massari D, Grigoletto F (1993) The mini-mental state examination: normative study on an Italian random sample. Dev Neuropsychol 9:77–85CrossRefGoogle Scholar
  16. 16.
    Appollonio I, Leone M, Isella V, Piamarta F, Consoli T, Villa ML, Forapani E, Russo A, Nichelli P (2005) The frontal assessment battery (FAB): normative values in an Italian population sample. Neurol Sci 26:108–116CrossRefPubMedGoogle Scholar
  17. 17.
    Carlesimo GA, Caltagirone C, Gainotti G (1996) The mental deterioration battery: normative data, diagnostic reliability and qualitative analyses of cognitive impairment. The group for the standardization of the mental deterioration battery. Eur Neurol 36:378–384CrossRefPubMedGoogle Scholar
  18. 18.
    Spinnler H, Tognoni G (1987) Standardizzazione e taratura italiana di test neuropsicologici. Ital J Neurol Sci 8(Suppl):1–120Google Scholar
  19. 19.
    Novelli G, Papagno C, Capitani E, Laiacona M, Vallar G, Cappa SF (1986) Tre test clinici di ricerca e produzione lessicale. Taratura su soggetti normali. Arch Psicol Neurol Psichiatr 47:477–506Google Scholar
  20. 20.
    Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, Ogar JM, Rohrer JD, Black S, Boeve BF, Manes F, Dronkers NF, Vandenberghe R, Rascovsky K, Patterson K, Miller BL, Knopman DS, Hodges JR, Mesulam MM, Grossman M (2011) Classification of primary progressive aphasia and its variants. Neurology 76:1006–1014CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J, van Swieten JC, Seelaar H, Dopper EGP, Onyike CU, Hillis AE, Josephs KA, Boeve BF, Kertesz A, Seeley WW, Rankin KP, Johnson JK, Gorno-Tempini ML, Rosen H, Prioleau-Latham CE, Lee A, Kipps CM, Lillo P, Piguet O, Rohrer JD, Rossor MN, Warren JD, Fox NC, Galasko D, Salmon DP, Black SE, Mesulam M, Weintraub S, Dickerson BC, Diehl-Schmid J, Pasquier F, Deramecourt V, Lebert F, Pijnenburg Y, Chow TW, Manes F, Grafman J, Cappa SF, Freedman M, Grossman M, Miller BL (2011) Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134:2456–2477CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Vergallo A, Carlesi C, Pagni C, Giorgi FS, Baldacci F, Petrozzi L, Ceravolo R, Tognoni G, Siciliano G, Bonuccelli U (2017) A single center study: Aβ42/p-Tau181 CSF ratio to discriminate AD from FTD in clinical setting. Neurol Sci 38:1791–1797CrossRefPubMedGoogle Scholar
  23. 23.
    Palmqvist S, Mattsson N, Hansson O, Alzheimer’s Disease Neuroimaging Initiative (2016) Cerebrospinal fluid analysis detects cerebral amyloid-ß accumulation earlier than positron emission tomography. Brain 139:1226–1236CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Engelborghs S et al (2017) Consensus guidelines for lumbar puncture in patients with neurological diseases. Alzheimers Dement 8:111–126Google Scholar
  25. 25.
    Walker Z, Moreno E, Thomas A, Inglis F, Tabet N, Rainer M, Pizzolato G, Padovani A (2015) Clinical usefulness of dopamine transporter SPECT imaging with 123I-FP-CIT in patients with possible dementia with Lewy bodies: randomised study. Br J Psychiatry 206:145–152CrossRefPubMedGoogle Scholar
  26. 26.
    McKheit IG et al (2017) Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB consortium. Neurology 89:88–100CrossRefGoogle Scholar
  27. 27.
    Mallik A, Drzezga A, Minoshima S (2017) Clinical amyloid imaging. Semin Nucl Med 47:31–43CrossRefPubMedGoogle Scholar
  28. 28.
    Zhao P, Zhang B, Gao S (2012) 18[F]-FDG PET study on the idiopathic Parkinson’s disease from several parkinsonian-plus syndromes. Parkinsonism Related Disorders 1(suppl) 18:S60–S62CrossRefGoogle Scholar
  29. 29.
    Niccolini F, Politis M (2016) A systematic review of lessons learned from PET molecular imaging research in atypical parkinsonism. Eur J Nucl Med Mol Imaging 43:2244–2254CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Palmqvist S, Zetterberg H, Mattsson N, Johansson P, For the Alzheimer's Disease Neuroimaging Initiative, Minthon L, Blennow K, Olsson M, For the Swedish BioFINDER study group, Hansson O (2015) Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology 85:1240–1249CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Da X et al (2013) Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. Neuroimage Clin 4:164–173CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2018

Authors and Affiliations

  • Gemma Lombardi
    • 1
  • Cristina Polito
    • 2
  • Valentina Berti
    • 2
  • Camilla Ferrari
    • 1
  • Giulia Lucidi
    • 1
    • 3
  • Silvia Bagnoli
    • 1
  • Irene Piaceri
    • 1
  • Benedetta Nacmias
    • 1
  • Alberto Pupi
    • 2
  • Sandro Sorbi
    • 1
    • 3
  1. 1.Department of Neuroscience, Psychology, Drug Research and Child HealthUniversity of FlorenceFlorenceItaly
  2. 2.Department of Biomedical, Experimental and Clinical Sciences “Mario Serio,” Nuclear Medicine UnitUniversity of FlorenceFlorenceItaly
  3. 3.IRCCS Don GnocchiFlorenceItaly

Personalised recommendations