Background
Alteration of the amyloid precursor protein (APP) forms ratio has been described in the platelets of patients with dementia of Alzheimer type (DAT) and in a subset of subjects with mild cognitive impairment (MCI).
Objective
To evaluate the potential role of the platelet APP forms ratio in predicting progression from MCI to DAT.
Design
Thirty subjects with MCI underwent a clinical and neuropsychological examination and a determination of the platelet APP forms ratio. Subjects were followed up periodically for 2 years, and the progression to dementia was evaluated.
Setting
Community population-based sample of patients admitted for memory complaints.
Results
Patients who progressed to DAT at the 2-year follow-up (n = 12) showed a significant decrease of baseline platelet APP forms ratio values (mean ± SD, 0.36 ± 0.28) compared with stable MCI subjects (mean ± SD, 0.73 ± 0.32) (P<.01) and patients who developed other types of dementia (mean ± SD, 0.83 ± 0.27) (P = .03). By fixing a cutoff score of 0.6, 10 (83%) of the 12 DAT patients showed baseline values below the cutoff, whereas 10 (71%) of 14 subjects who either developed non-Alzheimer–type dementia or maintained cognitive functions had values in the normal range.
Conclusion
Mild cognitive impairment is a major risk factor for DAT, and Alzheimer disease–related pathological changes can be identified in patients converting to DAT within a 2-year follow-up.
ASLIGHT impairment in cognitive functions, notably memory, with otherwise normal performances has been designated as mild cognitive impairment (MCI), and has become a topic of considerable research in the past few years.1-3
Individuals with MCI represent the population at higher risk to develop dementia of Alzheimer type (DAT), with a rate of progression 10 times faster than a healthy elderly subject. In this regard, some investigators4-6 have suggested that all MCI subjects have Alzheimer disease (AD).
Nevertheless, the proposed criteria for MCI may well apply to a heterogeneous population whose memory complaints could be secondary to systemic disease, a drug-induced state, affective disorders, or other neurodegenerative diseases, rather than to an ongoing AD-related process.
Detection of AD among MCI subjects is, therefore, mandatory to maximize the benefit of available therapies that maintain cognitive functions over time.7 In this view, biological and neuroimaging markers hold the promises to disclose the identification of the so-called preclinical stage.
Several researchers8,9 have tried to identify peripheral markers of AD, and high-accuracy diagnostic values since the mild stages have been obtained by the combination of tau and Aβ protein concentrations in cerebrospinal fluid. More recently, it has been demonstrated that altered tau and Aβ42 protein concentrations may already be detectable in those subjects who were clinically diagnosed as having MCI before developing dementia.10,11
Other researchers12 have focused attention on the amyloid precursor protein (APP), a protein expressed in several splice variants in neural compartment and in nonneural tissues.
Interestingly, it has been demonstrated that patients with sporadic DAT show an alteration of APP pattern forms expression in platelets when compared with age-matched control subjects and with patients affected by non-AD–related dementia.13-15 A platelet APP forms alteration has already been found in many MCI subjects, suggesting its potential role in identifying patients who will convert to DAT.16
This observation defined the frame of the present work, which aimed to investigate the platelet APP forms ratio (APPr) prospectively as a biomarker for the diagnosis of preclinical AD among MCI subjects. To this aim, a population of MCI subjects was observed through a 2-year follow-up study, and the platelet APPr at enrollment was evaluated.
Among a large sample of patients with memory complaints, 30 MCI patients were recruited from the Centre of Ageing Brain and Neurodegenerative Disorders, University of Brescia. The study was conducted in accord with local clinical research regulations, and informed consent was required from all subjects and caregivers when indicated.
All subjects underwent a somatic and neurologic examination and laboratory studies, including the determination of apolipoprotein E genotype. All individuals underwent a brain imaging study (computed tomography or magnetic resonance imaging). The behavioral and global cognitive evaluation was performed according to a standardized battery that included the following tools: Clinical Dementia Rating Scale,17 Mini-Mental State Examination,18 Alzheimer Disease Assessment Scale,19 Neuropsychiatric Inventory,20 Geriatric Depression Scale,21 Hamilton Anxiety Rating Scale,22 and instrumental activities of daily living23 and activities of daily living indexes.24 The diagnosis was accomplished by tests tapping different domains such as verbal and nonverbal memory, abstraction, executive functions, visuospatial skills, and language (data not presented).
The diagnosis of MCI was based on Mayo Clinic criteria.4,25
A diagnosis of probable DAT was based on National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer's Disease and Related Disorders Association criteria.26 A diagnosis of frontotemporal dementia and dementia with Lewy bodies was made according to standardized clinical criteria.27,28 The inclusion and exclusion criteria are reported elsewhere.15,16
This is a longitudinal open study. At baseline, subjects underwent the neuropsychological assessment previously described, and venipuncture for platelet collection was performed. Each subject was followed up periodically for 2 years.
Subjects with MCI were reexamined, and a final diagnosis according to clinical and neuropsychological features was determined by 2 independent raters (L.R. and L.B.) who were blind to the baseline experimental findings.
Platelet collection and preparation
The patient information and case diagnoses were unknown to the laboratory investigator (Dr Colciaghi) who received and analyzed the samples.
According to previous studies,15,16 platelets from each subject were processed for Western blot analysis by a monoclonal antibody (22C11) raised against the N-terminal domain of the APP, therefore recognizing all APP forms present in the samples. This antibody recognized 3 different APP forms, with the apparent molecular weight of 130, 110, and 106 kDa. The results were expressed as the platelet APPr between the optical density of the upper (130 kDa) and the lower (106-110 kDa) APP immunoreactive bands. The ratio was determined for each individual from at least 3 replications (SD among replications, <10%).
Comparisons among groups were performed using factor analysis of variance with post hoc analyses (Scheffé test) and Spearman rank correlation analysis. From a study15 performed on a large sample of DAT and control individuals, a cutoff score of 0.6 was calculated, and APPr values below this cutoff were considered pathological.
Results were averaged and expressed as mean ± SD. Differences were considered statistically significant at P<.05.
The 2-year follow-up data were available for 26 of 30 patients. Among these 26 patients, 12 (46%) progressed to DAT and 4 (15%) progressed to non-Alzheimer–type dementia (3 with frontotemporal dementia and 1 with Lewy body dementia); 10 (38%) were diagnosed as having stable MCI (percentages do not total 100 because of rounding).
The demographic and clinical characteristics at baseline of the sample, classified according to diagnosis at the 2-year follow-up, are reported in Table 1. At baseline, the APPr of the 26 MCI subjects was 0.58 ± 0.35.
Patients who progressed to DAT showed a significant decrease of baseline APPr values (0.36 ± 0.28) compared with stable MCI subjects (0.73 ± 0.32) (P<.01) and patients who developed other types of dementia (0.83 ± 0.27) (P = .03) (Figure 1).
In Table 2, the values of cognitive and functional performances at the 1- and 2-year follow-up are shown. Through Spearman rank correlation analysis, diagnosis at the 2-year follow-up was significantly associated with the APPr at baseline (P<.01), but there was no association with demographic and clinical variables and apolipoprotein E genotype (P = .52).
By fixing a cutoff score of 0.6, previously chosen among a large sample of subjects, at baseline, there were 14 (54%) of 26 MCI subjects with pathological APPr scores. Among these 14 subjects, 10 (71%) developed DAT, whereas 4 (29%) were classified as having stable MCI. In particular, 10 of 12 patients with DAT (sensitivity, 83%) showed baseline pathological values below the cutoff, whereas 10 of 14 subjects who either developed non-AD–type dementia (all 4 patients with non-Alzheimer–type dementia) or maintained cognitive functions (6 of 10 MCI patients) had normal values above the cutoff (specificity, 71%) (Figure 2).
In the present study, we confirmed that MCI is a major risk factor for developing dementia and that AD-related pathological changes can be identified in patients converting to DAT within the 2-year follow-up.
Mild cognitive impairment is a heterogeneous condition, and the literature data1,29 indicate a varying degree of rate of conversion toward DAT, ranging from 10% to 25%. To limit this heterogeneity, a consensus defined subtype features of MCI, such as amnestic MCI, to find a subgroup at a much higher risk of progression to dementia.25 In fact, in our sample, adopting strict criteria to keep confounding factors at a minimum and excluding patients with depression, somatic disorders, or cardiovascular pathological features, we found a high rate of progression (23% per year).
Moreover, the platelet APPr was significantly altered in those MCI subjects who progressed to DAT, as more than 80% of these subjects showed a pathological decrease of the platelet APPr at baseline.
Thus, the demonstration of an APPr decrease in MCI subjects and its association with progression to DAT suggests that a subgroup of these subjects already has the biological hallmarks of AD.
During the past 3 years, the research on biological and neuroimaging markers of AD has moved from early to preclinical diagnosis, because neuropathological studies30 supported the view that AD-related features precede the clinical onset of symptoms.
In this regard, it has been recently shown that cerebrospinal fluid markers, such as Aβ, tau, and phosphorylated tau proteins, correlate with either progressive cognitive decline or conversion to DAT with high accuracy values.10,11
Similar to these studies, we found high sensitivity (83%) and specificity (71%) values, thus supporting the view that biomarkers might represent a useful tool for identifying converter MCI from nonconverter MCI.
We acknowledge that our study has some limitations. A longer follow-up of a larger sample of subjects who still have MCI is needed, along with neuropathological data to confirm the clinical diagnosis of DAT. Furthermore, the inclusion and exclusion criteria might have determined a selection bias that favored the recruitment of MCI patients at higher risk of AD, thus limiting the generalization of our findings.
Despite these limitations, these observations have several implications at theoretical and clinical levels.
From the theoretical point of view, our data support the view that MCI represents a predementia stage, although not uniquely associated with AD. In fact, there is a relatively small proportion of MCI subjects who either do not progress or do convert to other forms of dementia than DAT, thus questioning the claim that criteria for MCI are highly specific for subjects with incipient AD. Nevertheless, converging evidence derived from an autopsy series clearly demonstrates that AD-related changes precede the stage at which standardized clinical criteria for DAT apply. In fact, typical AD neuropathological markers are found in persons with and without dementia; these persons are labeled as having preclinical AD. All together, these findings argue for a distinction to be made between AD and DAT, according to which AD should properly refer to a neuropathological entity that is distinct but overlapping with dementia.31
Indeed, taking into account all different prospective studies on MCI, accuracy values compare favorably with National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer's Disease and Related Disorders Association clinical criteria for probable DAT.32 Consequently, it might be speculated that the accommodation of the predementia stage on a biological profile argues for the real possibility of diagnosing AD before patients satisfy the National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer's Disease and Related Disorders Association criteria for dementia. It might well turn out that a single biomarker will not ever be reliable enough to become the gold standard for the diagnosis of AD before DAT, but rather that a combination of different biological markers will be required to identify AD before dementia develops.33,34 The operational approach to a preclinical diagnosis of AD by including markers from different sources will have relevant consequence in clinical practice because it might be the more appropriate approach through which to evaluate the effectiveness of therapeutic options for DAT prevention, emphasizing when the available treatments should take place.
In conclusion, our study suggests that in MCI patients, biological disease-related changes are already detectable and the APPr may represent a helpful predictor of progression. In the future, the better characterization of the biological and neuroimaging alterations in this population will open a new chapter on biomarker criteria for preclinical AD diagnosis and will prompt clinicians to communicate the diagnosis of AD when dementia symptoms are not already overt.
Corresponding author and reprints: Alessandro Padovani, MD, PhD, Clinica Neurologica, c/o Spedali Civili di Brescia, Università degli Studi di Brescia, Pza Spedali Civili, 1, Brescia, Italy (e-mail: padovani@bshosp.osp.unibs.it).
Accepted for publication July 3, 2003.
Author contributions: Study concept and design (Drs Borroni, Di Luca, and Padovani); acquisition of data (Drs Colciaghi, Rozzini, and Broglio); analysis and interpretation of data (Drs Borroni, Caltagirone, and Cattabeni); drafting of the manuscript (Drs Borroni, Di Luca, and Padovani); critical revision of the manuscript for important intellectual content (Drs Colciaghi, Caltagirone, Rozzini, Broglio, and Cattabeni); statistical expertise (Drs Borroni and Rozzini); obtained funding (Drs Caltagirone, Di Luca, and Padovani); administrative, technical, and material support (Drs Borroni and Colciaghi); study supervision (Dr Cattabeni).
This study was supported by a grant from the Ministry of Health (Progetto Finalizzato Alzheimer–Regione Lombardia and Progetto Finalizzato Alzheimer–Istituto Di Ricovero E Cura A Carattere Scientifico Santa Lucia), Rome, Italy; and grants 9906158271-004 (Dr Di Luca) and 9906158271-003 (Dr Padovani) from the Ministero dell'istruzione e dell'università e della ricerca, Roma, Italy.
We thank Elisabetta Cottini, MD; Chiara Agosti, MD; and Barbara Vicini Chilovi, MD, for performing the neuropsychological assessments.
1.Petersen
RCSmith
GEWaring
SC
et al Mild cognitive impairment: clinical characterization and outcome.
Arch Neurol.1999;56:303-308.
PubMedGoogle Scholar 2.Tierney
MCSzalai
JPSnow
WG
et al Prediction of probable Alzheimer's disease in memory-impaired patients: a prospective longitudinal study.
Neurology.1996;46:661-665.
PubMedGoogle Scholar 3.Ritchie
KTouchon
J Mild cognitive impairment: conceptual basis and current nosological status.
Lancet.2000;355:225-228.
PubMedGoogle Scholar 4.Petersen
RCStevens
JCGanguli
MTangalos
EGCummings
JLDeKosky
ST Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology.
Neurology.2001;56:1133-1142.
PubMedGoogle Scholar 5.Morris
JCStorandt
MMiller
JP
et al Mild cognitive impairment represents early-stage Alzheimer disease.
Arch Neurol.2001;58:397-405.
PubMedGoogle Scholar 6.Bozoki
AGiordani
BHeidebrink
JL
et al Mild cognitive impairments predict dementia in nondemented elderly patients with memory loss.
Arch Neurol.2001;58:411-416.
PubMedGoogle Scholar 7.Hogan
DBMcKeith
IG Of MCI and dementia: improving diagnosis and treatment.
Neurology.2001;56:1131-1132.
PubMedGoogle Scholar 8.Mehta
PDPirttila
TMehta
SP
et al Plasma and cerebrospinal fluid levels of amyloid β proteins 1-40 and 1-42 in Alzheimer disease.
Arch Neurol.2000;57:100-105.
PubMedGoogle Scholar 9.Andreasen
NMinthon
LDavidsson
P
et al Evaluation of CSF-tau and CSF-Aβ42 as diagnostic markers for Alzheimer disease in clinical practice.
Arch Neurol.2001;58:373-379.
PubMedGoogle Scholar 10.Riemenschneider
MLautenschlager
NWagenpfeil
SDiehl
JDrzezga
AKurz
A Cerebrospinal fluid tau and β-amyloid 42 proteins identify Alzheimer disease in subjects with mild cognitive impairment.
Arch Neurol.2002;59:1729-1734.
PubMedGoogle Scholar 11.Buerger
KTeipel
SJZinkowski
R
et al CSF tau protein phosphorylated at threonine 231 correlates with cognitive decline in MCI subjects.
Neurology.2002;59:627-629.
PubMedGoogle Scholar 12.Bush
AIMartins
RNRumble
B
et al The amyloid precursor protein of Alzheimer's disease is released by human platelets.
J Biol Chem.1990;265:15977-15983.
PubMedGoogle Scholar 13.Rosenberg
RNBaskin
FFosmire
JA
et al Altered amyloid protein processing in platelets of patients with Alzheimer disease.
Arch Neurol.1997;54:139-144.
PubMedGoogle Scholar 14.Baskin
FRosenberg
RNIyer
L
et al Platelet APP isoform ratios correlate with declining cognition in AD.
Neurology.2000;54:1907-1909.
PubMedGoogle Scholar 15.Padovani
APastorino
LBorroni
B
et al Amyloid precursor protein in platelets: a peripheral marker for the diagnosis of sporadic AD.
Neurology.2001;57:2243-2248.
PubMedGoogle Scholar 16.Padovani
ABorroni
BColciaghi
F
et al Abnormalities in the pattern of platelet amyloid precursor protein forms in patients with mild cognitive impairment and Alzheimer disease.
Arch Neurol.2002;59:71-75.
PubMedGoogle Scholar 17.Burke
WJMiller
JPRubin
EH
et al Reliability of the Washington University Clinical Dementia Rating.
Arch Neurol.1988;45:31-32.
PubMedGoogle Scholar 18.Folstein
MFFolstein
SEMcHugh
PR "Mini-Mental State": a practical method for grading the cognitive state of patients for the clinician.
J Psychiatr Res.1975;12:189-198.
PubMedGoogle Scholar 19.Rosen
WGMohs
RCDavis
KL
et al A new rating scale for Alzheimer's disease.
Am J Psychiatry.1984;141:1356-1364.
PubMedGoogle Scholar 20.Cummings
JLMega
MGray
KRosenberg-Thompson
SCarusi
DAGornbein
J The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia.
Neurology.1994;44:2308-2314.
PubMedGoogle Scholar 21.Sheikh
JIYesavage
JA Geriatric Depression Scale (GDS): recent evidence and development of a shorter version.
In: Brink
TL, ed.
Clinical Gerontology: A Guide to Assessment and Intervention. Binghamton, NY: Haworth Press Inc; 1986:165-173.
Google Scholar 22.Gjerris
ABech
PBojholm
S
et al The Hamilton Anxiety Scale: evaluation of homogeneity and inter-observer reliability in patients with depressive disorders.
J Affect Disord.1983;5:163-170.
PubMedGoogle Scholar 23.Lawton
MPBroody
EM Assessment of older people: self-maintaining and instrumental activities of daily living.
Gerontologist.1969;9:179-186.
PubMedGoogle Scholar 24.Sheikh
KSmith
DSMeade
TWGoldenberg
EBrennan
PJKinsella
G Repeatability and validity of a modified activities of daily living (ADL) index in studies of chronic disability.
Int Rehabil Med.1979;1:51-58.
PubMedGoogle Scholar 25.Petersen
RCDoody
RKurz
A
et al Current concepts in mild cognitive impairment.
Arch Neurol.2001;58:1985-1992.
PubMedGoogle Scholar 26.McKhann
GDrachman
DFolstein
MKatzman
RPrice
DStadlan
EM Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of the Department of Health and Human Services Task Force on Alzheimer's Disease.
Neurology.1984;34:939-944.
PubMedGoogle Scholar 27.The Lund and Manchester Groups Clinical and neuropathological criteria for fronto-temporal dementia.
J Neurol Neurosurg Psychiatry.1994;57:416-418.
PubMedGoogle Scholar 28.McKeith
GGalasko
DKosaka
K
et al Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop.
Neurology.1996;47:1113-1124.
PubMedGoogle Scholar 29.Flicker
CFerris
SHReisberg
B Mild cognitive impairment in the elderly: predictors of dementia.
Neurology.1991;41:1006-1009.
PubMedGoogle Scholar 30.Crystal
HDickson
DFuld
P
et al Clinico-pathological studies in dementia: nondemented subjects with pathologically confirmed Alzheimer's disease.
Neurology.1988;38:1682-1687.
PubMedGoogle Scholar 31.Blass
JP Alzheimer's disease and Alzheimer's dementia: distinct but overlapping entities.
Neurobiol Aging.2002;23:1077-1084.
PubMedGoogle Scholar 32.Knopman
DSDeKosky
STCummings
JL
et al Practice parameter: diagnosis of dementia (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology.
Neurology.2001;56:1143-1153.
PubMedGoogle Scholar 33.Okamura
NHiroyuki
AMaruyama
M
et al Combined analysis of CSF tau levels and [
123]I-iodoamphetamine SPECT in mild cognitive impairment: implications for a novel predictor of Alzheimer's disease.
Am J Psychiatry.2002;159:474-476.
PubMedGoogle Scholar 34.Growdon
JH Incorporating biomarkers into clinical drug trials in Alzheimer's disease.
J Alzheimers Dis.2001;3:287-292.
PubMedGoogle Scholar