Open access

Risk Factors for Disease Progression in Alzheimer's Disease

Written By

Schmidt C, Wolff M, Shalash A and Zerr I

Submitted: 07 November 2010 Published: 06 September 2011

DOI: 10.5772/20160

Chapter metrics overview

2,634 Chapter Downloads

View Full Metrics

1. Introduction

The most common form of dementia is Alzheimer’s disease (AD) (Blennow et al., 2006). Due to worldwide demographic aging, its incidence and socioeconomic impact is going to be growing noticeably within the next fifty years (Sloane et al., 2002). Typically the disease progresses slowly with a mean decline of about 3 MMSE (Mini Mental Status Examination) pts/yr (Morris et al., 1993). On average, patients survive 8 years after the diagnosis has been established (Goldberg, 2007). But sometimes fast progressive AD forms with distinct clinical features are observed (Caselli et al., 1998; Josephs et al., 2009; Mann et al., 1989; Schmidt et al., 2010; van Everbroeck et al., 2004).

During the past few years AD has increasingly being understood as a disease that appears in rather heterogeneous variants (Blennow et al., 2006; Wilkosz et al., 2010; van der Vlies et al., 2009a; Iqbal et al., 2005; Querfurth & LaFerla, 2010). This accounts for its clinical profile, biomarker patterns or neuropathological features. Still, studies sufficiently interrelating symptomatology to neuropathology, pathophysiology and biopathochemistry are lacking. Factors, which might cause heterogeneity, appear to be diverse. For instance, different deterioration speeds may occur in different disease stages (Wilkosz et al., 2010; Brooks et al., 1993; Storandt et al., 2002). Also differences in the so-called cognitive reserve (Stern, 2006; Mortimer et al., 2005; Paradise et al., 2009) could account for phenotypical disparities. But furthermore, different biological causes or processes that converge on a common final pathophysiological pathway might evoke heterogeneity (Ritchie & Touchon, 1992). With ever growing evidence of AD heterogeneity, rapidly progressive AD forms (rpAD) might very well be one representative of such AD subentities.

In this book chapter, we review clinical evidence regarding AD heterogeneity in general and rapidly progressive AD (rpAD) in particular. Questions arising regard the epidemiological evidence for rpAD, its predictability, the biological / pathophysiological basis and the impact on therapeutic decision-making (subtype adapted therapy).

Advertisement

2. Excursus: evidence of AD heterogeneity

Different disease courses, regarding speed and slope, as well as different phenotypes might represent distinct subtypes of AD (Davidson et al., 2010; Geldmacher et al., 2000; Mangone, 2004). Several attempts have been made to characterize those subtypes, by definition of cognitive subgroup patterns, biomarker profiles in the CSF and recently using neuroimaging (Wilkosz et al., 2010; Davidson et al., 2010; Boxer et al., 2003; Cummings, 2000).

Study Mean survival Age n (patients with rpAD), gender n, in parenthesis: n (subjects with prion disease)
Aksamit et al., 2001 n.a. n.a. 13 (not all neuropathologically confirmed) 152 (31)
van Everbroeck et al., 2004 22mn 71 clinically diagnosed: 45 (19m, 26f); thereof 30 confirmed by post mortem 201 (52)
Collins et al., 2000 n.a. n.a. 3 119 (14)
Gelpi et al., 2008 n.a. n.a. 6 "/900 (206)
Haïk et al., 2000 n.a. n.a. n.a. 465
Huang et al., 2003 n.a. n.a. 1, m 46 (17)
Jansen et al., 2009 n.a. n.a. 54 280 (146)
Jayaratnam et al., 2008 4.5mn 74 1, m 1
Josephs et al., 2009 3yrs
1.2yrs
72
74
1, m
1, m
22 (8)
Mahmoudi et al., 2010 21mn 74 1, m 1
Reinwald et al., 2004 40d 69 1, m 1
Schmidt et al., 2010 26.4mn 73 32 (15m,17f) 32
Tschampa et al., 2001 24mn 76 19 (4m, 15f) 56 (25)

Table 1.

Neuropathologically confirmed rpAD cases imitating features of prion disease in different studies of rapid dementias. (Abbreviations: d=days, f=female, m=male, mn=months, n.a.=not available, yrs=years). Table modified from Schmidt et al., 2011.

2.1. Heterogeneity in AD neuropsychology and imaging

In a comprehensive overview Cummings presents the knowledge about different phenotypes of AD, which also correlate with marked differences in the focal metabolism or distinct types of focal atrophy (Cummings, 2000). Firstly, he mentions cognitive heterogeneity. Different AD phenotypes may reflect subtypes characterized by marked aphasia (Gorno-Tempini et al., 2008; Price et al., 1993), pronounced visoconstructive disturbances (Furey-Kurkjian et al., 1996), the variant denominated as "posteriortcortical atrophy" (Benson et al, 1988; Tom et al., 1998) and a frontal variant (Foster et al., 1983). For all these speculative variants, different metabolism patterns have been demonstrated e.g. by means of FDG PET imaging (Foster et al., 1983; Grady et al., 1988; Haxby et al., 1988; Pietrini et al., 1996) - as a possible reflection of neurobiological heterogeneity. Boxer and colleagues for instance examined AD patients with similar cognitive profiles but marked differences in visuoconstructive abilities. More right than left cortical gray matter loss was seen in MRI imaging in the visuoconstructively impaired group (esp. right inferior temporal gyrus in contrast to the less spatially impaired group). Right inferotemporal atrophy might therefore be able to serve as an imaging surrogate marker for visuoconstructive disabilities. Another subtype might be AD with salient extrapyramidal signs. Those patients exhibit parkinsonoid features, more severe cognitive decline (Clark et al., 1997) and an increased number of neurofibrillary tangles in neuropathology (Liu et al., 1997). Lewy body (LB) pathology is common (McKeith et al., 1996) in AD, but the group mentioned here was free from such LB features. Behavioral symptoms such as delusion, aggression, depression etc. seem as well to be heterogeneous and also show differences especially regarding metabolism (Cummings, 2000).

2.2. CSF biomarker evidence of heterogeneity

Iqbal and colleagues defined disease subtypes based on CSF marker profiles, age at onset, clinical profile and disease course (Iqbal et al., 2005). Van der Vlies et al. could also identify three AD subtypes using CSF marker profiles (based on Tau, phosphorylated Tau (pTau), and Aβ1-42) - corrected for Apoε type, age, gender - showing distinct cognitive profiles on neuropsychologic testing (van der Vlies et al., 2009a, 2009b). Especially patients with very low Aβ1-42 and high Tau and pTau performed worse on Visual association testing (VAT), Trail Making Tests (TMT) and Word Fluency (WF).

The differences in CSF marker profiles might imply the underlying pathophysiology to differ between subtypes. Although this is not proven to date, some findings support this hypothesis: Cerebrospinal fluid (CSF) contains a dynamic and complex mixture of proteins, which reflects physiological and pathological state of the CNS (Gawinecka & Zerr, 2010; Weller, 2001). In AD, levels of both major key players in the disease pathogenesis, namely Tau protein and Aβ, are altered in the CSF. These CSF changes are assumed to mirror the pathophysiological process in the brain, however, direct comparisons are lacking due to a long period between lumbar puncture and CSF tests on the one side and potential autopsy and neuropathological workup on the other side.

2.3. AD heterogeneity in neuropathology

Also from a pathology point of view evidence has been found to support hypotheses of Alzheimer heterogeneity. The basis of neuropathological classification are: Braak's staging, describing the distribution of neurofibrillary tangles (NFT), CERAD staging, describing the densitiy of neuritic plaques and NIA-RIA criteria, being a synthesis of CERAD and Braak's criteria (Murayama & Saito, 2004). Regarding those criteria, neuropathological heterogeneity is observed. Ritchie et al. suggest three hypotheses to explain neuropathological heterogeneity in AD:

  1. subtypes

  2. disease stage effects

  3. "compensation" (differences in cause / origin and progression of AD) (Ritchie & Touchon, 1992).

Especially heterogeneous cortical atrophy, of which right inferotemporal atrophy correlates with visuoconstructive impairment, can be found (Boxer et al., 2003). Recent papers reported heterogeneous Aβ deposition patterns in the end stages of the disease with variations throughout the neocortex, which cannot be completely explained by a regular built up of the pathologic protein during the course of the disease. This implies that other biological factors might be involved to build certain phenotypes (Cupidi et al., 2010). The morphology of Aß deposits is influenced by the cyto- and fibroarchitectonics of the brain region in which they are found and by the amount of amyloid present (Wisniewski et al., 1989). Factors having an impact thereupon are not fully understood (Walker et al., 2008).

Studies, which focused on neurofibrillar tangles (NFT) in AD revealed significantly different NFT densities in various areas of the cerebral cortex without significant differences in the duration of illness, suggesting a possible existence of subgroups. Two distinct subentities in AD with different densities of neurofibrillary tangles - but apparently without distinct clinical courses could be differentiated (Mizuno et al., 2003). Even in patients with presenelin (PSEN) mutations, the neuropathological distribution of different types of plaques, intensity of cerebrovascular amyloid and the number of NFT substantially differed among individuals, implying that missense mutations in PSEN genes can alter a range of key gamma-secretase activities to produce an array of subtly different biochemical, neuropathological and clinical manifestations (Maarouf et al., 2008).

Although the pathological and clinical heterogeneity of AD has been recognized and addressed to some extent in the literature, direct studies on clinico-pathological phenotypes are sparse. Some authors are arguing against the hypotheses of neuropathological heterogeneity. Armstrong et al. for instance examined eighty cases (Armstrong et al., 2000). They found that neuropathological differences were rather continuously distributed in contrast to the subtype hypotheses. Heterogeneity in plaque and tangle distribution correlated more with disease stage (stage hypothesis) rather than being explained by the presence of AD subentities. Nonetheless plaque load and distribution was significantly influenced by the presence of Apoε type 4 allele.

Advertisement

3. Definition and epidemiology of rapidly progressive AD

AD has been a clinical diagnosis since the McKhann Criteria were established in 1984 (McKhann et al., 1984). Neuroimaging and CSF parameters increasingly came into use especially in the first decade of the new millennium leading to newly proposed research criteria finally being accepted as a validated instrument to support the diagnostic concept (de Meyer et al., 2010; Dubois et al., 2010; Dubois et al., 2007; Gauthier et al., 2008).

Alois Alzheimer first described the hallmarks of AD with plaques and neurofibrillary tangles (NFT) more than a hundred years ago. In synopsis with the clinical presentation, neuropathological work-up allows a definite diagnosis. But it has become obvious that AD pathology can also exist without significant simultaneous cognitive impairment (Price et al., 2009). In cases when AD was diagnosed clinically and by post mortem work-up, heterogeneity has also been found to exist e.g. in terms of tangle distribution (Mizuno et al., 2003). Until today it remains subject to controversy how to relate clinical signs and symptoms to specific neuropathological lesion patterns or profiles.

Hypothetically clinically differing disease course could represent distinct subentities of AD in terms of heterogeneity. This accounts especially for speed of decline and distinct trajectories of that deterioration speed (Davidson et al., 2010; Mangone, 2004). Some attempts have been made to characterize these subentities by defining cognitive subgroup profiles, CSF biomarker patterns and neuroimaging characteristics (Wilkosz et al., 2010; Davidson et al., 2010; Boxer et al., 2003; Cummings, 2000). ( see section 2)

Disease progression rates have also been used to distinguish AD subtypes. But at the moment there is no consensus about the definition of the term “rapidly progressive AD”. Moreover the term «rapid» has been used rather arbitrarily. It has been doubtful whether “rapid” should be applied to characterize either the rate of cognitive deterioration - and if so, on which scales - or the disease duration time (survival time). In addition, the trajectories of decline have not been and even are currently not clearly known. They might differ among subentities, making a clear definition very difficult. The majority of AD researchers assume a linear slope, but some investigators also suggest trilinear models of decline or even more trajectories (Wilkosz et al., 2010; Brooks et al., 1993).

A variety of definitions has been used in previous studies rather at will. The term “rapid” has been applied to describe a survival time below 4 years (Josephs et al., 2009), MMSE declines of >5 pts/yr (Doody et al., 2001), >3 pts/yr (Carcaillon et al., 2007), >4pts/0.5yrs (Dumont et al., 2005) or >2,56 pts/yr (Buccione et al, 2007) as well as CDR (Clinical Dementia Rating Scale) score progression from 1 to 2 or 3 within max. 3 yrs (Bhargava et al., 2006). Ito et al. observed an average MMSE loss of 5.5 pts/yr in mild to moderate AD in a metaanalysis (Ito et al., 2010). Encouraging a discussion and attempt to reach a consensus on the term "rapid cognitive decline”, a threshold of 3 or more MMSE pt loss per six months has been proposed (Schmidt et al., 2011; Soto et al., 2008).

Owed to different definitions of "rapid", rpAD seems to constitute approximately 10-30% of the AD population. In a longitudinal study with more than 600 AD patients over a two years period, Cortes et al. discovered that almost one third of the patients declined faster than 3 MMSE pts. per year. A tenth deteriorated twice as fast as the whole groups average decline of approx. 4.5 pts per year on the MMSE scale (Cortes et al, 2008). Dumont and colleagues, in another prospective study, saw one quarter of the cohort decline faster than 4 MMSE points within half a year (Dumont et al., 2005). Recently Åsa Wallin and her research group were able to show that approximately 8% of their AD study population were characterized by a significantly higher mortality and a mean speed of cognitive deterioration of almost 5 MMSE pts/yr (Wallin et al., 2010). Table 2 gives overview of different studies describing rapid progression and its frequency.

study definition of "rapid" [MMSE decline] proportion of study population, (n (total))
Carcaillon et al., 2007 "/3pt/yr 34% (254)
Ballard et al., 2001 "/4pt/yr * 60% (101)
Cortes et al., 2008 "/4.5/yr 11% (686)
Wallin et al., 2010 "/5pt/yr** 8% (151)
Ballard et al., 2001 "/7pt/yr * 32% (101)
Dumont et al., 2005 "/8pt/yr 25% (312)
Soto et al., 2008a multiple ("/3pts/6months) 10%-54%
Soto et al., 2008b "/4pts/first 6 months 14% (565)

Table 2.

*(«Rapid» is not explicitly defined in this study. The numbers given are mere observations.)

** Special CSF biomarker cluster

Frequency of rpAD in several clinical studies (longitudinal, cross-sectional, retrospective). «Rapid» has been defined by the authors in terms of MMSE decline (column 1) to specify a «rapid group» out of the AD continuum. (Abbreviations: MMSE=Minimental Status Examination, n=number, pts=points, yr=year). Table modified from Schmidt et al., 2011.

Advertisement

4. Factors associated with rapid progression

Much is known about clinical, pathobiochemical and hereditary factors altering the risk of developing Alzheimer’s disease, as well as how the risk to advance from Mild Cognitive Impairment (MCI) to manifest dementia is modulated by these. But there is a relative lack of knowledge about which signs and symptoms, blood and CSF marker values as well as genetic factors actually predict the speed of deterioration in AD.

4.1. Clinical signs, symptoms and comorbidity as predictors of fast progression

Several factors such as genetic properties, environmental circumstances, cerebral atherosclerosis, cognitive reserve, medical and social support contribute to disease progression (Etiene et al., 1998).

sign / comorbidity predictor of
slow progression no influence
or unclear
fast progression
apathy Starkstein et al., 2006 (354)
apraxia (constructional) Smith et al., 2001 (60)
atherosclerosis, atrial fibrillation, hypercholesterinemia, hypertension, microvascular disease, myocardial infarction Abellan et al., 2009 (686) Laukka et al., 2010 (138)Mielke et al., 2007 (135)Roselli et al., 2009 (162)Silvestrini et al., 2006 (53)
chronic systemic inflammation Holmes et al., 2010 (300)
diabetes mellitus Sanz et al., 2009 (608) Roselli et al., 2009 (162)
psychotic symptoms Mangone, 2004 (1000)Wilkosz et al., 2009 (201)
multitude of focal neurological signs Josephs et al., 2009 (1)Schmidt et al., 2010 (32)Tschampa et al., 2001 (19)van Everbroeck et al., 2004 (45)
high educational level Pavlik et al., 2009 (rate of decline) (478) Pavlik et al., 2009 (survival) (478) Roselli et al., 2009 (162)
low educational level Mangone, 2004 (1000)
motor signs Mangone, 2004 (1000)Portet et al., 2009 (388)Scarmeas et al., 2005 (533)
early fast decline Soto et al., 2008b (565)
seizures Volicer et al., 1995 (language function) (27)
severe cognitive impairment at disease onset Hui et al., 2003 (mortality) (354) Atchison et al., 2007 (150)Ito et al., 2010 (576)Marra et al., 2000 (45)
sex (male) Roselli et al., 2009 (162)

Table 3.

Clinical signs, symptoms and comorbidity as predictors of disease progression. Total number of subjects (AD) in the studies are given in parentheses. Table modified from Schmidt et al., 2011.

The role of comorbidity is subject to controversy. Diseases of the cardiovascular system and diabetes mellitus are commonly accepted as AD disease risk modulators. However, findings regarding their impact on disease progression are sometimes contradictory (Table 3) (Abellan van Kan et al., 2009; Mielke et al., 2007).

Fast deterioration also appears to be associated with the occurrence of certain signs and symptoms. Among those are especially early signs of the motor system. They are predictors of fast decline as well as poor outcome (Mangone, 2004; Portet et al., 2009; Scarmeas et al., 2005). Another potential indicator / predictor of a rapid disease course might be the presence of psychotic symptoms (Wilkosz et al. 2010). Table 3 provides an overview of the associations of comorbidity and symptoms with progression of AD.

Baseline cognitive status and preprogression rates in MMSE decline (estimated MMSE loss per time period from onset until diagnosis [pt/yr]) were used as predictive clinical markers as well. Another concept of predictive clinical markers has been demonstrated to be useful e.g. by Doody et al. in 2001. The baseline cognitive status as well as preprogression rates of MMSE decline were able to predict further speed of deterioration. Preprogression rates resemble the estimated MMSE loss per time period between the clinical onset to formal diagnosis (pts/yr).

It has been shown by Soto et al., that especially the early loss of 4 MMSE pts within half a year was predicting a poorer outcome (Soto et al., 2008b). Additionally, the baseline cognitive status is all the more capable of predicting the speed of decline regarding functional basic care abilities in AD (Atchison et al., 2007). The baseline level of cognition does not necessarily correlate with mortality, nonetheless, the cognitive decline rate features a considerable variability in some longitudinal studies (Hui et al., 2003). Recently a metaanalysis showed baseline ADAS-Cog values to be covariates of speed of decline (Ito et al., 2010). Santillan and coworkers proposed the use of a scale, consisting of the educational level, insight assessment, the presence of psychosis, the activities of daily living as well as MMSE. Measured at baseline this scale might be capable of estimating the risk of future deterioration (Santillan et al., 2003).

4.2. Imaging and prediction

An abundance of scientific work has been published regarding imaging in AD. The majority deals with either the early diagnosis of AD and differentiation MCI, AD and healthy subject, or makes statements about imaging and the risk of developing Alzheimer’s disease, or it correlates atrophy rates to stages of AD. Literature about baseline imaging characteristics that actually predict the future speed of decline of AD patients (and not the risk of progression from MCI to AD) is scarce. Table 4 gives an overview.

4.3. Predictive biomarkers

4.3.1. CSF

CSF markers have become an important part of AD diagnostics. But also as predictors of fast decline, they might harbor a certain potential. For instance, rapid cognitive deterioration has been demonstrated to be indicated by high total Tau (Tau) protein or hyperphosphorylated Tau (pTau) as well as low Aβ1-42 (411pg/ml or less) or a high Tau/Aβ1-42 ratio (0.81 or higher) in the cerebrospinal fluid (CSF) respectively (Mungas et al., 2002). Therefore attempts have been made to suggest and validate Tau as well as its phosphorylated isoforms in particular as prognostic markers. Kester et al. discovered that especially elevated Tau protein without proportionally elevated hyperphosphorylated Tau (pTau) might predict fast decline (Kester et al., 2009). Wallin and coworkers recently showed that subjects with very high levels of Tau (>1501 (±292) pg/ml) and pTau (>139 (±39) pg/ml) and at the same time low levels of Aβ1-42 (< 362 (± 66) pg/ml) deteriorate more rapidly and feature high mortality rates (Wallin et al., 2010).

study slower progression
or no influence
faster progression
Adak et al., 2004 (n=225, MRI) higher ventricular volume
Kinkingnehun et al., 2008 (n=41, MRI, voxel based morphometry) extensive cortical atrophy

Mungas et al., 2002 (n=120, MRI) hippocampal atrophy, cortical atrophy
Ridha et al., 2008 (n=52, MRI) hippocampal atrophy
Sluimer et al., 2008 (n=65, MRI) focal hippocampal shrinkage generalized global atrophy and early onset and Apoε4 negative
Swann et al., 1997
(n[AD]=24, MRI)
hippocampal atrophy

Table 4.

Imaging and the prediction of AD disease progression.

It has to be kept in mind that some studies the disease stage might be a confounder: Certain CSF marker levels or patterns could as well reflect the disease stage instead of being indicative or predictive for the deterioration rate. Data from serial, repeatedly performed lumbar punctures and CSF analyses are necessary to control this potential confounding factor. Only a small number of studies on this subject have been performed so far. The follow up intervals were short. Over a period of 24 months CSF Tau, pTau and Aβ1-42 appear to be quite constant (Sunderland et al., 1999; Blennow et al., 2007). This hypothesis has largely been undergirded by Buchave et al. However, they reported slightly increasing Tau values over two years (Buchhave et al., 2009). Contradicting these findings of constancy, Stomrud and colleagues demonstrated pTau to increase in a 4 years observation period. Furthermore this increment seemed to be associated with cognitive decline (Stomrud et al., 2010). Regarding Aβ1-42 levels, Huey and colleagues found these to slightly decrease while Tau staying stable observed over a period of 4 years (Huey et al., 2006).

4.3.2. Genetics

Efforts to investigate genetic predictors in AD have been significantly increased over the past years. A number of polymorphisms found seem to have predictive capability in regards of speed of decline. Nonetheless, several remain subject to discussion and controversy: Among those especially the Apoε gene. This polymorphism is a well established modulator of AD disease risk. But its significance as a predictor of progression is not yet as well examined. Some researchers claim, that the presence of the ε4 allele predicts fast deterioration especially in mild AD (Cosentino et al., 2008). But in opposition, according to van der Vlies, early onset AD is especially rapid, if the subjects are negative for Apoε4 (van der Vlies et al., 2009b). A recent study of our research group came to the same result: the ε4 allele was exceptionally infrequent among rpAD cases (Schmidt et al., 2010). Clues mount up that lacking Apoε4 in AD is not only associated with a faster decline but also a more atypical course (van der Flier et al., 2011).

Nevertheless, the research group of Kester and colleagues found no predictive capability of Apoε whatsoever (Kester et al., 2009). An overview of different genetic markers associated with speed of decline is provided in Table 5.

gene/polymorphism decline
slow no influence fast
Apoε4 Kester et al., 2009 (151) Cosentino et al., 2008 (570)
no Apoε4 van der Vlies et al., 2009b (291)
Schmidt et al., 2010 (32)
van der Flier et al., 2011
BuChE (K allele) Holmes et al., 2005 (339)
G51S PNP
(AA genotype)
Tumini et al., 2007 (321)
HMGCR (A allele) Porcellini et al., 2007 (190+586, 97, 296)
PSEN1 rs3025780
(TG genotype)
Belbin et al., 2009
(714, 169)
PSEN1 rs3025787
(CG genotype)
Belbin et al., 2009
(714, 169)
PSEN1 rs7152131
(CA genotype)
Belbin et al., 2009
(714, 169)
ACT7
(GC + CC genotype)
ACT-17 (AA genotype)
ACT promoter polymorphism
(TT genotype) + Apoε4
Belbin et al., 2008 (688+419)
Kamboh et al., 2006 (909)
Licastro et al., 2005 (422)
IL-1α -889
(*1/*1 genotype)
Murphy et al., 2001 (114)
IL-18 -137 (CC genotype) Bossu et al., 2007 (339)
FAS -1377
(AG + GG genotype)
Chiappelli et al., 2006 (137+144)
RAGE G82S
(GS + SS genotype)
Li et al., 2010 (276+254)

Table 5.

Genetic predictors of cognitive deterioration speed in AD Total number of subjects (AD) in the studies are given in parentheses.

Advertisement

5. Conclusion

Until recently, Alzheimer’s disease has been seen as a clinically rather homogeneous disease. But during the last decade several studies have differentiated early onset or late onset entities as well as fast declining forms. Classification and characterization of these disease subentities by means CSF biomarkers and search for indicative patterns as well as neuropsychological test batteries has been attempted. However, comprehensive approaches to characterize AD subtypes relating clinical characteristics to a neuropathological molecular level are lacking (Wilkosz et al., 2010; Doody et al., 2001). Latest pharmacological trials implicated that there may be different subtypes within Alzheimer’s disease exhibiting different susceptibilities to specific pharmacotherapies (Wallin et al., 2009). Hence, a superior characterization of the clinico-pathological heterogeneity and identification of predictive factors of disease progression should be able to improve our understanding of disease pathogenesis and allow better monitoring in therapeutic settings.

rpAD classical AD
survival few years (2-3) 8-10 years
onset still unclear, around the age of 73yrs in the study of Schmidt et al., 2010 around age 65yrs (below = early onset, above = late onset)
cognitive decline "/6 MMSE pts/yr à fast approx. 3-6 MMSE pts/yr à slow
focal neurological signs occurring in early stages, multiple (esp. extrapyramidal signs) occurring in late stages
CSF biomarkers very high Tau, very high pTau, very low A beta 1-42, proteins high Tau, high pTau, low A beta 1-42, proteins 14-3-3
ApoE4 controversial: its influence on decline see Table 4, sometimes seen negative in very rapid cases (Mann et al., 1989) established as a risk factor

Table 6.

Classic AD and rpAD in comparison. Table modified from Schmidt et al., 2011.

Advertisement

Acknowledgments

This book chapter is based on “Rapidly progressive Alzheimer`s Disease” (Schmidt et al. accepted for publication in Arch Neurol). The work was supported by the BMBF (Determinants for disease progression in AD, KNDD-2 (German Network for Degenerative Dementia) 2011-2013, grant 016/1010c

References

  1. 1. Abellan van Kan. G. Rolland Y. Nourhashémi F. Coley N. Andrieu S. Vellas B. 2009Cardiovascular disease risk factors and progression of Alzheimer’s disease. Dement Geriatr Cogn Disord., 27(3), 240 EOF 246 EOF
  2. 2. Adak S. Illouz K. Gorman W. Tandon R. Zimmerman E. A. Guariglia R. et al. 2004Predicting the rate of cognitive decline in aging and early Alzheimer disease. Neurology, 63(1), 108-114
  3. 3. Aksamit A. J. Preissner C. M. Homburger H. A. 2001Quantitation of 14-3-3 and neuron-specific enolase proteins in CSF in Creutzfeldt-Jakob disease. Neurology, 57(4), 728 EOF 30 EOF
  4. 4. Armstrong R. A. Nochlin D. Bird T. D. 2000Neuropathological heterogeneity in Alzheimer’s disease: a study of 80 cases using principal components analysis. Neuropathology, 20(1), 31 EOF 37 EOF
  5. 5. Atchison T. B. Massman P. J. Doody R. S. 2007Baseline cognitive function predicts rate of decline in basic-care abilities of individuals with dementia of the Alzheimer’s type. Arch Clin Neuropsychol, 22(1), 99 EOF 107 EOF
  6. 6. Ballard C. O’Brien J. Morris C. M. Barber R. Swann A. Neill D. et al. 2001The progression of cognitive impairment in dementia with Lewy bodies, vascular dementia and Alzheimer’s disease. Int J Geriatr Psychiatry,16(5), 499 EOF 503 EOF
  7. 7. Belbin, O., Beaumont, H., Warden, D., Smith, A.D., Kalsheker, N., & Morgan, K. (2009) PSEN1 polymorphisms alter the rate of cognitive decline in sporadic Alzheimer’s disease patients. Neurobiol Aging, 30(12), 1992 EOF 1999 EOF
  8. 8. Belbin O. Dunn J. L. Chappell S. Ritchie A. E. Ling Y. Morgan L. et al. 2008A SNP in the ACT gene associated with astrocytosis and rapid cognitive decline in AD. Neurobiol Aging, 29(8), 1167 EOF 1176 EOF
  9. 9. Benson D. F. Davis R. J. Snyder B. D. 1988Posterior cortical atrophy. Arc Neurol,45(7), 789-793
  10. 10. Bhargava D. Weiner M. F. Hynan L. S. Diaz-Arrastia R. Lipton A. M. 2006Vascular disease and risk factors, rate of progression, and survival in Alzheimer’s disease. J Geriatr Psychiatry Neurol, 19(2), 78 EOF 82 EOF
  11. 11. Blennow K. de Leon M. J. Zetterberg H. 2006Alzheimer’s disease. Lancet, 368(9533), 387-403
  12. 12. Blennow K. Zetterberg H. Minthon L. Lannfelt L. Strid S. Annas P. et al. 2007Longitudinal stability of CSF biomarkers in Alzheimer’s disease. Neurosci Lett, 419(1), 18-22
  13. 13. Bossù P. Ciaramella A. Moro M. L. Bellincampi L. Bernardini S. Federici G. et al. 2007Interleukin 18 gene polymorphisms predict risk and outcome of Alzheimer’s disease. J Neurol Neurosurg Psychiatr, 78(8), 807 EOF 11 EOF
  14. 14. Boxer A. L. Kramer J. H. Du A. T. Schuff N. Weiner M. W. Miller B. L. et al. 2003Focal right inferotemporal atrophy in AD with disproportionate visual constructive impairment. Neurology, 61(11), 1485 EOF 91 EOF
  15. 15. Brooks J. O. Kraemer H. C. Tanke E. D. Yesavage J. A. 1993The methodology of studying decline in Alzheimer’s disease. J Am Geriatr Soc, 41(6), 623 EOF 8 EOF
  16. 16. Buccione I. Perri R. Carlesimo G. A. Fadda L. Serra L. Scalmana S. et al. 2007Cognitive and behavioural predictors of progression rates in Alzheimer’s disease. Eur J Neurol, 14(4), 440 EOF 446 EOF
  17. 17. Buchhave P. Blennow K. Zetterberg H. Stomrud E. Londos E. Andreasen N. et al. 2009Longitudinal study of CSF biomarkers in patients with Alzheimer’s disease. PLoS ONE, 4(7), e6294 EOF
  18. 18. Carcaillon L. Pérès K. Péré J. J. Helmer C. Orgogozo J. M. Dartigues J. F. 2007Fast cognitive decline at the time of dementia diagnosis: a major prognostic factor for survival in the community. Dement Geriatr Cogn Disord, 23(6), 439 EOF 445 EOF
  19. 19. Caselli R. J. Couce M. E. Osborne D. Deen H. G. Parisi J. P. 1998From slowly progressive amnesic syndrome to rapidly progressive Alzheimer disease. Alzheimer Dis Assoc Disord, 12(3), 251 EOF 3 EOF
  20. 20. Chiappelli M. Nasi M. Cossarizza A. Porcellini E. Tumini E. Pinti M. et al. 2006Polymorphisms of fas gene: relationship with Alzheimer’s disease and cognitive decline. Dement Geriatr Cogn Disord, 22(4), 296 EOF 300 EOF
  21. 21. Clark C. M. Ewbank D. Lerner A. Doody R. Henderson V. W. Panisset M. et al. 1997The relationship between extrapyramidal signs and cognitive performance in patients with Alzheimer’s disease enrolled in the CERAD Study. Consortium to Establish a Registry for Alzheimer’s Disease. Neurology, 49(1), 70 EOF 75 EOF
  22. 22. Cortes F. Nourhashémi F. Guérin O. Cantet C. Gillette-Guyonnet S. Andrieu S. et al. 2008Prognosis of Alzheimer’s disease today: a two-year prospective study in 686 patients from the REAL-FR Study. Alzheimers Dement, 4(1), 22 EOF 29 EOF
  23. 23. Cosentino, S., Scarmeas, N., Helzner, E., Glymour, M.M., Brandt, J., Albert, M., et al. (2008) APOE epsilon 4 allele predicts faster cognitive decline in mild Alzheimer disease. Neurology, 70(19 Pt 2), 1842 EOF 9 EOF
  24. 24. Cummings J. L. 2000Cognitive and behavioral heterogeneity in Alzheimer’s disease: seeking the neurobiological basis. Neurobiol Aging, 21(6), 845 EOF 61 EOF
  25. 25. Cupidi C. Capobianco R. Goffredo D. Marcon G. Ghetti B. Bugiani O. et al. 2010Neocortical variation of Abeta load in fully expressed, pure Alzheimer’s disease. J Alzheimers Dis, 19(1), 57 EOF 68 EOF
  26. 26. Davidson J. E. Irizarry M. C. Bray B. C. Wetten S. Galwey N. Gibson R. et al. 2010An exploration of cognitive subgroups in Alzheimer’s disease. J Int Neuropsychol Soc, 16(2), 233 EOF 243 EOF
  27. 27. De Meyer G. Shapiro F. Vanderstichele H. Vanmechelen E. Engelborghs S. De Deyn P. P. et al. 2010Diagnosis-Independent Alzheimer Disease Biomarker Signature in Cognitively Normal Elderly People. Arch Neurol, 67(8), 949-956
  28. 28. Doody R. S. Massman P. Dunn J. K. 2001A method for estimating progression rates in Alzheimer disease. Arch Neurol, 58(3), 449 EOF 54 EOF
  29. 29. Dubois B. Feldman H. H. Jacova C. Cummings J. L. Dekosky S. T. Barberger-Gateau P. et al. 2010Revising the definition of Alzheimer’s disease: a new lexicon. Lancet Neurol, 9(11), 1118-1127
  30. 30. Dubois B. Feldman H. H. Jacova C. Dekosky S. T. Barberger-Gateau P. Cummings J. et al. 2007Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol, 6(8), 734 EOF 746 EOF
  31. 31. Dumont C. Voisin T. Nourhashemi F. Andrieu S. Koning M. Vellas B. 2005Predictive factors for rapid loss on the mini-mental state examination in Alzheimer’s disease. J Nutr Health Aging, 9(3), 163 EOF 7 EOF
  32. 32. Etiene D. Kraft J. Ganju N. Gomez-Isla T. Gemelli B. Hyman B. T. et al. 1998Cerebrovascular Pathology Contributes to the Heterogeneity of Alzheimer’s Disease. J Alzheimers Dis, 1(2), 119 EOF 134 EOF
  33. 33. Foster N. Chase T. N. Fedio P. Patronas N. J. Brooks R. A. Di Chiro G. 1983Alzheimer’s disease: focal cortical changes shown by positron emission tomography. Neurology, 33(8), 961 EOF 5 EOF
  34. 34. Furey-Kurkjian M. Pietrini P. Graff-Radford N. Alexander G. Freo U. Szczepanik J. et al. 1996The visual variant of Alzheimer’s disease: distinctive neuropsychological features. Neuropsychology, 10 294 300
  35. 35. Gauthier S. Dubois B. Feldman H. Scheltens P. 2008Revised research diagnostic criteria for Alzheimer’s disease. Lancet Neurol, 7(8), 668 EOF 670 EOF
  36. 36. Gawinecka J. Zerr I. 2010Cerebrospinal fluid biomarkers in human prion diseases. Future Neurology, 5(2), 301 EOF 316 EOF
  37. 37. Geldmacher D. Santillan C. Fritsch T. 2000Development of a scale to predict decline among mildly demented Alzheimer disease patients. Neurobiol Aging, 21(Suppl 1S), S93
  38. 38. Gelpi E. Heinzl H. Hoftberger R. Unterberger U. Strobel T. Voigtlander T. et al. 2008Creutzfeldt-Jakob disease in Austria: an autopsy-controlled study. Neuroepidemiology, 30(4), 215-221
  39. 39. Goldberg R. J. 2007Alzheimer’s disease. Compr Ther, 33(2), 58 EOF 64 EOF
  40. 40. Gorno-Tempini M. L. Brambati S. M. Ginex V. Ogar J. Dronkers N. F. Marcone A. et al. 2008The logopenic/phonological variant of primary progressive aphasia. Neurology, 71(16), 1227-1234
  41. 41. Grady C. L. Haxby J. V. Horwitz B. Sundaram M. Berg G. Schapiro M. et al. 1988Longitudinal study of the early neuropsychological and cerebral metabolic changes in dementia of the Alzheimer type. J Clin Exp Neuropsychol, 10(5), 576 EOF 96 EOF
  42. 42. Haïk S. Brandel J. P. Sazdovitch V. Delasnerie-Lauprêtre N. Peoc’h K. Laplanche J. L. et al. 2000Dementia with Lewy bodies in a neuropathologic series of suspected Creutzfeldt-Jakob disease. Neurology, 55(9), 1401 EOF 4 EOF
  43. 43. Haxby, J.V., Grady, C.L., Koss, E., Horwitz, B., Schapiro, M., Friedland, R.P., et al. (1988) Heterogeneous anterior-posterior metabolic patterns in dementia of the Alzheimer type. Neurology, 38(12), 1853-1863
  44. 44. Heinemann U. Krasnianski A. Meissner B. Varges D. Kallenberg K. Schulz-Schaeffer W. J. et al. 2007Creutzfeldt-Jakob disease in Germany: a prospective 12year surveillance. Brain, 130(Pt 5), 1350-1359
  45. 45. Holmes C. Ballard C. Lehmann D. David Smith. A. Beaumont H. Day I. N. et al. 2005Rate of progression of cognitive decline in Alzheimer’s disease: effect of butyrylcholinesterase K gene variation. J Neurol Neurosurg Psychiatr, 76(5), 640 EOF 643 EOF
  46. 46. Holmes C. Cunningham C. Zotova E. Woolford J. Dean C. Kerr S. et al. 2009Systemic inflammation and disease progression in Alzheimer disease. Neurology, 73(10), 768-774
  47. 47. Huang N. Marie S. K. Livramento J. A. Chammas R. Nitrini R. 2003protein in the CSF of patients with rapidly progressive dementia. Neurology, 61(3), 354 EOF 7 EOF
  48. 48. Huey E. D. Mirza N. Putnam K. T. Soares H. Csako G. Levy J. A. et al. 2006Stability of CSF beta-amyloid(1-42) and tau levels by APOE genotype in Alzheimer patients. Dement Geriatr Cogn Disord, 22(1), 48-53
  49. 49. Hui J. S. Wilson R. S. Bennett D. A. Bienias J. L. Gilley D. W. Evans D. A. 2003Rate of cognitive decline and mortality in Alzheimer’s disease. Neurology, 61(10), 1356-1361
  50. 50. Iqbal K. Flory M. Khatoon S. Soininen H. Pirttila T. Lehtovirta M. et al. 2005Subgroups of Alzheimer’s disease based on cerebrospinal fluid molecular markers. Ann Neurol, 58(5), 748 EOF 57 EOF
  51. 51. Ito K. Ahadieh S. Corrigan B. French J. Fullerton T. Tensfeldt T. 2010Disease progression meta-analysis model in Alzheimer’s disease. Alzheimers Dement, 6(1), 39-53
  52. 52. Jansen C. Schuur M. Spliet W. G. M. van Gool W. A. van Duijn C. M. Rozemuller A. J. M. 2009Eleven years of autopsy on account of Creutzfeldt-Jakob disease in the Netherlands]. Ned Tijdschr Geneeskd, 153, A172 EOF
  53. 53. Jayaratnam S. Khoo A. K. L. Basic D. 2008Rapidly progressive Alzheimer’s disease and elevated 14-3-3 proteins in cerebrospinal fluid. Age Ageing, 37(4), 467 EOF 469 EOF
  54. 54. Johnson J. K. Head E. Kim R. Starr A. Cotman C. W. 1999Clinical and pathological evidence for a frontal variant of Alzheimer disease. Arch Neurol, 56(10), 1233 EOF 9 EOF
  55. 55. Josephs K. A. Ahlskog J. E. Parisi J. E. Boeve B. F. Crum B. A. Giannini C. et al. 2009Rapidly progressive neurodegenerative dementias. Arch Neurol, 66(2), 201-207
  56. 56. Kamboh M. I. Minster R. L. Kenney M. Ozturk A. Desai P. P. Kammerer C. M. et al. 2006Alpha-1antichymotrypsin (ACT or SERPINA3) polymorphism may affect age-at-onset and disease duration of Alzheimer’s disease. Neurobiol Aging, 27(10), 1435-1439
  57. 57. Kester M. I. van der Vlies A. E. Blankenstein M. A. Pijnenburg Y. A. L. van Elk E. J. Scheltens P. et al. 2009CSF biomarkers predict rate of cognitive decline in Alzheimer disease. Neurology, 73(17), 1353-1358
  58. 58. Kinkingnéhun, S., Sarazin, M., Lehéricy, S., Guichart-Gomez, E., Hergueta, T., & Dubois, B. (2008) VBM anticipates the rate of progression of Alzheimer disease: a 3-year longitudinal study. Neurology, 70(23), 2201-2211
  59. 59. Laukka E. J. Fratiglioni L. Bäckman L. 2010The Influence of Vascular Disease on Cognitive Performance in the Preclinical and Early Phases of Alzheimer’s Disease. Dement Geriatr Cogn Disord, 29(6), 498 EOF 503 EOF
  60. 60. Li K. Dai D. Zhao B. Yao L. Yao S. Wang B. et al. 2010Association between the RAGE G82S polymorphism and Alzheimer’s disease. J Neural Transm, 117(1), 97 EOF 104 EOF
  61. 61. Licastro F. Chiappelli M. Grimaldi L. M. E. Morgan K. Kalsheker N. Calabrese E. et al. 2005A new promoter polymorphism in the alpha-1antichymotrypsin gene is a disease modifier of Alzheimer’s disease. Neurobiol Aging, 26(4), 449-453
  62. 62. Liu Y. Stern Y. Chun M. R. Jacobs D. M. Yau P. Goldman J. E. 1997Pathological correlates of extrapyramidal signs in Alzheimer’s disease. Ann Neurol, 41(3), 368-374
  63. 63. Maarouf C. L. Daugs I. D. Spina S. Vidal R. Kokjohn T. A. Patton R. L. et al. 20 EOF 2008Histopathological and molecular heterogeneity among individuals with dementia associated with Presenilin mutations. Mol Neurodegener, 3, 20
  64. 64. Mahmoudi, R., Manckoundia, P., Morrone, I., Lang, P.-O., Dramé, M., & Novella, J.-L. (2010) Atypical case of Alzheimer’s disease mimicking Creutzfeldt-Jakob disease: interest of cerebrospinal fluid biomarkers in the differential diagnosis. J Am Geriatr Soc, 58(9), 1821 EOF 3 EOF
  65. 65. Mangone C. A. 2004Clinical heterogeneity of Alzheimer’s disease. Different clinical profiles can predict the progression rate]. Rev Neurol, Apr 1;38(7), 675 EOF 81 EOF
  66. 66. Mann U. M. Mohr E. Chase T. N. 1989Rapidly progressive Alzheimer’s disease. Lancet, 2(8666), 799 EOF
  67. 67. Mc Keith I. G. Galasko D. Kosaka K. Perry E. K. Dickson D. W. Hansen L. A. et al. 1996Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology, 47(5), 1113-1124
  68. 68. Mc Khann G. Drachman D. Folstein M. Katzman R. Price D. Stadlan E. M. 1984Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34(7), 939 EOF 44 EOF
  69. 69. Mielke, M.M., Rosenberg, P.B., Tschanz, J., Cook, L., Corcoran, C., Hayden, K.M., et al. (2007) Vascular factors predict rate of progression in Alzheimer disease. Neurology, 69(19), 1850-1858
  70. 70. Mizuno Y. Ikeda K. Tsuchiya K. Ishihara R. Shibayama H. 2003Two distinct subgroups of senile dementia of Alzheimer type: quantitative study of neurofibrillary tangles. Neuropathology, 23(4), 282 EOF 289 EOF
  71. 71. Morris, J.C., Edland, S., Clark, C., Galasko, D., Koss, E., Mohs, R., et al. (1993) The consortium to establish a registry for Alzheimer’s disease (CERAD). Part IV. Rates of cognitive change in the longitudinal assessment of probable Alzheimer’s disease. Neurology, 43(12), 2457-2465
  72. 72. Mortimer J. A. Borenstein A. R. Gosche K. M. Snowdon D. A. 2005Very early detection of Alzheimer neuropathology and the role of brain reserve in modifying its clinical expression. J Geriatr Psychiatry Neurol, 18(4), 218 EOF 23 EOF
  73. 73. Mungas D. Reed B. R. Jagust W. J. De Carli C. Mack W. J. Kramer J. H. et al. 2002Volumetric MRI predicts rate of cognitive decline related to AD and cerebrovascular disease. Neurology, 59(6), 867 EOF 73 EOF
  74. 74. Murayama S. Saito Y. 2004Neuropathological diagnostic criteria for Alzheimer’s disease. Neuropathology, 24(3), 254 EOF 260 EOF
  75. 75. Murphy G. M. Claassen J. D. De Voss J. J. Pascoe N. Taylor J. Tinklenberg J. R. et al. 2001Rate of cognitive decline in AD is accelerated by the interleukin-1 alpha-889 *1 allele. Neurology, 56(11), 1595-1597
  76. 76. Paradise M. Cooper C. Livingston G. 2009Systematic review of the effect of education on survival in Alzheimer’s disease. Int Psychogeriatr, 21(1), 25 EOF 32 EOF
  77. 77. Pavlik V. N. Doody R. S. Massman P. J. Chan W. 2006Influence of premorbid IQ and education on progression of Alzheimer’s disease. Dement Geriatr Cogn Disord, 22(4), 367 EOF 377 EOF
  78. 78. Pietrini P. Furey M. L. Graff-Radford N. Freo U. Alexander G. E. Grady C. L. et al. 1996Preferential metabolic involvement of visual cortical areas in a subtype of Alzheimer’s disease: clinical implications. Am J Psychiatry, 153(10), 1261 EOF 1268 EOF
  79. 79. Porcellini E. Calabrese E. Guerini F. Govoni M. Chiappelli M. Tumini E. et al. 2007The hydroxy-methyl-glutaryl CoA reductase promoter polymorphism is associated with Alzheimer’s risk and cognitive deterioration. Neurosci Lett, 416(1), 66 EOF 70 EOF
  80. 80. Portet F. Scarmeas N. Cosentino S. Helzner E. P. Stern Y. 2009Extrapyramidal signs before and after diagnosis of incident Alzheimer disease in a prospective population study. Arch Neurol, 66(9), 1120 EOF 1126 EOF
  81. 81. Price B. H. Gurvit H. Weintraub S. Geula C. Leimkuhler E. Mesulam M. 1993Neuropsychological patterns and language deficits in 20 consecutive cases of autopsy-confirmed Alzheimer’s disease. Arch Neurol, 50(9), 931 EOF 7 EOF
  82. 82. Price J. L. Mc Keel D. W. Buckles V. D. Roe C. M. Xiong C. Grundman M. et al. 2009Neuropathology of nondemented aging: presumptive evidence for preclinical Alzheimer disease. Neurobiol Aging, 30(7), 1026 EOF 1036 EOF
  83. 83. Querfurth H. W. La Ferla F. M. 2010Alzheimer’s disease. N Engl J Med, 362(4), 329 EOF 344 EOF
  84. 84. Reinwald S. Westner I. M. Niedermaier N. 2004Rapidly progressive Alzheimer’s disease mimicking Creutzfeldt-Jakob disease. J Neurol, 251(8), 1020 EOF 2 EOF
  85. 85. Ridha B. H. Anderson V. M. Barnes J. Boyes R. G. Price S. L. Rossor M. N. et al. 2008Volumetric MRI and cognitive measures in Alzheimer disease : comparison of markers of progression. J Neurol, 255(4), 567 EOF 574 EOF
  86. 86. Ritchie K. Touchon J. 1992Heterogeneity in senile dementia of the Alzheimer type: individual differences, progressive deterioration or clinical sub-types? J Clin Epidemiol, 45(12), 1391 EOF 8 EOF
  87. 87. Roselli F. Tartaglione B. Federico F. Lepore V. Defazio G. Livrea P. 2009Rate of MMSE score change in Alzheimer’s disease: influence of education and vascular risk factors. Clin Neurol Neurosurg, 111(4), 327 EOF 330 EOF
  88. 88. Santillan C. E. Fritsch T. Geldmacher D. S. 2003Development of a scale to predict decline in patients with mild Alzheimer’s disease. J Am Geriatr Soc, Jan;51(1), 91 EOF 95 EOF
  89. 89. Sanz C. Andrieu S. Sinclair A. Hanaire H. Vellas B. 2009Diabetes is associated with a slower rate of cognitive decline in Alzheimer disease. Neurology, 73(17), 1359 EOF 1366 EOF
  90. 90. Scarmeas N. Albert M. Brandt J. Blacker D. Hadjigeorgiou G. Papadimitriou A. et al. 2005Motor signs predict poor outcomes in Alzheimer disease. Neurology, 64(10), 1696-1703
  91. 91. Schmidt C. Redyk K. Meissner B. Krack L. von Ahsen. N. Roeber S. et al. 2010Clinical features of rapidly progressive Alzheimer’s disease. Dement Geriatr Cogn Disord, 29(4), 371-378
  92. 92. Schmidt C. Wolff M. Weitz M. Bartlau T. Korth C. Zerr I. 2011On rapidly progressive Alzheimer’s disease. Arch Neurol. Accepted for publication.
  93. 93. Silvestrini M. Pasqualetti P. Baruffaldi R. Bartolini M. Handouk Y. Matteis M. et al. 2006Cerebrovascular reactivity and cognitive decline in patients with Alzheimer disease. Stroke, 37(4), 1010-1015
  94. 94. Sloane, P.D., Zimmerman, S., Suchindran, C., Reed, P., Wang, L., Boustani, M., et al. (2002) The public health impact of Alzheimer’s disease, 2000-2050: potential implication of treatment advances. Annu Rev Public Health,23 213 231
  95. 95. Sluimer, J.D., Vrenken, H., Blankenstein, M.A., Fox, N.C., Scheltens, P., Barkhof, F., et al. (2008) Whole-brain atrophy rate in Alzheimer disease: identifying fast progressors. Neurology, 70(19 Pt 2), 1836 EOF 41 EOF
  96. 96. Smith M. Z. Esiri M. M. Barnetson L. King E. Nagy Z. 2001Constructional apraxia in Alzheimer’s disease: association with occipital lobe pathology and accelerated cognitive decline. Dement Geriatr Cogn Disord, 12(4), 281 EOF 288 EOF
  97. 97. Soto M. E. Andrieu S. Arbus C. Ceccaldi M. Couratier P. Dantoine T. et al. 2008aRapid cognitive decline in Alzheimer’s disease. Consensus paper. J Nutr Health Aging, 12(10), 703 EOF 13 EOF
  98. 98. Soto M. E. Andrieu S. Cantet C. Reynish E. Ousset P. J. Arbus C. et al. 2008bPredictive value of rapid decline in mini mental state examination in clinical practice for prognosis in Alzheimer’s disease. Dement Geriatr Cogn Disord, 26(2), 109 EOF 116 EOF
  99. 99. Starkstein S. E. Jorge R. Mizrahi R. Robinson R. G. 2006A prospective longitudinal study of apathy in Alzheimer’s disease. J Neurol Neurosurg Psychiatr, 77(1), 8 EOF 11 EOF
  100. 100. Stern Y. 2006Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord, 20(3 Suppl 2), S69 74
  101. 101. Stomrud E. Hansson O. Zetterberg H. Blennow K. Minthon L. Londos E. 2010Correlation of longitudinal cerebrospinal fluid biomarkers with cognitive decline in healthy older adults. Arch Neurol, 67(2), 217 EOF 223 EOF
  102. 102. Storandt M. Grant E. A. Miller J. P. Morris J. C. 2002Rates of progression in mild cognitive impairment and early Alzheimer’s disease. Neurology, 59(7), 1034 EOF 41 EOF
  103. 103. Sunderland T. Wolozin B. Galasko D. Levy J. Dukoff R. Bahro M. et al. 1999Longitudinal stability of CSF tau levels in Alzheimer patients. Biol Psychiatry, 46(6), 750-755
  104. 104. Swann A. O’Brien J. Ames D. Schweitzer I. Desmond P. Tress B. 1997Does hippocampal atrophy on MRI predict cognitive decline? A prospective follow-up study. Int J Geriatr Psychiatry, 12(12), 1182 EOF 8 EOF
  105. 105. Tom T. Cummings J. L. Pollak J. 1998Posterior cortical atrophy: Unique features. NNCS, 4(1), 15 EOF 20 EOF
  106. 106. Tschampa H. J. Neumann M. Zerr I. Henkel K. Schröter A. Schulz-Schaeffer W. J. et al. 2001Patients with Alzheimer’s disease and dementia with Lewy bodies mistaken for Creutzfeldt-Jakob disease. J Neurol Neurosurg Psychiatr, 71(1), 33 EOF 9 EOF
  107. 107. Tumini E. Porcellini E. Chiappelli M. Conti C. M. Beraudi A. Poli A. et al. 2007The G51S purine nucleoside phosphorylase polymorphism is associated with cognitive decline in Alzheimer’s disease patients. Hum Psychopharmacol, 22(2), 75 EOF 80 EOF
  108. 108. van der Flier W. M. Pijnenburg Y. A. Fox N. C. Scheltens P. 2011Early-onset versus late-onset Alzheimer’s disease: the case of the missing APOE ε4 allele. Lancet Neurol, 10(3), 280-8
  109. 109. van der Vlies, A.E., Koedam, E.L.G.E., Pijnenburg, Y.A.L., Twisk, J.W.R., Scheltens, P., van der Flier, W.M. (2009a) Most rapid cognitive decline in APOE epsilon4 negative Alzheimer’s disease with early onset. Psychol Med, 39(11), 1907 EOF 11 EOF
  110. 110. van der Vlies A. E. Verwey N. A. Bouwman F. H. Blankenstein M. A. Klein M. Scheltens P. et al. 2009bCSF biomarkers in relationship to cognitive profiles in Alzheimer disease. Neurology, 72(12), 1056-1061
  111. 111. Van Everbroeck B. Dobbeleir I. De Waele M. De Deyn P. Martin J.. J. Cras P. 2004Differential diagnosis of 201 possible Creutzfeldt-Jakob disease patients. J Neurol, 251(3), 298-304
  112. 112. Volicer L. Smith S. Volicer B. J. 1995Effect of seizures on progression of dementia of the Alzheimer type. Dementia, 6(5), 258 EOF 63 EOF
  113. 113. Walker L. C. Rosen R. F. Levine H. 2008Diversity of Abeta deposits in the aged brain: a window on molecular heterogeneity? Rom J Morphol Embryol, 49(1), 5-11
  114. 114. Wallin A. K. Blennow K. Zetterberg H. Londos E. Minthon L. Hansson O. 2010CSF biomarkers predict a more malignant outcome in Alzheimer disease. Neurology, 74(19), 1531 EOF 1537 EOF
  115. 115. Wallin A. K. Hansson O. Blennow K. Londos E. Minthon L. 2009Can CSF biomarkers or pre-treatment progression rate predict response to cholinesterase inhibitor treatment in Alzheimer’s disease? Int J Geriatr Psychiatry, 24(6), 638-647
  116. 116. Weller R. O. 2001How well does the CSF inform upon pathology in the brain in Creutzfeldt-Jakob and Alzheimer’s diseases? J Pathol, 194(1), 1-3
  117. 117. Wilkosz P. A. Seltman H. J. Devlin B. Weamer E. A. Lopez O. L. De Kosky S. T. et al. 2010Trajectories of cognitive decline in Alzheimer’s disease. Int Psychogeriatr, 22(2), 281-290
  118. 118. Wisniewski H. M. Bancher C. Barcikowska M. Wen G. Y. Currie J. 1989Spectrum of morphological appearance of amyloid deposits in Alzheimer’s disease. Acta Neuropathol, 78(4), 337 EOF 47 EOF

Written By

Schmidt C, Wolff M, Shalash A and Zerr I

Submitted: 07 November 2010 Published: 06 September 2011