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Predicting Cognitive Decline in Alzheimer’s Disease (AD): The Role of Clinical, Cognitive Characteristics and Biomarkers

Written By

Mei Sian Chong and Tih-Shih Lee

Submitted: 26 April 2012 Published: 27 February 2013

DOI: 10.5772/54289

From the Edited Volume

Understanding Alzheimer's Disease

Edited by Inga Zerr

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1. Introduction

Given the rapid ageing of the population worldwide, global estimates of AD - generally considered to be the commonest subtype of dementia - are expected to increase from the current estimated 25 million to 63 million in 2030, and by 2050, a staggering 114 million [1]. Over the last two decades in particular, significant but modest breakthroughs in pharmacological treatment of this devastating condition have occurred. Presently, there is increasing conviction that intervention (especially disease-modifying therapy) will have to be instituted at the earliest possible stage of the illness to confer the greatest benefit.

Prevailing clinical criteria for Mild Cognitive Impairment (MCI) have low to moderate diagnostic accuracy in identifying and predicting progression to dementia. MCI is an unstable clinical construct where some patients convert (MCI-converters) while others remain relatively stable (MCI non-converters). As observed from neuropathological and recent biomarker studies, the accumulation of AD pathology (β-amyloid plaques and neurofibrillary tangles) may precede the onset of clinical disease by as long as 20-30 years [2,3]. This suggests that functional and structural brain changes may occur prior to apparent clinical manifestations of cognitive impairment (Figure 1). However, the current definition of MCI is based primarily on clinical and neuropsychological criteria, and this may have contributed to limited demonstration of efficacy in therapeutic and disease-modifying trials thus far. Supplementing existing criteria with information about biomarkers may enrich the definition of MCI This provided the impetus for the development of reliable biomarkers such as cerebrospinal fluid (CSF), neuroimaging and blood biomarkers to complement clinical approaches in early diagnosis and predicting progression. In support of this, the recent proposed criteria for symptomatic pre-dementia phase of AD (MCI), preclinical AD and presymptomatic AD have included biomarkers reflecting molecular pathology, downstream measures of structural and functional/metabolic changes, and associated biochemical changes in their research diagnostic armamentarium [4].

Longitudinal studies in AD subjects have also noted variability in disease progression. In one study, 11.9% of subjects exhibit rapid cognitive decline while some remained relatively stable [5]. Other studies that utilized parameters such as the decline in Mini Mental State Examination (MMSE) scores [6, 7] (≥3 point decline) also reported a distinctive difference in the clinical course between the fast-progressors and slow-progressors.

Figure 1.

Clinical Continuum of Alzheimer’s disease and hypothetical biomarker model

In this chapter, we will review the body of evidence on the use of various clinical and comorbid factors, alone and/or in combination with biomarkers, on predicting rapid cognitive decline across the spectrum of cognitive impairment – defined in terms of AD progression in MCI subjects and rapid cognitive decline in AD subjects. We will also look at longitudinal biomarker measurements as well as their role (alone and/ in combination with clinical and comorbid factors) in predicting cognitive decline and disease trajectories. We will discuss the implications of current research findings to their application in clinical and therapeutic trials. The chapter is not intended to be an exhaustive review of this burgeoning literature, but instead to highlight integrative and potentially novel lines of inquiry.


2. Clinical and cognitive/ behavioural characteristics (table 1)

A number of socio-demographic factors and vascular risk factors have been found to increase risk of development of AD.

Increased risk of cognitive decline in diabetes may reflect a dual pathologic process involving both cerebrovascular damage and neurodegenerative changes. Several possible pathophysiological mechanisms may include hyperglycemia, insulin resistance [8], oxidative stress, advanced glycation end products, and inflammatory cytokines. A shared clinicopathologic study alluded to the potential shared predisposition for developing amyloid in both the pancreas and brain [9]. This is supported by a study of intranasal insulin preventing cognitive decline, cerebral atrophy and white matter changes in mouse models [10]. Diabetes and pre-diabetes have been found to be associated with AD progression in MCI subjects, with progression from MCI to dementia accelerated by 3.18 years[11]. The stronger effect of pre-diabetes on MCI conversion may be caused by high glycemic level in pre-diabetes and increased insulin resistance [12]. Although antihypertensive therapy has been shown to be associated with reduced rate of conversion to AD in midregional proatrial natriuretic peptide-stratified subjects with MCI [13], there has been a paucity of data with regard to the individual effect of hypertension on MCI-converters[14]. A non-significant trend was found for cerebrovascular disease as a risk factor for MCI-converters[15]. Diabetes, hypertension and cerebrovascular disease have been found to be associated with faster progression rate in dementia [16-19]. Although mid-life hypercholesterolemia has been repeatedly shown to increase risk of late-life dementia, there is relatively little evidence of its influence on MCI-converters and the rate of AD decline [20].

Table 1.

Clinical and cognitive/ behavioural characteristics in predicting AD conversion in MCI patients and rapid AD progression/ decline

HR = Hazards ratio; PPV = Positive predictive value

95% CI= 95% confidence interval; NPV= Negative predictive value

WMC= White matter severity; MMSE =Mini Mental State Examination

RR= Relative risk; SIB = Severe Impairment Battery

OR= Odds ratio

Vascular risk factors, as a composite entity, have been shown to be associated with MCI conversion [21]. The individual risk factors of hypertension, diabetes, cerebrovascular disease and hypercholesterolemia in the study were associated with high risk of MCI conversion. Treatment of hypertension, diabetes and hypercholesterolemia showed reduced risk of MCI conversion. In the same Chongqing study, the authors showed separately the association of diabetes, baseline white matter changes (WMC), baseline moderate-to-severe carotid stenosis and carotid stenosis change during follow-up to be predictors of MCI conversion [22]. A separate longitudinal community study (ILSA- Italian Longitudinal Study on Aging) showed MCI progression to AD of 2.3 per 100 person-years with stroke as the only vascular risk factor associated with progression [15].

The heterogeneity of AD syndrome is likely related to, other than amyloid and tau pathology, a number of other factors, such as impaired energy metabolism, oxidative stress, neuro-inflammation, insulin and insulin growth factor (IGF) resistance, and insulin/ IGF-deficiency. These factors are often included as variables of interest in studies attempting to develop diagnostic and therapeutic targets for this disease. Brain insulin resistance promotes oxidative stress, reactive oxygen species (ROS) generation, DA damage and mitochondrial dysfunction, all of which drive pro-apoptosis, pro-inflammatory and pro-AβPP-Aβ cascades. Also, hyperinsulinaemia increases AβPP-Aβ and inflammatory indices in the brain, also promoting formation of advanced glycation end-products which lead to increased generation of ROS. Tau gene expression and phosphorylation are also regulated by insulin and IGF stimulation, where brain insulin and IGF resistance may result in decreased signaling through phosphoinositol-3-kinase (PI3K), Akt and Wnt/β-catenin and increased activation of GSK-3β – which is partly responsible for tau hyperphosphorylation. Hence, the focus on vascular factors in AD is justified based on chronic hyperglycemia, hyperinsulinemia, oxidative stress, advanced glycation end-products and inflammation promoting vascular disease [8].

The metabolic syndrome defined by the Third Adults Treatment Panel of the National Cholesterol Education Program as a combination of three or more of the following components: abdominal obesity (waist circumference >102cm for men and >88 cm for women; elevated plasma triglycerides (≥150mg/dl); low HDL cholesterol (<40mg/dl for men and <50mg/dl for women); high blood pressure (≥130/ ≥85mmHg) or being in hypertensive treatment; and high fasting plasma glucose (≥110mg/dl). This represents a clustering of vascular risk factors for morbidity and mortality. In addition, these factors may interact synergistically to influence cognition in a negative manner. Among MCI patients the presence of metabolic syndrome independently predicted an increased risk of progression to dementia over 3.5 years of follow-up. [23]

Older age has been shown to predict MCI-converters [24]. Latent class modeling methods and disease system analysis approach to characterize trajectories of cognitive decline in AD cohorts have also shown initial MMSE and age to best predict decline [25,26]. However, separate studies using AD clinical trial data with subjects on Donepezil have shown younger age to predict faster decline in placebo-treated patients [27]. Low education is a risk factor for AD. The cognitive reserve hypothesis predicts that persons with higher education delay the onset of accelerated cognitive decline; however, once AD disease process begins, it takes a more rapid course due to increased disease burden [28]. Pre-progression rate (calculated using clinician’s standardized assessment of symptom duration in years and baseline MMSE) has also been shown to predict cognitive decline trajectory [29]. Neuropsychiatric symptoms have also been shown to predict faster cognitive and functional decline [25,30,31].

Prospective studies of amnestic MCI (a-MCI) subjects have shown that episodic memory (such as delayed recall of word lists [32-34], spatial short term memory and visual recognition memory [35], and paired-associates learning [36,37]), semantic memory [37,38], attentional processing [39] and mental speed consistently predicted MCI converters. Within a very mild cognitive impairment group, higher CDR-sum of boxes and lower executive function predicted AD conversion [40]. Similarly, in a retrospective study of MCI-converters, verbal and visual memory, associative learning, vocabulary, executive functioning and other verbal tests of general intelligence were impaired at baseline [41]. An empirically weighted and combined set of neuropsychological tests involving domains of episodic memory, speeded executive functioning, recognition memory (false and true positives), visuospatial memory processing speed, and visual episodic memory together were strong predictors of MCI conversion to AD [42]. A recent study demonstrated that MCI individuals with learning deficits on the Rey Auditory Verbal Learning test showed widespread pattern of gray matter loss at baseline, as compared to retention deficits which was associated with more focal gray matter loss. However, impaired learning had modestly better predictive power than impaired retention, highlighting the importance of including learning measures in addition to retention measures when predicting outcomes in MCI subjects [43]. Verbal cued recall measured using the Memory Impairment Screen plus (MISplus) has also been shown to predict MCI conversion [44].

In subjects with AD, rapid disease progression was noted more frequently in subjects with higher education and those with moderate severity of global impairment. More severe memory impairment and executive dysfunctioning were associated with higher probabilities of progression at 2 years [45].

Longitudinally, follow-up of those who developed AD versus those who were non-demented prior to AD diagnosis, showed no evidence for accelerated decline of episodic memory from 6 to 3 years prior to incident dementia diagnosis [46]. Working memory (using digit span backward and forward as well as digit ordering) also did not show temporal change as a potentially useful marker of progression [47].

2.1. Summary

Age, vascular risk factors and metabolic syndrome affect AD conversion in MCI subjects. However, there is currently a lack of data on the effect of intensive vascular risk factor treatment in delaying/ halting the rate of progression in MCI subjects. Educational attainment plays an interesting role in AD. In support of the cognitive reserve hypothesis, higher educational attainment predicts delay of the onset of accelerated cognitive decline; however, once AD disease process begins, it takes a more rapid course due to increased disease burden.

Neuropsychological tests, especially episodic memory and executive functioning tests, seem to predict MCI-converters. When assessing MCI subjects, the inclusion of impaired learning in addition to retention measures may improve predictive power of AD progression from MCI. More severe cognitive impairment is associated with rapid AD progression.


3. Cerebrospinal fluid biomarkers (tables 2)

The most widely studied candidate CSF biomarkers include CSF total tau (t-tau), 42 amino acid form of Aβ (Aβ1-42) and phosphorylated tau protein (p-tau) [48]. They reflect respectively the corresponding central pathogenetic process of neuronal degeneration, amyloid-β peptide deposition in plaques, and hyperphosphorylation of tau with subsequent tangle formation. Fagan et al has also recently demonstrated that CSF Aβ and tau protein measurements, performed using INNOTEST enzyme-linked immunosorbent assay (ELISA) and INNO-BIA AlzBio3, were highly correlated with brain amyloid load, as assessed by PET and Pittsburgh compound B amyloid-imaging (r value from 0.77 to 0.94)[49]. This was further suggested, by a study of antemortem CSF concentrations of Aβ1-42 and t-tau/ Aβ1-42 ratio in an autopsy-confirmed AD cohort, that the standardization of biomarker techniques could potentially replace autopsy-confirmed AD for future diagnosis of definite AD [50].

Table 2.

Cerebrospinal fluid biomarkers in predicting AD conversion in MCI patients and rapid AD progression/ decline

HR = Hazards ratio

CRP = C-reactive protein

MMSE =Mini Mental State Examination

OR = Odds ratio

Sn = Sensitivity

Sp= Specific

LR+ = positive Likelihood ratio

LR - = negative Likelihood ratio

HR = Hazards ratio

95% CI= 95% confidence interval

3.1. Established CSF biomarkers

CSF biomarkers of elevated t-tau [51-56], high p-tau [52,53,57,58], low Aβ1-42 [52,53], and combinations of high t-tau/ p-tau and low Aβ1-42 concentrations [59-64], have been shown to be predictive of MCI-conversion to AD. The consistent feature in all of these studies is that increased CSF t-tau and p-tau concentrations are highly sensitive while low Aβ1-42 concentration is more specific. A recent longitudinal study showed that subjects with the lowest baseline Aβ42, highest tau and and p-tau concentration exhibited the most rapid MMSE decline. In addition, while there was little difference in the levels of these CSF biomarkers between stable MCI and cognitively healthy subjects, MCI-AD converters had the highest total tau concentrations [65].

High CSF t-tau and p-tau concentration (but not Aβ42) was associated with more rapid MMSE decline in a 3-year prospective longitudinal study. This suggests that increased t-tau levels reflect intensity of disease and hence rapidity of AD progression, while Aβ42 is more a diagnostic state marker, not associated with rate or stage of AD [65,66]. Another study showed p-tau to poorly differentiate between AD and vascular dementia, but to correlate with MMSE progression [67]. In contrast, another recent report showed lower Aβ42 levels to be associated with rapid-progressors compared with slow-progressors [68]. Wallin et al showed that AD subjects with a combination of low Aβ42 and very high CSF t-tau and p-tau levels performed worse on baseline cognitive tests, with faster deterioration, poorer outcome to cholinesterase inhibitor treatment and increased mortality [69].

With respect to serial biomarker measurements with disease progression, we found studies showing increasing p-tau 231 levels with disease progression in MCI subjects [70, 71] compared to controls over a period of 12-24 months. No definite trends were observed with Aβ40 and Aβ42 in the same studies [70,71]. A recent longitudinal study showed that nonspecific CSF biomarkers, in particular isoprostane, demonstrated an increase over time, which was correlated with AD conversion in MCI subjects and cognitive decline (as assessed by MMSE) [72].

Faster progression of brain atrophy (in terms of regional cortical thinning) has been found in the presence of lower Aβ1-42 levels and higher p-tau in Alzheimer’s Disease Neuroimaging Initiative (ADNI) data [73].

3.2. Novel CSF approaches

In a study in which novel CSF biomarkers were identified through mass spectrometry and re-evaluated by ELISA, it was found that NrCAM, YKL-40, chromogranin A and Carnosinase I were potentially able to improve the diagnostic accuracy of existing Aβ42 and tau CSF biomarkers. This could potentially improve characterization of clinic-pathological stages of the cognitive continuum from cognitive normalcy to mild dementia, with the promise of potential utility in clinical trials and monitoring disease progression [74]. Other potential CSF biomarkers include nanoparticle-based amyloid-β-derived diffusible ligands (ADDLs)[75], as well as a multiplexed immunoassay panel of a combination of a subset of markers, in particular, calbindin, which showed significant prognostic potential [76]. Preliminary data have also shown that soluble Aβ oligomers might inhibit long-term potentiation and hence, play an important role in AD pathogenesis. The increasing appreciation of Aβ oligomers (as compared to its native forms) in the pathogenesis of AD may suggest novel pathways to biomarkers, such as anti-oligomer antibodies that are specific for the soluble oligomeric state (as opposed to the fibrillar states). By quantifying Aβ oligomer formation, anti-oligomer antibodies may provide a promising strategy for monitoring disease progression [77,78].

Concerns with CSF biomarkers include measurement variability occurring through lack of standardization of CSF assays [79], high inter-laboratory and between-assay variance, sampling-handling factors, post lumbar-puncture headache, and poor acceptability to patients, especially if repeated measurements are involved. In an attempt to overcome these, the Alzheimer’s Association has launched a global quality-control program for AD CSF biomarkers, which will be administrated from the Clinical Neurochemistry Laboratory in Molndal, Sweden. This includes reference samples for use in studies, allowing normalization of biomarker levels and meta-analyses of published papers [80].

3.3. Summary

Elevated CSF total tau, p-tau, low Aβ and high tau: Aβ concentrations have been consistently shown to highly predict MCI-converters and AD progression. CSF Aβ and tau may reach a plateau at a relatively early stage of disease and remain fairly constant thereafter, limiting its utility for longitudinal measurement and in monitoring therapeutic response at the more advanced/ established stage of AD. However, it remains an important biomarker during the preclinical and prodromal stages of AD, reflecting the central pathogenic neurodegenerative process. Novel CSF biomarkers hold promise of circumventing this current limitation, especially Aβ oligomers and their potential use in documenting disease progression as well as being a potential therapeutic target. The invasive nature of lumbar puncture and standardization issues preclude its current routine clinical use.


4. Blood markers (table 3)

Peripheral blood is one of the most convenient sources of biomarkers. While the quest for a marker with high sensitivity and specificity has been ongoing for decades, no single blood-derived biomarker has been particularly outstanding in the diagnosis of AD, in predicting conversion from MCI to AD and in predicting slow and fast progression. The following are some of the most studied biomarkers. One should note that negative studies are usually not published and hence publication bias is possible.

Table 3.

Blood biomarkers in predicting AD conversion in MCI patients and rapid AD progression/ decline

HC = Healthy controls

SD = Standard deviation

OR = Odds ratio

95% CI= 95% confidence interval

Sn = Sensitivity

Sp= Specificity

OR = Odds ratio

PPV = Positive predictive value

NPV= Negative predictive value

LR+ = positive Likelihood Ratio

LR- = negative Likelihood Ratio

4.1. Plasma proteins/ peptides

Teleologically the most logical candidate is plasma Amyloid-beta (Aβ) and its derivatives, Aβ40 and Aβ42. They are the most studied of blood markers.

As Aβ accumulation is an early step in AD pathogenesis, such a biomarker would be potentially suitable for identifying patients in the earliest stage of disease process when intervention might be more effective.

Circulating Aβ is composed of Aβ produced by brain and peripheral tissue, and can be transported across the blood-brain barrier. They are derived from the amyloid precursor protein (APP). APP is catabolized via 2 pathways, one of which is amyloidogenic, and involves 3 enzyme systems, alpha, beta and gamma secretases. In the amyloidogenic pathway, APP is first cleaved by beta secretase to generate a secreted form of APP (sAPPbeta) and a C99 fragment. The C99 is then cleaved by gamma secretase to yield Aβ. Different cleavage sites on the C99 fragment produces two forms of Aβ – Aβ40 and Aβ42. While Aβ40 is the more common product, Aβ42 aggregates into amyloid fibrils more rapidly and is contained in both early diffuse plaques and fully formed neuritic plaques. In the non-amyloidogenic pathway, alpha secretase is involved and does not lead to Aβ formation [81].

Since elevation appears to be before or just at the onset of the clinically diagnosed disease, it has been hypothesized that high plasma Aβ42 is an antecedent risk indicator for AD, and its plasma levels declines with onset and progression. There have been many studies involving Aβ40 and Aβ42, though results have been inconclusive and at times contradictory refer to Table 1 [82, 83]. These inconsistent results may reflect variability due to technical reasons, such as timing of sample collection with reference to AD onset, the assay methods, and differential affinities of the antibodies used for different Aβ species. Koyama [84], in a large systematic review, concluded that plasma levels of Aβ40 and Aβ42 individually were not associated with development of AD and dementia. However the ratio of Aβ42:Aβ40 could predict development of AD and dementia, although the evidence is limited in MCI conversion and AD progression.

APP isoforms in platelets have been suggested to predict cognitive decline. APP metabolism has been found to be altered in the platelets of AD patients, specifically a reduced ratio of the upper (130kDa) to the lower (110-106 kDa) immunoreactivity band (APPr) [85].

The level of plasma C-reactive protein (CRP) rises in response to inflammation. Its role is primarily to activate the complement system. CRP by itself has been reported to be associated with accelerated cognitive deterioration and increased risk of conversion in MCI patients [86]. A combination of raised CRP with low Aβ has been associated with a significantly more rapid cognitive decline [87].

Homocysteine has been reported to be associated with human disease states, notably cardiovascular disease. Deficiencies of the B vitamins – B6(pyridoxine), B9(folic acid) and B12(cobalamin) are associated with high homocysteine levels. However, there is no data on homocysteine with MCI conversion and AD progression.

Clusterin, also called apolipoprotein J and coded by gene CLU, has been reported in genome-wide association studies (GWAS) to be associated with AD [83]. Clusterin is functionally associated with apoptosis and the clearance of cellular debris, including amyloid. Thambiesetty [88] found that higher clusterin levels were associated with slower brain atrophy in normal subjects who developed MCI during a 6-year follow-up. However, there is no current data with MCI conversion and AD progression.

Ceramides are a family of lipid molecules that are made up of sphingosine and a fatty acid. They are also constituent of sphinomyelin (SM). In addition to their structural function, they play a role as signaling molecules in regulating cell differentiation, proliferation, and programmed cell death. Mielke [89] found that high plasma levels of dihydroceramides (DHCer) and ceramide were associated with AD progression, though results did not reach significance. Nevertheless, higher plasma levels of SM, dihydrosphingomyelin (DHSM), SM/ceramide, and DHSM/DHCer ratios were associated with less progression on the MMSE and ADAS-Cog with the ratios being the strongest predictors of clinical progression. There is no current data on MCI progression.

4.2. Genetic and transcriptomic markers

APOEε4 is the best-established genetic risk factor for AD. APOE genotyping is not recommended for the routine diagnosis of AD. However many studies have investigated whether APOEε4 has a predictive value for progression from MCI to AD.

In a large meta-analysis, Elias-Sonnenschein [90] and co-workers found that APOEε4 is associated with a moderately increased risk of progression from MCI to AD.

Martins [91] found that the APOEε4 genotype predicts the age of onset of AD and neuropathic progression in a non-linear fashion. In their non-linear model, possession of an APOEε4 allele was related to earlier and faster cognitive decline, while possession of an APOEε4 was associated with slower decline. Homozygous APOEε4 showed faster cognitive decline than APOEε4 heterozygotes. The linear model was less sensitive and did not detect differences between APOEε4 homo- and heterozygotes.

Cosentino [92] also showed that the presence of at least one allele of APOEε4 was associated with faster decline in the incident population-based AD group. However the findings could not be extrapolated to prevalent AD in population or clinic-based samples. Hence APOEε4 influence may be more stage-dependent, with its effect on cognitive decline most evident in the earliest stages of disease and less so in moderate to severe stages.

Other genetic markers that have been identified in genome-wide association studies (GWAS) have not yet been shown to aid in diagnosis of AD or predict progression of disease in MCI or AD.

Unlike the static genome, the transcriptome comprises the dynamic expression of the genome over the course of the disease. Transcriptomic, or genome-wide gene expression studies, have been used to distinguish AD from healthy controls. One of the genes identified from transcriptomic studies is TOMM40, which has also been identified in GWAS studies [93]. We found that TOMM40 remained significantly downregulated over three time points in a longitudinal study (manuscript submitted for review). Transcriptomic products would ideally be used to track the progression of disease, identify markers that predict conversion of MCI to AD, and distinguish between fast and slow progressors. Hence this is a potential area of biomarker development in predicting MCI conversion and rapid AD progression.

4.3. Multiple marker arrays

Given the disappointing results achieved by single markers despite tremendous efforts, the field has now moved towards multiple markers that are obtained through high throughput technologies, sophisticated statistical analysis and bioinformatics. Ray [94] published a blood plasma-based proteomic screening tool to identify patients with AD and also to identify those likely to progress from MCI to AD. Biological analysis of the 18 proteins points to systemic dysregulation of hematopoiesis, immune responses, apoptosis and neuronal support. However efforts at independent validation of Ray’s findings have been discouraging [95].

Based on current literature, no single marker has been found to be significant in all the multiple marker arrays. Moreover one can expect that utilizing high throughput array technology, more multiple marker arrays will appear and dominate the blood biomarker landscape. To sound a note of caution, however, some panels may be derived from ‘over-fitting’ the dataset and may not survive generalization and independent validation. To date, multiple marker arrays have not been employed to study the conversion of MCI to AD and to differentiate between fast and slow progressors. This would be a logical next step for investigation.

4.4. Summary

Plasma Aβ is an appealing biomarker since many AD interventions under investigation are directed against Aβ. Thus an Aβ-based biomarker is attractive for those who will benefit from such treatments. However, many studies involving various blood biomarkers have conflicting and/or inconclusive results.

APOEε4 influence may be more stage-dependent, with its effect on disease trajectory most evident in the earliest stages of disease and less so in moderate to severe stages. Hence it should be included as a covariate in various clinical progression and therapeutic trials. A major challenge is that the literature thus far has focused on the use of blood biomarkers for diagnosis (requiring the identification of dichotomous - disease versus normal- states), which may not be applicable to the use of such biomarkers for tracking disease progression (for which an effective biomarker must show continuous change rather than merely being present or absent). Nevertheless blood biomarkers should be employed in combination with clinical assessment and neuroimaging to improve diagnostic and prognostic accuracy, especially given the peripheral nature and ease of blood sampling.


5. Neuroimaging (Table 4)

5.1. Structural imaging

Neuroimaging is now one of the most common tools used to aid the diagnosis of AD. It is a huge and burgeoning field and only select modalities and important studies on longitudinal imaging are discussed here.

Table 4.

Neuroimaging methods in predicting AD conversion in MCI patients and rapid AD progression/ decline

NC = Normal Controls

MRI = Magnetic Resonance Imaging

APC = Annual percent change

HC = Healthy Controls

SD = Standard deviation

MTL = Medial Temporal Lobe

aMCI = amnestic MCI

PIB = Pittsburgh Compound B

FDDNP = Fluoroethyl)methylamino]-2-napthyl}ethylidene) malononitrile

PET = Positron Emission Tomography

MMSE =Mini Mental State Examination

Sn = Sensitivity

Sp= Specific

-LR = negative Likelihood ratio

With technological advances over the past three decades, MRI is now readily available and relatively economical. Currently it is widely used as a diagnostic tool, to complement clinical assessment and neuropsychological testing. Moreover, MRI has also been considered for longitudinal tracking of the disease progression and to predict whether a MCI patient may go on to develop AD, or whether an AD patient will have an indolent or rapid course. Advances in technology have led to automated data-driven methods, such as automated measurement of whole brain volume over time, voxel-based morphometry (VBM), deformation-based morphometry (DBM) and analysis of cortical thickness. These technologies ameliorate the previous problems associated with manual measurement, inter-rater reliability and difficulties in cross-study comparisons.

In a seminal paper, Jack [96] studied annualized changes in volume of four structures in serial MRI studies: hippocampus, entorhinal cortex, whole brain and ventricles of normal, MCI and AD subjects. All four atrophy rates were greater among MCI-converters compared to non-converters and fast-progressors versus slow progressors. Although the differences in atrophy rates have been replicated consistently in several follow-up studies [97,98], given the overlap among those who did and did not convert, the authors cautioned that these measures were unlikely to provide absolute prognostic information for individual patients.

Using hippocampal volumetry, a prospective longitudinal cohort study found that greater atrophy in the CA1 hippocampal and subicular subfields predicted MCI conversion, whereas larger hippocampal volumes predicted cognitive stability and/or improvement [99].

Employing a 3-dimensional cortical mapping approach, Thompson [100], demonstrated a temporal-frontal-sensorimotor sequence of cortical atrophy with AD progression in a longitudinal series of 12 AD subjects, where left brain was found to degenerate faster than right.

Employing VBM technique, Risacher [101] found that AD and MCI converters demonstrated high atrophy across regions as compared to HC in global and hippocampal grey matter (GM) density, hippocampal and amygdalar volumes, and cortical thickness values from entorhinal cortex and other temporal and parietal lobe regions. MCI-stable showed intermediate atrophy. Degree of atrophy of medial temporal structures, especially the hippocampi, was found to be the best antecedent MRI marker of imminent conversion.

A separate study also showed that occipitoparietal (specifically precuneus, lingual gyrus and cuneus) atrophy at baseline better anticipated the rate of progression (fast decliners from slow decliners) over 3 years compared to clinical and neuropsychological assessment [102].

Cortical thickness is another measure of interest in structural neuroimaging where a normalized thickness index was computed using a subset of these regions, namely the right medial temporal, left lateral temporal and right posterior cingulate. Normalized thickness index at baseline differed significantly among all the four diagnosis groups (HC, stable MCI, progressive MCI and AD). Furthermore, normalized thickness index also correctly predicted evolution to AD for 76% of aMCI subjects after cross-validation [103].

5.2. Functional and molecular imaging

There are many functional imaging studies for AD though only a few specifically investigate longitudinal progression of MCI and AD using Fluorodeoxyglucose (18F) (FDG)-Positron Emission Tomography (PET) [104].

Lo [105] found that the rate of change of glucose metabolism and hippocampal volume accelerated as cognitive function deteriorated. Moreover, glucose metabolic decline and hippocampal atrophy were significantly slower in subjects with normal cognition compared to those with MCI or AD. Positive APOE4 status was also associated with accelerated hippocampal atrophy.

Molecular imaging utilizes small molecule ligands that bind with nanomolar affinity to amyloid and enters the brain for imaging with PET. It is a measure to detect and quantify cerebral beta-amyloidosis. It should be noted that besides AD, there are other disease conditions that may have cerebral Aβ.The most commonly used ligand is the carbon-11(11C)-based Pittsburgh compound B (PIB), which binds specifically to fibrillar Aβ but exhibits no demonstrable binding to neurofibrillary tangles. However, fluorine-18 (18F)-based tracers, e.g. 2-(1-{6-[(2-fluorine 18-labeled fluoroethyl)methylamino]-2-napthyl}ethylidene) malononitrile ([(18)F]FDDNP) have a considerably longer half-life compared to [11(C)]PIB and some types have been shown to also bind to neurofibrillary tangles.

Okello [106] showed that PIB-positive subjects with MCI are significantly more likely to convert to AD than PIB-negative ones. A separate longitudinal study showed that hippocampal atrophy and amyloid deposition (in posterior cingulate, lateral frontal cortex, temporal cortex, putamen and caudate nucleus) seem to dissociate during the evolution of MCI, the atrophy increasing clearly and [(11)C] PIB retention changing modestly when conversion to AD occurs [107]. Using [(18)F]FDDNP PET, higher baseline binding was associated with future decline in most cognitive domains. Specifically, frontal and parietal [(18)F]FDDNP binding yielded the greatest diagnostic accuracy in identifying MCI-converters versus non-converters [108]. With 18F florbetapir (18F-AV-45) tracer, baseline Aβ + scans were associated with greater clinical worsening on the AD Assessment Scale-Cognitive subscale (ADAS-Cog) and Clinical Dementia Rating-sum of boxes (CDR-SB). In MCI, Aβ + scans were also associated with greater decline in memory, Digit Symbol Substitution (DSS) and MMSE. Aβ + MCI subjects again tended to convert to AD at a higher rate than Aβ- subjects [109].

In a seminal comparison study of three modalities [110], using [(11)C]PIB, [(18)F]FDDNP and [18F]FDG, there was a significant increase in global cortical [(11)C]PIB binding (most prominent in the lateral temporal lobe) in MCI patients, but no changes in AD patients or controls. Interestingly, [(18)F]FDDNP did not show any changes in global binding potential. Moreover, changes in global [(11)C]PIB binding and posterior cingulate [(18)F]FDG uptake were correlated with changes in MMSE score over time across groups, but not with [(18)F]FDDNP binding. Hence it was postulated that [(11)C]PIB and [(18)F]FDDNP track molecular changes in different stages of AD. There was an increased amyloid load in MCI patients and progressive metabolic impairment in AD patients. The authors opined that [(18)F]FDDNP was less useful for examining disease progression.

To estimate the diagnostic accuracy of FDG-PET and PIB-PET for prediction of short-term conversion to AD in patients with MCI, Zhang [111] and co-workers performed a meta-analysis undertaken with a random-effects model. Overall diagnostic accuracy determined for both FDG-PET and PIB-PET suggests that they are potentially valuable techniques for prediction of progression in patients with MCI. Both have their advantages and their combined use is a promising option.

Villain et al recently published a longitudinal PIB study (testing conducted 18 months apart), showing a significant increase in amyloid-β accumulation in both PIB-positive and negative subjects (significantly higher in PIB-positive individuals) with a bimodal distribution of individual rates of neocortical amyloid- β accumulation [112].

5.3. Summary

MRI volumetry and brain atrophy rates have fairly good diagnostic and predictive value in MCI subjects. Longitudinal data on brain atrophy rates with disease progression are available and hence, can be used for monitoring disease progression in clinical trials. The limitations of structural neuroimaging as a biomarker include problems with the accurate delineation of regions of interest and lack of standardization of imaging and measurement techniques, making it difficult to compare data across the different institutions out of Europe, North America and Australia (all of which have their unified imaging consortiums). The advent of automated data-driven innovations for structural imaging holds promise. FDG-PET appears to be the leading candidate among the functional neuroimaging modalities, with available evidence for MCI diagnosis, prediction of MCI-converters and longitudinal data in monitoring serial progression. To date, [(11]C] PIB is the most extensively studied PET amyloid tracer, although 18F florbetapir proves to be an attractive alternative given the longer half-life. There is emerging evidence for amyloid imaging in the diagnosis of preclinical AD. From the standpoint of clinical trials of anti-amyloid therapy, in-vivo amyloid imaging pre-treatment allows selection of patients with demonstrable cerebral Aβ loads; repeated imaging during ongoing treatment allows detection of decrease in insoluble Aβ load in response to amyloid-clearing drugs such as immunotherapy. Amyloid imaging needs to be more practically accessible and affordable before it can be transferable to the clinical diagnostic routine.


6. Combinational biomarkers

Many of the aforementioned biomarker modalities are not separate discrete entities but have an effect on each other. For example, the association of hypertension with CSF tau and ptau-181, was found to be modified by APOEε4 phenotype, where hypertension is directly related to tau pathology (and not Aβ42) in APOEε4 homozygous carriers [113]. Elevated CSF t-tau and p-tau in presence of APOEε4/ε4 genotype has also been shown to influence faster AD progression in MCI subjects [114].

For the identification of MCI-converters, various literature showing combination biomarkers have been published. They include looking at clinical measures (such as cognitive or neuropsychological tests) in combination with CSF biomarkers [115], neuroimaging measures [116,117], or in combination with both CSF and neuroimaging measures [118-119].

A combination of CSF and neuroimaging biomarkers [120-4] has found improved predictive accuracy of MCI-converters, supported by slope analyses of annual cognitive decline [120]. Okamura showed that a high ratio between cerebrospinal fluid (CSF) tau and posterior cingulate perfusion on SPECT is useful in identifying MCI converters [125]. Using a machine-learning approach (support vector machines), Furney et al examined the utility of adding cytokine and neuroimaging biomarkers to conventional measures, and found that the combination of cytokine and neuroimaging with clinical and APOEε4 genotype improved accuracy [126]. Recent studies have also looked at multimodal neuroimaging techniques to predict MCI progression [127-129].

Other recent studies have used endophenotype-based approach and found single nucleotide polymorphism (SNP) such as rs1868402 to have strong, replicable association with CSFptau181 association with rate of AD progression [130].

Table 5.

Longitudinal biomarker studies

* expressed as % change per year compared to baseline values

** expressed as annual change β

MCI = Mild Cognitive Impairment

NC = Normal Controls

AD = Alzheimer’s Disease

CSF = Cerebrospinal fluid

PIB = Pittsburgh Compound B

FDG-PET = Fluorodeoxyglucose (18F)-Positron Emission Tomography

MMSE = Mini Mental State Examination

CDR-SB = Clinical Dementia Rating – Sum of Boxes

RAVLT = Rey Auditory Verbal Learning Tes


7. Conclusion and future directions

Clinical criteria alone, often subjective and dependent on clinical judgment, are insufficient to identify the pre-clinical stages of AD accurately. This has prompted the past decade-long intensive research into the use of more objective neuroimaging and biochemical markers to either replace, or complement, clinical approaches to facilitate an early and accurate diagnosis of the illness [131,132]. The chapter thus far details the rationale (most evident from Table 1) for the combined approach of clinical measures with other biomarkers in predicting AD progression; but in the earlier stages (prodromal and especially preclinical AD stages), biomarkers would play an increasingly important role. Combination biomarker approaches appear to be superior to a single biomarker approach, with the recent focus of researchers being on multimodal approach using various systems biology and multivariate modeling methods. Additionally, multi-site prospective studies, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), allow for global summary of results and patterns of change observed in clinical measures and candidate biomarkers [133] (Table 5). It must also be highlighted that some of the heterogeneity of biomarker findings thus far is related to the different periods of follow-up and hence AD conversion rates in MCI subjects.

The dynamic biomarker model, in the AD pathological cascade first proposed by Jack in 2010 [134], has been an area of intense interest. However, this inverse relationship between fibrillar amyloid plaque burden (on PIB imaging) and corresponding decrease in CSF Aβ42 and elevated tau, has led to the simplistic interpretation that the AD pathological cascade is purely driven by the amyloid cascade (Figure 1). This is partly due to extrapolation from cross-sectional studies, where in fact, longitudinal studies are required to determine the temporal order of the appearance of various pathogenic processes involved in this complex disease. Storandt et al [135] has recently demonstrated in a community cohort that CSF Aβ42 and tau were minimally correlated, suggesting that they represent independent processes. Additionally, they accounted for only 60% of variance on PIB imaging, suggesting that a third process may be related to brain atrophy or plaque formation [136].

In addition, understanding longitudinal biomarker change allows its potential inclusion in clinical trials, with recent studies advocating the use of neuroimaging biomarkers [137,138], CSF biomarkers [139] and/or combination biomarkers [137,140] to boost the power of clinical trials and decrease sample size in MCI trials. An integrated analyses approach using patient (age) severity- and disease-related (severe baseline cognitive, global or behavioural status) factors in established AD has been shown, with the potential of symptomatic AD therapy, to decrease likelihood of faster decline [141].

Further work on biomarkers is important because of their multiple potential roles. Biomarkers have the potential to be used as a prognostic tool for the prediction of AD conversion in MCI subjects and rapid AD progression, with translation into clinical practice by using a most practical algorithm, and as a diagnostic tool in prodromal/ preclinical stages of AD. Biomarkers may also lead to a deeper understanding of the complex pathogenesis of AD disease – including stage-specific and stage-independent processes. There is also currently an unfulfilled potential in biomarker-enriched clinical trials and the use of biomarkers in preclinical AD, especially in the advent of newer therapeutic targets. Finally there is also potential to extrapolate biomarker findings ‘backwards’ into the earliest stages of disease so that we may be able to identify those at risk and consider instituting interventions. This would enable earliest therapeutic intervention for at-risk subjects most amenable to disease-modifying treatments, and exclude those for whom the possible risks from investigational treatment would be more difficult to justify. At the very least, it would identify those who might benefit most from intensive monitoring and management of clinical factors, e.g. blood pressure, diabetes and lipids, and also non-invasive interventions, e.g. cognitive training. This vital work can only been done through multi-center studies and standardized evaluation techniques using various systems biology and statistical modeling approaches.


  1. 1. Wimo A, Winbald B, Aguero Torres H, von Strauss E. The magnitude of dementia occurrence in the world. Alz Dis Assoc Disord 2003; 17: 63-67.
  2. 2. Price JL, Morris JC. Tangles and plaques in nondemented aging and preclinical Alzheimer disease. Ann Neurol 1999; 45:358-68.
  3. 3. Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, Marcus DS, Cairns NJ, Xie X, Blazey TM, Holtzman DM, Santacruz A, Buckles V, Oliver A, Moulder K, Aisen PS, Ghetti B, Klunk WE, McDade E, Martins RN, Masters CL, Mayeux R, Ringman JM, Rossor MN, Schofield PR, Sperling RA, Salloway S, Morris JC; the Dominantly Inherited Alzheimer Network. Clinical and Biomarker Changes in Dominantly Inherited Alzheimer's Disease. N Engl J Med. 2012 Jul 11.
  4. 4. Jack CR Jr, Albert MS, Knopman DS, McKhann GM, Sperling RA, Carrillo MC, Thies B, Phelps CH. Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):257-62. Epub 2011 Apr 21.
  5. 5. Cores F, Nourhashemi F, Guerin O et al. Prognosis of Alzheimer’s disease today : a two-year prospective study in 686 patients from the REAL-FR Study. Alzheimers Dement 2008; 4(1): 22-29.
  6. 6. Roselli F, Tartaglione B, Federico F, Lepore V, Defazio G, Livrea P. Rate of MMSE score change in Alzheimer’s disease: Influence of education and vascular risk factors. Clin Neurol Neurosurg 2009; 111(4):327-30.
  7. 7. Soto ME, Anrieu S, Cantet C, Reynish E et al. Predictive value of rapid decline in Mini Mental State Examination in clinical practice for prognosis in Alzheimer’s disease. Dement Geriatr Cogn Disord 2008; 26(2): 109-16.
  8. 8. de la Monte SM. Contributions of Brain Insulin Resistance and Deficiency in Amyloid-Related Neurodegeneration in Alzheimer’s disease. Drugs 2012; 73(1):49-66.
  9. 9. Janson J, Laedtke T, Parisi JE, O/Brien P et al. Increased risk of type 2 diabetes in Alzheimer disease. Diabetes 2004; 53:474-481.
  10. 10. Francis GJ, Martinez JA, Liu WQ, Xu K, Ayer A, Fine J, Tuor UI, Glazner G, Hanson, LR, Frey WH 2nd, Toth C. Intranasal insulin prevents cognitive decline, cerebral atrophy and white matter changes in murine type I diabetic encephalopathy. Brain 2008;131(Pt 12):3311-34.
  11. 11. Xu E, Caracciolo B, Wang H, Winblad B et a. Accelerated progression from Mild Cognitive Impairment to Dementia in People with Diabetes. Diabetes 2010; 59:2928-2935.
  12. 12. Cole AR, Astell A, Green C, Sutherland C. Molecular connexions between dementia and diabetes. Neurosci Biobehav Rev 2007; 31:1046-63.
  13. 13. Schenider P, Buerger K, Teipel S, Uspenskaya O, Harmann O et al. Antihypertension Therapy is associated with reduced rate of conversion to Alzheimer’s disease in midregional proatrial natriuretic peptide strateified subjects with mild cognitive impairment. Biol Psy 2011; 70:145-51.
  14. 14. Siuda J, Gorzkowska A, Patalong-Ogiewa M, Krzystanek E, Czech E et al. From mild cognitive impairment to Alzheimer's disease - influence of homocysteine, vitamin B12 and folate on cognition over time: results from one-year follow-up. Neurol Neurochir Pol. 2009 Jul-Aug;43(4):321-9.
  15. 15. Solfrizzi V, Panza F, Colacicco Am, D’Introno A et al. Vascular risk factors, incidence of MCI, and rates of progression to dementia. Neurology 2004; 63(10): 1882-91.
  16. 16. Mielke MM, Rosenberg PB, Tschanz J et al. Vascular factors predict rate of progression in Alzheimer disease. Neurology 2007; 69:1850-58.
  17. 17. Musicco M, Palmer K, Salamone G, Lupo F, Perri R et al. Predictors of progression of cognitive decline in Alzheimer's disease: the role of vascular and sociodemographic factors." J Neurol. 2009 Aug;256(8):1288-95.
  18. 18. Bellew KM, Pigeon JG, Fleischman W, Gardner RM, Baker WW. Hypertension and the Rate of Cognitive Decline in Patients with Dementia of the Alzheimer Type. Alzheimer Dis Assoc Disord 2004; 18(4): 208-213.
  19. 19. Regan C, Katona C, Walker Z, Hooper J, Donovan J et al. Relationship of vascular risk to the progression of Alzheimer disease. Neurology 2006; 67:1357-62.
  20. 20. Helzner EP, Luchsinger JA, Scarmeas N et al. Contribution of vascular risk factors to disease progressionn in Alzheimer’s Disease. Arch Neurol 2009; 66(3):343-48.
  21. 21. Li J, Wang YJ, Zhang M, Xu ZQ et al. Vascular risk factors promote conversion from mild cognitive impairment to Alzheimer disease. Neurology 2011; 76:1485-91.
  22. 22. Li L, Wang Y, Yan J, Chen Y, Zhou R et al. Clinical predictors of cognitive decline in patients with mild cognitive impairment: the Chongqing aging study. J Neurol 2012; 259:1303-1311.
  23. 23. Solfrizzi V, Scafato E, Capurso C, D'Introno A, Colacicco AM, Frisardi V,Vendemiale G, Baldereschi M, Crepaldi G, Di Carlo A, Galluzzo L, Gandin C,Inzitari D, Maggi S, Capurso A, Panza F; Italian Longitudinal Study on Aging Working Group.Metabolic syndrome, mild cognitive impairment, and progression to dementia. The Italian Longitudinal Study on Aging. Neurobiol Aging 2011;32(11):1932-41.
  24. 24. Forlenza OV, Diniz BS, Talib LL, Radanovic M et al. Clinical and biologic predictors of Alzheimer’s disease in patients with amnestic mild cognitive impairment. Revista Brasilerira de Psiquiatria 2010; 32(3):216-22.
  25. 25. Wilkosz PA, Seltman HJ, Devlin B, Weamer EA, Lopez OL et al. Trajectories of Cognitive Decline in Alzheimer’s disease. Int Psychogeriatr 2010; 22(2):281-90.
  26. 26. Gomeni R, Simeoni M, Zvartau-Hind M, Irizarry MC, Austin D, Gold M. Modeling Alzheimer’s disease progression using the disease system analysis approach. Alz Dement 2012; 8:39-50.
  27. 27. Lopez OL, Schwam E, Cummings J, Gauthier S, Jones R, Wilkinson D et al. Predicting cognitive decline in Alzheimer’s disease: An integrated analysis. Alz Dement 2010; 6:431-39.
  28. 28. Hall CB, Derby C, LeValley A, Katz MH, Verghese J, Lipton RB. Education delays accelerated decline on a memory test in persons who develop dementia. Neurology 2007; 69:1657-64.
  29. 29. Doody RS, Pavlik V, Massman P, Rountree S et al. Predicting progression of Alzheimer’s disease. Alzheimer’s Res Therapy 2010;2:2.
  30. 30. Buccione I, Perri R, Carlesimo GA, Fadda L et al. Cognitive and behavioural predictors of progression rates in Alzheimer’s disease. Eur J Neurology 2007; 14:440-6.
  31. 31. Palmer K, Lupo F, Perri R, Salamone G et al. Predicting Disease progression in Alzheimer’s disease: The role of Neuropsychiatric syndromes on functional and cognitive decline. J Alz Dis 2011; 24:35-45.
  32. 32. De Jaeger CA, Hoegevorst E, Combrinck M, Budge MM. Sensitivity and specificity of neuropsychological tests for mild cognitive impairment, vascular cognitive impairment and Alzheimer’s disease. Psychological Medicine 2003; 33:1039–50.
  33. 33. Albert M, Blacker D, Moss MB, Tanzi R, McArdle JJ. Longitudinal change in cognitive performance among individuals with mild cognitive impairment. Neuropsychology 2007; 21(2):158-69.
  34. 34. Pozueta A, Rodriguez-Rodriguez E, Vazquez-Higuera J, Mateo I et al. Detection of early Alzheimer’s disease in MCI patients by combination of MMSE and an episodic memory test. BMC Neurology 2011; 11:78.
  35. 35. Gavett BE, Ozonoff A, Doktor V, Palmisano J et al. Predicting cognitive decine and conversion to Alzheimer’s disease in older adults using the NAB List Learning test. J Int Neuropsychol Soc 2010; 16(4): 651-60.
  36. 36. PJ Nestor, P Scheltens, JR Hodges. Advances in the early detection of Alzheimer’s disease. Nat Med. 2004 Jul;10 Suppl:S34–41. Review.
  37. 37. Fowler KS, Salling MM, Conway El, Semple JM, Louis WJ. Paired associate performance in the early detection of DAT. J Int Neuropsychol Soc 2002; 8(1):58-71.
  38. 38. DeCarli C, Mungas D, Harvey D, Reed B, Weiner M, Chui H, Jagust WC. Memory impairment, but not cerebrovascular disease, predicts progression of MCI to dementia. Neurology 2004; 63:220–7.
  39. 39. Amieva H, Letenneur L, Dartigues JF, Rouch-Leroyer I, Sourgen C, D Alchee-Biree F, Dib M, Barbeger-Gateau P, Orgogozo JM, Fabrigoule C. Annual Rate and Predictors of Conversion to Dementia in Subjects Presenting Mild Cognitive Impairment Criteria Defined according to a Population-Based Study. Dement Geriatr Cogn Disord 2004; 18:87–93.
  40. 40. Dickerson BC, Sperling RA, Hyman BT, Albert MS, Blacker D. Clinical Prediction of Alzheimer Disease Dementia across the spectrum of mild cognitive impairment. Arch Gen Psychiatry 2007; 64(12):1443-50.
  41. 41. Guarch J, Marcos T, Salamero M, Blesa R. Neuropsychological markers of dementia in patients with memory complaints. Int J Geriatr Psychiatry 2004; 19:352–58.
  42. 42. Chapman RM, Mapstone M, McCrary JW, Gardner MN, Porsteinnson AP et al. Predicting conversion from Mild Cognitive Impairment to Alzheimer’s disease using neuropsychological tests and multivariate methods. J Clin Exp Neuropsychol 2011; 33(2): 187-99.
  43. 43. Chan Y, Bondi MW, Fennema-Notestine C, McEvoy LK et al. Brain substrates of learning and retention in mild cognitive impairment diagnosis and progression to Alzheimer’s disease. Neuropsychologia 2010; 48(5):1237-47.
  44. 44. Dierckx E, Engelborghs S, De Raedt R, Van Buggenhout M, De Deyn PP et al. Verba; cued recall as a predictor of conversion to Alzheimer’s disease in Mild Cognitive Impairment. Int J Geriatr Psy 2009;24:1094-1100.
  45. 45. Musicco M, Salamone G, Caltagirone C, Cravello L et al. Neuropsychological Predictors of Rapidly Progressing Patients with Alzheimer’s disease. Dement Geriatr Cogn Disord 2010; 30:219-28.
  46. 46. Backman L, Small BJ, Fratiglioni L. Stability of the preclinical memory deficit in Alzheimer’s disease. Brain 2001; 124:96-102.
  47. 47. Bennett DA, Wilson RS, Schneider JA, Evans DA et al. Natural history of mild cognitive impairment in older persons. Neurology 2002; 59:198-205.
  48. 48. Blennow K, Hampal H. CSF markers for incipient Alzheimer’s disease. Lancet Neurol 2003; 2:605-13.
  49. 49. Fagan AM, Shaw LM, Xiong C, Vanderstichele H et al. Comparison of Analytical Platforms for cerebrospinal fluid measures of β-amyloid 1-42, total tau, and P-tau181 for identifying Alzheimer Disease Amyloid Plaque Pathology. Arch neurol doi:10.1001/archneurol.2011.105
  50. 50. Shaw LM, Vanderstichele H, Knapik-Czajka M et al. Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI. Acta Neuropathol 2011; 121:597-609.
  51. 51. Arai H, Nakagawa T, Kosaka Y et al. Elevated cerebrospinal fluid tau protein as a predictor of dementia in memory-impaired patients. Alzheimer’s Res 1997; 3:211-3.
  52. 52. Andreasen N, Vanmechelen E, Vanderstichele H, Davidsson P, Blennow K. Cerebrospinal fluid levels of total-tau, phosphor-tau and Aβ42 predicts development of Alzheimer’s disease in patients with mild cognitive impairment. Acta Neurol Scand 2003; 107(suppl 179):47-51.
  53. 53. Hampel H, Teipel SJ, Fuchsberger T, Andreasen N, Wiltfang J, Otto M, Shen Y, Dodel R, u Y, Farlow M, Moller HJ et al. Value of CSF beta-amyloid1-42 and tau as predictors of Alzheimer's disease in patients with mild cognitive impairment. Mol Psychiatry 2004; 9:705-10.
  54. 54. Maruyama M, Arai H, Sugita M, Tanji H, Higuchi M, Okamura N, Matsui T, Higuchi S, Matsushita S, Yoshida H, Sasaki H. Cerebrospinal fluid amyloid β1-42 in the mild cognitive impairment stage of Alzheimer’s disease. Exp Neurol 2001; 172:433-6.
  55. 55. Mattson N et al. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 2009; 302:485-93.
  56. 56. Visser PJ et al. Prevalence and prognostic value of CSF markers of Alzheimer’s disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: a prospective cohort study. Lancet Neurol 2009; 8:619-27.
  57. 57. Arai H, Idhiguro K, Ohno H, Moriyama M, Itoh N, Okamura N, Matsui T, Morikawa Y, Horikawa E, Kohno H, Sasaki H et al. CSF phosphorylated tau protein and mild cognitive impairment: a prospective study. Exp Neurol 2000; 1666:201-3.
  58. 58. Buerger K, Teipel SJ, Zinkoiwski R, Blennow K, Arai H, Engel R, Hofmann-Keiffer K, McCulloch C, Ptok U, Heun R, Andreasen N et al. CST tau protein phosphorylated at threonine 231 correlates with cognitive decline in MCI subjects. Neurology 2002; 59:627-9.
  59. 59. Riemenscheneider M, Lautenschlager N, Wagenpfeil S, Diehl J, Drzezga A, Kurz A. Cerebrospinal fluid tau and beta-amyloid 42 proteins identify Alzheimer disease in subjects with mild cognitive impairment. Arch Neurol 2002; 59:1729-34.
  60. 60. Andreasen N, Minthon L, Vanmechelen E, Vanderstichele H, Davidsson P, Winblad B, Blennow K. Cerebrospinal fluid tau and Aβ42 as predictors of development of Alzheimer’s disease in patients with mild cognitive impairment. Neurosci Letters 1999; 273:5-8.
  61. 61. Herruka S, Hallikainen M, Soininen H, Pirttila T. CSF Aβ42 and tau or phosphorylated tau and prediction of progressive mild cognitive impairment. Neurology 2005; 64:1294-7.
  62. 62. Zetterberg H, Wahlund LO, Blennow K. Cerebrospinal fluid markers for prediction of Alzheimer’s disease. Neurosci Letters 2003; 352: 67-9.
  63. 63. Parnetti L, Lanari A, Silvestrelli G, Saggese E, Reboldi P. Diagnosing prodromal Alzheimer’s disease: Role of CSF biochemical markers. Mechan Ageing Dev 2005 [EPub]
  64. 64. Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 2006:5:228-34.
  65. 65. Seppala TT, Koivisto AM, Hartikainen P et al. Longitudinal changes of CSF Biomarkers in Alzheimer’s disease. J Alz Iis 2011; 24:583-94.
  66. 66. Samgard K, Zetterberg H, Blennow K, Hansson O et al. Cerebrospinal fluid total tau as a marker of Alzheimer’s disease intensity. Int J Geriatr Psy 2010: 25:403-10.
  67. 67. Ravaglia S, Bini P, Sinforiani E, Franciotta D et al. Cerebrospinal fluid levels of tau phosphrylated at threonine 181 in patients with Alzheimer’s disease and vascular dementia. Neurol Sci 2008; 29:417-23.
  68. 68. Vlachos GSm Oarasjevas GP, Naoumis D, Kapaki E. Cerebrospinal fluid β-amyloid1-42 correlates with rate of progression in Alzheimer’s disease. J Neural Transm 2012; 119:799-804.
  69. 69. Wallin AK, Blennow K, Zetterberg J, Londos E et al. CSF biomarkers predict a more malignant course in Alzheimer disease. Neurology 2010; 74(19): 1531-7.
  70. 70. deLeon MJ, Segal S, Tarshish CY, DeSanti S, Zinkowski R, Mehta PD, Convit A, Caraos C, Rusinek H, Tsui W, Saint Louis LA et al. Longitudinal cerebrospinal fluid tau load increases in mild cognitive impairment. Neurosci Letters 2002; 333:183-6.
  71. 71. Leon MJ, Desanti S, Zinkowski R, Mehta PD, Pratico D, Segal S, Rusinek H, Li J, Tsui W, Saint Louis LA, Clark CM et al. Longitudinal CSF and MRI biomarkers improve the diagnosis of mild cognitive impairment. Neurobiol Aging. 2005 Aug 25.
  72. 72. Kester ML, Scheffer PG, Koel-Simmelink MJ, Twaalfhoven H et al. Serial CSF sampling in Alzheimer’s disease: specific versus non-specific markers. Neurobiol Aging 2012; 33:1591-98.
  73. 73. Rosun D, Schuff N, Shaw LM, Trojanowski JQ, Weiner MW et al. Relationship between CSF biomarkers of Alzheimer’s disease and Rates of Regional Cortical Thinning in ADNI data. J Alzheimer’s Dis 2011; 26:77-90.
  74. 74. Perrin RJ, Craig-Schapiro R, Malone JP, Shah AR, Gilmore P et al. Identification and Validation of Novel Cerebrospinal fluid biomarkers for staging early Alzheimer’s Disease. PLOS One 2011; 6(1):e16032.
  75. 75. Georganopoulou DG, Chang L, Nam J, Thaxton CS et al. Nanoparticle-based detection in cerebral spinal fluid of a soluble pathogenic biomarker for Alzheimer’s disease. PNAS 2005; 102(7):2273-6.
  76. 76. Craig-Scharpiro R, Kuhn M, Xiong C, Pickering Eh et al. Multiplexed Immunoassay Panel identified Novel CSF Biomarkers for Alzheimer’s Disease Diagnosis and Prognosis. PLOS One 2011; 6(4):e18850.
  77. 77. Glabe CG, Kayed R. Common structure and toxic function of amyloid oligomers implies a common mechanism of pathogenesis. Neurology 2006; 66(Suppl 1): S74-78.
  78. 78. Lemere CA, Maier M, Jiang L, Peng Y, Seabrook TJ. Amyloid-Beta Immunotherapy for the Prevention and Treatment of Alzheimer Disease: Lessons from mice, monkeys and humans. Rejuvenation Res 2006; 9(1): 77-84.
  79. 79. Olsson A et al. Simultaneous measurement of β-amyloid(1-42), total tau, and phosphorylated tau (Thr181) in cerebrospinal fluid by xMAP technology. Lin Chem 2005; 51:336-345.
  80. 80. Mattsson N, Zetterberg H, Blennow K. Lessons from multicenter studies on CSF biomarkers for Alzheimer’s disease. Int J Alz Dis 2010. doi:10.4061/2010/610613.
  81. 81. Lee TS, Chua SM, Ly P, Song W. Genomic and molecular characterization of Alzheimer Disease. Current Psych Reviews, 2010, 6, 104-113.
  82. 82. Hansson O, Zetterberg H, Vanmechelen E, Vanderstichele H, Andreasson U, Londos E, Wallin A, Minthon L, Blennow K. Evaluation of plasma Abeta(40) and Abeta(42) as predictors of conversion to Alzheimer's disease in patients with mild cognitive impairment. Neurobiol Aging. 2010 Mar;31(3):357-67. Epub 2008 May 19.
  83. 83. Mayeux R, Schupf N.Mayeux Blood-based biomarkers for Alzheimer's disease: plasma Aβ40 and Aβ42, and genetic variants. Neurobiol Aging. 2011 Dec;32 Suppl 1:S10-9. Review.
  84. 84. Koyama A, Okereke OI, Yang T, Blacker D, Selkoe DJ, Grodstein F. Plasma Amyloid-β as a Predictor of Dementia and Cognitive Decline: A Systematic Review and Meta-analysis.Arch Neurol. 2012 Mar 26.
  85. 85. Borroni B, Colciaghi F, Archetti S, Marcello E, Caimi L, Di Luca M, Padovani A. Predicting cognitive decline in Alzheimer disease. Role of platelet amyloid precursor protein. Alzheimer Dis Assoc Disord. 2004 Jan-Mar;18(1):32-4.
  86. 86. Xu G, Zhou Z, Zhu W, Fan X, Liu X. Plasma C-reactive protein (CRP) is related to cognitive deterioration and dementia in patients with mild cognitive impairment (Xu 2009) Neurol Sci. 2009 Sep 15;284(1-2):77-80. Epub 2009
  87. 87. Locascio JJ, Fukumoto H, Yap L, Bottiglieri T, Growdon JH, Hyman BT, Irizarry MC. Plasma amyloid beta-protein and C-reactive protein in relation to the rate of progression of Alzheimer disease. Arch Neurol. 2008 Jun;65(6):776-85
  88. 88. Thambisetty M, An Y, Kinsey A, Koka D, Saleem M, Güntert A, Kraut M, Ferrucci L, Davatzikos C, Lovestone S, Resnick SM. Plasma clusterin concentration is associated with longitudinal brain atrophy in mild cognitive impairment. Neuroimage. 2012 Jan 2;59(1):212-7. Epub 2011 Jul 28.
  89. 89. Mielke MM, Haughey NJ, Bandaru VV, Weinberg DD, Darby E, Zaidi N, Pavlik V, Doody RS, Lyketsos CG. Plasma sphingomyelins are associated with cognitive progression in Alzheimer's disease. J Alzheimers Dis. 2011;27(2):259-69.
  90. 90. Elias-Sonnenschein LS, Viechtbauer W, Ramakers IH, Verhey FR, Visser PJ. Predictive value of APOE-ε4 allele for progression from MCI to AD-type dementia: a meta-analysis. J Neurol Neurosurg Psychiatry. 2011 Oct;82(10):1149-56. Epub 2011 Apr 14.
  91. 91. Martins CA, Oulhaj A, de Jager CA, Williams JH. APOE alleles predict the rate of cognitive decline in Alzheimer disease: a nonlinear model. Neurology. 2005 Dec 27;65(12):1888-93.
  92. 92. Cosentino S, Scarmeas N, Helzner E, Glymour MM, Brandt J, Albert M, Blacker D, Stern Y.APOE epsilon 4 allele predicts faster cognitive decline in mild Alzheimer disease. Neurology. 2008 May 6;70(19 Pt 2):1842-9. Epub 2008 Apr 9
  93. 93. Lee TS, Goh L, Chong MS, Chua SM, Chen GB, Feng L, Lim WS, Chan M, Ng TP, Krishnan KR. Downregulation of TOMM40 expression in the blood of Alzheimer disease subjects compared with matched controls. J Psychiatr Res. 2012 Jun;46(6):828-30. Epub 2012 Apr 1.
  94. 94. Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, Blennow K, Friedman LF, Galasko DR, Jutel M, Karydas A, Kaye JA, Leszek J, Miller BL, Minthon L, Quinn JF, Rabinovici GD, Robinson WH, Sabbagh MN, So YT, Sparks DL, Tabaton M, Tinklenberg J, Yesavage JA, Tibshirani R, Wyss-Coray T. Nat Med. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. 2007 Nov;13(11):1359-62. Epub 2007 Oct 14.
  95. 95. Soares HD, Chen Y, Sabbagh M, Roher A, Schrijvers E, Breteler M. Identifying early markers of Alzheimer's disease using quantitative multiplex proteomic immunoassay panels. Ann N Y Acad Sci. 2009 Oct;1180:56-67.
  96. 96. Jack CR Jr, Shiung MM, Gunter JL, O'Brien PC, Weigand SD, Knopman DS, Boeve BF, Ivnik RJ, Smith GE, Cha RH, Tangalos EG, Petersen RC. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology. 2004 Feb 24;62(4):591-600.
  97. 97. Jack CR Jr, Petersen RC, Grundman M, Jin S, Gamst A, Ward CP, Sencakova D, Doody RS, Thal LJ; Members of the Alzheimer's Disease Cooperative Study (ADCS). Longitudinal MRI findings from the vitamin E and donepezil treatment study for MCI. Neurobiol Aging. 2008 Sep;29(9):1285-95. Epub 2007 Apr 23.
  98. 98. Jack CR Jr, Shiung MM, Weigand SD, O'Brien PC, Gunter JL, Boeve BF, Knopman DS, Smith GE, Ivnik RJ, Tangalos EG, Petersen RC. Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology. 2005 Oct 25;65(8):1227-31.
  99. 99. Apostolova LG, Dutton RA, Dinov ID, Hayashi KM, Toga AW, Cummings JL, Thompson PM Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol. 2006 May;63(5):693-9.
  100. 100. Thompson PM, Hayashi KM, de Zubicaray G, Janke AL, Rose SE, Semple J, Herman D, Hong MS, Dittmer SS, Doddrell DM, Toga AW. Dynamics of gray matter loss in Alzheimer's disease. J Neurosci. 2003 Feb 1;23(3):994-1005.
  101. 101. Risacher SL, Saykin AJ, West JD, Shen L, Firpi HA, McDonald BC; Alzheimer's Disease Neuroimaging Initiative (ADNI). Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res. 2009 Aug;6(4):347-61.
  102. 102. Kinkingnéhun S, Sarazin M, Lehéricy S, Guichart-Gomez E, Hergueta T, Dubois B.VBM anticipates the rate of progression of Alzheimer disease: a 3-year longitudinal study. Neurology. 2008 Jun 3;70(23):2201-11. Epub 2008 Apr 30.
  103. 103. Querbes O, Aubry F, Pariente J, Lotterie JA, Démonet JF, Duret V, Puel M, Berry I, Fort JC, Celsis P; Alzheimer's Disease Neuroimaging Initiative. Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve. Brain. 2009 Aug;132(Pt 8):2036-47. Epub 2009 May 12.
  104. 104. Silverman DH, Small GW, Chang CY, Lu CS, Kung De Aburto MA, Chen W, Czernin J, Rapoport SI, Pietrini P, Alexander GE, Schapiro MB, Jagust WJ, Hoffman JM, Welsh-Bohmer KA, Alavi A, Clark CM, Salmon E, de Leon MJ, Mielke R, Cummings JL, Kowell AP, Gambhir SS, Hoh CK, Phelps ME. Positron emission tomography in evaluation of dementia: Regional brain metabolism and long-term outcome. JAMA. 2001 Nov 7;286(17):2120-7.
  105. 105. Lo RY, Hubbard AE, Shaw LM, Trojanowski JQ, Petersen RC, Aisen PS, Weiner MW, Jagust WJ; Alzheimer's Disease Neuroimaging Initiative. Longitudinal change of biomarkers in cognitive decline. Arch Neurol. 2011 Oct;68(10):1257-66. Epub 2011 Jun 13.
  106. 106. Okello A, Koivunen J, Edison P, Archer HA, Turkheimer FE, Någren K, Bullock R, Walker Z, Kennedy A, Fox NC, Rossor MN, Rinne JO, Brooks DJ. Conversion of amyloid positive and negative MCI to AD over 3 years: an 11C-PIB PET study. Neurology. 2009 Sep 8;73(10):754-60. Epub 2009 Jul 8.
  107. 107. Koivunen J, Scheinin N, Virta JR, Aalto S, Vahlberg T, Någren K, Helin S, Parkkola R, Viitanen M, Rinne JO. Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study. Neurology. 2011 Mar 22;76(12):1085-90. Epub 2011 Feb 16.
  108. 108. Small GW, Siddarth P, Kepe V, Ercoli LM, Burggren AC, Bookheimer SY, Miller KJ, Kim J, Lavretsky H, Huang SC, Barrio JR.Prediction of cognitive decline by positron emission tomography of brain amyloid and tau. Arch Neurol. 2012 Feb;69(2):215-22.
  109. 109. Doraiswamy PM, Sperling RA, Coleman RE, Johnson KA, Reiman EM, Davis MD, Grundman M, Sabbagh MN, Sadowsky CH, Fleisher AS, Carpenter A, Clark CM, Joshi AD, Mintun MA, Skovronsky DM, Pontecorvo MJ; For the AV45-A11 Study Group. Amyloid-β assessed by florbetapir F 18 PET and 18-month cognitive decline: A multicenter study. Neurology. 2012 Aug 1.
  110. 110. Ossenkoppele R, Tolboom N, Foster-Dingley JC, Adriaanse SF, Boellaard R, Yaqub M, Windhorst AD, Barkhof F, Lammertsma AA, Scheltens P, van der Flier WM, van Berckel BN. Longitudinal imaging of Alzheimer pathology using [11C]PIB, [18F]FDDNP and [18F]FDG PET. Eur J Nucl Med Mol Imaging. 2012 Jun;39(6):990-1000. Epub 2012 Mar 23.
  111. 111. Zhang S, Han D, Tan X, Feng J, Guo Y, Ding Y. Diagnostic accuracy of 18 F-FDG and 11 C-PIB-PET for prediction of short-term conversion to Alzheimer's disease in subjects with mild cognitive impairment. Int J Clin Pract. 2012 Feb;66(2):185-98. doi: 10.1111/j.1742-1241.2011.02845.x.
  112. 112. Villain N, Chetelat G, Grassiot B, Bourgeat P et al. Regional dynamics of amyloid- β deposition in healthy elderly, mild cognitive impairment and Alzheimer’s disease: a voxelwise PiB-PET longitudinal study. Brain 2012; doi:10.1093/brain/aws125.
  113. 113. Kester MI, van der Lier WM, Mandic G, Blankenstein MA et al. Joint effect of hypertension and APOE genotype on CSF biomarkers for Alzheimer’s disease. J Alz IDis 2010;20:1083-90.
  114. 114. Blom ES, Gledraitis V, Zetterberg H, Fukumoto H et al. Rapid progression from mild cognitive impairment to Alzheimer’s Disease in subjects with elevated levels of tau in cerebrospinal fluid and the APOE e4/e4 genotype. Dement Geriatr Cogn Disord 2009; 27:458-464.
  115. 115. Palmqvist S, Hertze J, Minthon L, Wattmo C et al. Comparison of Brief Cognitive Tests and CSF biomarkers in prediciting Alzheimer’s disease in mild cognitive impairment: Six-year follow-up study. PLOS one 2012; 7(6):e38639
  116. 116. Ewers M, Walsh C, Trojanowski JQ, Shaw LM et al. Prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol Aging 2012; 33(7):1203-14.
  117. 117. Cui Y, Liu B, Luo S, Zhen X et al. Identification of Conversion from mild cognitive impairment to Alzheimer’s disease using multivariate predictors. PLOS One 2011; 6(7):e21896.
  118. 118. El Fakhri G, Kijewski MF, Johnson KA, Syrkin G, Killany RJ, Becker JA, Zimmerman RE, Albert MS. MRI-guided SPECT perfusion measures and volumetric MRI in Prodromal Alzheimer Disease. Arch Neurol 2003; 60:1066-72.
  119. 119. Huang C, Wahlund LO, Almkvist O, Elehu D, Svensson L, Jonsson T, Winblad B, Julin P. Voxel- and VOI-based analysis of SPECT CBF in relation to clinical and psychological heterogeneity of mild cognitive impairment. Neuroimage. 2003 Jul;19(3):1137-44.
  120. 120. Stephanie V, van Rossum I, Burns L, KNol D et al. Test sequence of CSF and MRI biomarkers for prediction of AD in subjects with MCI. Neurobiol Aging 2012; 33:2272-81.
  121. 121. Vemuri P, Wiste HJ, Weigand SD, Shaw LM et al. MRI and CSF biomarkers in normal, MCI and AD subjects. Neurology 2009;73:294-301.
  122. 122. Eckerstrom C, Andreasson U, Olsson E, Rolstad S, Blennow K et al. Combination of hippocampal volume and cerebrospinal fluid biomarkers improves predictive value in mild cognitive impairment. Dement Geriatr Cogn Disord 2010; 29:294-300.
  123. 123. Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ. Prediction of MCI to AD conversion, via MRI, CSF biomarkers and pattern classification. Neurobiol Aging 2011; 2322.e19-2322.e27
  124. 124. Hansson O, Buchhave P, Zetterberg H, Blennow K et al. Combined rCBF and CSF biomarkers predict preogression from mild cognitive impairment to Alzheimer’s disease. Neurobiol Aging 2009;30:165-173.
  125. 125. Okamura N, Arai H, Maruyama 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; 159:474-76.
  126. 126. Furney SJ, Kronenberg D, Simmons A, Guntert A, Dobson RJ et al. Combinatorial markers of mild cognitive impairment conversion to Alzheimer’s disease – cytokines and MRI measures together predict disease progression. J Alz Dis 2011; 26:395-405.
  127. 127. Zhang D, Shen D, Alzheimer’s Disease Neuroimaging Initiative. Predicting future clinical changes of MCI patients using Longitudinal and Multimodal Biomarkers. PLOS One 2012; 7(3):e33182.
  128. 128. Zhang D, Wang Y, Zhou L, Yuan H et al. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 2011; 55:856-67.
  129. 129. Hinrichs C, Singh V, Xu G, Johnson SC et al. Predictive markers for AD in a multi-modality Framework: An Analysis of MCI progression in the ADNI population. Neuroimage 2011; 55(2): 574-89.
  130. 130. Cruchaga C, Kauwe JSK, Mayo K, Spiegel N et al. SNPs associated with cerebrospinal fluid phosphor-tau levels influence rate of decline in Alzheimer Disease. PLOS Genetics 2010; 6(9):e1001101.
  131. 131. Chong MS, Sahadevan S. Preclinical Alzheimer’s disease: diagnosis and prediction of progression. Lancet Neurol 2005; 4: 576-79.
  132. 132. Chong MS, Lim WS, Sahadevan S. Biomarkers in prediction of progression of preclinical Alzheimer’s disease. Current Opinion in Investigational Drugs 2006: 7(7): 600-607.
  133. 133. Beckett LA, Harvey DJ, Gamst A, Donohue M et al. The Alzheimer’s Disease Neuroimaging Initiative: Annual Change in Biomarkers and Clinical Outcomes. Alz Dement 2010; 6(3): 257-64.
  134. 134. Jack CR, Knopman DS, Jagust WJ, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol 2010; 9:119-28
  135. 135. Storandt M, Head D, Fagan AM, Holtzman DM, Morris JC. Toward a multifactorial model of Alzheimer disease. Neurobiol Aging 2012; 33:2262-71.
  136. 136. Rowe CC, Ellisa KA, Rimajova M, Bourgeat P et al. Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging 2010; 31:1275-83.
  137. 137. Kohannim O, HUa X, Hibar DP, Lee S, Chou U, Toga AW et al. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol Aging 2010; 31:1429-42.
  138. 138. Lorenzi M, Donohue M, Paternico D, Scarpazza C et al. Enrichment through biomarkers in clinical trials of Alzheimer’s drugs in patients with mild cognitive impairment. Neurobiol Aging 2010; 31:1443-51.
  139. 139. van Rossum IA, Vos S, Handels R, Visser PJ. Biomarkers as Predictors for Conversion from Mild Cognitive Impairment to Alzheimer-Type Dementia: Implications for Trial Design. J Alz Dis 2010; 20:881-91.
  140. 140. Yu P, Dean RA, Hall SD, Qi Y et al. Enriching Amnestic Mild Cognitive Impairment Populations for Clinical Trials: Optimal Combination of Biomarkers to Predict Conversion to Dementia. J Alz Dis; doi 10.3233/JAD-2012-120832.
  141. 141. Lopez OL, Schqam E, Cummings J, Gautheir S, Jones R et al. Predicting cognitive decline in Alzheimer’s diseae: An integrated analysis. Alz Dement 2010; 6:431-9.

Written By

Mei Sian Chong and Tih-Shih Lee

Submitted: 26 April 2012 Published: 27 February 2013