**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 autopsyconfirmed AD cohort, that the standardization of biomarker techniques could potentially replace autopsy-confirmed AD for future diagnosis of definite AD [50].

#### **3.1. Established CSF biomarkers**

Prospective studies of amnestic MCI (a-MCI) subjects have shown that episodic memory (such asdelayedrecallofwordlists [32-34], spatial shorttermmemoryandvisualrecognitionmemory [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. Howev‐ er, impaired learning had modestly better predictive powerthan impaired retention, highlight‐ ing 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

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

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 educa‐ tional 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.

progression at 2 years [45].

380 Understanding Alzheimer's Disease

**2.1. Summary**

potentially useful marker of progression [47].

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 concentra‐ tion 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 concen‐ trations [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] com‐ pared 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 particularisoprostane, demonstrated an increase overtime, which was correlat‐ ed 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

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In a study in which novel CSF biomarkers were identified through mass spectrometry and reevaluated 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

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

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 standard‐

a promising strategy for monitoring disease progression [77,78].

of biomarker levels and meta-analyses of published papers [80].

ization issues preclude its current routine clinical use.

Initiative (ADNI) data [73].

**3.2. Novel CSF approaches**

**3.3. Summary**

**Table 2.** Cerebrospinal fluid biomarkers in predicting AD conversion in MCI patients and rapid AD progression/ decline

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**

**Study variable Population Results Key findings** 

Combination CSF 137 MCI subjects 42% converted - t-tau >350ng/L & Aβ42 <530 ng/L: biomarkers [64] compared to to AD Sn: 95%, Sp 83% of AD conversion 39 healthy HR 30, 95% CI 9.32-96.8, p<0.001 controls - p-tau >60ng/L & Aβ42 <530 ng/L: Sn 95%, Sp 81% of AD conversion HR 26.3, 95% CI 8.16-83.4, p<0.001 - t-tau/ Aβ42 ratio < 6.5 (t-tau>350ng/L) Sn 95%, Sp 87% of AD conversion HR 32.8 (10.2-105.6,p<0.001)

CSF biomarker 142 AD subjects 35 subjects had - High levels of t-tau correlated with concentration followed-up over t-tau>800ng/L lower baseline MMSE scores.

[66] 5 years More rapid decline in MMSE score correlated

CSF p-tau 70 AD and VD Cognitive decline 58% of probable AD patients showed p-tau concentration subjects with assessed 12 mth concentration higher than 36.08ng/L. [67] 36 age-matched (MMSE ≥ 5 point Cognitive decline correlated with p-tau conchealthy controls decline after 1yr) entration (x2

CSF Aβ42 74 AD subjects Rapid progressors Lower Aβ42 CSF concentration (mean 292 pg/ concentration defined at MMSE ml) in fast-progressors compared to slow- [68] decline >4/years progressors (mean 453 pg/ml) (p=0.042)

> and intermediate t-tau, p-tau Cluster 3 low Aβ42 and high t-tau, p-tau

**Table 2.** Cerebrospinal fluid biomarkers in predicting AD conversion in MCI patients and rapid AD progression/

Low CSF Aβ42 151 AD subjects k-means cluster Cluster 3 performed poorer on baseline and high CSF analysis done. cognitive tests. They exhibited poorer outcome t-tau and p-tau Cluster 1 low Aβ42 of cholinesterase inhibitor treatment. Cognition levels and low t-tau, p-tau deterioriated faster over time with substantially

[69] Cluster 2 low Aβ42 increased mortality rate.

with higher baseline t-tau (rs=-0.23,p=008). - p-tau>110ng/L showed lower baseline MMSE scores but no difference in progression. - Aβ42 showed no difference in baseline scores

=12.442, p=0.001).

or progression.

*Predicting AD conversion in MCI subjects* 

382 Understanding Alzheimer's Disease

*Predicting rapid AD progression/ decline* 

HR = Hazards ratio CRP = C-reactive protein

OR = Odds ratio Sn = Sensitivity Sp= Specific

HR = Hazards ratio

decline

MMSE =Mini Mental State Examination

LR+ = positive Likelihood ratio LR - = negative Likelihood ratio

95% CI= 95% confidence interval

In a study in which novel CSF biomarkers were identified through mass spectrometry and reevaluated 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 standard‐ ization 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 bloodderived 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.

**Study variable Population Results Key findings** 

– 4 years

 dementia respectively

ΑbetaCohort 1: 117 48 (41%) subjects No difference in plasma Abeta levels between Hansson [82] MCI subjects of cohort 1 MCI subjects that subsequently developed AD

 -7 years; 15 (14%) subjects HR (per SD decrease adjusted for age, sex): Cohort 2: 110 of cohort 2 Aβ40 1.08 (0.78-1.51), Aβ42 0.95 (0.71 followed up for 2 developed AD 1.27), Aβ42/42 ratio 0.83 (0.64-1.08)

Koyama [84] Meta-analysis Summary risk Association of low plasma Abeta42/Abeta40

C Reactive Protein 168 MCI subjects 58 subjects Association of high plasma CRP level with [86] followed up over developed accelerated cognitive deterioration and

 MMSE score was significantly lower for patients with high CRP levels than those with low CRP levels (-

APOE [90] 35 prospective 1236 developed APOE-ε4 allele is associated with a

with 10,303 ratio of 1.60 and ratio with AD and dementia.

MCI subjects, MCI to AD-type dementia. including 6095 OR for MCI subjects with APOE ε4 subjects over 2.9 progression to AD 2.29 (95% CI years of follow-up 1.88 to 2.80).Sn 0.53 (95% CI 0.4 to 0.61),

allele background prevalence or follow-up length

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

APP isoforms in 48 AD subjects Progression of AD Association of low APPr at baseline in platelets [85] followed up over predicting cognitive decline in AD.

Combination of 122 AD subjects Followed up Low plasma levels of Abeta40, Abeta42, and Aβ and CRP [87] 4.2 years high-sensitivity CRP were associated with a significantly more rapid cognitive decline. Plasma biomarkers contributed to 5-12% variance

Martins [91] 218 AD subjects In the non-linear APOE genotype strongly predicts the rate of

to earlier and faster cognitive decline. APOEε2 allele related to slower decline. Cosentino [92] 199 population- Presence of at APOEε4 influences cognitive decline most

> absent in prevalent AD subjects in population or clinic based group.

**Table 3.** Blood biomarkers in predicting AD conversion in MCI patients and rapid AD progression/ decline

Aβ40 and Aβ42. They are the most studied of blood markers.

Teleologically the most logical candidate is plasma Amyloid-beta (Aβ) and its derivatives,

215 population- faster cognitive based prevalent decline in the AD subjects, 156 populationclinic-based AD based incident AD subjects followed group (*p* = 0.01). up for an average However, this of 4 years association is

subjects SM/ceramide ratios declined1.35 points

model, possession cognitive decline in AD.

based incident least one ε4 allele significantly in the earliest stages of AD.

allele was related decline than heterozygotes.

 Living. Ceramides [89] 120 probable AD Follow-up 2.3y Highest tertiles of DHSM/DHCer and

∆MMSE = -2.8 ± 3.0, *p* < 0.05

∆MMSE = -0.9 ±2.3, *p* < 0.05

1 year APPr <0.40,

APPr ≥0.40,

AD subjects, associated with

4.9 ± 5.4 vs -3.2 ±4.2, *p* < 0.05)

 Sp 0.67 (95% CI 0.62 to 0.71), PPV 0.57 (95% CI 0.48 to 0.66), NPV 0.75 (95% CI 0.70 to 0.80). LR+ 1.60 (95% CI 1.48 to 1.72), and LR- 0.75 (95% CI 0.67 t o0.82). Meta-regression showed that Sn,Sp and NPV were dependent on age, APOE-ε4

on Blessed Dementia Scale and Activities of Daily

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385

 (p=0.001) and 1.19 (p=0.004) less per year on the MMSE and increased 3.18 points (p=0.001) and 2.42 (p=0.016) less per year on ADAS-Cog.

of an APOEε4 APOEε4 homozygotes showed faster cognitive

cohort studies of AD. moderately increased risk for progression from

followed up for 4 developed AD; and HC or stable MCI subjects.

subjects 1.67 for AD and

2 years dementia increased risk of AD.

*Predicting AD conversion in MCI subjects*

*Predicting rapid AD progression/ decline* 

APOEε4

HC = Healthy controls SD = Standard deviation OR = Odds ratio

Sn = Sensitivity Sp= Specificity OR = Odds ratio

95% CI= 95% confidence interval

PPV = Positive predictive value NPV= Negative predictive value LR+ = positive Likelihood Ratio LR- = negative Likelihood Ratio

**4.1. Plasma proteins/ peptides**


Combination of 122 AD subjects Followed up Low plasma levels of Abeta40, Abeta42, and Aβ and CRP [87] 4.2 years high-sensitivity CRP were associated with a significantly more rapid cognitive decline. Plasma biomarkers contributed to 5-12% variance

Martins [91] 218 AD subjects In the non-linear APOE genotype strongly predicts the rate of

to earlier and faster cognitive decline. APOEε2 allele related to slower decline. Cosentino [92] 199 population- Presence of at APOEε4 influences cognitive decline most

> absent in prevalent AD subjects in population or clinic based group.

215 population- faster cognitive based prevalent decline in the AD subjects, 156 populationclinic-based AD based incident AD subjects followed group (*p* = 0.01). up for an average However, this of 4 years association is

AD subjects, associated with

subjects SM/ceramide ratios declined1.35 points

model, possession cognitive decline in AD.

based incident least one ε4 allele significantly in the earliest stages of AD.

allele was related decline than heterozygotes.

 Living. Ceramides [89] 120 probable AD Follow-up 2.3y Highest tertiles of DHSM/DHCer and

APOEε4

on Blessed Dementia Scale and Activities of Daily

 (p=0.001) and 1.19 (p=0.004) less per year on the MMSE and increased 3.18 points (p=0.001) and 2.42 (p=0.016) less per year on ADAS-Cog.

of an APOEε4 APOEε4 homozygotes showed faster cognitive

platelets [85] followed up over predicting cognitive decline in AD. 1 year APPr <0.40, ∆MMSE = -2.8 ± 3.0, *p* < 0.05 Predicting Cognitive Decline in Alzheimer's Disease (AD): The Role of Clinical, Cognitive Characteristics and Biomarkers http://dx.doi.org/10.5772/54289 385

allele background prevalence or follow-up length

APP isoforms in 48 AD subjects Progression of AD Association of low APPr at baseline in

APPr ≥0.40,

MCI subjects, MCI to AD-type dementia. including 6095 OR for MCI subjects with APOE ε4 subjects over 2.9 progression to AD 2.29 (95% CI years of follow-up 1.88 to 2.80).Sn 0.53 (95% CI 0.4 to 0.61),

**Study variable Population Results Key findings** 

– 4 years

 dementia respectively

ΑbetaCohort 1: 117 48 (41%) subjects No difference in plasma Abeta levels between Hansson [82] MCI subjects of cohort 1 MCI subjects that subsequently developed AD

 -7 years; 15 (14%) subjects HR (per SD decrease adjusted for age, sex): Cohort 2: 110 of cohort 2 Aβ40 1.08 (0.78-1.51), Aβ42 0.95 (0.71 followed up for 2 developed AD 1.27), Aβ42/42 ratio 0.83 (0.64-1.08)

Koyama [84] Meta-analysis Summary risk Association of low plasma Abeta42/Abeta40

C Reactive Protein 168 MCI subjects 58 subjects Association of high plasma CRP level with [86] followed up over developed accelerated cognitive deterioration and

 MMSE score was significantly lower for patients with high CRP levels than those with low CRP levels (-

APOE [90] 35 prospective 1236 developed APOE-ε4 allele is associated with a

with 10,303 ratio of 1.60 and ratio with AD and dementia.

4.9 ± 5.4 vs -3.2 ±4.2, *p* < 0.05)

 Sp 0.67 (95% CI 0.62 to 0.71), PPV 0.57 (95% CI 0.48 to 0.66), NPV 0.75 (95% CI 0.70 to 0.80). LR+ 1.60 (95% CI 1.48 to 1.72), and LR- 0.75 (95% CI 0.67 t o0.82). Meta-regression showed that Sn,Sp and NPV were dependent on age, APOE-ε4

cohort studies of AD. moderately increased risk for progression from

followed up for 4 developed AD; and HC or stable MCI subjects.

subjects 1.67 for AD and

2 years dementia increased risk of AD.

*Predicting AD conversion in MCI subjects*

*Predicting rapid AD progression/ decline* 


**Table 3.** Blood biomarkers in predicting AD conversion in MCI patients and rapid AD progression/ decline

#### **4.1. Plasma proteins/ peptides**

**4. Blood markers (table 3)**

384 Understanding Alzheimer's Disease

published and hence publication bias is possible.

*Predicting AD conversion in MCI subjects*

*Predicting rapid AD progression/ decline* 

APOEε4

**Study variable Population Results Key findings** 

– 4 years

 dementia respectively

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 bloodderived 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

ΑbetaCohort 1: 117 48 (41%) subjects No difference in plasma Abeta levels between Hansson [82] MCI subjects of cohort 1 MCI subjects that subsequently developed AD

 -7 years; 15 (14%) subjects HR (per SD decrease adjusted for age, sex): Cohort 2: 110 of cohort 2 Aβ40 1.08 (0.78-1.51), Aβ42 0.95 (0.71 followed up for 2 developed AD 1.27), Aβ42/42 ratio 0.83 (0.64-1.08)

Koyama [84] Meta-analysis Summary risk Association of low plasma Abeta42/Abeta40

C Reactive Protein 168 MCI subjects 58 subjects Association of high plasma CRP level with [86] followed up over developed accelerated cognitive deterioration and

 MMSE score was significantly lower for patients with high CRP levels than those with low CRP levels (-

APOE [90] 35 prospective 1236 developed APOE-ε4 allele is associated with a

with 10,303 ratio of 1.60 and ratio with AD and dementia.

MCI subjects, MCI to AD-type dementia. including 6095 OR for MCI subjects with APOE ε4 subjects over 2.9 progression to AD 2.29 (95% CI years of follow-up 1.88 to 2.80).Sn 0.53 (95% CI 0.4 to 0.61),

allele background prevalence or follow-up length

APP isoforms in 48 AD subjects Progression of AD Association of low APPr at baseline in platelets [85] followed up over predicting cognitive decline in AD.

Combination of 122 AD subjects Followed up Low plasma levels of Abeta40, Abeta42, and Aβ and CRP [87] 4.2 years high-sensitivity CRP were associated with a significantly more rapid cognitive decline. Plasma biomarkers contributed to 5-12% variance

Martins [91] 218 AD subjects In the non-linear APOE genotype strongly predicts the rate of

to earlier and faster cognitive decline. APOEε2 allele related to slower decline. Cosentino [92] 199 population- Presence of at APOEε4 influences cognitive decline most

> absent in prevalent AD subjects in population or clinic based group.

215 population- faster cognitive based prevalent decline in the AD subjects, 156 populationclinic-based AD based incident AD subjects followed group (*p* = 0.01). up for an average However, this of 4 years association is

subjects SM/ceramide ratios declined1.35 points

model, possession cognitive decline in AD.

based incident least one ε4 allele significantly in the earliest stages of AD.

allele was related decline than heterozygotes.

 Living. Ceramides [89] 120 probable AD Follow-up 2.3y Highest tertiles of DHSM/DHCer and

∆MMSE = -2.8 ± 3.0, *p* < 0.05

∆MMSE = -0.9 ±2.3, *p* < 0.05

1 year APPr <0.40,

APPr ≥0.40,

AD subjects, associated with

4.9 ± 5.4 vs -3.2 ±4.2, *p* < 0.05)

 Sp 0.67 (95% CI 0.62 to 0.71), PPV 0.57 (95% CI 0.48 to 0.66), NPV 0.75 (95% CI 0.70 to 0.80). LR+ 1.60 (95% CI 1.48 to 1.72), and LR- 0.75 (95% CI 0.67 t o0.82). Meta-regression showed that Sn,Sp and NPV were dependent on age, APOE-ε4

on Blessed Dementia Scale and Activities of Daily

 (p=0.001) and 1.19 (p=0.004) less per year on the MMSE and increased 3.18 points (p=0.001) and 2.42 (p=0.016) less per year on ADAS-Cog.

of an APOEε4 APOEε4 homozygotes showed faster cognitive

cohort studies of AD. moderately increased risk for progression from

followed up for 4 developed AD; and HC or stable MCI subjects.

subjects 1.67 for AD and

2 years dementia increased risk of AD.

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 poten‐ tially suitable for identifying patients in the earliest stage of disease process when intervention might be more effective.

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 pro‐ grammed 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

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387

APOEε4 is the best-established genetic risk factor for AD. APOE genotyping is not recom‐ mended for the routine diagnosis of AD. However many studies have investigated whether

In a large meta-analysis, Elias-Sonnenschein [90] and co-workers found that APOEε4 is

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

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

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 tran‐ scriptomic 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

MCI progression.

**4.2. Genetic and transcriptomic markers**

between APOEε4 homo- and heterozygotes.

APOEε4 has a predictive value for progression from MCI to AD.

associated with a moderately increased risk of progression from MCI to AD.

the earliest stages of disease and less so in moderate to severe stages.

development in predicting MCI conversion and rapid AD progression.

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 cardio‐ vascular disease. Deficiencies of the B vitamins – B6(pyridoxine), B9(folic acid) and B12(coba‐ lamin) 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 genomewide association studies (GWAS) to be associated with AD [83]. Clusterin is functionally associated with apoptosis and the clearance of cellular debris, including amyloid. Thambie‐ setty [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 pro‐ grammed 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**

As Aβ accumulation is an early step in AD pathogenesis, such a biomarker would be poten‐ tially suitable for identifying patients in the earliest stage of disease process when intervention

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

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

Homocysteine has been reported to be associated with human disease states, notably cardio‐ vascular disease. Deficiencies of the B vitamins – B6(pyridoxine), B9(folic acid) and B12(coba‐ lamin) are associated with high homocysteine levels. However, there is no data on

Clusterin, also called apolipoprotein J and coded by gene CLU, has been reported in genomewide association studies (GWAS) to be associated with AD [83]. Clusterin is functionally associated with apoptosis and the clearance of cellular debris, including amyloid. Thambie‐ setty [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

secretase is involved and does not lead to Aβ formation [81].

homocysteine with MCI conversion and AD progression.

data with MCI conversion and AD progression.

might be more effective.

386 Understanding Alzheimer's Disease

cognitive decline [87].

APOEε4 is the best-established genetic risk factor for AD. APOE genotyping is not recom‐ mended 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 tran‐ scriptomic 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. How‐ ever efforts at independent validation of Ray's findings have been discouraging [95].

**Study variable Population Results Key findings** 

Jack et al. [96] 55 NC, 41 MCI, Atrophy rates of Rates of change from serial MRI studies together

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

years follow-up entorhinal progression in AD.

ventricle) Jack et al. [97] 133 MCI subjects 52 subjects MRI brain atrophy rate measures can be used as

carriers. Jack et al. [98] 72 aMCI 13 HC Larger ventricular APC (HR for a 1-SD increase

 MCI-converters. Apostolova et al. [99] 20 MCI subjects 6 subjects Smaller hippocampi and specifically CA1 and

Risascher et al. [101] 339 MCI (277 62 MCI Degree of neurodegeneration of MTL structures is

Querbes et al. [103] 122 aMCI (50 72 aMCI Normalised cortical thickness can predict AD

Lo et al. [105] 229 normal Rates of change Amyloid deposition is an early event before

Okello et al. [106] 31 aMCI 17 out of 31 MCI PIB-positive MCI subjects are more likely to

developed AD. Half (47%) converted to AD

Koivunen et al. [107] 29 MCI, 13 HC 17 MCI Hippocampal atrophy increases and amyloid

Small et al. [108] 22 HC and 21 Increases in [18F]FDDNP PET scanning may be useful in

memory decline yielded highest diagnostic accuracy. ROC

Doraiswamy et al. [109] 51 MCI, 69 HC, MCI Aβ+ and Florbetapir PET, which detects Aβ pathology, may

followed up over associated with for progression to AD.

MCI Aβ+ associated With greater decline in memory, DSS and MMSE (p < 0.05). Ossenkoppele et al. [110] 11 HC, 12 MCI, Global cortical [11C]PIB and [18F] FDG track molecular changes and 8 AD [11C]PIB BPND in different stages of AD.

followed up over increased

(82%)

within 1 year.

progressive MCI), 130 HC, 130 AD followed up over 24

months.

Molecular Imaging

64 AD four structures with standard clinical/psychometric measures can subjects; 1-5 (hippocampus, be used as surrogate markers of disease

(45 were APOEε4 therapeutic MCI setting.

subjects, 91 HC; developed MCI 1.4, p=0.007) increased risk of AD conversion. 1-2 years follow- or AD; Both ventricular APC (HR for a 1-SD increase 1.59, up. 39 MCI subjects p=0.001) and whole brain APC (HR for 1-SD increase developed AD 1.32, p=0.009) provided additional predictive

followed up over developed AD subicular subfields are associated with increased 3 years (MCI-c), 7 risk for conversion from MCI to AD.

MCI-stable, 62 developed AD the best antecedent MRI marker of imminent MCI-converters) conversion, with decreased hippocampal volume subjects, 206 (left > right) being the most robust structural MRI HC, 148 AD feature. Effect sizes of hippocampus (0.6) and MTL subjects structures (0.53) comparing MCI-stable and converters.

stable MCI, 72 developed AD. conversion with 76% cross-validated accuracy.

,397 MCI in CSF Aβ42 hypometabolism or hippocampal atrophy, and 193 AD , glucose meta- suggesting that biomarker prediction for cognitive

subjects, 26 HC (55%) had develop AD than PIB-negative subjects.

followed up hippocampal Positive APOE4 status accelerated hippocampal

3 years [11C]PIB Fast converters have higher PIB retention levels at

followed up over developed AD deposition changes modestly during conversion to 2 years AD, suggesting dissociation between the two

baseline.

MCI followed up frontal, posterior identifying people at risk for future cognitive over 2 years cingulate, and decline. Higher [18F]FDDNP binding at

correlated with P = 0.05 to 0.002).

and 31 AD HC Aβ+ be helpful in identifying individuals at increased risk

18 months. greater clinical Higher SUVr in MCI associated with greater decline

ADAS-Cog and MMSE (all p<0.05).

followed up over is significantly MCI subjects were found to have an increased 2.5 years. increased in amyloid load while AD subjects had increased MCI subjects, progressive metabolic impairment. but no changes [18F]FDDNP is less useful for examining disease

13 research pooled estimates: techniques for prediction of AD progression in MCI

14 HC 3 years sequence (temporal- frontal- sensorimotor)

subjects and 18 3 years than slow decliners, especially in the medial HC followed up occipitoparietal areas (specifically precuneus,

presenting dementia in regional brain metabolism was a sensitive indicator symptoms of 59% of AD. A negative PET scan indicated that dementia pathologic progression of cognitive impairment

as the disease progressed. Mirroring the sequence of neurofibrillary tangle accumulation observed in cross sections

single negative PET scan.

Left hemisphere degenerates faster (5.3 ± 2.3% per year in AD v.s. 0.9 ± 0.9% per year in controls; p<0.029) than the right

Lingual gyrus and cuneus which was not yet detected by clinical and neuropsychological

during the mean 3-year follow-up was unlikely to occur. Sn 93%, Sp 76%. –LR 0.1 (95% CI 0.06- 0.16) experiencing progressive course after a

was observed in progression.

global [18F] p=0.35).

FDDNP.

studies (7 FDG- 78.7% Sn (95% subjects.

Thompson et al. [100] 12 AD subjects, Followed up Cortical atrophy occurred in a well defined

at autopsy.

Kinkingnéhun et al.[103] 23 mild AD Followed up Fast decliners had a more extensive cortical atrophy

assessment.

Silverman et al. [107] 284 patients Progressive In patients presenting symptoms of dementia,

74% Sp (95%CI 67.0-80.3%) PIB-PET pooled estimates: 93.5% Sn (95% CI 71.3-99.9%) 56.2% Sp (95% CI 47.2-64.8%)

Zhang et al. [111] Meta-analysis of FDG-PET Both FDG-PET and PIB-PET are valuable

PET) CI 68.7-86.6%)

*Predicting rapid AD progression/ decline* 

Functional Imaging

PIB-positive MCI with faster conversion rates (p=0.035)

retention at baseline than slower converters in anterior cingulate, baseline (PIB- (p=0.027) and frontal cortex (p=0.031). positive). Only 1 out of 14 PIB-negative subjects develop AD. 14 of these 17 7 of 17 PIB-positive MCI, APOEε4 carriers associated

during evolution of MCI.

global binding at baseline is associated with future decline in follow-up most cognitive domains (r = -0.31 to -0.56,

progression of Frontal and parietal [18F]FDDNP binding

(r = -0.32 to -0.37, 0.88 (95% CI 0.72-1.00) compared with 0.68 P = 0.03 to 0.01). (95% CI 0.45-0.91) for medial temporal binding.

worsening on on ADAS-Cog, CDR-SB, memory measure (DSS)

and CDR-SB. MCI Aβ+ had higher risk of developing AD.

AD subjects or Reduction in [18F]FDG uptake at follow-up HC. Increase observed in AD subjects only (esp frontal, parietal, most prominent temporal lobes (all p<0.01). Changes in global in lateral [11C]PIB binding (p=-0.42, p<0.05) and cingulate temporal lobe [18F]FDG uptake (p=0.43, p<0.01) correlated (p < 0.05). with changes in MMSE score over time across No changes in groups but not for [18F] FDDNP binding (p=-0.18,

AD converters had greater [11C]PIB retention at baseline in posterior cingulate (p=0.022), putamen (p=0.041), caudate nucleus (p=0.025). Greater hippocampal atrophy in MCI converters at

subjects bolism and change is stage dependent.

3 years volume atrophy changes in MCI and AD.

remained stable Larger hippocampal volumes and relative (MCI-nc), and 7 preservation of both the subiculum and CA1 are improved (MCI- i). associated with cognitive stability or improvement.

Mean time APCs greater in APOE ε4 non-carriers.

cortex, whole Atrophy rates greater among MCI converters. brain, and Atrophy rates greater among AD fast progressors

developed AD indicators of disease progression in a multi-site

carriers). APC was greater in converters than non-converters.

information to covariate-adjusted sectional HC volume at baseline about risk of AD conversion. However, overlap present among those converters and non-converters indicate that these measures are unlikely to provide absolute prognosis for

http://dx.doi.org/10.5772/54289

389

to conversion APCs and changes in cognitive test performance 556 day in APOE uniformly correlated in expected direction (p<0.000)

*Predicting AD conversion in MCI subjects*

Structural Imaging

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.
