**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 pathophysio‐ logical mechanisms may include hyperglycemia, insulin resistance [8], oxidative stress, advanced glycation end products, and inflammatory cytokines. A shared clinicopathologic measures of structural and functional/metabolic changes, and associated biochemical

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 Ex‐ amination (MMSE) scores [6, 7] (≥3 point decline) also reported a distinctive difference in the

changes in their research diagnostic armamentarium [4].

376 Understanding Alzheimer's Disease

clinical course between the fast-progressors and slow-progressors.

**Figure 1.** Clinical Continuum of Alzheimer's disease and hypothetical biomarker model

but instead to highlight integrative and potentially novel lines of inquiry.

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

risk of development of AD.

In this chapter, we will review the body of evidence on the use of various clinical and co‐ morbid 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,

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

Increased risk of cognitive decline in diabetes may reflect a dual pathologic process involving both cerebrovascular damage and neurodegenerative changes. Several possible pathophysio‐ logical 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 prediabetes have been found to be associated with AD progression in MCI subjects, with pro‐ gression from MCI to dementia accelerated by 3.18 years[11]. The stronger effect of prediabetes 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 peptidestratified 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 cerebro‐ vascular disease as a risk factor for MCI-converters[15]. Diabetes, hypertension and cerebro‐ vascular 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].


Hypertension [16] 135 incident AD Rapid decline on Systolic BP ≥160 versus <160mmHg Patients in CDR-sum of boxes (controlling for other vascular variables) Cache Country and MMSE using for CDR-SB coeff X time 1.78 (95% CI 1.20-2.36) Dementia linear mixed models for MMSE coeff X time -2.38 (-3.23,-1.53)

Hypertension [17] 719 AD patients ADAS-cog increase OR 6.9 (95% CI 1.5-31.1, p=0.005)

Diabetes [18] 154 AD patients Disease progression Crude OR 0.38 (95% CI 0.2-0.9) attending Dementia of decrease of 5 pts Multivariate OR 0.36 (95% CI 0.1-0.9)

Cerebrovascular disease[19] 224 AD patients Decline in MMSE, No difference in vascular risk factors except ADAS-cog and cerebrovascular disease (mean difference in SIB difference MMSE 13.6 (-14.3—7.6); ADAS-cog 27 (-30.1- -13.7); SIB 54.4 (-62.3—29.9)

Vascular risk factors 156 AD patients AD decline Only higher LDL-cholesterol was independently including heart disease, living in using generalized associated with faster cognitive decline. stroke, diabetes, community estimating equa- Stratified according to APOEε4 showed higher hypertension), smoking, mean age 83 y tion models total cholesterol, higher LDL, stroke and heart pre-diagnosis blood lipid disease associated with faster decline.

Age [24] 201 Caucasian Latent class Best latent trajectory model: Initial MMSE and Probable/Possible mixture models age. Parameter estimate 0.85, p<.001 for MMSE, AD subjects at of quadratic Parameter estimate 0.04,p =0.04 for age.

Education [27] 127 persons in Change point Prior to diagnosis, lower levels of formal education Bronx Aging study models to test associated with poorer performance on memory developed dementia predictions of and verbal fluency. Accelerated decline in SRT (out of 488 comm- cognitive reserve shown by estimated annual rates of decline for unity dwelling hypothesis using 16 years, 9.5 years and 4 years of formal edu subjects) Buschke Selective cation was 3.22, 2.57 and 2.03 points/year

Neuropsychiatric symptoms 177 memory-clinic Rapid disease Affective syndrome increased risk of functional [30] AD outpatients progression defined decline (HR2.0, 95%CI 1.1-3.6) AND Manic as loss of ≥1 ability syndrome (HR 3.2, 95% CI 1.3-7.5)

Pre-progression rate- 798 probable AD Random effects Slopes of ADAScog and PSMS change for slow Clinician estimate of subjects from linear regression pre-progression smaller than fast pre-progression. of duration and baseline Alzheimer's Disease to calculate Rates of change on ADAScog slower for inter-MMSE [28] and Memory pre-progression mediate pre-progression group. Disorders categories and Slow progressors survived longer.

cog, VSAT Time, VSAT Errors, CDR Sum of boxes, PSMS and IADL scores Memory and executive 154 newly Rapid progression Memory moderate deficits: HR 1.3 (95%CI: Functioning [45] diagnosed AD of ≥ 5MMSE de- 0.4-4.5); severe deficits: HR 2.3 (95%CI: 0.6- Patients crease over 2yrs 9.0). Executive functions moderate deficits: HR 3.5 (95%CI 0.9-13.7); severe deficits: HR 5.7

 Progression Study

and LDL-C [20]

 In multi-center by ≥1 standard Trial deviation of baseline ADAS-cog score of

center or more on MMSE

 ADRC, Pittsburgh trajectories includ- Ing random intercept and concomitant variables (MMSE)

(SRT)

 in ADL or drop of ≥ 5 points on MMSE

Centre of change in ADAS-

(95%CI 1.4-23.2)

Reminding Test respectively.

**Study variable Population Results Key findings** 

over 9 years

 over 3 years to AD based on age strata rate (per 100 person years) Total 2.3

 70-74y 0 75-79 3.1 80-84y 2.0

> 72 without metabolic syndrome

followed up mean 38.8 mths

syndrome and

Diabetes and pre- 302 aMCI 155 subjects had HR 2.87 diabetes (95%CI 1.3-6.34) diabetes [11] and 182 CIND AD progression HR 4.96 pre-diabetes (95% CI 2.27 subjects aged -10.84)

Diabetes, baseline 257 MCI subjects MCI conversion Diabetes HR 2.92 (95% CI 1.12-7.6) white matter severity, over 3 years to AD 7.05%/year Baseline WMC severity (mild vs severe) baseline moderate-to- HR 0.04 ( 95% CI 0.006-0.242) severe carotid stenosis and Baseline carotid stenosis (moderate vs mild) carotid stenosis change [22] HR 8.46 (95% CI 2.1-34.14)

Stroke [15] 121 MCI subjects MCI conversion Stroke RR 4.0 (95% CI 0.92-13.87)

65-69y 0

Metabolic syndrome [8] 49 MCI subjects Progression to 67.6 (95% CI 35.17 – 129.93) Rate 1000 with metabolic dementia per person-years

Age [23] 97 amnestic MCI Annual rate of Odds ratio = 4.5 of AD progression

Empirically weighted and 43 MCI subjects 14 subsequently Multivariate combinations achieved 84% accuracy, Combined neuropsycholo- converted to AD 86% Sn, 83%Sp in predicting AD progression

learning and (high learning, high retention as reference

Learning measure and 607 MCI and HC Conversion to Low-learning, Low retention OR17.84, 95%CI retention measure [43] patients in ADNI AD at 2 years 7.37-43.10, p<0.001; Low-learning, High reten cohort divided tion OR 9.01, 95%CI2.98-27.21,p<0.001;

 retention) group) MISplus [44] 40MCI subjects Conversion to OR 0.28, 95%CI 0.099-0.79) to AD at 18 At cut-off of 2, PPV 71.5%,

Vascular risk factors [21] 837 MCI subjects 298 converters HR 2.04 (95% CI 1.33-3.11) followed annually 352 stable Hypertension HR 1.84 (95% CI 1.19-2.84)

≥ 75 years Accelerated progression by 3.18 years

over 5 years Diabetes HR 1.62 (95% CI 1.00 – 2.62)

Hypercholesterolemia HR 1.11 (95% CI

Cerebrovascular disease HR 1.60 (95% CI

Carotid stenosis change HR 124.1 (95% CI 0.95- 16,209.68)

1.04-1.18)

1,03 – 2.49)

88 cognitively- progression to Older age [exp(β)=1.11, SE(β)=0.7, WALD=4.2, unimpaired controls AD p=0.040] predictors of AD conversion

gical battery [42] (using episodic memory, speeded executive function, recognition memory (false positives).

recognition memory (true positives), speed in visuospatial memory, visuospatial episodic memory

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

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379

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, neuroinflammation, 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

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 compo‐ nents: 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

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].

glycation end-products and inflammation promoting vascular disease [8].

associated with progression [15].

over 3.5 years of follow-up. [23]

*Predicting AD conversion in MCI subjects* 


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

**Table 1.** Clinical and cognitive/ behavioural characteristics in predicting AD conversion in MCI patients and rapid AD progression/ decline

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 **Study variable Population Results Key findings** 

over 9 years

 over 3 years to AD based on age strata rate (per 100 person years) Total 2.3

 70-74y 0 75-79 3.1 80-84y 2.0

> 72 without metabolic syndrome

followed up mean 38.8 mths

syndrome and

 Progression Study

378 Understanding Alzheimer's Disease

and LDL-C [20]

 In multi-center by ≥1 standard Trial deviation of baseline ADAS-cog score of

center or more on MMSE

 ADRC, Pittsburgh trajectories includ- Ing random intercept and concomitant variables (MMSE)

(SRT)

 in ADL or drop of ≥ 5 points on MMSE

Centre of change in ADAS-

HR = Hazards ratio; PPV = Positive predictive value

RR= Relative risk; SIB = Severe Impairment Battery

OR= Odds ratio

progression/ decline

(95%CI 1.4-23.2)

95% CI= 95% confidence interval; NPV= Negative predictive value WMC= White matter severity; MMSE =Mini Mental State Examination

Reminding Test respectively.

Diabetes and pre- 302 aMCI 155 subjects had HR 2.87 diabetes (95%CI 1.3-6.34) diabetes [11] and 182 CIND AD progression HR 4.96 pre-diabetes (95% CI 2.27 subjects aged -10.84)

Diabetes, baseline 257 MCI subjects MCI conversion Diabetes HR 2.92 (95% CI 1.12-7.6) white matter severity, over 3 years to AD 7.05%/year Baseline WMC severity (mild vs severe) baseline moderate-to- HR 0.04 ( 95% CI 0.006-0.242) severe carotid stenosis and Baseline carotid stenosis (moderate vs mild) carotid stenosis change [22] HR 8.46 (95% CI 2.1-34.14)

Stroke [15] 121 MCI subjects MCI conversion Stroke RR 4.0 (95% CI 0.92-13.87)

65-69y 0

Metabolic syndrome [8] 49 MCI subjects Progression to 67.6 (95% CI 35.17 – 129.93) Rate 1000 with metabolic dementia per person-years

Age [23] 97 amnestic MCI Annual rate of Odds ratio = 4.5 of AD progression

Empirically weighted and 43 MCI subjects 14 subsequently Multivariate combinations achieved 84% accuracy, Combined neuropsycholo- converted to AD 86% Sn, 83%Sp in predicting AD progression

learning and (high learning, high retention as reference

Learning measure and 607 MCI and HC Conversion to Low-learning, Low retention OR17.84, 95%CI retention measure [43] patients in ADNI AD at 2 years 7.37-43.10, p<0.001; Low-learning, High reten cohort divided tion OR 9.01, 95%CI2.98-27.21,p<0.001; into 4 groups: High learning, low retention OR8.48, 95%CI

(based on 3.45-20.86, p<0.001

Hypertension [17] 719 AD patients ADAS-cog increase OR 6.9 (95% CI 1.5-31.1, p=0.005)

Diabetes [18] 154 AD patients Disease progression Crude OR 0.38 (95% CI 0.2-0.9) attending Dementia of decrease of 5 pts Multivariate OR 0.36 (95% CI 0.1-0.9)

Cerebrovascular disease[19] 224 AD patients Decline in MMSE, No difference in vascular risk factors except ADAS-cog and cerebrovascular disease (mean difference in SIB difference MMSE 13.6 (-14.3—7.6); ADAS-cog 27 (-30.1- -13.7); SIB 54.4 (-62.3—29.9)

Vascular risk factors 156 AD patients AD decline Only higher LDL-cholesterol was independently including heart disease, living in using generalized associated with faster cognitive decline. stroke, diabetes, community estimating equa- Stratified according to APOEε4 showed higher hypertension), smoking, mean age 83 y tion models total cholesterol, higher LDL, stroke and heart pre-diagnosis blood lipid disease associated with faster decline.

Age [24] 201 Caucasian Latent class Best latent trajectory model: Initial MMSE and Probable/Possible mixture models age. Parameter estimate 0.85, p<.001 for MMSE, AD subjects at of quadratic Parameter estimate 0.04,p =0.04 for age.

Education [27] 127 persons in Change point Prior to diagnosis, lower levels of formal education Bronx Aging study models to test associated with poorer performance on memory developed dementia predictions of and verbal fluency. Accelerated decline in SRT (out of 488 comm- cognitive reserve shown by estimated annual rates of decline for unity dwelling hypothesis using 16 years, 9.5 years and 4 years of formal edu subjects) Buschke Selective cation was 3.22, 2.57 and 2.03 points/year

Neuropsychiatric symptoms 177 memory-clinic Rapid disease Affective syndrome increased risk of functional [30] AD outpatients progression defined decline (HR2.0, 95%CI 1.1-3.6) AND Manic as loss of ≥1 ability syndrome (HR 3.2, 95% CI 1.3-7.5)

Pre-progression rate- 798 probable AD Random effects Slopes of ADAScog and PSMS change for slow Clinician estimate of subjects from linear regression pre-progression smaller than fast pre-progression. of duration and baseline Alzheimer's Disease to calculate Rates of change on ADAScog slower for inter-MMSE [28] and Memory pre-progression mediate pre-progression group. Disorders categories and Slow progressors survived longer.

cog, VSAT Time, VSAT Errors, CDR Sum of boxes, PSMS and IADL scores Memory and executive 154 newly Rapid progression Memory moderate deficits: HR 1.3 (95%CI: Functioning [45] diagnosed AD of ≥ 5MMSE de- 0.4-4.5); severe deficits: HR 2.3 (95%CI: 0.6- Patients crease over 2yrs 9.0). Executive functions moderate deficits: HR 3.5 (95%CI 0.9-13.7); severe deficits: HR 5.7

**Table 1.** Clinical and cognitive/ behavioural characteristics in predicting AD conversion in MCI patients and rapid AD

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

 retention) group) MISplus [44] 40MCI subjects Conversion to OR 0.28, 95%CI 0.099-0.79) to AD at 18 At cut-off of 2, PPV 71.5%, months (n=7) NPV 91.5%, Accuracy 87% Hypertension [16] 135 incident AD Rapid decline on Systolic BP ≥160 versus <160mmHg Patients in CDR-sum of boxes (controlling for other vascular variables) Cache Country and MMSE using for CDR-SB coeff X time 1.78 (95% CI 1.20-2.36) Dementia linear mixed models for MMSE coeff X time -2.38 (-3.23,-1.53)

Vascular risk factors [21] 837 MCI subjects 298 converters HR 2.04 (95% CI 1.33-3.11) followed annually 352 stable Hypertension HR 1.84 (95% CI 1.19-2.84)

≥ 75 years Accelerated progression by 3.18 years

over 5 years Diabetes HR 1.62 (95% CI 1.00 – 2.62)

Hypercholesterolemia HR 1.11 (95% CI

Cerebrovascular disease HR 1.60 (95% CI

Carotid stenosis change HR 124.1 (95% CI 0.95- 16,209.68)

1.04-1.18)

1,03 – 2.49)

88 cognitively- progression to Older age [exp(β)=1.11, SE(β)=0.7, WALD=4.2, unimpaired controls AD p=0.040] predictors of AD conversion

gical battery [42] (using episodic memory, speeded executive function, recognition memory (false positives).

recognition memory (true positives), speed in visuospatial memory, visuospatial episodic memory

*Predicting AD conversion in MCI subjects* 

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, neuroinflammation, 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 compo‐ nents: 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 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].

**3. Cerebrospinal fluid biomarkers (tables 2)**

**3.1. Established CSF biomarkers**

inhibitor treatment and increased mortality [69].

trations [65].

replace autopsy-confirmed AD for future diagnosis of definite AD [50].

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

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

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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‐

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

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].

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 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.
