**1. Introduction**

Given the rapid ageing of the population worldwide, global estimates of AD - generally con‐ sidered 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 convic‐ tion 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 diag‐ nostic accuracy in identifying and predicting progression to dementia. MCI is an unstable clinical construct where some patients convert (MCI-converters) while others remain rela‐ tively stable (MCI non-converters). As observed from neuropathological and recent bio‐ marker 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 sug‐ gests that functional and structural brain changes may occur prior to apparent clinical mani‐ festations 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 defini‐ tion of MCI This provided the impetus for the development of reliable biomarkers such as cerebrospinal fluid (CSF), neuroimaging and blood biomarkers to complement clinical ap‐ proaches in early diagnosis and predicting progression. In support of this, the recent pro‐ posed criteria for symptomatic pre-dementia phase of AD (MCI), preclinical AD and presymptomatic AD have included biomarkers reflecting molecular pathology, downstream

© 2013 Chong and Lee; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

measures of structural and functional/metabolic changes, and associated biochemical changes in their research diagnostic armamentarium [4].

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

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

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

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the rate of AD decline [20].

*Predicting AD conversion in MCI subjects* 

**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

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.

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

 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

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