3. Diagnostic challenges

#### 3.1. Accuracy of diagnosis

2.4. Challenges in comparing data sets

112 Alzheimer's Disease - The 21st Century Challenge

2.5. Drop outs and their risk factors

Yet death alters the probability of observing dementia.

results [13–17].

Retrofitting criteria and statistical models developed from experience with one cohort to another that has different demographic characteristics will end up with varying outcomes, not to mention the different combinations of measurements, cut-offs, number of subjects, and length of follow-up between samples that will further compound the variability of

Validity is gained when results are repeatable. Power is gained when shared data is combined. Sometimes data sets are easily comparable. For example, the ability of 3.0-Tesla (T) and 1.5-T scanners to track longitudinal atrophy in AD and MCI patients using tensor based morphometry are both similar and powerful enough to detect atrophy longitudinally [18], so it may not matter much that one cohort had their magnetic resonance imaging (MRI) on a 1.5-T scanner and another cohort had their MRI on a 3.0-T scanner. However in dementia studies, combining data sets is not a trivial issue. Comparing results from different studies that have used different methodologies is rather difficult. Combining data from different scanners introduces noise. Different positron emission tomography (PET) or MRI scanners have different scanner and software combinations. Inter scanner variability is excluded if all cross-sectional and longitu-

Lack of standardisation threatens to hamper the comparison and replication of results, increase analytical variability, and complicate the evaluation of methods [7]. Different methods of biomarker analyses give varying degrees of precision [19]. Drop outs or missing data are dealt with differently. Time lag between receiving a clinical diagnosis of subjective cognitive impairment (SCI) or MCI and enrolment differs between studies. If the time lag between diagnosis and recruitment is long, this might make one SCI or MCI cohort have more stable subjects, and so less likely to progress to a dementia subtype. Different population norms are used for

neuropsychological tests, and different batteries of neuropsychological tests are used.

Given that the stability of cognition can be affected by many factors in the short term, it is important to consider what variables are corrected for when we read published studies. As mentioned above, a down side to robustly designed studies which are generally informative as they control for many factors, is that they may not simulate routine clinical practice well.

Drop outs in research studies due to relocation and loss of interest should be classified as random dropouts. However drop outs from MCI studies are not entirely random [20]. Traditional survival analysis assumes censored observations are non-informative and ignorable [21].

Risk factors for cognitive and functional impairments in MCI can also be risk factors for dropping out early from MCI studies causing potential bias in the sample. For example, E4 is a risk factor for progression from a clinical dementia rating (CDR) of 0.5 to a CDR of 1 and above and a risk factor for cardiovascular mortality [22]. Heart failure is a risk factor for progression from mild cognitive to severe cognitive impairment, and for functional decline

[23]. Stroke is a risk factor for non-amnestic cognitive and functional decline [24].

dinal scans are performed on the same scanner—but this is not practical.

The dementia field is filled with many contradictory ideas and controversies. Accuracy of dementia diagnoses has been an unresolved challenge. For example, in the religious orders study involving over 1000 nuns, the majority of cases particularly in those over 85 have AD pathology as well as several other pathologies [25]. Of the phenotypes that look like clinically probable AD, some had Lewy bodies or other predominant neurodegenerative disorders at autopsy.

#### 3.2. Volatility of clinical outcomes

Diagnosing during the pre-dementia stages is challenged by fluctuations in cognitive ability over long periods of time [26]. In short term MCI studies, outcomes are rather volatile, such that one can revert to normal, remain MCI with improvement or deterioration in cognitive abilities, convert to dementia, improve after deteriorating further, or deteriorate again after improving. For example, in the Rochester Minnesota longitudinal study, as high as 35% of MCI reverted to normal when followed long enough [27]. However two-thirds of these ultimately progressed again to MCI or dementia. In the Pittsburgh longitudinal health study after over a decade of follow-up, a small percent return to normal after being diagnosed with MCI [28].

One way to account for the observed volatility is the rigid way disease and states are categorised. By taking a disease continuum and subjecting it to arbitrary boundaries, patients are likely to bounce in and out of them. Another cause of volatility is the random fluctuation of cognitive test scores up to half a standard deviation. Someone vulnerable near the cut-off could be having a good day and so their scores may be considered to be within the normal range, or having a bad day and so their scores may be considered to be within the MCI range. This variability of performance from day to day is not a trivial matter because it predicts future decline over and beyond cognitive performance [29]. Consecutive clinical information should be taken more seriously as it may discount initial diagnoses.

The entire trajectory of cognitive decline in one at risk of AD is not necessarily due solely to AD. To date only up to half of cognitive decline can be accounted for by neuropathology seen on autopsies of brains, e.g. AD, micro and macro infarcts, Lewy bodies, TDP-43, pre-synaptic proteins, and neuronal density and locus [30]. Pathology may trigger events or formation of other pathologies, thus causing people's brains to differ in how they respond to the predominant neurodegenerative pathology. For example, mixed AD with Lewy Bodies will have more variability in their cognition due to attention impairment [31].

### 3.3. The paradox of Alzheimer's disease biomarker validation studies

High quality studies validating the diagnostic utility of biomarkers involve blinding of clinicians to the biomarker results when making a clinical diagnosis, and blinding assessors of the biomarkers to the clinical diagnoses. However the diagnosis of clinically probable AD using standard criteria has an error rate of at least 20%, and definite diagnosis requires confirmatory pathology [32]. Hence no biomarker study can outweigh the quality of the clinical diagnosis even if double blinding is the gold standard. Unblinding a clinician to an amyloid PET scan result introduces circularity in the validation of the amyloid PET scan. However doing so has value as it may actually improve the certainty of an AD diagnosis or correct a wrong diagnosis of AD.

funding. If subjects are recruited after downstream processes have began, even though there is minimal disruption of neuronal integrity at enrolment, the treatment may not work. Yet it is

Challenges in Dementia Studies

115

http://dx.doi.org/10.5772/intechopen.72866

Phenotypes can range between being atypical to being unambiguous. Clinical labels lose credibility when challenged by biomarker evidence which are themselves not perfect. It is possible for an amyloid PET scan to be positive and the CSF Aβ level to be high, and vice versa. It is possible for tracer uptake to be concentrated only on one brain region unilaterally. It is possible for tracer uptake to increase rapidly between serial scans within a relatively short space of time. It is possible for tracer uptake to decrease between serial scans. False negatives, albeit rare, have been reported with Pittsburgh compound B (PiB) scans [34]. Even pathological confirmation, which is the gold standard, is not an exact science. Conflicting biomarkers add complexity to diagnosis and prognostication. It is important to apply Bayesian logic (i.e. post-test probability is affected by pre-test probability and the robustness of the test) when

Clinical diagnosis does not necessarily predict deterioration over time. It is appropriate to conclude that having a positive amyloid scan will result in AD patterns of deficits developing, but this does not exclude significant co-morbid conditions from becoming the predominant contributing factor in cognitive or functional decline. Older persons may be living long enough to accumulate another threat to the body. Thus neurodegenerative pathologies may be more relevant in pre-terminal decline than terminal decline. Death is a competing risk for seeing the

Cognitive tests demonstrate cognitive performance. They should be considered an adjunct tool in the assessment and management of an underlying neurodegenerative condition. All tests are based on paradigms on how we learn information. In order to detect deficits, tests are designed to push people until they make errors. A low score does not diagnose dementia. A high score does not exclude dementia. A single score cannot be considered in isolation.

Confidence that cognitive tests accurately reflect subject cognition is important. Tests require a wide response distribution and evenness of scale to enable sensitive detection of clinical changes and assessment of the degree of deficits. Sensitivity to cognitive disease and change over time, enables tracking of disease progression, evaluation of treatment effectiveness, and maintains focus on the symptoms and disease of interest. Measures should be able to capture deficits, have low noise, and relate to biological markers. Characterising early presenters based on neuropsychological test performance should be detailed enough to make sense, but not

overly precise—otherwise it can paradoxically complicate assessment and follow-up.

easier to raise money when subjects are considered to have a disease.

3.6. Discordant biomarker results

considering differentials.

4.1. Introduction

3.7. Clinical diagnosis versus clinical deterioration

clinical syndrome develop, even though the pathology is there.

4. Principles and challenges in cognitive testing

#### 3.4. Qualitative versus quantitative approach to diagnosis

The ability to accurately diagnose the clinical group to which a subject belongs is a crucial first step for appropriate management, and for clinical trial design. Categorising participants into MCI subtypes is heavily reliant on cross-sectional performance on neuropsychological tests as compared with a matched normal cohort. However, clinical assessment rather than quantitative variables takes precedence in assigning individuals into a dementia subtype. The problem with basing the MCI criteria on objective scores is that objective scores which are arbitrarily defined are required to support the subjective complains of symptoms which fluctuate. This system of categorising MCI helps to define MCI subgroups to facilitate research studies, but adds confusion when applied to assessing individuals. It has been observed in the ADNI cohorts that study variables have significant overlap between clinical groups, and that groups differ more qualitatively than quantitatively [33].

### 3.5. Conundrums in dementia studies

Even with histopathological confirmation of a definite AD diagnosis at death, it can be argued that there is always a degree of circularity in testing the predictive utility of any individual biomarker or clinical marker in high risk subjects for conversion to AD, unless each factor is not associated with each other. For example, if subjects are recruited from different sites, then regrouped by biomarker profile, those recruited from tertiary memory clinics are likely to both progress to AD faster and have positive biomarker or clinical marker profiles, whatever biomarker or clinical marker is used. Therefore in testing predictive utility for conversion to AD, comparing between at least two or more biomarkers or clinical markers, may enhance study quality.

All dementia neuropathological studies are designed based on neuropathologies we currently know how to identify. Neuropathologies that we do not know how to identify due to limitations in current histopathological staining techniques are pathologies that are not studied. Should they in fact be clinically relevant, we are unable to know this.

In order to test the concept that early intervention before disruption of neuronal integrity is key in successful therapy, subjects will have to be recruited at a stage where there is minimal disruption of neuronal integrity. However, if these subjects are recruited at too early stages of disease, they may not decline for the same reason that they are recruited, so results may be negative and they are not considered to have a disease but a syndrome. Having to recruit subjects with a syndrome but not a disease classification makes it harder to apply for research funding. If subjects are recruited after downstream processes have began, even though there is minimal disruption of neuronal integrity at enrolment, the treatment may not work. Yet it is easier to raise money when subjects are considered to have a disease.

### 3.6. Discordant biomarker results

3.3. The paradox of Alzheimer's disease biomarker validation studies

3.4. Qualitative versus quantitative approach to diagnosis

differ more qualitatively than quantitatively [33].

3.5. Conundrums in dementia studies

114 Alzheimer's Disease - The 21st Century Challenge

High quality studies validating the diagnostic utility of biomarkers involve blinding of clinicians to the biomarker results when making a clinical diagnosis, and blinding assessors of the biomarkers to the clinical diagnoses. However the diagnosis of clinically probable AD using standard criteria has an error rate of at least 20%, and definite diagnosis requires confirmatory pathology [32]. Hence no biomarker study can outweigh the quality of the clinical diagnosis even if double blinding is the gold standard. Unblinding a clinician to an amyloid PET scan result introduces circularity in the validation of the amyloid PET scan. However doing so has value as it may

The ability to accurately diagnose the clinical group to which a subject belongs is a crucial first step for appropriate management, and for clinical trial design. Categorising participants into MCI subtypes is heavily reliant on cross-sectional performance on neuropsychological tests as compared with a matched normal cohort. However, clinical assessment rather than quantitative variables takes precedence in assigning individuals into a dementia subtype. The problem with basing the MCI criteria on objective scores is that objective scores which are arbitrarily defined are required to support the subjective complains of symptoms which fluctuate. This system of categorising MCI helps to define MCI subgroups to facilitate research studies, but adds confusion when applied to assessing individuals. It has been observed in the ADNI cohorts that study variables have significant overlap between clinical groups, and that groups

Even with histopathological confirmation of a definite AD diagnosis at death, it can be argued that there is always a degree of circularity in testing the predictive utility of any individual biomarker or clinical marker in high risk subjects for conversion to AD, unless each factor is not associated with each other. For example, if subjects are recruited from different sites, then regrouped by biomarker profile, those recruited from tertiary memory clinics are likely to both progress to AD faster and have positive biomarker or clinical marker profiles, whatever biomarker or clinical marker is used. Therefore in testing predictive utility for conversion to AD, comparing

between at least two or more biomarkers or clinical markers, may enhance study quality.

Should they in fact be clinically relevant, we are unable to know this.

All dementia neuropathological studies are designed based on neuropathologies we currently know how to identify. Neuropathologies that we do not know how to identify due to limitations in current histopathological staining techniques are pathologies that are not studied.

In order to test the concept that early intervention before disruption of neuronal integrity is key in successful therapy, subjects will have to be recruited at a stage where there is minimal disruption of neuronal integrity. However, if these subjects are recruited at too early stages of disease, they may not decline for the same reason that they are recruited, so results may be negative and they are not considered to have a disease but a syndrome. Having to recruit subjects with a syndrome but not a disease classification makes it harder to apply for research

actually improve the certainty of an AD diagnosis or correct a wrong diagnosis of AD.

Phenotypes can range between being atypical to being unambiguous. Clinical labels lose credibility when challenged by biomarker evidence which are themselves not perfect. It is possible for an amyloid PET scan to be positive and the CSF Aβ level to be high, and vice versa. It is possible for tracer uptake to be concentrated only on one brain region unilaterally. It is possible for tracer uptake to increase rapidly between serial scans within a relatively short space of time. It is possible for tracer uptake to decrease between serial scans. False negatives, albeit rare, have been reported with Pittsburgh compound B (PiB) scans [34]. Even pathological confirmation, which is the gold standard, is not an exact science. Conflicting biomarkers add complexity to diagnosis and prognostication. It is important to apply Bayesian logic (i.e. post-test probability is affected by pre-test probability and the robustness of the test) when considering differentials.

#### 3.7. Clinical diagnosis versus clinical deterioration

Clinical diagnosis does not necessarily predict deterioration over time. It is appropriate to conclude that having a positive amyloid scan will result in AD patterns of deficits developing, but this does not exclude significant co-morbid conditions from becoming the predominant contributing factor in cognitive or functional decline. Older persons may be living long enough to accumulate another threat to the body. Thus neurodegenerative pathologies may be more relevant in pre-terminal decline than terminal decline. Death is a competing risk for seeing the clinical syndrome develop, even though the pathology is there.
