5. Principles and challenges in biomarker use

#### 5.1. Introduction

A biomarker is any identifiable biological measurement that can be objectively measured; that accurately represents underlying pathology associated with disease, like blood, CSF, or imaging; and that changes with risk or expression of disease. Biomarkers in dementia measure directly, the neuropathology that is primarily responsible, like the amount of β-amyloid (Aβ) plaques in the Alzheimer's disease brain (e.g. CSF Aβ<sup>42</sup> and Aβ amyloid PET), and indirectly, their downstream effects, like the amount of neuronal damage (e.g. CSF tau and volumetric MRI) or synaptic dysfunction (e.g. FDG PET). Biomarkers should not be confused with genetic risk factors, e.g. Apolipoprotein E ε4 polymorphism.

The diagnostic goals of biomarkers in dementia are to ensure significant neuropathology is present or not present in people at risk of developing dementia, so as to increase confidence in making a dementia subtype diagnosis like AD or non-AD in atypical cases, to reduce subject numbers in clinical studies, and to reduce heterogeneity in a study cohort. The prognostic goals of biomarkers are to assess risk and proximity of future decline by serving as surrogate outcome measures to demonstrate effects on downstream targets of neurodysfunction and neurodegeneration, to help define the disease stage, and reduce trial duration. The theragnostic goals of biomarkers are to serve as end point measures to prove engagement of disease modifying treatment with Aβ plaques, and to select drug of choice.

Due to the added value that biomarkers bring, they enable us to hypothesise in a much more rigorous way how we conduct dementia studies. For example, the development of diseasemodifying anti-amyloid therapies is now assisted by in vivo cerebral Aβ imaging to reduce the sample size by better selection of eligible volunteers for trials and to evaluate the efficacy of treatment. Biomarkers can help in planning which drugs are safe for AD drug trials by seeing if there had been some unexpected outcome in the brain. This would potentially improve safety, minimise cost which will in turn enable more drugs to be trialled while avoiding unsafe ones. Nonetheless, at this point in time, biomarkers are not used routinely in most clinical settings in dementia management. On top of limited access or support from current clinical guidelines, no neurodegenerative disease modifying drugs are currently licenced for routine use. However, should disease-modifying therapy become available, the issue of expanding infrastructure to meet the demands for biomarkers will be a subject of further debate. The potential for the usefulness of biomarkers is fully dependant on whether or not a cure for AD or non-AD dementias can be found.

The fundamental consideration with any assessment approach in dementia, whether with clinical bedside tests or with biomarkers is how precise a measure is in determining what it is meant to be detecting. To be used as surrogates for clinical measures, biomarkers need to be validated as reflecting clinical and/or pathological disease processes, taking into account the phase of disease where they have a high degree of specificity and sensitivity [52, 53]. Standardising procedures will reduce measurement errors in clinical trials. They should apply similarly to everyone no matter what race, language or culture they come from. Ideally, the biomarkers and clinical markers must be strongly associated, yet independent of each other, in order to be used as recruitment criteria and as outcome measures, yet avoiding circularity. However validating the relationship between biomarker change and cognitive outcome is an imperfect science. Considerable challenges remain in establishing the relationship between biological and cognitive measures throughout the chronology of the preclinical phase of AD.

A measurable biomarker needs to be operable clinically, have significant clinical implications if results are positive, and have clinical utility in terms of improving confidence in diagnosing, prognosticating or guiding treatment options. Unlike cognitive assessments, biomarkers offer more objective results and are considered complimentary to memory testing. They are highly valued for their ability to detect underlying structures or neuropathology in vivo. However the evaluation of biomarkers is an expensive endeavour, and cannot be carried out without collaboration between pharmaceuticals and public institutions.

The reproducibility of biomarker results can be affected by many factors. For example, discrepancy of biomarkers and cognitive tests can happen because of a plateau of biomarkers prior to cognitive change. Individual biomarkers of amyloid PET, MRI, FDG PET, and CSF in the ADNI cohort vary in their rate of change during disease progression, such that they fit better in sigmoidal models than linear models [54]. An ideal biomarker should have a sensitivity, specificity, as well as positive and negative predictive values above 80% for whatever is it supposed to be testing for [55, 56]. Biomarkers are expensive. Risks, benefits and costs have to be discussed with the patient.

### 5.2. Operationalisation challenges

practice effects. Practice or re-test effects occur in non-demented adults [48]. They involve episodic memory in learning test content, procedural non-declarative learning for familiarisation with task procedures, and anxiety reduction by desensitisation. Practice effects are not necessarily a nuisance as they themselves comprise a test. For example, one study showed that the loss of short-term practice effects portends a worse prognosis after 1 year in patients with MCI [49]. When the Cogstate was repeated four times a day, having attenuated practice effect in non-

A biomarker is any identifiable biological measurement that can be objectively measured; that accurately represents underlying pathology associated with disease, like blood, CSF, or imaging; and that changes with risk or expression of disease. Biomarkers in dementia measure directly, the neuropathology that is primarily responsible, like the amount of β-amyloid (Aβ) plaques in the Alzheimer's disease brain (e.g. CSF Aβ<sup>42</sup> and Aβ amyloid PET), and indirectly, their downstream effects, like the amount of neuronal damage (e.g. CSF tau and volumetric MRI) or synaptic dysfunction (e.g. FDG PET). Biomarkers should not be confused with genetic

The diagnostic goals of biomarkers in dementia are to ensure significant neuropathology is present or not present in people at risk of developing dementia, so as to increase confidence in making a dementia subtype diagnosis like AD or non-AD in atypical cases, to reduce subject numbers in clinical studies, and to reduce heterogeneity in a study cohort. The prognostic goals of biomarkers are to assess risk and proximity of future decline by serving as surrogate outcome measures to demonstrate effects on downstream targets of neurodysfunction and neurodegeneration, to help define the disease stage, and reduce trial duration. The theragnostic goals of biomarkers are to serve as end point measures to prove engagement of

Due to the added value that biomarkers bring, they enable us to hypothesise in a much more rigorous way how we conduct dementia studies. For example, the development of diseasemodifying anti-amyloid therapies is now assisted by in vivo cerebral Aβ imaging to reduce the sample size by better selection of eligible volunteers for trials and to evaluate the efficacy of treatment. Biomarkers can help in planning which drugs are safe for AD drug trials by seeing if there had been some unexpected outcome in the brain. This would potentially improve safety, minimise cost which will in turn enable more drugs to be trialled while avoiding unsafe ones. Nonetheless, at this point in time, biomarkers are not used routinely in most clinical settings in dementia management. On top of limited access or support from current clinical guidelines, no neurodegenerative disease modifying drugs are currently licenced for routine use. However, should disease-modifying therapy become available, the issue of expanding infrastructure to meet the demands for biomarkers will be a subject of further debate. The potential for the usefulness of biomarkers is fully dependant on whether or not a cure for AD

disease modifying treatment with Aβ plaques, and to select drug of choice.

demented participants detects MCI [50, 51].

118 Alzheimer's Disease - The 21st Century Challenge

5.1. Introduction

5. Principles and challenges in biomarker use

risk factors, e.g. Apolipoprotein E ε4 polymorphism.

or non-AD dementias can be found.

The challenges in operationalising biomarkers for clinical practice are: standardization of techniques; harmonising practices between settings; and developing infrastructure for community access to access them. In applying biomarkers in the clinical setting, we need to consider the noise and variability factors, whether these are going to present a critical issue when it comes to trying to apply this in cross-sectional or longitudinal evaluation. Different biomarkers provide different levels of certainty, are sensitive and specific at different disease stages and in different disease subtypes. Cross-sectional data of single time-point measures have less predictability than multiple measurements for seeing progression and outcomes in longitudinal data, which then in turn limits on-going participation. For most biomarkers, biomarker progressions are more associated with cognitive decline than baseline values [57]. This suggests that clinical trials which require recruiting at-risk subjects could be improved by using progression rather than baseline values in biomarkers to enrich the study subjects. Further studies are warranted to estimate the incremental effectiveness of improving clinical trial statistical power by using biomarker progression criteria.

Biomarkers should only offer additional information which we are unable to obtain during routine history-taking, physical examination, and investigations. Their use is more appropriate when there is some uncertainty in the clinical picture. All test results must be carefully interpreted in the context of a patient's clinical presentation. All tests have inherent limitations, so over-reliance on any test without first considering relevant clinical information is likely to lead to either over- or under-diagnosis, with potentially negative consequences. Hence we need to exercise our clinical judgement to consider how additional information helps in improving the probability of a dementia subtype diagnosis or in guiding treatment. Overemphasising biomarkers at the expense of appreciating the context of an individual case may end up inappropriately prioritising less important aspects of a case.

An analysis of within-site and inter-site assay reliability across seven centres using aliquots of CSF from normal control subjects and AD patients showed the coefficient of variation was 5.3% for Aβ, 6.7% for t-tau, and 10.8% for p-tau within centre, and it was 17.9, 13.1 and 14.6% for Aβ, t-tau, and p-tau respectively between centres [59]. The reason for the inter-laboratory

Challenges in Dementia Studies

121

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

Determining the threshold of a positive or negative biomarker result is arbitrary to some extent, and can be problematic. Yet it may significantly influence categories and outcomes. The essential difference between MCI and those considered to have normal cognition is evidence of objective impairment on cognitive test scores, even though cut-off scores are

Different approaches to determining cut-offs yield different degrees of positives, and form a band of intermediates close to where the cut-offs are. A case can be made for cut-offs to be modified by age rather than by merely depending on a simple number, but this will increase complexity in the analyses. Examples of cut-off approaches include clustering analysis, 95th percentile, iterative outlier approach, absolute cut-off (e.g. SUVR over 1.50 for PiB scans), and

CSF may be abnormal before PET and the discordance of low CSF Aβ<sup>42</sup> levels with PiB depends on the cut-offs for both [60]. Cases with discordance of both biomarkers are usually

Cut-offs can have implications in the design of AD trials. Lower cut-offs for amyloid positivity ensure the sample subjects are more likely to have AD, and high cut-offs might avoid exposing

By and large, the medical community tends to blur the distinction between that which is kept strictly for research, and that applied in routine clinical practice. At present, the boundaries between current research guidelines in dementia research and clinical practice are not distinct. Research criteria have a strong potential to impact clinical practice, such that terminologies

Biomarkers in dementia give risk information only, and results can be inconclusive. Until a cure is developed, the distance between advancements in diagnosis and treatment continues to grow. A positive result is not a diagnosis. Not all with positive biomarker results will develop AD. Potential harms with study participation include confusion over inconclusive results, being given wrong diagnoses, stigmatisation, exploitation, discrimination, negative affective reactions [61], escalation of insurance premiums [62], loss of the right to drive, additional work

precision is not well understood.

greater than control mean plus two standard deviations.

cases where one or both biomarker results are around the cut-off.

individuals to the risks of treatment with little chance of benefit.

6. Ethical challenges in the disclosure of biomarker results

used in research settings easily become adopted into routine clinical practice.

conditions, and over-protection by law which can disadvantage employers.

5.4. Cut-offs

arbitrarily defined.

6.1. Introduction

Until an effect on a particular biomarker is reasonably likely to predict clinical benefit by widespread evidence based agreement, it should not be used routinely as a surrogate outcome measure in AD. The specific potential benefits of biomarkers as individuals transit from normal to SCI, SCI to MCI, or MCI to dementia states need to be identified and measured. Although further validation for currently available biomarkers is still required, advancement in the biomarker field is currently approaching a plateau, as there is still no biomarker breakthrough that can capture processes upstream to Aβ accumulation.

Finally, it is wrongly assumed that biomarkers are just as sensitive and specific for detecting neuropathology across the age range and across the disease stage. For example, since the standardised uptake value ratio (SUVR) is calculated using cerebellar grey matter as the reference region, in late to advanced stages there will be amyloid build-up causing reduction of SUVR. This has implications for longitudinal studies. The general reduction in amyloid load after the plateau with ageing may falsely suggest that treatments are working.

#### 5.3. Cerebral spinal fluid biomarkers

CSF tau levels increase because of tau leaking from neuronal injury, and CSF Aβ levels decrease possibly because Aβ is crystallising in the cortices. The potential benefits of using cerebral spinal fluid biomarkers in AD research studies and prevention trials are the ability to: identify the presence of AD pathologies in the absence of cognitive symptoms; evaluate therapeutic target engagement; stage disease pathology; track progression of disease pathology; evaluate potential therapy-related disease modification; cost effectively assess multiple analytes in a single sample; and allow for better trial design with fewer subjects, shorter duration, and assessment of effects on the underlying disease pathologies.

CSF biomarkers are currently not routinely recommended for individual use in clinical practice. The disadvantage of CSF is that it requires a lumbar puncture. Not everyone is willing to have one, and also there is increased use of anticoagulation treatment in the elderly. Hence is it not suitable for population studies. Other challenges in the use of CSF include the lack of protocol and assay standardisation, sub-optimal assay reproducibility, difficulties in defining normal vs. abnormal cut-off values, misperception regarding safety, tolerability and utility of CSF collection and analysis, and the need for assay development and validity in the presence of a therapeutic agent, especially with antibody-based therapies. Agreement between CSF Aβ and florbetapir in ADNI subjects is reasonable but not great (κ = 0.72) cross-sectionally and longitudinally [58].

An analysis of within-site and inter-site assay reliability across seven centres using aliquots of CSF from normal control subjects and AD patients showed the coefficient of variation was 5.3% for Aβ, 6.7% for t-tau, and 10.8% for p-tau within centre, and it was 17.9, 13.1 and 14.6% for Aβ, t-tau, and p-tau respectively between centres [59]. The reason for the inter-laboratory precision is not well understood.

## 5.4. Cut-offs

Biomarkers should only offer additional information which we are unable to obtain during routine history-taking, physical examination, and investigations. Their use is more appropriate when there is some uncertainty in the clinical picture. All test results must be carefully interpreted in the context of a patient's clinical presentation. All tests have inherent limitations, so over-reliance on any test without first considering relevant clinical information is likely to lead to either over- or under-diagnosis, with potentially negative consequences. Hence we need to exercise our clinical judgement to consider how additional information helps in improving the probability of a dementia subtype diagnosis or in guiding treatment. Overemphasising biomarkers at the expense of appreciating the context of an individual case may

Until an effect on a particular biomarker is reasonably likely to predict clinical benefit by widespread evidence based agreement, it should not be used routinely as a surrogate outcome measure in AD. The specific potential benefits of biomarkers as individuals transit from normal to SCI, SCI to MCI, or MCI to dementia states need to be identified and measured. Although further validation for currently available biomarkers is still required, advancement in the biomarker field is currently approaching a plateau, as there is still no biomarker break-

Finally, it is wrongly assumed that biomarkers are just as sensitive and specific for detecting neuropathology across the age range and across the disease stage. For example, since the standardised uptake value ratio (SUVR) is calculated using cerebellar grey matter as the reference region, in late to advanced stages there will be amyloid build-up causing reduction of SUVR. This has implications for longitudinal studies. The general reduction in amyloid load

CSF tau levels increase because of tau leaking from neuronal injury, and CSF Aβ levels decrease possibly because Aβ is crystallising in the cortices. The potential benefits of using cerebral spinal fluid biomarkers in AD research studies and prevention trials are the ability to: identify the presence of AD pathologies in the absence of cognitive symptoms; evaluate therapeutic target engagement; stage disease pathology; track progression of disease pathology; evaluate potential therapy-related disease modification; cost effectively assess multiple analytes in a single sample; and allow for better trial design with fewer subjects, shorter duration, and assessment of effects

CSF biomarkers are currently not routinely recommended for individual use in clinical practice. The disadvantage of CSF is that it requires a lumbar puncture. Not everyone is willing to have one, and also there is increased use of anticoagulation treatment in the elderly. Hence is it not suitable for population studies. Other challenges in the use of CSF include the lack of protocol and assay standardisation, sub-optimal assay reproducibility, difficulties in defining normal vs. abnormal cut-off values, misperception regarding safety, tolerability and utility of CSF collection and analysis, and the need for assay development and validity in the presence of a therapeutic agent, especially with antibody-based therapies. Agreement between CSF Aβ and florbetapir in ADNI subjects is reasonable but not great (κ = 0.72) cross-sectionally and longitudinally [58].

end up inappropriately prioritising less important aspects of a case.

through that can capture processes upstream to Aβ accumulation.

5.3. Cerebral spinal fluid biomarkers

120 Alzheimer's Disease - The 21st Century Challenge

on the underlying disease pathologies.

after the plateau with ageing may falsely suggest that treatments are working.

Determining the threshold of a positive or negative biomarker result is arbitrary to some extent, and can be problematic. Yet it may significantly influence categories and outcomes. The essential difference between MCI and those considered to have normal cognition is evidence of objective impairment on cognitive test scores, even though cut-off scores are arbitrarily defined.

Different approaches to determining cut-offs yield different degrees of positives, and form a band of intermediates close to where the cut-offs are. A case can be made for cut-offs to be modified by age rather than by merely depending on a simple number, but this will increase complexity in the analyses. Examples of cut-off approaches include clustering analysis, 95th percentile, iterative outlier approach, absolute cut-off (e.g. SUVR over 1.50 for PiB scans), and greater than control mean plus two standard deviations.

CSF may be abnormal before PET and the discordance of low CSF Aβ<sup>42</sup> levels with PiB depends on the cut-offs for both [60]. Cases with discordance of both biomarkers are usually cases where one or both biomarker results are around the cut-off.

Cut-offs can have implications in the design of AD trials. Lower cut-offs for amyloid positivity ensure the sample subjects are more likely to have AD, and high cut-offs might avoid exposing individuals to the risks of treatment with little chance of benefit.
