Kevin T. Ong Kevin T. Ong

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

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

#### Abstract

Alzheimer's and other neurodegenerative diseases are generally incurable and often difficult to diagnose accurately. Yet early and accurate diagnosis of a neurodegenerative disease can potentially contribute to more effective treatment. Hence research efforts are moving towards early identification of high risk subjects and prevention of disease progression with biomarkers. Unfortunately dementia and biomarker studies are hampered by variables such as drop outs, challenges in comparing data sets, discordant biomarker sets, availability of histopathological confirmation at death, validity of cognitive testing, and nonlinear fluctuations in cognitive domains as disease progresses in vivo in subjects. This chapter is an assessment of the challenges in the early diagnosis of dementia, as well as a presentation of the issues faced in conducting dementia and biomarker studies.

DOI: 10.5772/intechopen.72866

Keywords: Alzheimer's disease, dementia, mild cognitive impairment, ageing, early diagnosis, biomarkers, research

#### 1. Introduction

Although dementia is a priority for research globally, dementia studies are very complicated to design [1, 2]. Patents have a time limit which might expire prior to completing a trial, thus complicating contracts with a pharmaceutical company to use their drugs. Drug studies may involve issues related to the use of biomarkers which have not been validated for such use, like disclosure of biomarker results to participants. The treatment target for best outcome is still unestablished, and there are no guarantees that any treatment will work. In addition the odds of success are poor based on a string of crushing defeats so far [3, 4]. Pharmaceuticals pull out of trials because of the price and risk of not succeeding. Due to the slowly progressive nature of dementia, there is a huge time-lag between the commencement of trials and obtaining results. Dementia covers a multitude of specialities, including neurologists, geriatricians, nuclear medicine physicians, radiologists, psychogeriatricians, pathologists, and psychologists. Collaboration with colleagues from different sub-specialities and with regulatory agencies is needed to successfully conduct studies.

© 2018 The Author(s). Licensee IntechOpen. 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.

© 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 eproduction in any medium, provided the original work is properly cited.

In any diagnostic entity, there is increased heterogeneity the earlier it is addressed, and so mild cognitive impairment (MCI) is a challenging population to study due to the heterogenous phenotypes, etiologies and prognosis, both cross-sectionally and longitudinally. Furthermore, similar symptoms can often be attributed to multiple different causes, each to varying degrees. Although there is a good amount of consistency between MCI studies themselves, increased heterogeneity in the actual early disease states does result in differences in outcome between MCI studies. The new research criteria for MCI due to Alzheimer's disease (AD) is an attempt to eventually move beyond highlighting MCI as a major risk factor for AD to operationalizing the prognostication of cognitive impairment in clinical settings.

collected and analysed the right way. We should remember that the modern science of genetics was founded on cross breeding yellow and green peas and their offsprings, at a time when

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

Research efforts are moving towards early identification of high risk subjects and prevention of progression. In the preclinical space, there is not yet a lot of longitudinal biomarker data. Longitudinal data provides important knowledge of biomarkers in predicting and monitoring cognitive and functional decline. To make the most of the limited data, use of both familiar as well as more sophisticated statistical techniques is required. There is a need for equations and

The Cox regression survival analysis is one statistical approach that can distill the heterogeneity of MCI aetiologies to determine independent risk factors for MCI conversion to AD. Cox regression is a survival analysis statistical technique that enables the simultaneous comparison and adjustment of the effects of several risk factors (i.e. the predictor variables or covariates) of an unwanted event occurring. It can also accommodate covariates that are dichotomous, continuous, and even if they might change in value. The required inputs are: time to an unwanted event of interest, the unwanted event of interest, and the predictor variables. The result is expressed as hazard ratios, which is the proportion of an unwanted event of interest between groups at an instantaneous moment in time. According to the Cox regression model, the hazard for an individual is a fixed hazard for any other individual. By inputting all known variables (risk factors) in a study cohort into the Cox model, we can adjust for all of them

The source of subjects is a significant point that affects rates of conversion to AD [8]. People seeking specialist care for memory loss are more selected compared with people in the community who happen to have some memory problems [9]. Different studies have different aims and designs, and different methods to operationalize criteria [7]. Cognitive complains can be spontaneous, yet not routinely elicited in some cases; and clinical assessments can be standardised in

Recruitment sites are an important consideration in designing studies. Cohorts at different sites are demographically different in some ways, so academic sites perform differently from commercial sites. Some cohorts like the Australian Imaging Biomarkers and Lifestyle healthy control cohort are Apolipoprotein E ε4 (E4) enriched [10]. The Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort consists of 398 MCI subjects, who were mostly white and highly educated, had intermediate cognitive measures and cerebral spinal fluid (CSF) biomarker levels between the

MCI cohorts recruited today may not be entirely relevant to tomorrow's world. Secular changes influence the predictive value of cognitive performance in dementia. For example, in the Flynn Effect [12], massive gains in IQ of Americans were observed between 1932 and 1978. Humanity seems to gain skills that make IQ tests outdated. Lifestyle technology development like software

ADNI controls and AD groups [11], and there was also a high proportion of E4 carriers.

many competing theories were making headway.

simultaneously.

formulas that can embrace heterogeneity without being too complex.

2.3. Source of subjects, where and when the study was conducted

some cases but based on more subjective clinical judgement in others.

apps may further leverage our function and so delay residential care.

This chapter considers the methodological issues, challenges and assumptions that need to be taken into consideration when evaluating dementia and biomarker studies.
