The aim of the thesis was to investigate the use of biomarkers for trial design in non-demented subjects with AD. In addition, we investigated the perceived usefulness of biomarkers in clinical practice. In chapter 2 .1 we found that biomarkers and cognitive markers showed a temporal order of change and that the markers showed different rates of decline in subjects with AD-asymptomatic, AD-MCI and AD-dementia. CSF Aß1-42 reached the maximum abnormality level in the asymptomatic stage and CSF tau in the MCI stage. The imaging and cognitive markers started to change in the asymptomatic stage and became abnormal in the MCI stage. The rate of change in these markers increased with increasing disease severity. The pattern of decline was distinct from that of subject without amyloid pathology, which showed a more benign pattern of change. Our results are, with some exceptions, in accordance to the hypothetical model of dynamic changes and may be helpful to determine stage specific outcome measures for clinical trials. In chapter 2.2 we studied the association of CSF Aβ 1-42 in non-demented subjects with SCD or MCI. We found that lower CSF Aβ 1-42 levels within the normal range are predictive for faster clinical progression in non-demented memory clinic subjects. This effect was more evident in subjects with MCI. For both SCD and MCI subjects, the predictive value of CSF Aβ 1-42 levels was stronger than CSF tau or p-tau levels. These results suggest that low normal CSF Aβ 1-42 levels could reflect the earliest changes in the brain that are relevant for AD. In chapter 2.3 we defined a cutoff for CSF aß1-42 with the use of data driven Gaussian mixture modelling. With Gaussian mixture modelling one assumes that the data are a mix sampled from two different distributions, which represent a normal and an abnormal population. With this method we determined a cutoff for abnormal CSF aß1-42 levels at 680 pg/ml. This cutoff was independent of the cognitive stage and APOE genotype. The cutoff was higher in older than in younger subjects. The data driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in non-demented subjects (HR 7.6 versus 5.2, p<0.01). Our new cutoff was similar to the amyloid PET derived cutoff for CSF aß1-42, implying a good correlation with amyloid pathology. II. Use of AD biomarkers for clinical trials and clinical practice. In chapter 3.1 we reviewed how AD biomarkers have been used in clinical trials for inclusion and as outcome so far. We showed that previous clinical trials used biomarkers mainly as outcome measure. In chapter 3.2 we investigated the influence of different inclusion criteria for preclinical and prodromal Alzheimer’s disease (AD) on changes in biomarkers and cognitive markers and on trial sample size estimates. We found that sample size varied widely according to the combination of inclusion criteria and outcome markers applied. The smallest sample size needed to show a treatment effect was estimated for subjects with normal cognition or MCI who had both abnormal amyloid and injury markers at baseline, using brain volumetric markers as outcome measure. In chapter 3.3 we performed a survey among European neurologists with specialisation in neurodegenerative diseases and assessed the attitude towards the utility of the concept MCI and usefulness of research criteria in clinical practice. Most respondents used the term MCI for diagnosis and in communication with patients and almost 70% of respondents used the research criteria. Use of research criteria for prodromal AD/MCI due to AD was perceived useful and considerable influenced the management of and communication to patients with MCI.
|Award date||14 Apr 2021|
|Place of Publication||amsterdam|
|Publication status||Published - 14 Apr 2021|
- Alzheimer's disease
- amyloid beta
- clinical trial
- clinical practice