**Author details**

on multimodal approach using various systems biology and multivariate modeling methods. Additionally, multi-site prospective studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), allow for global summary of results and patterns of change observed in clinical measures and candidate biomarkers [133] (Table 5). It must also be highlighted that some of the heterogeneity of biomarker findings thus far is related to the different periods of

The dynamic biomarker model, in the AD pathological cascade first proposed by Jack in 2010 [134], has been an area of intense interest. However, this inverse relationship between fibrillar amyloid plaque burden (on PIB imaging) and corresponding decrease in CSF Aβ42 and elevated tau, has led to the simplistic interpretation that the AD pathological cascade is purely driven by the amyloid cascade (Figure 1). This is partly due to extrapolation from crosssectional studies, where in fact, longitudinal studies are required to determine the temporal order of the appearance of various pathogenic processes involved in this complex disease. Storandt et al [135] has recently demonstrated in a community cohort that CSF Aβ42 and tau were minimally correlated, suggesting that they represent independent processes. Addition‐ ally, they accounted for only 60% of variance on PIB imaging, suggesting that a third process

In addition, understanding longitudinal biomarker change allows its potential inclusion in clinical trials, with recent studies advocating the use of neuroimaging biomarkers [137,138], CSF biomarkers [139] and/or combination biomarkers [137,140] to boost the power of clinical trials and decrease sample size in MCI trials. An integrated analyses approach using patient (age) severity- and disease-related (severe baseline cognitive, global or behavioural status) factors in established AD has been shown, with the potential of symptomatic AD therapy, to

Further work on biomarkers is important because of their multiple potential roles. Biomarkers have the potential to be used as a prognostic tool for the prediction of AD conversion in MCI subjects and rapid AD progression, with translation into clinical practice by using a most practical algorithm, and as a diagnostic tool in prodromal/ preclinical stages of AD. Biomarkers may also lead to a deeper understanding of the complex pathogenesis of AD disease – including stage-specific and stage-independent processes. There is also currently an unfulfil‐ led potential in biomarker-enriched clinical trials and the use of biomarkers in preclinical AD, especially in the advent of newer therapeutic targets. Finally there is also potential to extrap‐ olate biomarker findings 'backwards' into the earliest stages of disease so that we may be able to identify those at risk and consider instituting interventions. This would enable earliest therapeutic intervention for at-risk subjects most amenable to disease-modifying treatments, and exclude those for whom the possible risks from investigational treatment would be more difficult to justify. At the very least, it would identify those who might benefit most from intensive monitoring and management of clinical factors, e.g. blood pressure, diabetes and lipids, and also non-invasive interventions, e.g. cognitive training. This vital work can only been done through multi-center studies and standardized evaluation techniques using various

follow-up and hence AD conversion rates in MCI subjects.

396 Understanding Alzheimer's Disease

may be related to brain atrophy or plaque formation [136].

decrease likelihood of faster decline [141].

systems biology and statistical modeling approaches.

Mei Sian Chong1 and Tih-Shih Lee2

1 Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore

2 Duke University Medical School, USA
