Gerald Novak

Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

Background Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance. Methods Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included. Findings In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model. Interpretation There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.

Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease

Regulatory qualification of a biomarker for a defined context of use provides scientifically robust assurances to sponsors and regulators that accelerate appropriate adoption of biomarkers into drug development.

The ADAS-Cog revisited: novel composite scales based on ADAS-Cog to improve efficiency in MCI and early AD trials.

The Alzheimers Disease Assessment Scale‐Cognitive (ADAS‐Cog) has been used widely as a cognitive end point in Alzheimers Disease (AD) clinical trials. Efforts to treat AD pathology at earlier stages have also used ADAS‐Cog, but failure in these trials can be difficult to interpret because the scale has well‐known ceiling effects that limit its use in mild cognitive impairment (MCI) and early AD. A wealth of data exists in ADAS‐Cog from both historical trials and contemporary longitudinal natural history studies that can provide insights about parts of the scale that may be better suited for MCI and early AD trials.

Quantifying the Pathophysiological Timeline of Alzheimer's Disease

Hypothetical models of AD progression typically relate clinical stages of AD to sequential changes in CSF biomarkers, imaging, and cognition. However, quantifying the continuous trajectories proposed by these models over time is difficult because of the difficulty in relating the dynamics of different biomarkers during a clinical trial that is significantly shorter than the duration of the disease. We seek to show that through proper synchronization, it is possible to de-convolve these trends and quantify the periods of time associated with different pathophysiological changes associated with Alzheimers disease (AD). We developed a model that replicated the observed progression of ADAS-Cog 13 scores and used this as a more precise estimate of disease-duration and thus pathologic stage. We then synchronized cerebrospinal fluid (CSF) and imaging biomarkers according to our new disease timeline. By de-convolving disease progression via ADAS-Cog 13, we were able to confirm the predictions of previous hypothetical models of disease progression as well as establish concrete timelines for different pathobiological events. Specifically, our work supports a sequential pattern of biomarker changes in AD in which reduction in CSF Aβ(42) and brain atrophy precede the increases in CSF tau and phospho-tau.

Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes

AIM The objective is to develop a semi-mechanistic disease progression model for mild cognitive impairment (MCI) subjects. The model aims to describe the longitudinal progression of ADAS-cog scores from the Alzheimers disease neuroimaging initiative trial that had data from 198 MCI subjects with cerebrospinal fluid (CSF) information who were followed for 3 years. METHOD Various covariates were tested on disease progression parameters and these variables fell into six categories: imaging volumetrics, biochemical, genetic, demographic, cognitive tests and CSF biomarkers. RESULTS CSF biomarkers were associated with both baseline disease score and disease progression rate in subjects with MCI. Baseline disease score was also correlated with atrophy measured using hippocampal volume. Progression rate was also predicted by executive functioning as measured by the Trail B-test. CONCLUSION CSF biomarkers have the ability to discriminate MCI subjects into sub-populations that exhibit markedly different rates of disease progression on the ADAS-cog scale. These biomarkers can therefore be utilized for designing clinical trials enriched with subjects that carry the underlying disease pathology.

Evaluation of carisbamate, a novel antiepileptic drug, in photosensitive patients: an exploratory, placebo-controlled study.

PURPOSE Carisbamate, a novel neuromodulatory agent with antiepileptic properties, was evaluated in patients with photoparoxysmal responses to intermittent photic stimulation (IPS) in this multicenter, non-randomized, single-blind, placebo-controlled, proof-of-concept study. METHODS Eighteen Caucasian patients (14 females, 4 males) with a mean age of 30 years (range: 16-51 years) underwent standardized IPS under three eye conditions (during eye closure, eyes closed and eyes open) at hourly intervals for up to 8h after receiving placebo (Day 1), carisbamate (Day 2) and placebo (Day 3). Carisbamate was given at single doses of 250-1000 mg. All patients received one or two concomitant antiepileptic drugs, most commonly valproate. RESULTS Carisbamate produced a dose-dependent reduction in photosensitivity in the 13 evaluable patients, with abolishment of photoparoxysmal responses in 3 patients and clinically significant suppression of such responses in 7 additional patients. Photosensitivity was abolished or reduced in all five patients in the 1000-mg dose group. The onset of carisbamate occurred rapidly, with clinically significant suppression achieved before or near the time peak plasma drug levels were reached. The duration of action was dose-related and long-lasting, with clinically significant reductions of photosensitivity observed for up to 32 h after doses of 750 or 1000 mg. Carisbamate was generally well tolerated, with dizziness and nausea reported more frequently after active drug than placebo. CONCLUSION This study shows that carisbamate exhibits dose-related antiepileptic effects in the photosensitivity model. Randomized, controlled studies of carisbamate in epilepsy patients inadequately controlled by their existing AED therapy are warranted.

Age-Stratified Prevalence of Mild Cognitive Impairment and Dementia in European Populations: A Systematic Review

The prevalence of mild cognitive impairment (MCI) and dementia according to age remain uncertain. We systematically extracted age-stratified estimates of MCI and dementia prevalence reported in European studies published since 1995, and performed meta-analyses for dementia. We identified 10 relevant studies on MCI and 26 studies on dementia. Studies on MCI presented substantial heterogeneity preventing a meta-analysis, with a majority reporting an increase in prevalence at ≥75 years old. Pooled prevalence of dementia rose continuously from 55 years of age, reaching 44.7% (39.8; 49.6) in those ≥95 years of age. Homogenization of MCI criteria, and additional studies in Northern European population would be warranted.

Novel Statistically-Derived Composite Measures for Assessing the Efficacy of Disease-Modifying Therapies in Prodromal Alzheimer’s Disease Trials: An AIBL Study

BACKGROUND There is a growing consensus that disease-modifying therapies must be given at the prodromal or preclinical stages of Alzheimers disease (AD) to be effective. A major unmet need is to develop and validate sensitive measures to track disease progression in these populations. OBJECTIVE To generate novel statistically-derived composites from standard scores, which have increased sensitivity in the assessment of change from baseline in prodromal AD. METHODS An empirically based method was employed to generate domain specific, global, and cognitive-functional novel composites. The novel composites were compared and contrasted with each other, as well as standard scores for their ability to track change from baseline. The longitudinal characteristics and power to detect decline of the measures were evaluated. Data from participants in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study characterized as mild cognitively impaired with high neocortical amyloid-β burden were utilized for the study. RESULTS The best performing standard scores were CDR Sum-of-Boxes and MMSE. The statistically-derived novel composites performed better than the standard scores from which they were derived. The domain-specific composites generally did not perform as well as the global composites or the cognitive-functional composites. CONCLUSION A systematic method was employed to generate novel statistically-derived composite measures from standard scores. Composites comprised of measures including function and multiple cognitive domains appeared to best capture change from baseline. These composites may be useful to assess progression or lack thereof in prodromal AD. However, the results should be replicated and validated using an independent clinical sample before implementation in a clinical trial.

Changes in Brain Volume with Bapineuzumab in Mild to Moderate Alzheimer’s Disease

BACKGROUND Bapineuzumab, an anti-amyloid-β monoclonal antibody, was evaluated in two placebo-controlled trials in APOE*ɛ4 carriers and noncarriers, respectively, with Alzheimers disease. OBJECTIVES A volumetric magnetic resonance imaging substudy was performed to determine if bapineuzumab altered brain volume rate of change. METHODS Bapineuzumab dosages included 0.5 mg/kg in carriers and 0.5 or 1.0 mg/kg in noncarriers, every 13 weeks for 78 weeks. Volumetric outcomes included annualized brain, ventricular, and mean hippocampal boundary shift integrals (BBSI; VBSI; HBSI) up to Week 71. Treatment differences were estimated using mixed models for repeated measures. RESULTS For BBSI and HBSI, there were no significant treatment-related differences within either study, but, compared to pooled carriers and noncarriers receiving placebo, noncarriers receiving1.0 mg/kg bapineuzumab had greater increases in these measures. Bapineuzumab-treated patients showed significantly greater VBSI rates compared with placebo for 0.5 mg/kg in carriers and 1.0 mg/kg (but not 0.5 mg/kg) in noncarriers. CONCLUSIONS Bapineuzumab produced an increase in ventricular volume compared with placebo. Etiology for this increase is unclear but may be related to amyloid-β clearance or its consequences.

Disease progression model for Clinical Dementia Rating–Sum of Boxes in mild cognitive impairment and Alzheimer’s subjects from the Alzheimer’s Disease Neuroimaging Initiative

Background The objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale–Sum of Boxes (CDR–SB) scores. These were derived from the Alzheimer’s Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer’s disease and mild cognitive impairment patients who were followed for 2–3 years. Methods The model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications. Results Covariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR–SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR–SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer’s disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively. Conclusions In conclusion, this model describes disease progression in terms of CDR–SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations.