Evaluation of dose-dependent treatment effects after mid-trial dose escalation in biomarker, clinical, and cognitive outcomes for gantenerumab or solanezumab in dominantly inherited Alzheimer's disease

Introduction: While the Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU) was ongoing, external data suggested higher doses were needed to achieve targeted effects; therefore, doses of gantenerumab were increased 5-fold, and solanezumab was increased 4-fold. We evaluated to what extent mid-trial dose increases produced a dose-dependent treatment effect. Methods: Using generalized linear mixed effects (LME) models, we estimated the annual low- and high-dose treatment effects in clinical, cognitive, and biomarker outcomes. Results: Both gantenerumab and solanezumab demonstrated dose-dependent treatment effects (significant for gantenerumab, non-significant for solanezumab) in their respective target amyloid biomarkers (Pittsburgh compound B positron emission tomography standardized uptake value ratio and cerebrospinal fluid amyloid beta 42), with gantenerumab demonstrating additional treatment effects in some downstream biomarkers. No dose-dependent treatment effects were observed in clinical or cognitive outcomes. Conclusions: Mid-trial dose escalation can be implemented as a remedy for an insufficient initial dose and can be more cost effective and less burdensome to participants than starting a new trial with higher doses, especially in rare diseases. Highlights: We evaluated the dose-dependent treatment effect of two different amyloid-specific immunotherapies. Dose-dependent treatment effects were observed in some biomarkers. No dose-dependent treatment effects were observed in clinical/cognitive outcomes, potentially due to the fact that the modified study may not have been powered to detect such treatment effects in symptomatic subjects at a mild stage of disease exposed to high (or maximal) doses of medication for prolonged durations

Serum neurofilament light chain levels are associated with white matter integrity in autosomal dominant Alzheimer's disease

Neurofilament light chain (NfL) is a protein that is selectively expressed in neurons. Increased levels of NfL measured in either cerebrospinal fluid or blood is thought to be a biomarker of neuronal damage in neurodegenerative diseases. However, there have been limited investigations relating NfL to the concurrent measures of white matter (WM) decline that it should reflect. White matter damage is a common feature of Alzheimer's disease. We hypothesized that serum levels of NfL would associate with WM lesion volume and diffusion tensor imaging (DTI) metrics cross-sectionally in 117 autosomal dominant mutation carriers (MC) compared to 84 non-carrier (NC) familial controls as well as in a subset (N = 41) of MC with longitudinal NfL and MRI data. In MC, elevated cross-sectional NfL was positively associated with WM hyperintensity lesion volume, mean diffusivity, radial diffusivity, and axial diffusivity and negatively with fractional anisotropy. Greater change in NfL levels in MC was associated with larger changes in fractional anisotropy, mean diffusivity, and radial diffusivity, all indicative of reduced WM integrity. There were no relationships with NfL in NC. Our results demonstrate that blood-based NfL levels reflect WM integrity and supports the view that blood levels of NfL are predictive of WM damage in the brain. This is a critical result in improving the interpretability of NfL as a marker of brain integrity, and for validating this emerging biomarker for future use in clinical and research settings across multiple neurodegenerative diseases

Location of pathogenic variants in PSEN1 impacts progression of cognitive, clinical, and neurodegenerative measures in autosomal-dominant Alzheimer's disease

Although pathogenic variants in PSEN1 leading to autosomal-dominant Alzheimer disease (ADAD) are highly penetrant, substantial interindividual variability in the rates of cognitive decline and biomarker change are observed in ADAD. We hypothesized that this interindividual variability may be associated with the location of the pathogenic variant within PSEN1. PSEN1 pathogenic variant carriers participating in the Dominantly Inherited Alzheimer Network (DIAN) observational study were grouped based on whether the underlying variant affects a transmembrane (TM) or cytoplasmic (CY) protein domain within PSEN1. CY and TM carriers and variant non-carriers (NC) who completed clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF) as part of their participation in DIAN were included in this study. Linear mixed effects models were used to determine differences in clinical, cognitive, and biomarker measures between the NC, TM, and CY groups. While both the CY and TM groups were found to have similarly elevated Aβ compared to NC, TM carriers had greater cognitive impairment, smaller hippocampal volume, and elevated phosphorylated tau levels across the spectrum of pre-symptomatic and symptomatic phases of disease as compared to CY, using both cross-sectional and longitudinal data. As distinct portions of PSEN1 are differentially involved in APP processing by γ-secretase and the generation of toxic β-amyloid species, these results have important implications for understanding the pathobiology of ADAD and accounting for a substantial portion of the interindividual heterogeneity in ongoing ADAD clinical trials

Adiposity is Associated with Regional Cortical Thinning

BACKGROUND: Although obesity is associated with structural changes in brain grey matter, findings have been inconsistent and the precise nature of these changes is unclear. Inconsistencies may partly be due to the use of different volumetric morphometry methods, and the inclusion of participants with comorbidities that exert independent effects on brain structure. The latter concern is particularly critical when sample sizes are modest. The purpose of the current study was to examine the relationship between cortical grey matter and body mass index (BMI), in healthy participants, excluding confounding comorbidities and using a large sample size. SUBJECTS: A total of 202 self-reported healthy volunteers were studied using surface-based morphometry, which permits the measurement of cortical thickness, surface area and cortical folding, independent of each other. RESULTS: Although increasing BMI was not associated with global cortical changes, a more precise, region-based analysis revealed significant thinning of the cortex in two areas: left lateral occipital cortex (LOC) and right ventromedial prefrontal cortex (vmPFC). An analogous region-based analysis failed to find an association between BMI and regional surface area or folding. Participants' age was also found to be negatively associated with cortical thickness of several brain regions; however, there was no overlap between the age- and BMI-related effects on cortical thinning. CONCLUSIONS: Our data suggest that the key effect of increasing BMI on cortical grey matter is a focal thinning in the left LOC and right vmPFC. Consistent implications of the latter region in reward valuation, and goal control of decision and action suggest a possible shift in these processes with increasing BMI.We thank all the participants and the staff of the Wolfson Brain Imaging Centre. This work was supported by the Bernard Wolfe Health Neuroscience Fund (NM, HZ, ISF, PCF), the Wellcome Trust (RGAG/144 to N.M, RGAG/188 to ISF, RNAG/259 to PCF) and the Medical Research Council (G0701497 to KDE).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ijo.2016.42

A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium.

INTRODUCTION: We established a method for diagnostic harmonization across multiple studies of preclinical Alzheimer's disease and validated the method by examining its relationship with clinical status and cognition. METHODS: Cognitive and clinical data were used from five studies (N = 1746). Consensus diagnoses established in each study used criteria to identify progressors from normal cognition to mild cognitive impairment. Correspondence was evaluated between these consensus diagnoses and three algorithmic classifications based on (1) objective cognitive impairment in 2+ tests only; (2) a Clinical Dementia Rating (CDR) of ≥0.5 only; and (3) both. Associations between baseline cognitive performance and cognitive change were each tested in relation to progression to algorithm-based classifications. RESULTS: In each study, an algorithmic classification based on both cognitive testing cutoff scores and a CDR ≥0.5 provided optimal balance of sensitivity and specificity (areas under the curve: 0.85-0.95). Over an average 6.6 years of follow-up (up to 28 years), N = 186 initially cognitively normal participants aged on average 64 years at baseline progressed (incidence rate: 15.3 people/1000 person-years). Baseline cognitive scores and cognitive change were associated with future diagnostic status using this algorithmic classification. DISCUSSION: Both cognitive tests and CDR ratings can be combined across multiple studies to obtain a reliable algorithmic classification with high specificity and sensitivity. This approach may be applicable to large cohort studies and to clinical trials focused on preclinical Alzheimer's disease because it provides an alternative to implementation of a time-consuming adjudication panel