The Dominantly Inherited Alzheimer Network (DIAN) obtains longitudinal imaging and cerebrospinal fluid (CSF) biomarkers of Alzheimer disease (AD) in carriers of a pathogenic mutation in one of three genes (PSEN1, PSEN2, APP). A major goal of the DIAN study of autosomal dominant AD (ADAD) is to extrapolate its findings regarding the onset, sequence, and rates of change of biomarkers from the asymptomatic through the symptomatic stages of AD to advance the understanding of the far more common "sporadic" late onset AD (LOAD). It is unknown, however, whether ADAD and LOAD are sufficiently similar to permit such extrapolation. There were 282 mutation carriers (MCs) from DIAN and 297 participants from Alzheimer Disease Neuroimaging Initiative (ADNI) with longitudinal data on biomarkers. A CDR SumBox (SB) score of 1 defined a common "anchor point" for a linear mixed model on longitudinal scores of CDR SB. A piecewise linear mixed model aligning the longitudinal courses of each biomarker between individuals in DIAN and ADNI at the anchor point was used to compare the unadjusted rates of change prior to and after reaching CDR SB = 1. Prior to reaching the anchor point as defined by CDR SB = 1, both groups had decline in hippocampal volumes (HV) and increases in CSF tau and p-tau. MCs, but not ADNI participants, had significant accumulation of cerebral amyloid (amyloid PET, p<0.0001) and both groups decreased in CSF amyloid-beta (Aβ42, p<0.0001). The rate of decrease for CSF Aβ42 was significantly greater for MCs than for ADNI participants (p=0.0027). On a composite of shared cognitive tests, the MCs had declining performance (p=0.001) whereas ADNI participants did not. After progressing by CDR SB = 1, the subsequent rates of change in the aforementioned imaging and CSF biomarkers for both DIAN and ADNI participants were all significant, but the magnitude of the change rates is larger for MCs than for ADNI participants for all biomarkers except for the CSF analytes. Biomarker rates of change for ADAD (DIAN) and LOAD (ADNI) qualitatively are identical. However, ADAD appears to have greater rates of decline than LOAD.
Alzheimer disease (AD) is the leading cause of dementia worldwide. Of those at risk for AD, 1% have one of three autosomal dominant genetic mutations in presenilin 1 (PS1), presenilin 2 (PS2) or amyloid precursor protein (APP) which cause early dementia with ∼100% penetrance. Autosomal dominant AD (ADAD) offers unique opportunities to characterize and evaluate AD. However, the correspondence between ADAD and the far more prevalent sporadic AD (sAD) requires elucidation. We analyzed functional connectivity in multiple brain networks in a cross-sectional cohort of ADAD (N=87) and sAD (N=507) participants using resting state functional connectivity magnetic resonance imaging (rs-fcMRI). For both types of AD, we quantified rs-fcMRI changes in five resting state networks (RSNs) with respect to progressing clinical dementia rating (CDR) using general linear mixed models with factors CDR, AD type, and CDR ï,'ï€ AD type interaction. In the ADAD group, we investigated rs-fcMRI changes with respect to years from expected onset of symptoms. We also related rs-fcMRI differences to genetic mutation type (presenilin 1, presenilin 2, amyloid precursor protein). rs-fcMRI decreases with advancing clinical status were similar for both forms of AD in multiple RSNs. However, it is possible that ADAD causes more rapid loss with respect to CDR than sAD in certain RSNs. Within ADAD participants, functional connectivity in multiple RSNs was lower in individuals closer to their expected age of onset of symptoms. Within cognitively normal ADAD participants (N=33), mutation-specific effects may occur in particular RSNs. rs-fcMRI can elucidate specific AD subtypes and may help evaluate future therapeutic interventions.
One approach to estimate the number of participants needed to detect a treatment effect in Alzheimer's disease randomized clinical trials (RCTs) involves the subtraction of measurements acquired at two times over an observed time interval (ΔTo). While this approach may be suitable for RCT in which the proposed treatment time interval (ΔTt) is equal to ΔTo, its use need some cautions when a) ΔTt is longer or shorter than ΔTo or b) there are substantial individual variations in ΔTo. We considered the case in which each individual j had two observed serial measurements, xj1 and xj2, before and after time interval ΔToj for total of N subjects in an existing data set. The subtraction method multiplies the annualized subject group's mean change μ=(1/N)(∑(xj1 - xj2)/ΔToj) and its standard deviation σ by ΔTt to form ΔTt × μ and ΔTt × σ and use them to estimate the number of subjects needed to detect a particular treatment effect with a pre-defined statistical power and type-I error. As is known, the sample size estimate would then depend entirely on the std/mean ratio, with no additional consideration of the impact of longer or shorter ΔTt rather than serving as a common multiplier. 1), Using the simple subtraction method, sample size estimates would be the same regardless of RCT duration due to the common std/mean ratio and without additional models or assumptions. 2, The reason for this limitation is the misuse of the linear assumption (common multiplier) and the overlook of the measurement errors in forming the subtraction. 3, The simple subtraction approach works if one of several conditions holds. For example, a) the measurement error is ignorable or linearly related to trial duration; b) the proposed RCT duration ΔTt and the individualized ΔToj satisfy ∑Nj=1(1/ΔToj2)=N/ΔTt2; or c) if the between-subject variability of the annualized change is low, the std/mean ratio will be proportional to 1/ΔTt and the corresponding sample size decreases for longer ΔTt duration. The subtraction procedure should be used with caution. Importantly, this approach would require assumptions about the impact of treatment interval or use of an alternative (e.g., mixed model) approach.
Abstract Background In recent years there is increasing interest in modeling the effect of early longitudinal biomarker data on future time-to-event or other outcomes. Sometimes investigators are also interested in knowing whether the variability of biomarkers is independently predictive of clinical outcomes. This question in most applications is addressed via a two-stage approach where summary statistics such as variance are calculated in the first stage and then used in models as covariates to predict clinical outcome in the second stage. The objective of this study is to compare the relative performance of various methods in estimating the effect of biomarker variability. Methods A joint model and 4 different two-stage approaches (naïve, landmark analysis, time-dependent Cox model, and regression calibration) were illustrated using data from a large multi-center randomized phase III trial, the Ocular Hypertension Treatment Study (OHTS), regarding the association between the variability of intraocular pressure (IOP) and the development of primary open-angle glaucoma (POAG). The model performance was also evaluated in terms of bias using simulated data from the joint model of longitudinal IOP and time to POAG. The parameters for simulation were chosen after OHTS data, and the association between longitudinal and survival data was introduced via underlying, unobserved, and error-free parameters including subject-specific variance. Results In the OHTS data, joint modeling and two-stage methods reached consistent conclusion that IOP variability showed no significant association with the risk of POAG. In the simulated data with no association between IOP variability and time-to-POAG, all the two-stage methods (except the naïve approach) provided a reliable estimation. When a moderate effect of IOP variability on POAG was imposed, all the two-stage methods underestimated the true association as compared with the joint modeling while the model-based two-stage method (regression calibration) resulted in the least bias. Conclusion Regression calibration and joint modelling are the preferred methods in assessing the effect of biomarker variability. Two-stage methods with sample-based measures should be used with caution unless there exists a relatively long series of longitudinal measurements and/or strong effect size (NCT00000125).
Abstract Background The double‐blind (DB) period of the DIAN–TU‐001 phase 3 trial with gantenerumab provided evidence of significant but incomplete reduction of amyloid plaques, cerebrospinal fluid total tau, and phospho‐tau181 in dominantly inherited Alzheimer’s disease (DIAD). 1 Subsequently, eligible participants transitioned to an open‐label extension (OLE) period using higher doses of gantenerumab (1500mg SC‐administered every two weeks [q2w]). Method 73 DIAD participants entered the OLE period. All participants titrated gantenerumab doses and received at least 3 doses at 120, 255, 510, and 1020mg‐q4wk. Subsequently, titration continued with 1020mg‐q2w for 6 doses and then to 1500mg‐q2w. During the OLE period, 62/73 (84.9%) participants completed titration to the higher doses (1020‐1500mg‐q2w). Result Overall, 30.1% (22/73) of the participants experienced ARIA‐E during the OLE (Figure 1); among those, 72.7% (n = 16/22) experienced ARIA‐E at higher doses (1020‐1500mg‐q2w). 2/3 (67%) APOE4 homozygous participants developed ARIA‐E, while 5/19 (26%) and 10/51 (19%) heterozygous or noncarriers developed ARIA‐E. Radiologically, 56.3% of the ARIA‐E episodes at higher doses were multifocal and had a predominant occipital distribution. The largest cross‐sectional diameter of ARIA‐E at initial findings ranged from 3 to 81mm. ARIA‐E lesions improved, generally, while dosing was held with a mean time for ARIA‐E resolution of 59.5 days. ARIA‐E frequency was higher in PSEN1 and APP variants (14/61 (23.0%) and 2/7 (28.6%), respectively) compared to PSEN‐2 variants (0/5). Most of the ARIA‐E cases (16/22) co‐occur with ARIA‐H; however, ARIA‐H also occurred in 14/51 participants who never had ARIA‐E. ARIA‐H risk increases as the disease progresses and is less frequent during asymptomatic phases of the disease and EYO ←10 (Figure 2). Conclusion Safety data from the DIAN‐TU‐001 OLE study indicate that tripling the dose of gantenerumab is well tolerated in DIAD populations, with the incidence and severity of adverse events remaining comparable to the DB period 1,2 and without new/unexpected safety findings. Most ARIA‐E episodes occurred after titration to doses of 1020‐1500mg‐q2w (16 of 22 participants with ARIA‐E in the OLE) and within the initial 3‐4 months of receiving the higher doses. Risks for ARIA‐E in DIAD parallel reported risks for gantenerumab in sporadic AD, including ApoE4 status and number of MCH. 3
Gene model for the ortholog of glycogen synthase ( Glys ) in the Drosophila simulans May 2017 (Princeton ASM75419v2/DsimGB2) Genome Assembly (GenBank Accession: GCA_000754195.3 ). This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.
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