Simino, Jeannette

Abstract Background Alzheimer’s Disease (AD) progression is conceptualized as a cascade of sigmoidal (S‐shaped) curves, with biomarkers shifting sequentially from normal to abnormal states. Estimating these curves requires sophisticated modeling, with challenges including (1) selecting appropriate sigmoidal‐type functions, (2) achieving algorithmic convergence, and (3) producing clinically useful parameters. We compare commonly used sigmoidal‐functions and effects on novel estimates of early and late AD‐biomarker progression ages. Method Using 3,999 Amyloid PET (PiB SUVR) measurements from 1,367 Mayo Clinic Study of Aging participants, we compared model fits (AIC, BIC) between logistic and hyperbolic functions using Non‐Linear Mixed Effect (NLME) models with subject‐specific age‐shifts. We defined novel “initial” and “maximum” acceleration points (IAP and MAP) to compare clinically “earlier” and “later” AD‐progression ages between sigmoidal‐curves. The MAP uses the maximum second‐derivative point, reflecting the age of fastest acceleration in biomarker accumulation. The IAP is defined as the first point where the second derivative of the sigmoidal‐curve exceeds a clinical threshold (here, 5% velocity increase per year), indicating onset age of early accelerated biomarker accumulation. Estimates for C 2 N plasma p ‐tau217 were also attempted (1,306 measurements on 457 participants). Models adjusted for sex and APOE4‐positivity. Result In the larger PET sample, the hyperbola had lower AIC/BIC (better fit), but showed a decreasing early PiB‐SUVR trend (Table 1A, Figure 1). Ages of late MAP were comparable, as were sex and APOE4+ related PiB‐SUVR‐curve shifts. Age of early AD‐onset was estimated 1.5 years earlier for logistic (IAP=63.6) versus hyperbola (IAP=65.1) functions. The logistic function always converged, while the hyperbolic function did not converge for p ‐tau217 in the smaller C 2 N sample. Early‐onset and late‐progression ages were 7‐8 years later for p ‐tau217 versus PiB‐SUVR using logistic‐curves. Conclusion Late AD‐progression and covariate‐related shifts in progression were comparable across models, suggesting robustness to sigmoid choice. In contrast, estimates of early AD‐onset may be more dependent on functional form. While logistic‐sigmoids inherently increase monotonically, hyperbolas do not, here estimating a decline in initial amyloid trend. Future work may explore adding biologically informed constraints to flexible sigmoidal‐functions, and alternative Bayesian approaches, to optimize both model fit and estimation of early AD‐onset age.

Background: Previous studies suggest poor pulmonary function is associated with increased burden of cerebral white matter hyperintensities and brain atrophy among elderly individuals, but the results are inconsistent. Objective: To study the cross-sectional associations of pulmonary function with structural brain variables. Methods: Data from six large community-based samples (N = 11,091) were analyzed. Spirometric measurements were standardized with respect to age, sex, height, and ethnicity using reference equations of the Global Lung Function Initiative. Associations of forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and their ratio FEV1/FVC with brain volume, gray matter volume, hippocampal volume, and volume of white matter hyperintensities were investigated using multivariable linear regressions for each study separately and then combined using random-effect meta-analyses. Results: FEV1 and FVC were positively associated with brain volume, gray matter volume, and hippocampal volume, and negatively associated with white matter hyperintensities volume after multiple testing correction, with little heterogeneity present between the studies. For instance, an increase of FVC by one unit was associated with 3.5 ml higher brain volume (95% CI: [2.2, 4.9]). In contrast, results for FEV1/FVC were more heterogeneous across studies, with significant positive associations with brain volume, gray matter volume, and hippocampal volume, but not white matter hyperintensities volume. Associations of brain variables with both FEV1 and FVC were consistently stronger than with FEV1/FVC, specifically with brain volume and white matter hyperintensities volume. Conclusion: In cross-sectional analyses, worse pulmonary function is associated with smaller brain volumes and higher white matter hyperintensities burden.

Introduction: Cerebral microbleed (CMB) location may predict underlying pathology. Deep CMBs are more associated with hypertensive vascular disease, while lobar CMBs are more associated with cerebral amyloid angiopathy (CAA). The objective of this study was to determine the neuroimaging pathology associated with CMBs. Methods: We analyzed 1,831 nondemented ARIC participants (mean age=76.3 ±5.3 years, 40% male, 27% black) with 3T MRI scans at the fifth exam (2011-13). We fit multinomial logistic regression models to assess the effect of brain volumes (AD signature region atrophy, total gray matter, frontal, and log 2 -transformed white matter hyperintensities (WMH) volume), infarct frequencies (lacunar, non-lacunar, and total), and APOE (number of ε4 alleles) on CMB location (no, any deep, or lobar only CMBs). Models were weighted for the sample selection scheme and adjusted for age, male, education, hypertension, ever smoking status, diabetes, race-site membership, and estimated intracranial volume (brain volume models only). Results: The frequency of CMBs was 24.1%. A larger WMH volume, greater total infarct frequency, and smaller total grey volume increased the relative risks (RR) of both deep and lobar CMBs compared to no CMBs; increasing the WMH volume also increased the RR of deep to lobar CMBs. Additional lacunar infarcts increased the RR of deep compared to no CMBs, whereas AD signature region atrophy and APOE ε4 homozygosity increased the RR of lobar CMBs. Conclusion: CMBs are a common vascular pathology in the elderly. Deep CMB presence is associated with MRI features of hypertensive small vessel disease, while lobar CMB presence is associated with MRI features of CAA and Alzheimer’s disease.

Abstract Introduction There is increasing interest in plasma amyloid beta (Aβ) as an endophenotype of Alzheimer's disease (AD). Identifying the genetic determinants of plasma Aβ levels may elucidate important biological processes that determine plasma Aβ measures. Methods We included 12,369 non‐demented participants from eight population‐based studies. Imputed genetic data and measured plasma Aβ1‐40, Aβ1‐42 levels and Aβ1‐42/Aβ1‐40 ratio were used to perform genome‐wide association studies, and gene‐based and pathway analyses. Significant variants and genes were followed up for their association with brain positron emission tomography Aβ deposition and AD risk. Results Single‐variant analysis identified associations with apolipoprotein E ( APOE ) for Aβ1‐42 and Aβ1‐42/Aβ1‐40 ratio, and BACE1 for Aβ1‐40. Gene‐based analysis of Aβ1‐40 additionally identified associations for APP , PSEN2 , CCK , and ZNF397 . There was suggestive evidence for interaction between a BACE1 variant and APOE ε4 on brain Aβ deposition. Discussion Identification of variants near/in known major Aβ‐processing genes strengthens the relevance of plasma‐Aβ levels as an endophenotype of AD.

Background and Purpose— Cerebral microbleed (CMB) location (deep versus strictly lobar) may elucidate underlying pathology with deep CMBs being more associated with hypertensive vascular disease and lobar CMBs being more associated with cerebral amyloid angiopathy. The objective of this study was to determine whether neuroimaging signs of vascular disease and Alzheimer pathology are associated with different types of CMBs. Methods— Among 1677 nondemented ARIC (Atherosclerosis Risk in Communities) participants (mean age=76±5 years; 40% men; 26% black) with 3-Tesla MRI scans at the fifth examination (2011–2013), we fit multinomial logistic regression models to quantify relationships of brain volumes (Alzheimer disease signature regions, total gray matter, frontal gray matter, and white matter hyperintensity volumes), infarct frequencies (lacunar, nonlacunar, and total), and apolipoprotein E (number of ε4 alleles) with CMB location (none, deep/mixed, or strictly lobar CMBs). Models were weighted for the sample selection scheme and adjusted for age, sex, education, hypertension, ever smoking status, diabetes mellitus, race site membership, and estimated intracranial volume (brain volume models only). Results— Deep/mixed and strictly lobar CMBs had prevalences of 8% and 16%, respectively. Larger white matter hyperintensity burden, greater total infarct frequency, smaller frontal volumes (in women only), and smaller total gray matter volume were associated with greater risk of both deep and lobar CMBs relative to no CMBs. Greater white matter hyperintensity volume was also associated with greater risk of deep relative to lobar CMBs. Higher lacunar and nonlacunar infarct frequencies were associated with higher risk of deep CMBs, whereas smaller Alzheimer disease signature region volume and apolipoprotein E ε4 homozygosity were associated with greater risk of lobar CMBs. Conclusions— CMBs are a common vascular pathology in the elderly. Markers of hypertensive small-vessel disease may contribute to deep CMBs while cerebral amyloid angiopathy may drive development of lobar CMBs.