A flexible modeling approach for biomarker-based computation of absolute risk of Alzheimer's disease dementia.

INTRODUCTION: As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia. METHODS: We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis. RESULTS: A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5-year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aß)42/Aß40 (AUC = 0.82). DISCUSSION: An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults.

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.

Relative neuron loss in hippocampal sclerosis of aging and Alzheimer's disease.

OBJECTIVE: To characterize the pattern of neuron loss in hippocampal sclerosis of aging (HS-Aging) and age-related diseases and to evaluate its contribution to cognitive impairment in the elderly. METHODS: Participants (n = 1,361) came from longitudinal observational studies of aging at the Knight Alzheimer Disease Research Center, Washington University (St. Louis, MO). Relative neuron loss in the hippocampus of HS-Aging was measured using unbiased stereological methods. Transactive response DNA-binding protein of 43 kDa (TDP-43) proteinopathy, a putative marker of HS-Aging, was assessed. Clinical and cognitive data were analyzed using parametric statistical methods. RESULTS: Ninety-three cases had HS-Aging (6.8%), 8 cases had "pure" HS-Aging, and 37 cases had comorbid intermediate or high Alzheimer's disease neuropathological change (i/h ADNC). Relative neuron loss (ratio of neuron number in hippocampal subfield CA1 to the neuron number in parahippocampal gyrus) was 0.15 for HS-Aging; this was significantly lower than 0.64 for i/h ADNC and 0.66 for control cases (Kruskal-Wallis test, p < 0.0001; p = 0.0003, respectively). TDP-43 proteinopathy was present in 92.4% of HS-Aging cases, higher than that in i/h ADNC (52%) and control (25%) cases. Pure HS-Aging cases were more likely to have cognitive impairment in the memory domain. INTERPRETATION: Relative neuron loss in the hippocampus compared to the parahippocampus gyrus may be useful in distinguishing HS-Aging in the context of comorbid ADNC. HS-Aging contributes to cognitive impairment, which phenotypically resembles AD dementia. TDP proteinopathy is a frequent comorbidity in HS-Aging and may contribute to cognitive impairment to a modest degree. Ann Neurol 2018;84:749-761.

Emerging cerebrospinal fluid biomarkers in autosomal dominant Alzheimer's disease.

INTRODUCTION: Four less well-studied but promising "emerging" cerebrospinal fluid (CSF) biomarkers are elevated in late-onset Alzheimer disease (AD): neurogranin, synaptosomal-associated protein-25 (SNAP-25), visinin-like protein 1 (VILIP-1), and chitinase-3-like protein 1 (YKL-40). METHODS: CSF neurogranin, SNAP-25, VILIP-1, and YKL-40 were measured in families carrying autosomal-dominant AD mutations. RESULTS: The four emerging CSF biomarkers were significantly elevated in the mutation carriers (n = 235) versus noncarriers (n = 145). CSF SNAP-25, VILIP-1, and YKL-40 were altered very early in the AD time course, approximately 15-19 years before estimated symptom onset. All CSF biomarkers predicted important AD-related outcomes including performance on a cognitive composite, brain amyloid burden as measured by amyloid positron emission tomography, and the estimated years from symptom onset. DISCUSSION: Early abnormalities in CSF tTau, pTau, SNAP-25, VILIP-1, and YKL-40 suggest that synaptic damage, neuronal injury, and neuroinflammation begin shortly after the commencement of brain amyloid accumulation.

Physical activity and cognitive trajectories in cognitively normal adults: the adult children study.

Increased physical activity may protect against cognitive decline, the primary symptom of Alzheimer disease. In this study, we examined the relationship between physical activity and trajectories of cognitive functioning over serial assessments. Cognitively normal (Clinical Dementia Rating 0) middle-aged and older adults (N=173; mean age, 60.7 ± 7.8 y) completed a self-report measure of physical activity and a battery of standard neuropsychological tests assessing processing speed, attention, executive functioning, and verbal memory. At baseline, individuals with higher physical activity levels performed better on tests of episodic memory and visuospatial functioning. Over subsequent follow-up visits, higher physical activity was associated with small performance gains on executive functioning and working memory tasks in participants with one or more copies of the apolipoprotein ε4 allele (APOE4). In APOE4 noncarriers, slopes of cognitive performance over time were not related to baseline physical activity. Our results suggest that cognitively normal older adults who report higher levels of physical activity may have slightly better cognitive performance, but the potential cognitive benefits of higher levels of physical activity over time may be most evident in individuals at genetic risk for Alzheimer disease.

Antecedents of mind wandering states in healthy aging and mild cognitive impairment.

OBJECTIVE: Mind wandering refers to periods of internally directed attention and comprises up to 30% or more of our waking thoughts. Frequent mind wandering can be detrimental to ongoing task performance. We aim to determine whether rates of mind wandering change in healthy aging and mild cognitive impairment and how differences in mind wandering contribute to differences in attention and working memory. METHOD: We administered a standard behavioral task, the Sustained Attention to Response Test, to measure mind wandering in healthy younger adults (N = 66), healthy older adults (N = 51), and adults with cognitive impairment (N = 38), that was completed daily for 3 weeks. The N-back test was also administered at a reduced frequency as a measure of working memory performance. RESULTS: Generally speaking, averaged across 3 weeks of testing, relative to healthy older adults, mind wandering was higher in younger adults and in cognitive impairment, although the specific patterns varied across mind wandering states. Multiple states of mind wandering also predicted working memory performance; however, reaction time variability tended to be the best predictor based on model comparisons. Each state was also modestly associated with different dispositional factors including mood and Agreeableness. CONCLUSIONS: Patterns of mind wandering change across healthy aging and cognitive impairment and are related to individual differences in multiple dispositional factors and also working memory performance. These results suggest that different states of mind wandering should be measured and accounted for when modeling cognitive change in healthy and pathological aging. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease.

Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aß40lumi and Aß42/Aß40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aß40lumi and t-tau/Aß40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aß40lumi, p-tau181/Aß40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials.

Downstream Biomarker Effects of Gantenerumab or Solanezumab in Dominantly Inherited Alzheimer Disease: The DIAN-TU-001 Randomized Clinical Trial.

Importance: Effects of antiamyloid agents, targeting either fibrillar or soluble monomeric amyloid peptides, on downstream biomarkers in cerebrospinal fluid (CSF) and plasma are largely unknown in dominantly inherited Alzheimer disease (DIAD). Objective: To investigate longitudinal biomarker changes of synaptic dysfunction, neuroinflammation, and neurodegeneration in individuals with DIAD who are receiving antiamyloid treatment. Design, Setting, and Participants: From 2012 to 2019, the Dominantly Inherited Alzheimer Network Trial Unit (DIAN-TU-001) study, a double-blind, placebo-controlled, randomized clinical trial, investigated gantenerumab and solanezumab in DIAD. Carriers of gene variants were assigned 3:1 to either drug or placebo. The present analysis was conducted from April to June 2023. DIAN-TU-001 spans 25 study sites in 7 countries. Biofluids and neuroimaging from carriers of DIAD gene variants in the gantenerumab, solanezumab, and placebo groups were analyzed. Interventions: In 2016, initial dosing of gantenerumab, 225 mg (subcutaneously every 4 weeks) was increased every 8 weeks up to 1200 mg. In 2017, initial dosing of solanezumab, 400 mg (intravenously every 4 weeks) was increased up to 1600 mg every 4 weeks. Main Outcomes and Measures: Longitudinal changes in CSF levels of neurogranin, soluble triggering receptor expressed on myeloid cells 2 (sTREM2), chitinase 3-like 1 protein (YKL-40), glial fibrillary acidic protein (GFAP), neurofilament light protein (NfL), and plasma levels of GFAP and NfL. Results: Of 236 eligible participants screened, 43 were excluded. A total of 142 participants (mean [SD] age, 44 [10] years; 72 female [51%]) were included in the study (gantenerumab, 52 [37%]; solanezumab, 50 [35%]; placebo, 40 [28%]). Relative to placebo, gantenerumab significantly reduced CSF neurogranin level at year 4 (mean [SD] ß = -242.43 [48.04] pg/mL; P < .001); reduced plasma GFAP level at year 1 (mean [SD] ß = -0.02 [0.01] ng/mL; P = .02), year 2 (mean [SD] ß = -0.03 [0.01] ng/mL; P = .002), and year 4 (mean [SD] ß = -0.06 [0.02] ng/mL; P < .001); and increased CSF sTREM2 level at year 2 (mean [SD] ß = 1.12 [0.43] ng/mL; P = .01) and year 4 (mean [SD] ß = 1.06 [0.52] ng/mL; P = .04). Solanezumab significantly increased CSF NfL (log) at year 4 (mean [SD] ß = 0.14 [0.06]; P = .02). Correlation analysis for rates of change found stronger correlations between CSF markers and fluid markers with Pittsburgh compound B positron emission tomography for solanezumab and placebo. Conclusions and Relevance: This randomized clinical trial supports the importance of fibrillar amyloid reduction in multiple AD-related processes of neuroinflammation and neurodegeneration in CSF and plasma in DIAD. Additional studies of antiaggregated amyloid therapies in sporadic AD and DIAD are needed to determine the utility of nonamyloid biomarkers in determining disease modification. Trial Registration: ClinicalTrials.gov Identifier: NCT04623242.

Left atrial size is independently associated with cognitive function.

Left atrial (LA) diameter is easily attainable from echocardiograph and sensitive to underlying cardiovascular disease severity, although its association with neurocognitive outcomes is not well understood. Fifty older adults (64.50 ± 9.41 years), recruited from outpatient cardiology clinics and local papers who underwent magnetic resonance imaging, were administered the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and completed psychosocial self-report measures. LA diameter was quantified using echocardiogram. Hierarchical regression analyses revealed that greater LA size was independently associated with reduced performance on the following RBANS composites: language, delayed memory, and total index (p < 0.05 for all). Hierarchical regression analysis demonstrated no significant association between LA diameter and whole brain volume (p > 0.05). The current study suggests that greater LA size is associated with cognitive dysfunction in older adults and prospective studies are needed to validate these findings and elucidate underlying mechanisms.

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study.

Background: Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).

The brains of people with advanced Alzheimer's disease often look older than expected based on the patients' actual age. This 'brain age gap' (how old a brain appears compared to the person's chronological age) can be calculated thanks to machine learning algorithms which analyse images of the organ to detect changes related to aging. Traditionally, these models have relied on images of the brain structure, such as the size and thickness of various brain areas; more recent models have started to use activity data, such as how different brain regions work together to form functional networks. While the brain age gap is a useful measure for researchers who investigate aging and disease, it is not yet helpful for clinicians. For example, it is unclear whether the machine learning algorithm could detect changes in the brains of individuals in the initial stages of Alzheimer's disease, before they start to manifest cognitive symptoms. Millar et al. explored this question by testing whether models which incorporate structural and activity data could be more sensitive to these early changes. Three machine learning algorithms (relying on either structural data, activity data, or combination of both) were used to predict the brain ages of participants with no sign of disease; with biological markers of Alzheimer's disease but preserved cognitive functions; and with marked cognitive symptoms of the condition. Overall, the combined model was slightly better at predicting the brain age of healthy volunteers, and all three models indicated that patients with dementia had a brain which looked older than normal. For this group, the model based on structural data was also able to make predictions which reflected the severity of cognitive decline. Crucially, the algorithm which used activity data predicted that, in individuals with biological markers of Alzheimer's disease but no cognitive impairment, the brain looked in fact younger than chronological age. Exactly why this is the case remains unclear, but this signal may be driven by neural processes which unfold in the early stages of the disease. While more research is needed, the work by Millar et al. helps to explore how various types of machine learning models could one day be used to assess and predict brain health.

Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease.

BACKGROUND: "Brain-predicted age" estimates biological age from complex, nonlinear features in neuroimaging scans. The brain age gap (BAG) between predicted and chronological age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD), in which AD progression is highly predictable with minimal confounding age-related co-pathology. METHODS: We modeled BAG in 257 deeply-phenotyped ADAD mutation-carriers and 179 non-carriers from the Dominantly Inherited Alzheimer Network using minimally-processed structural MRI scans. We then tested whether BAG differed as a function of mutation and cognitive status, or estimated years until symptom onset, and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid-ß-42/40), phosphorylated tau (CSF and plasma pTau-181), neurodegeneration (CSF and plasma neurofilament-light-chain [NfL]), and cognition (global neuropsychological composite and CDR-sum of boxes). We compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of ADAD mutation variants, APOE Îµ4 carrier status, sex, and education. RESULTS: Advanced brain aging was observed in mutation-carriers approximately 7 years before expected symptom onset, in line with other established structural indicators of atrophy. BAG was moderately associated with amyloid PET and strongly associated with pTau-181, NfL, and cognition in mutation-carriers. Mutation variants, sex, and years of education contributed to variability in BAG. CONCLUSIONS: We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG associates well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, BAG offers unique benefits in simplicity of data processing and interpretation. Thus, results in this unique ADAD cohort with few age-related confounds suggest that brain aging attributable to AD neuropathology can be accurately quantified from minimally-processed MRI.

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 Aß 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 ß-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.

Cortical thickness of the cognitive control network in obesity and successful weight loss maintenance: a preliminary MRI study.

Cortical thickness of the cognitive control network was contrasted between obese (OB), successful weight loss maintainers (SWLM), and lean individuals. OB individuals had significant thinning, most notably in the anterior cingulate and posterior parietal cortices. SWLM individuals exhibited trends towards thicker cortex than OB individuals, which may be important in future studies.

Metabolic syndrome is associated with learning and recall impairment in middle age.

AIMS: To determine whether middle-aged individuals with metabolic syndrome, both with and without type 2 diabetes, exhibit cognitive impairments, and to determine the role of each metabolic syndrome component in those associations. METHODS: 143 participants were drawn from ongoing studies of normal aging. Metabolic syndrome was diagnosed in 73 participants (age: 60.4 +/- 8.4 years), who were contrasted with 70 age- and education-matched controls. RESULTS: Metabolic syndrome was associated with reductions in recall (p = 0.006), lower overall intellectual functioning (p = 0.013), and nearly significant reductions in learning (p = 0.066) and executive functioning (p = 0.050). These effects were only marginally attenuated when controlling for type 2 diabetes diagnosis. Of the 5 components of the metabolic syndrome, insulin resistance was the only significant predictor of variance in learning and recall. In addition, the number of metabolic syndrome criteria met was inversely associated with cognitive performance. CONCLUSIONS: These results indicate that impairments in cognitive functioning associated with metabolic syndrome and type 2 diabetes may begin as early as middle age and are primarily due to insulin resistance. These results demonstrate the importance of screening at-risk adults for insulin resistance in order to initiate lifestyle modifications to reverse or prevent these cognitive changes.

Neurofilament Light Predicts Decline in Attention but Not Episodic Memory in Preclinical Alzheimer's Disease.

BACKGROUND: Cerebrospinal fluid tau and neurofilament light (NfL) are two biomarkers of neurodegeneration in Alzheimer's disease. Previous reports have shown that the influence of tau on cognitive decline depends on levels of amyloid burden whereas NfL predicts decline independently of amyloid. Most studies use a global cognitive composite as the primary outcome, and it is unknown if critical cognitive domain scores are similarly sensitive to rates of decline due to neurodegeneration. OBJECTIVE: To examine the unique contribution of amyloid, tau, and NfL to rates of cognitive decline in multiple cognitive composites in a cognitively healthy, middle-aged to older adult cohort. METHODS: A total of 255 participants (55% female; mean age = 66.2 years, range = 42.5-86.7 years) completed CSF studies and serial cognitive assessments to measure global cognition, episodic memory, and attentional control. Linear mixed effects models were used to examine rates of change on each composite score as a function of baseline biomarker levels. RESULTS: Total tau predicted decline in attention regardless of amyloid status, but the relationship to global cognition and episodic memory was dependent on amyloid, replicating prior literature. NfL predicted decline in attention and global cognition, but not memory, and this effect was independent of amyloid status. CONCLUSIONS: These findings suggest that NfL can be used to monitor cognitive decline in aging and Alzheimer's disease and that an attentional control composite may be a better outcome for tracking general neurodegenerative effects on cognition.

Two-period linear mixed effects models to analyze clinical trials with run-in data when the primary outcome is continuous: Applications to Alzheimer's disease.

INTRODUCTION: Study outcomes can be measured repeatedly based on the clinical trial protocol before randomization during what is known as the "run-in" period. However, it has not been established how best to incorporate run-in data into the primary analysis of the trial. METHODS: We proposed two-period (run-in period and randomization period) linear mixed effects models to simultaneously model the run-in data and the postrandomization data. RESULTS: Compared with the traditional models, the two-period linear mixed effects models can increase the power up to 15% and yield similar power for both unequal randomization and equal randomization. DISCUSSION: Given that analysis of run-in data using the two-period linear mixed effects models allows more participants (unequal randomization) to be on the active treatment with similar power to that of the equal-randomization trials, it may reduce the dropout by assigning more participants to the active treatment and thus improve the efficiency of AD clinical trials.

Influence of tau PET, amyloid PET, and hippocampal volume on cognition in Alzheimer disease.

OBJECTIVE: To examine the independent and interactive influences of neuroimaging biomarkers on retrospective cognitive decline. METHODS: A total of 152 middle-aged and older adult participants with at least 2 clinical and cognitive assessments, a Clinical Dementia Rating score of 0 or 0.5, and a flortaucipir (18F-AV-1451) tau PET scan, a florbetapir (18F-AV-45) amyloid PET scan, and a structural MRI scan were recruited from the Knight Alzheimer Disease Research Center at Washington University in St. Louis. Cognition was assessed with standard measures reflecting episodic memory, executive functioning, semantic fluency, and processing speed. RESULTS: Results from retrospective longitudinal analyses showed that each biomarker had a univariate association with the global cognitive composite; however, when each marker was analyzed in a single statistical model, only tau was a significant predictor of global cognitive decline. There was an interaction between tau and amyloid such that tau-related cognitive decline was worse in individuals with high amyloid. There was also an interaction with hippocampal volume indicating that individuals with high levels of all 3 pathologies exhibited the greatest declines in cognition. Additional analyses within each cognitive domain indicated that tau had the largest negative influence on tests of episodic memory and executive functioning. CONCLUSIONS: Together, these results suggest that increasing levels of tau most consistently relate to declines in cognition preceding biomarker collection. These findings support models of Alzheimer disease (AD) staging that suggest that elevated ß-amyloid alone may be insufficient to produce cognitive change in individuals at risk for AD and support the use of multiple biomarkers to stage AD progression.

Neuropsychological measures that detect early impairment and decline in preclinical Alzheimer disease.

Identifying which neuropsychological measures detect early cognitive changes associated with Alzheimer disease (AD), brain pathology would be helpful clinically for the diagnosis of early AD and for the design of clinical trials. We evaluated which neuropsychological measures in our cognitive battery are most strongly associated with cerebrospinal fluid (CSF) biomarkers of AD brain pathology. We studied a large cohort (n = 233) of middle-to older-aged community-dwelling individuals (mean age 61 years) who had no clinical symptoms of dementia and underwent baseline CSF collection at baseline. Participants completed a battery of 9 neuropsychological measures at baseline and then every 1 to 3 years. CSF tau/Aß42 was associated with baseline performance on 5/9 neuropsychological measures, especially measures of episodic memory, and longitudinal performance on 7/9 neuropsychological measures, especially measures of global cognition. The free recall portion of the Free and Cued Selective Reminding Task (FCSRT-free) detected declining cognition in the high CSF tau/Aß42 group the earliest, followed by another measure of episodic memory and a sequencing task.

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.

Cerebrovascular perfusion among older adults is moderated by strength training and gender.

Cerebral perfusion is important in older adults as it is linked to cognitive declines. Physical activity can improve blood flow in the body but little is known about the relationship between physical activity and cerebral perfusion in older adults. In particular, no study has investigated the relation between strength training and cerebral perfusion. We examined whether different types of physical activity (assessed with the Rapid Assessment of Physical Activity questionnaire) were associated with MRI cerebrovascular perfusion in 59 older adults. There was a significant interaction between gender and strength training, such that women who engaged in strength training (weight lifting or calisthenics) at least once per week exhibited significantly greater cerebrovascular perfusion than women who did not. This interaction remained significant after controlling for other physical activity, demographics, and health variables. These findings suggest that regular strength training can be beneficial for cerebrovascular health in women.