Instrumental activities of daily living (IADLs) are used to diagnose older adults as having mild cognitive impairment (MCI) versus dementia. IADL rating scales, however, can be subject to biases. These biases, known as differential item functioning (DIF), occur when individuals receive different ratings based on demographic or contextual factors. Evaluating and addressing DIF is crucial because of the impact of IADL ratings on eligibility for clinical trials and access to FDA approved treatments. The Functional Activities Questionnaire (FAQ), an informant IADL rating scale, was available for 7,958 participants with MCI in the National Alzheimer's Coordinating Center Uniform Data Set. Data were analyzed using the Likelihood-based Investigation of Differential Item Functioning (LIDIF) model. This multivariate approach evaluates uniform and non-uniform DIF while simultaneously adjusting for multiple covariates, including participants’ age, sex, education, race, and informants’ sex and cohabitation status in a multivariate setting. DIF-adjusted IADL levels were estimated using posterior means to mitigate rating biases Significant DIF effects were identified across all 10 FAQ items, with race and informant characteristics emerging as key sources of bias. Black participants were consistently rated as less impaired than white participants, particularly at higher impairment levels, even after adjusting for age, education, and other covariates. This was most pronounced for items related to paying bills, shopping, games, meal preparation, paying attention, remembering appointments and travelling. Informants’ cohabitation status also influenced ratings, with non-cohabitating informants generally reporting less functional impairment compared to cohabitating informants. FAQ total scores underestimated impairment in Black participants due to a strong floor effect, where a large proportion of individuals received the lowest possible scores. DIF-adjusted IADL estimates reduced racial disparities, mitigated floor effects, and identified a greater number of individuals with impairment. These findings highlight the importance of accounting for demographic and informant-related characteristics in FAQ assessment. Adjusting for these biases improves the accuracy and fairness of IADL assessments, supporting equitable clinical decision-making and ensuring that functional impairment is appropriately recognized across diverse populations.

Chronic disease studies often collect data on biological and clinical markers at follow-up visits to monitor disease progression. Viewing such longitudinal measurements governed by latent continuous trajectories, we develop a new dynamic regression framework to investigate the heterogeneity pattern of certain features of the latent individual trajectory that may carry substantive information on disease risk or status. Employing the strategy of multi-level modeling, we formulate the latent individual trajectory feature of interest through a flexible pseudo B-spline model with subject-specific random parameters, and then link it with the observed covariates through quantile regression, avoiding restrictive parametric distributional assumptions that are typically required by standard multi-level longitudinal models. We propose an estimation procedure from adapting the principle of conditional score and develop an efficient algorithm for implementation. Our proposals yield estimators with desirable asymptotic properties as well as good finite-sample performance as confirmed by extensive simulation studies. An application of the proposed method to a cohort of participants with mild cognitive impairment (MCI) in the Uniform Data Set (UDS) provides useful insights about the complex heterogeneous presentations of cognitive decline in MCI patients.

OBJECTIVE:Alzheimer's disease (AD) is believed to be more common in African Americans (AA), but biomarker studies in AA populations are limited. This report represents the largest study to date examining cerebrospinal fluid AD biomarkers in AA individuals. METHODS:We analyzed 3,006 cerebrospinal fluid samples from controls, AD cases, and non-AD cases, including 495 (16.5%) self-identified black/AA and 2,456 (81.7%) white/European individuals using cutoffs derived from the Alzheimer's Disease Neuroimaging Initiative, and using a data-driven multivariate Gaussian mixture of regressions. RESULTS:Distinct effects of race were found in different groups. Total Tauand phospho181-Tau were lower among AA individuals in all groups (p < 0.0001), and Aβ42 was markedly lower in AA controls compared with white controls (p < 0.0001). Gaussian mixture of regressions modeling of cerebrospinal fluid distributions incorporating adjustments for covariates revealed coefficient estimates for AA race comparable with 2-decade change in age. Using Alzheimer's Disease Neuroimaging Initiative cutoffs, fewer AA controls were classified as biomarker-positive asymptomatic AD (8.0% vs 13.4%). After adjusting for covariates, our Gaussian mixture of regressions model reduced this difference, but continued to predict lower prevalence of asymptomatic AD among AA controls (9.3% vs 13.5%). INTERPRETATION:Although the risk of dementia is higher, data-driven modeling indicates lower frequency of asymptomatic AD in AA controls, suggesting that dementia among AA populations may not be driven by higher rates of AD. ANN NEUROL 2024;96:463-475.

Heterogeneous survival data are commonly present in chronic disease studies. Delineating meaningful disease subtypes directly linked to a survival outcome can generate useful scientific implications. In this work, we develop a latent class proportional hazards (PH) regression framework to address such an interest. We propose mixture proportional hazards modeling, which flexibly accommodates class-specific covariate effects while allowing for the baseline hazard function to vary across latent classes. Adapting the strategy of nonparametric maximum likelihood estimation, we derive an Expectation-Maximization (E-M) algorithm to estimate the proposed model. We establish the theoretical properties of the resulting estimators. Extensive simulation studies are conducted, demonstrating satisfactory finite-sample performance of the proposed method as well as the predictive benefit from accounting for the heterogeneity across latent classes. We further illustrate the practical utility of the proposed method through an application to a mild cognitive impairment (MCI) cohort in the Uniform Data Set.

BackgroundAlthough there are many possible causes for cervical dystonia (CD), a specific etiology cannot be identified in most cases. Prior studies have suggested a relationship between autoimmune disease and some cases of CD, pointing to possible immunological mechanisms.ObjectiveThe goal was to explore the potential role of multiple different immunological mechanisms in CD.MethodsFirst, a broad screening test compared neuronal antibodies in controls and CD. Second, unbiased blood plasma proteomics provided a broad screen for potential biologic differences between controls and CD. Third, a multiplex immunoassay compared 37 markers associated with immunological processes in controls and CD. Fourth, relative immune cell frequencies were investigated in blood samples of controls and CD. Finally, sequencing studies investigated the association of HLA DQB1 and DRB1 alleles in controls versus CD.ResultsScreens for anti-neuronal antibodies did not reveal any obvious abnormalities. Plasma proteomics pointed towards certain abnormalities of immune mechanisms, and the multiplex assay pointed more specifically towards abnormalities in T lymphocytes. Abnormal immune cell frequencies were identified for some CD cases, and these cases clustered together as a potential subgroup. Studies of HLA alleles indicated a possible association between CD and DRB1*15:03, which is reported to mediate the penetrance of autoimmune disorders.ConclusionsAltogether, the association of CD with multiple different blood-based immune measures point to abnormalities in cell-mediated immunity that may play a pathogenic role for a subgroup of individuals with CD.

Objective: To evaluate the impact of receptive vocabulary versus years of education on neuropsychological performance of Black and White older adults. Method: A community-based prospectively enrolled cohort (n = 1,007; 130 Black, 877 White) in the Emory Healthy Brain Study were administered the NIH Toolbox Picture Vocabulary Test and neuropsychological measures. Group differences were evaluated with age, sex, and education or age, sex, and Toolbox Vocabulary scores as covariates to determine whether performance differences between Black versus White participants were attenuated or eliminated. Results: With vocabulary as a covariate, the main effect of race was no longer significant for the MoCA, Phonemic Fluency, Rey Auditory Verbal Learning Test, and Rey Complex Figure Test immediate and delayed recall. Although still significantly different between groups, the effect sizes for Animal Fluency, Trails B-A, Symbol Digit Modalities Test, and Rey Copy were attenuated, with the greatest reductions occurring for the Multilingual Naming Test and Judgment of Line Orientation. Conclusions: Findings support the value of using receptive vocabulary as a proxy for premorbid ability level when comparing the cognitive performance of Black and White older adults. The results extend investigations using measures of single word reading to encompass measures assessing word meaning.

There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies. Unsupervised learning can provide insight into the pathophysiology of AD. A mixture of regressions can simultaneously identify clusters from multiple biomarkers while accounting for within-cluster demographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detection limits, which create additional challenges. We apply a mixture of regressions with a multivariate truncated Gaussian distribution (also called a censored multivariate Gaussian mixture of regressions or a mixture of multivariate tobit regressions) to over 3,000 participants from the Emory Goizueta Alzheimer's Disease Research Center and Emory Healthy Brain Study to examine amyloid-beta peptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSF with known detection limits. We address three gaps in the literature on mixture of regressions with a truncated multivariate Gaussian distribution: software availability; inference; and clustering accuracy. We discovered three clusters that tend to align with an AD group, a normal control profile and non-AD pathology. The CSF profiles differed by race, gender and the genetic marker ApoE4, highlighting the importance of considering demographic factors in unsupervised learning with detection limits. Notably, African American participants in the AD-like group had significantly lower tau burden.

Alzheimer’s disease (AD) progresses through a lengthy asymptomatic period during which pathological changes accumulate prior to development of clinical symptoms. As disease-modifying treatments are developed, tools to stratify risk of clinical disease will be required to guide their use. In this study, we examine the relationship of AD biomarkers in healthy middle-aged individuals to health history, family history, and neuropsychological measures and identify cerebrospinal fluid (CSF) biomarkers to stratify risk of progression from asymptomatic to symptomatic AD. CSF from cognitively normal (CN) individuals (N=1149) in the Emory Healthy Brain Study were assayed for Aβ 42 , total Tau (tTau), and phospho181-Tau (pTau), and a subset of 134 cognitively normal, but biomarker-positive, individuals were identified with asymptomatic AD (AsymAD) based on a locally-determined cutoff value for ratio of tTau to Aβ 42 . These AsymAD cases were matched for demographic features with 134 biomarker-negative controls (CN/BM-) and compared for differences in medical comorbidities and family history. Dyslipidemia emerged as a distinguishing feature between AsymAD and CN/BM- groups with significant association with personal and family history of dyslipidemia. A weaker relationship was seen with diabetes, but there was no association with hypertension. Examination of the full cohort by median regression revealed a significant relationship of CSF Aβ 42 (but not tTau or pTau) with dyslipidemia and diabetes. On neuropsychological tests, CSF Aβ 42 was not correlated with performance on any measures, but tTau and pTau were strongly correlated with visuospatial perception and visual episodic memory. In addition to traditional CSF AD biomarkers, a panel of AD biomarker peptides derived from integrating brain and CSF proteomes were evaluated using machine learning strategies to identify a set of 8 peptides that accurately classified CN/BM- and symptomatic AD CSF samples with AUC of 0.982. Using these 8 peptides in a low dimensional t-distributed Stochastic Neighbor Embedding analysis and k-Nearest Neighbor (k=5) algorithm, AsymAD cases were stratified into “Control-like” and “AD-like” subgroups based on their proximity to CN/BM- or AD CSF profiles. Independent analysis of these cases using a Joint Mutual Information algorithm selected a set of 5 peptides with 81% accuracy in stratifying cases into AD-like and Control-like subgroups. Performance of both sets of peptides was evaluated and validated in an independent data set from the Alzheimer’s Disease Neuroimaging Initiative. Based on our findings, we conclude that there is an important role of lipid metabolism in asymptomatic stages of AD. Visuospatial perception and visual episodic memory may be more sensitive than language-based abilities to earliest stages of cognitive decline in AD. Finally, candidate CSF peptides show promise as next generation biomarkers for predicting progression from asymptomatic to symptomatic stages of AD.

Recurrent event data frequently arise in chronic disease studies, providing rich information on disease progression. The concept of latent class offers a sensible perspective to characterize complex population heterogeneity in recurrent event trajectories that may not be adequately captured by a single regression model. However, the development of latent class methods for recurrent event data has been sparse, typically requiring strong parametric assumptions and involving algorithmic issues. In this work, we investigate latent class analysis of recurrent event data based on flexible semiparametric multiplicative modelling. We derive a robust estimation procedure through novelly adapting the conditional score technique and utilizing the special characteristics of multiplicative intensity modelling. The proposed estimation procedure can be stably and efficiently implemented based on existing computational routines. We provide solid theoretical underpinnings for the proposed method, and demonstrate its satisfactory finite sample performance via extensive simulation studies. An application to a dataset from research participants at Goizueta Alzheimer's Disease Research Center illustrates the practical utility of our proposals.

There are many possible etiologies for cervical dystonia (CD), but a cause cannot be identified in most cases. Most recent attention has focused on genetic causes, although a few prior studies have highlighted autoimmune mechanisms instead. Because autoimmune disorders frequently co-exist, the current study evaluated the hypothesis that autoimmune disorders might be more common in CD than neurological controls. The frequency of 32 common autoimmune disorders was evaluated using a systematic survey comparing 300 subjects with CD with 391 neurological controls. The frequency of thyroid disease was significantly higher in CD (20%) compared with controls (6%). Regression analyses that accounted for age and sex revealed an odds ratio of 4.5 (95% CI 2.5-8.1, p < 0.001). All other autoimmune disorders occurred with similar frequencies in CD and controls. Although these studies do not establish a mechanistic link between CD and autoimmune disease, they suggest the need for further attention to a potential relationship, and more specifically with thyroid disease.

Latent class analysis (LCA) is a useful tool to investigate the heterogeneity of a disease population with time-to-event data. We propose a new method based on non-parametric maximum likelihood estimator (NPMLE), which facilitates theoretically validated inference procedure for covariate effects and cumulative hazard functions. We assess the proposed method via extensive simulation studies and demonstrate improved predictive performance over standard Cox regression model. We further illustrate the practical utility of the proposed method through an application to a mild cognitive impairment (MCI) cohort dataset.

Heparin inhibits transport of electrons from reduced cytochrome c to cytochrome c oxidase. The effect is due to the interaction of heparin with cytochrome c. It has been observed that binding of heparin to the reduced or oxidized cytochrome c changes the spectrum of cytochrome c at the Soret region. Affinity chromatography of heparin in cytochrome c immobilized to thiol-Sepharose shows that commercial heparin is eluted in the low-affinity and high-affinity fractions. Both participate in the interaction with cytochrome c. Polylysine induces decay of the cytochrome c-heparin complex.

Recurrent event data frequently arise in chronic disease studies, providing rich information on disease progression. The concept of latent class offers a sensible perspective to characterize complex population heterogeneity in recurrent event trajectories that may not be adequately captured by a single regression model. However, the development of latent class methods for recurrent event data has been sparse, typically requiring strong parametric assumptions and involving algorithmic issues. In this work, we investigate latent class analysis of recurrent event data based on flexible semiparametric multiplicative modelling. We derive a robust estimation procedure through novelly adapting the conditional score technique and utilizing the special characteristics of multiplicative intensity modelling. The proposed estimation procedure can be stably and efficiently implemented based on existing computational routines. We provide solid theoretical underpinnings for the proposed method, and demonstrate its satisfactory finite sample performance via extensive simulation studies. An application to a dataset from research participants at Goizueta Alzheimer's Disease Research Center illustrates the practical utility of our proposals.

The locus coeruleus is the initial site of Alzheimer's disease neuropathology, with hyperphosphorylated Tau appearing in early adulthood followed by neurodegeneration in dementia. Locus coeruleus dysfunction contributes to Alzheimer's pathobiology in experimental models, which can be rescued by increasing norepinephrine transmission. To test norepinephrine augmentation as a potential disease-modifying therapy, we performed a biomarker-driven phase II trial of atomoxetine, a clinically-approved norepinephrine transporter inhibitor, in subjects with mild cognitive impairment due to Alzheimer's disease. The design was a single-centre, 12-month double-blind crossover trial. Thirty-nine participants with mild cognitive impairment and biomarker evidence of Alzheimer's disease were randomized to atomoxetine or placebo treatment. Assessments were collected at baseline, 6- (crossover) and 12-months (completer). Target engagement was assessed by CSF and plasma measures of norepinephrine and metabolites. Prespecified primary outcomes were CSF levels of IL1α and TECK. Secondary/exploratory outcomes included clinical measures, CSF analyses of amyloid-β42, Tau, and pTau181, mass spectrometry proteomics and immune-based targeted inflammation-related cytokines, as well as brain imaging with MRI and fluorodeoxyglucose-PET. Baseline demographic and clinical measures were similar across trial arms. Dropout rates were 5.1% for atomoxetine and 2.7% for placebo, with no significant differences in adverse events. Atomoxetine robustly increased plasma and CSF norepinephrine levels. IL-1α and TECK were not measurable in most samples. There were no significant treatment effects on cognition and clinical outcomes, as expected given the short trial duration. Atomoxetine was associated with a significant reduction in CSF Tau and pTau181 compared to placebo, but not associated with change in amyloid-β42. Atomoxetine treatment also significantly altered CSF abundances of protein panels linked to brain pathophysiologies, including synaptic, metabolism and glial immunity, as well as inflammation-related CDCP1, CD244, TWEAK and osteoprotegerin proteins. Treatment was also associated with significantly increased brain-derived neurotrophic factor and reduced triglycerides in plasma. Resting state functional MRI showed significantly increased inter-network connectivity due to atomoxetine between the insula and the hippocampus. Fluorodeoxyglucose-PET showed atomoxetine-associated increased uptake in hippocampus, parahippocampal gyrus, middle temporal pole, inferior temporal gyrus and fusiform gyrus, with carry-over effects 6 months after treatment. In summary, atomoxetine treatment was safe, well tolerated and achieved target engagement in prodromal Alzheimer's disease. Atomoxetine significantly reduced CSF Tau and pTau, normalized CSF protein biomarker panels linked to synaptic function, brain metabolism and glial immunity, and increased brain activity and metabolism in key temporal lobe circuits. Further study of atomoxetine is warranted for repurposing the drug to slow Alzheimer's disease progression.

Belatacept results in improved kidney transplant outcomes, but utilization has been limited by logistical barriers related to monthly (q1m) intravenous infusions. Every 2-month (q2m) belatacept has potential to increase utilization, therefore we conducted a randomized noninferiority trial in low immunologic risk renal transplant recipients greater than 1-year posttransplant. Patients on belatacept were randomly assigned to q1m or q2m therapy. The primary objective was a noninferiority comparison of renal function (eGFR) at 12 months with a noninferiority margin (NIM) of 6.0 ml/min/1.73 m 2 . One hundred and sixty-six participants were randomized to q1m ( n  = 82) or q2m ( n  = 84) belatacept, 163 patients received treatment, and 76 q1m and 77 q2m subjects completed the 12-month study period. Every 2-month belatacept was noninferior to q1m, as the difference in mean eGFR adjusted for baseline renal function did not exceed the NIM. Two-month dosing was safe and well tolerated, with no patient deaths or graft losses. Four rejection episodes and three cases of donor-specific antibodies (DSAs) occurred among q2m subjects; however, only one rejection and one instance of DSA were observed in subjects adherent to the study protocol. Every 2-month belatacept therapy may facilitate long-term utilization of costimulation blockade, but future multicenter studies with long-term follow-up will further elucidate immunologic risk. (ClinicalTrials.gov NCT02560558).

INTRODUCTION:Peripheral biomarkers to diagnose Alzheimer's disease (AD) have not been established. Given parallels between neuron and platelet biology, we hypothesized platelet membrane-associated protein changes may differentiate patients clinically defined with probable AD from noncognitive impaired controls.METHODS:Purified platelets, confirmed by flow cytometry were obtained from individuals before fractionation by ultracentrifugation. Following a comparison of individual membrane fractions by SDS-PAGE for general proteome uniformity, equal protein weight from the membrane fractions for five representative samples from AD and five samples from controls were pooled. AD and control protein pools were further divided into molecular weight regions by one-dimensional SDS-PAGE, prior to digestion in gel. Tryptic peptides were analyzed by reverse-phase liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Ionized peptide intensities were averaged for each identified protein in the two pools, thereby measuring relative protein abundance between the two membrane protein pools. Log2-transformed ratio (AD/control) of protein abundances fit a normal distribution, thereby permitting determination of significantly changed protein abundances in the AD pool.RESULTS:We report a comparative analysis of the membrane-enriched platelet proteome between patients with mild to moderate AD and cognitively normal, healthy subjects. A total of 144 proteins were determined significantly altered in the platelet membrane proteome from patients with probable AD. In particular, secretory (alpha) granule proteins were dramatically reduced in AD. Of these, we confirmed significant reduction of thrombospondin-1 (THBS1) in the AD platelet membrane proteome by immunoblotting. There was a high protein-protein connectivity of proteins in other pathways implicated by proteomic changes to the proteins that define secretory granules.CONCLUSIONS:Depletion of secretory granule proteins is consistent with a preponderance of post-activated platelets in circulation in AD. Significantly changed pathways implicate additional AD-related defects in platelet glycoprotein synthesis, lipid homeostasis, amyloidogenic proteins, and regulators of protease activity, many of which may be useful plasma membrane-expressed markers for AD. This study highlights the utility of LC-MS/MS to quantify human platelet membrane proteins and suggests that platelets may serve as a source of blood-based biomarkers in neurodegenerative disease.

Latent class analysis is an intuitive tool to characterize disease phenotype heterogeneity. With data more frequently collected on multiple phenotypes in chronic disease studies, it is of rising interest to investigate how the latent classes embedded in one phenotype are related to another phenotype. Motivated by a cohort with mild cognitive impairment (MCI) from the Uniform Data Set (UDS), we propose and study a time-dependent structural model to evaluate the association between latent classes and competing risk outcomes that are subject to missing failure types. We develop a two-step estimation procedure which circumvents latent class membership assignment and is rigorously justified in terms of accounting for the uncertainty in classifying latent classes. The new method also properly addresses the realistic complications for competing risks outcomes, including random censoring and missing failure types. The asymptotic properties of the resulting estimator are established. Given that the standard bootstrapping inference is not feasible in the current problem setting, we develop analytical inference procedures, which are easy to implement. Our simulation studies demonstrate the advantages of the proposed method over benchmark approaches. We present an application to the MCI data from UDS, which uncovers a detailed picture of the neuropathological relevance of the baseline MCI subgroups.

Sadan, Ofer; Singbartl, Kai; Kraft, Jacqueline; Plancher, Joao McONeil; Greven, Alexander; Kandiah, Prem; Pimentel, Cedric; Hall, Casey; Papangelou, Alexander; Asbury, William; Hanfelt, John; Samuels, Owen Author Information

AbstractBackgroundHypertension increases the risk of Alzheimer’s Disease (AD) but blood pressure (BP) in older adults and cognition associated has been conflicting. We reported that arterial stiffness, which may antedate hypertension, better predicts cognitive decline. We investigated the association between BP and arterial stiffness with cerebrospinal fluid (CSF) levels of Amyloid beta 1‐42 (Ab42), tau, and P‐tau in the Emory Healthy Brain study (EHBS).MethodsEHBS is an ongoing cohort study of cognitively normal adults (50‐75 years) established in 2016. Participants undergo CSF sampling and cognitive, BP and anthropometrics and arterial stiffness (carotid ‐femoral pulse wave velocity [PWV] measured using Sphygmocor device (Atcor Medical, Sydney, Australia) measurements. General Linear Model (GLM) analyses and logistic regression with receiver operator curve (ROC) were used. Preclinical AD was defined in this population at Ab42/tau<5.7 . Covariates included age, gender, race, hypertension diagnosis, and body mass index (BMI).ResultsIn this analysis, 630 participants were included (age= 63 (SD=7), 70% women, 87% White, education mean= 16 (2) years, BMI=25.36 (3.57) kg/m2, MoCA score=26.6 (2.3), mean BP = 143/83 mm Hg, 35% were hypertensive, and PWV=6.3 (1.8) m/sec. Mean Ab42=565.7 (151.3), total tau=52.0 (22.8), P‐tau= 29.4(12.5), and Ab42/tau=12.8 (6.2) and 48 (7.6%) participants fit criterion for preclinical AD. Higher PWV was associated with lower CSF Ab42 (p<0.0001, Ab42 was lower by ‐14.1 pg/dl per each 1 m/s increase in PWV). After adjusting for covariates this decreased to ‐10.6 pg/dl for each unit increase in PWV (p=0.003). Neither SBP (p=0.98) nor DBP (0.45) were associated with Ab42. Higher PWV, but not BP, was also associated with pre‐clinical AD: for each unit increase in PWV, adjusted odds ratio for preclinical AD=1.35, 95%CI (1.18‐1.56), ROC=0.68. This remained true after adjusting for SBP and for all covariates. There was no association between PWV or BP and total tau or P‐tau (p>0.5).ConclusionHigher PWV, reflecting increased arterial stiffness, but not brachial BP is associated with greater brain Ab42 levels and the odds of preclinical AD in cognitively normal adults. PWV may offer a non‐invasive method for identifying healthy individuals with higher likelihood of pre‐clinical AD.