Lower Prevalence of Asymptomatic Alzheimer's Disease Among Healthy African Americans.

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 Aß42 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.

Evaluating the association between latent classes and competing risks outcomes with multiphenotype data.

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.

A phase II study repurposing atomoxetine for neuroprotection in mild cognitive impairment.

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-ß42, 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-ß42. 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.

An Exploration of Subgroups of Neuropsychiatric Symptoms in Mild Cognitive Impairment and Their Risks of Conversion to Dementia or Death.

OBJECTIVE: To explore the heterogeneity of neuropsychiatric symptom (NPS) complexes in individuals with mild cognitive impairment (MCI) and assess the relative risks of converting to dementia or dying. DESIGN: Latent class analysis using 7,971 participants with MCI. SETTING: Participants in the Uniform Data Set (UDS) from 39 NIH Alzheimer's Disease Centers. PARTICIPANTS: Persons with a diagnosis of MCI at initial visit from each center and with either a Mini-Mental State Examination (MMSE) score of 22 or greater or an equivalent education-adjusted Montreal Cognitive Assessment (MoCA) score of 16 or greater. MEASUREMENTS: Neuropsychiatric Inventory Questionnaire (NPI-Q) administered at initial visit. RESULTS: In addition to a subgroup with mild or no NPS (relative frequency, 50%), three empirically-based subgroups of NPS were identified: 1) an "affect" or "negative mood" subgroup (27%) with depression, anxiety, apathy, nighttime disturbance, and change in appetite; 2) a "hyperactive" subgroup (14%) with agitation, irritability, and disinhibition; and 3) a "psychotic with additional severe NPS" subgroup (9%) with the highest risk of delusions and hallucinations, as well as highest risk of all other NPS. Each of these three subgroups had significantly higher risk of converting to dementia than the "mild NPS" class, with the "psychotic with additional severe NPS" subgroup possessing a 64% greater risk. The subgroups did not differ in their risks of death without dementia. CONCLUSION: Our findings of three NPS subgroups in MCI characterized by affect, hyperactive, or psychotic features are largely consistent with a previous 3-factor model of NPS found in a demented population. The consistency of these findings across studies and samples, coupled with our results on the associated risks of converting to dementia, suggests that the NPS structure is robust, and warrants further consideration in classification models of MCI.

Semiparametric Latent Class Analysis of Recurrent Event Data.

Recurrent events 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 events data has been sparse, typically requiring strong parametric assumptions and involving algorithmic issues. In this work, we investigate latent class analysis of recurrent events data based on flexible semiparametric multiplicative modeling. We derive a robust estimation procedure through novelly adapting the conditional score technique and utilizing the special characteristics of multiplicative intensity modeling. 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.

Every 2-month belatacept maintenance therapy in kidney transplant recipients greater than 1-year posttransplant: A randomized, noninferiority trial.

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 m2 . 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).

Scalable and robust latent trajectory class analysis using artificial likelihood.

Latent trajectory class analysis is a powerful technique to elucidate the structure underlying population heterogeneity. The standard approach relies on fully parametric modeling and is computationally impractical when the data include a large collection of non-Gaussian longitudinal features. We introduce a new approach, the first based on artificial likelihood concepts, that avoids undue modeling assumptions and is computationally tractable. We show that this new method provides reliable estimates of the underlying population structure and is from 20 to 200 times faster than conventional methods when the longitudinal features are non-Gaussian. We apply the approach to explore subgroups among research participants in the early stages of neurodegeneration.

Subjective Memory Complaints in White and African American Participants.

OBJECTIVE: Subjective memory complaints (SMCs) are associated with mild cognitive impairment and dementia but are understudied in African Americans (AAs). We compared SMC endorsement in white and AA participants and evaluated predictors of diagnostic progression. METHODS: Initial visit variables, including SMC and memory performance, were compared within a cognitively normal race-matched sample of white and AA participants (Ntotal = 912; 456each race) to assess the presence and predictors of SMC, the predictors of future diagnostic progression, and the change in memory performance over time. RESULTS: More white (32.9%) than AA (24.3%) participants reported SMC (P < .01, ϕ = -.10). Subjective memory complaint was predicted by memory performance (B = -0.03, standard error [SE] = 0.013, odds ratio [OR] = .968, P < .05) and race (B = -0.99, SE = 0.080, OR = .373, P < .001). Subjective memory complaints and memory performance were associated with progression, χ2 (3, n = 912) = 102.37, P < .001. African American race (-2.05 ± 0.24 SE) and SMC (-0.45 ± 0.21 SE) were associated with worse memory performance at baseline and over time, χ2(3) = 13.54, P < .01. CONCLUSIONS: In contrast to previous research, our study found that SMC is associated with diagnostic progression and objective memory declines in both white and AA participants.

The Relation of Psychosocial Distress With Myocardial Perfusion and Stress-Induced Myocardial Ischemia.

OBJECTIVE: Mental stress-induced myocardial ischemia is a frequent phenomenon in patients with coronary artery disease (CAD). The link between an integrated measure of chronic psychosocial distress and mental stress-induced myocardial ischemia, and whether it differs by sex, has not been examined before. METHODS: We used latent class analysis to derive a composite measure of psychosocial distress integrating scales of depression, posttraumatic stress, anxiety, anger, hostility, and perceived stress in 665 individuals with stable CAD. Participants underwent myocardial perfusion imaging with mental stress and perfusion defects were quantified at rest (summed rest score), with mental stress (summed stress score), and their difference (summed difference score), the latter being an index of inducible ischemia. RESULTS: The M (SD) age was 63 (9) years, and 185 (28%) were women. Latent class analysis characterized the study sample into four distinct classes of incremental psychosocial distress. In women, class 4 (highest distress) had an adjusted 4.0-point higher summed rest score (95% confidence interval = 0.2-7.7) as compared with class 1 (lowest distress), whereas no difference was observed in men (-0.87 points, 95% confidence interval = -3.74 to 1.99, p = .04 for interaction). There was no association between the psychosocial distress latent variable and summed difference score in either women or men. CONCLUSIONS: Among patients with CAD, a higher level of psychosocial distress is not associated with mental stress ischemia, but it is associated with more resting (fixed) perfusion abnormalities in women only, as well as with blunted hemodynamic response to mental stress in both men and women.

Rationale and Design of the Emory Healthy Aging and Emory Healthy Brain Studies.

Understanding the relationships between health and aging is essential for delaying morbidity and maximizing independence in aging populations as life expectancies increase. Loss of cognitive function is a feared age-associated condition and growing public health concern. Alzheimer's disease (AD), the most common cause of dementia, has no curative therapies. Characterizing the relationships between risk factors, biomarkers, and AD progression is critical for the development of effective disease prediction, clinical intervention, and ultimately, disease prevention. The Emory Healthy Aging Study (EHAS) and the Emory Healthy Brain Study (EHBS), which is nested within EHAS, aim to further the understanding of healthy aging and the pathogenesis of age-related illnesses in well-characterized, community-based prospective cohorts and to identify biomarkers for the earliest manifestations of AD for the facilitation of preventative interventions. The EHAS is an innovative, longitudinal, web-based study enrolling English-speaking adults in the U.S. who agree to be contacted for future studies. Using validated instruments, the annual questionnaire enquires about demographics, socioeconomics, self-reported cognitive function, personal and family medical history, lifestyle, and psychosocial factors. Cognitive assessments are also obtained using an ambulatory device. Nested within EHAS, the EHBS is enrolling up to 2,500 EHAS participants, 50-75 years old, who do not have a diagnosis of AD, mild cognitive impairment, or any other memory disorder. EHBS in-person, biennial study visits, include neuropsychological testing, cardiovascular measures, retinal and brain imaging, biospecimen collection (blood, cerebrospinal fluid, gut microbiome), and other assessments. Since spring 2016, EHAS and EHBS have enrolled 12,500 and 863 participants with completed baseline assessments, respectively. Data and biospecimens from EHBS participants will support a broad range of AD biomarker discovery efforts, and follow-up of EHAS participants will enable assessment of self-reported cognitive trajectories and accumulation of incident cases of a variety of health conditions. The EHAS design supports the interval deployment of new study instruments and targeted sampling for ancillary studies. This project will increase our knowledge about healthy aging, improve our understanding of risk factors for cognitive impairment and dementias, support development of biomarkers, and facilitate studies of age-associated disorders including AD.

Latent classes of mild cognitive impairment are associated with clinical outcomes and neuropathology: Analysis of data from the National Alzheimer's Coordinating Center.

Given the importance of identifying prodromes of dementia with specific etiologies, we assessed whether seven latent classes of mild cognitive impairment (MCI), defined empirically based on cognitive, functional, and neuropsychiatric information at initial visit, are associated with distinct clinical outcomes and neuropathological features. We separated 6034 participants with a baseline diagnosis of MCI into seven latent classes using previously defined criteria. We found that these latent classes of MCI differed significantly in their clinical outcomes, survival time, and neuropathology. Two amnestic multi-domain subgroups, as well as two other subgroups with functional impairments and neuropsychiatric disturbances, were at higher risk of not only a 'pure' form of Alzheimer's disease (AD) pathology, but also a 'mixed' pathology consisting of both AD and vascular features. Moreover, the seven latent classes had different risks of Lewy bodies, hippocampal sclerosis, and frontotemporal lobar degeneration (FTLD). This study indicates that data-driven subgroups of MCI are clinicopathologically informative and, with refinement, could lead to targeted interventions focused on each etiology.

A Randomized, Double-Blind, Placebo-Controlled Trial to Examine the Effectiveness of Lubiprostone on Constipation Symptoms and Colon Transit Time in Diabetic Patients.

OBJECTIVES: Constipation is the most common GI symptom in patients with diabetes mellitus (DM). Importantly, patients with constipation have lower health-related quality of life than those without constipation. Effective therapies for constipation are limited and there is a paucity of data evaluating the treatment of constipation in diabetics. METHODS: Diabetic patients with chronic idiopathic constipation (CIC) as defined by Rome III criteria were recruited from outpatient clinics at a tertiary-care center and a Veterans Administration Hospital. Demographic data, baseline stool patterns, and a constipation-specific quality of life survey (Patient Assessment of Constipation Quality of Life (PAC-QOL)) were obtained. Baseline colonic transit time (CTT) was evaluated utilizing the wireless motility capsule. Patients were randomized in a double-blind fashion to 48 mcg per day lubiprostone or placebo for 8 weeks. The primary end point measured was the difference in number of spontaneous bowel movements (SBMs) per week vs. baseline for each group at each week after initiation of therapy. Secondary end points included changes in CTT after 4 weeks of therapy, PAC-QOL after 8 weeks of therapy, and changes from baseline in associated gastrointestinal (GI) symptoms as well as need for rescue medication at 2, 4, and 8 weeks. RESULTS: Seventy-six patients (mean age, 56.9±9.1 years, 62% females) were randomized. There were no significant differences between the two groups' baseline data or demographics. During the 8-week treatment period, patients in the lubiprostone group experienced an average of 1.83±0.80 (P=0.02) more SBMs per week than those in the placebo group as compared with baseline. The duration of CTT at Week 4 was shorter by an average of 13 h compared with baseline in the lubiprostone group, and was prolonged by an average of 7 h compared with baseline in the placebo group, leading to a treatment effect of 20.3±7.3 h (P=0.006). PAC-QOL improved in both the groups; however, there was no significant difference between the groups. There was no difference in associated GI symptoms and need for rescue medication between the two groups after 8 weeks. There were no serious adverse events reported during the study. CONCLUSIONS: This study suggests that lubiprostone is a safe and effective treatment for increasing weekly SBMs and decreasing CTT in patients with DM and CIC.

The "shirt collar sign" of cervical dystonia.

The diagnosis of cervical dystonia (CD) is clinical. We describe a physical examination observation that has been noted in CD patients. There is a tendency for their shirt collars to be shifted to one side. We validated this apparently consistent finding by having blinded evaluators rating the symmetry of the shirt collars in CD and non-cervical dystonia control subjects. A high correlation was found between the physical finding which we call "shirt collar sign" and the diagnosis. "Shirt collar sign" may be a helpful sign in diagnosing CD.

Applicability of digital analysis and imaging technology in neuropathology assessment.

Alzheimer's disease (AD) is a progressive neurological disorder that affects more than 30 million people worldwide. While various dementia-related losses in cognitive functioning are its hallmark clinical symptoms, ultimate diagnosis is based on manual neuropathological assessments using various schemas, including Braak staging, CERAD (Consortium to Establish a Registry for Alzheimer's Disease) and Thal phase scoring. Since these scoring systems are based on subjective assessment, there is inevitably some degree of variation between readers, which could affect ultimate neuropathology diagnosis. Here, we report a pilot study investigating the applicability of computer-driven image analysis for characterizing neuropathological features, as well as its potential to supplement or even replace manually derived ratings commonly performed in medical settings. In this work, we quantitatively measured amyloid beta (Aß) plaque in various brain regions from 34 patients using a robust digital quantification algorithm. We next verified these digitally derived measures to the manually derived pathology ratings using correlation and ordinal logistic regression methods, while also investigating the association with other AD-related neuropathology scoring schema commonly used at autopsy, such as Braak and CERAD. In addition to successfully verifying our digital measurements of Aß plaques with respective categorical measurements, we found significant correlations with most AD-related scoring schemas. Our results demonstrate the potential for digital analysis to be adapted to more complex staining procedures commonly used in neuropathological diagnosis. As the efficiency of scanning and digital analysis of histology images increases, we believe that the basis of our semi-automatic approach may better standardize quantification of neuropathological changes and AD diagnosis, ultimately leading to a more comprehensive understanding of neurological disorders and more efficient patient care.

U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease.

Deposition of insoluble protein aggregates is a hallmark of neurodegenerative diseases. The universal presence of ß-amyloid and tau in Alzheimer's disease (AD) has facilitated advancement of the amyloid cascade and tau hypotheses that have dominated AD pathogenesis research and therapeutic development. However, the underlying etiology of the disease remains to be fully elucidated. Here we report a comprehensive study of the human brain-insoluble proteome in AD by mass spectrometry. We identify 4,216 proteins, among which 36 proteins accumulate in the disease, including U1-70K and other U1 small nuclear ribonucleoprotein (U1 snRNP) spliceosome components. Similar accumulations in mild cognitive impairment cases indicate that spliceosome changes occur in early stages of AD. Multiple U1 snRNP subunits form cytoplasmic tangle-like structures in AD but not in other examined neurodegenerative disorders, including Parkinson disease and frontotemporal lobar degeneration. Comparison of RNA from AD and control brains reveals dysregulated RNA processing with accumulation of unspliced RNA species in AD, including myc box-dependent-interacting protein 1, clusterin, and presenilin-1. U1-70K knockdown or antisense oligonucleotide inhibition of U1 snRNP increases the protein level of amyloid precursor protein. Thus, our results demonstrate unique U1 snRNP pathology and implicate abnormal RNA splicing in AD pathogenesis.

Consequences of impaired purine recycling on the proteome in a cellular model of Lesch-Nyhan disease.

The importance of specific pathways of purine metabolism for normal brain function is highlighted by several inherited disorders, such as Lesch-Nyhan disease (LND). In this disorder, deficiency of the purine recycling enzyme, hypoxanthine-guanine phosphoribosyltransferase (HGprt), causes severe neurological and behavioral abnormalities. Despite many years of research, the mechanisms linking the defect in purine recycling to the neurobehavioral abnormalities remain unclear. In the current studies, an unbiased approach to the identification of potential mechanisms was undertaken by examining changes in protein expression in a model of HGprt deficiency based on the dopaminergic rat PC6-3 line, before and after differentiation with nerve growth factor (NGF). Protein expression profiles of 5 mutant sublines carrying different mutations affecting HGprt enzyme activity were compared to the HGprt-competent parent line using the method of stable isotopic labeling by amino acids in cell culture (SILAC) followed by denaturing gel electrophoresis with liquid chromatography and tandem mass spectrometry (LC-MS/MS) of tryptic digests, and subsequent identification of affected biochemical pathways using the Database for Annotation, Visualization and Integrated Discovery (DAVID) functional annotation chart analysis. The results demonstrate that HGprt deficiency causes broad changes in protein expression that depend on whether the cells are differentiated or not. Several of the pathways identified reflect predictable consequences of defective purine recycling. Other pathways were not anticipated, disclosing previously unknown connections with purine metabolism and novel insights into the pathogenesis of LND.

Alleviating manoeuvres (sensory tricks) in cervical dystonia.

BACKGROUND: There is limited information on the phenomenology, clinical characteristics and pathophysiology of alleviating manoeuvres (AM), also called 'sensory tricks' in cervical dystonia (CD). METHODS: Individual data, collected from 10 sites participating in the Dystonia Coalition (http://clinicaltrials.gov/show/NCT01373424), included description of localisation and phenomenology of AM collected by systematic review of standardised video examinations. Analyses correlated demographic, neurologic, and psychiatric features of CD patients with or without effective AM. RESULTS: Of 154 people studied, 138 (89.6%) used AM, of which 60 (43.4%) reported partial improvement, 55 (39.8%) marked improvement, and 4 (0.03%) no effect on dystonic posture. Light touch, usually to the lower face or neck, was used by >90%. The presence or location of AM did not correlate with the severity of the dystonia. CONCLUSIONS: In this large and comprehensive study of CD, we found no clinical predictors of effective AM. Further studies of sensorimotor integration in dystonia are needed to better understand the pathophysiology of AM.

Blood Glucose Levels of Patients With Diabetes in the Immediate Post-Acute Hemodialysis Period: An Exploratory Study.

UNLABELLED: PROBLEM/PURPOSE: To establish the evidence on which to base a protocol for monitoring capillary blood glucose in hospitalized patients with diabetes mellitus in the immediate post-acute hemodialysis period. SAMPLE: Hospitalized, non-critically ill, adult patients (n = 68) with diabetes undergoing acute hemodialysis treatments. METHODS: Capillary blood glucose was tested 30 minutes prior to the end of the hemodialysis treatment, at the end of the treatment, and 30 minutes and 60 minutes post-treatment. Data were analyzed to determine both within and between patient variability. RESULTS: Glucose levels varied widely before, during, and after hemodialysis, with greatest variability at 60 minutes post-hemodialysis. Levels did not vary based on diabetes type or admitting diagnosis. Possible relationships were identified with length of treatment, insulin administration prior to treatment, and food consumed within an hour after treatment. CONCLUSIONS/NURSING IMPLICATIONS: Testing post-dialysis glucose levels earlier than 60 minutes post-treatment may miss the need for additional medication.

Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits.

There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies, which motivates the use of unsupervised learning. A mixture of regressions is an unsupervised method that can simultaneously identify clusters from multiple biomarkers while learning 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.

Latent Class Proportional Hazards Regression with Heterogeneous Survival Data.

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.