Clustered Data Research Articles

Regression analysis of clustered panel count data with additive mean models

Regression analysis of clustered panel count data with additive mean models

Robust compromise multicriteria decision model to evaluate the level of achievement of graduation profile

We propose an approach of a robust compromise multi-criteria decision-making model that evaluates the level of competency achievement in graduation profiles. Quantitative information on qualifications of specific and generic competencies of a graduation profile was collected from professors, graduates, employers, community partners, and students' self-perception of achievement. This information was categorized using ordinal Likert scales generated through Monte Carlo simulation methods and copula modeling. The proposed model is validated using optimization methods. It provides a single score for the level of competency achievement, and the analysis is conducted using descriptive statistics and multivariate analysis of clusters. Simulated scenarios revealed that higher weights given by stakeholders in the construction of the robust compromise score were associated with higher evaluation scores of competencies from the same stakeholders. Scenarios with negative correlations between stakeholder qualifications about level of achievement of graduation profile yielded lower scores in the robust compromise multi-criteria decision-making model. Our proposal for Robust Compromise Multi-Criteria Decision Making Model offers more precise single scores, reducing the epistemological uncertainty associated with the input information.

The impact of halo concentration on the Sunyaev Zel’dovich effect signal from massive galaxy clusters

ABSTRACT The Sunyaev Zel’dovich (SZ) effect is sensitive to the pressure of ionized gas inside galaxy clusters, which is in turn controlled largely by the gravitational potential of the cluster. Changing the concentration parameter describing the cluster mass distribution impacts the gravitational potential and thus the cluster SZ signal, with implications for cosmological and other analyses of SZ-selected clusters. We investigate the concentration–SZ relation in theory and simulations. We find that the impact of concentration on the inner SZ profile (R ≲ 0.75R200c) can be captured with standard polytropic gas models. However, we find that such models do a poor job of reproducing the outer SZ profiles (R ≳ 0.75R200c) and the relation between the integrated SZ signal, Y, and concentration. This disagreement results from a sharp truncation of the gas pressure profile near the splashback radius, likely caused by virial shocks. We develop a simple description of the truncation that leads to a good match with the simulated SZ profiles out to several R200c for clusters of varying mass and concentration, and that also accurately predicts the concentration–Y relationship. Finally, we determine how inference of the linear bias parameter and splashback radius for SZ-selected clusters can be biased by ignoring the concentration dependence of the SZ signal, finding that bias to the former is essentially negligible, while bias to the latter can be as much as 2 per cent.

The Role of Green Finance in Fostering the Sustainability of the Economy and Renewable Energy Supply: Recent Issues and Challenges

Nowadays, European countries, while implementing the goals of sustainable development, are trying to be energy secure by reducing energy consumption and switching to renewable energy sources; the issue of green bonds is essential in the financing of these projects. This study aims to investigate the current situation and trends in green finance, identifies its problems and challenges, as well as determines the importance of green bonds in sustainability in general and renewable energy supply in particular. The authors conducted a bibliometric analysis of green finance and renewable energy, followed by an extensive literature review identifying the benefits and challenges of green bond investments, continued with an empirical study that included hypothesis testing and the creation and analysis of the EU countries clusters, which have not been sufficiently studied to date. A study examining trends in green finance, identifying its benefits and challenges, found that green bond investments have a positive impact on carbon reduction and renewable energy supply in the EU OECD countries, and cluster analysis of the European OECD countries indicated a positive relationship between economic performance and overall ESG risk.

Adherence to key recommendations for design and analysis of stepped-wedge cluster randomized trials: A review of trials published 2016-2022.

The stepped-wedge cluster randomized trial (SW-CRT), in which clusters are randomized to a time at which they will transition to the intervention condition - rather than a trial arm - is a relatively new design. SW-CRTs have additional design and analytical considerations compared to conventional parallel arm trials. To inform future methodological development, including guidance for trialists and the selection of parameters for statistical simulation studies, we conducted a review of recently published SW-CRTs. Specific objectives were to describe (1) the types of designs used in practice, (2) adherence to key requirements for statistical analysis, and (3) practices around covariate adjustment. We also examined changes in adherence over time and by journal impact factor. We used electronic searches to identify primary reports of SW-CRTs published 2016-2022. Two reviewers extracted information from each trial report and its protocol, if available, and resolved disagreements through discussion. We identified 160 eligible trials, randomizing a median (Q1-Q3) of 11 (8-18) clusters to 5 (4-7) sequences. The majority (122, 76%) were cross-sectional (almost all with continuous recruitment), 23 (14%) were closed cohorts and 15 (9%) open cohorts. Many trials had complex design features such as multiple or multivariate primary outcomes (50, 31%) or time-dependent repeated measures (27, 22%). The most common type of primary outcome was binary (51%); continuous outcomes were less common (26%). The most frequently used method of analysis was a generalized linear mixed model (112, 70%); generalized estimating equations were used less frequently (12, 8%). Among 142 trials with fewer than 40 clusters, only 9 (6%) reported using methods appropriate for a small number of clusters. Statistical analyses clearly adjusted for time effects in 119 (74%), for within-cluster correlations in 132 (83%), and for distinct between-period correlations in 13 (8%). Covariates were included in the primary analysis of the primary outcome in 82 (51%) and were most often individual-level covariates; however, clear and complete pre-specification of covariates was uncommon. Adherence to some key methodological requirements (adjusting for time effects, accounting for within-period correlation) was higher among trials published in higher versus lower impact factor journals. Substantial improvements over time were not observed although a slight improvement was observed in the proportion accounting for a distinct between-period correlation. Future methods development should prioritize methods for SW-CRTs with binary or time-to-event outcomes, small numbers of clusters, continuous recruitment designs, multivariate outcomes, or time-dependent repeated measures. Trialists, journal editors, and peer reviewers should be aware that SW-CRTs have additional methodological requirements over parallel arm designs including the need to account for period effects as well as complex intracluster correlations.

Genome mining of metabolic gene clusters in the Rubiaceae family

The Rubiaceae plant family, comprising 3 subfamilies and over 13,000 species, is known for producing significant bioactive compounds such as caffeine and monoterpene indole alkaloids. Despite an increase in available genomes from the Rubiaceae family over the past decade, a systematic analysis of the metabolic gene clusters (MGCs) encoded by these genomes has been lacking. In this study, we aim to identify and analyze metabolic gene clusters within complete Rubiaceae genomes through a comparative analysis of eight species. Applying two bioinformatics pipelines, we identified 2372 candidate MGCs, organized into 549 gene cluster families (GCFs). To enhance the reliability of these findings, we developed coexpression networks and conducted orthology analyses. Using genomic data from Solanum lycopersicum (Solanaceae) for comparative purposes, we provided a detailed view of predicted metabolic enzymes, pathways, and coexpression networks. We bring some examples of MGCs and GCFs involved in biological pathways of terpenes, saccharides and alkaloids. Such insights lay the groundwork for discovering new compounds and associated MGCs within the Rubiaceae family, with potential implications in developing more robust crop species and expanding the understanding of plant metabolism. This large-scale exploration also provides a new perspective on the evolution and structure-function relationship of these clusters, offering opportunities for the highly efficient utilization of these unique metabolites. The outcome of this study contributes to a broader comprehension of the biosynthetic pathways, elucidating multiple aspects of specialized metabolism and offering innovative avenues for biotechnological applications.

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots.

A table tennis serve versus rally hit elicits differential hemispheric electrocortical power fluctuations.

Human visuomotor control requires coordinated interhemispheric interactions to exploit the brain's functional lateralization. In right-handed individuals, the left hemisphere (right arm) is better for dynamic control and the right hemisphere (left arm) is better for impedance control. Table tennis is a game that requires precise movements of the paddle, whole body coordination, and cognitive engagement, providing an ecologically valid way to study visuomotor integration. The sport has many different types of strokes (e.g., serve, return, and rally shots), which should provide unique cortical dynamics given differences in the sensorimotor demands. The goal of this study was to determine the hemispheric specialization of table tennis serving - a sequential, self-paced, bimanual maneuver. We used time-frequency analysis, event-related potentials, and functional connectivity measures of source-localized electrocortical clusters and compared serves with other types of shots, which varied in the types of movement required, attentional focus, and other task demands. We found greater alpha (8-12 Hz) and beta (13-30 Hz) power in the right sensorimotor cortex than in the left sensorimotor cortex, and we found a greater magnitude of spectral power fluctuations in the right sensorimotor cortex for serve hits than return or rally hits, in all right-handed participants. Surprisingly, we did not find a difference in interhemispheric functional connectivity between a table tennis serve and return or rally hits, even though a serve could arguably be a more complex maneuver. Studying real-world brain dynamics of table tennis provides insight into bilateral sensorimotor integration.NEW & NOTEWORTHY We found different spectral power fluctuations in the left and right sensorimotor cortices during table tennis serves, returns, and rallies. Our findings contribute to the basic science understanding of hemispheric specialization in a real-world context.

Promoting healthy cooking patterns in China: Analysis of consumer clusters and the evolution of cooking pattern trends.

Cooking methods can change the composition of foods and have important effects on human health. The Chinese people have developed many distinct and unique cooking methods. However, the daily cooking patterns of Chinese people and the characteristics and evolution of trends in cooking patterns commonly used by Chinese consumers remain unclear. The objective of this study was to identify the major cooking patterns and discuss their effects on human health, as well as to identify the cooking pattern consumer clusters and the evolution of trends in Chinese consumer cooking patterns. From March to June 2021, this study interviewed 4,710 residents in Eastern China regarding the consumption frequency of each cooking method when food is prepared at home or when eating out. Exploratory factor analysis, K-Means cluster analysis, Chi-square test, pairwise comparisons of multiple sample rates, and multivariate linear regression were used to identify the cooking patterns and cooking pattern consumer clusters, to assess differences in consumption preferences between consumer clusters, and to examine the relationship between demographic characteristic variables and different cooking patterns. Results revealed three major cooking patterns, namely traditional Chinese (cooking methods with native Chinese characteristics), bland, and high-temperature cooking patterns, as well as seven cooking pattern consumer clusters and their demographic characteristics in the Eastern Chinese population. With increases in age, education level, and income, consumers tended to choose the healthy "Bland" cooking pattern. Further, there was a higher proportion of people aged 36-65 years in the C3 cluster, which is characterized by the "Bland" cooking pattern. However, participants who were male and younger made fewer healthy choices in their cooking patterns. Specifically, a higher proportion of participants aged 21-35 years were found in the C5 cluster, which is characterized by the unhealthy "High-temperature" cooking pattern. Therefore, culinary health education should focus on individuals who are male and young. Specifically, the shift in cooking patterns among people aged 21-35 years should receive special attention.

Heterogeneous analysis for clustered data using grouped finite mixture models

Heterogeneous analysis for clustered data using grouped finite mixture models

A practical guide to selecting and blending approaches for clustered data: Clustered errors, multilevel models, and fixed-effect models.

A practical guide to selecting and blending approaches for clustered data: Clustered errors, multilevel models, and fixed-effect models.

ANGI-06. BIOLOGY OF GLIOBLASTOMA INVASION: A SINGLE-CELL LEVEL ANALYSIS OF TUMOR CORE VS. INFILTRATING EDGE

Abstract Glioblastoma (GBM) is a highly proliferative and diffusely infiltrating tumor, consisting of a necrotic core showing extensive microvascular proliferation and an infiltrating edge that extends beyond the surgical resection margin. The aggressiveness of GBM can be attributed to the existence of glioma stem-like cell populations (GSC) at this infiltrating edge. Although many studies have characterized GSC biology in culture, their contributions to invasion in vivo, and in the context of their microenvironment, remains poorly understood. In this study, we used macro-dissected biopsies of the tumor core and the infiltrative edge from six genetically-distinct glioblastoma tumors, to generate a spatiotemporal map of tumor invasion at a single-cell level. Unbiased clustering of ~40,000 nuclei and integrated analysis identified various cell types composing the tumor microenvironment, with predominance of tumor cells at the core and non-neoplastic cells at the edge, as expected. Inferred copy number alterations based on gene expression confirmed tumor cell identity in the core and the edge datasets. Differential expression analysis of core vs. edge tumor clusters revealed distinct transcriptomic signatures including several genes previously implicated in GBM invasion, such as EGFR, matrix metalloproteinases, and MAPK signaling pathway members. Several genes from this newly defined “GBM invasion signature” were further validated in a PDX invasion model, in which the transcriptome within the tumor core and infiltrative edge were similarly sequenced and analyzed at the single cell level. We are currently employing 10X-visium combined with immunohistochemistry to spatially deconvolute and corroborate the defined GBM invasion/infiltration signature at the tissue level in tumor biopsies. Overall, our study aims to provide high resolution map of glioma tumor invasion that can prioritize new and unique targetable pathways.

EXTH-05. ANTI-TUMOR IMMUNE RESPONSE OF ONCOMAGNETIC MONOTHERAPY IN GLIOBLASTOMA

Abstract BACKGROUND Glioblastoma (GBM) is the most lethal primary malignant brain tumor with a median survival of 15–20 months. Concurrent temozolomide (TMZ) chemotherapy and radiation (XRT) remain the current standard of care (SOC) treatment for newly diagnosed GBM. The addition of tumor treating fields (TTFs) improves median survival for newly diagnosed GBM from 16 to 20.9 months. This modest improvement underscores the urgent need to identify a new treatment modality as a safe and effective therapy. PURPOSE The noninvasive Oncomagnetic device developed in our laboratory selectively kills glioblastoma (GBM) in vitro by generating the spinning oscillating magnetic field (sOMF) which disrupts electron transport chain, leading to a marked increase in reactive oxygen species (ROS) and consequently to cancer cell death. We studied the cellular and molecular effects of sOMF treatment on GBM cell lines and investigated the immune response to Oncomagnetic monotherapy (OMT) in a syngeneic mouse model. RESULTS sOMF significantly reduces cell proliferation and cell survival in vitro . It induces DNA damage and arrests cells in G1-phase of the cell cycle. Whole-body sOMF stimulation caused a substantial retardation of tumor growth and reduction of the contrast-enhanced tumor volume in 9.4T MRI scans. OMT showed significant increase in overall survival in treated mice. We also observed upregulated immune response indicated by RNA sequencing data obtained from sOMF-treated LN229 cells as well as in treated GBM mouse tumor sections by imaging mass cytometry analysis. The tSNE analysis of various immune clusters suggests significant increase in immune response by activating CD4+, CD8+, murine-macrophages and M1-macrophages located at tumor site in the treated group. CONCLUSION The obtained data indicate significant anti-tumor response by inhibiting cancer signaling pathways and an increased immune response. Our findings suggest unique mechanism of action for sOMF stimulation and provide a strong rationale for standalone OMT for GBM.

Impact of CHA2DS2-VASc score on smartphone-based screening for atrial fibrillation: A pre-specified subgroup analysis of the eBRAVE-AF trial

Abstract Background The eBRAVE-AF trial showed that digital smart-device based screening for atrial fibrillation (AF) more than doubled the detection rate of treatment-relevant AF when compared to usual care. Purpose In this pre-specified subgroup analysis, we investigated the effect of CHA2DS2-VASc score on compliance, efficacy of digital screening, and the interaction of CHA2DS2-VASc score and AF-triggered major adverse cardiovascular complications (MACCE). Methods 5.551 individuals, free of AF were randomized to digital screening (n=2.860) or usual care (n=2.691). For digital screening, participants used a certified app to screen for irregularities in their pulse waves by means of photoplethysmography (PPG). Abnormal findings were confirmed by a 14-day Holter-ECG. The primary endpoint was newly diagnosed AF treated with oral anticoagulation (OAC) within 6 months. After 6 months, participants crossed over to a second study phase with reverse assignment. For this analysis we combined the two phases using multilevel clustered Cox-regression analysis. CHA2DS2-VASc score was dichotomized at a cut-off of ≤ /> 3. Results The median CHA2DS2-VASc score was 3 ± 1, the median age was 65 ±11 years, 31% participants were females. Participants performed a median of 53 ± 62 PPG-measurements with no significant difference in participants with CHA2DS2-VASc score ≤ 3 (52 ± 61) and CHA2DS2-VASc score > 3 (54 ± 65, p=0.269). During a median follow-up time of 12 months 102 participants reached the primary endpoint (including 2 endpoints in the cross-over window). A CHA2DS2-VASc score > 3 (n=1.205; 33 events) compared to CHA2DS2-VASc score ≤ 3 (n=4.346; 69 events) was a significant predictor for reaching the primary endpoint (HR 1.78; 95% CI 1.18-2.70; p=0.006). Digital screening significantly increased the detection rate of AF requiring OAC in participants with CHA2DS2-VASc score > 3 (HR 2.11; 95% CI 1.00-4.42; p=0.049; Fig 1A) and CHA2DS2-VASc score ≤ 3 (HR 2.43; 95% CI 1.43-4.13; p=0.001; Fig 1B). There was no interaction (p=0.758) between CHA2DS2-VASc score and the efficacy of digital screening. The rate of MACCE in participants with CHA2DS2-VASc score > 3 (5.2%; 95% CI 3.8-6.6%) and CHA2DS2-VASc score ≤ 3 (4.6%; 95% CI 4.0%-5.3%) was not significantly different (HR 1.14; 95% CI 0.76-1.71; p=0.539). However, only in participants with CHA2DS2-VASc score ≤ 3 MACCE were associated with previously diagnosed AF (HR 9.77; 95% CI 5.08-18.79; p<0.001 vs. 1.43; 95% CI 0.28-7.37; p=0.618 in participants with CHA2DS2-VASc score > 3; p-interaction=0.034). Conclusion In a large-scale clinical trial, comparing digital screening to usual care for detecting AF requiring OAC a CHA2DS2-VASc score >3 did not influence compliance but was associated with higher risk for developing AF requiring OAC. CHA2DS2-VASc score >3 was not associated with higher rate of MACCE. However, only in participants with CHA2DS2-VASc score ≤ 3, these MACCE were triggered by previously diagnosed AF.

Differences of women and men in smartphone-based screening for atrial fibrillation: a pre-specified subgroup analysis of the eBRAVE-AF trial

Abstract Background eBRAVE-AF, a siteless, randomized, controlled trial, showed that digital smart-device based screening for atrial fibrillation (AF) significantly increased the detection rate of treatment-relevant AF when compared to usual care. Purpose In this pre-specified subgroup analysis, we aimed to investigate the effect of sex on compliance, efficacy of digital screening, as well as the interaction between sex and AF-triggered major adverse cardiovascular complications (MACCE). Methods 5.551 individuals, free of AF at baseline were randomized to digital screening (n=2.860) or usual care (n=2.691). For digital screening, participants used a certified app to screen for irregularities in their pulse waves by means of photoplethysmography (PPG). Abnormal findings were confirmed by a 14-day Holter-ECG. The primary endpoint was newly diagnosed AF treated with oral anticoagulation (OAC) within 6 months. After 6 months, participants were invited to cross-over for a second study phase with reverse assignment. For this analysis we combined the two phases of the study using multilevel clustered Cox-regression analysis. Results The median age was 65 ± 11 years, the median CHA2DS2-VASc score was 3 ± 1 and 1.725 participants (31%) were females. Female sex was associated with better compliance in terms of more frequent PPG-measurements (57 ± 57) compared to male sex (50 ± 62; p<0.001). During a median follow-up time of 12 months 102 subjects reached the primary endpoint (including 2 endpoints in the cross-over window). Male sex (81 events) compared to female sex (21 events) was a significant predictor for reaching the primary endpoint (HR 1.74; 95% CI 1.08-2.82, p=0.023). While digital screening significantly increased the detection rate of AF requiring OAC in men (HR 2.48; 95% CI 1.52-4.05, p <0.001; Fig 1A), this effect did not reach the level of statistical significance in women (HR 1.83; 95% CI 0.74-4.54, p=0.193; Fig 1B), most probably due to the lower event-rate. The interaction between sex and the efficacy of digital screening was not statistically significant (p=0.563). Next to a higher incidence of AF in men (3.0% per year; 95% CI 2.4-4.5%) compared to women (1.8% per year; 95% CI 1.1%-2.4%; p=0.013), male participants were also at a higher risk for developing MACCE (106 vs. 20 events) (HR 2.57; 95% CI 1.57-4.20, p <0.001). AF was a significant predictor for developing MACCE in men (HR 6.52; 95% CI 3.42-12.43, p<0.001). Only one female participant with AF developed MACCE. Therefore, this analysis cannot be applied in females. Conclusion In a large-scale clinical trial, comparing digital screening to usual care for detecting AF requiring OAC, female sex was associated with better compliance to the study protocol and lower risk for developing AF requiring OAC. There was no significant interaction between sex and the efficacy of digital screening for detecting AF requiring OAC. In men, newly-diagnosed AF was associated with increased risk for MACCE.

Abstract 18766: Optimizing Stroke Prophylaxis in the Emergency Department With an Electronic Clinical Decision Support Tool: A Preliminary Analysis of a Multi-Stage Multi-Center Stepped-Wedge Cluster Randomized Trial

BACKGROUND: Atrial fibrillation (AF) is a common arrhythmia that is often first diagnosed in the Emergency Department (ED). AF is associated with an increased risk of stroke that can be reduced by 64% if an oral anticoagulant (OAC) is prescribed appropriately. RESEARCH QUESTION: Will education and implementation of a clinical decision support (CDS) tool in the ED increase OAC prescription for appropriate candidates diagnosed with new-onset or paroxysmal AF (pAF)? METHODS: This is the first phase of a multi-stage multi-center stepped-wedge cluster-randomized clinical trial implementing a CDS tool into the electronic health record (EHR). Step one implements an external browser link to the CDS tool in the EHR with targeted provider education, whereas steps 2 and 3 will motivate provider engagement with triggers. Data were collected from an urban academic tertiary care center and a community hospital. Patients aged greater than 17 years were selected if they had a primary diagnosis of AF/pAF during ED visits from Jan 2020-Apr 2023. The academic and community sites transitioned from the retrospective phase to Step 1 in Jan 2022 and Apr 2022, respectively. Appropriate OAC prescription was determined based on elevated stroke risk and low-moderate bleed risk. A logistic GEE model was used to study the effect of the proposed intervention on OAC prescription. ED providers were treated as a random effect to capture clustering. The model includes the CDS intervention, site, and patient characteristics sex, race, ethnicity, and age. RESULTS: Of 2031 patients, 312 met inclusion criteria and 150 were selected as appropriate OAC candidates, with 72 (52% male, median age 74) in the retrospective step and 78 (52% male, median age 76) in Step 1. Below are odds ratio estimates with corresponding 95% CI’s: CDS intervention 3.12 [1.57, 6.21] and sex female vs male 2.04 [1.04, 4.00]. Non-significant variables site, race, ethnicity, and age were included as adjusting covariates. CONCLUSIONS: Limited CDS tool education and integration is associated with improved OAC prescribing for appropriate AF/pAF patients. Future studies should assess the impact of additional CDS integration on OAC prescribing and patient outcomes.

Abstract 18296: Insights From the LURIC Study: Decoding the Peripheral Immune Cell Composition in Atherosclerosis Through Single Cell RNA-Sequencing

Introduction: Atherosclerosis and its clinical sequelae, myocardial infarction and stroke, represent the main causes of death worldwide. Although preclinical evidence has suggested the existence of a sustained inflammatory and immune response driving disease and complications, the extend of cellular alterations in human atherosclerosis remains enigmatic. Hypothesis: Here, we employ single cell RNA-sequencing (scRNA-seq) on peripheral blood mononucleated cells (PBMCs) in a well-defined cardiovascular risk cohort from the Ludwigshafen Risk and Cardiovascular Health (LURIC) trial to define changes in the immune cell landscape in atherosclerotic patients Methods: Of 3317 patients enrolled in the LURIC trial, we selected individuals with stable coronary-artery disease (CAD) (n=31) and healthy patients (no CAD) (n=16) by propensity score matching, adjusted for CRP, NT-proBNP, Troponin T, LDL-C, and Cystatin-C, in a case-control design. scRNA-seq was performed by droplet sequencing on PBMCs stored in liquid nitrogen. Individual data sets were integrated and changes between healthy individuals and patients with CAD were evaluated on numeric and transcriptional levels. Results: We identified several regulated leukocyte populations that were differentially regulated between both conditions. Interestingly, small cell populations like Tregs (>1.4-fold more), platelets (>10-fold more), stem cells (>3-fold more), and proliferating cells (>2-fold-more) were significantly increased in PBMCs from patients suffering from CAD. On the contrary, naïve B cell and T cell populations were considerably overrepresented in healthy control patients (>1.4-fold more). Pathway analyses of distinct clusters in patients with CAD revealed increased signalling for cellular exhaustion, response to stress, and leukocyte activation, which strikingly resembles the underlying pathology in atherosclerosis. Conclusions: Employing scRNAseq, we demonstrate that atherosclerosis is associated with a profound change in the circulating immune cell landscape even in the absence of measurable differences in inflammatory biomarkers. These findings propose the usage of scRNAseq for the discovery of outcome-relevant cellular biomarkers in the future.

Perceptions of Hospital Care for Persons With Dementia During the COVID-19 Pandemic: A Social Media Sentiment Analysis.

The coronavirus disease 2019 (COVID-19) pandemic led to many hospital service disruptions and strict visitor restrictions that affected care of older adult populations. This study investigates perceptions of hospital care for persons with dementia during the COVID-19 pandemic as shared on Reddit's social media platform. This study combined an Opinion Mining Framework with linguistic processing to conduct a sentiment analysis of word clusters and care-based content in a sample of 1,205 posts shared between February 2020 and March 2023 in Reddit's English-language corpus. Data were classified based on reoccurring contiguous sequences of 2 words from our text sample. Hospital dementia care discourse on Reddit advanced 4 negative sentiment themes: (1) fear of poor medication management, hydration, and hygiene, (2) loss of patient advocacy, (3) precipitation of advance directive discussions, and (4) delayed discharge and loss of nursing home bed. One positive sentiment theme also emerged: gratitude toward hospital staff. Negative sentiment Reddit posts constituted a larger share of the posts than positive posts regarding hospital care for persons with dementia. People who posted about their experiences shared their concerns about hospital care deficiencies and the importance of including informal caregivers in hospital settings, particularly in the context of a pandemic. Implications exist for dementia training, improved quality of care, advance care planning, and transitions in care policies.

Abstract 14943: Trans-Biobank Mendelian Randomization Analyses Identify Distinct and Opposing Pathways in the Association of Lower Plasma Low-Density Lipoprotein-Cholesterol With Risk of Gallstone Disease

Background: Higher plasma LDL-cholesterol increases coronary artery disease risk, while biliary cholesterol promotes gallstone formation. Trial evidence suggests that lowering plasma LDL-cholesterol with bempedoic acid increases gallstone risk. Hypothesis: Different plasma LDL-cholesterol lowering pathways have distinct effects on biliary cholesterol and thereby gallstone disease risk. Methods: We conducted a Mendelian randomization (MR) study using data from the UK Biobank (30,547 gallstone disease cases/336,742 controls), FinnGen (34,461 cases/301,383 controls) and Biobank Japan (9,305 cases/168,253 controls). We firstly performed drug-target MR analyses substantiated by colocalization to investigate the effects of plasma LDL-cholesterol lowering therapies on gallstone risk. We then performed clustered MR analyses followed by pathway analyses to identify distinct pathways in the association of lower plasma LDL-cholesterol with gallstone risk. Results: Genetic mimics of statins were associated with lower gallstone risk, but genetic mimics of PCSK9 inhibitors, mipomersen and bile acid sequestrants were associated with higher gallstone risk. The association for statins was supported by colocalization (posterior probability 98.7%). Clustered MR analyses consistently identified clusters showing opposing associations of lower plasma LDL-cholesterol with gallstone risk. Among variants predicting lower plasma LDL-cholesterol, those associated with lower gallstone risk were mapped to glycosphingolipid biosynthesis pathway, while those associated with higher gallstone risk were mapped to pathways relating to ATP-binding cassette transporters and plasma lipoprotein assembly, remodelling, and clearance. Conclusions: Genetic evidence suggests that different plasma LDL-cholesterol lowering pathways may have opposing effects on gallstone disease risk. Specifically, statins may reduce gallstone disease risk.

Evaluating tests for cluster-randomized trials with few clusters under generalized linear mixed models with covariate adjustment: A simulation study.

Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small-sample corrections have been proposed for continuous or binary outcomes without covariate adjustment. However, appropriate tests to use for count outcomes or under covariate-adjusted models remains unknown. An important setting in which this issue arises is in cluster-randomized trials (CRTs). Because many CRTs have just a few clusters (eg, clinics or health systems), covariate adjustment is particularly critical to address potential chance imbalance and/or low power (eg, adjustment following stratified randomization or for the baseline value of the outcome). We conducted simulations to evaluate GLMM-based tests of the treatment effect that account for the small (10) or moderate (20) number of clusters under a parallel-group CRT setting across scenarios of covariate adjustment (including adjustment for one or more person-level or cluster-level covariates) for both binary and count outcomes. We find that when the intraclass correlation is non-negligible ( 0.01) and the number of covariates is small ( 2), likelihood ratio tests with a between-within denominator degree of freedom have type I error rates close to the nominal level. When the number of covariates is moderate ( 5), across our simulation scenarios, the relative performance of the tests varied considerably and no method performed uniformly well. Therefore, we recommend adjusting for no more than a few covariates and using likelihood ratio tests with a between-within denominator degree of freedom.