Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Changes in expression of breast cancer tumor biomarkers between primary tumors and corresponding metastatic sites: common patterns and relationships with survival.

PURPOSE: In metastatic breast cancer, differences in expression patterns of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) between the primary tumor (PT) and metastatic site (MET) have been reported. However, there is limited understanding of the relationship of tumor subtype discordance and overall survival (OS). We evaluated patterns of ER/PR/HER2 in PTs and corresponding METs and assessed the relationship between these patterns and OS. METHODS: Patients diagnosed at our center with metastatic breast cancer (2011-2020) were included. ER/PR were stratified as < 1%/1-10%/ > 10% by immunohistochemistry and HER2 as positive/negative by immunohistochemistry/FISH. Tumor subtypes were classified as ER or PR + /HER2-, HER2+ , or triple-negative. Biomarker discordance data from PTs to METs were analyzed for expression patterns. OS was assessed. RESULTS: Of 254 patients, 41 (16.1%) had synchronous and 213 (83.9%) had metachronous METs. Category change of ER/PR/HER2 expression was observed in 56 (22.0%), 117 (40.5%), and 30 (11.8%) patients, respectively. Tumor subtype changed in 56 (22.0%) patients. We identified a difference between PT and MET from ER > 10% to ER < 1% (n = 28,16.2% p < 0.01); PR > 10% to PR < 1% (n = 54,48.2%, p < 0.001); PR > 10% to PR 1-10% (n = 18,16.1%, p < 0.001), and ER or PR+/HER2- to triple-negative (n = 19,13.0%, p = 0.03). In log-rank analysis, change from an ER or PR+/HER2- (5-year OS 88.6%) PT to a HER2+(67.5%) or triple-negative (54.6%) MET was associated with decreased survival (p < 0.01); however, in multivariate analysis, discordant biomarker expression was not associated with decreased survival (p > 0.05). CONCLUSION: Tumor expression of ER/PR/HER2 can differ between the PT and MET. Loss of ER/PR expression is common and may be related to worse survival. Routine assessment of MET tumor markers could inform prognosis and therapeutic decision-making.

Parcellating the human brain using resting-state dynamic functional connectivity.

Brain cartography has expanded substantially over the past decade. In this regard, resting-state functional connectivity (FC) plays a key role in identifying the locations of putative functional borders. However, scant attention has been paid to the dynamic nature of functional interactions in the human brain. Indeed, FC is typically assumed to be stationary across time, which may obscure potential or subtle functional boundaries, particularly in regions with high flexibility and adaptability. In this study, we developed a dynamic FC (dFC)-based parcellation framework, established a new functional human brain atlas termed D-BFA (DFC-based Brain Functional Atlas), and verified its neurophysiological plausibility by stereo-EEG data. As the first dFC-based whole-brain atlas, the proposed D-BFA delineates finer functional boundaries that cannot be captured by static FC, and is further supported by good correspondence with cytoarchitectonic areas and task activation maps. Moreover, the D-BFA reveals the spatial distribution of dynamic variability across the brain and generates more homogenous parcels compared with most alternative parcellations. Our results demonstrate the superiority and practicability of dFC in brain parcellation, providing a new template to exploit brain topographic organization from a dynamic perspective. The D-BFA will be publicly available for download at https://github.com/sliderplm/D-BFA-618.

Exploring the causal relationship between periodontitis and gut microbiome: Unveiling the oral-gut and gut-oral axes through bidirectional Mendelian randomization.

AIM: This Mendelian randomization (MR) study was performed to explore the potential bidirectional causal relationship between the gut microbiome (GM) and periodontitis. MATERIALS AND METHODS: We used genetic instruments from the genome-wide association study of European descent for periodontitis from the GeneLifestyle Interactions in Dental Endpoints (GLIDE) consortium (17,353 cases and 28,210 controls) and the FinnGen consortium (4434 cases and 259,234 controls) to investigate the causal relationship with GM (the MiBioGen consortium, 18,340 samples), and vice versa. Several MR techniques, which include inverse variance weighting (IVW), MR-Egger, weighted median, simple mode and weighted mode approaches, were employed to investigate the causal relationship between the exposures and the outcomes. Cochran's Q-test was performed to detect heterogeneity. The MR-Egger regression intercept and MR pleiotropy residual sum and outlier test (MR-PRESSO) were conducted to test potential horizontal pleiotropy. Leave-one-out sensitivity analyses were used to assess the stabilities of single nucleotide polymorphisms (SNPs). Finally, the IVW results from the two databases were analysed using meta-analysis. RESULTS: We confirmed three potential causal relationships between GM taxa and periodontitis at the genus level. Among them, the genera Alistipes and Holdemanella were genetically associated with an increased risk of periodontitis. In reverse, periodontitis may lead to a decreased abundance of the genus Ruminococcaceae UCG014. CONCLUSIONS: The demonstration of a causal link between GM and periodontitis provides compelling evidence, highlighting the interconnectivity and interdependence of the gut-oral and oral-gut axes.

Instrumental variable estimation of complier causal treatment effect with interval-censored data.

Assessing causal treatment effect on a time-to-event outcome is of key interest in many scientific investigations. Instrumental variable (IV) is a useful tool to mitigate the impact of endogenous treatment selection to attain unbiased estimation of causal treatment effect. Existing development of IV methodology, however, has not attended to outcomes subject to interval censoring, which are ubiquitously present in studies with intermittent follow-up but are challenging to handle in terms of both theory and computation. In this work, we fill in this important gap by studying a general class of causal semiparametric transformation models with interval-censored data. We propose a nonparametric maximum likelihood estimator of the complier causal treatment effect. Moreover, we design a reliable and computationally stable expectation-maximization (EM) algorithm, which has a tractable objective function in the maximization step via the use of Poisson latent variables. The asymptotic properties of the proposed estimators, including the consistency, asymptotic normality, and semiparametric efficiency, are established with empirical process techniques. We conduct extensive simulation studies and an application to a colorectal cancer screening data set, showing satisfactory finite-sample performance of the proposed method as well as its prominent advantages over naive methods.

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.

Tensor response quantile regression with neuroimaging data.

Collecting neuroimaging data in the form of tensors (i.e. multidimensional arrays) has become more common in mental health studies, driven by an increasing interest in studying the associations between neuroimaging phenotypes and clinical disease manifestation. Motivated by a neuroimaging study of post-traumatic stress disorder (PTSD) from the Grady Trauma Project, we study a tensor response quantile regression framework, which enables novel analyses that confer a detailed view of the potentially heterogeneous association between a neuroimaging phenotype and relevant clinical predictors. We adopt a sensible low-rank structure to represent the association of interest, and propose a simple two-step estimation procedure which is easy to implement with existing software. We provide rigorous theoretical justifications for the intuitive two-step procedure. Simulation studies demonstrate good performance of the proposed method with realistic sample sizes in neuroimaging studies. We conduct the proposed tensor response quantile regression analysis of the motivating PTSD study to investigate the association between fMRI resting-state functional connectivity and PTSD symptom severity. Our results uncover non-homogeneous effects of PTSD symptoms on brain functional connectivity, which cannot be captured by existing tensor response methods.

DNER and GNL2 are differentially m6A methylated in periodontitis in comparison with periodontal health revealed by m6A microarray of human gingival tissue and transcriptomic analysis.

OBJECTIVE: This study aims to investigate the differences in the epigenomic patterns of N6-methyladenosine (m6A) methylation in gingival tissues between patients with periodontitis (PD) and healthy controls, identifying potential biomarkers. BACKGROUND: As a multifactorial disease, PD involves multiple genetic and environmental effects. The m6A modification is the most prevalent internal mRNA modification and linked to various inflammatory diseases. However, the m6A modification pattern and m6A-related signatures in PD remain unclear. MATERIALS AND METHODS: An m6A microarray of human gingival tissues was conducted in eight subjects: four diagnosed with PD and four healthy controls. Microarray analysis was performed to identify the differentially m6A methylated mRNAs (DMGs) and the differentially expressed mRNAs (DEGs). The differentially methylated and expressed mRNAs (DMEGs) were subjected to functional enrichment analysis by Metascape. The weighted gene co-expression network analysis (WGCNA) algorithm, the least absolute shrinkage and selection operator (LASSO) regression, and univariate logistic regression were performed to identify potential biomarkers. The cell type localization of the target genes was determined using single-cell RNA-seq (scRNA-seq) analysis. The m6A methylation level and gene expression of hub genes were subsequently verified by m6A methylated RNA immunoprecipitation (MeRIP) and quantitative real-time PCR (qRT-PCR). RESULTS: In total, 458 DMGs, 750 DEGs, and 279 DMEGs were identified based on our microarray. Pathway analyses conducted for the DMEGs revealed that biological functions were mainly involved in the regulation of stem cell differentiation, ossification, circadian rhythm, and insulin secretion pathways. Besides, the genes involved in crucial biological processes were mainly expressed in fibroblast and epithelial cells. Furthermore, the m6A methylation and expression levels of two hub biomarkers (DNER and GNL2) were validated. CONCLUSION: The current study exhibited a distinct m6A epitranscriptome, identified and verified two PD-related biomarkers (DNER and GNL2), which may provide novel insights into revealing the new molecular mechanisms and latent targets of PD.

Revealing a potential necroptosis-related axis (RP11-138A9.1/hsa-miR-98-5p/ZBP1) in periodontitis by construction of the ceRNA network.

BACKGROUND AND OBJECTIVES: Periodontitis, a prevalent chronic inflammatory condition, poses a significant risk of tooth loosening and subsequent tooth loss. Within the realm of programmed cell death, a recently recognized process known as necroptosis has garnered attention for its involvement in numerous inflammatory diseases. Nevertheless, its correlation with periodontitis is indistinct. Our study aimed to identify necroptosis-related lncRNAs and crucial lncRNA-miRNA-mRNA regulatory axes in periodontitis to further understand the pathogenesis of periodontitis. MATERIALS AND METHODS: Gene expression profiles in gingival tissues were acquired from the Gene Expression Omnibus (GEO) database. Selecting hub necroptosis-related lncRNA and extracting the key lncRNA-miRNA-mRNA axes based on the ceRNA network by adding novel machine-learning models based on conventional analysis and combining qRT-PCR validation. Then, an artificial neural network (ANN) model was constructed for lncRNA in regulatory axes, and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Weighted correlation network analysis (WGCNA) and single-sample gene set enrichment analysis (ssGSEA) was performed to explore how these lncRNAs work in periodontitis. RESULTS: Seven hub necroptosis-related lncRNAs and three lncRNA-miRNA-mRNA regulatory axes (RP11-138A9.1/hsa-miR-98-5p/ZBP1 axis, RP11-96D1.11/hsa-miR-185-5p/EZH2 axis, and RP4-773 N10.4/hsa-miR-21-5p/TLR3 axis) were predicted. WGCNA revealed that RP11-138A9.1 was significantly correlated with the "purple module". Functional enrichment analysis and ssGSEA demonstrated that the RP11-138A9.1/hsa-miR-98-5p/ZBP1 axis is closely related to the inflammation and immune processes in periodontitis. CONCLUSION: Our study predicted a crucial necroptosis-related regulatory axis (RP11-138A9.1/hsa-miR-98-5p/ZBP1) based on the ceRNA network, which may aid in elucidating the role and mechanism of necroptosis in periodontitis.

Influence of METTL3 knockdown on PDLSC osteogenesis in E. coli LPS-induced inflammation.

OBJECTIVE: This study aimed to investigate the effect of METTL3 knockdown on osteogenic differentiation of human periodontal ligament stem cells (PDLSCs) in the weak inflammation microenvironments, as well as the underlying mechanisms. MATERIALS AND METHODS: PDLSCs were stimulated by lipopolysaccharide from Escherichia coli (E. coli LPS), followed by quantification of METTL3. METTL3 expression was assessed using RT-qPCR and Western blot analysis in periodontitis. METTL3 knockdown PDLSCs were stimulated with or without E. coli LPS. The evaluation included proinflammatory cytokines, osteogenic markers, ALP activity, and mineralized nodules. Bioinformatics analysis and Western blot determined the association between METTL3 and the PI3K/Akt pathway. RESULTS: METTL3 was overexpressed in periodontitis. METTL3 knockdown in PDLSCs reduced proinflammatory cytokines, osteogenic markers, ALP activity, and mineralized nodules in both environments. Bioinformatics analysis suggested a link between METTL3 and the PI3K/Akt pathway. METTL3 knockdown inhibited PI3K/Akt signaling pathway activation. CONCLUSION: METTL3 knockdown might inhibit osteogenesis in PDLSCs through the inactivation of PI3K/Akt signaling pathway. Concomitant findings might shed novel light on the roles and potential mechanisms of METTL3 in the LPS-stimulated inflammatory microenvironments of PDLSCs.

Globally Adaptive Longitudinal Quantile Regression with High Dimensional Compositional Covariates.

In this work, we propose a longitudinal quantile regression framework that enables a robust characterization of heterogeneous covariate-response associations in the presence of high-dimensional compositional covariates and repeated measurements of both response and covariates. We develop a globally adaptive penalization procedure, which can consistently identify covariate sparsity patterns across a continuum set of quantile levels. The proposed estimation procedure properly aggregates longitudinal observations over time, and ensures the satisfaction of the sum-zero coefficient constraint that is needed for proper interpretation of the effects of compositional covariates. We establish the oracle rate of uniform convergence and weak convergence of the resulting estimators, and further justify the proposed uniform selector of the tuning parameter in terms of achieving global model selection consistency. We derive an efficient algorithm by incorporating existing R packages to facilitate stable and fast computation. Our extensive simulation studies confirm the theoretical findings. We apply the proposed method to a longitudinal study of cystic fibrosis children where the association between gut microbiome and other diet-related biomarkers is of interest.

Exploration of cross-talk and pyroptosis-related gene signatures and molecular mechanisms between periodontitis and diabetes mellitus via peripheral blood mononuclear cell microarray data analysis.

OBJECTIVES: This bioinformatics study is aimed at identifying cross-talk genes, pyroptosis-related genes, and related pathways between periodontitis (PD) and diabetes mellitus (DM), which includes type 1 diabetes (T1DM) and type 2 diabetes (T2DM). METHODS: GEO datasets containing peripheral blood mononuclear cell (PBMC) data of PD and DM were acquired. After batch correction and normalization, differential expression analysis was performed to identify the differentially expressed genes (DEGs). And cross-talk genes in the PD-T1DM pair and the PD-T2DM pair were identified by overlapping DEGs with the same trend in each pair. The weighted gene coexpression network analysis (WGCNA) algorithm helped locate the pyroptosis-related genes that are related to cross-talk genes. Receiver-operating characteristic (ROC) curve analysis confirmed the predictive accuracy of these hub genes in diagnosing PD and DM. The correlation between hub genes and the immune microenvironment of PBMC in these diseases was investigated by Spearman correlation analysis. The experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed. Subnetwork analysis helped identify the key pathway connecting DM and PD. RESULTS: Hub genes in the PD-T1DM pair (HBD, NLRC4, AIM2, NLRP2) and in the PD-T2DM pair (HBD, IL-1Β, AIM2, NLRP2) were identified. The similarity and difference in the immunocytes infiltration levels and immune pathway scores of PD and DM were observed. ROC analysis showed that AIM2 and HBD exhibited pleasant discrimination ability in all diseases, and the subnetwork of these genes indicated that the NOD-like receptor signaling pathway is the most potentially relevant pathway linking PD and DM. CONCLUSION: HBD and AIM2 could be the most relevant potential cross-talk and pyroptosis-related genes, and the NOD-like receptor signaling pathway could be the top candidate molecular mechanism linking PD and DM, supporting a potential pathophysiological relationship between PD and DM.

Degludec hospital trial: A randomized controlled trial comparing insulin degludec U100 and glargine U100 for the inpatient management of patients with type 2 diabetes.

AIMS: Limited data exist about the use of insulin degludec in the hospital. This multicentre, non-inferiority, open-label, prospective randomized trial compared the safety and efficacy of insulin degludec-U100 and glargine-U100 for the management of hospitalized patients with type 2 diabetes. METHODS: In total, 180 general medical and surgical patients with an admission blood glucose (BG) between 7.8 and 22.2 mmol/L, treated with oral agents or insulin before hospitalization were randomly allocated (1:1) to a basal-bolus regimen using degludec (n = 92) or glargine (n = 88), as basal and aspart before meals. Insulin dose was adjusted daily to a target BG between 3.9 and 10.0 mmol/L. The primary endpoint was the difference in mean hospital daily BG between groups. RESULTS: Overall, the randomization BG was 12.2 ± 2.9 mmol/L and glycated haemoglobin 84 mmol/mol (9.8% ± 2.0%). There were no differences in mean daily BG (10.0 ± 2.1 vs. 10.0 ± 2.5 mmol/L, p = .9), proportion of BG in target range (54·5% ± 29% vs. 55·3% ± 28%, p = .85), basal insulin (29.6 ± 13 vs. 30.4 ± 18 units/day, p = .85), length of stay [median (IQR): 6.7 (4.7-10.5) vs. 7.5 (4.7-11.6) days, p = .61], hospital complications (23% vs. 23%, p = .95) between treatment groups. There were no differences in the proportion of patients with BG <3.9 mmol/L (17% vs. 19%, p = .75) or <3.0 mmol/L (3.7% vs. 1.3%, p = .62) between degludec and glargine. CONCLUSION: Hospital treatment with degludec-U100 or glargine-U100 is equally safe and effective for the management of hyperglycaemia in general medical and surgical patients with type 2 diabetes.

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.

Pyroptosis may play a crucial role in modifications of the immune microenvironment in periodontitis.

BACKGROUND AND OBJECTIVE: Published studies proved that both pyroptosis and periodontitis owned a substantial relationship with immunity, and recent research revealed a solid correlation between periodontitis and pyroptosis. While abundant findings have confirmed pyroptosis has a strong impact on the tumor microenvironment, the function of pyroptosis in influencing the periodontitis immune microenvironment remains poorly understood. Thus, we aimed to identify pyroptosis-related genes whose expression signature can well discriminate periodontitis from healthy controls and to comprehend the role of pyroptosis in the periodontitis immune microenvironment. MATERIALS AND METHODS: The periodontitis-related datasets were acquired from the Gene Expression Omnibus (GEO) database. A series of bioinformatics analyses were conducted to investigate the underlying mechanism of pyroptosis in the periodontitis immune microenvironment. Infiltrating immunocytes, immunological reaction gene sets, and the human leukocyte antigen (HLA) gene were all investigated as potential linkages between periodontitis immune microenvironment and pyroptosis. RESULTS: Twenty-one pyroptosis-related genes were dysregulated. A four-mRNA combined classification model was constructed, and the receiver operating characteristic (ROC) curve analysis demonstrated its prominent classification capabilities. Subsequently, the mRNA levels of the four hub markers (CYCS, CASP3, NOD2, CHMP4B) were validated by quantitative real-time PCR (qRT-PCR). The correlation coefficients between each hub gene and immune characteristics were calculated, and CASP3 exhibited the most significant correlations with the immune characteristics. Furthermore, distinct pyroptosis-related expression patterns were revealed, along with immunological features of each pattern. Afterward, we discovered 1868 pyroptosis phenotype-related genes, 134 of which were related to immunity. According to the functional enrichment analysis, these 134 genes were closely related to cytokine signaling in immune system, and defense response. Finally, a co-expression network was constructed via the 1868 gene expression profiles. CONCLUSION: Four hub mRNAs (CYCS, CASP3, NOD2, and CHMP4B) formed a classification model and concomitant results revealed the crucial role of pyroptosis in the periodontitis immune microenvironment, providing fresh insights into the etiopathogenesis of periodontitis and potential immunotherapy.

Assessing dynamic covariate effects with survival data.

Dynamic (or varying) covariate effects often manifest meaningful physiological mechanisms underlying chronic diseases. However, a static view of covariate effects is typically adopted by standard approaches to evaluating disease prognostic factors, which can result in depreciation of some important disease markers. To address this issue, in this work, we take the perspective of globally concerned quantile regression, and propose a flexible testing framework suited to assess either constant or dynamic covariate effects. We study the powerful Kolmogorov-Smirnov (K-S) and Cramér-Von Mises (C-V) type test statistics and develop a simple resampling procedure to tackle their complicated limit distributions. We provide rigorous theoretical results, including the limit null distributions and consistency under a general class of alternative hypotheses of the proposed tests, as well as the justifications for the presented resampling procedure. Extensive simulation studies and a real data example demonstrate the utility of the new testing procedures and their advantages over existing approaches in assessing dynamic covariate effects.

[Effects of Multicomponent Case Management on the Problem Behaviors of Patients With Dementia and Family Caregivers' Distress, Self-Efficacy, Depression, Burden and Health Promotion Behaviors].

BACKGROUND: Few of the interventions currently available for family caregivers (FCGs) of persons with dementia (PWDs) with long-term follow-ups have a grounding in theory and incorporate multicomponent case management formats. PURPOSE: Based on Pearlin's Caregiving and Stress Process model, this study was developed to examine the effectiveness of a family-centered case management program for PWDs with early to moderate dementia in terms of reducing PWDs behavioral problems and improve FCG outcomes, including distress, self-efficacy, depression, caregiver burden, and health-promoting behaviors. METHODS: This randomized, single-blind, parallel-controlled trial included 76 dyads of PWDs and their FCGs. The dyads were recruited from outpatient clinics at dementia centers in three district hospitals in northern Taiwan. The dyads were randomly assigned to the intervention group (IG, n = 39) and control group (CG, n = 37). The dyads in the IG received a four-month intervention with two home or clinic visits and two telephone interviews. The multi-component interventions provided assessment, education, consultations, support, and referrals to long-term care resources. The CG received routine care and two social phone calls. Data were collected upon enrollment (T0 = baseline) and at 4-,6-, and 12-months post-intervention (T1, T2, and T3, respectively). Generalized estimating equations were conducted to analyze the effects of the intervention. RESULTS: By controlling for the interaction between group and time, we made a comparison between IG and the CG. The results showed significant improvements from baseline measures in behavioral problems in the PWDs for mood, psychosis, and social engagement, and improvements in the FCGs for distress and self-efficacy for obtaining respite as well as for better control of distressing thoughts, feelings of depression, caregiver burden, and overall health promoting behaviors at T1 and T2 (p < 0.5). Significant improvements were also found in the IG for psychomotor regulation among PWDs and the self-efficacy of FCGs in managing the PWDs' disturbing behaviors and health promotion behaviors for nutrition at T1 (p < 0.5). There were no significant improvements in the outcome variables at T3. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: Significant interactions between group and time were found at the 6-month assessment (T2) for improvements in problem behaviors of PWDs and depression, caregiver burden, and distress in the FCGs. Positive effects on self-efficacy and health promotion behaviors among the FCGs were also achieved. The results suggest that a multicomponent case management intervention should be referenced in dementia care policymaking for FCGs and PWDs.

Liraglutide hospital discharge trial: A randomized controlled trial comparing the safety and efficacy of liraglutide versus insulin glargine for the management of patients with type 2 diabetes after hospital discharge.

AIM: To compare a glucagon-like peptide-1 receptor agonist with basal insulin at hospital discharge in patients with uncontrolled type 2 diabetes in a randomized clinical trial. METHODS: A total of 273 patients with glycated haemoglobin (HbA1c) 7%-10% (53-86 mol/mol) were randomized to liraglutide (n = 136) or insulin glargine (n = 137) at hospital discharge. The primary endpoint was difference in HbA1c at 12 and 26 weeks. Secondary endpoints included hypoglycaemia, changes in body weight, and achievement of HbA1c <7% (53 mmol/mol) without hypoglycaemia or weight gain. RESULTS: The between-group difference in HbA1c at 12 weeks and 26 weeks was -0.28% (95% CI -0.64, 0.09), and at 26 weeks it was -0.55%, (95% CI -1.01, -0.09) in favour of liraglutide. Liraglutide treatment resulted in a lower frequency of hypoglycaemia <3.9 mmol/L (13% vs 23%; P = 0.04), but there was no difference in the rate of clinically significant hypoglycaemia <3.0 mmol/L. Compared to insulin glargine, liraglutide treatment was associated with greater weight loss at 26 weeks (-4.7 ± 7.7 kg vs -0.6 ± 11.5 kg; P < 0.001), and the proportion of patients with HbA1c <7% (53 mmol/mol) without hypoglycaemia was 48% versus 33% (P = 0.05) at 12 weeks and 45% versus 33% (P = 0.14) at 26 weeks in liraglutide versus insulin glargine. The proportion of patients with HbA1c <7% (53 mmol/mol) without hypoglycaemia and no weight gain was higher with liraglutide at 12 (41% vs 24%, P = 0.005) and 26 weeks (39% vs 22%; P = 0.014). The incidence of gastrointestinal adverse events was higher with liraglutide than with insulin glargine (P < 0.001). CONCLUSION: Compared to insulin glargine, treatment with liraglutide at hospital discharge resulted in better glycaemic control and greater weight loss, but increased gastrointestinal adverse events.

Sitagliptin for the prevention and treatment of perioperative hyperglycaemia in patients with type 2 diabetes undergoing cardiac surgery: A randomized controlled trial.

AIM: To assess whether treatment with sitagliptin, starting before surgery and continued during the hospital stay, can prevent and reduce the severity of perioperative hyperglycaemia in patients with type 2 diabetes undergoing coronary artery bypass graft (CABG) surgery. MATERIALS AND METHODS: We conducted a double-blinded, placebo-controlled trial in adults with type 2 diabetes randomly assigned to receive sitagliptin or matching placebo starting 1 day prior to surgery and continued during the hospital stay. The primary outcome was difference in the proportion of patients with postoperative hyperglycaemia (blood glucose [BG] > 10 mmol/L [>180 mg/dL]) in the intensive care unit (ICU). Secondary endpoints included differences in mean daily BG in the ICU and after transition to regular wards, hypoglycaemia, hospital complications, length of stay and need of insulin therapy. RESULTS: We included 182 participants randomized to receive sitagliptin or placebo (91 per group, age 64 ± 9 years, HbA1c 7.6% ± 1.5% and diabetes duration 10 ± 9 years). There were no differences in the number of patients with postoperative BG greater than 10 mmol/L, mean daily BG in the ICU or after transition to regular wards, hypoglycaemia, hospital complications or length of stay. There were no differences in insulin requirements in the ICU; however, sitagliptin therapy was associated with lower mean daily insulin requirements (21.1 ± 18.4 vs. 32.5 ± 26.3 units, P = .007) after transition to a regular ward compared with placebo. CONCLUSION: The administration of sitagliptin prior to surgery and during the hospital stay did not prevent perioperative hyperglycaemia or complications after CABG. Sitagliptin therapy was associated with lower mean daily insulin requirements after transition to regular wards.

Causal Proportional Hazards Estimation with a Binary Instrumental Variable.

Instrumental variables (IV) are a useful tool for estimating causal effects in the presence of unmeasured confounding. IV methods are well developed for uncensored outcomes, particularly for structural linear equation models, where simple two-stage estimation schemes are available. The extension of these methods to survival settings is challenging, partly because of the nonlinearity of the popular survival regression models and partly because of the complications associated with right censoring or other survival features. Motivated by the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer screening trial, we develop a simple causal hazard ratio estimator in a proportional hazards model with right censored data. The method exploits a special characterization of IV which enables the use of an intuitive inverse weighting scheme that is generally applicable to more complex survival settings with left truncation, competing risks, or recurrent events. We rigorously establish the asymptotic properties of the estimators, and provide plug-in variance estimators. The proposed method can be implemented in standard software, and is evaluated through extensive simulation studies. We apply the proposed IV method to a data set from the Prostate, Lung, Colorectal and Ovarian cancer screening trial to delineate the causal effect of flexible sigmoidoscopy screening on colorectal cancer survival which may be confounded by informative noncompliance with the assigned screening regimen.