A Brief Adherence Intervention that Improved Glycemic Control: Mediation by Patterns of Adherence

This study examined whether longitudinal adherence profiles mediated the relationship between a brief adherence intervention and glycemic control among patients with type 2 diabetes. Adherence was assessed using the Medication Event Monitoring System. Longitudinal analysis via growth curve mixture modeling was carried out to classify patients according to patterns of adherence to oral hypoglycemic agents. Hemoglobin A1c assays were used to measure glycemic control as the clinical outcome. Across the whole sample, longitudinal adherence profiles mediated 35.2% (13.2, 81.0%) of the effect of a brief adherence intervention on glycemic control [from odds ratio (OR) = 8.48, 95% confidence interval (CI) (3.24, 22.2) to 4.00, 95% CI (1.34, 11.93)]. Our results suggest that patients in the intervention had better glycemic control largely due to their greater likelihood of adherence to oral hypoglycemic agents

Patterns of Adherence to Oral Hypoglycemic Agents and Glucose Control among Primary Care Patients with Type 2 Diabetes

Researchers sought to examine whether there are patterns of oral hypoglycemic-agent adherence among primary-care patients with type 2 diabetes that are related to patient characteristics and clinical outcomes. Longitudinal analysis via growth curve mixture modeling was carried out to classify 180 patients who participated in an adherence intervention according to patterns of adherence to oral hypoglycemic agents across 12 weeks. Three patterns of change in adherence were identified: adherent, increasing adherence, and nonadherent. Global cognition and intervention condition were associated with pattern of change in adherence (p \u3c .05). Patients with an increasing adherence pattern were more likely to have an Hemoglobin A1c) \u3c 7%; adjusted odds ratio = 14.52, 95% CI (2.54, 82.99) at 12 weeks, in comparison with patients with the nonadherent pattern. Identification of patients with type 2 diabetes at risk of nonadherence is important for clinical prognosis and the development and delivery of interventions

Patterns of Adherence to Oral Hypoglycemic Agents and Glucose Control among Primary Care Patients with Type 2 Diabetes

Researchers sought to examine whether there are patterns of oral hypoglycemic-agent adherence among primary-care patients with type 2 diabetes that are related to patient characteristics and clinical outcomes. Longitudinal analysis via growth curve mixture modeling was carried out to classify 180 patients who participated in an adherence intervention according to patterns of adherence to oral hypoglycemic agents across 12 weeks. Three patterns of change in adherence were identified: adherent, increasing adherence, and nonadherent. Global cognition and intervention condition were associated with pattern of change in adherence (p \u3c .05). Patients with an increasing adherence pattern were more likely to have an Hemoglobin A1c) \u3c 7%; adjusted odds ratio = 14.52, 95% CI (2.54, 82.99) at 12 weeks, in comparison with patients with the nonadherent pattern. Identification of patients with type 2 diabetes at risk of nonadherence is important for clinical prognosis and the development and delivery of interventions

Neighborhood Social Environment and Patterns of Adherence to Oral Hypoglycemic Agents among Patients with Type 2 Diabetes Mellitus

This study examined whether neighborhood social environment was related to patterns of adherence to oral hypoglycemic agents among primary care patients with type 2 diabetes mellitus. Residents in neighborhoods with high social affluence, high residential stability, and high neighborhood advantage, compared to residents in neighborhoods with one or no high features present, were significantly more likely to have an adherent pattern compared to a nonadherent pattern. Neighborhood social environment may influence patterns of adherence. Reliance on a multilevel contextual framework, extending beyond the individual, to promote diabetic self-management activities may be essential for notable public health improvements

Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response

Contains fulltext : 220669.pdf (publisher's version ) (Open Access)Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection

Developing Effective Questionnaire-Based Prediction Models for Type 2 Diabetes for Several Ethnicities

Background: Type 2 diabetes disproportionately affects individuals of non-white ethnicity through a complex interaction of multiple factors. Early disease prediction and detection is therefore essential and requires tools that can be deployed at large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes for multiple ethnicities.Methods: Logistic regression models, using questionnaire-only features, were trained on the White population of the UK Biobank, and validated in five other ethnicities and externally in Lifelines. In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Predictive accuracy was assessed and a detailed sensitivity analysis was conducted to assess potential clinical utility. Furthermore, we compared the questionnaire algorithms to clinical non-laboratory type 2 diabetes risk tools.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC=0·901) and eight-year incidence (AUC=0·873) in the White UK Biobank population. Both models replicate well in Lifelines, with AUCs of 0·917 and 0·817 for prevalence and incidence. Both models performed consistently well across ethnicities, with AUCs of 0·855 to 0·894 for prevalence and from 0·819 to 0·883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 type 2 diabetes cases. Model performance improved with the addition of blood biomarkers, but not with the addition of physical measurements.Interpretation: Easy-to-implement, questionnaire-based models can predict prevalent and incident type 2 diabetes with high accuracy across all ethnicities, providing a highly-scalable solution for population-wide risk stratification

Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure

Not just differential gene expression but also differential gene regulation in immune cells account for individual differences in the immune response. Authors show here by single-cell RNA-sequencing of peripheral blood mononuclear cells from a large cohort of genetically diverse individuals that gene expression and regulatory changes in these cells depend on the context of and interactions between cell types, genetics, type of pathogen and time after exposure. The host's gene expression and gene regulatory response to pathogen exposure can be influenced by a combination of the host's genetic background, the type of and exposure time to pathogens. Here we provide a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens. These analyses indicate that cell-type-specificity is a more prominent factor than pathogen-specificity regarding contexts that affect how genetics influences gene expression (i.e., eQTL) and co-expression (i.e., co-expression QTL). In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes. This indicates widespread, context-specific changes in gene expression level and its regulation that are driven by genetics. Pathway analysis on the CLEC12A gene that exemplifies cell-type-, exposure-time- and genetic-background-dependent co-expression interactions, shows enrichment of the interferon (IFN) pathway specifically at 3-h post-exposure in monocytes. Similar genetic background-dependent association between IFN activity and CLEC12A co-expression patterns is confirmed in systemic lupus erythematosus by in silico analysis, which implies that CLEC12A might be an IFN-regulated gene. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes

Mapping local and global variability in plant trait distributions

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration - specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways - without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means