Exploration of hematological and immunological changes associated with the severity of type 2 diabetes mellitus in Japan

ANGPTL4 May Regulate the Crosstalk Between Intervertebral Disc Degeneration and Type 2 Diabetes Mellitus: A Combined Analysis of Bioinformatics and Rat Models

Introduction

The crosstalk between intervertebral disc degeneration (IVDD) and type 2 diabetes mellitus (T2DM) has been investigated. However, the common mechanism underlying this phenomenon has not been clearly elucidated. This study aimed to explore the shared gene signatures of IVDD and T2DM.

Methods

The expression profiles of IVDD (GSE27494) and T2DM (GSE20966) were acquired from the Gene Expression Omnibus database. Five hub genes including ANGPTL4, CCL2, CCN3, THBS2, and INHBA were preliminarily screened. GO (Gene Ontology) enrichment analysis, functional correlation analysis, immune filtration, Transcription factors (TFs)-mRNA-miRNA coregulatory network, and potential drugs prediction were performed following the identification of hub genes. RNA sequencing, in vivo and in vitro experiments on rats were further performed to validate the expression and function of the target gene.

Results

Five hub genes (ANGPTL4, CCL2, CCN3, THBS2, and INHBA) were identified. GO analysis demonstrated the regulation of the immune system, extracellular matrix (ECM), and SMAD protein signal transduction. There was a strong correlation between hub genes and different functions, including lipid metabolism, mitochondrial function, and ECM degradation. The immune filtration pattern grouped by disease and the expression of hub genes showed significant changes in the immune cell composition. TFs-mRNA-miRNA co-expression networks were constructed. In addition, pepstatin showed great drug-targeting relevance based on potential drugs prediction of hub genes. ANGPTL4, a gene that mediates the inhibition of lipoprotein lipase activity, was eventually determined after hub gene screening, validation by different datasets, RNA sequencing, and experiments.

Discussion

This study screened five hub genes and ANGPTL4 was eventually determined as a potential target for the regulation of the crosstalk in patients with IVDD and T2DM.

Assessing the severity of Type 2 diabetes using clinical data-based measures: a systematic review

AIMS

To identify and critically appraise measures that use clinical data to grade the severity of Type 2 diabetes.

METHODS

We searched MEDLINE, Embase and PubMed between inception and June 2018. Studies reporting on clinical data-based diabetes-specific severity measures in adults with Type 2 diabetes were included. We excluded studies conducted solely in participants with other types of diabetes. After independent screening, the characteristics of the eligible measures including design and severity domains, the clinical utility of developed measures, and the relationship between severity levels and health-related outcomes were assessed.

RESULTS

We identified 6798 studies, of which 17 studies reporting 18 different severity measures (32 314 participants in 17 countries) were included: a diabetes severity index (eight studies, 44%); severity categories (seven studies, 39%); complication count (two studies, 11%); and a severity checklist (one study, 6%). Nearly 89% of the measures included diabetes-related complications and/or glycaemic control indicators. Two of the severity measures were validated in a separate study population. More severe diabetes was associated with increased healthcare costs, poorer cognitive function and significantly greater risks of hospitalization and mortality. The identified measures differed greatly in terms of the included domains. One study reported on the use of a severity measure prospectively.

CONCLUSIONS

Health records are suitable for assessment of diabetes severity; however, the clinical uptake of existing measures is limited. The need to advance this research area is fundamental as higher levels of diabetes severity are associated with greater risks of adverse outcomes. Diabetes severity assessment could help identify people requiring targeted and intensive therapies and provide a major benchmark for efficient healthcare services.

Using electronic health records to quantify and stratify the severity of type 2 diabetes in primary care in England: rationale and cohort study design

INTRODUCTION

The increasing prevalence of type 2 diabetes mellitus (T2DM) presents a significant burden on affected individuals and healthcare systems internationally. There is, however, no agreed validated measure to infer diabetes severity from electronic health records (EHRs). We aim to quantify T2DM severity and validate it using clinical adverse outcomes.

METHODS AND ANALYSIS

Primary care data from the Clinical Practice Research Datalink, linked hospitalisation and mortality records between April 2007 and March 2017 for patients with T2DM in England will be used to develop a clinical algorithm to grade T2DM severity. The EHR-based algorithm will incorporate main risk factors (severity domains) for adverse outcomes to stratify T2DM cohorts by baseline and longitudinal severity scores. Provisionally, T2DM severity domains, identified through a systematic review and expert opinion, are: diabetes duration, glycated haemoglobin, microvascular complications, comorbidities and coprescribed treatments. Severity scores will be developed by two approaches: (1) calculating a count score of severity domains; (2) through hierarchical stratification of complications. Regression models estimates will be used to calculate domains weights. Survival analyses for the association between weighted severity scores and future outcomes-cardiovascular events, hospitalisation (diabetes-related, cardiovascular) and mortality (diabetes-related, cardiovascular, all-cause mortality)-will be performed as statistical validation. The proposed EHR-based approach will quantify the T2DM severity for primary care performance management and inform the methodology for measuring severity of other primary care-managed chronic conditions. We anticipate that the developed algorithm will be a practical tool for practitioners, aid clinical management decision-making, inform stratified medicine, support future clinical trials and contribute to more effective service planning and policy-making.

ETHICS AND DISSEMINATION

The study protocol was approved by the Independent Scientific Advisory Committee. Some data were presented at the National Institute for Health Research School for Primary Care Research Showcase, September 2017, Oxford, UK and the Diabetes UK Professional Conference March 2018, London, UK. The study findings will be disseminated in relevant academic conferences and peer-reviewed journals.

Fasting glucose level modulates cell surface expression of CD11b and CD66b in granulocytes and monocytes of patients with type 2 diabetes

INTRODUCTION

Cardiovascular complications are the leading cause of mortality in type 2 diabetes (T2DM), in which onset and progression of atherosclerosis is linked to chronic inflammation. Activation status of innate immune cells (granulocytes [Gc], monocytes [Mc]), as reflected by increased CD11b, CD66b, and other surface markers, increases their endothelial and cytokines/chemokines release. Whereas this inflammatory activation seems inversely related to poor glycemic control, the effect of acute spontaneous hyperglycemia on innate immune cell activation remains unclear.

METHODS

Expression of key markers (CD11b, CD14, CD16, CD62L, and CD66b) was therefore determined by flow cytometry on whole blood of healthy subjects and patients with T2DM with spontaneous fasting euglycemia or hyperglycemia both at baseline and after 30, 90, and 240 minutes of incubation at room temperature.

RESULTS

Hyperglycemic patients with T2DM had significantly higher Gc and Mc CD11b and Gc CD66b surface mean fluorescence intensity compared with the euglycemic patients with T2DM whose values were similar to those of the healthy controls. CD16 expression in CD14+CD16+ Mc was elevated in all patients with T2DM, regardless of glycemic levels.

CONCLUSION

Our data suggest that whereas the presence of diabetes per se may have a proinflammatory effect, hyperglycemia seems to further acutely exacerbate innate cell inflammatory status and their consequent endothelial adhesion and vascular damage potential.

Differences in the haematological profile of healthy 70 year old men and women: normal ranges with confirmatory factor analysis

Background

Reference ranges are available for different blood cell counts. These ranges treat each cell type independently and do not consider possible correlations between cell types.

Methods

Participants were identified from the Community Health Index as survivors of the 1947 Scottish Mental Survey, all born in 1936, who were resident in Lothian (potential n = 3,810) and invited to participate in the study. Those who consented were invited to attend a Clinical Research Facility where, amongst other assessments, blood was taken for full blood count. First we described cell count data and bivariate correlations. Next we performed principal components analysis to identify common factors. Finally we performed confirmatory factor analysis to evaluate suitable models explaining relationships between cell counts in men and women.

Results

We examined blood cell counts in 1027 community-resident people with mean age 69.5 (range 67.6-71.3) years. We determined normal ranges for each cell type using Q-Q plots which showed that these ranges were significantly different between men and women for all cell types except basophils. We identified three principal components explaining around 60% of total variance of cell counts. Varimax rotation indicated that these could be considered as erythropoietic, leukopoietic and thrombopoietic factors. We showed that these factors were distinct for men and women by confirmatory factor analysis: in men neutrophil count was part of a 'thrombopoietic' trait whereas for women it was part of a 'leukopoietic' trait.

Conclusions

First, normal ranges for haematological indices should be sex-specific; at present this only pertains to those associated with erythrocytes. Second, differences between individuals across a range of blood cell counts can be explained to a considerable extent by three major components, but these components are not the same in men and women.