RIPK3-deficient mice were not protected from nephrotoxic nephritis

Background

Necrotizing glomerular lesions are a feature of severe glomerulonephritis. Unlike apoptosis, cellular necrosis has the potential to release damage-associated proteins into the microenvironment, thereby potentiating inflammation. Until recently necrosis was thought to be an unregulated cellular response to injury. However, recent evidence suggests that under certain circumstances receptor mediated necrosis occurs in response to death ligand signalling, one form of which is termed necroptosis. RIPK3, a receptor interacting protein, is a limiting step in the intracellular signalling pathway of necroptosis. A non-redundant role for RIPK3 has been implicated in mouse models of renal ischaemia reperfusion injury and toxic renal injury. The aim of this study was to investigate the role of RIPK3 in nephrotoxic nephritis (NTN), a model of immune complex glomerulonephritis in mice.

Methods

We induced NTN in RIPK3−/− and WT mice, comparing histology and renal function in both groups.

Results

There was no improvement in urinary albumin creatinine ratio, serum urea, glomerular thrombosis or glomerular macrophage infiltration in the RIPK3−/− mice compared to WT. There was also no difference in number of apoptotic cells in glomeruli as measured by TUNEL staining between the RIPK3−/− and WT mice.

Conclusion

The data suggests that RIPK3 is not on a critical pathway in the pathogenesis of nephrotoxic nephritis.

Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK)

BACKGROUND

Screening for undiagnosed diabetes is not widely undertaken due to the high costs and invasiveness of blood sampling. Simple non-invasive tools to identify high risk individuals can facilitate screening. The main objectives of this study are to develop and validate a risk score for screening undiagnosed diabetes among Sri Lankan adults and to compare its performance with the Cambridge Risk Score (CRS), the Indian Diabetes Risk Score (IDRS) and three other Asian risk scores.

METHODS

Data were available from a representative sample of 4276 adults without diagnosed diabetes. In a jack-knife approach two thirds of the sample was used for the development of the risk score and the remainder for the validation. Age, waist circumference, BMI, hypertension, balanitis or vulvitis, family history of diabetes, gestational diabetes, physical activity and osmotic symptoms were significantly associated with undiagnosed diabetes (age most to osmotic symptoms least). Individual scores were generated for these factors using the beta coefficient values obtained in multiple logistic regression. A cut-off value of sum = 31 was determined by ROC curve analysis.

RESULTS

The area under the ROC curve of the risk score for prevalent diabetes was 0.78 (CI 0.73-0.82). In the sample 36.3 % were above the cut-off of 31. A risk score above 31 gave a sensitivity, specificity, positive predictive value and negative predictive value of 77.9, 65.6, 9.4 and 98.3 % respectively. For Sri Lankans the AUC for the CRS and IDRS were 0.72 and 0.66 repectively.

CONCLUSIONS

This simple non-invasive screening tool can identify 80 % of undiagnosed diabetes by selecting 40 % of Sri Lankan adults for confirmatory blood investigations.

CD14 expression is increased on monocytes in patients with anti-neutrophil cytoplasm antibody (ANCA)-associated vasculitis and correlates with the expression of ANCA autoantigens

Monocyte subsets with differing functional properties have been defined by their expression of CD14 and CD16. We investigated these subsets in anti-neutrophil cytoplasm antibody (ANCA)-associated vasculitis (AAV) and determined their surface expression of ANCA autoantigens. Flow cytometry was performed on blood from 14 patients with active AAV, 46 patients with AAV in remission and 21 controls. The proportion of classical (CD14(high) CD16(neg/low)), intermediate (CD14(high) CD16(high)) and non-classical (CD14(low) CD16(high)) monocytes and surface expression levels of CD14 and CD16 were determined, as well as surface expression of proteinase 3 (PR3) and myeloperoxidase (MPO) on monocyte subsets. There was no change in the proportion of monocytes in each subset in patients with AAV compared with healthy controls. The expression of CD14 on monocytes from patients with active AAV was increased, compared with patients in remission and healthy controls (P < 0.01). Patients with PR3-ANCA disease in remission also had increased monocyte expression of CD14 compared with controls (P < 0.01); however, levels in patients with MPO-ANCA disease in remission were lower than active MPO-ANCA patients, and not significantly different from controls. There was a correlation between CD14 and both PR3 and MPO expression on classical monocytes in AAV patients (r = 0.79, P < 0.0001 and r = 0.42, P < 0.005, respectively). In conclusion, there was an increase in monocyte CD14 expression in active AAV and PR3-ANCA disease in remission. The correlation of CD14 expression with ANCA autoantigen expression in AAV may reflect cell activation, and warrants further investigation into the potential for increased CD14 expression to trigger disease induction or relapse.

Glycaemic risk assessment in children and young people with Type 1 diabetes mellitus

AIM

To ascertain if those with diabetes (and their carers) ascribe a similar level of risk to blood glucose control as healthcare professionals.

METHODS

We used a structured questionnaire to ask fifty healthcare professionals how 'dangerous' a given blood glucose value was. Their answers were modelled to produce an algorithm of assessed risk. To examine if patients (and their carers) would apportion a similar level of risk to that of healthcare professionals, the same questionnaire was issued to fifty children and adolescents with Type 1 diabetes. For patients under 8 years old the carers completed the questionnaires (n = 23). Both patient and carers together completed the questionnaire for those aged 8-11 years (n = 15) and patients over the age of 11 years completed the questionnaire themselves (n = 12). The median results and interquartile range of the assessed level of risk, as determined by the two groups, were compared using a generalized linear model.

RESULTS

A significant difference (P < 0.0001) was identified between the median risk assessments of the two groups. The zero level of assessed risk was upward shifted in the patient group by 0.8 mmol/l and indicated the patients' view of risk increased.

CONCLUSIONS

Patients with Type 1 diabetes (and their carers) evaluate the risk from blood glucose values differently from healthcare professionals. The euglycaemic state (zero ascribed risk) that patients chose was 0.8 mmol/l greater than that of healthcare professionals, indicating, perhaps, hypoglycaemia avoidance, a more pragmatic approach or less exposure to current trends in glycaemic control.

A method for assessing quality of control from glucose profiles

AIM

As the practice of multiple assessments of glucose concentration throughout the day increases for people with diabetes, there is a need for an assessment of glycaemic control weighted for the clinical risks of both hypoglycaemia and hyperglycaemia.

METHODS

We have developed a methodology to report the degree of risk which a glycaemic profile represents. Fifty diabetes professionals assigned risk values to a range of 40 blood glucose concentrations. Their responses were summarised and a generic function of glycaemic risk was derived. This function was applied to patient glucose profiles to generate an integrated risk score termed the Glycaemic Risk Assessment Diabetes Equation (GRADE). The GRADE score was then reported by use of the mean value and the relative percent contribution to the weighted risk score from the hypoglycaemic, euglycaemic, hyperglycaemic range, respectively, e.g. GRADE (hypoglycaemia%, euglycaemia%, hyperglycaemia%).

RESULTS

The GRADE scores of indicative glucose profiles were as follows: continuous glucose monitoring profile non-diabetic subjects GRADE = 1.1, Type 1 diabetes continuous glucose monitoring GRADE = 8.09 (20%, 8%, 72%), Type 2 diabetes home blood glucose monitoring GRADE = 9.97 (2%, 7%, 91%).

CONCLUSIONS

The GRADE score of a glucose profile summarises the degree of risk associated with a glucose profile. Values < 5 correspond to euglycaemia. The GRADE score is simple to generate from any blood glucose profile and can be used as an adjunct to HbA1c to report the degree of risk associated with glycaemic variability.

A method for assessing quality of control from glucose profiles

AIM

As the practice of multiple assessments of glucose concentration throughout the day increases for people with diabetes, there is a need for an assessment of glycaemic control weighted for the clinical risks of both hypoglycaemia and hyperglycaemia.

METHODS

We have developed a methodology to report the degree of risk which a glycaemic profile represents. Fifty diabetes professionals assigned risk values to a range of 40 blood glucose concentrations. Their responses were summarised and a generic function of glycaemic risk was derived. This function was applied to patient glucose profiles to generate an integrated risk score termed the Glycaemic Risk Assessment Diabetes Equation (GRADE). The GRADE score was then reported by use of the mean value and the relative percent contribution to the weighted risk score from the hypoglycaemic, euglycaemic, hyperglycaemic range, respectively, e.g. GRADE (hypoglycaemia%, euglycaemia%, hyperglycaemia%).

RESULTS

The GRADE scores of indicative glucose profiles were as follows: continuous glucose monitoring profile non-diabetic subjects GRADE = 1.1, Type 1 diabetes continuous glucose monitoring GRADE = 8.09 (20%, 8%, 72%), Type 2 diabetes home blood glucose monitoring GRADE = 9.97 (2%, 7%, 91%).

CONCLUSIONS

The GRADE score of a glucose profile summarises the degree of risk associated with a glucose profile. Values < 5 correspond to euglycaemia. The GRADE score is simple to generate from any blood glucose profile and can be used as an adjunct to HbA1c to report the degree of risk associated with glycaemic variability.

Synthesis of colensan-1-one

Colensan-I-one (I), an isomer of the naturally occurring norditerpene, colens-14-en-2-one, has been synthesized from 2-oxomanoyl oxide (II).