Mutations in the gene encoding NLRP3 cause a spectrum of autoinflammatory diseases known as the cryopyrin-associated periodic syndromes (CAPS)1. NLRP3 is a key component of one of several distinct cytoplasmic multiprotein complexes (inflammasomes) that mediate the maturation of the proinflammatory cytokine interleukin-1β (IL-1β) by activating caspase-1. Although several models for inflammasome activation, such as K+ efflux2, generation of reactive oxygen species3 and lysosomal destabilization4, have been proposed, the precise molecular mechanism of NLRP3 inflammasome activation, as well as the mechanism by which CAPS-associated mutations activate NLRP3, remain to be elucidated. Here we show that the murine calcium-sensing receptor (CASR) activates the NLRP3 inflammasome, mediated by increased intracellular Ca2+ and decreased cellular cyclic AMP (cAMP). Ca2+ or other CASR agonists activate the NLRP3 inflammasome in the absence of exogenous ATP, whereas knockdown of CASR reduces inflammasome activation in response to known NLRP3 activators. CASR activates the NLRP3 inflammasome through phospholipase C, which catalyses inositol-1,4,5-trisphosphate production and thereby induces release of Ca2+ from endoplasmic reticulum stores. The increased cytoplasmic Ca2+ promotes the assembly of inflammasome components, and intracellular Ca2+ is required for spontaneous inflammasome activity in cells from CAPS patients. CASR stimulation also results in reduced intracellular cAMP, which independently activates the NLRP3 inflammasome. cAMP binds to NLRP3 directly to inhibit inflammasome assembly, and downregulation of cAMP relieves this inhibition. The binding affinity of cAMP for CAPS-associated mutant NLRP3 is substantially lower than for wild-type NLRP3, and the uncontrolled mature IL-1β production from CAPS patients' peripheral blood mononuclear cells is attenuated by increasing cAMP. Taken together, these findings indicate that Ca2+ and cAMP are two key molecular regulators of the NLRP3 inflammasome that have critical roles in the molecular pathogenesis of CAPS.

Background: Multiple laboratory tests are used in the diagnosis and management of patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. Approach: An expert committee drafted evidence-based recommendations for the use of laboratory analysis in patients with diabetes. An external panel of experts reviewed a draft of the guidelines, which were modified in response to the reviewers’ suggestions. A revised draft was posted on the Internet and was presented at the AACC Annual Meeting in July, 2000. The recommendations were modified again in response to oral and written comments. The guidelines were reviewed by the Professional Practice Committee of the American Diabetes Association. Content: Measurement of plasma glucose remains the sole diagnostic criterion for diabetes. Monitoring of glycemic control is performed by the patients, who measure their own plasma or blood glucose with meters, and by laboratory analysis of glycated hemoglobin. The potential roles of noninvasive glucose monitoring, genetic testing, autoantibodies, microalbumin, proinsulin, C-peptide, and other analytes are addressed. Summary: The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are of minimal clinical value at the present time, and measurement of them is not recommended.

The endothelial nitric-oxide synthase (eNOS) is a key determinant of vascular homeostasis. Like all known nitric-oxide synthases, eNOS enzyme activity is dependent on Ca 2؉ -calmodulin. eNOS is dynamically targeted to specialized cell surface signal-transducing domains termed plasmalemmal caveolae and interacts with caveolin, an integral membrane protein that comprises a key structural component of caveolae. We have previously reported that the association between eNOS and caveolin is quantitative and tissue-specific (Feron, O., Belhassen, L., Kobzick, L., Smith, T. W., Kelly, R. A., and Michel, T. (1996) J. Biol. Chem. 271, 22810 -22814). We now report that in endothelial cells the interaction between eNOS and caveolin is importantly regulated by Ca 2؉ -calmodulin. Addition of calmodulin disrupts the heteromeric complex formed between eNOS and caveolin in a Ca 2؉ -dependent fashion. In addition, overexpression of caveolin markedly attenuates eNOS enzyme activity, but this inhibition is reversed by purified calmodulin. Caveolin overexpression does not affect the activity of the other NOS isoforms, suggesting eNOSspecific inhibition of NO synthase by caveolin. We propose a model of reciprocal regulation of eNOS in endothelial cells wherein the inhibitory eNOS-caveolin complex is disrupted by binding of Ca 2؉

Background Trends in the prevalence and control of diabetes defined by hemoglobin A1c (HbA1c) are important for health care policy and planning. Objective To update trends in prevalence of diabetes, pre-diabetes, and glycemic control in persons with diagnosed diabetes over the past two decades in U.S. adults. Design Cross-sectional. Setting The National Health and Nutrition Examination Surveys 1988-1994 and 1999-2010. Participants Adults 20 years or older. Measurements We used calibrated HbA1c to define undiagnosed diabetes (≥6.5%), pre-diabetes (5.7-6.4%), and, among persons with diagnosed diabetes, glycemic control (<7.0% or <8.0%). Trends in HbA1c categories were compared to fasting glucose (≥7.0 and 5.6-6.9 mmol/l [≥126 and 100-125 mg/dL]). Results In 2010, there were ~21 million adults aged 20 years or older with total confirmed diabetes (self-reported diabetes or diagnostic values for both fasting glucose and calibrated HbA1c) in the U.S. The prevalence of total confirmed diabetes increased but the prevalence of undiagnosed diabetes has remained fairly stable, reducing the proportion of total diabetes cases that are undiagnosed to 11% in 2005-10. The prevalence of pre-diabetes was lower when defined by calibrated HbA1c compared to when defined by fasting glucose but has increased from 5.8% in 1988-1994 to 12.4% in 2005-2010 when defined by HbA1c. Glycemic control has improved overall but total diabetes prevalence was higher and diabetes was less controlled among non-Hispanic blacks and Mexican Americans compared to non-Hispanic whites. Limitations Cross-sectional design. Conclusions Over the past two decades, the prevalence of total diabetes has increased substantially. However, the proportion of diabetes cases that are undiagnosed has decreased suggesting improvements in screening and diagnosis. Among the growing number of persons with diagnosed diabetes, glycemic control improved but remains a challenge, particularly among non-Hispanic blacks and Mexican Americans.

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While A1C is well established as an important risk marker for diabetes complications, with the increasing use of continuous glucose monitoring (CGM) to help facilitate safe and effective diabetes management, it is important to understand how CGM metrics, such as mean glucose, and A1C correlate. Estimated A1C (eA1C) is a measure converting the mean glucose from CGM or self-monitored blood glucose readings, using a formula derived from glucose readings from a population of individuals, into an estimate of a simultaneously measured laboratory A1C. Many patients and clinicians find the eA1C to be a helpful educational tool, but others are often confused or even frustrated if the eA1C and laboratory-measured A1C do not agree. In the U.S., the Food and Drug Administration determined that the nomenclature of eA1C needed to change. This led the authors to work toward a multipart solution to facilitate the retention of such a metric, which includes renaming the eA1C the glucose management indicator (GMI) and generating a new formula for converting CGM-derived mean glucose to GMI based on recent clinical trials using the most accurate CGM systems available. The final aspect of ensuring a smooth transition from the old eA1C to the new GMI is providing new CGM analyses and explanations to further understand how to interpret GMI and use it most effectively in clinical practice. This Perspective will address why a new name for eA1C was needed, why GMI was selected as the new name, how GMI is calculated, and how to understand and explain GMI if one chooses to use GMI as a tool in diabetes education or management.

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