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Background Although cardiovascular mortality continued declining from 2000 to 2019, the rate of this decrease decelerated. We aimed to assess the trends and disparities in risk factor control and treatment among US adults with atherosclerotic cardiovascular disease to find potential causes of the deceleration. Methods and Results A total of 55 ,021 participants, aged ≥20 years, from the 1999 to 2018 National Health and Nutrition Examination Survey were included, of which 5717 were with atherosclerotic cardiovascular disease. Risk factor control was defined as hemoglobin A1c <7%, blood pressure <140/90 mm Hg, and non–high‐density lipoprotein cholesterol <100 mg/dL. The prevalence of atherosclerotic cardiovascular disease oscillated between 7.3% and 8.9% from 1999 to 2018. A significant increasing trend was observed in the prevalence of diabetes, obesity, heavy alcohol consumption, and self‐reported hypertension within the population with atherosclerotic cardiovascular disease ( P trend ≤0.001). Non–high‐density lipoprotein cholesterol <100 mg/dL increased from 7.1% in 1999 to 2002 to 15.7% in 2003 to 2006, before plateauing. Blood pressure control (<140/90 mm Hg) increased until 2011 to 2014, but declined to 70.1% in 2015 to 2018 ( P trend <0.001, P joinpoint =0.14). Similarly, the proportion of participants achieving hemoglobin A1c control began to decrease after 2006 ( P joinpoint =0.05, P trend =0.001). The percentage of participants achieving all 3 targets increased significantly from 4.5% to 18.6% across 1999 to 2018 ( P trend =0.02), but the increasing trend decelerated after 2005 to 2006 ( P joinpoint <0.001). Striking disparities in risk factor control and medication use persisted between sexes, and between different racial and ethnic populations. Conclusions Worsened control of glycemia, blood pressure, obesity, and alcohol consumption, leveled lipid control, and persistent socioeconomic disparities may be contributing factors to the observed deceleration in decreasing cardiovascular mortality trends.

In vivo MRS is a non‐invasive measurement technique used not only in humans, but also in animal models using high‐field magnets. MRS enables the measurement of metabolite concentrations as well as metabolic rates and their modifications in healthy animals and disease models. Such data open the way to a deeper understanding of the underlying biochemistry, related disturbances and mechanisms taking place during or prior to symptoms and tissue changes. In this work, we focus on the main preclinical 1H, 31P and 13C MRS approaches to study brain metabolism in rodent models, with the aim of providing general experts’ consensus recommendations (animal models, anesthesia, data acquisition protocols). An overview of the main practical differences in preclinical compared with clinical MRS studies is presented, as well as the additional biochemical information that can be obtained in animal models in terms of metabolite concentrations and metabolic flux measurements. The properties of high‐field preclinical MRS and the technical limitations are also described.

ObjectiveTo discover biomarkers involved in the pathophysiology of antineutrophil cytoplasmic antibody–associated vasculitis (AAV) and to determine whether low‐density granulocytes (LDGs) contribute to gene expression signatures in AAV.MethodsThe source of clinical data and linked biologic specimens was a randomized controlled treatment trial in AAV. RNA sequencing of whole blood from patients with AAV was performed during active disease at the baseline visit and during remission 6 months later. Gene expression was compared between patients who met versus those who did not meet the primary trial outcome of clinical remission at 6 months (responders versus nonresponders). Measurement of neutrophil‐related gene expression was confirmed in peripheral blood mononuclear cells (PBMCs) to validate the findings in whole blood. A negative‐selection strategy isolated LDGs from PBMC fractions.ResultsDifferential expression between responders (n = 77) and nonresponders (n = 35) was detected in 2,346 transcripts at the baseline visit (P < 0.05). Unsupervised hierarchical clustering demonstrated a cluster of granulocyte‐related genes, including myeloperoxidase (MPO) and proteinase 3 (PR3). A granulocyte multigene composite score was significantly higher in nonresponders than in responders (P < 0.01) and during active disease than during remission (P < 0.01). This signature strongly overlapped an LDG signature identified previously in lupus (false discovery rate by gene set enrichment analysis <0.01). Transcription of PR3 measured in PBMCs was associated with active disease and treatment response (P < 0.01). LDGs isolated from patients with AAV spontaneously formed neutrophil extracellular traps containing PR3 and MPO.ConclusionIn AAV, increased expression of a granulocyte gene signature is associated with disease activity and decreased response to treatment. The source of this signature is likely LDGs, a potentially pathogenic cell type in AAV.