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Background: The objectives of this study were to (1) evaluate telemetry use pre- and postimplementation of clinical decision support tools to support American Heart Association practice standards for telemetry monitoring and (2) understand the factors that may contribute to variation of telemetry monitoring in practice., Methods and Results: First, we captured overall variability in telemetry use pre- and postimplementation of the clinical decision support intervention. We then conducted semistructured interviews with telemetry-ordering providers to identify key barriers and facilitators to adoption. During the study period, 399 physicians met criteria for inclusion and were divided into excessive and nonexcessive orderers. Distribution of telemetry use was bimodal. Among nonexcessive users, 24.4% of patient days were with telemetry compared with 51.6% among excessive users. On average, both excessive (6.1% reduction) and nonexcessive users (2.8% reduction) decreased telemetry use postimplementation, and these reductions were sustained over a 16-month period. Sixteen interviews were conducted. Physicians believed that the tool was successful because it caused them to more closely consider if telemetry was indicated for each patient. Physicians also voiced frustration with interruptions to their workflow, and some noted that they commonly use telemetry outside of practice standards to monitor patients who were acutely but not critically ill., Conclusions: Embedding telemetry practice standards into the electronic health record in the form of clinical decision support is effective at reducing excess telemetry use. Although the intervention was well received, there are persistent barriers, such as preexisting views on telemetry and existing workflow habits, that may inhibit higher adoption of standards.

Background: Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability., Objectives: We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery., Methods: During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership., Implementation Results: Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2)., Conclusions: Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team., Competing Interests: The authors have no relevant conflicts of interest to report., (© 2024 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Importance: The Deterioration Index (DTI), used by hospitals for predicting patient deterioration, has not been extensively validated externally, raising concerns about performance and equitable predictions., Objective: To locally validate DTI performance and assess its potential for bias in predicting patient clinical deterioration., Design, Setting, and Participants: This retrospective prognostic study included 13 737 patients admitted to 8 heterogenous Midwestern US hospitals varying in size and type, including academic, community, urban, and rural hospitals. Patients were 18 years or older and admitted between January 1 and May 31, 2021., Exposure: DTI predictions made every 15 minutes., Main Outcomes and Measures: Deterioration, defined as the occurrence of any of the following while hospitalized: mechanical ventilation, intensive care unit transfer, or death. Performance of the DTI was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Bias measures were calculated across demographic subgroups., Results: A total of 5 143 513 DTI predictions were made for 13 737 patients across 14 834 hospitalizations. Among 13 918 encounters, the mean (SD) age of patients was 60.3 (19.2) years; 7636 (54.9%) were female, 11 345 (81.5%) were White, and 12 392 (89.0%) were of other ethnicity than Hispanic or Latino. The prevalence of deterioration was 10.3% (n = 1436). The DTI produced AUROCs of 0.759 (95% CI, 0.756-0.762) at the observation level and 0.685 (95% CI, 0.671-0.700) at the encounter level. Corresponding AUPRCs were 0.039 (95% CI, 0.037-0.040) at the observation level and 0.248 (95% CI, 0.227-0.273) at the encounter level. Bias measures varied across demographic subgroups and were 14.0% worse for patients identifying as American Indian or Alaska Native and 19.0% worse for those who chose not to disclose their ethnicity., Conclusions and Relevance: In this prognostic study, the DTI had modest ability to predict patient deterioration, with varying degrees of performance at the observation and encounter levels and across different demographic groups. Disparate performance across subgroups suggests the need for more transparency in model training data and reinforces the need to locally validate externally developed prediction models.

Objectives: Seasonal influenza vaccines protect against 3 (trivalent influenza vaccine [IIV3]) or 4 (quadrivalent influenza vaccine [IIV4]) viruses. IIV4 costs more than IIV3, and there is a trade-off between incremental cost and protection. This is especially the case in low- and middle-income countries (LMICs) with limited budgets; previous reviews have not identified studies of IIV4-IIV3 comparisons in LMICs. We summarized the literature that compared health and economic outcomes of IIV4 and IIV3, focused on LMICs., Methods: We systematically searched 5 databases for articles published before October 6, 2021, that modeled health or economic effects of IIV4 versus IIV3. We abstracted data and compared findings among countries and models., Results: Thirty-eight studies fit our selection criteria; 10 included LMICs. Most studies (N = 31) reported that IIV4 was cost-saving or cost-effective compared with IIV3; we observed no difference in health or economic outcomes between LMICs and other countries. Based on cost differences of influenza vaccines, only one study compared coverage of IIV3 with IIV4 and reported that the maximum IIV4 price that would still yield greater public health impact than IIV3 was 13% to 22% higher than IIV3., Conclusions: When vaccination coverage with IIV4 and IIV3 is the same, IIV4 tends to be not only more effective but more cost-effective than IIV3, even with relatively high price differences between vaccine types. Alternatively, where funding is limited as in most LMICs, higher vaccine coverage can be achieved with IIV3 than IIV4, which could result in more favorable health and economic outcomes., (Copyright © 2022. Published by Elsevier Inc.)

Importance: The SARS-CoV-2 pandemic has overwhelmed hospital capacity, prioritizing the need to understand factors associated with type of discharge disposition., Objective: Characterization of disposition associated factors following SARS-CoV-2., Design: Retrospective study of SARS-CoV-2 positive patients from March 7th, 2020, to May 4th, 2022, requiring hospitalization., Setting: Midwest academic health-system., Participants: Patients above the age 18 years admitted with PCR + SARS-CoV-2., Intervention: None., Main Outcomes: Discharge to home versus PAC (inpatient rehabilitation facility (IRF), skilled-nursing facility (SNF), long-term acute care (LTACH)), or died/hospice while hospitalized (DH)., Results: We identified 62,279 SARS-CoV-2 PCR+ patients; 6,248 required hospitalizations, of whom 4611(73.8%) were discharged home, 985 (15.8%) to PAC and 652 (10.4%) died in hospital (DH). Patients discharged to PAC had a higher median age (75.7 years, IQR: 65.6-85.1) compared to those discharged home (57.0 years, IQR: 38.2-69.9), and had longer mean length of stay (LOS) 14.7 days, SD: 14.0) compared to discharge home (5.8 days, SD: 5.9). Older age (RRR:1.04, 95% CI:1.041-1.055), and higher Elixhauser comorbidity index [EI] (RRR:1.19, 95% CI:1.168-1.218) were associated with higher rate of discharge to PAC versus home. Older age (RRR:1.069, 95% CI:1.060-1.077) and higher EI (RRR:1.09, 95% CI:1.071-1.126) were associated with more frequent DH versus home. Blacks, Asians, and Hispanics were less likely to be discharged to PAC (RRR, 0.64 CI 0.47-0.88), (RRR 0.48 CI 0.34-0.67) and (RRR 0.586 CI 0.352-0.975). Having alpha variant was associated with less frequent PAC discharge versus home (RRR 0.589 CI 0.444-780). The relative risks for DH were lower with a higher platelet count 0.998 (CI 0.99-0.99) and albumin levels 0.342 (CI 0.26-0.45), and higher with increased CRP (RRR 1.006 CI 1.004-1.007) and D-Dimer (RRR 1.070 CI 1.039-1.101). Increased albumin had lower risk to PAC discharge (RRR 0.630 CI 0.497-0.798. An increase in D-Dimer (RRR1.033 CI 1.002-1.064) and CRP (RRR1.002 CI1.001-1.004) was associated with higher risk of PAC discharge. A breakthrough (BT) infection was associated with lower likelihood of DH and PAC., Conclusion: Older age, higher EI, CRP and D-Dimer are associated with PAC and DH discharges following hospitalization with COVID-19 infection. BT infection reduces the likelihood of being discharged to PAC and DH., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ikramuddin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This analysis describes the prevalence of contraindications to nirmatrelvir/ritonavir among 66 007 patients with coronavirus disease 2019 in a large health care system. A possible contradiction was present in 9830 patients (14.8%), with the prevalence of contraindications increasing with higher acuity of illness., Competing Interests: Potential conflicts of interest. D.R.B. has conducted clinical trials of outpatient COVID-19 therapies. Otherwise, the authors declare no conflicts of interest related to this work. The authors: no reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Objective: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED)., Methods: We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance., Results: The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05)., Conclusion: A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation., Competing Interests: The authors have declared that no competing interests exist.

Background: Despite past and ongoing efforts to achieve health equity in the USA, racial and ethnic disparities persist and appear to be exacerbated by COVID-19., Objective: Evaluate neighborhood-level deprivation and English language proficiency effect on disproportionate outcomes seen in racial and ethnic minorities diagnosed with COVID-19., Design: Retrospective cohort study SETTING: Health records of 12 Midwest hospitals and 60 clinics in Minnesota between March 4, 2020, and August 19, 2020 PATIENTS: Polymerase chain reaction-positive COVID-19 patients EXPOSURES: Area Deprivation Index (ADI) and primary language MAIN MEASURES: The primary outcome was COVID-19 severity, using hospitalization within 45 days of diagnosis as a marker of severity. Logistic and competing-risk regression models assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race, effects of ADI and primary language were measured using logistic regression., Results: A total of 5577 individuals infected with SARS-CoV-2 were included; 866 (n = 15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 vs. 40.4 years, p < 0.001) and more likely to be male (n = 425 [49.1%] vs. 2049 [43.5%], p = 0.002). Of those requiring hospitalization, 43.9% (n = 381), 19.9% (n = 172), 18.6% (n = 161), and 11.8% (n = 102) were White, Black, Asian, and Hispanic, respectively. Independent of ADI, minority race/ethnicity was associated with COVID-19 severity: Hispanic patients (OR 3.8, 95% CI 2.72-5.30), Asians (OR 2.39, 95% CI 1.74-3.29), and Blacks (OR 1.50, 95% CI 1.15-1.94). ADI was not associated with hospitalization. Non-English-speaking (OR 1.91, 95% CI 1.51-2.43) significantly increased odds of hospital admission across and within minority groups., Conclusions: Minority populations have increased odds of severe COVID-19 independent of neighborhood deprivation, a commonly suspected driver of disparate outcomes. Non-English-speaking accounts for differences across and within minority populations. These results support the ongoing need to determine the mechanisms that contribute to disparities during COVID-19 while also highlighting the underappreciated role primary language plays in COVID-19 severity among minority groups., (© 2021. Society of General Internal Medicine.)

Objective: Ensuring an efficient response to COVID-19 requires a degree of inter-system coordination and capacity management coupled with an accurate assessment of hospital utilization including length of stay (LOS). We aimed to establish optimal practices in inter-system data sharing and LOS modeling to support patient care and regional hospital operations., Materials and Methods: We completed a retrospective observational study of patients admitted with COVID-19 followed by 12-week prospective validation, involving 36 hospitals covering the upper Midwest. We developed a method for sharing de-identified patient data across systems for analysis. From this, we compared 3 approaches, generalized linear model (GLM) and random forest (RF), and aggregated system level averages to identify features associated with LOS. We compared model performance by area under the ROC curve (AUROC)., Results: A total of 2068 patients were included and used for model derivation and 597 patients for validation. LOS overall had a median of 5.0 days and mean of 8.2 days. Consistent predictors of LOS included age, critical illness, oxygen requirement, weight loss, and nursing home admission. In the validation cohort, the RF model (AUROC 0.890) and GLM model (AUROC 0.864) achieved good to excellent prediction of LOS, but only marginally better than system averages in practice., Conclusion: Regional sharing of patient data allowed for effective prediction of LOS across systems; however, this only provided marginal improvement over hospital averages at the aggregate level. A federated approach of sharing aggregated system capacity and average LOS will likely allow for effective capacity management at the regional level., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Super-utilizers with 4 or more admissions per year frequently receive low-quality care and disproportionately contribute to healthcare costs. Inpatient care fragmentation (admission to multiple different hospitals) in this population has not been well described., Objective: To determine the prevalence of super-utilizers who receive fragmented care across different hospitals and to describe associated risks, costs, and health outcomes., Research Design: We analyzed inpatient data from the Health Care Utilization Project's State Inpatient and Emergency Department database from 6 states from 2013. After identifying hospital super-utilizers, we stratified by the number of different hospitals visited in a 1-year period. We determined how patient demographics, costs, and outcomes varied by degree of fragmentation. We then examined how fragmentation would influence a hospital's ability to identify super-utilizers., Subjects: Adult patients with 4 or more inpatient stays in 1 year., Measures: Patient demographics, cost, 1-year hospital reported mortality, and probability that a single hospital could correctly identify a patient as a super-utilizer., Results: Of the 167,515 hospital super-utilizers, 97,404 (58.1%) visited more than 1 hospital in a 1-year period. Fragmentation was more likely among younger, non-white, low-income, under-insured patients, in population-dense areas. Patients with fragmentation were more likely to be admitted for chronic disease management, psychiatric illness, and substance abuse. Inpatient fragmentation was associated with higher yearly costs and lower likelihood of being identified as a super-utilizer., Conclusions: Inpatient care fragmentation is common among super-utilizers, disproportionately affects vulnerable populations. It is associated with high yearly costs and a decreased probability of correctly identifying super-utilizers.

Purpose: Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes., Methods: This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes., Results: The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III., Conclusion: We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design., Competing Interests: The authors have declared that no competing interests exist.

Observational studies, from multiple countries, repeatedly demonstrate an association between obesity and severe COVID-19, which is defined as need for hospitalization, intensive care unit admission, invasive mechanical ventilation (IMV) or death. Meta-analysis of studies from China, USA, and France show odds ratio (OR) of 2.31 (95% CI 1.3-4.1) for obesity and severe COVID-19. Other studies show OR of 12.1 (95% CI 3.25-45.1) for mortality and OR of 7.36 (95% CI 1.63-33.14) for need for IMV for patients with body mass index (BMI) ≥ 35 kg/m 2 . Obesity is the only modifiable risk factor that is not routinely treated but treatment can lead to improvement in visceral adiposity, insulin sensitivity, and mortality risk. Increasing the awareness of the association between obesity and COVID-19 risk in the general population and medical community may serve as the impetus to make obesity identification and management a higher priority.

Background: Patients transferred between hospitals are at high risk of adverse events and mortality. The relationship between insurance status, transfer practices, and outcomes has not been definitively characterized., Objective: To identify the association between insurance coverage and mortality of patients transferred between hospitals., Design: We conducted a single-institution observational study, and validated results using a national administrative database of inter-hospital transfers., Setting: Three ICUs at an academic tertiary care center validated by a nationally representative sample of inter-hospital transfers., Patients: The single-institution analysis included 652 consecutive patients transferred from 57 hospitals between 2011 and 2012. The administrative database included 353,018 patients transferred between 437 hospitals., Measurements: Adjusted inpatient mortality and 24-h mortality, stratified by insurance status., Results: Of 652 consecutive transfers to three ICUs, we observed that uninsured patients had higher adjusted inpatient mortality (OR 2.67, p = 0.021) when controlling for age, race, gender, Apache-II, and whether the patient was transferred from an ED. Uninsured were more likely to be transferred from ED (OR 2.3, p = 0.026), and earlier in their hospital course (3.9 vs 2.0 days, p = 0.002). Using an administrative dataset, we validated these observations, finding that the uninsured had higher adjusted inpatient mortality (OR 1.24, 95% CI 1.13-1.36, p < 0.001) and higher mortality within 24 h (OR 1.33 95% CI 1.11-1.60, p < 0.002). The increase in mortality was independent of patient demographics, referral patterns, or diagnoses., Limitations: This is an observational study where transfer appropriateness cannot be directly assessed., Conclusions: Uninsured patients are more likely to be transferred from an ED and have higher mortality. These data suggest factors that drive inter-hospital transfer of uninsured patients have the potential to exacerbate outcome disparities.

Background: Mineralocorticoid receptor (MR) antagonists have protective effects in the brain during experimental ischemic stroke, and we have previously demonstrated a key role for myeloid MR during stroke pathogenesis. In this study, we explore both model- and sex-specific actions of myeloid MR during ischemic stroke., Methods and Results: The MR antagonist eplerenone significantly reduced the infarct size in male (control, 99.5 mm(3); eplerenone, 74.2 mm(3); n=8 to 12 per group) but not female (control, 84.0 mm(3); eplerenone, 83.7 mm(3); n=6 to 7 per group) mice after transient (90-minute) middle cerebral artery occlusion. In contrast to MR antagonism, genetic ablation of myeloid MR in female mice significantly reduced infarct size (myeloid MR knockout, 9.4 mm(3) [5.4 to 36.6]; control, 66.0 mm(3) [50.0 to 81.4]; n=6 per group) after transient middle cerebral artery occlusion. This was accompanied by reductions in inflammatory gene expression and improvement in neurological function. In contrast to ischemia-reperfusion, myeloid MR-knockout mice were not protected from permanent middle cerebral artery occlusion. The infarct size and inflammatory response after permanent occlusion showed no evidence of protection by myeloid MR knockout in photothrombotic and intraluminal filament models of permanent occlusion., Conclusions: These studies demonstrate that MR antagonism is protective in male but not female mice during transient middle cerebral artery occlusion, whereas genetic ablation of myeloid MR is protective in both male and female mice. They also highlight important mechanistic differences in the role of myeloid cells in different models of stroke and confirm that specific myeloid phenotypes play key roles in stroke protection.

Inappropriate excess of the steroid hormone aldosterone, which is a mineralocorticoid receptor (MR) agonist, is associated with increased inflammation and risk of cardiovascular disease. MR antagonists are cardioprotective and antiinflammatory in vivo, and evidence suggests that they mediate these effects in part by aldosterone-independent mechanisms. Here we have shown that MR on myeloid cells is necessary for efficient classical macrophage activation by proinflammatory cytokines. Macrophages from mice lacking MR in myeloid cells (referred to herein as MyMRKO mice) exhibited a transcription profile of alternative activation. In vitro, MR deficiency synergized with inducers of alternatively activated macrophages (for example, IL-4 and agonists of PPARgamma and the glucocorticoid receptor) to enhance alternative activation. In vivo, MR deficiency in macrophages mimicked the effects of MR antagonists and protected against cardiac hypertrophy, fibrosis, and vascular damage caused by L-NAME/Ang II. Increased blood pressure and heart rates and decreased circadian variation were observed during treatment of MyMRKO mice with L-NAME/Ang II. We conclude that myeloid MR is an important control point in macrophage polarization and that the function of MR on myeloid cells likely represents a conserved ancestral MR function that is integrated in a transcriptional network with PPARgamma and glucocorticoid receptor. Furthermore, myeloid MR is critical for blood pressure control and for hypertrophic and fibrotic responses in the mouse heart and aorta.

Aims/hypothesis: The aim of this study was to determine the dependency on peroxisome proliferator-activated receptor-gamma (PPAR-gamma) of insulin sensitisation and glucose homeostasis by thiazolidinediones using a global Ppar-gamma (also known as Pparg)-knockout mouse model., Methods: Global Mox2-Cre-Ppar-gamma-knockout (MORE-PGKO) mice were treated with rosiglitazone and analysed for insulin sensitivity and glucose metabolism. Metabolic and hormonal variables were determined. Adipose and other tissues were measured and analysed for gene expression., Results: Rosiglitazone induced regrowth of fat in female but not male MORE-PGKO mice, and only in specific depots. Insulin sensitivity increased but, surprisingly, was not associated with the typical changes in adipokines, plasma NEFA or tissue triacylglycerol. However, increases in alternatively activated macrophage markers, which have been previously associated with metabolic improvement, were observed in the regrown fat. Rosiglitazone improved glucose homeostasis but not insulin sensitivity in male MORE-PGKO mice, with further increase of insulin associated with an apparent expansion of pancreatic islets., Conclusions/interpretation: Stimulating fat growth by rosiglitazone is sufficient to improve insulin sensitivity in female mice with 95% PPAR-gamma deficiency. This increase in insulin sensitivity is not likely to be due to changes typically seen in adipokines or lipids but may involve changes in macrophage polarisation that occur independent of PPAR-gamma. In contrast, rosiglitazone improves glucose homeostasis in male mice with similar PPAR-gamma deficiency by increasing insulin production independent of changes in adiposity. Further, the insulin-sensitising effect of rosiglitazone is dependent on PPAR-gamma in this male lipodystrophic model.

Peroxisome proliferator-activated receptor (PPAR)-gamma is a nuclear receptor and transcription factor in the steroid superfamily. PPAR-gamma agonists, the thiazolidinediones, are clinically used to treat type 2 diabetes. In addition to its function in adipogenesis and increasing insulin sensitivity, PPAR-gamma also plays critical roles in the vasculature. In vascular endothelial cells, PPAR-gamma activation inhibits endothelial inflammation by suppressing inflammatory gene expression and therefore improves endothelial dysfunction. In vascular smooth muscle cells, PPAR-gamma activation inhibits proliferation and migration and promotes apoptosis. In macrophages, PPAR-gamma activation suppresses inflammation by regulating gene expression and increases cholesterol uptake and efflux. A recurring theme in many cell types is the modulation of the innate immunity system particularly through altering the activity of the nuclear factor kappaB. This system is likely to be even more prominent in modulating disease in vascular cells. The effects of PPAR-gamma in the vascular cells translate into the beneficial function of this transcription factor in vascular disorders, including hypertension and atherosclerosis. Both human genetic studies and animal studies using transgenic mice have demonstrated the importance of PPAR-gamma in these disorders. However, recent clinical studies have raised significant concerns about the cardiovascular side effects of thiazolidinediones, particularly rosiglitazone. Weighing the potential benefit and harm of PPAR-gamma activation and exploring the functional mechanisms may provide a balanced view on the clinical use of these compounds and new approaches to the future therapeutics of vascular disorders associated with diabetes.

Peroxisome proliferator-activated receptor-gamma (PPAR-gamma), an essential transcriptional mediator of adipogenesis, lipid metabolism, insulin sensitivity, and glucose homeostasis, is increasingly recognized as a key player in inflammatory cells and in cardiovascular diseases (CVD) such as hypertension, cardiac hypertrophy, congestive heart failure, and atherosclerosis. PPAR-gamma agonists, the thiazolidinediones (TZDs), increase insulin sensitivity, lower blood glucose, decrease circulating free fatty acids and triglycerides, lower blood pressure, reduce inflammatory markers, and reduce atherosclerosis in insulin-resistant patients and animal models. Human genetic studies on PPAR-gamma have revealed that functional changes in this nuclear receptor are associated with CVD. Recent controversial clinical studies raise the question of deleterious action of PPAR-gamma agonists on the cardiovascular system. These complex interactions of metabolic responsive factors and cardiovascular disease promise to be important areas of focus for the future.

Peroxisome proliferator-activated receptor-gamma (PPAR-gamma) agonists, such as the thiazolidinediones (TZDs), decrease acute inflammation in both pancreatic cell lines and mouse models of acute pancreatitis. Since PPAR-gamma agonists have been shown to exert some of their actions independent of PPAR-gamma, the role of PPAR-gamma in pancreatic inflammation has not been directly tested. Furthermore, the differential role of PPAR-gamma in endodermal derivatives (acini, ductal cells, and islets) as opposed to the endothelial or inflammatory cells is unknown. To determine whether the effects of a TZD, rosiglitazone, on caerulein-induced acute pancreatitis are dependent on PPAR-gamma in the endodermal derivatives, we created a cell-type specific knock out of PPAR-gamma in pancreatic acini, ducts, and islets. PPAR-gamma knockout animals show a greater response in some inflammatory genes after caerulein challenge. The anti-inflammatory effect of rosiglitazone on edema, macrophage infiltration, and expression of the proinflammatory cytokines is significantly decreased in pancreata of the knockout animals compared with control animals. However, rosiglitazone retains its effect in the lungs of the pancreatic-specific PPAR-gamma knockout animals, likely due to direct anti-inflammatory effect on lung parenchyma. These data show that the PPAR-gamma in the pancreatic epithelia and islets is important in suppressing inflammation and is required for the anti-inflammatory effects of TZDs in acute pancreatitis.