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Hyperglycemia and inflammation

Hyperglycemia and inflammation

J Hepatol. Hypergoycemia Hyperglycemia and inflammation begins with the formation of the autophagosome, a Infllammation structure that surrounds the cytoplasmic cargo macromolecules and organelles and then fuses with the lysosome, resulting in the degradation of the cargo 8. Intern Med. Hyperglycemia and inflammation

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Davies, J. Inflammatory response after total pancreatectomy and islet autotransplantation. Transplantation proceedings 30 , Download references. GS is supported by the CAPES Brazilian Foundation PDSE - LU is supported by the NIH RGM, Eastern Scholarship JZ, and NSFC Department of Surgery, Rutgers-New Jersey Medical School, Newark, NJ, , USA.

Department of Physiology, Medical School - University of São Paulo, Ribeirão Preto, SP, , Brazil. Center for Immunity and Inflammation, Rutgers-New Jersey Medical School, Newark, NJ, , USA. You can also search for this author in PubMed Google Scholar. and G. performed animal experiments, figures preparation, and article editing.

and M. performed diabetes animal experiments. performed cytokine analyses. and H. contributed to glucose vagal electrical recording, animal experiment interpretation, and article editing. directed the study and wrote the article.

Correspondence to Luis Ulloa. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Joseph, B. Glucose Activates Vagal Control of Hyperglycemia and Inflammation in Fasted Mice.

Sci Rep 9 , Download citation. Received : 22 August Accepted : 14 November Published : 30 January Anyone you share the following link with will be able to read this content:.

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nature scientific reports articles article. Download PDF. Subjects Experimental models of disease Sepsis. Abstract Sepsis is a leading cause of death in hospitalized patients.

Introduction Sepsis represents a leading cause of death in the ICU of modern hospitals killing around , Americans per year 1 , 2 , 3. Results Metabolic fasting increased systemic inflammation and worsened survival in endotoxemia Epidemiological studies show an association between metabolic and immune alterations, but the mechanism linking these systems is unknown.

Figure 1. Full size image. Figure 2. Figure 3. Figure 4. Discussion Our results indicate that metabolic fasting affects both hyperglycemic and systemic inflammatory responses to bacterial endotoxin by modulating vagal neuromodulation.

Material and Methods Chemicals and Reagents LPS E. Statistical Analyses Tests were performed with GraphPad Prism Software® GraphPad Software, La Jolla, CA. Availability of Materials and Data The authors will make materials, data and associated protocols promptly available to readers. References Angus, D.

Article CAS Google Scholar Hotchkiss, R. Article CAS Google Scholar Ulloa, L. Article CAS Google Scholar Rice, T. Article CAS Google Scholar Martin, G.

Article Google Scholar Tracey, K. Article CAS ADS Google Scholar Marshall, J. Article Google Scholar Carré, J. Article Google Scholar Eskandari, M. CAS PubMed Google Scholar Brealey, D. Article CAS Google Scholar Wolfe, R. CAS PubMed Google Scholar Machado, F. Article Google Scholar Cerra, F.

Article CAS Google Scholar van Vught, L. Article Google Scholar Finfer, S. Article Google Scholar Novosad, S. Article Google Scholar Kosteli, A.

Article CAS Google Scholar Weisberg, S. Article CAS Google Scholar Arkan, M. Article CAS Google Scholar Solinas, G. Article CAS Google Scholar Buras, J.

Article CAS Google Scholar Young, H. Article CAS ADS Google Scholar Vida, G. Article CAS Google Scholar Wang, H. Article CAS ADS Google Scholar Huston, J. Article CAS Google Scholar Borovikova, L. Article CAS ADS Google Scholar Ulloa, L. Article Google Scholar Kanashiro, A.

Article Google Scholar Ahren, B. Data collected were medical history, including DM type type 1 or type 2 ; demographics; laboratory tests; medications; clinical characteristics; inpatient medical therapy; hospitalization course; and outcomes.

Estimated glomerular filtration rate eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation.

The Michigan Medicine COVID Cohort M 2 C 2 is the largest ISIC subcohort. In addition to the variables collected for ISIC, serial laboratory measurements, frequently monitored blood glucose levels, and daily insulin dose administered throughout hospitalization were collected for M 2 C 2 as part of a standardized inpatient management protocol for hyperglycemia Blood samples were collected and analyzed for several inflammatory biomarkers, including soluble urokinase plasminogen activator receptor suPAR , interleukin-6 IL-6 , C-reactive protein CRP , D-dimer, ferritin, lactate dehydrogenase, and procalcitonin levels within 48 h of admission.

CRP, ferritin, D-dimer, lactate dehydrogenase, and procalcitonin levels were measured by the central laboratory at the respective institution of enrollment at the request of the clinical team. Samples underwent up to two thaw cycles.

Technicians performing assays were blinded to clinical data. The primary outcome was the composite end point of in-hospital death, need for mechanical ventilation, and need for renal replacement therapy. Secondary outcomes included each individual component of the primary outcome. We report clinical characteristics for the overall cohort stratified by DM history using categorical variables expressed as a number and percentage and continuous variables expressed as mean SD and median 25th—75th interquartile range for normally and nonnormally distributed continuous data, respectively.

The characteristics between individuals with and without DM were compared using χ 2 tests for categorical variables and unpaired t tests or Mann-Whitney U tests for normal and nonnormal continuous variables, respectively.

The incidences of the individual outcomes in-hospital death, need for mechanical ventilation, and need for renal replacement therapy were compared between individuals with and without DM at admission using χ 2 tests. To determine whether DM history was independently associated with higher thromboinflammation marker levels, separate linear regression models were created, with each biomarker as the dependent variable and DM, age, sex, BMI, race, hypertension, coronary artery disease, and heart failure as independent variables.

Standardization was performed to compare the effect sizes for DM across each biomarker. We examined the association between DM and the composite outcome using stepwise logistic regression models. Model 0 was unadjusted. Model 1 included age, sex, and race.

Model 2 included the variables in model 1 as well as clinical characteristics, including BMI, history of hypertension, coronary artery disease, congestive heart failure, and admission eGFR.

We repeated this analysis to explore the association between DM and each outcome individually. We then performed mediation analysis to assess whether the effect of DM on the composite outcome is mediated by suPAR, after adjusting for the clinical variables in model 3 age, sex, race, BMI, and history of hypertension, coronary artery disease, congestive heart failure, and admission eGFR We first examined the association of each clinical characteristic and the composite outcome in univariable analysis.

Biomarkers of inflammation log 2 transformed were each examined in the multivariable risk model separately. We also explored risk factors for each individual outcome using the same multivariable risk model.

Finally, we calculated the relative importance of clinical characteristics, biomarkers of inflammation, and glucose levels for predicting the composite outcome on the basis of the Gini index using a random forest approach The coefficient of variation is expressed as the SD divided by the mean of all glucose measurements during hospitalization.

The average amount of insulin administered was calculated as the total insulin dose units divided by patient weight kilograms multiplied by the total number of in-hospital days. We used Spearman rank correlation to examine the correlation between each biomarker of inflammation with glucose coefficient of variation and the average insulin dose received during hospitalization.

To assess the association between each exposure and the composite outcome, we used multivariable regression models.

For glucose variables, the coefficients are expressed as a unit difference, whereas insulin dose is expressed as a difference in 0. Models were adjusted for age, sex, race, BMI, and history of hypertension, coronary artery disease, and congestive heart failure.

Separate models were additionally adjusted for suPAR within 48 h of admission and corticosteroid use. We performed a complete case analysis for multivariable models. There were no missing data for any demographic or clinical characteristic.

All analyses were performed using R 4. Compared with individuals without DM, those with DM were older mean age 64 vs. On hospital admission, individuals with DM were less likely to present with fever COPD, chronic obstructive pulmonary disease; FEU, fibrinogen-equivalent units; IQR, interquartile range.

HbA 1c measured within 1 year of hospital admission was available in individuals without DM and with DM. In unadjusted analyses, the levels of several inflammatory biomarkers, including suPAR, CRP, procalcitonin, and D-dimer, were higher on admission in individuals with DM than in those without DM Table 1.

On average, participants with DM had Overall, the primary composite outcome was observed in There was a total of In unadjusted analyses, individuals with DM had a significantly higher cumulative incidence of the primary composite outcome In multivariable analyses, adjusting for demographics model 1 and clinical characteristics model 2 heavily attenuated the association between DM and the primary outcome adjusted odds ratio [aOR] 1.

When these outcomes were examined individually, a similar pattern was seen Fig. Risk of in-hospital outcomes in individuals with COVID and with and without DM. Four different models were used: model 0 unadjusted ; model 1 demographics adjusted for age, sex, and race; model 2 clinical characteristics additionally adjusted for BMI and history of hypertension, coronary artery disease, and congestive heart failure clinical characteristics ; and model 3 inflammation further adjusted for suPAR level.

The proportion of the effect of DM on the primary outcome mediated by suPAR was We found similar associations when examining outcomes individually, with a few notable exceptions Supplementary Table 3. Older age was strongly associated with in-hospital death aOR 1. Type 1 DM, prior insulin use, and medications for hyperglycemia were not associated with an increased odds in the primary outcome.

Levels of all inflammatory biomarkers were associated with an increased odds of the primary outcome when examined separately in a multivariable model adjusted for demographic and clinical risk factors Supplementary Table 2.

We identified suPAR level as the most important variable associated with the primary outcome in individuals with DM and COVID, followed by BMI, admission glucose, and age in descending order of importance Fig. Variable importance plot to predict composite outcome in individuals with DM and COVID The variable importance plot is based on the Gini index using a random forest approach.

Shown are data from model 3 adjusted for age, sex, race, BMI, admission suPAR, and history of preexisting coronary artery disease, hypertension, and heart failure for predicting the composite outcome of in-hospital death, need for mechanical ventilation, and need for renal replacement therapy.

We also examined whether glucose ranges, glucose variation, and insulin requirements were associated with the primary outcome. The glucose coefficient of variation in individuals with DM was The glucose coefficient of variation, a greater percentage of glucose values outside the target range, a greater percentage of high glucose values, and a higher required insulin dose were all associated with a greater odds of the primary outcome in individuals with DM Fig.

Per every 0. Including suPAR or corticosteroid use in the models did not affect estimates significantly Supplementary Table 5. Associations among glucose, insulin, and combined outcome in individuals with DM in the M 2 C 2 subset.

All ORs are compared using the following reference categories for each variable: 0—1. The glucose coefficient of variation is calculated as the SD divided by the mean of all glucose measurements taken during hospitalization and then multiplied by Percent in glucose range and high glucose are expressed as the percentage of all glucose measurements within each category during hospitalization.

In this in-depth examination of the interplay among DM, inflammation, hyperglycemia, and outcomes in individuals hospitalized for COVID, we found that the impact of DM on outcomes is tightly linked to heightened inflammation.

First, individuals with DM had a greater incidence of in-hospital outcomes and higher levels of inflammatory markers notably suPAR compared with those without DM. The association between DM and outcomes was abrogated, however, by including suPAR in the model, with mediation analysis suggesting that the effect of DM on outcomes is largely mediated by suPAR.

Among individuals with DM, suPAR, BMI, admission glucose levels, and age were the most important risk factors in that order. The correlation between inflammatory markers and hyperglycemia was modest at best, while hyperglycemia and higher insulin requirements during hospitalization were associated with worse outcomes.

This association was not attenuated after adjusting for suPAR, implying that hyperglycemia affects COVID—related outcomes through noninflammatory processes.

DM is a well-established risk factor for COVID 2 , 17 ; however, the underlying mechanisms are unclear. In susceptible individuals, SARS-CoV-2 infection is thought to trigger a prolonged hyperinflammatory response, dubbed the cytokine storm 4 , 18 — DM, as a chronic inflammatory condition, may predispose individuals to a heightened inflammatory response 23 , Mitochondrial disruption, rather than changes to glucose metabolism, has been found to lead to altered T-cell cytokine production notably by T-helper 17 cells in type 2 DM Consistently, we found that individuals with DM had higher levels of inflammatory biomarkers, including suPAR, CRP, procalcitonin, and D-dimer.

After adjusting for comorbidities, we noted a singular association between DM and suPAR, suggesting that suPAR represents the inflammatory biomarker most reflective of the hyperinflammatory state in DM and COVID Our mediation analysis supports this finding in that we found that suPAR levels accounted for Conversely, another study found that CRP accounted for only SuPAR is an immune-derived signaling glycoprotein, which is notorious for its role in kidney disease 25 — 27 , cardiovascular disease 28 — 30 , and most recently, COVID 13 , Blood suPAR levels are notably high in individuals with type 1 or type 2 DM, even in the nonacute setting, and are strongly predictive of DM-related outcomes, such as nephropathy and atherosclerotic events 28 , 32 , Several studies have identified a correlation between T-helper 17 cells and suPAR levels 34 , 35 , which may explain the predilection for individuals with DM to have higher suPAR levels 23 , SuPAR differs from other biomarkers of inflammation in that it is not an acute-phase reactant: Levels remain stable in highly proinflammatory situations, such as acute myocardial infarction or cardiac surgery An increased suPAR level, however, is triggered by specific stimuli, such as smoking and RNA viruses e.

Accordingly, individuals with DM and COVID have four- to eightfold higher suPAR levels median 8. Overall, these findings suggest that suPAR levels may reflect more specifically the burden of inflammation in COVID compared with other biomarkers.

Hyperglycemia has traditionally been thought to be a major driver of inflammation through several mechanisms, including increased oxidative stress 8. In our study, hyperglycemia and higher insulin requirements are independently associated with in-hospital outcomes in individuals with DM and COVID, consistent with earlier studies 2 , Surprisingly, we found only a weak correlation between suPAR or other inflammatory biomarkers with hyperglycemia, and the association between hyperglycemia and outcomes was not mitigated by adjusting for suPAR.

The association between hyperglycemia and COVID—related outcomes likely occurs through mechanisms not reflected by inflammatory biomarkers. This is consistent with a study showing that nonmitochondrial glycolysis did not affect the inflammatory signature in type 2 DM Whether aggressive glucose control would improve COVID—related outcomes remains to be shown in a clinical trial setting This study has several important strengths.

It is the largest study to investigate the role of inflammatory biomarkers in individuals with DM hospitalized for COVID In addition, in contrast with other studies, it includes a diverse cohort of individuals specifically hospitalized for COVID rather than defined by SARS-CoV-2 positivity alone.

Blood samples were collected on admission, without being confounded by anti-inflammatory therapies, and thus, reflect more accurately the inflammatory state.

The clinical data were collected through careful and adjudicated review of individual medical records rather than through administrative data sets. The study benefited from standardized glucose and insulin data collected continuously throughout the hospitalization through the Michigan Medicine hyperglycemia management protocol.

This study also had some limitations. Given the small number of patients with type 1 DM in this cohort, the findings cannot be extended to these individuals. The diagnosis of DM was based on medical chart review and available HbA 1c levels at the time of admission; thus, it is possible that some individuals classified as not having DM could have had undiagnosed DM.

Finally, mechanistic studies are warranted to validate the inferences based on the epidemiologic observations noted in our study. In summary, these data show that COVID—related in-hospital outcomes in individuals with DM are driven by a hyperinflammatory state reflected best by suPAR levels.

SuPAR levels were the most important predictor of outcomes in individuals with DM, followed by obesity, hyperglycemia, and age. Hyperglycemia and higher insulin requirements correlated weakly with inflammatory biomarkers and were associated with outcomes independently of suPAR, suggesting that they likely impact outcomes through other mechanisms.

Further study is needed to determine whether suPAR and hyperglycemia are therapeutic targets for the management of COVID in individuals with DM.

Clinical trial reg. NCT , clinicaltrials. is supported by a National Heart, Lung, and Blood Institute—funded postdoctoral fellowship T32HL is funded by National Heart, Lung, and Blood Institute grant 1R01HL, National Institute of Diabetes and Digestive and Kidney Diseases NIDDK grants 1R01DKA1 and U01DKS1, and the Frankel Cardiovascular Center COVID Impact Research Ignitor award U-M G is supported by NIDDK grants 1R01DK and U01DK, JDRF Australia grant 5-COES-B, and Michigan Diabetes Research Center pilot and feasibility NIDDK grant PDK is supported by the Hellenic Institute for the Study of Sepsis.

is supported through intramural funds from Charité Universitätsmedizin Berlin and the Berlin Institute of Health. The funders had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; and preparation, review, or decision to publish the manuscript.

Duality of Interest. and S. are members of the scientific advisory board of Walden Biosciences. is a cofounder, shareholder, and chief scientific officer of ViroGates and a named inventor on patents related to suPAR. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. wrote the first draft. performed the statistical analyses. collected the data and performed quality control. provided expert interpretation of the findings.

All authors reviewed the initial draft and provided critical revisions and approved the final version of the manuscript. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Data Sharing. Study protocol, statistical code, and data set summary data are available upon request after publication through a collaborative process.

Data sets can be accessed upon approval of a submitted research proposal. Please contact penegonz med. edu for additional information. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest.

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Metabolic Health. Ultimate Guide. Hyperglycemia and inflammation helps heal your body, but chronic inflammation can cause Hyperlycemia damage. Nad Hyperglycemia and inflammation, Infkammation. You can inflammatlon an Hyperglycemia and inflammation response to Satisfy your thirst cravings injury like a cut or a splinterto an infection from bacteria or a virus, or from other exposures that the body may see as a threat, such as stressdietary sugarand environmental toxins. Inflammation is usually divided into two types: acute and chronic. Acute is the type that most people think of when they hear inflammation. Low GI gluten-free options VasbinderElizabeth AndersonHyperglycemia and inflammation ShadidHanna InflammatoonMichael PanTariq U. Hyperglycemia and inflammationAthanasios Inflaammation Hyperglycemia and inflammation, Frank TackeEvangelos J. Giamarellos-BourboulisJochen ReiserJesper Eugen-OlsenEva L. FeldmanRodica Pop-BusuiSalim S. Hayek; on behalf of the ISIC Study Group, Inflammation, Hyperglycemia, and Adverse Outcomes in Individuals With Diabetes Mellitus Hospitalized for COVID

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