Serum biomarkers correlated with liver stiffness assessed in a multicenter study of pediatric cholestatic liver disease.

BACKGROUND AND AIMS: Detailed investigation of the biological pathways leading to hepatic fibrosis and identification of liver fibrosis biomarkers may facilitate early interventions for pediatric cholestasis. APPROACH AND RESULTS: A targeted enzyme-linked immunosorbent assay-based panel of nine biomarkers (lysyl oxidase, tissue inhibitor matrix metalloproteinase (MMP) 1, connective tissue growth factor [CTGF], IL-8, endoglin, periostin, Mac-2-binding protein, MMP-3, and MMP-7) was examined in children with biliary atresia (BA; n = 187), alpha-1 antitrypsin deficiency (A1AT; n = 78), and Alagille syndrome (ALGS; n = 65) and correlated with liver stiffness (LSM) and biochemical measures of liver disease. Median age and LSM were 9 years and 9.5 kPa. After adjusting for covariates, there were positive correlations among LSM and endoglin ( p = 0.04) and IL-8 ( p < 0.001) and MMP-7 ( p < 0.001) in participants with BA. The best prediction model for LSM in BA using clinical and lab measurements had an R2 = 0.437; adding IL-8 and MMP-7 improved R2 to 0.523 and 0.526 (both p < 0.0001). In participants with A1AT, CTGF and LSM were negatively correlated ( p = 0.004); adding CTGF to an LSM prediction model improved R2 from 0.524 to 0.577 ( p = 0.0033). Biomarkers did not correlate with LSM in ALGS. A significant number of biomarker/lab correlations were found in participants with BA but not those with A1AT or ALGS. CONCLUSIONS: Endoglin, IL-8, and MMP-7 significantly correlate with increased LSM in children with BA, whereas CTGF inversely correlates with LSM in participants with A1AT; these biomarkers appear to enhance prediction of LSM beyond clinical tests. Future disease-specific investigations of change in these biomarkers over time and as predictors of clinical outcomes will be important.

Gut Microbiome in Obesity, Metabolic Syndrome, and Diabetes.

PURPOSE OF REVIEW: Obesity and diabetes are worldwide epidemics. There is also a growing body of evidence relating the gut microbiome composition to insulin resistance. The purpose of this review is to delineate the studies linking gut microbiota to obesity, metabolic syndrome, and diabetes. RECENT FINDINGS: Animal studies as well as proof of concept studies using fecal transplantation demonstrate the pivotal role of the gut microbiota in regulating insulin resistance states and inflammation. While we still need to standardize methodologies to study the microbiome, there is an abundance of evidence pointing to the link between gut microbiome, inflammation, and insulin resistance, and future studies should be aimed at identifying unifying mechanisms.

(-)-Epicatechin attenuates high-glucose-induced inflammation by epigenetic modulation in human monocytes.

PURPOSE: Diabetes is a pro-inflammatory state associated with increased monocyte activity. NF-κB is the master switch of inflammation and is activated during diabetes. (-)-Epicatechin (EC), the main cocoa flavonol, displays anti-inflammatory and anti-diabetic effects under high glucose conditions. Recently, it has been suggested that dietary polyphenols might modulate chromatin remodelling by epigenetic changes and regulate monocyte NF-κB activation and cytokine expression under diabetic conditions. The aim of the study was to test the potential anti-inflammatory role of EC via inducing posttranslational histone changes in the presence of a high glucose (HG) concentrations. METHODS: Human monocytic cells (THP-1 cells) were pre-treated with EC (5 µM) and 4 h later exposed to 25 mM glucose (HG) for a total of 24 h. Control cells were grown under normoglycemic conditions (NG, 5.5 mM glucose). Acetyl CBP/p300, HDAC4, total histone 3 (HH3), H3K9ac, H3K4me2 and H3K9me2, and phosphorylated and total levels of p65-NF-κB were analysed by Western blot. Histone acetyltransferase (HAT) activity was measured in nuclear lysates, and TNF-α release was evaluated in culture media. RESULTS: EC incubation restored to control levels (NG) the changes induced by HG in p-p65/p65-NF-ĸB ratio, acetyl CBP/p300 values and HAT activity. Moreover, EC pre-treatment counteracted the increased acetylation of H3K9 and H3K4 dimethylation and attenuated the diminished H3K9 dimethylation triggered by HG. EC also significantly decreased HG-enhanced HDAC4 levels and TNF-α release, respectively. CONCLUSIONS: EC induces epigenetic changes and decreased NF-κB and TNF-α levels in human monocytes cultured in HG conditions such as in diabetes.

COUP-TFII regulates tumor growth and metastasis by modulating tumor angiogenesis.

Tumor growth depends on nutrients and oxygen supplied by the vasculature through angiogenesis. Here, we show that the chicken ovalbumin upstream promoter-transcription factor II (COUP-TFII), a member of the nuclear receptor family, is a major angiogenesis regulator within the tumor microenvironment. Conditional ablation of COUP-TFII in adults severely compromised neoangiogenesis and suppressed tumor growth in xenograft mouse models. In addition, tumor growth and tumor metastasis were also impaired in a spontaneous mammary-gland tumor model in the absence of COUP-TFII. We showed that COUP-TFII directly regulates the transcription of Angiopoietin-1 in pericytes to enhance neoangiogenesis. Importantly, provision of Angiopoietin-1 partially restores the angiogenic defects exhibited by the COUP-TFII-deficient mice, which supports the notion that COUP-TFII controls Angiopoietin-1/Tie2 signaling to regulate tumor angiogenesis. Because COUP-TFII has little impact on normal adult physiological function, our results raise an interesting possibility that inhibition of COUP-TFII may offer a therapeutic approach for anticancer intervention.

Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control.

BACKGROUND: The nonstationary property of electromyography (EMG) signals usually makes the pattern recognition (PR) based methods ineffective after some time in practical application for multinational prosthesis. The conventional EMG PR, which is accomplished in two separate steps: training and testing, ignores the mismatch between training and testing conditions and often discards the useful information in testing dataset. METHOD: This paper presents a novel self-enhancing approach to improve the classification performance of the electromyography (EMG) pattern recognition (PR). The proposed self-enhancing method incorporates the knowledge beyond the training condition to the classifiers from the testing data. The widely-used linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are extended to self-enhancing LDA (SELDA) and self-enhancing QDA (SEQDA) by continuously updating their model parameters such as the class mean vectors, the class covariances and the pooled covariance. Autoregressive (AR) and Fourier-derived cepstral (FC) features are adopted. Experimental data in two different protocols are used to evaluate performance of the proposed methods in short-term and long-term application respectively. RESULTS: In protocol of short-term EMG, based on AR and FC, the recognition accuracy of SEQDA and SELDA is 2.2% and 1.6% higher than conventional that of QDA and LDA respectively. The mean results of SEQDA(C) and SEQDA (M) are improved by 2.2% and 0.75% for AR, and 1.99% and 1.1% for FC respectively when compared to QDA. The mean results of SELDA(C) and SELDA (M) are improved by 0.48% and 1.55% for AR, and 0.67% and 1.22% for FC when compared to LDA. In protocol of long-term EMG, the mean result of SEQDA is 3.15% better than that of QDA. CONCLUSION: The experimental results show that the self-enhancing classifiers significantly outperform the original versions using both AR and FC coefficient feature sets. The performance of SEQDA is superior to SELDA. In addition, preliminary study on long-term EMG data is conducted to verify the performance of SEQDA.

Growth and differentiation factor 15 (GDF15) levels predict adverse respiratory outcomes in premature neonates.

Growth and differentiation factor 15 (GDF15) is a stress-responsive cytokine, and its expression increases during inflammation, hyperoxia, and senescence. Significantly, GDF15 is secreted by the placenta, and maternal levels increase throughout pregnancy. Serum GDF15 level is a promising biomarker for many lung diseases like pulmonary hypertension and pulmonary fibrosis. However, circulating GDF15 levels in preterm infants and their role as a predictor of respiratory outcomes have not been studied. We hypothesized that GDF15 levels would increase with gestational age at birth, and that postnatal GDF15 will be correlated with adverse respiratory outcomes in preterm infants. Scavenged blood samples were retrieved from 57 preterm infants at five time points, from birth until 36-weeks postmenstrual age (PMA). GDF15 levels were measured using ELISA in 114 samples. We performed two-sample t-test, correlation and linear regression, logistic regression, and mixed-effects linear models for statistical analysis, and significance was identified when p < 0.05. Contrary to our hypothesis, for every 1-week increase in gestational age at birth, the predicted GDF15 level decreased by 475.0 pg/ml (p < 0.001). Greater PMA was significantly associated with lower serum GDF15 levels (p < 0.001). Interestingly, higher GDF15 levels were associated with a longer need for mechanical ventilation (p = 0.034), prolonged respiratory support need (p < 0.001), and length of hospital stay (p = 0.006). In conclusion, in preterm infants, GDF15 levels show an inverse correlation with gestational age at birth, with higher levels in more preterm babies, and levels trend down postnatally. Furthermore, longitudinal GDF15 levels through 36 weeks PMA predict adverse respiratory outcomes in preterm infants.

Stratification of Pediatric COVID-19 cases by inflammatory biomarker profiling and machine learning.

An objective method to identify imminent or current Multi-Inflammatory Syndrome in Children (MIS-C) infected with SARS-CoV-2 is highly desirable. The aims was to define an algorithmically interpreted novel cytokine/chemokine assay panel providing such an objective classification. This study was conducted on 4 groups of patients seen at multiple sites of Texas Children's Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 66 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January-May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August-October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021-January 2022 infected with delta and/or omicron variants. Group 1 was used to train a L1-regularized logistic regression model which was validated using 5-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the algorithmically interpreted cytokine/chemokine assay panel. Standard laboratory markers predict MIS-C with a 5-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a 5-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC =0.98, F1=0.93, on Group 3 it yielded AUROC=0.89, F1 = 0.89, and on Group 4 AUROC= 0.99, F1= 0.97). Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a sensitive, specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date.

Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning.

While pediatric COVID-19 is rarely severe, a small fraction of children infected with SARS-CoV-2 go on to develop multisystem inflammatory syndrome (MIS-C), with substantial morbidity. An objective method with high specificity and high sensitivity to identify current or imminent MIS-C in children infected with SARS-CoV-2 is highly desirable. The aim was to learn about an interpretable novel cytokine/chemokine assay panel providing such an objective classification. This retrospective study was conducted on four groups of pediatric patients seen at multiple sites of Texas Children's Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 70 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January and May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August and October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021 andJanuary 2022 infected with delta and/or omicron variants. Group 1 was used to train an L1-regularized logistic regression model which was tested using five-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the cytokine/chemokine assay-based classifier. Standard laboratory markers predict MIS-C with a five-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a five-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC = 0.98 and F1 = 0.93, on Group 3 it yielded AUROC = 0.89 and F1 = 0.89, and on Group 4 AUROC = 0.99 and F1 = 0.97. Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a highly sensitive, and specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date.

A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor.

Purpose: To propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis. Methods: We retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC (n = 60), breast cancer (BC, n = 60) and other tumor types (n = 20). All patients underwent contrast-enhanced brain MRI scans. The brain metastasis was subdivided into phenotypically consistent subregions using patient-level and population-level clustering. A residual network with a global average pooling layer (RN-GAP) was proposed to calculate deep learning-based features. Features from each subregion were selected with least absolute shrinkage and selection operator (LASSO) to build logistic regression models (LRs) for predicting primary tumor types (LR-NSCLC for the NSCLC origin and LR-BC for the BC origin), EGFR mutation status (LR-EGFR) and HER2 status (LR-HER2). Results: The brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set. Conclusion: Our proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis.

Effects of Team Size and a Decision Support Tool on Healthcare Providers' Workloads in Simulated Neonatal Resuscitation: A Randomized Trial.

INTRODUCTION: Optimal resuscitation team size for workload distribution among team members is not known. In addition, decision support tools (DSTs) are available to improve team performance, but the effect on workload is not known. Because increased workload can impair performance, we aimed to determine whether team size or use of a DST alters workload in healthcare providers (HCPs) while performing neonatal resuscitation. METHODS: We report a substudy of a randomized, 2 × 2 factorial design study using 109 Neonatal Resuscitation Program-trained HCPs. Healthcare providers were randomized to 1 of 4 permutations, including team size of 2 versus 3 and using DST versus memory alone while performing 2 simulated neonatal resuscitations. The HCPs' workload was assessed by the National Aeronautics and Space Administration Task Load Index obtained after each scenario. Mixed effects linear models compared the effect of team size and DST use on National Aeronautics and Space Administration Task Load Index scores. RESULTS: When all team members were combined, there was an increased workload in teams of 2 HCPs compared with teams of 3 and was primarily due to an increase in workload on the team leaders. Decision support tool use increased workload for the other team members in the first of the 2 scenarios but did not increase workload in the second scenario. CONCLUSIONS: Teams of 2 HCPs reported a higher workload compared with teams of 3 HCPs. Decision support tool use can increase workload for other team members when first introduced as a new task. This study highlights the need to consider factors that negatively affect mental workload when determining the composition of a resuscitation team.

Challenges in Using the Posterior Inferior Cerebellar Artery for Revascularization of the Anterior Inferior Cerebellar Artery: A Microsurgical Anatomic Study.

BACKGROUND: A bypass is usually required to prevent ischemia during the treatment of anterior inferior cerebellar artery (AICA) aneurysms. The intracranial (IC)-to-IC bypass provides several advantages over the extracranial-to-IC bypass in the posterior fossa. However, there are only 2 case reports about AICA revascularization with the posterior inferior cerebellar artery (PICA). We aimed to investigate the microsurgical anatomical challenges for PICA to AICA anastomosis. METHODS: Ten cadaveric heads injected with colored silicone were inspected on both sides using a lateral transcondylar approach. After the donor and recipient arteries were examined from the posterior side, neurovascular contents of the posterior fossa were excised and the origin, course, and variations of both arteries were investigated from the anterior view. The diameters of the AICA and PICA segments and the intersegment distance were measured. RESULTS: PICA variations and posteromedial origins from the vertebral artery were identified in 8 of the 20 right and 6 of the 20 left sides, and the first segment of the PICA was not present in 7 sides. Furthermore, in 18 sides, the PICA was trapped between the lower cranial nerves and dentate ligaments. Therefore the donor artery could not be brought closer than 1 cm to the recipient artery in 19 sides. Moreover, AICA variations were identified in 6 sides, and in 12 sides, the diameter of the recipient artery was <1 mm. CONCLUSIONS: The mostly PICA-related issues made PICA-to-AICA anastomosis unfeasible in all cadaveric heads included in the study.

Misidentification of unstable, low oxygen affinity hemoglobin variant.

BACKGROUND: We present a case of Hemoglobin (Hgb) Louisville in a 6-y old girl presenting with hypoxia and mild hemolytic anemia. METHODS: Hgb Louisville is a rare, unstable hemoglobin variant with low oxygen affinity, but initial workup of this case using widely-available hemoglobin fractionation methods was unable to differentiate this variant from normal hemoglobins. RESULTS: This resulted in a delayed diagnosis. CONCLUSION: This case highlights the limitations of routine hemoglobin fractionation methods for correct diagnosis of hemoglobin variants, and subsequent impact on patient care and management.

From Ibni Sina (Avicenna) to Present, History of International Fellowship and Observership: University of Wisconsin-Madison Experience.

Since the inception of the International Fellowship (IF) Program in the Department of Neurological Surgery at the University of Wisconsin-Madison in 2006, training has been provided to 219 residents, neurosurgeons, and medical students from 18 countries and five continents. These IFs took a long academic and geographic journey to improve their skills in patient care. The advanced training, they received lead to 14 of these IF neurosurgeons to return to their hometowns with higher academic appointments, including two chairmen, seven professors, two associate professors, two assistant professors, and one consultant neurosurgeon. An additional measure of success for the IF Program is that fellows continue to communicate with their mentors and with their prior fellow international colleagues long after their fellowship ends.

Gut Microbiome and Inflammation: A Study of Diabetic Inflammasome-Knockout Mice.

AIMS: Diabetes is a proinflammatory state, evidenced by increased pattern recognition receptors and the inflammasome (NOD-like receptor family pyrin domain (NLRP)) complex. Recent reports have elucidated the role of the gut microbiome in diabetes, but there is limited data on the gut microbiome in NLRP-KO mice and its effect on diabetes-induced inflammation. METHODS: Gut microbiome composition and biomarkers of inflammation (IL-18, serum amyloid A) were assessed in streptozotocin- (STZ-) induced diabetic mice on a NLRP3-knockout (KO) background versus wild-type diabetic mice. RESULTS: SAA and IL-18 levels were significantly elevated in diabetic mice (STZ) compared to control (WT) mice, and there was a significant attenuation of inflammation in diabetic NLRP3-KO mice (NLRP3-KO STZ) compared to control mice (p < 0.005). Principal coordinate analysis clearly separated controls, STZ, and NLRP3-KO STZ mice. Among the different phyla, there was a significant increase in the Firmicutes : Bacteroidetes ratio in the diabetic group compared to controls. When compared to the WT STZ group, the NLRP3-KO STZ group showed a significant decrease in the Firmicutes : Bacteroidetes ratio. Together, these findings indicate that interaction of the intestinal microbes with the innate immune system is a crucial factor that could modify diabetes and complications.

Monocytes from metabolic syndrome subjects exhibit a proinflammatory M1 phenotype.

BACKGROUND: The metabolic syndrome is a highly prevalent state affecting one in three US adults and comprises a cluster of cardiometabolic risk factors. While metabolic syndrome is a proinflammatory state, there is a paucity of studies examining monocyte/macrophage phenotype in metabolic syndrome subjects. This was the aim of this study. METHODS: Following informed consent, metabolic syndrome and age- and gender-matched healthy subjects (n=15/group) were recruited, and monocytes were obtained for phenotypic characterization of the classical M1 phenotype and alternative M2 phenotype. Biomarkers of inflammation, C-reactive protein (CRP), and proinflammatory cytokines were examined. RESULTS: Metabolic syndrome subjects had significantly higher waist circumference (WC), significantly increased systolic blood pressure, higher fasting glucose, triglycerides, and free fatty acids levels, and lower high-density lipoprotein cholesterol (HDL-C) levels compared to matched controls. Also, CRP and endotoxin levels were significantly elevated in metabolic syndrome compared to controls. Metabolic syndrome subjects had significantly higher levels of the M1 phenotype and significantly decreased levels of the M2 phenotype compared to controls, even after adjusting for WC. Among the other biomarkers of inflammation, there were significant increases in the proinflammatory cytokines and chemokines interleukin-1ß (IL-1ß), IL-6, and monocyte chemoattractant protein-1 (MCP-1) and decreased IL-10 in metabolic syndrome compared to controls. The M1 phenotype was significantly correlated to levels of CRP, endotoxin, MCP-1, and WC and negatively with HDL-C. CONCLUSIONS: Monocytes from metabolic syndrome subjects display a proinflammatory M1 phenotype that could promote the increased cardiometabolic burden in these subjects.

Modulation of endothelial progenitor cell number and function with n-3 polyunsaturated fatty acids.

Endothelial dysfunction is pivotal in atherosclerosis. Endothelial progenitor cells (EPC) predict cardiovascular events and could serve as a cellular biomarker of endothelial function. Epidemiological studies suggest the benefits of omega 3 polyunsaturated fatty acids (n-3 PUFA), mainly docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) on cardiovascular health. However, there is a paucity of data on the effect of n-3 PUFA on EPC number and functionality. Incubation with DHA and EPA, either alone or in combination significantly increased the number of EPCs and colony forming units (CFU). In addition, co-incubation with DHA + EPA, significantly enhanced EPC migratory capacity, adhesive properties and greater incorporation into tubules. Thus, EPA + DHA are effective in improving EPC number and functionality in-vitro. Future studies will test the effect of n-3 PUFA supplementation on EPC number and function in-vivo and will elucidate plausible mechanisms.

Increased expression of Fc-γ receptors on monocytes in patients with nascent metabolic syndrome.

CONTEXT: The metabolic syndrome (MetS) confers an increased propensity for diabetes and cardiovascular disease. C-reactive protein (CRP) levels are increased in MetS and predict cardiovascular events. Fc-γ receptors (FcgRs) are immune receptors on macrophages and include FcgRI (CD64), FcgRII (CD32), and FcgRIII (CD16). OBJECTIVE: CRP has been shown to be processed by CD32 and CD64 in human monocytes. Although MetS is characterized by increased CRP and monocyte proinflammatory activity, data on FcgR expression on monocytes in patients with MetS compared with those of control subjects are sparse. Thus, the aim of the study was to examine this expression and the correlation with CRP. DESIGN AND SETTING: After informed consent was given, patients with MetS (n = 50) and control subjects (n = 40) were studied. CD32 and CD64 expression on monocytes was examined by both flow cytometry and Western blotting. Immunoprecipitation studies were performed to examine spleen tyrosine kinase (Syk) activity as a measure of CD32 and CD64 activity. RESULTS: Surface expression of CD32 and CD64 was significantly increased in patients with MetS compared with that in control subjects even after adjustment for body mass index and waist circumference. Expression of these FcgRs increased significantly with the number of features of MetS (Ptrend < .001) and correlated with levels of high-sensitivity CRP (P < .01). CRP after engagement of CD32 and CD64 signals via Syk. Syk phosphorylation was increased in monocytes of patients with MetS compared with that in control subjects. CONCLUSIONS: We provide novel evidence of increased FcgR expression and activity in monocytes of patients with MetS that correlates with the number of features of MetS and with high-sensitivity CRP.

Increased cellular and circulating biomarkers of oxidative stress in nascent metabolic syndrome.

CONTEXT: Metabolic syndrome (MetS) confers a greater risk for both cardiovascular disease (CVD) and diabetes. Oxidative stress (OS) could contribute to this excess risk. However, there are few data examining both cellular and circulating biomarkers of OS in MetS uncomplicated by diabetes and CVD. OBJECTIVE: The aim of the study was to evaluate both cellular and circulating biomarkers of OS in MetS uncomplicated by diabetes or CVD. DESIGN AND SETTING: At an academic medical center, we compared MetS (n=43) vs. control subjects (n=33). Fasting blood was collected for monocyte isolation and assay of OS biomarkers. MAIN OUTCOME: Monocyte nicotinamide adenine dinucleotide phosphate oxidase activity (p22 phox and p47), superoxide anion release, oxidized-low-density lipoprotein (Ox-LDL), nitrotyrosine, and nuclear factor erythroid 2-related factor were measured. RESULTS: There was significantly increased release of superoxide from the monocytes (basal and after activation) of MetS compared with controls adjusted for body mass index. Body mass index-adjusted plasma levels of Ox-LDL and nitrotyrosine were significantly increased in MetS. There was a linear trend between biomarkers of oxidative stress and increasing number of features of MetS. Also, there was a significant increase in nicotinamide adenine dinucleotide phosphate oxidase membrane expression of p22 phox and p47 phox in MetS. The major cellular antioxidant defense, nuclear factor erythroid 2-related factor was significantly decreased. There were significant correlations between homeostasis model assessment insulin resistance index and both Ox-LDL and nitrotyrosine and superoxide and Ox-LDL. CONCLUSIONS: Collectively, nascent MetS is associated with increased OS as evidenced by both circulating and cellular biomarkers, and this could contribute to the risk for both diabetes and CVD.