Clinical Medicine
Abstract
Background: The complement system plays a key role in host defense but is activated by ischemia-reperfusion injury (IRI). Primary graft dysfunction (PGD) is a form of acute lung injury occurring predominantly due to IRI, which worsens survival after lung transplantation (LTx). Local complement activation is associated with acute lung injury, but whether it is more reflective of allograft injury compared to systemic activation remains unclear. We proposed that local complement activation would help identify those who develop PGD post-LTx. We also aimed to identify which complement activation pathways are associated with PGD. Methods: We performed a multicenter cohort study at the University of Pennsylvania and Washington University. Bronchoalveolar lavage (BAL) and plasma specimens were obtained from recipients within 24 h post-LTx. PGD was scored based on the consensus definition. Complement activation products and components of each arm of the complement cascade were measured using ELISA. Results: In both cohorts, sC4d and sC5b-9 levels were increased in BAL of subjects with PGD compared to those without PGD. Subjects with PGD also had higher C1q, C2, C4, and C4b, compared to subjects without PGD, suggesting classical and lectin pathway involvement. Ba levels were higher in subjects with PGD, suggesting alternative pathway activation. Among lectin pathway-specific components, MBL and FCN-3 had a moderate-to-strong correlation with the terminal complement complex in the BAL but not in the plasma. Conclusion: Complement activation fragments are detected in the BAL within 24 h post-LTx. Components of all three pathways are locally increased in subjects with PGD. Our findings create a precedent for investigating complement-targeted therapeutics to mitigate PGD. Funding: This research was supported by the National Institutes of Health (NIH), American Lung Association, Children’s Discovery Institute, the Robert Wood Johnson Foundation, the Cystic Fibrosis Foundation, the Barnes-Jewish Hospital Foundation, The Danish Hearth Foundation], The Danish Research Foundation of Independent Research, The Svend Andersen Research Foundation and the Novo Nordisk Research Foundation.
Authors
Hrishikesh S. Kulkarni, Kristy Ramphal, Lina Ma, Melanie Brown, Michelle L. Oyster, Kaitlyn Speckhart, Tsuyoshi Takahashi, Derek E. Byers, Mary K. Porteous, Laurel Kalman, Ramsey R. Hachem, Melanie Rushefski, Ja'Nia McPhatter, Marlene Cano, Daniel Kreisel, Masina Scavuzzo, Brigitte Mittler, Edward Cantu, Katrine Pilely, Peter Garred, Jason D. Christie, John Atkinson, Andrew E. Gelman, Joshua M. Diamond
Abstract
Background: Elevated levels of inflammatory cytokines have been associated with poor outcomes among COVID-19 patients. It is unknown, however, how these levels compare to those observed in critically ill patients with ARDS or sepsis due to other causes. Methods: We used a luminex assay to determine expression of 76 cytokines from plasma of hospitalized COVID-19 patients and banked plasma samples from ARDS and sepsis patients. Our analysis focused on detecting statistical differences in levels of 6 cytokines associated with cytokine storm (IL-1b, IL-1RA, IL-6, IL-8, IL-18, and TNFα) between patients with moderate COVID-19, severe COVID-19, and ARDS or sepsis. Results: 15 hospitalized COVID-19 patients, 9 of whom were critically ill, were compared to critically ill patients with ARDS (n = 12) or sepsis (n = 16). There were no statistically significant differences in baseline levels of IL-1b, IL-1RA, IL-6, IL-8, IL-18, and TNFα between patients with COVID-19 and critically ill controls with ARDS or sepsis. Conclusions: Levels of inflammatory cytokines were not higher in severe COVID-19 patients than in moderate COVID-19 or critically ill patients with ARDS or sepsis in this small cohort. Broad use of immunosuppressive therapies in ARDS has failed in numerous Phase 3 studies; use of these therapies in unselected patients with COVID-19 may be unwarranted. Funding: A.J.R.: Stanford ICU Biobank NHLBI K23 HL125663. C.A.B.: Burroughs Wellcome Fund Investigators in the Pathogenesis of Infectious Diseases #1016687; NIH/NIAID U19AI057229-16 (PI MM Davis); Stanford Maternal Child Health Research Institute; Chan Zuckerberg Biohub.
Authors
Jennifer G. Wilson, Laura J. Simpson, Anne-Maud Ferreira, Arjun Rustagi, Jonasel A. Roque, Adijat Asuni, Thanmayi Ranganath, Philip M. Grant, Aruna K. Subramanian, Yael Rosenberg-Hasson, Holden Maecker, Susan Holmes, Joseph E. Levitt, Catherine Blish, Angela J. Rogers
Abstract
BACKGROUND. Metabolically healthy obesity (MHO) and metabolically healthy overweight (MH-OW) have been suggested to be an important and emerging phenotype with an increased risk of cardiovascular disease (CVD). However, whether MHO and MH-OW are associated with all-cause mortality remains inconsistent. METHODS. The association of MHO and MH-OW and all-cause mortality was determined in China community-based prospective cohort study (Kailuan Study) including 93,272 adults at baseline. Data were analyzed from 2006 to 2017. Participants were categorized into six mutually exclusive groups according to the body mass index (BMI) and metabolic syndrome (MetS) status. The primary outcome is all-cause death, whereas accidental deaths were excluded. RESULTS. During a median follow-up of 11.04 years (interquartile range: 10.74-11.22 years), 8,977 deaths occurred. Compared to healthy participants with normal BMI (MH-NW), MH-OW had lowest risk of all-cause mortality (multivariate-adjusted hazard ratio [aHR]: 0.926; 95% confidence interval [CI]: 0.861 to 0.997), whereas there was no increased or decreased risk for MHO (aHR: 1.009; 95% CI: 0.886 to 1.148). Stratified analyses and sensitivity analyses further validated that nonsignificant association between MHO and all-cause mortality. CONCLUSIONS. Overweight and obesity do not predicate increased risk of all-cause mortality in metabolic healthy Chinese individuals.
Authors
Qiuyue Tian, Anxin Wang, Yingting Zuo, Shuohua Chen, Haifeng Hou, Wei Wang, Shouling Wu, Youxin Wang
Abstract
Background: Our objective is to investigate whether primary Sjogren’s syndrome (pSS) is associated with multiple system atrophy (MSA). Methods: We performed a retrospective cohort study assessing rates of (a) MSA in a cohort of patients with pSS, and (b) and rates of pSS in a cohort of patients with MSA. These data were, compared to rates in respective control groups. We additionally reviewed the neuropathologic findings in two patients with pSS, cerebellar degeneration, parkinsonism, and autonomic dysfunction. Results: Our cohort of 308 pSS patients had a greater incidence of MSA compared with four large population-based studies and had had a significantly higher prevalence of at least probable MSA (1% vs. 0%, p = 0.02) compared to 776 patients in a control cohort of patients with other autoimmune disorders. Our cohort of 26 autopsy-proven MSA patients had a significantly higher prevalence of pSS compared with a cohort of 115 patients with other autopsy-proven neurodegenerative disorders (8% vs. 0%, p = 0.03). The two patients we described with pSS and progressive neurodegenerative disease showed classic MSA pathology at autopsy. Conclusion: Our findings provide evidence for an association between MSA and pSS that is specific to both pSS, among autoimmune disorders, and MSA, among neurodegenerative disorders. The two cases we describe of autopsy-proven MSA support that MSA pathology can explains neurologic disease in a subset of pSS patients. These findings together support the hypothesis that systemic autoimmune disease plays role in neurodegeneration. Study funding: The Michigan Brain Bank is supported in part through an NIH grant P30AG053760.
Authors
Kyle S. Conway, Sandra Camelo-Piragua, Amanda O. Fisher-Hubbard, William Perry, Vikram G. Shakkottai, Sriram Venneti
Abstract
Background: A treatment option for ADPKD has highlighted the need to identify rapidly progressive patients. Kidney size/age and genotype have predictive power for renal outcomes, but their relative and additive value, plus associated trajectories of disease progression, are not well defined. Methods: The value of genotypic and/or kidney imaging data (Mayo Imaging Class) to predict the time to functional (end stage kidney disease; ESKD, or decline in estimated glomerular filtration rate; eGFR) or structural (increase in height adjusted total kidney volume; htTKV) outcomes were evaluated in a Mayo Clinic PKD1/PKD2 population; and eGFR and htTKV trajectories from 20-65 years of age modeled and independently validated in similarly defined CRISP and HALT PKD patients. Results: Both genotypic and imaging groups strongly predicted ESKD and eGFR endpoints, with genotype improving the imaging predictions, and vice versa; a multivariate model had strong discriminatory power (C statistic = 0.845). However, imaging but not genotypic groups predicted htTKV growth, although more severe genotypic and imaging groups had larger kidneys at a young age. The trajectory of eGFR decline was linear from baseline in the most severe genotypic and imaging groups, but curvilinear in milder groups. Imaging class trajectories differentiated htTKV growth rates; severe classes had rapid early growth and large kidneys but growth later slowed. Conclusions: The value of imaging, genotypic, and combined data to identify rapidly progressive patients was demonstrated, and reference values for clinical trials provided. Our data indicates that differences in kidney growth rates before adulthood significantly define patients with severe disease. Funding: NIDDK grants: Mayo DK058816, DK090728; CRISP DK056943, DK056956, DK056957, DK056961; HALT PKD DK062410, DK062408, DK062402, DK082230, DK062411, DK062401.
Authors
Sravanthi Lavu, Lisa E. Vaughan, Sarah R. Senum, Timothy L. Kline, Arlene B. Chapman, Ronald D. Perrone, Michal Mrug, William E. Braun, Theodore I. Steinman, Frederic F. Rahbari-Oskoui, Godela M. Brosnahan, Kyongtae T. Bae, Douglas Landsittel, Fouad T. Chebib, Alan S. L. Yu, Vicente E. Torres, Peter C. Harris
Abstract
Reprogramming of host metabolism supports viral pathogenesis by fueling viral proliferation, by providing, for example, free amino acids and fatty acids as building blocks. To investigate metabolic effects of SARS-COV-2 infection, we evaluated serum metabolites of COVID-19 patients (n = 33; diagnosed by nucleic acid testing), as compared to COVID-19-negative controls (n = 16). Targeted and untargeted metabolomics analyses identified altered tryptophan metabolism into the kynurenine pathway, which regulates inflammation and immunity. Indeed, these changes in tryptophan metabolism correlated with interleukin-6 (IL-6) levels. Widespread dysregulation of nitrogen metabolism was also seen in infected patients, with altered levels of most amino acids, along with increased markers of oxidant stress (e.g., methionine sulfoxide, cystine), proteolysis, and renal dysfunction (e.g., creatine, creatinine, polyamines). Increased circulating levels of glucose and free fatty acids were also observed, consistent with altered carbon homeostasis. Interestingly, metabolite levels in these pathways correlated with clinical laboratory markers of inflammation (i.e., IL-6 and C-reactive protein) and renal function (i.e., blood urea nitrogen). In conclusion, this initial observational study identified amino acid and fatty acid metabolism as correlates of COVID-19, providing mechanistic insights, potential markers of clinical severity, and potential therapeutic targets.
Authors
Tiffany Thomas, Davide Stefanoni, Julie A. Reisz, Travis Nemkov, Lorenzo Bertolone, Richard O. Francis, Krystalyn E. Hudson, James C. Zimring, Kirk C. Hansen, Eldad A. Hod, Steven L. Spitalnik, Angelo D’Alessandro
Abstract
Background Currently recommended traditional spirometry outputs do not reflect their relative contributions to airflow, and we hypothesized that machine learning algorithms can be trained on spirometry data to identify these structural phenotypes. Methods Participants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep learning model to predict structural phenotypes of COPD on computed tomography (CT), and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convolutional neural network, Random Forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10>median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (fSAD>20% on parametric response mapping). Results Among 8,980 individuals, neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79-0.81) than traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69-0.71) and FEV1 %predicted (AUC 0.70, 95%CI 0.68-0.71) ), and random forest classifier (AUC 0.78, 95%CI 0.77-0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90-0.92) than FEV1/FVC (AUC 0.80, 95%CI 0.78-0.82), FEV1 %predicted (AUC 0.83, 95%CI 0.80-0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88-0.91). Conclusions Structural phenotypes of COPD can be identified from spirometry using deep learning and machine learning approaches, demonstrating their potential to identify individuals for targeted therapies.
Authors
Sandeep Bodduluri, Arie Nakhmani, Joseph M. Reinhardt, Carla G. Wilson, Merry-Lynn N. McDonald, Ramaraju Rudraraju, Byron C Jaeger, Nirav R. Bhakta, Peter J. Castaldi, Frank C. Sciurba, Chengcui Zhang, Purushotham V. Bangalore, Surya P. Bhatt
Abstract
BACKGROUND Prediction of adverse outcomes in cerebral malaria (CM) is difficult. We hypothesized that cell-free DNA (cfDNA) levels would facilitate identification of severe and potentially fatal CM cases.METHODS In this retrospective study, plasma from Malawian children with CM (n = 134), uncomplicated malaria (UM, n = 77), and healthy controls (HC, n = 60) was assayed for cfDNA using a fluorescence assay. Host and parasite cfDNA was measured by quantitative PCR. Immune markers were determined by ELISA, Luminex, or cytometric bead array.RESULTS Total cfDNA increased with malaria severity (HC versus UM, P < 0.001; HC versus CM, P < 0.0001; UM versus CM, P < 0.0001), was elevated in retinopathy-positive (Ret+) CM relative to Ret– CM (7.66 versus 5.47 ng/μL, P = 0.027), and differentiated Ret+ fatal cases from survivors (AUC 0.779; P < 0.001). cfDNA levels in patients with non–malarial febrile illness (NMF, P = 0.25) and non–malarial coma (NMC, P = 0.99) were comparable with UM. Host DNA, rather than parasite DNA, was the major cfDNA contributor (UM, 268 versus 67 pg/μL; CM, 2824 versus 463 pg/μL). Host and parasite cfDNA distinguished CM by retinopathy (host, AUC 0.715, P = 0.0001; parasite, AUC 0.745, P = 0.0001), but only host cfDNA distinguished fatal cases (AUC 0.715, P = 0.0001). Total cfDNA correlated with neutrophil markers IL-8 (rs = 0.433, P < 0.0001) and myeloperoxidase (rs = 0.683, P < 0.0001).CONCLUSION Quantifying plasma cfDNA is a simple assay useful in identifying children at risk for fatal outcome and has promise as a point-of-care assay. Elevated cfDNA suggests a link with host inflammatory pathways in fatal CM.FUNDING NIH NCATS (AK), Burroughs-Wellcome (AK), and National Health and Medical Research Council of Australia (SJR).
Authors
Iset Medina Vera, Anne Kessler, Li-Min Ting, Visopo Harawa, Thomas Keller, Dylan Allen, Madi Njie, McKenze Moss, Monica Soko, Ajisa Ahmadu, Innocent Kadwala, Stephen Ray, Tonney S. Nyirenda, Wilson L. Mandala, Terrie E. Taylor, Stephen J. Rogerson, Karl B. Seydel, Kami Kim
Abstract
BACKGROUND. The numbers of fatal cases of Coronavirus Disease 2019 (COVID-19) continue to increase rapidly around the world. We aim to retrospectively investigate potential roles of factors, mainly immunologic parameters, in early predicting outcomes of patients with COVID-19. METHODS. A total of 1,018 patients confirmed COVID-19 were enrolled in our retrospective study from two centers. The data of clinical features, laboratory tests, immunological tests, radiological findings, and outcomes were collected. Univariate and multivariable logistic regression analysis were performed to evaluate factors associated with in-hospital mortality. Receiver operator characteristic (ROC) curves and survival curves were plotted to evaluate the clinical usefulness. RESULTS. Compared to the survival patients, the counts of all T lymphocytes subsets were markedly lower in non-survivors(P < 0.001), especially in CD8+ T cells (96.89 vs 203.98 cells/μl, P < 0.001) . Among all tested cytokines, IL-6 elevated most significantly with an upward trend of more than ten times (56.16 vs 5.36 pg/mL, P < 0.001). By a multivariable logistic regression analysis, two immunological indicators were found to be associated with in-hospital mortality, including IL-6 > 20 pg/mL (OR = 9.781; 95%CI, 6.304–15.174; P < 0.001) and CD8+ T cell count < 165 cells/μl (OR = 5.930; 95%CI, 3.677–9.562; P < 0.001), after adjusting confounding factors (age, gender, and underlying diseases). All the patients were divided into four groups according to levels of IL-6 and CD8+ T cells. The group with IL-6 > 20 pg/mL and CD8+ T cell count < 165 cells/μl had more old and male patients, as well as more proportion of patients with comorbidities, ventilation, ICU admission, shock, and death than those of any other group (P < 0.001). Furthermore, the ROC curve of the model combining IL-6 (>20 pg/mL) and CD8+ T cell count(<165 cells/μl) displayed more favorable discrimination than that of CURB-65 score (area under curve (AUC) = 0.907 vs 0.843, P < 0.001). Hosmer-Lemeshow test showed a good fitting of the model with no statistical significance (P = 0.581). CONCLUSIONS. We firstly identify two reliable prognostic indicators, IL-6 (>20 pg/mL) and CD8+ T cell count (<165 cells/μl), which can accurately stratify patients into risk categories and predict mortality of patients with COVID-19. Those two indicators combined may guide clinicians to evaluate patient prognosis and make appropriate decisions.
Authors
Miao Luo, Jing Liu, Weiling Jiang, Shuang Yue, Huiguo Liu, Shuang Wei
Abstract
BACKGROUND. Dysregulation of L-arginine metabolism has been proposed to occur in severe asthma patients. The effects of L-arginine supplementation on L-arginine metabolite profiles in these patients is unknown. We hypothesized that severe asthmatics with low fractional exhaled nitric oxide (FeNO) would have fewer asthma exacerbations with the addition of L-arginine to their standard asthma medications compared to placebo and would demonstrate the greatest changes in metabolite profiles. METHODS. Participants were enrolled in a single-center, cross-over, double-blinded, L-arginine intervention trial at the University of California-Davis (NCT01841281). Subjects received placebo or L-arginine, dosed orally at 0.05mg/kg (ideal body weight) twice daily. The primary endpoint was moderate asthma exacerbations. Longitudinal plasma metabolite levels were measured using mass spectrometry. A linear mixed-effect model with subject-specific intercepts was used for testing treatment effects. RESULTS. A cohort of 50 subjects was included in the final analysis. L-arginine did not significantly decrease asthma exacerbations in the overall cohort. Higher citrulline levels and a lower arginine availability index (AAI) were associated with higher FeNO (P-value = 0.005 and 2.51 x 10–9 respectively). Higher AAI was associated with lower exacerbation events. The eicosanoid prostaglandin H2 (PGH2) and Nα-Acetyl-L-arginine were found to be good predictors for differentiating clinical responders and non-responders. CONCLUSIONS. There was no statistically significant decrease in asthma exacerbations in the overall cohort with L-arginine intervention. PGH2, Nα-Acetyl-L-arginine and the AAI could serve as predictive biomarkers in future clinical trials that intervene in the arginine metabolome.
Authors
Shu-Yi Liao, Megan R. Showalter, Angela L. Linderholm, Lisa M. Franzi, Celeste Kivler, Yao Li, Michael R. Sa, Zachary A. Kons, Oliver Fiehn, Lihong Qi, Amir A. Zeki, Nicholas J. Kenyon
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