Skunk River Review Fall 1997, Vol 9

https://openspace.dmacc.edu/skunkriver/1018/thumbnail.jp

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Förbättrad diagnostik och prediktion vid samhällsförvärvad pneumoni

Community-acquired pneumonia (CAP) is a major cause of morbidity and mortality worldwide. Although there is wide variation in the microbial etiology, CAP may manifest with similar symptoms, making institution of proper treatment challenging. Therefore, etiological diagnosis is important to ensure that correct treatment and necessary infection control measures are instituted. This provides a challenge for conventional microbial diagnostic methods, typically based on culture and direct antigen tests. Moreover, existing molecular biomarkers have poor prognostic value. Few studies have investigated the global metabolic response during infection and virtually nothing is known about early responses after the start of antimicrobial treatment. The aim of this work was to improve diagnostic and predictive methods for CAP. In paper I, a qPCR panel targeting 15 pathogens known to cause CAP was developed and evaluated. It combined identification of bacterial pathogens and viruses in the same diagnostic platform. The method proved to be robust and the results consistent with those obtained by standard methods. The panel approach, compared to conventional, selective diagnostics, detected a larger number of pathogens. In Paper II, whole blood samples from 65 patients with bacteremic sepsis were analyzed for metabolite profiles. Forty-nine patients with symptoms of sepsis, but later attributed to other diagnoses, were matched according to age and sex and served as a control group. Six metabolites were identified, all of which predicted growth of bacteria in blood culture. One of the metabolites, myristic acid, alone predicted bacteremic sepsis with a sensitivity of 100% and a specificity of 95%. Paper III and IV were based on a clinical study enrolling 35 patients with suspected CAP in need of hospital care. The aim was to study the metabolic response during the early phase of acute infection. The qPCR panel developed in Paper I was used to obtain the microbial etiological diagnosis. Paper IV focused on the global metabolic response and highlighted the dynamics of changes in major metabolic pathways during early recovery. A specific metabolite pattern for M. pneumoniae etiology was found. Four metabolites accurately predicted all but one patient as either M. pneumoniae etiology or not. Paper III looked at phospholipid levels during the first 48 hours after hospital admission. It was found that all major phospholipid species, especially the lysophosphatidyl-cholines, were pronouncedly decreased during acute infection. Levels started to increase the day after admission, reaching statistical significance at 48 hours. Paper II-IV showed that metabolomics might be used to study a number of different aspects of infection, such as etiology, disease progress and recovery. Knowledge of the metabolic profiles of patients may not only be utilized for biomarker discovery, as proposed in this work, but also for the future development of targeted therapies and supportive treatment

Förbättrad diagnostik och prediktion vid samhällsförvärvad pneumoni

Community-acquired pneumonia (CAP) is a major cause of morbidity and mortality worldwide. Although there is wide variation in the microbial etiology, CAP may manifest with similar symptoms, making institution of proper treatment challenging. Therefore, etiological diagnosis is important to ensure that correct treatment and necessary infection control measures are instituted. This provides a challenge for conventional microbial diagnostic methods, typically based on culture and direct antigen tests. Moreover, existing molecular biomarkers have poor prognostic value. Few studies have investigated the global metabolic response during infection and virtually nothing is known about early responses after the start of antimicrobial treatment. The aim of this work was to improve diagnostic and predictive methods for CAP. In paper I, a qPCR panel targeting 15 pathogens known to cause CAP was developed and evaluated. It combined identification of bacterial pathogens and viruses in the same diagnostic platform. The method proved to be robust and the results consistent with those obtained by standard methods. The panel approach, compared to conventional, selective diagnostics, detected a larger number of pathogens. In Paper II, whole blood samples from 65 patients with bacteremic sepsis were analyzed for metabolite profiles. Forty-nine patients with symptoms of sepsis, but later attributed to other diagnoses, were matched according to age and sex and served as a control group. Six metabolites were identified, all of which predicted growth of bacteria in blood culture. One of the metabolites, myristic acid, alone predicted bacteremic sepsis with a sensitivity of 100% and a specificity of 95%. Paper III and IV were based on a clinical study enrolling 35 patients with suspected CAP in need of hospital care. The aim was to study the metabolic response during the early phase of acute infection. The qPCR panel developed in Paper I was used to obtain the microbial etiological diagnosis. Paper IV focused on the global metabolic response and highlighted the dynamics of changes in major metabolic pathways during early recovery. A specific metabolite pattern for M. pneumoniae etiology was found. Four metabolites accurately predicted all but one patient as either M. pneumoniae etiology or not. Paper III looked at phospholipid levels during the first 48 hours after hospital admission. It was found that all major phospholipid species, especially the lysophosphatidyl-cholines, were pronouncedly decreased during acute infection. Levels started to increase the day after admission, reaching statistical significance at 48 hours. Paper II-IV showed that metabolomics might be used to study a number of different aspects of infection, such as etiology, disease progress and recovery. Knowledge of the metabolic profiles of patients may not only be utilized for biomarker discovery, as proposed in this work, but also for the future development of targeted therapies and supportive treatment

Förbättrad diagnostik och prediktion vid samhällsförvärvad pneumoni

Community-acquired pneumonia (CAP) is a major cause of morbidity and mortality worldwide. Although there is wide variation in the microbial etiology, CAP may manifest with similar symptoms, making institution of proper treatment challenging. Therefore, etiological diagnosis is important to ensure that correct treatment and necessary infection control measures are instituted. This provides a challenge for conventional microbial diagnostic methods, typically based on culture and direct antigen tests. Moreover, existing molecular biomarkers have poor prognostic value. Few studies have investigated the global metabolic response during infection and virtually nothing is known about early responses after the start of antimicrobial treatment. The aim of this work was to improve diagnostic and predictive methods for CAP. In paper I, a qPCR panel targeting 15 pathogens known to cause CAP was developed and evaluated. It combined identification of bacterial pathogens and viruses in the same diagnostic platform. The method proved to be robust and the results consistent with those obtained by standard methods. The panel approach, compared to conventional, selective diagnostics, detected a larger number of pathogens. In Paper II, whole blood samples from 65 patients with bacteremic sepsis were analyzed for metabolite profiles. Forty-nine patients with symptoms of sepsis, but later attributed to other diagnoses, were matched according to age and sex and served as a control group. Six metabolites were identified, all of which predicted growth of bacteria in blood culture. One of the metabolites, myristic acid, alone predicted bacteremic sepsis with a sensitivity of 100% and a specificity of 95%. Paper III and IV were based on a clinical study enrolling 35 patients with suspected CAP in need of hospital care. The aim was to study the metabolic response during the early phase of acute infection. The qPCR panel developed in Paper I was used to obtain the microbial etiological diagnosis. Paper IV focused on the global metabolic response and highlighted the dynamics of changes in major metabolic pathways during early recovery. A specific metabolite pattern for M. pneumoniae etiology was found. Four metabolites accurately predicted all but one patient as either M. pneumoniae etiology or not. Paper III looked at phospholipid levels during the first 48 hours after hospital admission. It was found that all major phospholipid species, especially the lysophosphatidyl-cholines, were pronouncedly decreased during acute infection. Levels started to increase the day after admission, reaching statistical significance at 48 hours. Paper II-IV showed that metabolomics might be used to study a number of different aspects of infection, such as etiology, disease progress and recovery. Knowledge of the metabolic profiles of patients may not only be utilized for biomarker discovery, as proposed in this work, but also for the future development of targeted therapies and supportive treatment

Evaluation of the Biofire Filmarray Pneumonia panel plus for lower respiratory tract infections

Background: Standard diagnostic methods for lower respiratory tract infections are currently too slow and insensitive to guide early clinical decisions concerning treatment and isolation. Syndrome-specific, diagnostic panels have potential to provide information about aetiology quickly. Available panels have been of limited use in lower respiratory tract infections due to slow turn-around-time, lack of quantification of important pathogens and lack of detection of resistance genes. Materials/methods: We evaluated the newly developed Biofire(R) Filmarray(R) Pneumonia Panel plus (Biomerieux). Eighty-eight consecutive lower respiratory tract samples were analyzed by both standard microbiological methods, as requested by the referring clinician, and by the panel. The agreement with standard methods, empirical treatment coverage and possible impact on isolation practices were assessed by comparing the results from standard diagnostic methods with the panel results in relation to clinical data and information of antimicrobial therapy. Results: Both qualitative and semi-quantitative results from the panel generally displayed good agreement with standard methods and by combining methods, a possible aetiology was detected in 73% of patients. Due to the panel approach, the panel detected viruses more frequently. In 25% of the 60 patients assessed for empirical treatment coverage, a pathogen not covered by current therapy was detected and in 30% of in-house patients the panel results were found to potentially influence clinical decisions related to isolation care. Conclusions: The new diagnostic panel shows promise in improving aetiological diagnostics of lower respiratory tract infections. Correctly applied it has potential to offer support in clinical decision-making within hours of sampling

Phospholipid Levels in Blood during Community-Acquired Pneumonia

Phospholipids, major constituents of bilayer cell membranes, are present in large amounts in pulmonary surfactant and play key roles in cell signaling. Here, we aim at finding clinically useful disease markers in community-acquired pneumonia (CAP) using comprehensive phospholipid profiling in blood and modeling of changes between sampling time points. Serum samples from 33 patients hospitalized with CAP were collected at admission, three hours after the start of intravenous antibiotics, Day 1 (at 12–24 h), Day 2 (at 36–48 h), and several weeks after recovery. A profile of 75 phospholipid species including quantification of the bioactive lysophosphatidylcholines (LPCs) was determined using liquid chromatography coupled to time-of-flight mass spectrometry. To control for possible enzymatic degradation of LPCs, serum autotaxin levels were examined. Twenty-two of the 33 patients with a clinical diagnosis of CAP received a laboratory-verified CAP diagnosis by microbial culture or microbial DNA detection by qPCR. All major phospholipid species, especially the LPCs, were pronouncedly decreased in the acute stage of illness. Total and individual LPC concentrations increased shortly after the initiation of antibiotic treatment, concentrations were at their lowest 3h after the initiation, and increased after Day 1. The total LPC concentration increased by a change ratio of 1.6–1.7 between acute illness and Day 2, and by a ratio of 3.7 between acute illness and full disease resolution. Autotaxin levels were low in acute illness and showed little changes over time, contradicting a hypothesis of enzymatic degradation causing the low levels of LPCs. In this sample of patients with CAP, the results demonstrate that LPC concentration changes in serum of patients with CAP closely mirrored the early transition from acute illness to recovery after the initiation of antibiotics. LPCs should be further explored as potential disease stage biomarkers in CAP and for their potential physiological role during recovery.Originally included in thesis in manuscript form </p

Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69-0.99) and a specificity 0.84 (95% CI 0.58-0.94) with an AUC of 0.93 (95% CI 0.89-1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85-1.00) and specificity of 0.95 (95% CI 0.74-0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics

Olfactory dysfunction as an early predictor for post-COVID condition at 1-year follow-up

Background: Olfactory dysfunction together with neurological and cognitive symptoms are common after COVID-19. We aimed to study whether performance on olfactory and neuropsychological tests following infection predict post-COVID condition (PCC), persisting symptoms, and reduced health-related quality of life. Methods: Both hospitalized (N = 10) and non-hospitalized individuals (N = 56) were enrolled in this prospective cohort study. Participants were evaluated 1–3 months after infection with an olfactory threshold test and neuropsychological tests, which was used as predictors of PCC. A questionnaire outlining persisting symptoms and the validated instrument EuroQol five-dimension five-level for health-related quality of life assessment were used as outcome data 1 year after infection (N = 59). Principal component analysis was used to identify relevant predictors for PCC at 1 year. Results: Objectively assessed olfactory dysfunction at 1–3 months post infection, but not subjective olfactory symptoms, predicted post-COVID condition with reduced health-related quality of life (PCC+) at 1 year. The PCC+ group scored more often below the cut off for mild cognitive impairment on the Montreal Cognitive Assessment (61.5% vs. 21.7%) and higher on the Multidimensional Fatigue Inventory-20, compared to the group without PCC+. Conclusion: Our results indicate that objectively assessed, olfactory dysfunction is a predictor for PCC+. These findings underscore the importance of objective olfactory testing. We propose that olfactory screening in the early post-acute phase of COVID-19 infection might identify individuals that are at higher risk of developing long-term health sequalae