Search

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Arsenic exposure has been associated with low birth weight. However, the underlying mechanisms are not well understood. Alterations to metabolites may act as causal mediators of the effect of arsenic exposure on low birth weight. This pilot study aimed to explore the role of metabolites in mediating the association of arsenic exposure on infant birth weight. Study samples were selected from a well-established prospectively enrolled cohort in Bangladesh comprising 35 newborns and a subset of 20 matched mothers. Metabolomics profiling was performed on 35 cord blood samples and 20 maternal peripheral blood samples collected during the second trimester of pregnancy. Inorganic arsenic (iAs) exposure was evaluated via cord blood samples and maternal toenail samples collected during the first trimester. Multiple linear regression and mediation analyses were used to explore the relationship between iAs exposure, metabolite alterations, and low birth weight. Cord blood arsenic level was correlated with elevated levels of 17-methylstearate, laurate (12:0) and 4-vinylphenol sulfate along with lower birth weight. Prenatal maternal toenail iAs level was associated with two peripheral blood metabolites (butyrylqlycine and tartarate), which likely contributed to higher cord blood iAs levels both independently and interactively. Findings of this pilot study indicate that both intrauterine and maternal peripheral blood metabolites appear to influence the toxic effect of inorganic arsenic exposure on low birth weight.

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level. [ABSTRACT FROM AUTHOR]

Background: ARDS is a devastating syndrome with heterogeneous subtypes, but few causal biomarkers have been identified.Research Question: Would multistage Mendelian randomization identify new causal protein biomarkers for ARDS 28-day mortality?Study Design and Methods: Three hundred moderate to severe ARDS patients were selected randomly from the Molecular Epidemiology of ARDS cohort for proteomics analysis. Orthogonal projections to latent structures discriminant analysis was applied to detect the association between proteins and ARDS 28-day mortality. Candidate proteins were analyzed using generalized summary data-based Mendelian randomization (GSMR). Protein quantitative trait summary statistics were retrieved from the Efficiency and safety of varying the frequency of whole blood donation (INTERVAL) study (n = 2,504), and a genome-wide association study for ARDS was conducted from the Identification of SNPs Predisposing to Altered Acute Lung Injury Risk (iSPAAR) consortium study (n = 534). Causal mediation analysis detected the role of platelet count in mediating the effect of protein on ARDS prognosis.Results: Plasma insulin-like growth factor binding protein 7 (IGFBP7) moderately increased ARDS 28-day mortality (OR, 1.11; 95% CI, 1.04-1.19; P = .002) per log2 increase. GSMR analysis coupled with four other Mendelian randomization methods revealed IGFBP7 as a causal biomarker for ARDS 28-day mortality (OR, 2.61; 95% CI, 1.33-5.13; P = .005). Causal mediation analysis indicated that the association between IGFBP7 and ARDS 28-day mortality is mediated by platelet count (OR, 1.03; 95% CI, 1.02-1.04; P = .01).Interpretation: We identified plasma IGFBP7 as a novel causal protein involved in the pathogenesis of ARDS 28-day mortality and platelet function in ARDS, a topic for further experimental and clinical investigation. [ABSTRACT FROM AUTHOR]

>bold<>italic/italic<>/bold< (1) To investigate atrophy patterns of hippocampal subfield volume and Alzheimer's disease (AD)-signature cortical thickness in mild cognitive impairment (MCI) patients; (2) to explore the association between the neuropsychological (NP) and the brain structure in the MCI and older normal cognition group; (3) to determine whether these associations were modified by the apolipoprotein E (APOE) ε4 gene and cognitive status. >bold<>italic/italic<>/bold< The FreeSurfer software was used for automated segmentation of hippocampal subfields and AD-signature cortical thickness for 22 MCI patients and 23 cognitive normal controls (NC). The volume, cortical thickness, and the neuropsychological scale were compared with two-sample >italic/italic< tests. Linear regression models were used to determine the association between the NP and the brain structure. >bold<>italic/italic<>/bold< Compared with the NC group, MCI patients showed a decreased volume of the left presubiculum, subiculum and right CA2_3 and CA4_DG (>italic/italic< < 0.05, FDR corrected). The volume of these regions was positively correlated with NP scores. Of note, these associations depended on the cognitive status but not on the APOE ε4 status. The left subiculum and presubiculum volume were positively correlated with the Montreal Cognitive Assessment (MoCA) scores only in the MCI patients. >bold<>italic/italic<>/bold< Atrophy of the hippocampal subfields may be a powerful biomarker for MCI in the Chinese population. [ABSTRACT FROM AUTHOR]

Background: MicroRNAs (miRNAs) can play important roles in inflammation and infection, which are common manifestations of acute respiratory distress syndrome (ARDS). We assessed if whole blood miRNAs were potential diagnostic biomarkers for human ARDS. Methods: This nested case-control study ( N = 530) examined a cohort of ARDS patients and critically ill at-risk controls. Whole blood miRNA profiles and logistic regression analyses identified miRNAs correlated with ARDS. Stratification analysis also assessed selected miRNA markers for their role in sepsis and pneumonia associated with ARDS. Receiver operating characteristic (ROC) analysis evaluated miRNA diagnostic performance, along with Lung Injury Prediction Score (LIPS). Results: Statistical analyses were performed on 294 miRNAs, selected from 754 miRNAs after quality control screening. Logistic regression identified 22 miRNAs from a 156-patient discovery cohort as potential risk or protective markers of ARDS. Three miRNAs-miR-181a, miR-92a, and miR-424-from the discovery cohort remained significantly associated with ARDS in a 373-patient independent validation cohort (FDR q < 0.05) and meta-analysis ( p < 0.001). ROC analyses demonstrated a LIPS baseline area-under-the-curve (AUC) value of ARDS of 0.708 (95% CI 0.651-0.766). Addition of miR-181a, miR-92a, and miR-424 to LIPS increased baseline AUC to 0.723 (95% CI 0.667-0.778), with a relative integrated discrimination improvement of 2.40 ( p = 0.005) and a category-free net reclassification index of 27.21% ( p = 0.01). Conclusions: miR-181a and miR-92a are risk biomarkers for ARDS, whereas miR-424 is a protective biomarker. Addition of these miRNAs to LIPS can improve the risk estimate for ARDS. [ABSTRACT FROM AUTHOR]

Background: Early detection of the Acute Respiratory Distress Syndrome (ARDS) has the potential to improvethe prognosis of critically ill patients admitted to the intensive care unit (ICU). However, no reliable biomarkers are currently available for accurate early detection of ARDS in patients with predisposing conditions. Objectives: This study examined risk factors and biomarkers for ARDS development and mortality in two prospective cohort studies. Methods: We examined clinical risk factors for ARDS in a cohort of 178 patients in Beijing, China who were admitted to the ICU and were at high risk for ARDS. Identified biomarkers were then replicated in a second cohort of1,878 patients in Boston, USA. Results: Of 178 patients recruited from participating hospitals in Beijing, 75 developed ARDS. After multivariate adjustment, sepsis (odds ratio [OR]:5.58, 95% CI: 1.70–18.3), pulmonary injury (OR: 3.22; 95% CI: 1.60–6.47), and thrombocytopenia, defined as platelet count <80×103/µL, (OR: 2.67; 95% CI: 1.27–5.62)were significantly associated with increased risk of developing ARDS. Thrombocytopenia was also associated with increased mortality in patients who developed ARDS (adjusted hazard ratio [AHR]: 1.38, 95% CI: 1.07–1.57) but not in those who did not develop ARDS(AHR: 1.25, 95% CI: 0.96–1.62). The presence of both thrombocytopenia and ARDS substantially increased 60-daymortality. Sensitivity analyses showed that a platelet count of <100×103/µLin combination with ARDS provide the highest prognostic value for mortality. These associations were replicated in the cohort of US patients. Conclusions: This study of ICU patients in both China and US showed that thrombocytopenia is associated with an increased risk of ARDS and platelet count in combination with ARDS had a high predictive value for patient mortality. [ABSTRACT FROM AUTHOR]

Laser printer-emitted nanoparticles (PEPs) generated from toners during printing represent one of the most common types of life cycle released particulate matter from nano-enabled products. Toxicological assessment of PEPs is therefore important for occupational and consumer health protection. Our group recently reported exposure to PEPs induces adverse cardiovascular responses including hypertension and arrythmia via monitoring left ventricular pressure and electrocardiogram in rats. This study employed genome-wide mRNA and miRNA profiling in rat lung and blood integrated with metabolomics and lipidomics profiling in rat serum to identify biomarkers for assessing PEPs-induced disease risks. Whole-body inhalation of PEPs perturbed transcriptional activities associated with cardiovascular dysfunction, metabolic syndrome, and neural disorders at every observed time point in both rat lung and blood during the 21 days of exposure. Furthermore, the systematic analysis revealed PEPs-induced transcriptomic changes linking to other disease risks in rats, including diabetes, congenital defects, auto-recessive disorders, physical deformation, and carcinogenesis. The results were also confirmed with global metabolomics profiling in rat serum. Among the validated metabolites and lipids, linoleic acid, arachidonic acid, docosahexanoic acid, and histidine showed significant variation in PEPs-exposed rat serum. Overall, the identified PEPs-induced dysregulated genes, molecular pathways and functions, and miRNA-mediated transcriptional activities provide important insights into the disease mechanisms. The discovered important mRNAs, miRNAs, lipids and metabolites may serve as candidate biomarkers for future occupational and medical surveillance studies. To the best of our knowledge, this is the first study systematically integrating in vivo, transcriptomics, metabolomics, and lipidomics to assess PEPs inhalation exposure-induced disease risks using a rat model. [ABSTRACT FROM AUTHOR]