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COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications.In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done.We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile.A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study.Eric and Wendy Schmidt.

Objectives Vitamin D deficiency (VDD) is associated with coronary artery disease (CAD) directly by augmenting atherosclerosis and indirectly through cardiovascular risk factors. The present study was aimed to find an association of 25 hydroxyvitamin D (25(OH)D) with lipid profile among established CAD. Methods A cross-sectional study was conducted among 73 patients of angiographically confirmed CAD aged between 35 and 55 years of both gender. Serum 25(OH)D and lipid profile were estimated by ELISA kit and Roche autoanalyzer respectively. Atherogenic index of plasma (AIP) and sdLDL (small dense low-density lipoprotein) were calculated using the accepted formula. Results The mean 25(OH)D level was 17.95 ± 13.51. Only 15% had sufficient 25(OH)D level. There was a significant negative correlation of 25(OH)D with TC/HDL (T.cholesterol/High-density lipoprotein) ratio (p=0.022). Multivariate logistic regression analysis showed no statistically significant impact of 25(OH)D with lipid biomarkers. Conclusions We found low 25(OH)D mean value among CAD and a significant negative correlation of 25(OH)D with TC/HDL. This study suggests VDD may affect primary lipid target resulting in unfavorable outcomes in CAD.

To discover lipidomic alterations during pregnancy in mothers who subsequently delivered small for gestational age (SGA) neonates and identify predictive lipid markers that can help recognize and manage these mothers, we carried out untargeted lipidomics on maternal serum samples collected between 24-28 weeks of gestation. We used a nested case-control study design and serum from mothers who delivered SGA and appropriate for gestational age babies. We applied untargeted lipidomics using mass spectrometry to characterize lipids and discover changes associated with SGA births during pregnancy. Multivariate pattern recognition software Collaborative Laboratory Integrated Reports (CLIR) was used for the post-analytical recognition of range differences in lipid ratios that could differentiate between SGA and control mothers and their integration for complete separation between the two groups. Here, we report changes in lipids from serum collected during pregnancy in mothers who delivered SGA neonates. In contrast to normal pregnancies where lysophosphatidic acid increased over the course of the pregnancy owing to increased activity of lysophospholipase D, we observed a decrease (32%; P = 0.05) of 20:4-lysophosphatidic acid in SGA mothers, which could potentially compromise fetal growth and development. Integration of lipid ratios in an interpretive tool (CLIR) could completely separate SGA mothers from controls demonstrating the power of untargeted lipidomic analyses for identifying novel predictive biomarkers. Additional studies are required for further assessment of the lipid biomarkers identified in this report.

Context: Diabetic foot ulcer (DFU) is one of the most dreaded complication of diabetes which often affects the morbidity and mortality of a diabetic. Biomarkers are required for follow-up of these patients to prevent further complications to the affected limb. Aim: The aim of this study is to evaluate ischemia-modified albumin (IMA), fibrinogen in relation with high sensitive-C-reactive protein (hs-CRP) in patients with DFUs. Methods: a hospital-based cross-sectional study was carried out among 30 patients with DFU admitted in the surgery department of tertiary care teaching hospital. The duration of the study was 2 months. Serum IMA, hs-CRP, and plasma fibrinogen levels were measured. Results: The present study has demonstrated that IMA levels (P < 0.05) and fibrinogen levels (P < 0.05) are statistically significantly elevated in patients with DFU and had significant correlation with albumin and hs-CRP (P < 0.05). Conclusions: The use of IMA, hs-CRP, and fibrinogen may be incorporated during the follow-up of type 2 diabetes mellitus patients and may probably prevent the development of DFU and also possibly prevent lower limb amputation. Further studies with a larger number of patients with DFU are necessary to reach a definitive judgment.