Many countries have seen a two-wave pattern in reported cases of coronavirus disease-19 during the 2020 pandemic, with a first wave during spring followed by the current second wave in late summer and autumn. Empirical data show that the characteristics of the effects of the virus do vary between the two periods. Differences in age range and severity of the disease have been reported, although the comparative characteristics of the two waves still remain largely unknown. Those characteristics are compared in this study using data from two equal periods of 3 and a half months. The first period, between 15th March and 30th June, corresponding to the entire first wave, and the second, between 1st July and 15th October, corresponding to part of the second wave, still present at the time of writing this article. Two hundred and four patients were hospitalized during the first period, and 264 during the second period. Patients in the second wave were younger and the duration of hospitalization and case fatality rate were lower than those in the first wave. In the second wave, there were more children, and pregnant and post-partum women. The most frequent signs and symptoms in both waves were fever, dyspnea, pneumonia, and cough, and the most relevant comorbidities were cardiovascular diseases, type 2 diabetes mellitus, and chronic neurological diseases. Patients from the second wave more frequently presented renal and gastrointestinal symptoms, were more often treated with non-invasive mechanical ventilation and corticoids, and less often with invasive mechanical ventilation, conventional oxygen therapy and anticoagulants. Several differences in mortality risk factors were also observed. These results might help to understand the characteristics of the second wave and the behaviour and danger of SARS-CoV-2 in the Mediterranean area and in Western Europe. Further studies are needed to confirm our findings.

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SummaryBackgroundObesity can influence hepatic mitochondrial function, and cause non‐alcoholic steatohepatitis (NASH). Diagnosis and follow‐up rely on invasive liver biopsy so blood‐based markers are urgently required.AimTo investigate whether values of circulating metabolites from energy and one‐carbon (1‐C) metabolism may: (a) reflect hepatic mitochondrial flexibility failure and (b) act as NASH biomarkers.MethodsPatients with severe obesity undergoing bariatric surgery (n = 270) were investigated using quantitative targeted plasma metabolomics. Comparisons were with non‐obese controls without liver disease (n = 50). Obese patients with NASH (n = 53) and without NASH (n = 130) representing extreme groups of liver disease were assessed to test the diagnostic ability of the measured circulating metabolites. Paired liver biopsy and plasma samples from NASH patients were available 1 year post‐surgery and were evaluated to monitor metabolomic changes with liver damage resolution.ResultsWe identified correlations between human liver metabolism and obesity. High‐plasma α‐ketoglutarate (α‐KG) and lactate concentrations in NASH patients indicating citric acid cycle replenishment via glutaminolysis might also be a crucial point in NASH onset. Plasma measurements of α‐KG, β‐hydroxybutyrate, pyruvate and oxaloacetate reduced the uncertainty in clinical diagnosis of NASH [area under receiver operating characteristic curve (AUC) of 0.826] and predicted NASH resolution without ambiguity (AUC of 0.999).ConclusionChanges in plasma mitochondrial metabolites appear to be associated with NASH. These metabolic responses may be dynamically remodelled following resolution of liver damage through massive weight loss.

Infectious and many non-infectious diseases share common molecular mechanisms. Among them, oxidative stress and the subsequent inflammatory reaction are of particular note. Metabolic disorders induced by external agents, be they bacterial or viral pathogens, excessive calorie intake, poor-quality nutrients, or environmental factors produce an imbalance between the production of free radicals and endogenous antioxidant systems; the consequence being the oxidation of lipids, proteins, and nucleic acids. Oxidation and inflammation are closely related, and whether oxidative stress and inflammation represent the causes or consequences of cellular pathology, both produce metabolic alterations that influence the pathogenesis of the disease. In this review, we highlight two key molecules in the regulation of these processes: Paraoxonase-1 (PON1) and chemokine (C-C motif) ligand 2 (CCL2). PON1 is an enzyme bound to high-density lipoproteins. It breaks down lipid peroxides in lipoproteins and cells, participates in the protection conferred by HDL against different infectious agents, and is considered part of the innate immune system. With PON1 deficiency, CCL2 production increases, inducing migration and infiltration of immune cells in target tissues and disturbing normal metabolic function. This disruption involves pathways controlling cellular homeostasis as well as metabolically-driven chronic inflammatory states. Hence, an understanding of these relationships would help improve treatments and, as well, identify new therapeutic targets.

SARS-CoV-2 infection produces a response of the innate immune system causing oxidative stress and a strong inflammatory reaction termed ‘cytokine storm’ that is one of the leading causes of death. Paraoxonase-1 (PON1) protects against oxidative stress by hydrolyzing lipoperoxides. Alterations in PON1 activity have been associated with pro-inflammatory mediators such as the chemokine (C-C motif) ligand 2 (CCL2), and the glycoprotein galectin-3. We aimed to investigate the alterations in the circulating levels of PON1, CCL2, and galectin-3 in 126 patients with COVID-19 and their interactions with clinical variables and analytical parameters. A machine learning approach was used to identify predictive markers of the disease. For comparisons, we recruited 45 COVID-19 negative patients and 50 healthy individuals. Our approach identified a synergy between oxidative stress, inflammation, and fibrogenesis in positive patients that is not observed in negative patients. PON1 activity was the parameter with the greatest power to discriminate between patients who were either positive or negative for COVID-19, while their levels of CCL2 and galectin-3 were similar. We suggest that the measurement of serum PON1 activity may be a useful marker for the diagnosis of COVID-19.

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We investigated alterations in the levels of the antioxidant paraoxonase-1 (PON1) and the lipoprotein profile (analyzed by nuclear magnetic resonance) in patients with lung cancer (LC) or head and neck cancer (HNC), and the effects produced thereon by radiotherapy (RT). We included 33 patients with LC and 28 patients with HNC. Before irradiation, and one month after completion of RT, blood samples were obtained. The control group was composed of 50 healthy subjects. Patients had significantly lower serum PON1 activity and concentration before RT than the control group. PON1-related variables were good predictors of the presence of LC or HNC, with analytical sensitivities and specificities greater than 80%. Patients showed a significant increase in the number of particles of all subclasses of very-low-density lipoproteins (large, medium and small). However, these changes were not maintained when adjusted for age, sex, and other clinical and demographic variables. Irradiation was associated with a significant increase in PON1 concentration and, only in patients with HNC, with an increase in high-density lipoprotein-cholesterol concentration. Our results suggest that determinations of the levels of PON1-related variables may constitute good biomarkers for the evaluation of these diseases. Studies with a larger number of patients are needed to fully confirm this hypothesis.

We report a pilot study on the feasibility of determinations of circulating levels of paraoxonase-1 (PON1) and compounds related to energy metabolism as biomarkers for the evaluation of patients with rectal cancer (RC), and the effects produced by neoadjuvant radiochemotherapy (NRCT). We studied 32 patients treated with radiotherapy plus capecitabine concomitant chemotherapy and 48 control subjects. We identified pre-NRCT PON1 and α-ketoglutarate as the parameters that best discriminated between RC patients and the control group. Receiver operating characteristics analysis of the combination of the two parameters showed an area under the curve (AUC) of 0.918. Moreover, patients who presented a pathological complete response (pCR) to treatment had lower plasma pre-NRCT valine concentrations (AUC of 0.826). Patients who had a relapse had lower concentrations of succinate (AUC of 0.833). The results of the present study illustrate the usefulness of investigating alterations in oxidative stress and metabolism in RC. Due to the small number of patients studied, our results must be considered preliminary, but they suggest that the determination of circulating levels of PON1 and α-ketoglutarate might be a valuable tool for the early diagnosis of RC, while the determination of valine and succinate might effectively predict pCR and the appearance of relapse.