An Interplay between Oxidative Stress (Lactate Dehydrogenase) and Inflammation (Anisocytosis) Mediates COVID-19 Severity Defined by Routine Clinical Markers.

Viral infections activate the innate immune response and the secretion of inflammatory cytokines. They also alter oxidative stress markers, which potentially can have an involvement in the pathogenesis of the disease. The aim of this research was to study the role of the oxidative stress process assessed through lactate dehydrogenase (LDH) on the severity of COVID-19 measured by oxygen saturation (SaO2) and the putative interaction with inflammation. The investigation enrolled 1808 patients (mean age of 68 and 60% male) with COVID-19 from the HM Hospitals database. To explore interactions, a regression model and mediation analyses were performed. The patients with lower SaO2 presented lymphopenia and higher values of neutrophils-to-lymphocytes ratio and on the anisocytosis coefficient. The regression model showed an interaction between LDH and anisocytosis, suggesting that high levels of LDH (>544 U/L) and an anisocytosis coefficient higher than 10% can impact SaO2 in COVID-19 patients. Moreover, analysis revealed that LDH mediated 41% (p value = 0.001) of the effect of anisocytosis on SaO2 in this cohort. This investigation revealed that the oxidative stress marker LDH and the interaction with anisocytosis have an important role in the severity of COVID-19 infection and should be considered for the management and treatment of the oxidative phenomena concerning this within a precision medicine strategy.

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Hematological- and Immunological-Related Biomarkers to Characterize Patients with COVID-19 from Other Viral Respiratory Diseases.

COVID-19 has overloaded health system worldwide; thus, it demanded a triage method for an efficient and early discrimination of patients with COVID-19. The objective of this research was to perform a model based on commonly requested hematological variables for an early featuring of patients with COVID-19 form other viral pneumonia. This investigation enrolled 951 patients (mean of age 68 and 56% of male) who underwent a PCR test for respiratory viruses between January 2019 and January 2020, and those who underwent a PCR test for detection of SARS-CoV-2 between February 2020 and October 2020. A comparative analysis of the population according to PCR tests and logistic regression model was performed. A total of 10 variables were found for the characterization of COVID-19: age, sex, anemia, immunosuppression, C-reactive protein, chronic obstructive pulmonary disease, cardiorespiratory disease, metastasis, leukocytes and monocytes. The ROC curve revealed a sensitivity and specificity of 75%. A deep analysis showed low levels of leukocytes in COVID-19-positive patients, which could be used as a primary outcome of COVID-19 detection. In conclusion, this investigation found that commonly requested laboratory variables are able to help physicians to distinguish COVID-19 and perform a quick stratification of patients into different prognostic categories.

Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort.

The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the "COVID Data Save Lives" were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11-30.54, and Cluster C 14.29 CI: 6.66-34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64-3.01, and Cluster-C 1.71 CI: 1.08-2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.

Interaction of ACEI antihypertensive agent's administration with the inflammatory status at admission concerning COVID-19 clinical stay outcomes.

Interactions between anti-hypertensive agents (ACEI), comorbidities, inflammation, and stress status may impact hospital stay duration in COVID-19 patients. This retrospective study analyzed epidemiological data, comorbidities, metabolic/inflammatory markers, and clinical information from 165 SARS-CoV-2 positive patients. In a multiple linear regression model, an IL-6 higher than 100 mg/L, glucose at admission (baseline levels at the hospital entry), and the interaction between ACEI administration and LDH predicted the days of hospital admission (P < 0.001). In conclusion, hypertensive patients suffering more severe inflammatory condition assessed by LDH levels clinically benefited more and reduced the hospital stay when prescribed ACEI agents than those with lower systemic baseline inflammation at admission.

Proinflammatory and Hepatic Features Related to Morbidity and Fatal Outcomes in COVID-19 Patients.

OBJECTIVE: to screen putative associations between liver markers and proinflammatory-related features concerning infectious morbidity and fatal outcomes in COVID-19 patients. METHODS: a total of 2094 COVID-19 positive patients from the COVID-DATA-SAFE-LIFES cohort (HM hospitals consortium) were classified according to median values of hepatic, inflammatory, and clinical indicators. Logistic regression models were fitted and ROC cures were generated to explain disease severity and mortality. RESULTS: intensive care unit (ICU) assistance plus death outcomes were associated with liver dysfunction, hyperinflammation, respiratory insufficiency, and higher associated comorbidities. Four models including age, sex, neutrophils, D-dimer, oxygen saturation lower than 92%, C-reactive protein (CRP), Charlson Comorbidity Index (CCI), FIB-4 and interactions with CRP, neutrophils, and CCI explained ICU plus death variance in more than 28%. The predictive values of ROC curves were: FIB-4 (0.7339), AST/ALT ratio (0.7107), CRP (0.7003), CCI index (0.6778), neutrophils (0.6772), and platelets (0.5618) concerning ICU plus death outcomes. CONCLUSIONS: the results of this research revealed that liver and proinflammatory features are important determinants of COVID-19 morbidity and fatal outcomes, which could improve the current understanding of the COVID-19 physiopathology as well as to facilitate the clinical management and therapy decision-making of this disease under a personalized medicine scope.

[Evolution of patients infected with SARS-CoV-2 according to previous metabolic status]. / Evolución de la infección por SARS-CoV-2 en función del estado metabólico previo del paciente.

INTRODUCTION: Introduction: coronavirus disease 2019 (COVID-19) encompasses a wide spectrum of symptoms, including respiratory, gastrointestinal, hematological, and dermatological manifestations. The virus interaction with cells located in the respiratory tract causes the release of inflammatory mediators, whose involvement could be exacerbated by co-existing obesity, diabetes, and cardiovascular events. Objectives: the objective of this research was to analyze the clinically metabolic status in patients who have suffered COVID-19 disease in order to predict the outcome. Methods: this research is a retrospective study based on a cohort of 165 consecutively admitted patients with criteria for COVID-19 pneumonia according to WHO guidelines at the Hospital Universitario Puerta de Hierro between March and April 2020. Recorded variables included demographic and epidemiological data plus diagnoses as well as morbid complications during hospitalization. The Biochemistry Unit Laboratory carried out laboratory analyses according to validated operational procedures. The statistical tests included univariate and multivariate models adjusted for baseline characteristics and clinically relevant features. Results: the most frequent comorbidity in our cohort was arterial hypertension (44.0 %), followed by dyslipidemia (32.1 %), obesity (30.9 %), and diabetes mellitus (20.0 %). The association between admission to the intensive care unit (ICU) with body mass index (BMI) in a multivariate model was statistically significant, evidencing that obese subjects (BMI ≥ 30 kg/m2) have a 19 % higher risk of requiring ICU care. The univariate model revealed a statistically significant association between obesity and ICU admission and length of hospital stay (p < 0.05). The relationship between baseline blood glucose and in-hospital mortality was also statistically significant (p = 0.03), as well as with total cholesterol and ICU admission (p = 0.007). Conclusions: obesity is related to a longer time of hospitalization and a higher rate of admissions to the ICU. Low total cholesterol levels and abnormal baseline blood glucose were risk factors for ICU requirement and in-hospital mortality. Patient categorization based on obesity could be valuable in the development of a precision medicine model within the COVID-19 pandemic.

INTRODUCCIÓN: Introducción: la enfermedad por COVID-19 engloba un amplio espectro de síntomas entre los que destacan los trastornos respiratorios, digestivos, hematológicos y dermatológicos. La interacción del virus con las células ubicadas en el tracto respiratorio provoca la liberación de mediadores inflamatorios cuya producción podría estar relacionada con la obesidad, la diabetes y los eventos cardiovasculares. Objetivos: analizar el estado metabólico al ingreso de los pacientes infectados por SARS-CoV-2 y su capacidad para predecir el desenlace clínico. Métodos: este trabajo consiste en un estudio retrospectivo basado en una cohorte de 165 pacientes ingresados consecutivamente en el Hospital Universitario Puerta de Hierro Majadahonda entre marzo y abril de 2020 con criterios de neumonía COVID-19 según las pautas de la OMS. Las variables registradas incluyeron datos socio-demográficos y epidemiológicos, herramientas diagnósticas y complicaciones durante el ingreso hospitalario. El Servicio de Bioquímica del centro realizó los análisis de laboratorio empleando procedimientos validados. El estudio estadístico incluye modelos univariantes y multivariados, ajustados por las características basales clínicamente relevantes de la población. Resultados: la comorbilidad más frecuente en nuestra población fue la hipertensión arterial (44,0 %), seguida por la dislipemia (32,1 %), la obesidad (30,9 %) y la diabetes mellitus (20,0 %). En el análisis multivariante, la asociación del ingreso en la Unidad de Cuidados Intensivos (UCI) con el índice de masa corporal (IMC) resultó estadísticamente significativa, con un 19 % más de riesgo en aquellos pacientes con IMC ≥ 30 kg/m2. El modelo univariante reveló la asociación estadísticamente significativa de la obesidad y el ingreso en la UCI con la duración de la estancia hospitalaria (p < 0,05). La relación entre glucemia basal y mortalidad intrahospitalaria también resultó estadísticamente significativa (p = 0,03). Los niveles bajos de colesterol total se asociaron a una tasa mayor de ingresos en la UCI (p = 0,007). Conclusiones: la obesidad se asocia a una mayor estancia hospitalaria y necesidad de ingreso en la UCI en los pacientes infectados por el SARS-CoV-2. El descenso en las cifras de colesterol total y una glucemia basal alterada son factores de riesgo del ingreso en la UCI y la mortalidad intrahospitalaria. La categorización en función del grado de obesidad de los pacientes podría ser de utilidad en el desarrollo de un modelo de medicina de precisión en el contexto de la COVID-19.

Exploring Host Genetic Polymorphisms Involved in SARS-CoV Infection Outcomes: Implications for Personalized Medicine in COVID-19.

OBJECTIVE: To systematically explore genetic polymorphisms associated with the clinical outcomes in SARS-CoV infection in humans. METHODS: This comprehensive literature search comprised available English papers published in PubMed/Medline and SCOPUS databases following the PRISMA-P guidelines and PICO/AXIS criteria. RESULTS: Twenty-nine polymorphisms located in 21 genes were identified as associated with SARS-CoV susceptibility/resistance, disease severity, and clinical outcomes predominantly in Asian populations. Thus, genes implicated in key pathophysiological processes such as the mechanisms related to the entry of the virus into the cell and the antiviral immune/inflammatory responses were identified. CONCLUSIONS: Although caution must be taken, the results of this systematic review suggest that multiple genetic polymorphisms are associated with SARS-CoV infection features by affecting virus pathogenesis and host immune response, which could have important applications for the study and understanding of genetics in SARS-CoV-2/COVID-19 and for personalized translational clinical practice depending on the population studied and associated environments.