The relationship between systemic lupus erythematosus (SLE) and low-grade metabolic inflammation (MI) with the microbiota is crucial for understanding the pathogenesis of these diseases and developing effective therapeutic interventions. In this context, it has been observed that the gut microbiota plays a key role in the immune regulation and inflammation contributing to the exacerbation through inflammatory mediators. This research aimed to describe similarities/differences in anthropometric, biochemical, inflammatory, and hepatic markers as well as to examine the putative role of gut microbiota concerning two inflammatory conditions: SLE and MI.

Data were obtained from a cohort comprising adults with SLE and MI. Faecal samples were determined by 16S technique. Statistical analyses compared anthropometric and clinical variables, and LEfSe and MetagenomeSeq were used for metagenomic data. An interaction analysis was fitted to investigate associations of microbiota with fatty liver index (FLI) depending on the inflammatory condition.

Participants with low-grade MI showed worse values in anthropometry and biochemicals compared with patients with SLE. The liver profile of patients with MI was unhealthier, while no relevant differences were found in most of the inflammatory markers between groups. LEfSe analysis revealed an overrepresentation of Bifidobacteriaceae family in SLE group. An interactive association between gut Bifidobacterium abundance and type of disease was identified for FLI values, suggesting an effect modification of the gut microbiota concerning liver markers depending on the inflammatory condition.

This study found phenotypical and microbial similarities and disparities between these two inflammatory conditions, evidenced in clinical and hepatic markers, and showed the interactive interplay between gut Bifidobacterium and liver health (measured by FLI) that occur in a different manner depending on the type of inflammatory disease. These results underscore the importance of personalized approaches and individual microbiota in the screening of different inflammatory situations, considering unique hepatic and microbiota profiles.

Metabolic disorders and autoimmune diseases elicit distinct yet interconnected manifestations of inflammation, which may be boosted by an excess of body adiposity. The purpose of this investigation was to analyze anthropometric, biochemical, and inflammatory/coagulation variables concerning patients diagnosed with systemic lupus erythematosus (SLE) exploiting low-grade metabolic inflammation (MI), as reference.

A population stratification by body mass index (BMI), allowed to assess the impact of adiposity on the putative role of gut microbiota composition on coagulation markers. A total of 127 participants with MI and SLE were categorized into two main groups based on their BMI, following WHO criteria: a low BMI group (<30 kg/m2) and a high BMI group (≥30 kg/m2). Each group included recorded data on demographics, comorbidities, and key clinical markers. Anthropometric and body composition variables, clinical features, and inflammatory/coagulation markers were measured while fecal 16S rRNA sequencing was examined at the genus Bifidobacterium. Regression models were fitted to evaluate the relationship between gut microbiota, inflammatory/coagulation markers, and body weight in these types of diseases.

The study revealed worse clinical outcomes in anthropometric, body composition, and clinical markers in low-grade MI conditions as compared to SLE. However, inflammatory and coagulation markers such as C-reactive protein (CRP) and fibrinogen were significantly more elevated in patients with SLE, which was exacerbated by high BMI/ body fat as compared to the other screened groups. An interaction analysis revealed that fibrinogen levels showed different trends when Bifidobacterium was increased depending on BMI/adiposity, which evidenced an effect modification by this microorganism in patients with SLE.

These findings underline that gut microbiota composition, particularly the presence of Bifidobacterium, may play a crucial role in modulating inflammation and coagulation processes in patients with SLE and high fat. These insights highlight the potential of targeting gut microbiota as a therapeutic strategy to mitigate inflammation and improve clinical outcomes in SLE patients.

Background: Metabolic inflammation (MI), long COVID (LC) and systemic lupus erythematosus (SLE) share some metabolic common manifestations and inflammatory pathophysiological similarities. Health-related quality of life (HRQoL) and metabolic age are indicators of health status. The "METAINFLAMMATION-CM Y2020/BIO-6600" project, a prospective controlled study, aimed to identify differential diagnostic tools and clinical features among three inflammatory conditions by comparing obesity status (low BMI vs. high BMI). Methods: A total of 272 adults of both Caucasian and Hispanic descent, diagnosed with MI, LC or SLE, and a range of BMI, were recruited. Clinical and phenotypic traits were measured to analyze body composition, metabolic and inflammatory markers, HRQoL data, metabolic age and lifestyle habits using a 3 × 2 (disease × BMI) factorial design. Results: Some inflammatory related variables, such as fibrinogen, RDW (red cell blood distribution width), ESR (erythrocyte sedimentation rate) and NLR (neutrophil/lymphocyte ratio), showed effect modifications depending on the BMI and disease type. In relation to HRQoL, the Physical Component Summary (PCS12) showed no relevant changes, while the Mental Component Summary (MCS12) showed a significant effect modification according to the disease type and BMI (p < 0.05). Furthermore, a significant interaction was identified between the disease type and BMI in relation to metabolic age (p = 0.02). Conclusions: Assessing the impact of BMI on these three inflammatory diseases may help to prevent clinical complications and to design personalized treatments, especially for patients with SLE, who have a worse prognosis with an increased BMI compared to the other two inflammatory diseases.

Patients with Post-viral long hauler encompass lasting symptoms and comorbid complexities, often exacerbated in individuals with excessive body weight. The aim was to study gut microbiota in 130 patients with post-viral long hauler stratified by body mass index (BMI) and the relationship between inflammation and microbiota. Significant higher values were found for anthropometric variables and markers of glucose and dyslipidemia in individuals with higher BMI, as well as elevated levels of C-reactive protein, fibrinogen, IL-6, uric acid, and D-dimer. An interactive association showed an interplay between Faecalibacterium, D-dimer levels, and insulin resistance. This investigation showed that anthropometric, biochemical, and inflammatory variables were impaired in patients with post-viral long haulers with higher BMI. In addition, gut microbiota differences were found between groups and a modification effect on Faecalibacterium abundance regarding insulin resistance and D-dimer. These findings suggest that considering adiposity and gut microbiota structure and composition may improve personalized clinical interventions in patients with chronic inflammation.

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.

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.

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.

An increasing number of scientific journals have proposed a connection between tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) and the severity of COVID-19. Vitamin D has been discussed as a potential therapy for COVID-19 due to its immunomodulatory effects. This meta-analysis aims to determine the relationship, if any, between TNF-α, IL-6, vitamin D, and COVID-19 severity and mortality.

The design of the study is a systematic review and meta-analysis. A literature search is performed using PubMed, Cochrane, ProQuest, and Google Scholar.

TNF-α insignificantly increases the risk of COVID-19 severity (adjusted odds ratio (aOR) = 1.0304; 95% CI 0.8178-1.2983; p = 0.80) but significantly increases the risk of COVID-19 mortality (crude hazard ratio (HR) = 1.0640; 95% CI 1.0259-1.1036; p = 0.0009). IL-6 significantly increases the risk of COVID-19 severity (aOR = 1.0284; 95% CI 1.0130-1.0441; p = 0.0003) and mortality (aOR = 1.0076; 95% CI 1.0004-1.0148; p = 0.04; adjusted hazard ratio (aHR) = 1.0036; 95% CI 1.0010-1.0061; p = 0.006). There is a statistically insignificant difference of the mean vitamin D levels between patients with severe COVID-19 and non-severe COVID-19 (mean difference (MD) = -5.0232; 95% CI 11.6832-1.6368; p = 0.14). A vitamin D deficiency insignificantly increases the risk of mortality of COVID-19 patients (aOR = 1.3827; 95% CI 0.7103-2.6916; p = 0.34).

IL-6 is an independent prognostic factor towards COVID-19 severity and mortality.

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.

to screen putative associations between liver markers and proinflammatory-related features concerning infectious morbidity and fatal outcomes in COVID-19 patients.

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.

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.

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.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent for the Coronavirus Disease 2019 (COVID-19). The COVID-19 pandemic has created unimaginable and unprecedented global health crisis. Since the outbreak of COVID-19, millions of dollars have been spent, hospitalization overstretched with increasing morbidity and mortality. All these have resulted in unprecedented global economic catastrophe. Several drugs and vaccines are currently being evaluated, tested, and administered in the frantic efforts to stem the dire consequences of COVID-19 with varying degrees of successes. Zinc possesses potential health benefits against COVID-19 pandemic by improving immune response, minimizing infection and inflammation, preventing lung injury, inhibiting viral replication through the interference of the viral genome transcription, protein translation, attachment, and host infectivity. However, this review focuses on the various mechanisms of action of zinc and its supplementation as adjuvant for vaccines an effective therapeutic regimen in the management of the ravaging COVID-19 pandemic. PRACTICAL APPLICATIONS: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent for the Coronavirus Disease 2019 (COVID-19), has brought unprecedented untold hardship to both developing and developed countries. The global race for vaccine development against COVID-19 continues with success in sight with attendant increasing hospitalization, morbidity, and mortality. Available drugs with anti-inflammatory actions have become alternative to stem the tide of COVID-19 with attendant global financial crises. However, Zinc is known to modulate several physiological functions including intracellular signaling, enzyme function, gustation, and olfaction, as well as reproductive, skeletal, neuronal, and cardiovascular systems. Hence, achieving a significant therapeutic approach against COVID-19 could imply the use of zinc as a supplement together with available drugs and vaccines waiting for emergency authorization to win the battle of COVID-19. Together, it becomes innovative and creative to supplement zinc with currently available drugs and vaccines.