[Evolution of patients infected with SARS-CoV-2 according to previous metabolic status]

Comparative assessment of phenotypic markers in patients with chronic inflammation: Differences on Bifidobacterium concerning liver status

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Body mass index and fat influences the role of Bifidobacterium genus in lupus patients concerning fibrinogen levels

Introduction

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.

Methods

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.

Results

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.

Discussion

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.

Comparison of Metabolic Syndrome, Autoimmune and Viral Distinctive Inflammatory Related Conditions as Affected by Body Mass Index

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.

Modulatory role of Faecalibacterium on insulin resistance and coagulation in patients with post-viral long haulers depending on adiposity

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.

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.