Chronic viral inflammation is associated with oxidative stress and changes in gut microbiota. The Mediterranean diet (MD), with recognized anti-inflammatory and antioxidant properties, modulates gut microorganisms, specifically on the interaction between extra virgin olive oil, a key component of the MD with well-documented antioxidant effects. This study investigated the influence of adherence to MD and antioxidant-rich foods (extra virgin olive oil) on biochemical, inflammatory, and microbiota profiles in patients with chronic inflammation defined as a prolonged inflammatory response due to immune dysregulation following the acute phase of the viral infection. Participants were classified into low (n = 54) and high (n = 134) MD adherence groups (cut-off of 7 points based on previous studies utilizing the same threshold in the assessment of MD adherence). Gut microbiota was sequenced using the 16S technique, and the adherence to MD was assessed using a validated questionnaire for a Spanish population. High adherence to the MD was linked to significant improvements in inflammatory and oxidative stress markers, including reductions in LDL-cholesterol, glucose, and lactate dehydrogenase (LDH) levels, an indicative of redox balance, as well as a significant higher consumption of antioxidant foods. Moreover, gut microbiota analysis revealed distinct compositional shifts and a lower abundance of the Oscillibacter genus in the high adherence group. Notably, a significant interaction was observed between MD adherence and extra virgin olive oil consumption, with Oscillibacter abundance influencing LDH levels, suggesting that the MD antioxidant properties may modulate inflammation through gut microbiota-mediated mechanisms. These findings provide new evidence that adherence to the Mediterranean diet can reduce inflammatory markers in patients with long-COVID-19, a population that has not been extensively studied, while also highlighting the potential role of the bacterial genus Oscillibacter in modulating this effect.

Background: Obesity is a major global public health issue with no fully satisfactory solutions. Most nutritional interventions rely on caloric restriction, with varying degrees of success. Very low-calorie ketogenic diets (VLCKD) have demonstrated rapid and sustained weight loss by inducing ketone bodies through lipolysis, reducing appetite, and preserving lean mass while maintaining metabolic health. Methods: A prospective clinical study analyzed sociodemographic, anthropometric, and adherence data from 7775 patients undergoing a multidisciplinary nutritional single-arm intervention based on a commercial weight-loss program. This method, using protein preparations with a specific balanced nutritional profile, aimed to identify key predictors of weight-loss success and classify population phenotypes with shared baseline characteristics and weight-loss patterns to optimize treatment personalization. Results: Statistical and machine learning analyses revealed that male gender (−9.2 kg vs. −5.9 kg) and higher initial body weight (−8.9 kg vs. −4.0 kg) strongly predict greater weight loss on a VLCKD, while age has a lesser impact. Two distinct population clusters emerged, differing in age, sex, follow-up duration, and medical visits, demonstrating unique weight-loss success patterns. These clusters help define individualized strategies for optimizing outcomes. Conclusions: These findings translationally support associations with the efficacy of a multidisciplinary VLCK weight-loss program and highlight predictors of success. Recognizing variables such as sex, age, and initial weight enhances the potential for a precision-based approach in obesity management, enabling more tailored and effective treatments for diverse patient profiles and prescribe weight loss personalized recommendations.

Metabolic syndrome (MS) and systemic lupus erythematosus (SLE) represent two pathophysiologically distinct chronic conditions associated with elevated cardiovascular risk. Emerging evidence links gut microbiota to host lipid metabolism and lipoprotein function. This investigation aimed to analyze disease-specific relations between gut microbiota and an advanced lipoprotein profiling in an overweight population affected by MS and SLE. A total of 85 individuals with MS and 66 with SLE, all overweight, were included. Anthropometric, body composition, and biochemical parameters were assessed. Lipoprotein profiles were quantified by ¹ H-NMR spectroscopy, and gut microbiota composition via 16S rRNA sequencing. Machine learning Boruta feature selection identified microbial taxa linked to lipid traits. Regression models evaluated microbiota–LDL particle associations by disease type. Participants in the MS group showed more unfavorable values in anthropometric, body composition and clinical biochemistry. Advanced lipoprotein analysis revealed that MS individuals had higher levels of IDL-C, VLDL-TG, small LDL-P, and total LDL-P, with decreased HDL-C, reflecting a more atherogenic profile. Gut microbiota analysis identified Alistipes as a key discriminant taxon. A significant association was observed between Alistipes abundance and small LDL particle concentrations, being modulated by disease type, suggesting that the association between gut microbiota and lipid metabolism may differ by disease type. Participants with metabolic syndrome showed unfavorable anthropometric, clinical, and lipoprotein profiles, along with distinct gut microbiota, compared to individuals with the autoimmune-driven SLE. The results highlight condition-specific host–microbiota–lipid relationship and supports the use of novel approaches to guide precision strategies for cardiovascular risk reduction in high-risk populations.

Translational research has documented the conjoint beneficial relationships between dietary and physical activity habits concerning weight maintenance. However, the precise interplay between diet and exercise impacting body composition remains unclear, challenging personalized interventions. This study aimed to explore potential interactions and effect modifications of these factors affecting the body mass index (BMI) within an online adult cohort. Data from 11,883 NUTRiMDEA cohort participants were analyzed in this cross-sectional study, categorizing individuals by age, sex, and BMI using linear regression models to assess the interactions between lifestyle factors and adiposity. Significant differences emerged in anthropometry, lifestyle, and health-related quality of life (HRQoL) across categories. The combined effect of diet and physical activity had a greater impact on BMI than physical activity or Mediterranean diet adherence alone, with lower BMI as physical activity levels increased (β: −0.5) and adherence to the Mediterranean diet decreased, where a modification effect between them was identified (β: −0.28). Participants with lower Mediterranean diet adherence displayed superior BMI when physical activity was low, but when activity levels were higher, their BMI aligned with those with healthier dietary habits. An interaction link between lifestyle factors and BMI was found, showing the differential effects of the Mediterranean diet and physical activity combination concerning adiposity.

Childhood obesity continues to rise worldwide. Family gut microorganisms may be associated with childhood obesity. The aim of the study was to analyze bacterial similarities in fecal microbiota composition between parent-offspring pairs as linked to body weight. A total of 146 father/mother and offspring pairs were categorized into four groups according to the weight status of the parent-child pair as follows: group 1, parent and child with normal weight; group 2, parent and child with overweight/obesity; group 3, parent with normal weight and child with overweight/obesity; group 4, parent with overweight/obesity and child with normal weight. Anthropometric measurements and lifestyle assessments were performed in all participants. Microbiota characteristics were determined by 16S ribosomal RNA gene sequencing. Logistic regression models were performed to determine whether the abundance of any bacteria was able to predict childhood obesity. Moreover, receiver operating characteristic curves were fitted to define the relative diagnostic strength of bacterial taxa for the correct identification of childhood obesity. The absence/abundance of Catenibacterium mitsuokai, Prevotella stercorea, Desulfovibrio piger, Massiliprevotella massiliensis, and Phascolarctobacterium succinatutens was involved in body weight family associations. A positive relationship between P. succinatutens richness from parents and M. massiliensis from children was observed with regard to body weight status (odds ratio, 1.14, P = 0.013). This study describes five potential gut bacteria that may be putatively involved in family weight status relationships and appear to be useful for predicting obesity.

Background/Objectives: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a multifactorial origin involving genetic, epigenetic, and environmental determinants as well as some risk factors. Genetic predisposition has been quantified through polygenic risk scores (PRS), which integrate the cumulative effect of multiple single nucleotide variants (SNVs) associated with disease risk. Despite extensive research on immune and inflammatory pathways in SLE, the interplay between genetic susceptibility and metabolic dysfunction remains poorly understood. This study aimed to explore associations between SLE-related PRS and metabolic, inflammatory, and clinical parameters in adults participating in the METAINFLAMACIÓN-CM project (Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain). Methods: Ninety-three participants were included: 56 SLE patients and 37 individuals with metabolic syndrome (MetS) as a reference group. PRS were computed based on validated lupus-associated SNVs. Results: SLE patients showed a distinct metabolic profile compared with the MetS group, characterized by lower BMI, visceral fat, blood pressure, glucose, and liver enzyme levels. Within the SLE cohort, PRS values varied markedly and correlated with specific clinical and biochemical features. Linear regression models revealed a significant inverse association between PRS in SLE and ferritin levels, whereas other metabolic and inflammatory markers (glucose, IL-6, LDL, CRP, neutrophils) were directly influenced by clinical factors. Conclusions: Polygenic predisposition contributes to variability in SLE metabolic phenotype but does not independently drive most inflammatory parameters. SLE patients displayed metabolic and inflammatory alterations relevant to cardiovascular risk, highlighting the importance of comprehensive cardiometabolic assessment. Integrating PRS with metabolic profiling may support precision personalized management and improve cardiovascular risk evaluation in SLE.