Medical treatment of coronavirus 19 disease (COVID-19) is a therapeutic challenge. The available data strongly suggest that calcifediol treatment may reduce the severity of COVID-19, and corticosteroids are the treatment of choice worldwide for severe COVID-19. Both have a very similar action profile, and their combined use in patients may modify the contribution of each administered compound. Objective: To evaluate how treatment with calcifediol and/or corticosteroids in medical practice modified the need for ICU admission, death, or poor prognosis of patients hospitalized with COVID-19 during the first outbreaks. Design, patients and setting: A retrospective observational cohort study of patients admitted for COVID-19 to the Pneumology Unit of the Hospital Universitario Reina Sofía (Córdoba, Spain). Interventions: Patients were treated with calcifediol or/and corticosteroids with the best available therapy and standard care, according to clinical practice guidelines. Measurements: Admission to the intensive care unit (ICU) or death during hospitalization and poor prognosis. Results: Seven hundred and twenty-eight patients were included. According to the treatment received, they were included in four groups: calcifediol (n = 68), glucocorticoids (n = 112), both (n = 510), or neither (n = 38). Of the 578 patients treated with calcifediol, 88 were admitted to the ICU (15%), while of the 150 not treated with calcifediol, 39 required ICU admission (26%) (p < 0.01). Among the patients taking calcifediol without glucocorticoids, only 4 of 68 (5.8%) required ICU admission, compared to 84 of 510 (16.5%) treated with both (p = 0.022). Of the 595 patients who had a good prognosis, 568 (82.01%) had received treatment with calcifediol versus the 133 patients with a poor prognosis, of whom 90 (67.66%) had received calcifediol (p < 0.001). This difference was not found for corticosteroids. Interpretation: The treatment of choice for hospitalized patients with moderate or mild COVID-19 could be calcifediol, not administering corticosteroids, until the natural history of the disease reaches a stage of hyperinflammation.

Recent advances in proteomics have enabled the identification of early protein biomarkers and metabolic disturbances associated with type 2 diabetes (T2D), a major global health challenge. This systematic review and meta-analysis synthesize evidence from 27 studies comparing proteomic profiles of individuals with T2D and normoglycemic controls, selected from 2,422 initial records. The QUADOMICS assessment showed good methodological reporting for sample handling and proteomic analysis (>70% of studies), but over 60% lacked information on confounding clinical factors and biomarker validation. A qualitative synthesis focused on 85 recurrently reported proteins (≥8 studies), which showed strong interconnectivity and were involved in immune response, lipid-protein organization, detoxification, proteolysis, and coagulation, key pathways implicated in T2D. An omics-based meta-analysis identified seven promising protein biomarkers for T2D related to lipid/glucose metabolism (Q12907_LMAN2, P02652_POA2, P07602_PSPA, P09622_DLD); cell binding/adhesion (P12109_COL6A1, P12830_CDH1); and translational regulation and mitochondrial function (P35232_PHB). Random-effects meta-analysis revealed variation in effect sizes across studies for previously highlighted biomarkers, but three of them (P02763_ORM1, P00738_HP, P25311_AZGP1) exhibited considerable consistency. To enhance accessibility and further exploration of findings, we provide the interactive web tool metaMarkersT2D: https://jgcurras.shinyapps.io/metaMarkersT2D/.

Preterm delivery affects approximately 10% of pregnancies worldwide and remains a major clinical challenge due to the lack of reliable early predictive tools. Existing strategies are often invasive, relying on blood or amniotic fluid samples and requiring complex processing. In this study, we describe a novel non-invasive approach based on the multiplex detection of inflammatory cytokines in small urine volumes from pregnant women. To account for clinical and temporal variability, we applied Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to adjust for gestational age at sampling and obstetric factors. Correlation network analyses revealed cytokine interactions that distinguished preterm from term deliveries, with macrophage-derived cytokines—MIP-1α, MIP-1β, IL-15, and IL-22—emerging as central nodes. These findings highlight the involvement of the IL-1 pathway in the pathophysiology of preterm labor. Furthermore, urinary IL-5 and IL-31 levels correlated positively with pregnancy duration, whereas IL-1β and IL-1Ra in urine and TNFα in amniotic fluid showed inverse associations. Altogether, this non-invasive methodology provides insight into immune dynamics during pregnancy and offers a foundation for future studies focused on biomarker discovery and mechanistic understanding of preterm birth.