Background: SGLT2 inhibitors are key therapies in heart failure (HF), but their combined multidomain effects have not been analyzed together. Methods: We conducted a PROSPERO-registered (CRD420251235850) systematic review and meta-analysis of all randomized controlled trials (RCTs) comparing SGLT2i (dapagliflozin, empagliflozin, canagliflozin, sotagliflozin) with placebo in adults with HF, regardless of ejection fraction or diabetes status. We searched PubMed/MEDLINE, Embase, Cochrane CENTRAL, and Web of Science through 1 February 2025. Outcomes were grouped into four domains: (1) clinical events, (2) symptoms/health status (Kansas City Cardiomyopathy Questionnaire, KCCQ), (3) functional capacity (6 min walk distance, peak VO₂), and (4) cardiac remodeling/energetics (cardiac MRI, 31P-MRS). We used random-effects models with Hartung–Knapp adjustment and assessed heterogeneity by I² and prediction intervals. Results: Eleven RCTs with 23,812 patients (HFrEF, HFmrEF, HFpEF, and acute or recently decompensated HF) were included. SGLT2i lowered the risk of cardiovascular death or first HF hospitalization by 23% (HR 0.77, 95% CI 0.72–0.82; p < 0.0001; I² = 28%; prediction interval 0.68–0.87), with similar effects across ejection fraction, diabetes status, and presentation type. All-cause and cardiovascular mortality dropped by 12% (HR 0.88, 95% CI 0.81–0.96) and 14% (HR 0.86, 95% CI 0.78–0.95), respectively. SGLT2i improved KCCQ—Clinical Summary Score by 4.6 points (95% CI 3.4–5.8; p < 0.0001) and increased the odds of a ≥5-point improvement (OR 1.49, 95% CI 1.32–1.68; NNT = 12). Six-minute walk distance increased by 21.8 m (95% CI 9.4–34.2; p = 0.001), and mechanistic trials showed significant reverse remodeling (ΔLVEDV −19.8 mL, ΔLVEF +6.1%; both p < 0.001). No improvement was observed in myocardial PCr/ATP ratio. Safety was favorable, with no excess ketoacidosis or severe hypoglycemia. Conclusions: This multidomain synthesis demonstrates that SGLT2 inhibitors provide consistent, robust reductions in mortality and hospitalizations, while also delivering early, clinically meaningful improvements across multiple patient-centered domains. These results establish SGLT2i as a foundational component of contemporary HF management.

Background/Objectives: Antibiotic exposure is highly prevalent in patients hospitalized with COVID-19, yet the relationship between specific prescribing patterns, microbiologically confirmed secondary infections, and clinical outcomes remains incompletely understood, particularly in settings with high antimicrobial resistance. Methods: This single-center retrospective observational cohort included 395 consecutive adults hospitalized with RT-PCR-confirmed COVID-19 in a tertiary infectious diseases hospital. Data on demographics, comorbidities, baseline disease severity, antimicrobial prescribing (timing, WHO AWaRe class, duration, monotherapy/combination, escalation/de-escalation), microbiological results, and outcomes were extracted from electronic records and the microbiology information system. The primary outcome was microbiologically confirmed secondary infection; secondary outcomes were ICU admission, invasive mechanical ventilation, length of stay, and in-hospital mortality. Multivariable logistic regression and survival analyses assessed associations between antibiotic exposure and outcomes. Results: Overall, 88.4% of patients received systemic antibiotics, predominantly initiated within 24 h of admission and mostly empirical; 58.7% received combination regimens, with frequent use of Watch/Reserve agents. Secondary infections occurred in 28.4% of patients, were hospital-acquired in 82.1%, and involved multidrug-resistant organisms in 41.1% of cases. Any antibiotic exposure was independently associated with secondary infection (adjusted odds ratio, aOR 2.15; 95% CI 1.42–3.27), while prolonged therapy (≥7 days), Watch/Reserve use, and early initiation showed additional risk gradients. Antibiotic exposure was also associated with higher odds of ICU admission, invasive mechanical ventilation, prolonged hospitalization, and in-hospital mortality after adjustment. Conclusions: In this real-world COVID-19 cohort, broad and largely empirical antibiotic use was common and strongly associated with hospital-acquired, often multidrug-resistant secondary infections and worse clinical outcomes. These findings highlight the need for reinforced antimicrobial stewardship focusing on restrictive early broad-spectrum use, AWaRe-guided agent selection, systematic 48–72 h reassessment with de-escalation, and minimization of treatment duration.

Background and Objectives: Dysglycemia is a major determinant of adverse outcomes in COVID-19, yet the separate contributions of poor glycemic control and glycemic variability (GV) remain incompletely defined. We conducted a systematic review and meta-analysis of observational cohort studies (both prospective and retrospective) to quantify the impact of chronic hyperglycemia and glucose instability on disease severity, intensive care requirements, and mortality in patients with COVID-19. Materials and Methods: We searched PubMed, Scopus, and Web of Science from January 2020 to October 2024 for observational cohort studies reporting clinically relevant COVID-19 outcomes stratified by glycemic control or GV. Dysglycemia definitions varied across studies (HbA1c-based chronic hyperglycemia, fasting glucose, or admission/in-hospital hyperglycemia). GV was assessed using metrics including mean amplitude of glycemic excursions (MAGE), standard deviation (SD), coefficient of variation (CV), or maximum daily glucose difference. Twelve studies met inclusion criteria and were included in qualitative synthesis; five studies were eligible for quantitative synthesis of clinical outcomes. Random-effects DerSimonian–Laird models were applied due to anticipated clinical heterogeneity. Heterogeneity was evaluated using Cochran’s Q, τ², and I² statistics. Results: Overall, 12 observational studies (9 prospective and 3 retrospective cohorts; n = 1,008,310 patients) were included. In quantitative analyses of five eligible cohorts, poor glycemic control was associated with a significantly increased risk of severe or critical COVID-19 (pooled RR = 1.75, 95% CI: 1.45–2.11; I² = 29%), ICU admission (RR = 1.54, 95% CI: 1.18–2.01), and mechanical ventilation (RR = 1.72, 95% CI: 1.31–2.26). Three studies evaluating GV demonstrated a strong association with adverse outcomes (pooled RR = 2.07, 95% CI: 1.71–2.50; I² = 0%); this low heterogeneity should be interpreted cautiously given the limited number of studies. GV remained associated with mortality in multivariable models, indicating that glycemic variability is separately associated with mortality as a clinically relevant prognostic risk marker in hospitalized COVID-19 patients. Conclusions: Both chronic hyperglycemia and elevated glycemic variability are each associated with increased risk of severe COVID-19 outcomes. Glycemic variability appeared to be a consistent, low-heterogeneity prognostic marker of mortality, being separately associated with higher death risk in hospitalized COVID-19 patients, highlighting its potential utility as a dynamic metabolic biomarker. Early identification and targeted management of dysglycemia—especially glucose instability—may improve prognosis in hospitalized COVID-19 patients. PROSPERO: CRD420251250718.

Background/Objectives: Orthodontic treatment induces controlled mechanical forces that alter the periodontal environment, including changes in oral microbiota composition and activation of local inflammatory pathways. Despite the widespread and growing use of orthodontic appliances across all age groups, the magnitude, timing, and multi-domain biological impact of these changes have not been comprehensively quantified in a single systematic synthesis. This systematic review and meta-analysis aimed to synthesize the available evidence on periodontal clinical parameters, oral microbiota composition, and local inflammatory biomarkers associated with orthodontic treatment using fixed appliances and clear aligners, and to provide a structured, GRADE-rated evidence base for clinical practice. Methods: A systematic review and meta-analysis was conducted in accordance with PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus, and Web of Science were searched from inception to March 2026. Prospective cohort studies, longitudinal clinical studies, and randomized controlled trials evaluating periodontal parameters, oral microbiota, and inflammatory biomarkers during orthodontic treatment were included. Quantitative synthesis was performed using mean differences or standardized mean differences with 95% confidence intervals, primarily assessing within-group (pre–post) changes. Results: Eighteen studies (n = 812 patients; follow-up 3–12 months) met inclusion criteria. Fixed orthodontic appliances were consistently associated with transient increases in plaque index (MD 0.45, 95% CI 0.32–0.58; I² = 62%), gingival index (MD 0.38, 95% CI 0.25–0.51; I² = 55%), and bleeding on probing (MD 15.2%, 95% CI 10.1–20.3%; I² = 48%), particularly during early treatment phases. Microbiological analyses demonstrated within-group shifts toward increased prevalence of periodontopathogenic species (Streptococcus mutans OR 2.45, 95% CI 1.89–3.18; Porphyromonas spp. OR 2.14, 95% CI 1.67–2.75) in patients treated with fixed appliances. Local inflammatory responses were characterized by elevated IL-1β (MD 1.2, 95% CI 0.8–1.6) and IL-6 (MD 0.9, 95% CI 0.6–1.2) in gingival crevicular fluid. Certainty of evidence was rated moderate for plaque and gingival indices and low for microbiological and inflammatory outcomes (GRADE). Conclusions: Orthodontic treatment—particularly with fixed appliances—is associated with transient, reversible deterioration of periodontal indices, shifts toward a more dysbiotic oral microbiome, and elevation of local inflammatory mediators in gingival crevicular fluid during active treatment phases. These changes are manageable through structured preventive protocols and regular periodontal monitoring. Future prospective studies with concurrent control groups and standardized multi-domain outcome measures are needed to better define the magnitude and reversibility of these biological responses. PROSPERO: CRD420261336117.