Cristian Tebé

Objectives: People with gout exhibit significant clinical heterogeneity due to diverse comorbidity profiles. This study aimed to identify distinct clinical phenotypes using cluster analysis and to evaluate differences in management, specifically urate-lowering therapy (ULT) patterns and sustained serum urate (SU) control. Methods: A population-based retrospective cohort study was conducted using the SIDIAP database (Catalonia, Spain), including 94 759 patients with incident gout (2012–23). A K-prototypes algorithm identified clusters based on demographics and major comorbidities. Outcomes included sustained SU control (defined as SU <6 mg/dl for ≥80% of the follow-up time) and ULT adherence (Medication Possession Ratio). Results: Three distinct clusters were identified. Cluster 1 (37.0%) was comprised of younger patients (mean age 54) with fewer comorbidities and the highest SU control. Cluster 2 (22.8%) included older patients (mean age 74) characterized by 100% type 2 diabetes prevalence, high obesity (51.3%), and the greatest cardiovascular burden. Cluster 3 (40.2%) featured older patients with hypertension and dyslipidaemia but no diabetes. Overall, management was suboptimal in all groups; only 12% of patients achieved sustained SU control. While Cluster 2 had higher ULT prescription rates, Cluster 1 showed better adherence and SU control. Conclusion: Gout manifests in three clearly differentiated clinical phenotypes, each potentially reflecting a distinct predominant aetiology of hyperuricaemia (isolated/genetic, insulin-resistance-mediated, or renal-vascular). The identification of these high-risk metabolic and hypertensive clusters underscores the need for comorbidity-driven management and tailored therapeutic strategies.

Two Cox‐based multistate modeling approaches are compared for modeling a complex multicohort event history process. The first approach incorporates cohort information as a fixed covariate, thereby providing a direct estimation of the cohort‐specific effects. The second approach includes the cohort as a stratum variable, which offers an extra flexibility in estimating the transition probabilities. Additionally, both approaches may include possible interaction terms between the cohort and a given prognostic predictor. Furthermore, the Markov property conditional on observed prognostic covariates is assessed using a global score test. Whenever departures from the Markovian assumption are revealed for a given transition, the time of entry into the current state is incorporated as a fixed covariate, yielding a semi‐Markov process. The two proposed methods are applied to a three‐wave dataset of COVID‐19‐hospitalized adults in the southern Barcelona metropolitan area (Spain), and the corresponding performance is discussed. While both semi‐Markovian approaches are shown to be useful, the preferred one will depend on the focus of the inference. To summarize, the cohort–covariate approach enables an insightful discussion on the behavior of the cohort effects, whereas the stratum–cohort approach provides flexibility to estimate transition‐specific underlying risks according to the different cohorts.

Pasireotide long-acting release (PAS-LAR) is a second-generation somatostatin receptor ligand (SRL) approved for acromegaly treatment. This meta-analysis aimed to evaluate the real-world effectiveness and safety of PAS-LAR in patients with acromegaly resistant to first-generation somatostatin receptor ligands (fgSRL). A systematic literature search was conducted in PubMed and Web of Science for real-world studies on PAS-LAR in acromegaly published between 2014 and 2023. Random-effects meta-analyses were performed on biochemical control rates, tumor shrinkage, and metabolic parameters. Twelve studies comprising 409 patients were included. The pooled rate of insulin-like growth factor 1 (IGF-1) control was 57.9% [95% CI: 48.4–66.8] and the percentage of patients with tumor shrinkage was 33.3% [95%CI: 19.7–50.4]. Significant reductions were observed in growth hormone standardized mean difference (SMD) 0.6 ng/mL [95% CI: 0.3 to 1.0] and IGF-1 levels SMD 0.9 ULN [95% CI: 0.4 to 1.4]. However, as expected, a worsening in glucose metabolism was noted as an increase in fasting glucose SMD − 0.8 mg/dL [95% CI: -1.0 to -0.5, p < 0.01], glycated hemoglobin SMD − 0.5% [95% CI: -0.7 to -0.2]. and type 2 diabetes mellitus prevalence SMD − 11.5% (95% CI: -17.5 to -5.5). PAS-LAR demonstrated higher effectiveness in real-world settings, with over 60% of patients achieving IGF-1 control compared to the around 30% efficacy observed in clinical trials. These findings suggest that PAS-LAR is an effective option for acromegaly patients resistant to fgSRL, but careful monitoring of glucose levels is essential. The high heterogeneity observed across studies emphasizes the need for identifying PAS-LAR response biomarkers to set-up individualized treatment approaches for optimizing patient outcomes.

Importance: Ambient air pollution and antimicrobial resistance pose significant global public health challenges. It is not known whether ambient air pollution is associated with increased consumption of antimicrobials.Objective: To assess whether a short-term association exists between ambient air pollution levels and antimicrobial consumption among the general population seeking primary care consultations for acute respiratory symptoms.Design, Setting, and Participants: This 2-stage cross-sectional ecological time series analysis study using data on daily ambient air pollution and antimicrobial consumption was conducted in the 11 largest cities in Catalonia, Spain, from June 23, 2012, to December 31, 2019, among all inhabitants aged 12 years or older. Statistical analysis was performed from November 2022 to December 2023.Exposures: Daily ambient air pollution (particulate matter of 10 μg/m³ [PM₁₀], particulate matter of 2.5 μg/m³ [PM_2.5], and nitrogen dioxide [NO₂]).Main Outcomes and Measures: The main outcome was antimicrobial consumption associated with primary care consultations for acute respiratory symptoms in the 30 days before and after the dispensing of the antimicrobial. Antimicrobial consumption was measured as defined daily doses (DDDs) per 1000 inhabitants per day.Results: Among 1 938 333 inhabitants (median age, 48 years [IQR, 34-65 years]; 55% female participants), there were 8 421 404 antimicrobial dispensations, with a median of 12.26 DDDs per 1000 inhabitants per day (IQR, 6.03-15.32 DDDs per 1000 inhabitants per day). The median adjusted morbidity score was 2.0 (IQR, 1.0-5.0). For the 1 924 814 antimicrobial dispensations associated with primary care consultations for acute respiratory symptoms, there was a significant correlation between increases of 10 μg/m³ in the concentration of the 3 pollutants studied and heightened antimicrobial consumption at day 0 (PM₁₀: relative risk [RR], 1.01 [95% CI, 1.01-1.02]; PM_2.5: RR, 1.03 [95% CI, 1.01-1.04]; NO₂: RR, 1.04 [95% CI, 1.03-1.05]). A delayed association emerged between increases in PM_2.5 concentration and antimicrobial consumption between day 7 (RR, 1.00 [95% CI, 1.00-1.01]) and day 10 (RR, 1.00 [95% CI, 1.00-1.01]) after exposure.Conclusions and Relevance: In this 2-stage cross-sectional study using ecological time series analysis, short-term exposure to air pollution was associated with increased antimicrobial use associated with primary care consultations for acute respiratory symptoms in the general population. This finding could contribute to informing policy decisions aimed at reducing air pollution and its associated risks, thereby promoting respiratory health and reducing antimicrobial use.

Meta‐analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta‐analysis in various clinical research topics, the number of published meta‐analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta‐analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy‐Amanida is a new tool that combines two R packages, “amanida” and “webchem”, to enable meta‐analysis of aggregate statistical data, like p‐value and fold‐change, while ensuring the compounds naming harmonization. The Easy‐Amanida app is implemented in Shiny, an R package add‐on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta‐analysis using Easy‐Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy‐Amanida beyond the metabolomics field.

Metabolic syndrome (MetS) is associated with alterations of lipoprotein structure and function that can be characterized with advanced lipoprotein testing (ADLT). The effect of the Mediterranean diet (MedDiet) and weight loss on the lipoprotein subclass profile has been scarcely studied. Within the PREDIMED-Plus randomized controlled trial, a sub-study conducted at Bellvitge Hospital recruiting center evaluated the effects of a weight loss program based on an energy-reduced MedDiet (er-MedDiet) and physical activity (PA) promotion (intervention group) compared with energy-unrestricted MedDiet recommendations (control group) on ADLT-assessed lipoprotein subclasses. 202 patients with MetS (n = 107, intervention; n = 95, control) were included. Lipid profiles were determined, and ADLT was performed at baseline, 6, and 12 months. Linear mixed models were used to assess the effects of intervention on lipoprotein profiles. Compared to the control diet, at 12 months, the er-MedDiet+PA resulted in a significant additional 4.2 kg of body weight loss, a decrease in body mass index by 1.4 kg/m², reduction in waist circumference by 2.2 cm, decreased triglycerides, LDL-cholesterol and non-HDL-cholesterol, and increased HDL-cholesterol. In er-MedDiet+PA participants, ADLT revealed a decrease in small dense-LDL-cholesterol (sd-LDL-C), intermediate-density lipoproteins, VLDL-triglyceride, and HDL-Triglyceride, and an increase in large LDL and large VLDL particles. In conclusion, compared to an ad libitum MedDiet (control group), er-MedDiet+PA decreased plasma triglycerides and the triglyceride content in HDL and VLDL particles, decreased sd-LDL-C, and increased large LDL particles, indicating beneficial changes against cardiovascular disease.

Introduction The profiles of patients with COVID-19 have been widely studied, but little is known about differences in baseline characteristics and in outcomes between subjects with a ceiling of care assigned at hospital admission and subjects without a ceiling of care. The aim of this study is to compare, by ceiling of care, clinical features and outcomes of hospitalized subjects during four waves of COVID-19 in a metropolitan area in Catalonia. Methods Observational study conducted during the first (March–April 2020), second (October–November 2020), third (January–February 2021), and fourth wave (July–August 2021) of COVID-19 in five centers of Catalonia. All subjects were adults (> 18 years old) hospitalized with a proven SARS-CoV-2 infection and with therapeutic ceiling of care assessed by the attending physician at hospital admission. Results A total of 5813 subjects were analyzed. Subjects with a ceiling of care were mainly older (difference in median age of 20 years), with more comorbidities (Charlson index 3 points higher) and with fewer clinical signs at baseline than patients without a ceiling of care. Some features of their clinical profiles changed among waves. There were differences in treatments received during hospital admission across waves, but not between subjects with and without a ceiling of care. Subjects with a ceiling of care had a death incidence more than four times the death incidence of subjects a without a ceiling of care (risk ratio (RR) ranging from 3.5 in the first wave to almost 6 in the third and fourth). Incidence of severe pneumonia and complications for subjects with a ceiling of care was around 1.5 times the incidence in subjects without a ceiling of care. Discussion Analysis of hospitalized subjects with SARS-CoV-2 infection should be stratified according to therapeutic ceiling of care to avoid bias and outcome misestimation.