Background: Healthcare workers (HCWs) are a historical key target of influenza vaccination programs. For the 2021–2022 season, WHO considered the coadministration of a flu and a COVID-19 vaccine as acceptable and recommended it to allow for higher uptake of both vaccines. The aim of this study was to investigate demographic and occupational features of vaccinated HCWs, reasons behind flu vaccine acceptance and a possible effect of the coadministration of a COVID-19 vaccine, in order to potentially draw general conclusions on HCWs’ attitude towards flu vaccination and inform further strategies for consistent improvement of vaccine acceptance. Methods: a promotional and educational campaign, a gaming strategy, and vaccination delivery through both a large central hub and on-site ambulatories, were the implemented strategies. In the central hub, the flu/COVID-19 vaccine coadministration was offered. Statistical descriptive analysis, multiple correspondence analysis (MCA) and logistic regression models were performed. Results: 2381 HCWs received the flu vaccine, prompting a vaccination coverage rate (VCR) of 52.0% versus 43.1% in the 2020–2021 campaign. Furthermore, 50.6% vaccinated HCWs belonged to the 18–39 years-old age group. The most expressed reasons for vaccine uptake were “Vaccination is the most effective strategy of prevention” (n = 1928, 81.0%), “As HCW it’s my duty to get vaccinated to protect my patients” (n = 766, 32.2%), and the group of COVID-19-related reasons (n = 586, 24.6%). In addition, 23.3% HCWs received the flu vaccine in the current campaign but not in the previous one (newly vaccinated) and the flu/COVID-19 vaccine coadministration was more frequent in this group. A total of 51.0% HCWs were hesitant towards the coadministration, while residents and nurses showed the highest propensity to receive it. Conclusions: in the second year of the COVID-19 pandemic, the Fondazione’s influenza VCR continued to increase, with the greatest participation among HCWs aged 18–39 years. A potential propelling role of the COVID-19 vaccine coadministration was highlighted.

Background: Metabolic syndrome (MS) is closely linked to obesity; however, not all individuals with obesity will develop obesity-related complications and a metabolically healthy obesity (MHO) group is also described. Objective: To perform a multivariate analysis (MVA) of the anthropometric and biochemical data in pediatric patients with obesity to reveal a “phenotype” predictive for MS. Methods: We analyzed 528 children with obesity (OB) and 119 normal-weight pediatric patients (NW). Adiposity indices were recorded, and MS was detected. MVA was performed. Results: Analysis of the structure of correlation of the variables showed that the variables of waist circumference (WC), body mass index (BMI), and estimated fat mass (eFM) were positively correlated with each other as a whole. In addition, the variables of the triglycerides (TG), triglyceride–glucose (TyG) index, and visceral adiposity index were positively correlated with each other as a whole, although none were correlated with the variables of BMI z-score, waist-to-height ratio, WC, eFM, or weight. The variables that related to insulin resistance (IR) and dyslipidemia were crucial for the early stratification of patients at risk of MS. Conclusions: Independently of body weight, IR, dyslipidemia, hypertriglyceridemia, and fat distribution seem to be the strongest MS risk factors. The early detection of and intervention in these modifiable risk factors are useful to protect children’s health.

Thyroid function plays a crucial role in nervous system integrity and metabolic homeostasis. We evaluated the pattern of TSH, FT4 and FT3 release in children with neuromotor impairment (NI) in relationship with metabolic syndrome (MS). We enrolled 55 patients with NI and 30 controls. Clinical parameters, thyroid function and MS presence were recorded. Principal component analysis (PCA), cluster analysis, and logistic regression models were performed. MS was detected in 54.5% of patients. Four clusters were identified: the first one included only controls and, contrasting with cluster 4, was exclusively characterized by children with disability and MS. This latter showed increased FT4 and FT3 and decreased TSH levels. Cluster 2, characterized by disability without MS showed high FT4 and FT3, whereas cluster 3 with low FT4 and FT3 mainly included disability (90%) and showed prevalent MS (57%). The association between TSH and NI is represented by a U-shape structure. The TSH, FT3 and FT4 release patterns may reflect thyrotropic adaptation, allostatic response and compensatory mechanisms. These mechanisms, found in both MS and disability, show that the odds of having a condition of NI with or without MS increase as the TSH values deviate, in both directions, from a value of 2.5 mLU/mL.

Background: The prevalence of pediatric metabolic syndrome is usually closely linked to overweight and obesity; however, this condition has also been described in children with disabilities. We performed a multivariate pattern analysis of metabolic profiles in neurologically impaired children and adolescents in order to reveal patterns and crucial biomarkers among highly interrelated variables. Patients and methods: We retrospectively reviewed 44 cases of patients (25M/19F, mean age 12.9 ± 8.0) with severe disabilities. Clinical and anthropometric parameters, body composition, blood pressure, and metabolic and endocrinological assessment (fasting blood glucose, insulin, total cholesterol, high-density lipoprotein cholesterol, triglycerides, glutamic oxaloacetic transaminase, glutamate pyruvate transaminase, gamma-glutamyl transpeptidase) were recorded in all patients. As a control group, we evaluated 120 healthy children and adolescents (61M/59F, mean age 12.9 ± 2.7). Results: In the univariate analysis, the children-with-disabilities group showed a more dispersed distribution, thus with higher variability of the features related to glucose metabolism and insulin resistance (IR) compared to the healthy controls. The principal component (PC1), which emerged from the PC analysis conducted on the merged dataset and characterized by these variables, was crucial in describing the differences between the children-with-disabilities group and controls. Conclusion: Children and adolescents with disabilities displayed a different metabolic profile compared to controls. Metabolic syndrome (MetS), particularly glucose metabolism and IR, is a crucial point to consider in the treatment and care of this fragile pediatric population. Early detection of the interrelated variables and intervention on these modifiable risk factors for metabolic disturbances play a central role in pediatric health and life expectancy in patients with a severe disability.