Corina Delia

Background/Objectives: Childhood obesity is associated with early metabolic complications, particularly insulin resistance (IR), which significantly elevates the long-term risk for type 2 diabetes and cardiovascular disease. Standard measures such as BMI may inadequately capture metabolic risk, particularly in children with atypical phenotypes such as TOFI (Thin Outside, Fat Inside). This study aimed to evaluate the prevalence and predictors of IR in a pediatric population with overweight and obesity, using both conventional biomarkers and bioelectrical impedance analysis (BIA). We also examined the predictive value of lipid ratios and fasting glucose and applied Principal Component Analysis (PCA) to identify underlying body composition dimensions. Methods: A retrospective cohort of 210 children aged 1–18 years, assessed in a tertiary pediatric endocrinology center in Romania, was analyzed. Clinical data included anthropometric measures, fasting laboratory results, and body composition parameters obtained via Tanita PRO DC430 MA BIA. Insulin resistance was defined as HOMA-IR > 2. ROC analysis assessed the predictive performance of triglyceride-to-HDL (Tg/HDL) ratio, fasting glucose, and BIA metrics. PCA was applied to BIA variables to explore dimensional structure. Results: Insulin resistance was present in 54.8% of the cohort. It was significantly associated with higher age, pubertal status, ALT, LDL-cholesterol, triglycerides, and BIA-derived fat-free mass (FFM), TBW, and PMM. ROC analyses showed moderate predictive power for Tg/HDL (AUROC = 0.645) and triglycerides (AUROC = 0.656) in identifying IR. BIA metrics had comparable discriminatory performance (AUROC~0.61). PCA reduced eight BIA parameters into two components: a fat-free mass axis (TBW, FFM, PMM, WATERM) and an adiposity axis (BMI, FATP, FATM, WATERP). Conclusions: This study highlights the high burden of insulin resistance among children with excess weight and supports the integration of BIA and composite biomarkers into early screening protocols. PCA-derived components may improve metabolic phenotyping in pediatric obesity.

Background/Objectives: Childhood obesity is associated with early metabolic complications, particularly insulin resistance (IR), which significantly elevates the long-term risk for type 2 diabetes and cardiovascular disease. Standard measures such as BMI may inadequately capture metabolic risk, particularly in children with atypical phenotypes such as TOFI (Thin Outside, Fat Inside). This study aimed to evaluate the prevalence and predictors of IR in a pediatric population with overweight and obesity, using both conventional biomarkers and bioelectrical impedance analysis (BIA). We also examined the predictive value of lipid ratios and fasting glucose and applied Principal Component Analysis (PCA) to identify underlying body composition dimensions. Methods: A retrospective cohort of 210 children aged 1–18 years, assessed in a tertiary pediatric endocrinology center in Romania, was analyzed. Clinical data included anthropometric measures, fasting laboratory results, and body composition parameters obtained via Tanita PRO DC430 MA BIA. Insulin resistance was defined as HOMA-IR >2. ROC analysis assessed the predictive performance of triglyceride-to-HDL (TG/HDL) ratio, fasting glucose, and BIA metrics. PCA was applied to BIA variables to explore dimensional structure. Results: Insulin resistance was present in 54.8% of the cohort. It was significantly associated with higher age, pubertal status, ALT, LDL-cholesterol, triglycerides, and BIA-derived fat-free mass (FFM), TBW, and PMM. ROC analyses showed moderate predictive power for TG/HDL (AUROC = 0.645) and triglycerides (AUROC = 0.656) in identifying IR. BIA metrics had comparable discriminatory performance (AUROC ~0.61). PCA reduced eight BIA parameters into two components: a fat-free mass axis (TBW, FFM, PMM, WATERM) and an adiposity axis (BMI, FATP, FATM, WATERP). Conclusions: This study highlights the high burden of insulin resistance among children with excess weight and supports the integration of BIA and composite biomarkers into early screening protocols. PCA-derived components may improve metabolic phenotyping in pediatric obesity.

There is an increasing interest in dyslipidemia in adult patients since it is known to contribute to early cardiovascular disease. Often, dyslipidemia starts in childhood, and it is associated with aggravating lifestyle choices concerning eating habits, such as the tendency to consume processed food and fast food, as well as the tendency to be more and more sedentary. We conducted a retrospective cross-sectional study describing the prevalence of dyslipidemia in a single medical center in Romania and the associated pathology. We evaluated all lipid profiles that were ordered in our clinic over nine years. We included 2413 patients that were evaluated in our clinic in the timeframe 2011–2020. Out of them, 18.23% had high values for LDL-cholesterol. More than a quarter (25.91%) were diagnosed with obesity. 11.37% of the patients with high LDL-cholesterol levels had various metabolic disorders including primary dyslipidemia. A small number of patients with hypercholesterolemia had thyroid disorders (4.10%). Patients with high LDL-cholesterol had various diagnoses ranging from metabolic to neurologic disorders, keeping in mind that there are multiple pathologies that can lead to dyslipidemia. Evaluating children for dyslipidemia is at hand for medical professionals. Screening for dyslipidemia in children would provide the opportunity to prevent rather than treat cardiovascular events.

Familial hypercholesterolemia (FH) is a genetic disease marked by high levels of LDL-cholesterol. This condition has long-term clinical implications, such as cardiovascular events, that are evident during adult life. Here, we report on a single-center cross-sectional showcase study of genetic testing for FH in a Romanian pediatric group. Genetic testing for FH was performed on 20 Romanian pediatric patients, 10 boys and 10 girls, admitted with LDL-cholesterol levels over 130 mg/mL to the National Institute for Mother and Child Health “Alesssandrescu-Rusescu” in 2020. Genetic testing was performed using the Illumina TruSight Cardio panel. We identified pathogenic/likely pathogenic variants that could explain the phenotype in 5/20 cases. The involved genes were LDLR and APOB. Clinical signs that suggest the diagnosis of FH are scarce for the pediatric patient, although it can be diagnosed early during childhood by lipid panel screening. Prevention could prove lifesaving for some of these patients.

It is known that cigarette smoking is correlated with medical associated inquires. New electronic cigarettes are intensively advertised as an alternative to conventional smoking, but only a few studies demonstrate their harmful potential. A cross-sectional study was designed using 150 subjects from Brasov (Romania), divided into three groups: non-smokers (NS = 58), conventional cigarettes smokers (CS = 58) and electronic cigarettes users (ECS = 34). The aim of this study was to determine levels of some plasma lipophilic and hematological components, and the total antioxidant status that could be associated with the smoking status of the subjects. Serum low density lipoproteins (LDL) cholesterol increased significantly for ECS participants versus NS group (18.9% difference) (p < 0.05). Also, the CS group is characterized by an increase of serum LDL cholesterol (7.9% difference vs. NS), but with no significant statistical difference. The variation of median values of serum very low density lipoproteins (VLDL) was in order NS < ECS < CS, with statistical difference between NS and CS groups (34.6% difference; p = 0.023). When comparing the antioxidant status of the three groups, significant differences (p < 0.05) were obtained between NS vs. CS and NS vs. ECS. Similar behavior was identified for CS and ECS. Statistically significant changes (p < 0.0001) for both vitamin A and vitamin E were identified in the blood of NS vs. CS and NS vs. ECS, and also when comparing vitamin A in the blood of the CS group versus the ECS group (p < 0.05). When all groups were compared, the difference in the white blood cell (WBC) was (p = 0.008). A slight increase in the red blood cell (RBC) count was observed, but with no statistical difference between groups. These results indicated that conventional cigarette and e-cigarette usage promotes the production of excess reactive oxygen species, involving different pathways, different antioxidants and bioactive molecules.