Background/Objectives: One of the metabolic complications of obesity is known as Metabolic Syndrome (MetS). This narrative review aims to synthesize current research on MetS in young populations, identify knowledge gaps, and guide future studies and funding priorities. It provides evidence-based insights into screening, diagnosis, and treatment, offering practical guidance for healthcare professionals. Methods: A comprehensive search of the literature was conducted to identify relevant studies on obesity in MetS in PubMed and Google Academic electronic database. The search was performed using a combination of “obesity”, “complications”, “metabolic syndrome”, “children”, and “adolescents” keywords. Studies were selected based on predefined inclusion and exclusion criteria to ensure relevance and methodological rigor. Results: The lack of universally accepted diagnostic criteria for MetS in children has led to inconsistencies in its definition across medical literature. Irrespective of the chosen diagnostic approach, the prevalence of MetS in children and adolescents has exhibited a concurrent rise with the increasing rates of obesity in this age group. The pathophysiology of MetS remains incompletely understood, with ongoing discussions on the interplay of genetic, epigenetic, environmental, dietary, and lifestyle factors. Screening for MetS is recommended for overweight and obese children. Conclusions: Establishing global, consensus-driven criteria that account for ethnicity, gender, and age would enhance diagnostic accuracy and treatment approaches. The prevention of excessive weight among children and adolescents stands as a paramount objective within modern society’s healthcare system. Considering the complexity of the disease and the treatment, the team must be multidisciplinary.

Background: Diabetic neuropathy, particularly in its autonomic form, is often underdiagnosed despite its clinical significance. Electrochemical skin conductance (ESC), measured by SUDOSCAN, offers a non-invasive way to assess the autonomic dysfunction. Methods: A total of 288 diabetic patients were assessed using SUDOSCAN to measure ESC in the hands and feet. Clinical and laboratory parameters, including glycated hemoglobin (HbA1c), body mass index (BMI), blood pressure, lipid profile, and cardiovascular risk, were analyzed for correlations with ESC. Neuropathy status was evaluated, and ROC analysis was performed to assess diagnostic accuracy. Results: Sudomotor dysfunction was prevalent, particularly in patients with a diabetes duration exceeding 20 years (p < 0.05). Men showed significantly higher right foot ESC than women (76.5 ± 13.1 vs. 74.0 ± 13.5 µS, p = 0.041). A strong inverse correlation was found between cardiovascular risk score and right foot ESC (r = −0.455, p < 0.001). Left foot ESC also correlated inversely with cardiovascular risk (r = −0.401, p < 0.001) and HbA1c (r = −0.150, p = 0.049), while a weak positive correlation was seen with BMI (r = 0.145, p = 0.043). ROC analysis showed the highest area under the curve (AUC) in right foot ESC for autonomic neuropathy (AUC = 0.750, 95% CI: 0.623–0.877, p < 0.001). Conclusions: This study is among the few to systematically correlate ESC with validated cardiovascular risk scores in a diabetic outpatient cohort, highlighting its potential as a novel early screening biomarker for autonomic and cardiovascular complications.

The use of quick and non-invasive techniques for detecting chronic kidney disease (CKD) in patients with type 2 diabetes mellitus is desirable and has recently garnered attention. One of these techniques is the evaluation of nephropathy risk based on electrochemical skin conductance (ESC) measured with a SUDOSCAN device. This paper aims to evaluate the possibility of using SUDOSCANs in chronic kidney disease prediction in diabetic patients and to investigate the relationships between clinical characteristics and SUDOSCAN parameters. The number of patients with type 2 diabetes included in this study was 254. Clinical metabolic characteristics like glycated hemoglobin, total and LDL cholesterol, triglyceride, blood pressure, and creatinine were determined along with body mass index, diabetes duration, and age. The estimated glomerular filtration rate (EGFR) was calculated and patients were grouped into three CKD stages based on EGFR values. Electrochemical skin conductance in hands and feet was determined with a SUDOSCAN device. The results showed that patients with symptomatic CKD (S2 and 3) presented lower ESC values, along with lower EFGRs and higher creatinine levels. A significant positive but weak correlation (p < 0.05) was observed between SUDOSCAN nephropathy risk and EGFR. The general linear model indicated that the SUDOSCAN nephropathy risk score could be used in CKD diagnosis only if considering age, diabetes duration, and body mass index. The area under the curve (AUC) of the receiver operating characteristic (ROC) analysis revealed the moderate possibility of using the SUDOSCAN nephropathy risk score to predict CKD, since it was 0.61 (p < 0.01, 95% CI 0.54–0.68), but only if the other factors mentioned above are included. Based on the cut-off value of 59.50 identified, patients were grouped (values above and below cut-off), and the results showed that patients with a SUDOSCAN nephropathy risk score of <59.50 have lower SUDOSCAN-ESC values measured in their hands and feet, lower EGFR and higher creatinine levels. These results indicated the possibility of using SUDOSCAN as a supporting tool to identify CKD if it is correlated with other factors like age, diabetes duration, and body mass index. This is important for medical progress regarding the use of novel non-invasive technologies in identifying CKD associated with type 2 diabetes.

Type 1 Diabetes Mellitus (T1D) can be related to various factors, including neonatal and perinatal conditions. This study investigated the impact of neonatal and perinatal factors—Apgar score, birth weight, feeding type, sex, and delivery type—on the risk of Type 1 Diabetes Mellitus and evaluated predictive models. A cohort of 327 patients was analyzed using correlations, General Linear Model, and artificial neural network. T1D patients showed higher birth weight, lower Apgar score, a predominance of formula feeding, and more cesarean deliveries. Diabetes risk showed a moderate positive correlation with birth weight class and nutrition type (p < 0.05) and a weak negative correlation with Apgar score (p < 0.05), while birth weight, birth weight class, and nutrition type were all weakly positively correlated with each other and with delivery type (p < 0.05). General linear model identified nutrition type, birth weight, and Apgar score as key predictors, with significant interactions among them (R² = 0.88). Artificial neural network achieved high accuracy (84.2%), AUC (0.95), sensitivity (89.1%), and specificity (76.7%). ANN successfully modeled the complex non-linear interactions among early-life factors, allowing it to discriminate between high-risk and low-risk cases, as evidenced by the low prediction errors (RMSE 0.31 and MAE 0.09) and strong agreement (Kappa 0.81). The models’ strong internal predictive performance points to potential applications in early T1D diagnosis and personalized management, although confirmation in larger and independent datasets is needed.

Background/Objectives: Diabetic neuropathy is a prevalent complication of type 2 diabetes mellitus (T2DM), but the contribution of dietary factors to neuropathy severity remains insufficiently characterized. This study investigated associations between dietary patterns, nutrient intake, and neuropathy severity in 300 adults with T2DM from Northeastern Romania. Methods: Dietary intake was assessed using a validated food frequency questionnaire, and five dietary patterns were derived using principal component analysis. Neuropathy severity was analyzed as an ordinal outcome using logistic regression models adjusted for age, sex, HbA1c, diabetes duration, and treatment. Results: Higher adherence to Western/fast-food and alcohol and animal fat dietary patterns was associated with greater neuropathy severity in unadjusted analyses, whereas a healthy/prudent pattern showed inverse associations; however, these relationships were attenuated after multivariable adjustment. In contrast, higher intakes of protein (OR = 0.98, 95% CI: 0.97–0.99), magnesium (OR = 0.99, 95% CI: 0.98–1.00), zinc (OR = 0.80, 95% CI: 0.69–0.94), vitamin B3 (OR = 0.94, 95% CI: 0.89–0.99), and vitamin B12 (OR = 0.76, 95% CI: 0.62–0.93) remained independently associated with lower neuropathy severity after adjustment. Conclusions: These findings suggest that specific nutrient intakes may be more strongly associated with diabetic neuropathy severity than overall dietary patterns, highlighting potential nutritional targets for neuropathy risk reduction and clinical management in patients with T2DM.

Background/Objectives: Diabetes mellitus is associated with complex behavioral, psychosocial, and functional challenges that may influence disease burden and complication profiles. This study aimed to identify latent dimensions of diabetes self-management and health-related functioning and to evaluate their association with the type of diabetes-related complications. Methods: A cross-sectional study was conducted in 300 patients with type 2 diabetes, classified into microvascular and macrovascular complication groups. A questionnaire comprising DSMQ (diabetes self-management questionnaire) derived items, SF-36-derived items, and other questions was administered to all participants. Exploratory factor analysis was performed to identify latent dimensions related to self-management, emotional burden, dietary behavior, glycemic control challenges, glucose monitoring, and functional status. Composite scores were calculated for factors with acceptable internal consistency. Between-group comparisons, Spearman correlation analysis, and binary logistic regression models adjusted for age, sex, residence area, marital status, education, diabetes duration, and treatment type were applied. Results: Six latent factors were identified: Emotional Burden, Dietary Self-Management, Functional Limitations, Glycemic Control Challenges, Glucose Monitoring, and General Health Perception. However, only the factors demonstrating acceptable internal consistency were retained for subsequent analyses: Emotional Burden, Functional Limitations, and Glycemic Control Challenges. Functional Limitations showed the strongest association with complication type, with patients with macrovascular complications reporting greater functional impairment. After adjustment for potential confounders and correction for multiple comparisons, higher adherence to the recommended meal plan (DSMQ2) and greater functional limitations remained the principal differences between the complication groups. Functional Limitations Score remained independently associated with complication type in both unadjusted and adjusted logistic regression models. Conclusions: These cross-sectional findings support the integration of functional, psychosocial, and behavioral assessment into comprehensive diabetes care and highlight differences in patient-reported outcomes across complication groups.