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.

Sensors are versatile technologies that provide rapid and efficient diagnostic results, making them invaluable tools in public health for measuring and monitoring community exposure to environmental contaminants. Heavy metals such as lead, mercury, and cadmium, commonly found in food and water, can accumulate in the body and have toxic effects, contributing to the development of conditions like obesity and diabetes. Traditional methods for detecting these metals often require invasive blood samples; however, sensors can utilize saliva, offering a noninvasive and simplified approach for public health screening. The use of saliva as a diagnostic fluid represents a major advance in population health monitoring due to its low cost, noninvasiveness, and ease of collection. Recent advances in sensor technology have enabled the development of diagnostic tests that link heavy metal levels in saliva with the risk of developing obesity and diabetes. Optimizing these sensors could facilitate the identification of individuals or groups at risk, enabling targeted, personalized preventive measures. Sensors that use saliva for detecting heavy metals hold promise for diagnosing and preventing metabolic diseases, providing valuable insights into the link between heavy metal exposure and metabolic health.

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.

A dangerous side effect of diabetes that can significantly lower quality of life and raise the death rate of diabetic individuals is diabetic autonomic neuropathy. It is essential to identify and anticipate this disease early on for prompt intervention and care. This study aims to predict this diabetic complication using Sudoscan and artificial intelligence. In this study, 172 individuals with type 1 or type 2 diabetes mellitus provided clinical and demographic information. Sudoscan was used to evaluate the subjects’ sudomotor dysfunction. Statistical methods were used to link various electrochemical skin conductance values with risk factors for neuropathy such as age, BMI, age of diabetes, or biochemical values such as cholesterol and triglycerides. Different machine-learning algorithms were used to predict the risk of diabetic autonomic neuropathy based on the collected data. The accuracy achieved with Logistic Regression is 92.6%, and with the Random Forest model is 96.3%. Lazzy Classifiers also show that six classifiers have a high performance of 97%. Thus, the use of machine learning algorithms in this field of metabolic diseases offers new perceptions for diagnosis, treatment, and prevention, and improves the quality of life of diabetic patients by reducing the incidence of complications related to this disease.

Humans are facing a devastating epidemic of metabolic syndrome that is linked to the worldwide dramatic increase in obesity and diabetes. Significant evidence suggests that the intestinal microbiota plays a major role in the pathogenesis of metabolic diseases. Due to the gut–brain axis link, dysbiosis in the gut microbiota have been demonstrated in both metabolic and neurological disease. Increasing evidence suggests that the gut microbiota is very important in maintaining health and changes in its composition may contribute to psychiatric and neurodegenerative disorders. It is also in research that changes in microbiota composition profile due to diabetes are modulated by the vagus nerve. Therefore, it is plausible that disruptions in the gut microbiota may be captured through electroencephalography signaling. Several studies which used standard methods of signal processing have highlighted some changes in electroencephalographic rhythms on patients with diabetes.