Background and Objectives: This study aimed to investigate the novel adiponectin–resistin (AR) index as a predictor of the development of metabolic syndrome (MetS) in individuals with type 2 diabetes mellitus (T2DM). MetS is common in T2DM and increases cardiovascular risk. Adiponectin and resistin, adipokines with opposing effects on insulin sensitivity and inflammation, make the AR index a potential marker for metabolic risk. Materials and Methods: This prospective observational study included 80 T2DM participants (ages 30–60) from Sarajevo, Bosnia and Herzegovina, over 24 months. The participants were divided into two groups: T2DM with MetS (n = 48) and T2DM without MetS (n = 32). Anthropometric data, biochemical analyses, and serum levels of adiponectin and resistin were measured at baseline and every six months. The AR index was calculated using the formula AR = 1 + log10(R) − 1 + log10(A), where R and A represent resistin and adiponectin concentrations. Logistic regression identified predictors of MetS. Results: T2DM patients who developed MetS showed a significant decline in adiponectin levels (40.19 to 32.49 ng/mL, p = 0.02) and a rise in resistin levels (284.50 to 315.21 pg/mL, p = 0.001). The AR index increased from 2.85 to 2.98 (p = 0.001). The AR index and resistin were independent predictors of MetS after 18 months, with the AR index showing a stronger predictive value (p = 0.007; EXP(B) = 1.265). Conclusions: The AR index is a practical marker for predicting MetS development in T2DM participants, improving metabolic risk stratification. Incorporating it into clinical assessments may enhance early detection and treatment strategies.

ABSTRACT Background: The triglyceride/high-density lipoprotein (TG/HDL) ratio emerges as a promising marker for cardiovascular risk. However, the relationship between overall serum lipid levels and hemorrhagic stroke (HS) remains uncertain. Therefore, our study aims to explore the association between this novel index and mortality in HS patients. Methods: Utilizing a retrospective-prospective framework from January 2020 to August 2023, we scrutinized data from 104 hospitalized patients diagnosed with HS, with particular attention to their medical backgrounds and lipid profiles. Results: Age (odds ratio [OR], 1.078; 95% confidence interval [CI], 1.032–1.125; P = 0.001), atrial fibrillation (OR, 0.237; 95% CI, 0.074–0.760; P = 0.015), glucose level (OR, 1.121; 95% CI, 1.007–1.247; P = 0.037), and TG/HDL index (OR, 0.368; 95% CI, 0.173–0.863; P = 0.020) emerged as independent predictors for in-hospital mortality, as determined by both univariable and multivariable logistic regression analyses. Conclusion: Our results add weight to the growing evidence backing the utility of the TG/HDL index in assessing cardiovascular risk among HS patients. They emphasize the necessity of adopting a comprehensive risk assessment and management strategy that incorporates both traditional markers and novel indicators.

Background/Objectives: The study of microbiome composition shows positive indications for application in the diagnosis and treatment of many conditions and diseases. One such condition is autism spectrum disorder (ASD). We aimed to analyze gut microbiome samples from children in Bosnia and Herzegovina to identify microbial differences between neurotypical children and those with ASD. Additionally, we developed machine learning classifiers to differentiate between the two groups using microbial abundance and predicted functional pathways. Methods: A total of 60 gut microbiome samples (16S rRNA sequences) were analyzed, with 44 from children with ASD and 16 from neurotypical children. Four machine learning algorithms (Random Forest, Support Vector Classification, Gradient Boosting, and Extremely Randomized Tree Classifier) were applied to create eight classification models based on bacterial abundance at the genus level and KEGG pathways. Model accuracy was evaluated, and an external dataset was introduced to test model generalizability. Results: The highest classification accuracy (80%) was achieved with Random Forest and Extremely Randomized Tree Classifier using genus-level taxa. The Random Forest model also performed well (78%) with KEGG pathways. When tested on an independent dataset, the model maintained high accuracy (79%), confirming its generalizability. Conclusions: This study identified significant microbial differences between neurotypical children and children with ASD. Machine learning classifiers, particularly Random Forest and Extremely Randomized Tree Classifier, achieved strong accuracy. Validation with external data demonstrated that the models could generalize across different datasets, highlighting their potential use.

AIM To determine whether demographic data, clinical features, and laboratory variables at disease onset can predict the response to methotrexate in juvenile idiopathic arthritis (JIA) patients. METHODS A cohort of 143 newly diagnosed JIA patients initially treated with methotrexate was enrolled in this study. Demographic, clinical, and laboratory parameters were analysed using univariate and multivariate logistic regression to identify predictors of response to methotrexate. The variables included erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), platelets, IgA, IgG, the number of active joints and age at disease onset. Treatment response was assessed at six months, with patients classified as responders (those who achieved clinically inactive disease according to the American College of Rheumatology - ACR criteria) or non-responders. RESULTS Poor response to methotrexate was associated with the number of active joints (p=0.0001; OR=2.7), baseline levels of CRP (p=0.044; OR=1.138), IgA (p=0.004; OR=2.159), and platelet count (p=0.01; OR=1.05). IgG level (P=0.236) did not correlate with the treatment response. CONCLUSION We identified widely available and clinically acceptable biomarkers that can be utilized as predictive indicators of response to methotrexate in JIA patients.