👤 Mohammadreza Torabi

Obesity is a complex disease resulting from the interaction of genetic and environmental factors. In this study, 414 single nucleotide polymorphism (SNPs) were analyzed in DNA samples obtained from 48 obese patients and 50 healthy controls of Turkish origin to identify genetic variants associated with obesity. Genotype frequency analysis revealed 18 variants significantly or near-significantly associated with obesity. Among these, rs12199580 (PNPLA1), rs34911341 (GHRL), and rs116843064 (ANGPTL4) emerged as novel candidate variants not previously reported in the context of obesity. Functional annotation analyses confirmed that most of the significant variants were located in exonic or regulatory regions, and the related genes were primarily involved in neuroendocrine control, lipid metabolism, and energy homeostasis. Pathway enrichment analysis indicated significant overrepresentation of pathways such as PPAR-alpha-regulated lipid metabolism, ghrelin synthesis and secretion, and cholesterol transport, which are all closely linked to obesity pathophysiology. Polygenic risk score models constructed from the significant SNPs demonstrated a markedly increased genetic risk burden when rare high-effect variants were included. In regression analyses adjusted for age, sex, and Body Mass Index (BMI), the variant rs17024258 in the GNAT2 gene maintained a statistically significant and independent association with BMI (P < .02), whereas most other variants lost significance after covariate adjustment. Furthermore, certain variants were found to exhibit markedly different allele frequencies in the Turkish cohort compared to global reference populations, highlighting potential population-specific genetic architecture. This study contributes to the identification of both previously known and novel genetic variants associated with obesity and underscores the importance of population-specific genomic data in understanding genetic predisposition to complex diseases such as obesity. Show less

White matter hyperintensity (WMH), indicative of cerebral small vessel disease, has emerged as a potential biomarker for cognitive decline in Alzheimer's disease (AD). However, their predictive role across specific cognitive domains within the AD spectrum remains unclear. This study investigates the relationship between WMH volume and cognitive performance in memory, executive function, and language across the AD continuum. A cross-sectional analysis was conducted using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), comprising 557 participants categorized into cognitively normal (CN; n = 158), mild cognitive impairment (MCI; n = 334), and Alzheimer's dementia (AD; n = 65) groups. Cognitive function was assessed using composite scores for memory (ADNI-MEM), executive function (ADNI-EF), and language (ADNI-LAN). WMH volume was quantified through validated Bayesian segmentation of MRI data. Associations between cognitive scores and WMH volume, adjusted for age, gender, APOE ε4 status, and vascular risk factors, were evaluated via multiple linear regression analyses. WMH volume showed numerically progressive increases from CN to MCI and AD groups; however, between-group differences did not reach statistical significance. Within the MCI group, significant negative associations emerged between WMH volume and memory (β=-0.13, adjusted p = 0.045) and language scores (β=-0.12, adjusted p = 0.045). Conversely, these relationships were absent in both the CN and AD groups. WMH volume relates specifically to declines in memory and language abilities, particularly in individuals with MCI. These results support using WMH measurements as early markers to identify cognitive decline in AD, potentially helping to guide earlier diagnosis and treatment decisions. Show less

This study aims to develop a receptor-dependent 4D-QSAR model to overcome key limitations of traditional QSAR, including its dependency on molecular alignment and poor performance with small datasets, by integrating ligand - target interaction information. Angiogenesis-related receptors, including VEGFR2, FGFR1-4, EGFR, PDGFR, RET, and HGFR (MET) were chosen based on the biological relevance in cancer. Ligand datasets with known IC₅₀ values were extracted from PubChem. One hundred docked conformers per ligand were generated using AutoDock. Protein - ligand interaction fingerprints were computed and encoded as 4D-descriptors. After evaluation via multiple classification algorithms, Random Forest was selected for model construction. The results shown that the proposed model outperformed traditional 2D-QSAR approaches across all targets. Accuracy exceeded 70% in most datasets, including those with fewer than 30 compounds. Besides, the model performance was significantly improved via using all conformers versus using a single best pose. The model demonstrated robust predictive power across varying receptor classes under consistent assay conditions. The proposed receptor-dependent 4D-QSAR model provides enhanced accuracy and generalizability for small, diverse datasets. Its integration of LTI-derived descriptors makes it a valuable tool for early-stage lead optimization and supports rational multi-target drug design in oncology. Show less