Search Results (162)

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations. Full article

Background: Periodontitis is a multifactorial inflammatory disease with a complex interplay between microbial, environmental, and host-related factors. Among host factors, genetic susceptibility plays a significant role in influencing both disease onset and progression. Over the past two decades, a wide range of genetic tests, ranging from single-nucleotide polymorphism (SNP) analysis to genome-wide association studies (GWAS), have been explored to assess individual risk profiles and potential treatment responses. However, despite initial enthusiasm, the clinical integration of genetic testing in periodontics remains limited. This narrative review aims to critically examine the current landscape of genetic testing in periodontitis, including commercially available tests, their scientific validity, and their clinical utility. Methods: Most relevant studies which were published in recent years were identified by using the major scientific search engines, including PubMed, Scopus, and Web of Science. Articles discussing genetic susceptibility, key gene polymorphisms, and emerging technologies were included in this narrative review. Results: Polymorphisms in genes coding for IL-1, IL-6, TNF-α, and in others involved in immune modulation and bone metabolism, are associated with periodontitis. Nevertheless, there are limitations related to heterogeneity in study design, population stratification, and gene–environment interactions. Moreover, emerging technologies, including polygenic risk scoring and machine learning approaches, may enhance the predictive value of genetic tools in periodontology. Conclusions: A deeper understanding of genetic susceptibility could pave the way for precision dentistry and personalized periodontal care, but significant hurdles remain before genetic testing can become a routine component of periodontal diagnostics. Full article

Obesity is a multifaceted disorder influenced by various factors, with heredity being a significant contributor. Bariatric surgery is the most effective long-term intervention for morbid obesity and associated comorbidities, while outcomes vary significantly across individuals. Recent studies indicate that genetic and molecular determinants, particularly alterations in the leptin–melanocortin signalling pathway involving the fat mass and obesity-associated gene (FTO), pro-opiomelanocortin (POMC), melanocortin 4 receptor (MC4R), leptin (LEP), and leptin receptor (LEPR), influence the efficacy of weight loss and metabolic adaptations post-surgery. This narrative review consolidates evidence from peer-reviewed papers available in PubMed and Scopus until July 2025. The emphasis was on novel research and systematic reviews examining genetic polymorphisms, gene–environment interactions, and outcomes following bariatric procedures such as Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). Recent research emphasised the integration of genetic screening and precision medicine models into clinical bariatric workflows. Variants in FTO (e.g., rs9939609), MC4R (e.g., rs17782313), LEPR, and POMC are associated with diminished weight loss post-surgery, an increased likelihood of weight regain, and reduced metabolic enhancement. Patients with bi-allelic mutations in MC4R, POMC, or LEPR exhibited poor long-term outcomes despite receiving effective physical interventions. Furthermore, genes regulating mitochondrial metabolism (such as PGC1A), adipokine signalling (such as ADIPOQ), and glucose regulation (such as GLP1R) have been demonstrated to influence the body’s response to sugar and the extent of weight gain or loss. Two recent systematic reviews elucidate that candidate gene investigations are beneficial; however, larger genome-wide association studies (GWAS) and machine learning techniques are necessary to enhance predictive accuracy. Integrating genetic and molecular screening with bariatric surgery planning possesses significant therapeutic potential. Genotyping can assist in patient selection, procedural decisions, and medication additions, particularly for those with variants that influence appetite regulation or metabolic flexibility. Advancements in precision medicine, including the integration of polygenic risk scores, omics-based profiling, and artificial intelligence, will enhance the customisation of surgical interventions and extend the lifespan of individuals with severe obesity. The epigenetic regulators of energy balance DNA methylation, histone changes, and microRNAs that may affect individual differences in weight-loss patterns after bariatric surgery are also briefly contextualised. We discuss the concept that epigenetic modulation of gene expression, mediated by microRNAs in response to food and exercise, may account for variations in metabolic outcomes post-surgery. Full article

Chronic dermatological conditions such as acne vulgaris, androgenetic alopecia (AGA), and alopecia areata (AA) affect hundreds of millions worldwide and contribute substantially to quality-of-life impairment. Despite the availability of systemic retinoids, anti-androgens, and JAK inhibitors, therapeutic responses remain heterogeneous and relapse is common, underscoring the need for biologically grounded stratification. Over the past decade, large genome-wide association studies and functional analyses have clarified disease-specific and cross-cutting mechanisms. In AA, multiple independent HLA class II signals and immune-regulatory loci such as BCL2L11 and LRRC32 establish antigen presentation and interferon-γ/JAK–STAT signalling as central drivers, consistent with clinical responses to JAK inhibition. AGA is driven by variation at the androgen receptor and 5-α-reductase genes alongside WNT/TGF-β regulators (WNT10A, LGR4, RSPO2, DKK2), explaining follicular miniaturisation and enabling polygenic risk prediction. Acne genetics highlight an immune–morphogenesis–lipid triad, with loci in TGFB2, WNT10A, LGR6, FASN, and FADS2 linking follicle repair, innate sensing, and sebocyte lipid metabolism. Barrier modulators such as FLG and OVOL1, first described in atopic dermatitis, further shape inflammatory thresholds across acne and related phenotypes. Together, these findings position genetics not as an abstract catalogue of risk alleles but as a map of tractable biological pathways. They provide the substrate for patient-stratified interventions ranging from JAK inhibitors in AA, to endocrine versus morphogenesis-targeted strategies in AGA, to lipid- and barrier-directed therapies in acne, while also informing cosmetic practices focused on barrier repair, sebaceous balance, and follicle health. Full article

Type 1 Diabetes Mellitus (T1D) is an autoimmune disease characterized by the destruction of pancreatic β-cells, predominantly manifesting in childhood or adolescence. The lack of clearly interpretable biological markers in the early stages, combined with the insidious onset of the disease, poses significant challenges to early diagnosis and the implementation of preventive strategies. The applicability of classic T1D biomarkers for understanding the mechanisms of the autoimmune process, preclinical diagnostics and treatment efficiency is limited. Despite advances in next-generation sequencing (NGS) technologies, which have enabled large-scale genome-wide association studies (GWASs) and the identification of polygenic risk scores (PRSs) associated with T1D predisposition, as well as progress in bioinformatics approaches for assessing dysregulated gene expression, no universally accepted risk assessment model or definitive predictive biomarker has been established. Until now, the use of new promising biomarkers for T1D diagnostics is limited by insufficient evidence base. However, they have great potential for the development of diagnostic methods on their basis, which has been shown in single or serial large-scale studies. This critical review covers both well-known biomarkers widely used in clinical practice, such as HLA-haplotype, non-HLA SNPs, islet antigen autoantibodies, C-peptide, and the promising ones, such as cytokines, cfDNA, microRNA, T1D-specific immune cells, islet-TCR, and T1D-specific vibrational bands. Additionally, we highlight new approaches that have been gaining popularity and have already demonstrated their potential: GWAS, single-cell transcriptomics, identification of antigen-specific T cells using scRNA-seq, and FTIR spectroscopy. Although some of the biomarkers, in our opinion, are still limited to a research context or are far from being implemented in clinical diagnostics of T1D, they have the greatest potential of being applied in clinical practice. When integrated with the monitoring of the classical autoimmune diabetes markers, they would increase the sensitivity and specificity during diagnostics of early and preclinical stages of the disease. This critical review aims to evaluate the current landscape of classical and emerging biomarkers in autoimmune diabetes, with a focus on those enabling early detection—prior to extensive destruction of pancreatic islets. Another goal of the review is to focus the attention of the scientific community on the gaps in early T1D diagnostics, and to help in the selection of markers, targets, and methods for scientific studies on creating novel diagnostic panels. Full article

Vitamin D deficiency is highly prevalent in the Middle East despite abundant sunlight; however, most genetic studies have focused on common variants in Europeans only. We analyzed whole-genome sequences from 13,808 Qatar Biobank participants, evaluating rare variants (minor allele frequency 0.01–0.0001) for associations with serum 25-hydroxyvitamin D (25(OH)D) levels and deficiency risk (≤20 ng/mL) in independent discovery (n = 5885) and replication (n = 7767) cohorts, followed by meta-analyses. In quantitative analyses, the discovery cohort identified 41 genome-wide significant signals, including CD36 rs192198195 (p = 2.48 × 10⁻⁸), and replication found 46, including SLC16A7 rs889439631 (p = 2.19 × 10⁻⁸), implicating lipid metabolism pathways. In binary analyses, replication revealed POTEB3 rs2605913 (p = 2.8 × 10⁻⁸), while meta-analysis (n = 13,652) uncovered SLC25A37 rs952825245 (p = 5.15 × 10⁻¹²), a locus associated with cancer and vitamin D signaling. Rare-variant polygenic scores derived from discovery significantly predicted continuous (R² = 0.146, p = 9.08 × 10⁻¹²) and binary traits (AUC = 0.548, OR = 0.99, p = 9.22 × 10⁻⁶) in replication. This first rare-variant GWAS of vitamin D in Middle Easterners identifies novel loci and pathways, underscores the contribution of ancestry-specific rare alleles, and supports integrating rare and common variants to guide precision management in high-burden populations. Full article

Background: Polygenic risk scores (PRSs) aggregate the effects of many common genetic variants and are being investigated as tools to refine coronary artery disease (CAD) risk prediction beyond traditional clinical models. Methods and Results: We review the development of PRS from early unweighted scores to contemporary genome-wide models and summarize evidence from major studies. We identified key studies through PubMed searches using the terms “polygenic risk score,” “genetic risk prediction,” and “coronary artery disease,” supplemented by citation chaining of highly cited articles and recent reviews. Large cohorts, such as the UK Biobank, show that individuals in the highest PRS percentiles have a 3–5-fold higher risk of CAD, and may gain the greatest benefit from statin therapy. PRS can also reclassify younger adults at borderline or intermediate risk and may complement coronary artery calcium (CAC) scoring. Conclusions: PRSs hold promise for lifetime risk stratification and targeted prevention in CAD but are limited by ancestry bias in GWAS, underrepresentation of diverse populations, inconsistency in individual estimates, and lack of standardized reporting. Future research should focus on expanding multi-ancestry databases, standardizing methods, prospective validation, and effective communication strategies to support equitable and evidence-based clinical use. Full article

Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality globally. Early detection and personalised prevention strategies are crucial for reducing the burden of CVD. The use of biomarkers plays a pivotal role in identifying individuals at risk and facilitating timely interventions. This rapid scoping review aims to identify and evaluate current research on biomarkers used for primary and secondary personalised prevention of CVD, highlighting evidence gaps and the integration of digital technologies. A comprehensive search was conducted in Medline and Embase databases from January 2020 to February 2023. Joanna Briggs Institute (JBI) Manual for Evidence Synthesis and PRISMA-ScR guidelines were followed. A total of 775 studies were included, with ischemic heart disease (IHD) and stroke being the most investigated CVDs. Molecular, cellular, imaging, physiological, and anthropometric biomarkers were included. Molecular biomarkers, particularly genetic and biochemical, were the most researched. For secondary prevention, there was considerable research using imaging biomarkers. Genetic biomarker research was the most frequent category of biomarker identified, particularly using variant analysis and polygenic scores, followed by biochemical, imaging, and physiological biomarkers. There was also evidence of the integration of artificial intelligence to enhance the predictive capabilities of these biomarkers. Despite progress, research gaps were identified for less common CVDs, such as aortic aneurysm and nonrheumatic valvular heart disease, and limited research investigating other molecular biomarker types, such as epigenetics and transcriptomics. Full article

Background/Objectives: While single variants may have only small effects on common heritable traits like schizophrenia, methods for combining such effects over multiple variants have been proposed for more than 30 years. The currently favored approaches are polygenic risk scores. Their main aim is the genetic prediction of phenotypes. Methods: To accommodate the inherent genetic heterogeneity between males and females, we separated them into two independent datasets and in each developed allelic polygenic risk scores. We focused on variants with high predictability rather than high statistical significance and derived a statistical test to assess the significance of results obtained in one sex and replicated in the other sex. Results: As few as 5000 highly predictive variants achieved accuracy exceeding 95% in each of males and females, and only 2.8% and 3.3% of cases and controls were misclassified in females and males, respectively. Conclusions: Our allelic polygenic risk scores are based on individual genotypes rather than summary statistics and produce highly accurate, cross-validated phenotype predictions. Although variants were originally selected as being highly predictive rather than statistically significant, 544 disease-associated variants were shown to be significantly shared between males and females, which represents a replication in an independent dataset. Full article

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery. Full article

Obesity is an epidemic that currently impacts many nations. The persistence of this disease is shaped by both genetic and epigenetic factors that extend beyond calorie balance. Research in the past year has revealed that epigenetic and cellular memory within adipose tissue can predispose individuals to weight regain after initial fat loss, as shown by studies indicating persistent transcriptional and chromatin changes even after fat mass reduction. Independent studies also demonstrate long-lasting metabolic shifts, such as those triggered by glucose-dependent insulinotropic polypeptide receptor (GIPR)-induced thermogenesis and sarcolipin (SLN) stabilization that also support a form of “metabolic memory” that is associated with sustained weight loss. At the neural level, rare variants in synaptic genes like BSN (Bassoon presynaptic cytomatrix protein), a presynaptic scaffold protein, and APBA1 (amyloid beta precursor protein binding family A member 1), a neuronal adaptor involved in vesicular trafficking, disrupt communication in feeding circuits, elevating obesity risk and illustrating how synaptic integrity influences food intake regulation. Similarly, the spatial compartmentalization of metabolic signaling within neuronal cilia is emerging as crucial, with cilia-localized receptors G protein-coupled receptor 75 (GPR75) and G protein-coupled receptor 45 (GPR45) exerting opposing effects on energy balance and satiety. Meanwhile, genome-wide association studies (GWAS) have advanced through larger, more diverse cohorts and better integration of environmental and biological data. These studies have identified novel obesity-related loci and demonstrated the value of polygenic risk scores (PRS) in predicting treatment responses. For example, genetic variants in GLP-1R (glucagon-like peptide-1 receptor) and GIPR (glucose-dependent insulinotropic polypeptide receptor) may modulate the effectiveness of incretin-based therapies, while PRS for satiation can help match individuals to the most appropriate anti-obesity medications. This review focuses on studies in the last two years that highlight how advances in obesity genetics are driving a shift toward more personalized and mechanism-based treatment strategies. Full article

Osteoporosis is a multifactorial, polygenic disease characterized by reduced bone mineral density (BMD) and increased fracture risk. Genome-wide association studies (GWASs) have identified numerous loci associated with BMD and/or bone fractures, but functional characterization of these target genes is essential to understand the biological mechanisms underlying osteoporosis. This review focuses on current methodologies and key examples of successful functional studies aimed at evaluating gene function in osteoporosis research. Functional evaluation typically follows a multi-step approach. In silico analyses using omics datasets expression quantitative trait loci (eQTLs), protein quantitative trait loci (pQTLs), and DNA methylation quantitative trait loci (mQTLs) help prioritize candidate genes and predict relevant biological pathways. In vitro models, including immortalized bone-derived cell lines and primary mesenchymal stem cells (MSCs), are used to explore gene function in osteogenesis. Advanced three-dimensional culture systems provide additional physiological relevance for studying bone-related cellular processes. In situ analyses of patient-derived bone and muscle tissues offer validation in a disease-relevant context, while in vivo studies using mouse and zebrafish models enable comprehensive assessment of gene function in skeletal development and maintenance. Integration of these complementary methodologies helps translate GWAS findings into biological insights and supports the identification of novel therapeutic targets for osteoporosis. Full article

Background/Objectives: The implementation of polygenic scores (PGSs) and multifactorial risk assessments (MFRAs) has the potential to enhance breast cancer risk stratification, particularly in carriers of moderate-penetrance pathogenic variants (PVs), whose risk profiles often remain unclear if testing is limited to monogenic risk factors. Methods: To enhance breast cancer risk stratification, we included the BCAC313 polygenic score, together with MFRA, for carriers of moderate-penetrance pathogenic variants (PVs) during routine diagnostics and assessed its effect on the classification of patients’ risk categories in a real-world cohort at our center in its first year of implementation. Seventeen carriers with PVs in moderate-risk breast cancer genes were included in this study. Thirteen of them qualified for analysis for a full MFRA, including PGS, according to ancestry estimation and clinical criteria. The MFRA was performed using the CanRisk tool, which incorporates clinical, lifestyle, familial, and genetic data, including the BCAC313 score. Results: PGS z-scores were significantly higher in breast cancer patients compared to the unaffected control cohort (p = 0.016). The MFRA, including PGS, increased risk estimates for contralateral breast cancer in seven of eight patients with breast cancer and for primary breast cancer in three of five healthy carriers, compared to the risk conferred by the MFRA and moderate-penetrance pathogenic variant alone. Risk estimates varied widely, demonstrating the value of MFRA in personalized care. In five cases, one with a CHEK2-PV and four with an ATM-PV, the modified risk assessment contributed to the surgical decision for a prophylactic mastectomy. Conclusions: The MFRA, including PGS, provides the clinically meaningful refinement of breast cancer risk estimates in individuals with moderate-risk PVs. Personalized risk predictions can inform clinical management and support decision-making, which highlights the utility of this approach in clinical practice. Full article

Ischemic stroke is a complex, multifactorial disorder with a significant heritable component. Recent developments in genome-wide association studies (GWASs) have identified several common variants associated with clinical outcomes, stroke subtypes, and overall risk. Key loci implicated in biological pathways related to vascular integrity, lipid metabolism, inflammation, and atherogenesis include 9p21 (ANRIL), HDAC9, SORT1, and PITX2. Although polygenic risk scores (PRSs) hold promise for early risk prediction and stratification, their clinical utility remains limited by Eurocentric bias and missing heritability. Integrating multiomics approaches, such as functional genomics, transcriptomics, and epigenomics, enhances our understanding of stroke pathophysiology and paves the way for precision medicine. This review summarizes the current genetic landscape of ischemic stroke, emphasizing how evolving methodologies are shaping its prevention, diagnosis, and treatment. Full article

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global morbidity and mortality, prompting significant interest in individualized prevention and treatment strategies. This review synthesizes recent advances in genomic and precision medicine approaches relevant to ASCVD, with a focus on genetic risk scores, lipid metabolism genes, and emerging gene editing techniques. A structured literature search was conducted across PubMed, Scopus, and Web of Science databases to identify key publications from the last decade addressing genomic mechanisms, therapeutic targets, and computational tools in ASCVD. Notable findings include the identification of causal genetic variants such as PCSK9 and LDLR, the development of polygenic risk scores for early prediction, and the use of deep learning algorithms for integrative multi-omics analysis. In addition, we highlight current and future therapeutic applications including PCSK9 inhibitors, RNA-based therapies, and CRISPR-based genome editing. Collectively, these advances underscore the promise of precision medicine in tailoring ASCVD prevention and treatment to individual genetic and molecular profiles. Full article