## 3 Discussion  
The polygenic risk scoring approach has become truly innovative in the study of the genetics of multifactorial diseases. Compared to individual SNPs, PRS models often yield highly reproducible results, which holds promise as a prognostic factor. It is advisable to investigate the information content of the developed models in the intended population before implementation. In this study, we employed the PRS model introduced by [PERSON] et al. [9], which was initially intended for familial late-onset Alzheimer's disease but subsequently found to share a common architecture with early-onset and sporadic forms [30]. To identify the SNPs included in the utilized PRS model and the \(\varepsilon\) alleles of the _APOE_ gene, we developed a molecular approach using the established hydrogel microarray technology. The microarray analysis can be completed in less than 12 h. The software that executes the SNP identification algorithm, PRS calculation, and _APOE_ allele determination automatically analyzes the microarray hybridization pattern. Compared to next-generation sequencing (NGS) assays, the cost of microarray analysis is significantly lower and comparable to PCR (less than USD 10 per sample). However, microarray-based assays are limited by the incomplete automation of all procedural steps and a lack of standardization that is critical for routine genetic testing in clinical laboratory settings. Nevertheless, several hydrogel microarray-based assays have been approved for in vitro clinical diagnostics by the Russian regulatory agency [31]. In this study, the microarray results were validated via Sanger sequencing and real-time PCR, with SNP identification showing 100% concordance. The developed assay was applied to investigate the impact of PRS and the \(\varepsilon\) alleles of the _APOE_ on dementia risk among the residents of the Moscow region in the Russian population, utilizing a sample of 348 individuals diagnosed with dementia and 519 cognitively normal participants. For the first time, the AD polygenic risk model was tested on the European part of the Russian population, revealing its capacity to accurately assess the risk of dementia. The OR of 1.26 (per standard deviation) obtained in this study is equivalent to [PERSON] et al. 's OR of 1.29 for the European population. Additionally, we achieved similar AUC values (0.559 compared to 0.57) [9]. Of the studied genetic factors, as expected, homozygotes for _APOE-\(\varepsilon\)4_ had the highest risk of dementia (OR = 7.98). The risk for individuals in the fourth quartile of PRS (OR = 1.85) was approximately equal to the risk for the heterozygous carriers of _APOE-\(\varepsilon\)4_ (OR = 1.81). Our data regarding the impact of PRS on disease development risk in _APOE-\(\varepsilon\)4_ carriers (Table 2) support previous findings indicating that individuals carrying \(\varepsilon\)4_ with high PRS values exhibit a higher risk [8; 32]. Thus, there is compelling evidence that genetic factors, including PRS and _APOE-\(\varepsilon\)4_, influence the risk of developing dementia. However, considering the small AUC values achieved solely using genetic factors, it is improbable that they can be utilized as an independent predictor. In certain studies [8; 12], clinical and social factors such as age, gender, _APOE-\(\varepsilon\)4_, and education were more crucial for risk assessment than PRS. We showed that AUC values greater than 0.7 were achieved by including social factors in the analysis, such as the presence of higher education, family, and intellectual work. Nevertheless, the statistical significance of PRS as a risk factor for dementia implies that it can enhance the stratification of individuals into risk groups when incorporated into multivariate scales. Such scales should consider biochemical biomarkers that reflect the preclinical stages of the disease [33].