Aim. To study the prevalence of dyslipidemias and their association with various risk factors in the Russian population of men and women aged 35-74 years in 2020-2022.Material and methods. This work was carried out as part of the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation-3 (ESSE-RF3) study. The sample included 28731 men and women aged 35-74 years living in 15 Russian regions. Hypercholesterolemia (HC) was diagnosed with total cholesterol (TC) ≥5,0 mmol/l, while elevated low-density lipoprotein cholesterol (LDL-C) was considered ≥3,0 mmol/l, hypertriglyceridemia — with triglyceride levels ≥1,7 mmol/l, reduced high-density lipoprotein cholesterol (HDL-C) <1,0 mmol/l in men and <1,2 mmol/l in women. Associations were assessed using logistic regression after adjustment for socio-demographic characteristics, drinking status, presence of hypertension (HTN) and stroke.Results. The prevalence of hypertriglyceridemia in the Russian Federation in 2020-2022 was 58,8%, hypertriglyceridemia — 32,2%. The incidence of lipid-lowering therapy increased with age from 1% in the group of 35-44 years to 16% in the group of 65-74 years. On average, only 7,6% of study participants received lipid-lowering therapy. Significant associations of HC with HTN, obesity and alcohol abuse were identified. Similar results were obtained for elevated LDL-C levels, with the exception of alcohol abuse. In turn, a reduced HDL-C level was significantly associated with the lack of higher education, marriage, physical activity, smoking and the presence of diseases.Conclusion. The prevalence of lipid disorders in the Russian Federation in 2020-2022 remained at a high level. These disorders occurred more often in women, and they were better informed about their cholesterol levels and more often received lipid-lowering therapy. HTN, obesity, and some behavioral and social risk factors were associated with dyslipidemia.

SCORE2 (Systematic COronary Risk Evaluation 2) is a risk assessment scale for cardiovascular events, presented in 2021 by the European Society of Cardiology. Both for training and validation of the SCORE2 model, representative samples from the Russian population were not used. Therefore, we aimed to validate SCORE2 on a such sample. For this purpose, we used a sample from the ESSE-RF epidemiological study consisting of 7251 participants aged 40–69 years without history of CVDs. We performed the validation by comparing SCORE2 risk estimates for ESSE-RF participants with the observed incidence of cardiovascular events in the study, adjusted for event information losses. The validation demonstrated that SCORE2 risk estimates were accurate for Russian men and inaccurate for Russian women. Together with the quantitative assessment of risk, SCORE2 offers its interpretation in terms of 10-year CVD risk group: low-moderate, high, and very high. For Russian men we considered the original interpretation of the SCORE2 estimates to be questionable because almost none of the men would be categorized as having “low-to-moderate” 10-year CVD risk. This problem would be typical for all countries of the very high CVD risk region. Therefore, we proposed a new interpretation of the SCORE2 risk estimates for men from the very high risk region. According to the proposed interpretation, the fraction of men in ESSE-RF in “low-to-moderate” 10-year CVD risk increased from 2% to 18% and the fraction of men in “very high” CVD risk decreased from 63% to 20% as compared to the original interpretation. The proposed interpretation would allow a more personalized approach to CVD treatment and optimize the burden on primary healthcare in the very high risk region countries.

Complex diseases often share genetic susceptibility factors, molecular pathways, and pathological mechanisms. Understanding these commonalities through systematic cross‐disease comparisons can reveal both disease‐specific and shared biomarkers, potentially suggesting new therapeutic targets and opportunities for drug repurposing. In recent years, the growth of multi‐omics datasets across diverse diseases, coupled with advances in computational systems biology, has enabled sophisticated cross‐disease analyses. New methodological frameworks have emerged for integrating and comparing disease‐specific molecular signatures, from gene‐level analyses to complex network‐based approaches. Here, we present a comprehensive framework for computational cross‐disease comparison and integration of omics data, covering established and emerging methodologies. These include gene‐level comparative analyses, pathway‐based approaches, network biology methods, matrix factorization techniques, and machine learning approaches. We examine important aspects of data preprocessing, normalization, and integration, suggesting practical solutions to common technical challenges. We provide a detailed overview of relevant software tools and databases, discussing their strengths, limitations, and optimal use cases for cross‐disease analysis. Finally, we explore current trends in cross‐disease omics analysis, particularly through deep learning methods, highlighting new opportunities for methodological innovation and biological discovery in this field. This compilation of computational methods and practical insights aims to serve as a resource both for bioinformaticians seeking guidance on optimal method selection and biomedical researchers interested in applied cross‐disease analyses. In addition to highlighting practical recommendations and common pitfalls, it provides an entry point to the extensive literature in the field, supporting readers in identifying and further exploring suitable methods for their research needs.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Databases and Expert Systems

Aim. To perform a population analysis of Non-High Density Lipoprotein Cholesterol level (non-HDL-c) in Russian population and to evaluate its association with cardiovascular events.Material and Methods. The material consisted of results obtained from 11 regions of the ESSE-RF1 Study and from 4 regions of the ESSE-RF2 Study. Study protocols were identical. The studies were performed in 2012-2014 and 2017, respectively. Endpoints were assessed in 19041 people aged 35-64 years. The median follow-up was 6.5 years in ESSE RF (1) and 3.8 years in ESSE RF(2). Analysis was performed for three lipid variables: total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and non-HDLC in two samples: the general population sample and the same sample without individuals with coronary heart disease (CHD), myocardial infarction (MI) and/or stroke history and not taking statins (the population sample of "without a history of cardiovascular diseases [CVD]". The analysis of nonlinear associations was performed using the generalized additive Cox model. The combined cardiovascular endpoint was represented by cardiovascular death and nonfatal MI and stroke. Traditional and laboratory FRs, socio-demographic parameters were analyzed. The significance level for all tested hypotheses was set to be 0.05.Results. The prevalence of elevated non-HDL-C level (>3.7 mmol/l) was found to be 74.6%. No gender differences were found: there was 74.6% for men and 74.5% for women. Both mean values and prevalence of elevated non-HDL-C were increased with age in women, and its level was slightly decreased in men after 55 years old. Almost all analyzed RFs were significantly associated with elevated non-HDL-C in these two population samples. In both samples elevated total CH and elevated LDL-C were associated with all-cause mortality after correction for all RFs. On the contrary, the non-HDL-C was associated with CVD combined end pints. It has been shown that the risk of these end points increases uniformly with increase in levels of non HDL cholesterol, no nonlinear associations were found.Conclusion. The results of a population-based analysis of non-HDL-C performed in the Russian population for the first time confirmed that elevated non-HDL-C levels contribute significantly to determining the risk of cardiovascular events in the medium term. It can be assumed that the new risk scales (SCORE2 and SCORE OP) proposed by the European Society of Cardiology and the European Society of Preventive Cardiology, which include non-HDL C instead of TC, will allow adequate assessment of 10-year cardiovascular risk for Russians. However, continued monitoring of endpoints in order to obtain stable associations is required.

Aim. To establish reference values of handgrip test for a middle-aged and elderly population (35-74 years), as well as study the association of health characteristics and muscle strength.Material and methods. For the analysis, we used data from the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation-3 (ESSE-RF3) study, conducted in 2020-2022. The sample was selected similarly to the previous ESSE-RF stages. The sample included 27689 men and women aged 35-74 years from 14 Russian regions. Of the individual variables, the characteristics with the highest evidence level of influence on muscle strength were taken as covariates. Muscle strength was assessed by a handgrip test using a MEGEON 34090 device with an accuracy of 0,1 kg. Three measurements were taken for each arm in a standing position, and the maximum value was used in the analysis. Normative sex-specific handgrip test values were established regardless of age: a participant's handgrip value was considered reduced if it was 2 standard deviations below the average value of participants at the age with the best/highest scores.Results. The average handgrip test values for the entire male sample were 43,4±11,1 kg, while for the female sample — 27,6±6,1 kg. In men under 53 years of age, muscle strength decreases at a rate of 0,12 kg/year, then the decline accelerates to 0,48 kg/year. In women under 45,5 years of age, a slight and insignificant increase in muscle strength was detected by 0,02 kg/year, and after 45,5 years, handgrip value falls at a rate of 0,2 kg/year. Therefore, both men and women lose 1,3% of muscle strength per year to year and the decline rate do not differ. The peak age can be considered 35 years old, since at this age, the average muscle strength in men and women is 47,6±11,5 and 29,2±5,7 kg, respectively, and the calculated norm (M–2SD) for men was 47,6–2´11,5≈24 kg using rounding down, and for women it was 29,2– 2´5,7≈17 kg. With age, as expected, the proportion of people of both sexes with a metabolic syndrome level below the threshold increases, reaching 7,5% after 65 years. Multivariate regression analysis showed that, taking into account adjustments for covariates, for every 10 years, muscle strength decreases by an average of 3,1 kg in men and by 1,7 kg in women. Negative associations of metabolic syndrome with inflammation markers (fibrinogen and C-reactive protein) and noncommunicable diseases are also significant, but only men have significantly lower rates of metabolic syndrome in the presence of anxiety and depression, low income and urban residence.Conclusion. The use of handgrip test in any age range during medical examinations or preventive examinations of the adult population and informing primary care specialists about the results obtained can help improve the general health of the population.

Identifying causal relationships in omics data is essential for understanding underlying biological processes. However, detecting these relationships remains challenging due to the complexity of molecular networks and observational data limitations. To guide researchers, we conducted a systematic literature review of data-driven causal omics analysis methods that use structured prior knowledge from regulatory and interaction databases. We grouped methods into three approaches based on the extent of prior knowledge integration: regulon-level (direct regulator–target links, straightforward interpretation, but with the risk of oversimplification), flow-level (multi-step propagation from regulators to targets, broader mechanism explanation, but lacking uncertainty modeling), and network-level (system-wide interactions and crosstalk, most comprehensive, but with increased computational complexity and requiring particularly careful interpretation). These methods have demonstrated utility across diverse applications, including identification of therapeutic targets in acute myeloid leukemia, elucidation of mechanisms in IgA nephropathy, and detection of regulatory perturbations in Alzheimer’s disease. We discuss the strengths, limitations, and representative use cases of each approach, and address general limitations and outline future research directions. This review serves as a practical guide for the entire analysis process, from selecting prior knowledge databases (PKDBs) to choosing and applying causal analysis methods for different research questions.