Alexey V. Churov

Aging induces profound physiological changes that alter drug disposition and response, narrowing therapeutic windows and increasing susceptibility to adverse drug reactions (ADRs). This review synthesizes current evidence on age-related pharmacokinetic and pharmacodynamic changes, with a focus on pharmacogenomic determinants of drug response in older adults. Progressive decline in renal function and hepatic metabolic capacity and alterations in body composition collectively necessitate age-appropriate dose adjustments. Polypharmacy, prevalent in community-dwelling and hospitalized older adults, compounds these challenges through drug–drug and drug–drug–gene interactions. Pharmacogenomic testing—particularly for CYP2D6, CYP2C19, CYP2C9, and SLCO1B1—offers a promising strategy with which to personalize therapy, while deprescribing frameworks provide practical approaches to mitigating medication-related risks. This review provides a comprehensive framework for understanding the mechanistic basis of altered drug response in older adults and proposes actionable strategies for optimizing pharmacotherapy in this vulnerable population. Throughout, pharmacogenomic claims are graded across four levels: biological plausibility, measured pharmacokinetic effect, pharmacogenetic actionability, and demonstrated clinical utility. Furthermore, selected gene–drug examples are explicitly identified as either geriatric-specific evidence or extrapolations from general adult pharmacogenomics, clarifying which findings are truly modified by the aging process.

In this paper, we develop a non-Hermitian coupling method for integrating heterogeneous datasets. Using the completeness of the root-vector system of the operator associated with the coupled graph Laplacian, we construct a generalization of a bi-orthogonal basis. Decomposing the trace functional into regular and nilpotent parts, we elaborate an analogue of the gradient ascent and descent method for the extremal trace problem. The central point of the concept is that the extremal values coincide with the trace values and their difference yields an intrinsic characteristic of dataset heterogeneity. The results are illustrated by single-cell multi-omics integration, where the indicators distinguish stable systems from stratified populations.

Chronological age can be estimated from molecular profiles; however, predictive accuracy alone does not establish a biologically validated ageing clock. We therefore evaluated whether plasma protein intensities measured by data-independent acquisition mass spectrometry (DIA-MS) support chronological-age estimation in a cross-sectional cohort from Moscow, Russia, while explicitly examining acquisition-order and MS-batch confounding. Plasma samples from 350 adult participants aged 19.8 to 99.1 years were analyzed as single-shot, single-injection DIA-MS acquisitions on a Q Exactive HFX platform. The final matrix comprised 222 protein groups and was generated with DIA-NN version 1.8.1 using a two-pass predicted-library-assisted workflow, cross-run precursor-level normalization, normalized MaxLFQ-like protein–group quantification, and 1% precursor and protein–group false-discovery-rate thresholds. Descriptive associations were assessed using Spearman rank correlation with Bonferroni correction. Feature selection and missing-value imputation were confined to each training subset. Generalization was assessed primarily by holding out complete MS batches, with repeated, nested, and random five-fold analyses retained as internal comparators. Of the 222 proteins, 148 were associated with chronological age at the nominal threshold p < 0.05, and 98 remained significant after Bonferroni correction. The strongest descriptive associations involved fibrinogen beta chain (FGB; rs = 0.590), SERPINA3 (rs = 0.560), ITIH4 (rs = 0.542), complement C5 (rs = 0.538), and ceruloplasmin (rs = 0.518). In internal random-split analyses, a 50-feature Gradient Boosting strategy yielded MAE = 8.90 ± 1.06 years in repeated five-fold cross-validation and MAE = 9.10 ± 1.10 years in nested cross-validation. However, when complete MS batches were held out, aggregate MAE increased to 10.56 years (R² = 0.554) using a batch-stratified assignment and 11.87 years (R² = 0.461) using GroupKFold. These batch-held-out results constitute the primary estimate of generalization from the present dataset. To assess the trade-off between error and assay complexity, strategies using 5, 8, 10, 12, 15, 20, 30, and 50 proteins selected within each training fold were compared. The 8-feature strategy yielded MAE = 11.83 years in repeated random cross-validation, whereas the 50-feature strategy yielded MAE = 8.90 years. These estimates concern feature-number strategies, not fixed panels. The post hoc eight-protein list is reported only as an exploratory candidate set for future independent validation. Acquisition order was associated with chronological age (rs = 0.336, p = 1.03 × 10⁻¹⁰), and age and sex distributions differed strongly across 27 MS batches. Order alone predicted age with MAE = 3.98 years in a negative-control model, demonstrating that age information was encoded in the acquisition design. Accordingly, the present findings support only a proof-of-concept for plasma-proteomic prediction of chronological age within this dataset; they do not establish a batch-independent model, a clinical tool, or a biologically validated ageing clock.

Atherosclerosis is an age-related immune-metabolic disease in which lipid retention, endothelial dysfunction, innate immune activation, and vascular remodeling interact over decades. In older adults, inflammaging, mitochondrial dysfunction, defective autophagy/mitophagy, and cellular senescence may lower the threshold for persistent inflammasome activation in the vascular wall. The NLR family pyrin domain containing 3 (NLRP3) inflammasome and interleukin-1β (IL-1β) are central to this process because they convert sterile danger signals, including cholesterol crystals, oxidized lipids, mitochondrial stress, and disturbed flow, into caspase-1 activation, IL-1β and interleukin-18 (IL-18) maturation, pyroptosis, endothelial dysfunction, macrophage activation, and plaque destabilization. Clinical proof-of-concept from IL-1β blockade supports inflammatory targeting in atherosclerotic cardiovascular disease, but direct cytokine inhibition leaves upstream inflammasome-prone states insufficiently addressed. This narrative review synthesizes evidence published from 2010 to 2026, together with older landmark studies, on mechanistic, translational, and clinical links between the NLRP3/IL-1β axis and age-related atherosclerosis. Traditional Chinese medicine-derived formulas and isolated natural compounds are considered because they may modulate upstream priming, mitochondrial reactive oxygen species, autophagy, lipid handling, macrophage polarization, endothelial pyroptosis, and inflammasome-associated cytokine release. However, most data remain preclinical, and translation requires stronger causal validation, chemical standardization, pharmacokinetic characterization, age-relevant models, and systematic safety assessment.

Aging induces profound physiological changes that alter drug disposition and response, narrowing therapeutic windows and increasing susceptibility to adverse drug reactions (ADRs). This review synthesizes current evidence on age-related pharmacokinetic and pharmacodynamic changes, with a focus on pharmacogenomic determinants of drug response in older adults. Progressive decline in renal function, hepatic metabolic capacity, and alterations in body composition collectively necessitate age-appropriate dose adjustments. Polypharmacy, prevalent in community-dwelling and hospitalized older adults, compounds these challenges through drug–drug and drug–drug–gene interactions. Pharmacogenomic testing—particularly for CYP2D6, CYP2C19, CYP2C9, and SLCO1B1—offers a promising strategy to personalize therapy, while deprescribing frameworks provide practical approaches to mitigate medication-related risks. This review presents a comprehensive framework for understanding the mechanistic basis of altered drug response in older adults and proposes actionable strategies for optimizing pharmacotherapy in this vulnerable population.

Regulatory CD4+ T cells (Tregs) are essential for immune tolerance, tissue repair, and control of inflammation, but functional human Tregs cannot be identified reliably by a single protein. This targeted narrative review evaluates protein-level and proteomics-supported Treg signatures with explicit attention to species, sample source, analytical platform, validation strategy, and intended use. Primary human LC-MS/MS studies reveal pathway-level differences involving T-cell receptor signaling, metabolism, lysosomal activity, and lineage protection, whereas murine proteomic studies provide mechanistic candidates such as Themis1 but do not establish human biomarkers. CyTOF, functional single-cell protein profiling, spatially resolved protein imaging, and multi-omics further resolve phenotypic and tissue heterogeneity. Established CD25high/CD127low/FOXP3-based panels support enrichment and phenotyping; CTLA-4, ICOS, TIGIT, GITR, PD-1, chemokine receptors, suppressive enzymes, metabolic proteins, and emerging candidates report functional or tissue states but are not Treg-exclusive. Cancer currently provides the strongest tissue-level and prognostic evidence, whereas data in healthy aging, cardiovascular and metabolic disease, osteoarthritis, and neurodegeneration remain heterogeneous and mainly exploratory. Across contexts, validated diagnostic sensitivity, specificity, reference ranges, prospective clinical utility, and inter-laboratory reproducibility are largely absent. Treg immunoproteomics is therefore best regarded as a discovery and stratification framework rather than a standardized clinical diagnostic test.

This review is devoted to diagonal integration of multi-omics data, an approach enabling to combine data from different molecular modalities obtained in independent studies without pairwise measurements. Twenty-two diagonal integration methods classified by mathematical basis are analyzed. A comparative analysis of international and Russian platforms is presented. A concept of a biocentric approach based on coupled Laplacians and kernel methods in reproducing Hilbert space is substantiated.

In this paper, we consider the extremal trace problem for the coupled Laplacian on the sets homeomorphic to the Stiefel manifold defined on the complex Euclidean space. The study is implemented via various mathematical methods, including topological and probabilistic approaches. A detailed, comprehensive classification of the stationary points is given, which itself deserves to be considered as a general method in the framework of the optimization theory. Finally, an application to biologically meaningful integration of heterogeneous datasets, in which the structure of molecular interactions serves as a significant constraint for the mathematical model, is proposed. The main advantage of the elaborated method in comparison with the previously used ones is the absence of any conditions on the structure of the initial heterogeneous datasets. This paper is a continuation of a series of papers by our research group devoted to the development of new mathematical methods for integrating multi-omics data.

Background/Objectives: Population aging necessitates a shift from disease-focused paradigms to a holistic characterization of biological aging processes. While chronological age remains the primary metric, it poorly captures inter-individual variability in physiological resilience and health trajectories. This study aimed to identify robust, multidimensional aging phenotypes independent of chronological age and sex using integrative factor analysis of heterogeneous biomedical data from a Russian cohort—a population underrepresented in aging research. Methods: We analyzed data from 1201 conditionally healthy adults (aged 18–99 years) enrolled in the RUSS AGE study. A comprehensive dataset comprising 118 variables across 11 modalities—including biochemical markers, anthropometry, physical function, cognitive-emotional assessments, lifestyle factors, and psychosocial indicators—was integrated using Multi-Omics Factor Analysis v2 (MOFA2). Following the extraction of 16 latent factors and residualization for demographic confounders, consensus clustering was performed to identify distinct aging phenotypes. Phenotype stability was internally recapitulated using gradient-boosting classifiers (XGBoost, CatBoost) in a stratified five-fold cross-validation and on a held-out test set. Results: MOFA2 identified 16 stable latent factors, explaining 21.3% of the total variance and capturing coordinated variation across metabolic, inflammatory, cardiovascular, cognitive, and behavioral domains. Consensus clustering revealed five reproducible phenotypes—Anemic (n = 82), Metabolically Subcompensated (n = 99), Metabolically Decompensated (n = 304), Overloaded (n = 302), and Balanced (n = 414)—characterized by distinct multisystem profiles independent of age (p > 0.05 after FDR correction) and sex. Supervised classification achieved high discriminative performance (macro F1-score = 0.75, OvR ROC-AUC = 0.93 on the held-out test set), quantifying the internal reconstructability of the phenotype labels from the original feature space rather than external generalization to an independent cohort. Conclusions: This study demonstrates the feasibility of data-driven, biologically coherent phenotyping of healthy aging using integrative factor analysis. The identified phenotypes represent stable configurations of physiological, functional, and psychosocial characteristics that transcend chronological age, providing a foundation for the future development of risk-stratification tools, preventive interventions, and biological-age calculators, subject to subsequent validation in longitudinal and independent external cohorts.

Aging is characterized by progressive loss of physiological resilience accompanied by increased susceptibility to chronic diseases. Among the interconnected hallmarks of aging, cellular senescence has emerged as a central driver of systemic inflammation through the senescence-associated secretory phenotype (SASP). Senescent cells accumulate across multiple tissues with advancing age and secrete complex mixtures of cytokines, growth factors, and proteases that reshape tissue microenvironments and propagate inflammatory signaling locally and systemically. Increasing evidence indicates that SASP composition is highly heterogeneous and depends on cell lineage, metabolic state, and the nature of the senescence-inducing stressor. Recent discoveries further demonstrate that inflammatory signaling in senescent cells is sustained by multiple nucleic acid–sensing pathways, including both cGAS–STING–dependent DNA sensing and mitochondrial RNA–mediated activation of RIG-I–like receptors. Concurrently, senescent cells deploy immune-evasion mechanisms that limit clearance by cytotoxic lymphocytes and natural killer cells, facilitating their persistence within aging tissues. Accumulation of senescent cells therefore represents a critical mechanistic link between molecular damage and the systemic inflammatory state known as inflammaging. This review synthesizes current understanding of tissue-specific SASP programs across immune, vascular, metabolic, hepatic, and neural systems. Particular emphasis is placed on mechanisms that amplify local senescence into organism-wide inflammation, including endocrine signaling, extracellular vesicle trafficking, and sex-dependent modulation of senescence pathways. Graphical abstract The alternative text for this image may have been generated using AI.