There is no single universal biomarker yet to estimate overall health status and longevity prospects. Moreover, a consensual approach to the very concept of aging and the means of its assessment are yet to be developed. Markers of aging could facilitate effective health control, more accurate life expectancy estimates, and improved health and quality of life. Clinicians routinely use several indicators that could be biomarkers of aging. Duly validated in a large cohort, models based on a combination of these markers could provide a highly accurate assessment of biological age and the pace of aging. Biological aging is a complex characteristic of chronological age (usually), health-to-age concordance, and medically estimated life expectancy. This study is a review of the most promising techniques that could soon be used in routine clinical practice. Two main selection criteria were applied: a sufficient sample size and reliability based on validation. The selected biological age calculators were grouped according to the type of biomarker used: (1) standard clinical and laboratory markers; (2) molecular markers; and (3) epigenetic markers. The most accurate were the calculators, which factored in a variety of biomarkers. Despite their demonstrated effectiveness, most of them require further improvement and cannot yet be considered for use in standard clinical practice. To illustrate their clinical application, we reviewed their use during the COVID-19 pandemic.

Schizophrenia varies greatly from person to person, mainly because of its polygenic nature. Consequently, schizophrenia patients form distinct subphenotypes of schizophrenia, with specific symptom patterns and outcomes. This study included 4257 adults, with long-term schizophrenia (control - 8955 individuals) who were assessed for schizophrenia with potentially severe outcomes based on following criteria: disability in functional and/or physical domains before the age of 40; severe negative symptoms (present in infancy or shortly after onset); a continuous course of the disease. Additionally, the time of the onset and aggressive/antisocial tendencies were assessed as one the predictors of potentially severe outcomes. A total of 817 participants met at least three of these criteria, i.e., had disruptive schizophrenia. A genome-wide and transcriptome-wide association study was conducted using linear regression and the PrediXcan algorithm. The obtained data were used to develop a polygenic risk model for early risk prediction of schizophrenia with potentially severe outcomes. Significant associations were found between schizophrenia and variants in CAMTA1, TRHDE, NELFE, and others. The PRS model demonstrated high performance in training, internal and external validation (ROC AUC of 0.9, 0.89, and 0.68, respectively). The functional pathway analysis highlighted pathways involved in ATP metabolism, myeloid cell differentiation, and apoptotic processes. Subphenotyping schizophrenia may enhance the discovery of genetic factors affecting its development and progression. The GWAS and TWAS findings revealed general mechanisms involved in the development of schizophrenia with potentially severe outcomes, such as synapse regulation, inflammation, and apoptosis.