Association between housing type and accelerated biological aging in different sexes: moderating effects of health behaviors.

INTRODUCTION: Despite associated with multiple geriatric disorders, whether housing type, an indicator of socioeconomic status (SES) and environmental factors, is associated with accelerated biological aging is unknown. Furthermore, although individuals with low-SES have higher body mass index (BMI) and are more likely to smoke, whether BMI and smoking status moderate the association between SES and biological aging is unclear. We examined these questions in urbanized low-SES older community-dwelling adults. METHODS: First, we analyzed complete blood count data using the cox proportional hazards model and derived measures for biological age (BA) and biological age acceleration (BAA, the higher the more accelerated aging) (N = 376). Subsequently, BAA was regressed on housing type, controlling for covariates, including four other SES indicators. Interaction terms between housing type and BMI/smoking status were separately added to examine their moderating effects. Total sample and sex-stratified analyses were performed. RESULTS: There were significant differences between men and women in housing type and BAA. Compared to residents in ≥3 room public or private housing, older adults resided in 1-2 room public housing had a higher BAA. Furthermore, BMI attenuated the association between housing type and BAA. In sex-stratified analyses, the main and interaction effects were only significant in women. In men, smoking status instead aggravated the association between housing type and BAA. CONCLUSION: Controlling for other SES indicators, housing type is an independent socio-environmental determinant of BA and BAA in a low-SES urbanized population. There were also sex differences in the moderating effects of health behaviors on biological aging.

Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts human lifespan limit.

We investigated the dynamic properties of the organism state fluctuations along individual aging trajectories in a large longitudinal database of CBC measurements from a consumer diagnostics laboratory. To simplify the analysis, we used a log-linear mortality estimate from the CBC variables as a single quantitative measure of the aging process, henceforth referred to as dynamic organism state indicator (DOSI). We observed, that the age-dependent population DOSI distribution broadening could be explained by a progressive loss of physiological resilience measured by the DOSI auto-correlation time. Extrapolation of this trend suggested that DOSI recovery time and variance would simultaneously diverge at a critical point of 120 - 150 years of age corresponding to a complete loss of resilience. The observation was immediately confirmed by the independent analysis of correlation properties of intraday physical activity levels fluctuations collected by wearable devices. We conclude that the criticality resulting in the end of life is an intrinsic biological property of an organism that is independent of stress factors and signifies a fundamental or absolute limit of human lifespan.

Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience.

Biological age acceleration (BAA) models based on blood tests or DNA methylation emerge as a de facto standard for quantitative characterizations of the aging process. We demonstrate that deep neural networks trained to predict morbidity risk from wearable sensor data can provide a high-quality and cheap alternative for BAA determination. The GeroSense BAA model was trained and validated using steps per minute recordings from 103,830 one-week long and 2,599 of up to 2 years-long longitudinal samples and exhibited a superior association with life-expectancy over the average number of steps per day in, e.g., groups stratified by professional occupations. The association between the BAA and effects of lifestyles, the prevalence of future incidence of diseases was comparable to that of BAA from models based on blood test results. Wearable sensors let sampling of BAA fluctuations at time scales corresponding to days and weeks and revealed the divergence of organism state recovery time (resilience) as a function of chronological age. The number of individuals suffering from the lack of resilience increased exponentially with age at a rate compatible with Gompertz mortality law. We speculate that due to the stochastic character of BAA fluctuations, its mean and auto-correlation properties together comprise the minimum set of biomarkers of aging in humans.

Genetic and phenotypic analysis of the causal relationship between aging and COVID-19.

Background: Epidemiological studies revealed that the elderly and those with comorbidities are most affected by COVID-19, but it is important to investigate shared genetic mechanisms between COVID-19 risk and aging. Methods: We conducted a multi-instrument Mendelian Randomization analysis of multiple lifespan-related traits and COVID-19. Aging clock models were applied to the subjects with different COVID-19 conditions in the UK-Biobank cohort. We performed a bivariate genomic scan for age-related COVID-19 and Mendelian Randomization analysis of 389 immune cell traits to investigate their effect on lifespan and COVID-19 risk. Results: We show that the genetic variation that supports longer life is significantly associated with the lower risk of COVID-19 infection and hospitalization. The odds ratio is 0.31 (P = 9.7 × 10-6) and 0.46 (P = 3.3 × 10-4), respectively, per additional 10 years of life. We detect an association between biological age acceleration and future incidence and severity of COVID-19 infection. Genetic profiling of age-related COVID-19 infection indicates key contributions of Notch signaling and immune system development. We reveal a negative correlation between the effects of immune cell traits on lifespan and COVID-19 risk. We find that lower B-cell CD19 levels are indicative of an increased risk of COVID-19 and decreased life expectancy, which is further validated by COVID-19 clinical data. Conclusions: Our analysis suggests that the factors that accelerate aging lead to an increased COVID-19 risk and point to the importance of Notch signaling and B cells in both. Interventions that target these factors to reduce biological age may reduce the risk of COVID-19.

Quantitative characterization of biological age and frailty based on locomotor activity records.

We performed a systematic evaluation of the relationships between locomotor activity and signatures of frailty, morbidity, and mortality risks using physical activity records from the 2003-2006 National Health and Nutrition Examination Survey (NHANES) and UK BioBank (UKB). We proposed a statistical description of the locomotor activity tracks and transformed the provided time series into vectors representing physiological states for each participant. The Principal Component Analysis of the transformed data revealed a winding trajectory with distinct segments corresponding to subsequent human development stages. The extended linear phase starts from 35-40 years old and is associated with the exponential increase of mortality risks according to the Gompertz mortality law. We characterized the distance traveled along the aging trajectory as a natural measure of biological age and demonstrated its significant association with frailty and hazardous lifestyles, along with the remaining lifespan and healthspan of an individual. The biological age explained most of the variance of the log-hazard ratio that was obtained by fitting directly to mortality and the incidence of chronic diseases. Our findings highlight the intimate relationship between the supervised and unsupervised signatures of the biological age and frailty, a consequence of the low intrinsic dimensionality of the aging dynamics.

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Age-related physiological changes in humans are linearly associated with age. Naturally, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. In this work, we used one-week long physical activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) to compare three increasingly accurate biological age models: the unsupervised Principal Components Analysis (PCA) score, a multivariate linear regression, and a state-of-the-art deep convolutional neural network (CNN). We found that the supervised approaches produce better chronological age estimations at the expense of a loss of the association between the aging acceleration and all-cause mortality. Consequently, we turned to the NHANES death register directly and introduced a novel way to train parametric proportional hazards models suitable for out-of-the-box implementation with any modern machine learning software. As a demonstration, we produced a separate deep CNN for mortality risks prediction that outperformed any of the biological age or a simple linear proportional hazards model. Altogether, our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

Structure-based design of small-molecule ligands of phosphofructokinase-2 activating or inhibiting glycolysis.

Glycolysis lies at the basis of metabolism and cell energy supply. The disregulation of glycolysis is involved in such pathological processes as cancer proliferation, neurodegenerative diseases, and amplification of ischemic damage. Phosphofructokinase-2 (PFK-2), a bifunctional enzyme and regulator of glycolytic flux, has recently emerged as a promising anticancer target. Herein, the computer-aided design of a new class of aminofurazan-triazole regulators of PFK-2 is described along with the results of their in vitro evaluation. The aminofurazan-triazoles differ from other recently described inhibitors of PFK-2 and demonstrate the ability to modulate glycolytic flux in rat muscle lysate, producing a twofold decrease by inhibitors and fourfold increase by activators. The most potent compounds in the series were shown to inhibit the kinase activity of the hypoxia-inducible form of PFK-2, PFKFB3, as well as proliferation of HeLa, lung adenocarcinoma, colon adenocarcinoma, and breast cancer cells at concentrations in the low micromolar range.

PLATINUM: a web tool for analysis of hydrophobic/hydrophilic organization of biomolecular complexes.

UNLABELLED: The PLATINUM (Protein-Ligand ATtractions Investigation NUMerically) web service is designed for analysis and visualization of hydrophobic/hydrophilic properties of biomolecules supplied as 3D-structures. Furthermore, PLATINUM provides a number of tools for quantitative characterization of the hydrophobic/hydrophilic match in biomolecular complexes e.g. in docking poses. These complement standard scoring functions. The calculations are based on the concept of empirical Molecular Hydrophobicity Potential (MHP). AVAILABILITY: The PLATINUM web tool as well as detailed documentation and tutorial are available free of charge for academic users at http://model.nmr.ru/platinum/. PLATINUM requires Java 5 or higher and Adobe Flash Player 9. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Docking of ATP to Ca-ATPase: considering protein domain motions.

Most standard molecular docking algorithms take into account only ligand flexibility, while numerous studies demonstrate that receptor flexibility may be also important. While some efficient methods have been proposed to take into account local flexibility of protein side chains, the influence of large-scale domain motions on the docking results still represents a challenge for computational methods. In this work we compared the results of ATP docking to different models of Ca-ATPase: crystallographic apo- and holo-forms of the enzyme as well as "flexible" target models generated via molecular dynamics (MD) simulations in water. MD simulations were performed for two different apo-forms and one holo-form of Ca2+-ATPase and reveal large-scale domain motions of type "closure", which is consistent with experimental structures. Docking to a set of MD-conformers yielded correct solutions with ATP bound in both domains regardless of the starting Ca2+-ATPase structure. Also, special attention was paid to proper ranking of docking solutions and some particular features of different scoring functions and their applicability for the model of "flexible" receptor. Particularly, the results of docking ATP were ranked by a scoring criterion specially designed to estimate ATP-protein interactions. This criterion includes stacking and hydrophobic interactions characteristic of ATP-protein complexes. The performance of this ligand-specific scoring function was considerably better than that of a standard scoring function used in the docking algorithm.

Molecular lipophilicity in protein modeling and drug design.

Hydrophobic interactions play a key role in the folding and maintenance of the 3-dimensional structure of proteins, as well as in the binding of ligands (e.g. drugs) to protein targets. Therefore, quantitative assessment of spatial hydrophobic (lipophilic) properties of these molecules is indispensable for the development of efficient computational methods in drug design. One possible solution to the problem lies in application of a concept of the 3-dimensional molecular hydrophobicity potential (MHP). The formalism of MHP utilizes a set of atomic physicochemical parameters evaluated from octanol-water partition coefficients (log P) of numerous chemical compounds. It permits detailed assessment of the hydrophobic and/or hydrophilic properties of various parts of molecules and may be useful in analysis of protein-protein and protein-ligand interactions. This review surveys recent applications of MHP-based techniques to a number of biologically relevant tasks. Among them are: (i) Detailed assessment of hydrophobic/hydrophilic organization of proteins; (ii) Application of this data to the modeling of structure, dynamics, and function of globular and membrane proteins, membrane-active peptides, etc. (iii) Employment of the MHP-based criteria in docking simulations for ligands binding to receptors. It is demonstrated that the application of the MHP-based techniques in combination with other molecular modeling tools (e.g. Monte Carlo and molecular dynamics simulations, docking, etc.) permits significant improvement to the standard computational approaches, provides additional important insights into the intimate molecular mechanisms driving protein assembling in water and in biological membranes, and helps in the computer-aided drug discovery process.

Complementarity of hydrophobic properties in ATP-protein binding: a new criterion to rank docking solutions.

ATP is an important substrate of numerous biochemical reactions in living cells. Molecular recognition of this ligand by proteins is very important for understanding enzymatic mechanisms. Considerable insight into the problem may be gained via molecular docking simulations. At the same time, standard docking protocols are often insufficient to predict correct conformations for protein-ATP complexes. Thus, in most cases the native-like solutions can be found among the docking poses, but current scoring functions have only limited ability to discriminate them from false positives. To improve the selection of correct docking solutions obtained with the GOLD software, we developed a new ranking criterion specific for ATP-protein binding. The method is based on detailed analysis of the intermolecular interactions in 40 high-resolution 3D structures of ATP-protein complexes (the training set). We found that the most important factors governing this recognition are hydrogen-bonding, stacking between adenine and aromatic protein residues, and hydrophobic contacts between adenine and protein residues. To address the latter, we applied the formalism of 3D molecular hydrophobicity potential. The results obtained were used to construct an ATP-oriented scoring criterion as a linear combination of the terms describing these intermolecular interactions. The criterion was then validated using the test set of 10 additional ATP-protein complexes. As compared with the standard scoring functions, the new ranking criterion significantly improved the selection of correct docking solutions in both sets and allowed considerable enrichment at the top of the list containing docking poses with correct solutions.