Robustness during Aging-Molecular Biological and Physiological Aspects

Drugs against metabolic diseases as potential senotherapeutics for aging-related respiratory diseases

Recent advances in aging research have provided novel insights for the development of senotherapy, which utilizes cellular senescence as a therapeutic target. Cellular senescence is involved in the pathogenesis of various chronic diseases, including metabolic and respiratory diseases. Senotherapy is a potential therapeutic strategy for aging-related pathologies. Senotherapy can be classified into senolytics (induce cell death in senescent cells) and senomorphics (ameliorate the adverse effects of senescent cells represented by the senescence-associated secretory phenotype). Although the precise mechanism has not been elucidated, various drugs against metabolic diseases may function as senotherapeutics, which has piqued the interest of the scientific community. Cellular senescence is involved in the pathogenesis of chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), which are aging-related respiratory diseases. Large-scale observational studies have reported that several drugs, such as metformin and statins, may ameliorate the progression of COPD and IPF. Recent studies have reported that drugs against metabolic diseases may exert a pharmacological effect on aging-related respiratory diseases that can be different from their original effect on metabolic diseases. However, high non-physiological concentrations are needed to determine the efficacy of these drugs under experimental conditions. Inhalation therapy may increase the local concentration of drugs in the lungs without exerting systemic adverse effects. Thus, the clinical application of drugs against metabolic diseases, especially through an inhalation treatment modality, can be a novel therapeutic approach for aging-related respiratory diseases. This review summarizes and discusses accumulating evidence on the mechanisms of aging, as well as on cellular senescence and senotherapeutics, including drugs against metabolic diseases. We propose a developmental strategy for a senotherapeutic approach for aging-related respiratory diseases with a special focus on COPD and IPF.

A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure-Klemera and Doubal method-BA (KDM-BA) we previously developed-with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.

Characterizing the Heterogeneity of Aging: A Vision for a Staging System for Aging

Introduction: Older adulthood encompasses several decades of change and heterogeneity. Primary care providers need a geriatric comprehensive vision for defining older adult subpopulations.

Methods: Using PubMed and Google searches, we reviewed the literature on epidemiology of age-related physiological changes, age-related diseases and geriatric syndromes, functional state, and emotional and social changes. We divided old age into strata based on chronological age and strata based on functional state, disease burden, and geriatric syndromes.

Results: We describe 4 chronological-age strata beginning at age 60, and 4 functional-age strata based on frailty according to a modified clinical frailty scale. We provide clinical considerations and anticipatory guidance topics for each of the age strata and functional strata.

Conclusion: Chronological age, functional status, chronic disease burden and geriatric syndromes, and life expectancy are all important domains that impact clinical care and appropriate anticipatory guidance for individual older adults. Better knowledge for differentiating subpopulations of older adults may improve clinical care, reduce medical overuse, improve personalized anticipatory guidance, and focus on the impact of functional state on the quality of life.

Influence of spatial structure on protein damage susceptibility: a bioinformatics approach

Aging research is a very popular field of research in which the deterioration or decline of various physiological features is studied. Here we consider the molecular level, which can also have effects on the macroscopic level. The proteinogenic amino acids differ in their susceptibilities to non-enzymatic modification. Some of these modifications can lead to protein damage and thus can affect the form and function of proteins. For this, it is important to know the distribution of amino acids between the protein shell/surface and the core. This was investigated in this study for all known structures of peptides and proteins available in the PDB. As a result, it is shown that the shell contains less susceptible amino acids than the core with the exception of thermophilic organisms. Furthermore, proteins could be classified according to their susceptibility. This can then be used in applications such as phylogeny, aging research, molecular medicine, and synthetic biology.

Amantadine: reappraisal of the timeless diamond-target updates and novel therapeutic potentials

The aim of the current review was to provide a new, in-depth insight into possible pharmacological targets of amantadine to pave the way to extending its therapeutic use to further indications beyond Parkinson's disease symptoms and viral infections. Considering amantadine's affinities in vitro and the expected concentration at targets at therapeutic doses in humans, the following primary targets seem to be most plausible: aromatic amino acids decarboxylase, glial-cell derived neurotrophic factor, sigma-1 receptors, phosphodiesterases, and nicotinic receptors. Further three targets could play a role to a lesser extent: NMDA receptors, 5-HT3 receptors, and potassium channels. Based on published clinical studies, traumatic brain injury, fatigue [e.g., in multiple sclerosis (MS)], and chorea in Huntington's disease should be regarded potential, encouraging indications. Preclinical investigations suggest amantadine's therapeutic potential in several further indications such as: depression, recovery after spinal cord injury, neuroprotection in MS, and cutaneous pain. Query in the database http://www.clinicaltrials.gov reveals research interest in several further indications: cancer, autism, cocaine abuse, MS, diabetes, attention deficit-hyperactivity disorder, obesity, and schizophrenia.