Effects of Sex, Strain, and Energy Intake on Hallmarks of Aging in Mice.

Calorie restriction (CR) is the most robust non-genetic intervention to delay aging. However, there are a number of emerging experimental variables that alter CR responses. We investigated the role of sex, strain, and level of CR on health and survival in mice. CR did not always correlate with lifespan extension, although it consistently improved health across strains and sexes. Transcriptional and metabolomics changes driven by CR in liver indicated anaplerotic filling of the Krebs cycle together with fatty acid fueling of mitochondria. CR prevented age-associated decline in the liver proteostasis network while increasing mitochondrial number, preserving mitochondrial ultrastructure and function with age. Abrogation of mitochondrial function negated life-prolonging effects of CR in yeast and worms. Our data illustrate the complexity of CR in the context of aging, with a clear separation of outcomes related to health and survival, highlighting complexities of translation of CR into human interventions.pre-print5,92 M

Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers

Abstract There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results andthe recent advances in artificial intelligence (AI). By employing age-prediction models developed using supervised deep learning techniques, we found that smokers exhibited higher aging rates than nonsmokers, regardless of their cholesterol ratios and fasting glucose levels. We further used those models to quantify the acceleration of biological aging due to tobacco use. Female smokers were predicted to be twice as old as their chronological age compared to nonsmokers, whereas male smokers were predicted to be one and a half times as old as their chronological age compared to nonsmokers. Our findings suggest that deep learning analysis of routine blood tests could complement or even replace the current error-prone method of self-reporting of smoking status and could be expanded to assess the effect of other lifestyle and environmental factors on aging