Longevity Papers

Nicotinamide adenine dinucleotide (NAD

Why people age at different rates is a fundamental, unsolved problem in biology. We created a model that predicts an individuals age from physiological traits that change with age in the large UK Biobank dataset, such as blood pressure, lung function, strength and stimulus- reaction time. The model best predicted a persons age when it heavily-weighted traits that together query multiple organ systems, arguing that most or all physiological systems (lung, heart, brain, etc.) contribute to the global phenotype of chronological age. Differences between calculated "biological" age and chronological age ({Delta}Age) appear to reflect an individuals relative youthfulness, as people predicted to be young for their age had a lower subsequent mortality rate and a higher parental age at death, even though no mortality data were used to calculate {Delta}Age. Remarkably, the effect of each year of physiological {Delta}Age on Gompertz mortality risk was equivalent to that of one chronological year. A Genome-Wide Association Study (GWAS) of {Delta}Age, and analysis of environmental factors associated with {Delta}Age identified known as well as new factors that may influence human aging, including genes involved in synapse biology and a tendency to play computer games. We identify a small number of readily measured physiological traits that together assess a persons biological age and may be used clinically to evaluate therapeutics designed to slow aging and extend healthy life.

Organ function declines with age, and large-scale transcriptomic analyses have highlighted differential aging trajectories across tissues. The mechanism underlying shared and organ-selective functional changes across the lifespan, however, still remains poorly understood. Given the central role of mitochondria in powering cellular processes needed to maintain tissue health, we therefore undertook a systematic assessment of respiratory activity across 33 different tissues in young (2.5 months) and old (20 months) mice of both sexes. Our high-resolution mitochondrial respiration atlas reveals: (1) within any group of mice, mitochondrial activity varies widely across tissues, with the highest values consistently seen in heart, brown fat, and kidney; (2) biological sex is a significant but minor contributor to mitochondrial respiration, and its contributions are tissue-specific, with major differences seen in the pancreas, stomach, and white adipose tissue; (3) age is a dominant factor affecting mitochondrial activity, especially across most brain regions, different fat depots, skeletal muscle groups, eyes, and different regions of the gastrointestinal tract; (4) age effects can be sex- and tissue-specific, with some of the largest effects seen in pancreas, heart, adipose tissue, and skeletal muscle; and (5) while aging alters the functional trajectories of mitochondria in a majority of tissues, some are remarkably resilient to age-induced changes. Altogether, our data provide the most comprehensive compendium of mitochondrial respiration and illuminate functional signatures of aging across diverse tissues and organ systems.

The geroscience hypothesis proposes that underlying biological processes, such as the accumulation of senescent cells, have deleterious effects on multiple tissues and increase the risk of many chronic conditions with aging. Senescent cells produce heterogenous biomarkers, also called senescence-associated secretory phenotype (SASP). Circulating concentrations of senescence biomarkers may reflect an underlying burden of senescent cells in various tissues. Plasma levels of these proteins have been associated with increased mortality and poorer physical function. The associations of them with the incidence of major age-related conditions including heart failure, cardiovascular disease, stroke, and dementia, have not been studied. We measured 35 senescence biomarkers in baseline plasma samples from 1678 participants aged 70-79 years old in the longitudinal Health ABC cohort study. Clinical outcomes were ascertained and validated over an average 11.5 year follow-up. In models adjusted for age, sex, and race, higher levels of most of senescence biomarkers were associated with increased risk of all-cause mortality, mobility limitation, and heart failure. Several were also associated with an increased risk of coronary heart disease, stroke, and dementia. Very few were associated with the risk of cancer. Proteins that were selected by Lasso regression for each outcome that commonly included GDF15 and IL6, significantly improved the prediction of mortality, mobility limitation, and heart failure compared with age, sex, and race alone. These results indicate that levels of senescence biomarkers predict an increased risk of several age-related clinical outcomes and may identify individuals most likely to benefit from senotherapeutics.

Studies using mitophagy reporter mice have established steady-state landscapes of mitochondrial destruction in mammalian tissues, sparking intense interest in basal mitophagy. Yet how basal mitophagy is modified by healthy aging in diverse brain cell types has remained a mystery. We present a comprehensive spatiotemporal analysis of mitophagy and macroautophagy dynamics in the aging mammalian brain, reporting critical region- and cell-specific turnover trajectories in a longitudinal study. We demonstrate that the physiological regulation of mitophagy in the mammalian brain is cell-specific, dynamic and complex. Mitophagy increases significantly in the cerebellum and hippocampus during midlife, while remaining unchanged in the prefrontal cortex (PFC). Conversely, macroautophagy decreases in the hippocampus and PFC, but remains stable in the cerebellum. We also describe emergent lysosomal heterogeneity, with subsets of differential acidified lysosomes accumulating in the aging brain. We further establish midlife as a critical inflection point for autophagy regulation, which may be important for region-specific vulnerability and resilience to aging. By mapping

In this paper, we advance the network theory of aging and mortality by developing a causal mathematical model for the mortality rate. First, we show that in large networks, where health deficits accumulate at nodes representing health indicators, the modeling of network evolution with Poisson processes is universal and can be derived from fundamental principles. Second, with the help of two simplifying approximations, which we refer to as mean-field assumption and homogeneity assumption, we provide an analytical derivation of Gompertz law under generic and biologically relevant conditions. We identify the parameters in Gompertz law as a function of the parameters driving the evolution of the network, and illustrate our computations with simulations and analytic approximations.

Evidence that life-extending interventions are not uniformly effective across the lifespan calls for an analytic tool that can estimate age-specific treatment effects on mortality hazards. Here we report such a tool, applying it to mouse data from 42 compounds tested in the NIA Interventions Testing Program. This tool identified agents that either reduced (22) or increased (15) mortality hazards or did both (2) in at least one sex, most with marked variation in the duration of efficacy and magnitude of effect size. Only 8 reduced mortality hazards after 90% mortality, when the burden of senescence is the greatest. Sex differences were common. This new analytic tool complements the commonly used log-rank test. It detects more potential life-extending candidates (22 versus 10) and indicates when during the life course they are effective. It also uncovers adverse effects.

Mosaic loss of Y chromosome (mLOY) is an acquired condition wherein a sizeable proportion of an organ's cells have lost their Y. Large-scale cohort studies have shown that mLOY is age-dependent and a strong risk factor for all-cause mortality and adverse outcomes of age-related diseases. Emerging multi-omics approaches that combine gene expression, epigenetic and mutational profiling of human LOY cell populations at single-cell levels, and contemporary work in in vitro cell and preclinical mouse models have provided important clues into how mLOY mechanistically contributes to disease onset and progression. Despite these advances, what has been missing is a system-level insight into mLOY. By integrating the most recent advances in wide-ranging aspects of mLOY research, we summarize a unified model to understanding the cause and consequence of mLOY at the molecular, cellular, and organismal levels. This model, referred to as the "Unstable Y Cascade model," states that (i) the rise and expansion of LOY result from interaction by the inherently unstable Y, germline genetic and epigenetic variants, and numerous cell-intrinsic and external factors; (ii) LOY initiates genomic, epigenomic, and transcriptomic alterations in X and autosomes, thereafter induces a cascade of tissue-specific cellular alterations that contribute locally to the onset and progression of diseases; and (iii) LOY cells exert paracrine effects to non-LOY cells, thereby amplifying LOY-associated pathological signaling cascades to remote non-LOY cells. This new model has implications in the development of therapeutic interventions that could prevent or delay age-related diseases via mitigating mLOY burden.

Age-dependent changes in DNA methylation allow chronological and biological age inference, but the underlying mechanisms remain unclear. Using ultra-deep sequencing of >300 blood samples from healthy individuals, we show that age-dependent DNA methylation changes are regional and occur at multiple adjacent CpG sites, either stochastically or in a coordinated block-like manner. Deep learning analysis of single-molecule patterns in two genomic loci achieved accurate age prediction with a median error of 1.46-1.7 years on held-out human blood samples, dramatically improving current epigenetic clocks. Factors such as gender, BMI, smoking and other measures of biological aging do not affect chronological age inference. Longitudinal 10-year samples revealed that early deviations from epigenetic age are maintained throughout life and subsequent changes faithfully record time. Lastly, the model inferred chronological age from as few as 50 DNA molecules, suggesting that age is encoded by individual cells. Overall, DNA methylation changes in clustered CpG sites illuminate the principles of time measurement by cells and tissues, and facilitate medical and forensic applications.