Aging involves progressive loss of cellular function and integrity, presumably caused by accumulated stochastic damage to cells. Alterations in energy metabolism contribute to aging, but how energy metabolism changes with age, how these changes affect aging, and whether they can be modified to modulate aging remain unclear. In locomotory muscle of post-fertile Caenorhabditis elegans, we identified a progressive decrease in cytosolic phosphoenolpyruvate carboxykinase (PEPCK-C), a longevity-associated metabolic enzyme, and a reciprocal increase in glycolytic pyruvate kinase (PK) that were necessary and sufficient to limit lifespan. Decline in PEPCK-C with age also led to loss of cellular function and integrity including muscle activity, and cellular senescence. Genetic and pharmacologic interventions of PEPCK-C, muscle activity, and AMPK signaling demonstrate that declines in PEPCK-C and muscle function with age interacted to limit reproductive life and lifespan via disrupted energy homeostasis. Quantifications of metabolic flux show that reciprocal changes in PEPCK-C and PK with age shunted energy metabolism toward glycolysis, reducing mitochondrial bioenergetics. Last, calorie restriction countered changes in PEPCK-C and PK with age to elicit antiaging effects via TOR inhibition. Thus, a programmed metabolic event involving PEPCK-C and PK is a determinant of aging that can be modified to modulate aging.Aging is characterized by the progressive decline in cellular function and integrity that leads to disease vulnerability and eventually death of organisms (1). The leading proposed cause of decline in cellular function and integrity with age is the accumulation of stochastic damage of molecules and organelles by reactive molecules, such as reactive oxygen species (ROS). Whether ROS are detrimental to organisms and whether ROS limit lifespan, however, are in debate (2).Energy metabolism supplies ATP for cellular function and maintenance. Alterations in energy metabolism are linked to the aging process and aging-associated diseases (3). In model organisms, environmental and genetic factors that change energy metabolism, such as calorie restriction (CR) (4), inhibition of target of rapamycin (TOR) (5), and 5Ј AMP kinase (AMPK) (6) are determinants of longevity. A large body of aging research has been focusing on the signaling of CR, TOR inhibition, and AMPK in regulating longevity. The exact alterations in energy metabolism that occur with age, how these changes impact aging, and whether they can be modified to modulate aging are understudied and remain poorly understood, largely due to the intrinsic complexity of energy metabolism, and the indirect impact of these longevity paradigms on energy metabolism. This impedes the understanding of aging mechanisms and the development of mechanism-based strategies to modulate aging.A key regulation of energy metabolism at the cellular level is the reciprocal changes of PK and PEPCK-C (7). Whether this regulation of cellular energy metabolism contributes to organismal aging is...

Objective Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition that has emerged as a significant public health problem given its increased prevalence over the past decade. The high prevalence of obesity and large waist circumference among NFL players are two risk factors that might contribute to the high susceptibility of football players to develop OSA. National Football League linemen might be particularly vulnerable since they tend to have a higher body mass index. In this scoping review, we aim to bring attention to the limited research regarding OSA among National Football League players and highlight the negative consequences of OSA in an attempt to increase awareness of the urgent need for further research in this area. Methods Search terms associated with obstructive sleep apnea and football were used to examine Google Scholar, EMBASE, CINAHL, PubMed, ProQuest, and Web of Science Plus for relevant studies. All relevant studies were included and documented. Results Findings included (n=4) studies of interest. All 4 studies revealed a near or slightly above 50% prevalence of OSA in the investigated cohorts (mostly retired NFL linemen). Most participants in the study (active NFL players) showed symptoms associated with a sleep-disorder breathing condition (snoring). Conclusion OSA requires more attention from the research and medical community. As suggested by results in the 4 studies included in this paper, OSA and associated symptoms are prevalent in the NFL population. Further research is required to investigate the extent of OSA and OSA risk in this population. There is an urgent need to conduct OSA risk surveillance in the athletic community.

No abstract

No abstract

One of the central elements of any causal inference is an object called structural causal model (SCM), which represents a collection of mechanisms and exogenous sources of random variation of the system under investigation (Pearl, 2000). An important property of many kinds of neural networks is universal approximability: the ability to approximate any function to arbitrary precision. Given this property, one may be tempted to surmise that a collection of neural nets is capable of learning any SCM by training on data generated by that SCM. In this paper, we show this is not the case by disentangling the notions of expressivity and learnability. Specifically, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020), which describes the limits of what can be learned from data, still holds for neural models. For instance, an arbitrarily complex and expressive neural net is unable to predict the effects of interventions given observational data alone. Given this result, we introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences. Building on this new class of models, we focus on solving two canonical tasks found in the literature known as causal identification and estimation. Leveraging the neural toolbox, we develop an algorithm that is both sufficient and necessary to determine whether a causal effect can be learned from data (i.e., causal identifiability); it then estimates the effect whenever identifiability holds (causal estimation). Simulations corroborate the proposed approach.Preprint. Under review.