Metabolism is not just about energy—how the body handles nutrient fuel and converts it to useable energetic currency. Metabolism also encompasses synthesis, modification, and exchange of the building blocks for all aspects of cellular function and acts as a sensor and regulator of cellular activities, in which individual moieties within metabolic pathways influence cellular responses. A substantial amount of the energy taken in each day is required to simply sustain life; the energetic demands of physical activity are superimposed on a vastly integrated machinery. Metabolic status has been linked to innumerable diseases and disorders, including those most prevalent with age ([ 1 ][1]–[ 3 ][2]). On page 808 of this issue, Pontzer et al. ([ 4 ][3]) analyze energy expenditure in more than 6400 males and females from 29 countries across the globe, aged between 8 days and 95 years, and show distinct metabolic phases during development and aging. An understanding of energy expenditure across the life span must grapple with the diversity of humans, including sex, race, body composition, and their environment. Estimates of energy expenditure can be captured with indirect calorimetry that measures gas exchange and heat production of sequestered individuals, or by the doubly labeled water (DLW) method in free living individuals. The DLW technique is based on the relative bodily elimination rates of isotopes of oxygen and hydrogen ([ 5 ][4]). In the time since methods were developed for application in humans ([ 6 ][5]), the use of DLW has been steadily growing. Associated costs of isotope dosing have limited most studies to relatively small cohorts, but there has been commitment among the research community to share data and to develop integrative methods so that large-cohort data analysis might be undertaken ([ 7 ][6]). In the study of Pontzer et al. , energy expenditure was adjusted to fat-free mass to account for differences in body size, revealing intrinsic shifts in metabolic status over the course of development, maturation, and aging. The authors identify inflection points that are the boundaries for four distinct phases. It seems clear from their data that infants and adolescents form two different metabolic categories. It has been said before, but children are not just small adults ([ 8 ][7]). That young people represent separate metabolic status categories has important implications for recommendations about diet and physical activity, not to mention pharmaceutical dose recommendations for younger persons. The remaining two phases cover adulthood and advanced age. Adjusted energy expenditure is notably stable from 20 years of age up to about 60 years of age, at which point a gradual decline is observed (see the figure). The decline from age 60 is thought to reflect a change in tissue-specific metabolism, the energy expended on maintenance. It cannot be a coincidence that the increase in incidence of noncommunicable diseases and disorders begins in this same time frame ([ 9 ][8]). These findings indicate that life stage needs to be carefully considered when choosing disease models. This is particularly important for research on the etiology of age-associated diseases and disorders ([ 10 ][9]). Pathways and factors that are readily targetable in young growing animals may not be as sensitive or even responsive in older animals, and young models fail to capture the aged environment and may miss interactions that emerge as a result of intrinsic differences in metabolic status. ![Figure][10] Life span of metabolism Measures of energy expenditure (adjusted for fat-free mass) identify three inflection points over the human life span. Energy expenditure increases during infancy and childhood and then declines through adolescence, a plateau phase lasts throughout adulthood, and a second declining phase occurs from 60 years of age. The marked rise in incidence of chronic disease from late middle age aligns with the shift in energy expenditure and loss of adiposity, suggesting that metabolism may be a driver in aging biology. GRAPHIC: A. MASTIN/ SCIENCE The impact of body size on metabolic rate has been discussed and explored for decades ([ 11 ][11]). Total energy expenditure is sex dimorphic, with lower levels in females than in males; however, accounting for fat-free mass removes this distinction. It is important that contributions from physical activity and tissue-specific metabolic rates, both of which change over the human life span, must be accounted for if computational models are to fit the observed data. Although not the focus of the work, Pontzer et al. identified substantial heterogeneity in body composition among individuals. Challenges arising from heterogeneity among individuals are reflected in the growing enthusiasm for precision medicine ([ 12 ][12]). It is abundantly clear that one size does not fit all. By adjusting for fat-free mass, this study peels some of this variance away to reveal intrinsic shifts in metabolism that are matched to life phase. There is considerable heterogeneity in how and when aging manifests in terms of disease incidence ([ 13 ][13]). It would be interesting to explore how mid-life disposition informs outcomes in advanced age and how well disease burden among individuals links to age-associated changes in their tissue-specific metabolism. The causal factors in age-related vulnerability to disease no doubt reside in the documented changes in cellular biology, tissue physiology, and systemic homeostasis. Studies of laboratory animals have honed in on metabolism as a central theme in aging and in delayed aging through caloric restriction ([ 14 ][14]). Differences in metabolism are predicted to affect derivation of energy from nutrient sources, foundational material for synthesis of cellular machinery and communication relays, and the ability to optimize cellular activities according to prevailing conditions, whether external or internal. It will come as no surprise then that recent efforts to identify pharmacological agents that positively affect health in aging converge on metabolism ([ 15 ][15]). The Pontzer et al. study provides important new insights into human metabolism; the unprecedented scale and scope of the study is matched by the outstanding collaborative spirit that made it possible. 1. [↵][16]1. N. N. Pavlova, 2. C. B. Thompson , Cell Metab. 23, 27 (2016). [OpenUrl][17][CrossRef][18][PubMed][19] 2. 1. S. Costantino, 2. F. Paneni, 3. F. Cosentino , J. 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[OpenUrl][48][CrossRef][49][PubMed][50][Web of Science][51] 12. [↵][52]1. M. A. Haendel, 2. C. G. Chute, 3. P. N. Robinson , N. Engl. J. Med. 379, 1452 (2018). [OpenUrl][53] 13. [↵][54]1. D. J. Lowsky, 2. S. J. Olshansky, 3. J. Bhattacharya, 4. D. P. Goldman , J. Gerontol. A Biol. Sci. Med. Sci. 69, 640 (2014). [OpenUrl][55][CrossRef][56][PubMed][57][Web of Science][58] 14. [↵][59]1. P. Balasubramanian, 2. P. R. Howell, 3. R. M. Anderson , EBioMedicine 21, 37 (2017). [OpenUrl][60] 15. [↵][61]1. L. Partridge, 2. M. Fuentealba, 3. B. K. Kennedy , Nat. Rev. Drug Discov. 19, 513 (2020). [OpenUrl][62][CrossRef][63] Acknowledgments: T.W.R. and R.M.A. are supported by NIH/NIA grants AG040178, AG057408, and AG067330; the Department for Veterans Affairs BX003846; and the Simons Foundation. 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