Digital Gems

Polypharmacy — taking five or more meds at a time — leads to side effects, unnecessary hospitalizations and premature deaths. Researchers and pharmacists are seeking solutions to this serious public health problem that disproportionately affects older adults.

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. Physiol. 594, 2061 (2016). [OpenUrl][20] 3. [↵][21]1. S. Camandola, 2. M. P. Mattson , EMBO J. 36, 1474 (2017). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. H. Pontzer et al ., Science 373, 808 (2021). [OpenUrl][25][Abstract/FREE Full Text][26] 5. [↵][27]1. J. R. Speakman , Am. J. Clin. Nutr. 68, 932S (1998). [OpenUrl][28][Abstract][29] 6. [↵][30]1. D. A. Schoeller , J. Nutr. 118, 1278 (1988). [OpenUrl][31][Abstract/FREE Full Text][32] 7. [↵][33]1. J. R. Speakman et al ., Cell Rep. Med. 2, 100203 (2021). [OpenUrl][34][CrossRef][35][PubMed][36] 8. [↵][37]1. P. F. Saint-Maurice, 2. Y. Kim, 3. G. J. Welk, 4. G. A. Gaesser , Eur. J. Appl. Physiol. 116, 29 (2016). [OpenUrl][38] 9. [↵][39]NCD Countdown collaborators, Lancet 392, 1072 (2018). [OpenUrl][40][CrossRef][41][PubMed][42] 10. [↵][43]1. B. K. Kennedy et al ., Cell 159, 709 (2014). [OpenUrl][44][CrossRef][45][PubMed][46] 11. [↵][47]1. M. Kleiber , Physiol. Rev. 27, 511 (1947). [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. [1]: #ref-1 [2]: #ref-3 [3]: #ref-4 [4]: #ref-5 [5]: #ref-6 [6]: #ref-7 [7]: #ref-8 [8]: #ref-9 [9]: #ref-10 [10]: pending:yes [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14 [15]: #ref-15 [16]: #xref-ref-1-1 "View reference 1 in text" [17]: {openurl}?query=rft.jtitle%253DCell%2BMetab.%26rft.volume%253D23%26rft.spage%253D27%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cmet.2015.12.006%26rft_id%253Dinfo%253Apmid%252F26771115%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [18]: /lookup/external-ref?access_num=10.1016/j.cmet.2015.12.006&link_type=DOI [19]: /lookup/external-ref?access_num=26771115&link_type=MED&atom=%2Fsci%2F373%2F6556%2F738.atom [20]: {openurl}?query=rft.jtitle%253DJ.%2BPhysiol.%26rft.volume%253D594%26rft.spage%253D2061%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [21]: #xref-ref-3-1 "View reference 3 in text" [22]: {openurl}?query=rft.jtitle%253DEMBO%2BJ.%26rft_id%253Dinfo%253Adoi%252F10.15252%252Fembj.201695810%26rft_id%

With infections increasing once more, and hospitalization rising among older adults, health experts offer a timely warning: a coronavirus infection can look different in older patients.

Coronavirus cases that send vaccinated people to the hospital or cause death have been relatively rare, a New York Times analysis of state data shows.

Provincetown, Mass., the quirky community at the tip of Cape Cod, thought it was safe to return to prepandemic partying. It wasn’t.

In a Times simulation, we modeled Delta-driven Covid outbreaks in two communities, one with a high vaccination rate and another with a low rate. Their levels of serious illness and death were starkly different.

As another heat wave builds in the U.S. Northwest, a New York Times analysis has found that recent scorching temperatures in the area may have been more lethal than officially reported.

Ultra-processed foods -- such as frozen pizza, microwave meals, packaged snacks and desserts -- accounted for 67% of calories consumed in 2018 up from 61% in 1999, according to research.

Is the FDA too lax? Do bears go to the bathroom in spotless marble-floored lavatories? Is the Pope an Anabaptist?

But it’s never too late (nor too soon) to take these preventive steps

How the Brazilian butt lift, one of the world’s most dangerous plastic surgery procedures, went mainstream.

A new Yale study shows that limited upward mobility has serious consequences on the life expectancy of young adults, particularly among Black males.

Some have been used in hospitals, despite not being properly tested. But the pandemic could help make medical AI better.

A variant discovered in Colombia is now showing up in South Florida hospitals. Time and vaccination efforts will determine how quickly it could spread, experts say.

On this episode, we dive into the changing conversation about race and ethnicity in medicine. We hear stories about why it’s harder for Black Americans to get kidney transplants, why “Asian” is too broad of a category when it comes to public health, and how we could collect better, more meaningful data.

Yes, even for healthy people.

Infections in vaccinated Americans are rare, compared with those in unvaccinated people, the document said. But when they occur, vaccinated people may spread the virus just as easily.

In communities with growing caseloads, vaccinated and unvaccinated people should return to wearing masks indoors in public areas, health officials said.

Volume 44 (2021) - Article 52 | Pages 1271–1294

A new study finds that health care has become the country’s largest source of debt in collections. Those debts are largest where Medicaid wasn’t expanded.

COVID-19 consistently displays a higher mortality in males. This sex-specific difference in outcomes is seen not only in the current COVID-19 pandemic, but also in prior viral epidemics and pandemics. Sex hormones, such as testosterone, play a clear role ...

Some anti-vaccination groups on Facebook are changing their names to euphemisms like “Dance Party” or “Dinner Party,” and using code words to fit those themes.

Cases of the variant have popped up in several states. But neither the WHO nor the CDC considers it a variant of concern, and the fast-spreading delta variant continues to dominate U.S. cases.

Today's pollution is very different from the Brown Cloud of the '70s and '80s but it could be just as bad.

Objective To estimate changes in life expectancy in 2010-18 and during the covid-19 pandemic in 2020 across population groups in the United States and to compare outcomes with peer nations. Design Simulations of provisional mortality data. Setting US and 16 other high income countries in 2010-18 and 2020, by sex, including an analysis of US outcomes by race and ethnicity. Population Data for the US and for 16 other high income countries from the National Center for Health Statistics and the Human Mortality Database, respectively. Main outcome measures Life expectancy at birth, and at ages 25 and 65, by sex, and, in the US only, by race and ethnicity. Analysis excluded 2019 because life table data were not available for many peer countries. Life expectancy in 2020 was estimated by simulating life tables from estimated age specific mortality rates in 2020 and allowing for 10% random error. Estimates for 2020 are reported as medians with fifth and 95th centiles. Results Between 2010 and 2018, the gap in life expectancy between the US and the peer country average increased from 1.88 years (78.66 v 80.54 years, respectively) to 3.05 years (78.74 v 81.78 years). Between 2018 and 2020, life expectancy in the US decreased by 1.87 years (to 76.87 years), 8.5 times the average decrease in peer countries (0.22 years), widening the gap to 4.69 years. Life expectancy in the US decreased disproportionately among racial and ethnic minority groups between 2018 and 2020, declining by 3.88, 3.25, and 1.36 years in Hispanic, non-Hispanic Black, and non-Hispanic White populations, respectively. In Hispanic and non-Hispanic Black populations, reductions in life expectancy were 18 and 15 times the average in peer countries, respectively. Progress since 2010 in reducing the gap in life expectancy in the US between Black and White people was erased in 2018-20; life expectancy in Black men reached its lowest level since 1998 (67.73 years), and the longstanding Hispanic life expectancy advantage almost disappeared. Conclusions The US had a much larger decrease in life expectancy between 2018 and 2020 than other high income nations, with pronounced losses among the Hispanic and non-Hispanic Black populations. A longstanding and widening US health disadvantage, high death rates in 2020, and continued inequitable effects on racial and ethnic minority groups are likely the products of longstanding policy choices and systemic racism. Data sharing: Requests for additional data and analytic scripts used in this study should be emailed to RKM (Ryan.Masters@colorado.edu).

The decline, which is the largest seen in a single year since World War II, reflects the pandemic’s sustained toll on Americans, particularly the disproportionate impact of covid-19 on communities of color.