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Renormalized basal metabolic rate describes the human aging process and longevity

aging biomarkers

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#1 Engadin

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Posted 19 June 2019 - 10:07 AM

Published at Biology of Aging News



F U L L    T E X T :   Willey Online Library






The question of why we age and finally die has been a central subject in the life, medical, and health sciences. Many aging theories have proposed biomarkers that are related to aging. However, they do not have sufficient power to predict the aging process and longevity. We here propose a new biomarker of human aging based on the mass‐specific basal metabolic rate (msBMR). It is well known by the Harris–Benedict equation that the msBMR declines with age but varies among individual persons. We tried to renormalize the msBMR by primarily incorporating the body mass index into this equation. The renormalized msBMR (RmsBMR) which was derived in one cohort of American men (n = 25,425) was identified as one of the best biomarkers of aging, because it could well reproduce the observed respective American, Italian, and Japanese data on the mortality rate and survival curve. A recently observed plateau of the mortality rate in centenarians corresponded to the lowest value (threshold) of the RmsBMR, which stands for the final stage of human life. A universal decline of the RmsBMR with age was associated with the mitochondrial number decay, which was caused by a slight fluctuation of the dynamic fusion/fission system. This decay form was observed by the measurement in mice. Finally, the present approach explained the reason why the BMR in mammals is regulated by the empirical algometric scaling law.





Recent aging theories have proposed various causative biomarkers such as reactive oxygen species (Harman, 1956), calorie restriction (Faulks, Turner, Else, & Hulbert, 2006), telomere length (Aubert & Lansdorp, 2008), insulin signaling (Junnila, List, Berryman, Murrey, & Kopchick, 2013), mitochondrial (mt) DNA mutations (Linnane, Marzuki, Ozawa, & Tanaka, 1989; Trifunovic et al., 2004), fatty acid composition of membranes (Hulbert, Pamplona, Buffenstein, & Buttemer, 2007), and methylation (Hannum et al., 2013). To date, the validity of these biomarkers has been examined mainly by investigating their age dependency. However, they are not satisfactory for an accurate description of the aging process, and they seem to interact with each other in a complex way (Bratic & Larsson, 2013; Ernst, Haes, Cardoen, & Schoofs, 2014; Payne & Chinnery, 2015; Perez et al., 2009). Thus, it is essential to explain how these biomarkers can show that the survival curve and mortality rate are directly related to longevity. Indeed, the probability of survival drops markedly in individuals over the age of 80, and the mortality rate increases exponentially up to the age of 100 (Barbieri et al., 2015). In particular, a recent study reported that the mortality rate forms a plateau in centenarians and suggested that humans have the potential to live beyond the age of 120 (Barbi, Lagona, Marsili, Vaupel, & Wachter, 2018). However, this interesting result has to be confirmed by a robust theoretical analysis.


We here propose a new biomarker to describe the mortality rate and survival curve of the elderly. The basal metabolic rate (BMR) has long been known to decline with age, in line with the Harris–Benedict equation (HBE), which is useful for statistical analysis of a large amount of data (Harris & Benedict, 1918). The mass‐specific BMR (msBMR; BMR per unit mass) confers the standard normalization of BMR to decrease the variation based on the body weight of individual persons. However, the obtained msBMR still varies among them. We developed an approach in which a universal metabolic rate function of age was derived by renormalizing the msBMR. The first renormalization was attained by incorporating the body mass index (BMI) into the HBE. Interestingly, the variation of the msBMR was thus markedly decreased. We further performed a second renormalization to remove the remaining variation due to individual height by a little readjustment of the BMI. As a result, the renormalized msBMR (RmsBMR) revealed an exponential decline with only age (T) as a universal metabolic rate function irrespective of individual persons, F(T) = F(0) × e−uT(T ≥ 16 years), with “u” as a decay constant.


First of all, we showed that the logistic model of the metabolic rate function F(T) accurately approximated the mortality rate of old Americans (>80 years of age; Barbieri et al., 2015) and also well reproduced the survival curve. Here, we recognized that the plateau of the mortality rate in centenarians (Barbi et al., 2018) was a critical signal for the lower limit (threshold) of the msBMR to maintain human life. The plateau effect prolonged the longevity especially without exceeding the age of 120.


The organ metabolic rates and weights of a reference male (Elia, 1992; Snyder et al., 1975) were useful to explain the reason why the RmsBMR provides the exponential decline with age. We found that renormalization of the msBMR corresponded to extraction of the core parts of organ weights from the body weights of individuals. This extraction was made by mainly adjusting the organ weights with low metabolic rates such as adipose tissue and skeletal muscle. We estimated the core organ weights and their msBMR of a number of American samples.


The RmsBMR is likely proportional to cellular metabolism and then to the mitochondrial number (mt density) within the standard cell. The exponential decay form of this density was shown to be a solution of the transport equation for the mitochondrial dynamical fusion/fission flow. This decay form was proven to be based on the Markov process, although the basic mechanism behind the occurrence of the mitochondrial dysfunction has remained unresolved (Payne & Chinnery, 2015; Seo et al., 2010; Westermann, 2012). The exponential decay of the mt density was supported by the measurements made in mice. Finally, the present approach also gave a theoretical basis to the empirical allometric scaling law in mammals.






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