Zero order correlations were performed to determine relations between variables in the overall study population. To identify significant independent predictors of RMR adj , stepwise multiple regression analysis also was performed. Adjusted maximal oxygen consumption values VO 2 max adj , covaried for fat mass and fat-free mass, were used in correlational analyses.
Because RMR did not differ between users and nonusers of hormone replacement within and across groups, subjects were pooled for all analyses. Table 1 presents selected characteristics of the sedentary women.
Fat-free mass did not differ with age. Based on analysis of diet records, there were no significant differences in absolute carbohydrate, fat, protein, or total estimated energy intake in the premenopausal vs. Plasma concentrations of norepinephrine, T 3 , and T 4 also were not different in the two groups Table 3. NA, not applicable. A, Pre- vs. Percentages are given in parentheses. Current RDA recommendations for protein, carbohydrate, and fat intake are 0. Subject characteristics for the runners are presented in Table 1.
Fat-free mass, BMI, and waist to hip ratio were not different between the groups. Estimated absolute energy intake and composition also did not differ with age, although the postmenopausal women had lower carbohydrate and protein intakes when normalized per kg BM Table 2.
No differences were observed in plasma norepinephrine, T 3 , and T 4 levels between the groups Table 3. Table 4 presents characteristics for the postmenopausal swimmers and the matched subgroup of postmenopausal runners. The two groups did not differ in age, hormone replacement use, fat-free mass, hours of exercise per week either specific training or other exercise , or relative performance.
No group differences were observed in estimated energy intake and composition or in plasma levels of norepinephrine, T 3 , or T 4. The relationship between RMR and maximal oxygen consumption aerobic fitness , normalized for fat mass and fat-free mass, in the overall population A and the individual subgroups B.
The primary new finding from the present study is that a significant decrease in RMR adj with age in healthy sedentary women is not observed in women who perform endurance exercise on a regular basis.
This is supported by the facts that both the postmenopausal runners and swimmers demonstrated levels of RMR adj not different from those in young adult endurance athletes, whereas a significantly lower RMR adj was observed in the postmenopausal compared to the premenopausal sedentary women.
The present data in women are consistent with a previous report by Poehlman and colleagues in men The differences in RMR adj observed with age in the sedentary and active women in the present study, when viewed over time, may have considerable physiological significance. Given this, our findings may have important clinical implications.
For example, body weight and fatness increase with age in women 24 — 26 , and recent evidence indicates that even modest weight gain in women with advancing age is associated with markedly increased risks of noninsulin-dependent diabetes mellitus 27 , coronary artery disease 28 , and overall mortality 29 , We 13 , 14 and others 11 , 12 have shown that the age-related increases in body weight and body fatness are either smaller or absent in endurance exercise-trained compared to sedentary women.
Moreover, meeting nutritional requirements is a problem for some older women In this context, the higher RMR in physically active postmenopausal women would allow them to maintain a higher total energy intake and, therefore, have a greater likelihood of meeting their dietary needs.
Several factors not directly related to exercise have been reported to be associated with RMR in certain populations or conditions, including thyroid hormones 32 , 33 , total energy and macronutrient intake 34 , and sympathetic nervous system activity 35 , In the present study, however, there was no consistent relation between RMR and any of these putative mechanisms.
Fat-free mass and fat mass are important determinants of RMR 7 , 8 , However, we found that RMR declined with age in the sedentary, but not in the physically active, women after adjusting for these factors. Thus, our findings suggest that RMR per unit of metabolically active tissue is higher in exercise-trained vs.
A significant relationship between VO 2 max and RMR across the adult age range has been found previously in active and sedentary men 37 , 38 , but not in sedentary women Our results indicate that aerobic fitness is significantly related to RMR among healthy females varying in age and exercise status.
We should emphasize at least three limitations of the present study. First, using a model that we have employed in the past 13 , 16 , we attempted to minimize constitutional differences between the pre- and postmenopausal endurance runners by matching them for age-adjusted performance.
Despite this, however, because of the cross-sectional nature of our study design we cannot discount the possibility that genetic or constitutional factors influenced our findings. Secondly, it is unknown whether RMR declines significantly in endurance-trained women beyond the age range studied i.
Lastly, we compared groups who were very different in their activity levels i. Thus, our results do not address the question of the minimum level of habitual exercise that is associated with a diminished age-related decline in RMR. In conclusion, the results of the present study provide experimental evidence that is consistent with the concept that the age-related decline in RMR in sedentary women is not observed in women who regularly perform endurance exercise.
The absence of a significant decline in RMR in middle-aged and older endurance-trained women may play a role in the maintenance of their lower levels of body weight and fatness compared to those in sedentary women.
The authors thank Drs. Christopher L. Melby and James O. Hill for their consultation throughout the study, and Dr. Melby for his preliminary review of this manuscript. Br J Nutr. Google Scholar. Najjar M, Rowland M. United States, — Vital Health Statistics Despres J-P , Lamarche B. J Intern Med. J Gerontol. Poehlman ET. J Am Geriat Soc. Arch Intern Med. Tataranni P , Ravussin E. Int J Obesity. Am J Physiol. Am J Clin Nutr. N Engl J Med. Med Sci Sport Exer.
J Appl Physiol. Ann NY Acad Sci. Wilmore JH. Champaign : Human Kinetics. Research also shows that HIIT can help your body build and preserve muscle mass with age Research shows a lack of sleep can slow down your metabolism. One study found that 4 hours of sleep reduced metabolism by 2.
Fortunately, a night of long sleep 12 hours helped restore metabolism It also seems that poor sleep may increase muscle loss. Since muscle influences your RMR, losing muscle can slow down your metabolism If you struggle to fall asleep, try unplugging from technology at least one hour before bed. Alternatively, try a sleep supplement.
Eating more protein-rich foods can help fight a slowing metabolism. This is known as the thermic effect of food TEF. Protein-rich foods have a higher TEF than carb- and fat-rich foods Protein is also essential to fight sarcopenia. Thus, a protein-rich diet can fight an aging metabolism by preserving muscle Older adults also tend to have a lower appetite, which may decrease calorie intake and slow metabolism If you struggle to eat enough calories, try eating smaller portions more frequently.
It is also great to have high-calorie snacks like cheese and nuts handy. This is because green tea contains caffeine and plant compounds, which have been shown to increase your resting metabolism Summary: Although your metabolism slows down with age, there are many ways to combat this. This includes resistance training, high-intensity training, getting plenty of rest, eating enough protein and calories and drinking green tea. Being less active, losing muscle mass and the aging of your internal components all contribute to a sluggish metabolism.
This includes weight lifting, high-intensity interval training, eating enough calories and protein, getting plenty of sleep and drinking green tea. Try adding a few of these strategies into your daily routine to help keep your metabolism fast and even give it a boost. Your metabolism determines how many calories you burn each day.
Here are 9 easy ways to boost your metabolism, backed by science. Metabolism tests can tell you how effectively your body burns calories, and uses oxygen during workouts. They're a valuable tool which can help you…. Metabolic rates vary by individual. This article explains why some people have a fast metabolism and how you can speed up yours to burn more calories.
This article…. Researchers say the type 2 diabetes drug semaglutide can help people lose weight by decreasing appetite and energy intake. Others say it can be used as a starting point for health assessments. In comparison to the subjects investigated by Van Pelt et al , subjects in our study on average were neither sedentary nor especially active but showed moderate PALs.
Despite the significant differences we observed in PAL between young and elderly subjects, these were very small elderly vs younger women: 1. Next, we examined by two different statistical procedures whether the lower RMR of the elderly subjects is independent of changes in body composition when compared to RMR of the young adults. Piers et al determined body composition with dual energy X-ray absorptiometry in 38 young and 24 elderly subjects and used a model incorporating four tissue compartments: fat, bone mineral, appendicular lean tissue mass and nonappendicular lean tissue mass.
This difference is remarkably similar to our results. The authors concluded that the age-related decline in RMR is partly explained by a reduction in the quantity as well as the metabolic activity of lean tissue components. Besides the decline in FFM, age-related changes in its composition, especially in relative masses of metabolically active organs like the heart, liver, kidneys or brain and of metabolically less active tissues like muscle, bone or skin could also be responsible for the decline in RMR during aging.
This was discussed in some recent studies Gallagher et al, ; Bosy-Westphal et al, In our second approach, we therefore considered the detailed composition of the FFM and investigated whether age-related differences in the proportions of the miscellaneous organ masses on FFM could account for the lower RMR in elderly subjects.
Both in women and men, differences between measured RMR and RMR calculated on the basis of the detailed body composition were significantly larger in the elderly compared to young subjects. This indicates that the lower RMR in elderly subjects cannot be entirely due to age-related differences in the organ masses or different proportions of the miscellaneous organ masses on FFM. These results support the hypothesis that the specific organs estimated in this study do not account for the lower RMR in the elderly.
The reason for the greater sex difference of the differences between measured and calculated RMR in the young subjects is unclear. There is not much evidence in the literature for sex-specific factors influencing RMR independently from differences in body composition.
However, as our data were analysed separately for the two sexes, potential sex-specific causes cannot have an impact on our results. Our results regarding RMR that is to be expected theoretically confirm those of Gallagher et al , who used the same approach in only seven elderly women and six elderly men. This correspondence of the results is even more remarkable in that by contrast to the study of Gallagher et al organ masses were not measured in our study but derived from the regression equations developed by Garby et al Bosy-Westphal et al also measured RMR and body composition including several organ masses in 26 young 13 females, 13 males and 26 elderly subjects 15 females, 11 males and specific organ metabolic rates were taken from the literature.
However, the exclusion of five elderly subjects with cardiac hypertrophy resulted in agreement between measured and calculated RMR in the elderly. The authors therefore argued that the age-related decline in RMR is not caused by a decreasing organ metabolic rate, but is attributed to a reduction in FFM as well as in proportional changes in its metabolically active components.
The reason for this disagreement to the results of Gallagher et al and our study is not clear. Bosy-Westphal et al discuss that subject bias might have posed limitations on their study as in female subjects there was no decrease in FFM with age.
Thus, the relatively small number of subjects investigated could have biased the results in this study. However, there might also be a possibility that we overestimated calculated RMR of the elderly. Although the sum of organ masses as calculated with the equations from Garby et al in our study was significantly lower in the elderly when compared to young subjects, heart mass was nevertheless significantly higher in the elderly.
As discussed by Bosy-Westphal et al , an increased heart mass could also add to the observed age-group differences between measured and calculated RMR in case the heart metabolic rate decreased with an increasing heart mass in the elderly. If an increased heart mass was more frequently present in Garby's autopsy population than in the GISELA subjects, the algorithm derived from Garby et al could have resulted in an overestimation of the calculated RMR of the subjects in our study.
Several findings support the observation that aging is associated with a decline in metabolic rate per unit of tissue mass. Poehlman et al b examined the hypothesis that a decline in Na—K pump activity contributes to the lower RMR in older males, independent of the loss of FFM. They found that the age-related reduction in Na—K pump activity is a partial contributor to the decline in RMR in older men.
Conley et al determined the decline in oxidative capacity per volume of human vastus lateralis muscle between younger and elderly human subjects. They reported mitochondrial volume density and oxidative capacity per mitochondrial volume to be significantly lower in muscles of the elderly compared to younger subjects. Furthermore, sympathetic nervous system SNS activity may also account for a lower RMR adjusted for body composition in elderly subjects. There are some limitations in the assessment of body composition in our study.
Results for FFM and FM obtained by bioelectrical impedance analysis can vary substantially depending on the respective algorithms used to estimate these compartments. In order to select the most appropriate equation, experimental conditions under which the various equations were derived were evaluated with regard to comparability of the study population, experimental protocol, impedance equipment and electrode placement. On the basis of these criteria, and also as it has been validated by densitometry we found the equation of Deurenberg et al to be most appropriate for use in our study.
A further limitation of our study is that weights of the brain, heart, liver and kidneys were not measured but calculated by using the regression equations from Garby et al This approach was used, because within the scope of our study, including a rather large number of subjects, it was not possible to assess the weights of the different organs by extensive methods like computer tomography or magnetic resonance imaging.
The literature on equations for calculating weights of these organs is scarce; thus, we used the regression equations from Garby et al , which, however, were derived from a large number of subjects from a country bordering the one of our subjects.
Furthermore, in our study, skeletal muscle mass was calculated from the data of FFM according to Forbes and therefore can be considered only as an estimate. In summary, our results obtained in a relatively large sample support these findings indicating a decline in RMR with advancing age, which cannot be totally explained by changes in body composition. Irrespective of the approach employed, there is a striking correspondence in the magnitude of differences between adjusted RMR of elderly and younger subjects and of differences between measured and calculated RMR of elderly subjects detected in our study and those reported by Piers et al and by Gallagher et al This is even more remarkable when considering that in these latter studies, results were derived from much smaller samples and by using different methodological procedures.
However, neither with the results of our investigation in which weights of the brain, heart, liver and kidneys were calculated by regression equations according to Garby et al nor with the results of Gallagher et al , who measured the volumes of the organs and tissues by magnetic resonance imaging, can it be explained whether the decline in RMR independently of changes in body composition relates to a reduction in the organs' metabolic rate or whether this is due, for example, to morphological changes like infiltration of the organs with fat, oedema or cystic structures Gallagher et al, Future studies should focus therefore on oxygen consumption of specific organs and its relation to respective anatomical, physiological or biochemical changes in these tissues associated with age.
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