Total energy expenditure TEE is a combination of BMR, plus energy used for physical activities and energy used to digest food known as dietary thermogenesis. We know certain factors affect energy expenditure, such as age, sex, body mass, body composition, physical activity, and illness, yet the latest comprehensive study , which included data from people around the world, revealed surprising information about the timing of age-related metabolism changes over the lifespan.

Researchers calculated TEE in all subjects using doubly labeled water measurements the gold standard for measuring energy expenditure. They used additional datasets, mathematical models, and adjustments to account for differences in body size, age, and reproductive status.

Their findings revealed four distinct phases of adjusted total and basal energy expenditure over the lifespan. Neonatal 1 month to 1 year : Neonates in the first month of life had size-adjusted energy expenditure similar to that of adults. Energy expenditure increased rapidly over the first year, reaching a peak at 0.

Childhood and adolescence 1 to 20 years : Although total and basal expenditure as well as fat-free mass continued to increase with age throughout childhood and adolescence, size-adjusted expenditures steadily declined throughout this period. Sex had no effect on the rate of decline.

At Of note, there was no increase in adjusted total or basal energy expenditure during the pubertal ages of 10 to 15 years old. We all know that regular aerobic exercise a.

Just 30 minutes of at least moderate intensity exercise, five days per week, improves virtually all aspects of metabolic health.

But what you may not know is that emerging evidence also suggests that reducing the time you spend being sedentary a. sitting and avoiding prolonged, continuous sedentary periods may also improve your metabolism. Muscle-strengthening activities are safe for older adults and proven to maintain the integrity of muscle and bone, and improve balance, coordination and mobility.

In addition, because muscles are highly metabolically active, building and maintaining healthy muscle mass will help keep your metabolism going. These exercises may even reduce the signs and symptoms of common metabolic diseases such as diabetes and obesity.

Guidelines from the department of Health and Human Services recommend performing muscle-strengthening activities, such as lifting weights, resistance band exercises or body weight exercise like push-ups, sit-ups, etc. at least two days per week. To fuel a healthy metabolism you should eat a variety of nutrient dense foods from each food group.

Nutrient dense foods are foods that have a high nutrient to calorie ratio such as lean meats, vegetables and whole grains. Consuming fiber-rich foods and drinking plenty of water may particularly benefit metabolic health by aiding digestion and lowering blood sugar and cholesterol.

The reasons for this are not entirely understood, but experts believe a combination of physiological, environmental and behavioral changes are to blame.

Research indicates that the following tips can help promote longer, more restful sleep:. However, research indicates that if we start early, we are much more likely to maintain a consistent, healthy body weight, which can be vital for preserving metabolic function, reducing our risk for many chronic conditions and maintaining functional independence well into our golden years.

Kate Lyden, Ph. In this role she designs, implements and supports clinical studies related to physical activity and sleep. She works in close collaboration with academic partners to bring together industry know-how with academic innovation.

Prior to joining Misfit, Inc. Her research examined the effects of interrupting sedentary time with short and continuous bouts of moderate intensity walking on metabolic outcomes in overweight adults.

She developed methodologies to quantify physical activity, sedentary behavior and sleep using wearable sensors, and used these techniques to understand the dose-response relationship between physical activity and sedentary behavior and chronic disease.

On the contrary, conditions characterized by pro-inflammatory status and immunological activation, which are strong predictors of mortality, are associated with an increased BMR attributable, at least in part, to bioenergetic dysregulation 21 , Taken as a whole, these observations suggest that higher BMR may be associated with higher mortality.

In a clinical setting, BMR may be considered as the energy requirement to maintain a structural and functional homeostasis at rest, in fasting and thermoneutral conditions. We hypothesized that pathology and accelerated aging increase the BMR because extra energy is required to counteract wide fluctuations in the homeostatic equilibrium 6.

Thus, a high BMR late in life would be a marker of the effort to maintain homeostasis and the failure to reduce BMR over time a risk factor for mortality.

This hypothesis has never been tested in humans, mainly due to a lack of suitable data. We evaluated whether high BMR is a risk factor for mortality in the healthy participants enrolled in the Baltimore Longitudinal Study of Aging BLSA from through and whose follow-up for mortality was conducted over more than 40 years.

Participants were community-dwelling persons healthy at the enrollment in the BLSA. They were continually recruited from , primarily from the Baltimore—Washington, DC area. From through the cohort included exclusively men because of staff and budget limitation. Women were enrolled after Minorities were enrolled from the s.

A general description of the sample and recruitment criteria of the BLSA have been previously reported These analyses were based on persons men and women enrolled from through and followed up to for mortality. Participants with known or suspected thyroid or adrenal dysfunction, as evidenced by history, drug use, or physical examination, were excluded.

Each participant contributed data on BMR on one or more visits. Participants were evaluated approximately every 2 years at the Gerontology Research Center in Baltimore, Maryland.

Follow-up visits lasted 2—3 days and included medical evaluations and physiological and cognitive tests. Blood and urine samples were collected in the morning after the participants had fasted for at least 12 hours.

Aliquots of serum and plasma were immediately obtained and analyzed by the clinical laboratory staff of the Johns Hopkins Bayview Medical Center. Participants were housed on a ward and underwent indirect calorimetry in fasting, resting state for the two following mornings between and am in a temperature-controlled room.

BMR was estimated from basal O 2 consumption and CO 2 production measured by the open-circuit method described by Shock and Yiengst 25 and Tzankoff and Norris Samples of expired air were collected for 6—8 minutes during three collection periods. Until , O 2 and CO 2 were analyzed by using a Haldane apparatus, and after that time by paramagnetic method for O 2 Paramagnetic O 2 analyzer model G-2; Beckman Instruments, Fullerton, CA and by infrared absorption gas analyzer Model LB-1, Beckman Instruments for CO 2.

After the two analytical systems were shown to be equivalent, all subsequent analyses were done by the more modern method. Both instruments were calibrated daily with standardized gas mixtures obtained commercially in standard pressure tanks and checked by the Haldane method.

Mortality data were collected by telephone follow-up and correspondence with participants and their relatives. Every year, regular searches of the National Death Index were conducted to ascertain the vital status of the participants.

The cause of death was determined by a consensus of three physicians who reviewed death certificates, medical records, and other available documentation on each participant. Weight in kilograms and height in centimeters were measured after overnight fasting with participants wearing a hospital gown, on a standard physician's balance scale and stadiometer, respectively.

Body mass index BMI was calculated by dividing the weight in kilograms measured at each visit by the square of height in meters. Waist circumference was measured as the minimal circumference between the inferior rib cage and iliac crests. Creatinine excretion, an indirect measure of total amount of muscle mass, was determined [using the method described by Hare 28 ] from hour urine collection that began on the day of the participant's arrival at the Gerontology Research Center.

Diabetes mellitus was defined by American Diabetes Association criteria. Leisure time physical activity LTPA was estimated from self-reported information on time spent performing 97 activities including work-related physical activities on a typical day, averaged over the previous 2 years To measure muscle isometric strength, participants were seated with their upper arms perpendicular to the floor and the forearm parallel to the anterior—posterior axis and perpendicular to the head-to-seat axis.

Shoulders were supported by a backboard and by shoulder straps. Hands lay on 1-inch-thick wooden grips connected by wires to a supporting frame. Participants pulled against the grips in four ways: up, down, forward, and backward along the axis of the forearm.

Each direction was tested three times, and the maximal value was recorded. A minute rest period occurred between trials. Grip strength was measured with a Smedley Hand Dynamometer Stoelting, Wood Dale, IL calibrated to known weights and adjusted to fit each participant's grip.

Test—retest reliability for total strength was estimated by repeated measurements on 2 consecutive days. The Pearson R correlation for total muscle strength was 0. Participants were categorized as smokers, former smokers, or never smokers at each visit based on self-report questionnaires.

Blood pressure was measured at the brachial artery using a sphygmomanometer that was calibrated at regular intervals with a mercury standard Cancer diagnosis was based on medical evaluation and clinical reports. The cross-sectional part of this study used data from both sexes, whereas the longitudinal analyses were performed only in men.

In Table 1 and Table 2 , baseline and follow-up characteristics of participants were reported as means and standard deviations if continuous variables, and as proportions and percentages if categorical variables. Differences between survivors and decedents were tested using Student t or chi-square tests.

Mixed effect models were used to study the age-related change of BMR for the entire sample of male participants. These models were also tested separately for those who died and those who were censored at the end of the follow-up period, using the Loess approach Figure 1C The contribution of BMR to mortality was estimated in proportional hazards regression models using the survival functions developed by Therneau and Brambsch 34 , which allow for time-dependent covariates using a counting process.

In all models, BMR was included both as a linear and as a quadratic term to allow for nonlinear association.

Model 1 was adjusted for age, date of visit, race, weight, and BMI. Model 2 was also adjusted for smoking, and Model 3 was adjusted for all covariates of Model 2 plus total physical activity, creatinine excretion, muscle strength, and white blood cell count.

Because at least one of the covariates in Model 3 and Model 4 had missing data for approximately one third of the time points at which BMR was collected, the value of these covariates was imputed after examining the pattern of the missing data in relationship with the other covariates.

The statistical procedure used for imputation was aregImpute in S-PLUS version 6. This algorithm generates values for missing data based on all the available data collected longitudinally in each participant Table 3 34 , The nature of the relationship between BMR and mortality was explored by looking at the functional form of the relationship of Martingale residuals and BMR.

Risk proportionality was tested by plotting Martingale residuals according to time. The statistical procedure used for this analysis was cox. zph in S-PLUS. To explore the relationship between BMR and mortality, we grouped participants into four BMR groups using the following cutoffs: These cutoffs were identified by looking at the functional form of the relationship between the Martingale residuals and BMR.

The distribution of BMR groups resembled the quartile distribution of participants at baseline and fitted very well the critical thresholds for mortality. The characteristics of participants according to BMR groups are reported in Tables 4 and 5.

Mortality risk ratios were estimated using a longitudinal time-dependent model according to the survival functions developed by Therneau and Brambsch The probability of death for participants who were in the different BMR groups was estimated in comparison to the second group These analyses were conducted using all-cause mortality, cancer mortality, and cardiovascular mortality.

In all survival analyses, a time-to-event approach was used. Time-to-event was estimated as the difference between the date of death and the date of visit at baseline evaluation in participants who died during the follow-up, and as the difference between date of visit of last updating of death index which was in the data set and date of visit of baseline evaluation in those participants who were still alive at the end of the follow-up period.

All analyses were performed using S-PLUS version 6. The baseline characteristics of participants according to final vital status are presented in Table 1. Overall, A total of evaluations over 24 years were performed in men and evaluations over 4 years were performed in women.

On average, 4. Only few women had more then one BMR evaluation Table 2. The decedent participants were older at both the first and last evaluations, had lower creatinine excretion and muscle strength, were less physically active, and had a significantly higher waist circumference compared to those who were still alive at the end of the follow-up period Tables 1 and 2.

The longitudinal trajectory of BMR was evaluated only in men because women started the study at a later stage. On average, the rate of BMR decline was 0. However, the rate of decline accelerated from 0. Noteworthy, among participants who entered the study before the age of 65 years, those who died had higher BMR than those who survived Figure 1C , and such a difference became progressively larger over the follow-up period.

All participants who were enrolled after age 65 years died during the follow-up period, and the average slope of their BMR decline was steeper than that in the younger groups data not shown.

The excess risk of mortality associated with higher BMR was statistically significant after further adjustment for smoking Table 3 , Model 2 , total physical activity, creatinine excretion used as a measure of muscle mass , muscle strength, and white blood cell count Table 3 , Model 3.

The strength of the association between BMR and mortality was substantially unchanged after adjustment for systolic and diastolic blood pressure, diabetes, and cancer Table 3 , Model 4. The functional form of the relationship between BMR and mortality was nonlinear, and no excess mortality was found in the range Independent of age, participants with BMR in the range These findings remained substantially unchanged after adjustment for visit date, race, BMI, physical activity, muscle mass, smoking status, and white blood cell count data not shown.

Interestingly, the critical thresholds identified in this analysis approximately divided the BMR distribution into groups Table below Figure 2 , Table 4.

Using baseline and longitudinal data analysis, the lowest age-adjusted mortality rate was found in the second group BMR within Participants in the highest and those in the lowest BMR groups had higher mortality risk compared to those in the reference group Table 4.

Independent of age, race, calendar date, BMI, smoking status, muscle mass, and white blood cell count, participants in the The percentage of cardiovascular and cancer deaths were similar across the BMR groups.

However, based on a time-to-event analysis, persons in the highest versus those in the reference BMR group had significantly higher estimated HR values for cardiovascular mortality HR: 1. Persons in the highest versus those in the reference BMR group tended to have a higher HR value for cancer mortality HR: 1.

On the basis of data collected longitudinally from healthy community-dwelling persons who participated in the BLSA, we found that BMR declined nonlinearly with age.

Independent of age, participants who died during the follow-up period tended to have a higher BMR compared to those who survived.

Accordingly, higher BMR was associated with increased risk of mortality independent of age, weight, BMI, smoking status, total physical activity, muscle mass and strength, white blood cell count, diabetes, blood pressure, and calendar date.

The relationship between BMR and mortality was curvilinear with minimum mortality between These findings are the first evidence based on longitudinal data showing that a blunted capacity to reduce BMR with age is a significant risk factor for mortality in the general population.

We confirmed that BMR declines with age 25 , 26 , 36 , 37 and showed that the rate of decline accelerates at older ages Interestingly, the age-related decline of BMR was blunted in participants who died compared to those who were still alive at the end of the follow-up period.

These results are compatible with the notion that long-lived individuals are able to preserve a low energy metabolism, perhaps reflecting good health status.

The discrepancy in BMR between individuals who died and those who survived may indicate pathological conditions causing a homeostatic dysregulation that substantially increase minimum energy requirements. A potential clinical application of this concept stems from the hypothesis that an excessively high BMR, such as one caused by uncontrolled inflammation, is an early index of the perturbation of health status.

As a consequence, BMR may help clinicians to monitor the effectiveness of their interventions. We acknowledge that the present study has limitations. The BLSA sample is not representative of the general population because it mostly includes highly educated individuals of high socioeconomic status who are considered healthy at study entry.

Data were collected from through Women were enrolled in the study only from The longitudinal BMR evaluation based on the same methodology and performed in the same clinical setting at the Gerontology Research Center in Baltimore over a period of decades is a unique feature of the BLSA.

However, we caution the readers about the difficulty of detecting early thyroid dysfunction based only on physical examination. During —, thyroid-stimulating hormone TSH radioimmunoassay or triiodothyronine T 3 determinations were not available for the BLSA participants, and later they were measured only in a subgroup.

However, to our knowledge this study is unique because of the open panel design and the length of follow-up of the participants. Additionally, the BLSA is particularly suited to address the scientific question of this manuscript, and our findings have important potential scientific implications.

By showing that BMR is independently related to mortality in humans, and that persons who died had a blunted age-related BMR decline compared to those who survived, our study may open new research perspectives to investigate the role of energy expenditure in influencing both health status and duration of life.

Decision Editor: Darryl Wieland, PhD, MPH. Changes in basal metabolic rate BMR with aging. A, Data collected on BMR in all participants and at all time-points using nonparametric smoothing functions dashed line for men; solid line for women.

B, Longitudinal trend of BMR by age decade solid lines and limited to men.