Resting Metabolic Rate

Introduction

Background

Resting metabolic rate (RMR) represents the energy expended by the body to maintain essential physiological functions at rest, such as breathing, circulation, organ function, and cellular processes. It constitutes the largest portion of an individual’s total daily energy expenditure. RMR is typically assessed after an overnight fast and a period of inactivity, commonly using techniques like a ventilated hood system or within a whole-room calorimeter.^[1]These methods quantify oxygen consumption and carbon dioxide production, allowing for the calculation of energy expenditure.^[1]

Biological Basis

The primary determinant of RMR is an individual’s body size and composition, with fat-free mass (FFM) being a significant factor, accounting for approximately 80% of the variance observed in RMR and overall 24-hour energy expenditure.^[1] Beyond these physiological attributes, genetic factors also contribute considerably to the interindividual differences in RMR. Studies, including those on twins, have consistently demonstrated a heritable component to metabolic rates.^{[2], [3], [4]}Research indicates that the heritability of 24-hour energy expenditure, which encompasses RMR, can be estimated at 0.52.^[1] Specific genetic variants, such as rs11014566 in the GPR158 gene, have been linked to lower RMR, highlighting a direct genetic influence on energy metabolism.^[1]

Clinical Relevance

RMR is a crucial clinical indicator, particularly in the context of weight management and metabolic health. A lower RMR has been identified as a risk factor for future weight gain, predicting long-term increases in both body weight and fat mass.^{[1], [5], [6]}Genetic variations that lead to a reduced RMR can therefore predispose individuals to a higher body mass index (BMI) and increased percent body fat (PFAT), as evidenced by the association with the G allele ofrs11014566 .^[1]A thorough understanding of an individual’s RMR can inform personalized dietary and exercise strategies aimed at preventing or managing obesity and associated conditions like type 2 diabetes.

Social Importance

The widespread prevalence of obesity and metabolic syndrome globally underscores the significant social importance of understanding factors such as RMR. Genetic predispositions to a lower RMR can contribute to the development of obesity in vulnerable populations, as observed in American Indians where specific variants like those inGPR158 have been investigated.^[1]Continued research into the genetic and physiological determinants of RMR is vital for developing targeted public health interventions and personalized medicine approaches to combat the rising rates of obesity and its associated health burdens, thereby promoting healthier lifestyles and improved outcomes across diverse communities.

Methodological and Statistical Constraints

The research, despite utilizing one of the largest existing samples for energy expenditure (EE) measurements in a genome-wide association study (GWAS) comprising 419 Pima Indians, faced significant statistical power limitations.^[1] This low power meant the study was largely unable to detect the typically modest effect sizes of genetic variants at the stringent genome-wide statistical significance threshold (P < 5 x 10^-8).^[1] Consequently, while some variants showed nominal associations, the risk of false positives remains higher, necessitating cautious interpretation of findings not reaching this threshold.^[1]To address the low statistical power and reduce spurious findings, the study employed a strategy of prioritizing variants with consistent, directionally aligned associations across two independent EE measures (24-h EE and resting metabolic rate, RMR).^[1] While this approach enhances confidence in the identified variants, the modest effect sizes reported (e.g., -96 to -21 kcal/day for 24-h EE and -132 to -25 kcal/day for RMR) underscore the need for further validation.^[1] A major weakness highlighted is the lack of other existing datasets with both genotypic data and EE measures, which severely limits the ability to independently replicate the primary EE findings and confirm the generalizability of the observed genetic associations.^[1]

Generalizability and Population-Specific Findings

The study’s primary cohort consists of American Indians, specifically Pima Indians, and utilized a custom genotyping array designed to capture common genetic variation within this population.^[1] This population-specific design, while valuable for understanding genetic influences in this group, inherently limits the direct generalizability of the findings to populations of different ancestries.^[1] For instance, the identified rs11014566 G allele, which associated with reduced EE, had a frequency of 0.60 in full-heritage Pima Indians but was considerably lower in other populations (e.g., 0.23 in Americans, 0.11 in Africans, and <0.01 in Europeans).^[1]While the study employed precise and reproducible methods for measuring RMR and 24-h EE using metabolic chambers and ventilated hood systems, the specific characteristics of the Pima Indian cohort, including their known susceptibility to obesity and type 2 diabetes, could influence the observed genetic effects and their phenotypic expression.^[1] The replication efforts for BMI were conducted in Mexican populations, which, while related, are distinct from the primary Pima Indian cohort.^[1] This suggests that while some associations might extend to broader Amerindian populations, the direct applicability of the EE findings to diverse global populations requires further investigation.^[1]

Unexplained Heritability and Complex Interactions

The study reported substantial heritability for 24-h EE (0.52) and maximum BMI (0.55), indicating a strong genetic component to these traits.^[1] However, despite these high heritability estimates, no single genetic variant achieved genome-wide statistical significance for EE.^[1] The identified variants, such as rs11014566 in GPR158, explain only a small fraction of this heritable variance, contributing to the broader phenomenon of “missing heritability” in complex traits.^[1]This suggests that a multitude of other common variants with even smaller effects, rare variants, or complex epistatic interactions likely contribute to the remaining genetic influence on resting metabolic rate.

While the analysis adjusted for several key confounders including age, sex, body composition, and genetic relatedness, the intricate interplay between genetic predispositions and environmental factors remains largely unexplored.^[1]Environmental influences, such as dietary patterns, physical activity levels, and broader lifestyle factors, could significantly modify the expression of genetic variants affecting energy expenditure. The identification ofGPR158as a gene associated with reduced EE represents a crucial step, but the precise biological mechanisms through which this gene influences energy metabolism and its potential interactions with specific environmental contexts leading to variations in resting metabolic rate warrant extensive future research.^[1]

Variants

Genetic variations contribute significantly to the diversity observed in human resting metabolic rate (RMR), a fundamental component of daily energy expenditure that influences body weight regulation. The geneSULT2B1 encodes a sulfotransferase enzyme involved in the metabolism of various endogenous and exogenous substances, including steroid hormones and cholesterol. A variant such as rs10401347 within or near SULT2B1 could potentially alter the enzyme’s activity or expression, thereby influencing lipid and steroid metabolism pathways critical for maintaining cellular energy balance and overall metabolic efficiency.^[1] Similarly, intergenic variants like rs4698250 , situated between long intergenic non-coding RNAs (LINC01182 and LINC00504), may affect the regulatory functions of these non-coding RNAs. These lncRNAs are known to play roles in gene expression, and alterations can impact a broad range of biological processes, including those governing energy metabolism. The genetic influence on metabolic rates is well-established, with studies demonstrating significant heritability.^[2] Other variants are implicated in pathways crucial for nutrient processing and energy production. The rs1997885 variant, located between GOLGA2P4 and ADAMTS7P5, may influence the activity or regulation of nearby genes involved in cellular structure or extracellular matrix remodeling, which can indirectly affect tissue metabolism. GOLGA2P4 is a pseudogene related to golgin proteins, while ADAMTS7P5 is a pseudogene related to ADAMTS proteases, both of which have complex cellular roles. Meanwhile, the rs3760788 variant, found near BCAT2 and HSD17B14, is of particular interest. BCAT2(Branched-Chain Amino Acid Transaminase 2) is essential for the catabolism of branched-chain amino acids (BCAAs), which are important fuel sources and signaling molecules in metabolism. Variations here could alter BCAA breakdown, affecting energy substrate utilization and potentially RMR.^[5] HSD17B14(Hydroxysteroid 17-Beta Dehydrogenase 14) is involved in steroid hormone metabolism, further linking this region to hormonal regulation of energy balance. The interplay of genetic factors and energy expenditure is a key area of metabolic research.

Further affecting metabolic regulation, variants in genes like FGF21 are critical. Fibroblast Growth Factor 21 (FGF21) is a hormone known to regulate glucose and lipid metabolism, often described as a metabolic stress hormone. Thers655772 variant located near FGF21 and RNU6-317P could impact FGF21expression or function, thereby influencing whole-body energy expenditure, insulin sensitivity, and fat metabolism. AlteredFGF21 signaling due to this variant might lead to differences in RMR and susceptibility to metabolic disorders.^[7] Additionally, the rs77373510 variant near PRDX5P1 and LINC02005 may play a role in oxidative stress response or gene regulation. PRDX5P1is a pseudogene related to Peroxiredoxin 5, an antioxidant enzyme, suggesting a potential link to cellular redox state and metabolic health. Long non-coding RNAs such asLINC02005 and LINC02026 (with variant rs9828253 ) are increasingly recognized for their diverse regulatory functions in cellular processes, including metabolism. Variations in these regions could affect the expression of neighboring genes or their own regulatory capacity, contributing to individual differences in RMR.^[4] The rs281377 variant in FUT2(Fucosyltransferase 2) is notable for its role in determining secretor status, influencing the composition of gut microbiota and potentially impacting nutrient absorption and host metabolism. Individuals with differentFUT2genotypes may exhibit variations in their metabolic profiles and RMR due to altered gut microbiome interactions and systemic inflammatory states.^[8] Furthermore, the rs3754705 variant, near RBM44 and RAMP1, could influence RNA processing and receptor signaling. RBM44 (RNA Binding Motif Protein 44) is involved in RNA metabolism, while RAMP1(Receptor Activity Modifying Protein 1) is a critical component of calcitonin gene-related peptide (CGRP) receptor function, involved in vasodilation and inflammation. Changes in these processes can have systemic effects that indirectly modulate RMR. Lastly, thers11668163 variant in NFILZ(Nuclear Factor, Interleukin 3 Regulated) may affect immune and inflammatory responses, which are closely intertwined with metabolic regulation. Chronic low-grade inflammation can influence energy expenditure and contribute to metabolic dysregulation, making such variants relevant to RMR variability.^[1]

Key Variants

RS ID	Gene	Related Traits
rs10401347	SULT2B1	resting metabolic rate
rs4698250	LINC01182 - LINC00504	resting metabolic rate
rs1997885	GOLGA2P4 - ADAMTS7P5	resting metabolic rate ceramide amount
rs3760788	BCAT2 - HSD17B14	resting metabolic rate
rs655772	FGF21 - RNU6-317P	resting metabolic rate
rs77373510	PRDX5P1 - LINC02005	resting metabolic rate
rs9828253	LINC02026	resting metabolic rate
rs281377	FUT2	alkaline phosphatase resting metabolic rate schizophrenia FAM3C/NECTIN2 protein level ratio in blood blood protein amount
rs3754705	RBM44 - RAMP1	resting metabolic rate
rs11668163	NFILZ	resting metabolic rate

Defining Resting Metabolic Rate and Core Energy Expenditure Concepts

Resting metabolic rate (RMR) represents the energy expended by the body while at rest, serving as a fundamental component of overall energy expenditure (EE). Operationally, RMR is precisely defined as the average EE measured over a 40-minute period upon awakening after an overnight fast, during which the subject is instructed to remain awake and motionless.^[1] This , calculated using the equations of Lusk, is then extrapolated to a full day to provide a daily kilocalorie value.^[1]While often discussed alongside basal metabolic rate (BMR) in broader scientific literature, RMR is a distinct and widely utilized operational definition for assessing energy metabolism under specific, standardized resting conditions.

Beyond RMR, several related terms delineate different facets of energy expenditure. Total daily EE, referred to as 24-hour EE, encompasses all energy expended over a full day, including periods of activity and sleep.^[1]Within this framework, sleeping metabolic rate (Sleeping EE) is specifically defined as the average EE recorded during 15-minute nightly intervals between 01:00 and 05:00, provided that spontaneous physical activity (SPA) remains below 1.5%.^[1]Another key component is “awake and fed” thermogenesis (AFT), which is derived as the difference between the EE during an inactive awake state and the sleeping metabolic rate.^[1] These terms collectively provide a comprehensive classification system for understanding human energy metabolism across various physiological states.

Methodological Frameworks and Parameters

The precise of resting metabolic rate and other energy expenditure components relies on standardized methodological approaches and strict operational criteria. RMR is typically assessed using a respiratory hood system: subjects undergo a 10-minute acclimation period before EE is calculated every 5 minutes from respiratory gas exchange data, with the 40-minute average then extrapolated.^[1] For comprehensive 24-hour EE and substrate oxidation measurements, a whole-room calorimeter, also known as a respiratory chamber, is employed.^{[1], [5]} In this setting, volunteers remain in the chamber for 23 hours and 15 minutes, with EE continuously measured and calculated at 15-minute intervals, subsequently averaged and extrapolated to a 24-hour period.^[1]To ensure consistency and accuracy, subjects adhere to a standard weight-maintaining diet for three days prior to metabolic testing, typically consisting of 50% carbohydrates, 30% fats, and 20% proteins.^{[1], [9], [10]}Body weight is monitored daily, and food intake is adjusted to maintain weight within 1% of the baseline.^[1]Body composition, including percent body fat (PFAT), fat mass (FM), and fat-free mass (FFM), is also a critical ; historically, this was determined by underwater weighing, but later transitioned to total body dual-energy X-ray absorptiometry (DXA), with conversion equations applied to ensure comparability between methods.^[1] These rigorous protocols are essential for providing reliable data on energy metabolism in clinical and research settings.

Determinants, Classifications, and Clinical Implications

Resting metabolic rate and overall energy expenditure are influenced by a complex interplay of physiological factors, forming a basis for classifying metabolic states. The largest determinant of RMR and 24-hour EE is body size and composition, with fat-free mass (FFM) accounting for approximately 80% of the variance due to its role as metabolically active tissue.^[1] Beyond anthropometric measures, age and sex also contribute to EE variability.^[1] Importantly, genetic factors play a measurable role in the interindividual variance of EE, a finding supported by twin studies.^{[1], [2], [3]}The clinical significance of RMR and EE lies in their predictive capacity for health outcomes, particularly concerning weight regulation. A lower EE has been consistently identified as a risk factor for future body-weight gain and an increase in fat mass.^{[1], [5]}This relationship underscores the utility of RMR measurements in identifying individuals at risk for obesity. Furthermore, genetic studies aim to classify individuals based on variants that influence EE, such as those inGPR158associated with reduced energy expenditure, to uncover novel metabolic pathways impacting body weight and fatness.^[1] Such classifications aid in understanding the complex etiology of metabolic disorders and developing targeted interventions.

Fundamental Principles of Energy Metabolism

Resting metabolic rate (RMR) represents the energy expended by the body to maintain essential physiological functions at rest, such as breathing, circulation, and cellular activity, after an overnight fast and in a thermoneutral environment. This baseline energy expenditure is critical for sustaining life and reflects the sum of countless molecular and cellular processes. At the cellular level, metabolic pathways continuously convert nutrients into adenosine triphosphate (ATP), the primary energy currency, through processes like glycolysis, the Krebs cycle, and oxidative phosphorylation. Key biomolecules, including enzymes, facilitate these reactions, while hormones such as thyroid hormones and insulin regulate their pace, ensuring energy supply meets demand. Disruption in these delicate regulatory networks can lead to imbalances in energy homeostasis, where the body’s ability to maintain a stable internal environment for energy use is compromised.^[1]The overall energy expenditure (EE), including RMR, is primarily determined by an individual’s body size and composition, with fat-free mass (FFM) being a major contributor, accounting for approximately 80% of the variance in both RMR and 24-hour EE. FFM, composed largely of muscle and organs, represents the metabolically active tissues that consume significant energy even at rest.^[1] The continuous oxidation of carbohydrates and fats, quantified using equations like those developed by Lusk, provides a direct measure of this energy output.^[11] Furthermore, the body’s ability to predict weight gain is influenced by metabolic factors such as fasting respiratory exchange ratio, the thermic effect of food, and overall fuel utilization.^[8]

Genetic Architecture of Metabolic Rate

Genetic factors play a measurable role in the interindividual variability of energy expenditure, including RMR. Twin studies have demonstrated a significant genetic effect on resting metabolic rates, indicating that inherited traits influence how efficiently an individual’s body utilizes energy.^[2]Heritability estimates for 24-hour EE have been reported around 0.52, and for maximum Body Mass Index (BMI) around 0.55, highlighting the substantial genetic contribution to these metabolic traits.^[1] This genetic influence extends to specific genes and their variants, which can impact metabolic pathways and predispose individuals to certain metabolic profiles.

For instance, a genome-wide association study identified variants in the GPR158 gene, such as rs11014566 , that are associated with reduced energy expenditure. Specifically, individuals carrying two copies of the G allele atrs11014566 exhibited lower 24-hour EE and RMR, with the effect being particularly pronounced during sleep.^[1] This genetic variation in GPR158is also linked to a higher maximum BMI, increased percent body fat (PFAT), and greater fat mass (FM), suggesting a direct genetic pathway influencing energy balance and body composition.^[1] Other genes, such as LEPR and MC4R, have also been nominally associated with variations in BMI and 24-hour EE, indicating a complex genetic regulatory network underlying metabolic rate and its impact on body weight.^[12]

Tissue-Specific Contributions and Systemic Regulation

The overall resting metabolic rate is an aggregate of energy consumption across various tissues and organs, each contributing differently based on its metabolic activity and mass. Organs like the brain, liver, heart, and kidneys, despite their relatively small mass, are highly metabolically active and contribute disproportionately to RMR. Muscle tissue, while less metabolically active per unit of mass than organs, contributes significantly due to its larger overall mass, particularly fat-free mass.^[1]The interactions among these tissues are coordinated through systemic regulatory mechanisms, including the nervous and endocrine systems, which adjust energy expenditure in response to internal and external cues.

For example, the regulation of glucose tolerance involves complex interactions between different organ systems, as evidenced by studies showing increased glucose tolerance in mice deficient in the N-type Ca2+ channel alpha(1B)-subunit gene, suggesting a role for calcium channels in metabolic control.^[13]Hormones released by endocrine glands, such as the pancreas and thyroid, act on various target tissues to modulate nutrient uptake, storage, and utilization, thereby influencing systemic energy balance. These tissue interactions and systemic consequences highlight the intricate biological network that underpins an individual’s resting metabolic rate, making it a complex trait influenced by both genetics and environmental factors.

Metabolic Rate in Health and Disease

Resting metabolic rate is a crucial determinant of an individual’s susceptibility to weight gain and obesity, representing a fundamental aspect of pathophysiological processes related to energy balance. A consistently lower energy expenditure has been identified as a significant risk factor for subsequent increases in body weight and fat mass.^[5]This inverse relationship suggests that individuals with a naturally lower RMR may be more prone to accumulating excess energy as fat if energy intake is not correspondingly reduced. The concept of “awake and fed thermogenesis,” which is the energy expended above sleeping metabolic rate while awake and fed, also plays a role, with lower levels predicting future weight gain in individuals with abdominal adiposity.^[14] Disruptions in energy homeostasis, whether due to genetic predispositions or environmental factors, can lead to compensatory responses that affect overall metabolic health. For example, a lower RMR can be a primary contributor to weight gain, rather than just a consequence, indicating a fundamental biological mechanism at play.^[7]Understanding these disease mechanisms and developmental processes related to metabolic rate is vital for addressing the global challenge of obesity and associated metabolic disorders. Identifying genetic variants, such as those inGPR158, that influence EE and body composition provides insights into novel metabolic pathways that could be targeted for therapeutic interventions.^[1]

RMR as a Predictor of Weight Gain and Obesity Risk

Resting metabolic rate (RMR) measurements hold significant prognostic value in predicting an individual’s susceptibility to weight gain and the development of obesity. Research consistently indicates that a lower rate of energy expenditure, including RMR, serves as a risk factor for future increases in body weight and fat mass over the long term.^{[5], [14]} This predictive capability allows for early risk assessment, identifying individuals who may be metabolically predisposed to weight accumulation, even in lean adult populations.^[15]Such insights are crucial for personalized prevention strategies, enabling clinicians to intervene with targeted lifestyle modifications before substantial weight gain occurs. The utility of RMR in predicting weight changes has been explored in various studies, underscoring its role as a metabolic predictor of obesity.^{[7], [8], [16]}

Genetic Determinants and Comorbidity Stratification

The of RMR also offers a window into the genetic underpinnings of energy expenditure, which can inform risk stratification for metabolic comorbidities. RMR exhibits familial dependence and a notable genetic component, suggesting inherited predispositions to certain metabolic profiles.^{[2], [4]} For instance, a genome-wide association study in American Indians identified variants in the GPR158 gene, specifically the G allele at rs11014566 , that were associated with significantly reduced 24-hour energy expenditure and lower RMR.^[1]Individuals carrying two copies of this G allele demonstrated lower overall energy expenditure, particularly during sleep.^[1]This genetic variant was also linked to a higher maximum body mass index (BMI), increased percent body fat (PFAT), and greater fat mass, highlighting its role in obesity risk.^[1]Given that the frequency of this G allele is notably higher in populations like Pima Indians compared to other ancestries, understanding such genetic influences can aid in stratifying individuals at higher risk for obesity and related metabolic conditions, enabling more precise, population-specific prevention and management approaches.^[1]

RMR in Personalized Metabolic Management

Beyond risk prediction, RMR measurements provide critical diagnostic utility and guide personalized treatment and monitoring strategies in metabolic health. By quantifying an individual’s baseline energy expenditure, clinicians can identify those with an inherently lower metabolic rate who may require more stringent caloric control or increased physical activity to maintain or achieve a healthy weight. This diagnostic insight informs the selection of appropriate dietary and exercise interventions, moving beyond generalized recommendations to truly personalized medicine approaches. Furthermore, serial RMR measurements can serve as a monitoring tool, assessing the effectiveness of weight management programs, pharmacological treatments, or lifestyle modifications over time. Observing changes in RMR can indicate shifts in metabolic adaptation, disease progression, or response to therapy, thereby allowing for timely adjustments to optimize patient care and long-term outcomes in conditions like obesity and type 2 diabetes.

Longitudinal Studies and Metabolic Predictors of Weight Gain

Longitudinal cohort studies have extensively investigated the relationship between resting metabolic rate (RMR) and long-term weight changes, establishing RMR as a significant predictor of weight gain in various populations. Research from the Baltimore Longitudinal Study on Aging, for instance, indicated that both fasting respiratory exchange ratio and RMR can predict future weight gain, highlighting the importance of metabolic efficiency in weight regulation.^[8]Similarly, studies in Pima Indians demonstrated that a reduced rate of energy expenditure (EE) serves as a risk factor for body-weight gain, with lower EE consistently predicting long-term increases in both weight and fat mass.^[5]While some studies suggest that RMR may not universally predict weight gain in all contexts, a positive association between resting energy expenditure and weight gain has also been observed in lean adult populations, indicating complex interactions between metabolism and body weight trajectories.^[7] Further epidemiological investigations have revealed that familial factors significantly influence RMR, with studies demonstrating a strong familial dependence of this metabolic measure.^[4]Twin studies further support a genetic component, showing measurable genetic effects on both resting and exercise metabolic rates, as well as significant concordance in RMR among monozygotic twins.^[2]These findings underscore that while body size and composition, particularly fat-free mass, are the largest determinants of RMR and 24-hour EE, genetic predisposition plays a crucial, albeit smaller, role in interindividual variations in energy expenditure and, consequently, in the susceptibility to weight gain.^[5]

Ancestry-Specific Genetic Determinants and Cross-Population Comparisons

Cross-population genetic studies have been instrumental in identifying ancestry-specific variants that influence energy expenditure. A genome-wide association study (GWAS) conducted in American Indians, specifically the Pima Indian population, identified variants in theGPR158gene associated with reduced energy expenditure.^[1]This study, which included both full-heritage and mixed-heritage Pima Indians, revealed that the G allele of the single nucleotide polymorphismrs11014566 was associated with lower 24-hour EE, particularly during sleeping, and consequently with a higher maximum body mass index (BMI) and percent body fat.^[1] The allele frequency of this variant varied significantly across ethnic groups, being high in American Indians but considerably lower in Americans, Africans, and nearly absent in Europeans, suggesting population-specific genetic influences on metabolic traits.^[1] Replication efforts for the association of rs11014566 with BMI were undertaken in the Slim Initiative in Genomic Medicine for the Americas (SIGMA) consortium, comprising Mexican individuals without diabetes. These studies further supported the association of the BMI risk alleles with higher BMI, indicating that such genetic effects might extend to other populations of Native American ancestry.^[1]The identification of such ancestry-specific genetic variants not only highlights the genetic heterogeneity in metabolic pathways across different ethnic groups but also provides insights into population-specific susceptibilities to obesity and related metabolic disorders.

Methodological Approaches in Energy Expenditure Research

Population studies on resting metabolic rate and energy expenditure rely on precise and standardized methodologies to ensure robust and generalizable findings. RMR is typically measured upon awakening after an overnight fast using a respiratory hood system, with energy expenditure calculated over a defined period (e.g., 40 minutes) and extrapolated.^[1]For comprehensive energy expenditure assessments, 24-hour EE and substrate oxidation are often measured in whole-room calorimeters, where volunteers remain for extended periods under controlled conditions, with continuous monitoring of EE and spontaneous physical activity (SPA).^[5]Body composition, including fat mass (FM) and fat-free mass (FFM), is crucial for adjusting EE measurements and is estimated through methods like underwater weighing or dual-energy X-ray absorptiometry (DXA).^[1] These studies often involve large sample sizes, such as the 7,701 Pima Indian samples used for genome-wide association analysis, with subsets undergoing detailed inpatient metabolic testing.^[1]To account for confounding factors and genetic relatedness within family-based cohorts, analyses typically adjust for demographic factors like age and sex, body composition parameters (FM, FFM), SPA, and genetic principal components.^[1] While such rigorous study designs enhance the representativeness and generalizability of findings, the power to detect subtle genetic effects can vary, necessitating careful interpretation of nominally significant associations and replication in independent cohorts.^[1]

Frequently Asked Questions About Resting Metabolic Rate

These questions address the most important and specific aspects of resting metabolic rate based on current genetic research.

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1. Why can my friend eat so much and stay thin, but I can’t?

This often comes down to individual differences in resting metabolic rate (RMR), which is how many calories your body burns at rest. A significant portion of these differences, about 52% of your total energy expenditure, is influenced by genetics. Some people naturally have a higher RMR due to their genes, allowing them to burn more calories even when inactive.

2. My sibling is thin, but I’m not. Why are we so different?

Even among family members, genetic variations can lead to different metabolic rates. While overall energy expenditure has a strong heritable component (around 52%), specific genetic variants you inherit can influence your RMR. This means your body might burn calories differently than your sibling’s, even with similar lifestyles.

3. Could my slow metabolism be why I keep gaining weight?

Yes, a naturally lower resting metabolic rate (RMR) is a known risk factor for future weight gain. Studies have shown that individuals with lower RMR tend to gain more body weight and fat mass over time. Your genetic makeup plays a considerable role in determining your RMR.

4. If my family struggles with weight, will I too?

There’s a strong genetic component to metabolic rates and body weight, with heritability for 24-hour energy expenditure estimated around 52% and BMI at 55%. So, if your family has a history of weight issues, it suggests you might have a genetic predisposition to a lower RMR or higher BMI. However, lifestyle choices still play a crucial role.

5. Are my kids doomed to have my slow metabolism?

Not necessarily “doomed,” but there’s a strong likelihood they will inherit some of your metabolic predispositions. Genetics account for about 52% of the variance in energy expenditure. While they may inherit genes linked to RMR, their ultimate metabolic health will also be significantly shaped by their diet, activity levels, and environment.

6. Does my ethnic background affect my weight risks?

Yes, research suggests that genetic influences on metabolism can vary across different ethnic groups. For example, some genetic variants associated with reduced energy expenditure, like one in theGPR158gene, are more common in certain populations, such as American Indians, which can influence their susceptibility to obesity.

7. Would a genetic test help me understand my metabolism?

A genetic test could potentially offer insights into your metabolic predispositions. While no single gene fully explains your RMR, identifying specific variants linked to lower energy expenditure might help personalize dietary and exercise strategies. However, current genetic tests only capture a small part of the complex picture, and more research is needed for comprehensive understanding.

8. Can exercise or diet really overcome my ‘slow’ metabolism?

Absolutely. While your resting metabolic rate has a strong genetic component (around 52% heritable), diet and exercise are powerful tools. Regular physical activity and a balanced diet can significantly influence your overall energy expenditure and help manage weight, even if you have a genetic predisposition to a lower RMR.

9. Why do weight loss diets work for others but not me?

It could be due to differences in your resting metabolic rate (RMR), which is significantly influenced by your genetics. Some people naturally burn fewer calories at rest, making weight loss more challenging even with similar diets. Genetic variations can affect how efficiently your body uses energy.

10. Why isn’t there a simple ‘fix’ for my slow metabolism?

There isn’t a single “fix” because your metabolism, particularly your resting metabolic rate (RMR), is a complex trait influenced by many factors. While genetics play a significant role, accounting for about 52% of energy expenditure, many different genes with small effects contribute, and their interactions are still being understood. This complexity means a personalized approach, rather than a universal fix, is often most effective.

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This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.

Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.

References

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[2] Bouchard C, Tremblay A, Nadeau A, et al. “Genetic effect in resting and exercise metabolic rates.” Metabolism. 1989;38:364–370.

[3] Fontaine, E., et al. “Resting metabolic rate in monozygotic and dizygotic twins.”Acta Genet Med Gemellol (Roma), vol. 34, 1985, pp. 41–47.

[4] Bogardus C, Lillioja S, Ravussin E, et al. “Familial dependence of the resting metabolic rate.” N Engl J Med. 1986;315:96–100.

[5] Ravussin E, Lillioja S, Knowler WC, et al. “Reduced rate of energy expenditure as a risk factor for body-weight gain.” N Engl J Med. 1988;318:467–472.

[6] Tataranni, P. A., et al. “Body weight gain in free-living Pima Indians: effect of energy intake vs expenditure.”Int J Obes Relat Metab Disord, vol. 27, 2003, pp. 1578–1583.

[7] Anthanont P, Jensen MD. “Does basal metabolic rate predict weight gain?” Am J Clin Nutr. 2016;104:959–963.

[8] Seidell JC, Muller DC, Sorkin JD, Andres R. “Fasting respiratory exchange ratio and resting metabolic rate as predictors of weight gain: the Baltimore Longitudinal Study on Aging.” Int J Obes Relat Metab Disord. 1992;16:667–674.

[9] Pannacciulli, N., et al. “The 24-h carbohydrate oxidation rate in a human respiratory chamber predicts ad libitum food intake.”The American Journal of Clinical Nutrition, vol. 86, no. 3, 2007, pp. 625-632.

[10] Penesova, A., et al. “Short-term isocaloric manipulation of carbohydrate intake: effect on subsequent ad libitum energy intake.”European Journal of Nutrition, vol. 50, no. 6, 2011, pp. 455-463.

[11] Lusk, G. “Animal calorimetry: analysis of oxidation of mixtures of carbohydrates and fat.” J Biol Chem, vol. 59, 1924, pp. 41–42.

[12] Traurig, M. T., et al. “Variants in the LEPR gene are nominally associated with higher BMI and lower 24-h energy expenditure in Pima Indians.”Obesity (Silver Spring), vol. 20, 2012, pp. 2426–2430.

[13] Takahashi, E., et al. “Increased glucose tolerance in N-type Ca2+ channel alpha(1B)-subunit gene-deficient mice.”Int J Mol Med, vol. 15, 2005, pp. 937–944.

[14] Piaggi P, Thearle MS, Bogardus C, Krakoff J. “Lower energy expenditure predicts long-term increases in weight and fat mass.” J Clin Endocrinol Metab. 2013;98:E703–E707.

[15] Luke, A., Durazo-Arvizu, R., Cao, G., et al. (2006). Positive association between resting energy expenditure and weight gain in a lean adult population.American Journal of Clinical Nutrition, 83(5), 1076–1081.

[16] Weinsier, R. L., Nelson, K. M., Hensrud, D. D., et al. (1995). Metabolic predictors of obesity. Contribution of resting energy expenditure, thermic effect of food, and fuel utilization to four-year weight gain of post-obese and never-obese women.Journal of Clinical Investigation, 95(2), 980–985.