Does basal metabolic rate predict weight gain?

Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris-Benedict Equation with Indirect Calorimetry

Background: The main objective of the work was the analysis and description of data on body composition and resting energy expenditure (REE) values of selected groups of patients with obesity whose REE measurement results using indirect calorimetry reached a level below 95% of the predicted REE calculated using the Harris-Benedict (H-B) equation. The sub-goals were to describe the dependence of body composition on the size of the REE and to find out if the deviations between the number of the total measured REE and the REE calculated using H-B in the adapted group (patients with altered REE values, lower than expected caused by long caloric restriction) are significant. Methods: For the research, 71 (39 women and 32 men) patients treated in obesitology were selected. Patients underwent the measurement of resting metabolism using indirect calorimetry (IC) and body composition measurement on the bioimpedance device and, at the same time, the value of resting metabolism was calculated for everyone using the H-B equation. The whole group was divided into five groups according to the deviation of the measurement using IC and the calculation of the H-B equation. Results: In the total set of examined individuals, there were 32.4% with a reduced REE value compared to the REE calculation according to the H-B equation, which corresponds to 23 individuals. In the adapted group, the average measured REE was 2242 ± 616 kcal compared to the H-B calculation of 2638 ± 713 kcal. Statistically, these results were not significant, but a high case-to-case variation was found. The highest deviation from the H-B predictive calculation was -42% and +43% in the whole research group. The amount of muscle tissue in the adapted group averaged 44.3 ± 11.9 kg and the amount of fat-free mass (FFM) 77.9 ± 20.1 kg. When statistically testing the dependence of REE on FFM and muscle tissue in the adapted group, a strong correlation was found. Conclusions: The H-B equation alone is not suitable for setting a suitable diet therapy for an individual with obesity. In order to select and characterize a group of adapted individuals, it will be necessary to use other methods or a larger research sample, and preferably examine and divide patients with specific comorbidities or include their health status.

Association between basal metabolic rate and cardio-metabolic risk factors: Evidence from a Mendelian Randomization study

Background

Cardio-metabolic risk factors play a crucial role in the development of cardiovascular and metabolic diseases. Basal metabolic rate (BMR) is a fundamental physiological parameter that affects energy expenditure and might contribute to variations in these risk factors. However, the exact relationship between BMR and cardio-metabolic risk factors has remained unclear.

Methods

We employed Mendelian Randomization (MR) analysis to explore the association between BMR (N: 534,045) and various cardio-metabolic risk factors, including body mass index (BMI, N: 681,275), fasting glucose (N: 200,622), high-density lipoprotein (HDL) cholesterol (N = 403,943), low-density lipoprotein (LDL) cholesterol (N = 431,167), total cholesterol (N: 344,278), and triglycerides (N: 441,016), C-reactive protein (N: 436,939), waist circumference (N: 232,101), systolic blood pressure (N: 810,865), diastolic blood pressure (N: 810,865), glycated haemoglobin (N: 389,889), and N-terminal prohormone brain natriuretic peptide (N: 21,758). We leveraged genetic variants strongly associated with BMR as instrumental variables to investigate potential causal relationships, with the primary analysis using the Inverse Variance Weighted (IVW) method.

Results

Our MR analysis revealed compelling evidence of a causal link between BMR and specific cardio-metabolic risk factors. Specifically, genetically determined higher BMR was associated with an increased BMI (β = 0.7538, 95% confidence interval [CI]: 0.6418 to 0.8659, p < 0.001), lower levels of HDL cholesterol (β = -0.3293, 95% CI: 0.4474 to -0.2111, p < 0.001), higher levels of triglycerides (β = 0.1472, 95% CI: 0.0370 to 0.2574, p = 0.0088), waist circumference (β = 0.4416, 95% CI: 0.2949 to 0.5883, p < 0.001), and glycated haemoglobin (β = 0.1037, 95% CI: 0.0080 to 0.1995, p = 0.0377). However, we did not observe any significant association between BMR and fasting glucose, LDL cholesterol, total cholesterol, C-reactive protein, systolic blood pressure, diastolic blood pressure, or N-terminal prohormone brain natriuretic peptide (all p-values>0.05).

Conclusion

This MR study provides valuable insights into the relationship between BMR and cardio-metabolic risk factors. Understanding the causal links between BMR and these factors could have important implications for the development of targeted interventions and therapies.

Characteristics of Normal Weight Insulin-Resistant Adults with Unfavorable Health Outcomes

Background: Insulin resistance can be present in otherwise healthy, normal weight adults. Whether there are phenotype/sex-differences between normal weight insulin-resistant (NWIR) and normal weight insulin-sensitive (NWIS) Caucasians and whether there are differences in adverse health outcomes are unknown. Our goal was to define phenotypes and intermediate-term health outcomes of NWIR versus NWIS Caucasian adults. Methods: We analyzed data from 227 healthy volunteers body mass index 18 to <25.0 kg/m2 who underwent insulin clamp studies between January 1987 and January 2017 at Mayo Clinic to identify those in the top (NWIS, n = 56) and bottom (NWIR, n = 56) quartiles of insulin action. We compared the phenotypical characteristics and were able to collect medical records data for 80% of NWIS and 88% of NWIR to identify time to onset of hypertension, hyperglycemia, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and all cause death; the follow-up averaged 11 (4, 20) years. Results: Body fat was significantly greater and peak VO2 was significantly less in both NWIS than NWIR males and females. Only in females was abdominal subcutaneous fat by computed tomography significantly greater in NWIR than NWIS. In NWIR males high-density lipoprotein-cholesterol and fat free mass were significantly less, and fasting insulin was greater than NWIS males. For the entire NWIS population, Kaplan-Meier disease-free survival analysis showed longer times free of hypertension, hyperglycemia, and some cardiovascular diseases than for NWIR. Conclusions: There are sex-specific phenotypes of NWIR in Caucasian adults. NWIR may be associated with accelerated onset of some adverse medical outcomes.

Resting energy expenditure of a diverse group of South African men and women

BACKGROUND

In South Africa, overweight/obesity is a public health concern, disproportionally affecting Black females. A contributory role of a lower resting energy expenditure (REE) is suggested for African Americans. The present study assessed the REE of Black and White South African adults aiming to better understand the underlying predictors to overweight/obesity and transform this into locally appropriate recommendations.

METHODS

In 328 (63% female; 39% Black) healthy South African adults, REE was measured with indirect calorimetry and body composition with multifrequency bioelectrical impedance analysis. The REE was estimated with 30 sets of published equations. Black-White differences in REE, as measured and adjusted (analysis of covariance), were determined with quantile regression. Reliability/agreement of estimated (against measured) REE was determined with intra-class correlations (ICCs) and Bland-Altman analysis. A new equation was developed by median regression followed by preliminary validation.

RESULTS

Measured REE (adjusted for age along with fat-free mass [FFM], FFM index, FFM plus fat mass, FFM index plus fat mass index) in White subjects was significantly higher (p < 0.001) than in Black subjects for men and women alike, regardless of obesity class. None of the sets of estimation equations had good agreement with measured REE for Black, White, male and female subjects simultaneously. A new estimation equation, based on whole-body variables, had good reliability (ICC = 0.79) and agreement (mean difference: 27 kJ) and presents practical opportunities for groups at the local grass-roots level.

CONCLUSIONS

The REE in Black South African adults is lower than in White adults. Tailored REE equations may improve REE estimation of racially/ethnically diverse South African groups and contribute to improved obesity management.

Thirty Obesity Myths, Misunderstandings, and/or Oversimplifications: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) is intended to provide clinicians an overview of 30 common obesity myths, misunderstandings, and/or oversimplifications.

Methods

The scientific support for this CPS is based upon published citations, clinical perspectives of OMA authors, and peer review by the Obesity Medicine Association leadership.

Results

This CPS discusses 30 common obesity myths, misunderstandings, and/or oversimplifications, utilizing referenced scientific publications such as the integrative use of other published OMA CPSs to help explain the applicable physiology/pathophysiology.

Conclusions

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) on 30 common obesity myths, misunderstandings, and/or oversimplifications is one of a series of OMA CPSs designed to assist clinicians in the care of patients with the disease of obesity. Knowledge of the underlying science may assist the obesity medicine clinician improve the care of patients with obesity.

Metabolic changes in aging humans: current evidence and therapeutic strategies

Aging and metabolism are inextricably linked, and many age-related changes in body composition, including increased central adiposity and sarcopenia, have underpinnings in fundamental aging processes. These age-related changes are further exacerbated by a sedentary lifestyle and can be in part prevented by maintenance of activity with aging. Here we explore the age-related changes seen in individual metabolic tissues - adipose, muscle, and liver - as well as globally in older adults. We also discuss the available evidence for therapeutic interventions such as caloric restriction, resistance training, and senolytic and senomorphic drugs to maintain healthy metabolism with aging, focusing on data from human studies.

Basal metabolic rate and risk of multiple sclerosis: a Mendelian randomization study

To determine the relationship between basal metabolic rate (BMR) and multiple sclerosis (MS) susceptibility, we analyzed genome-wide association study (GWAS) summary statistics data from the International Multiple Sclerosis Genetics Consortium on a total of 115,803 participants of European descent, including 47,429 patients with MS and 68,374 controls. We selected 378 independent genetic variants strongly associated with BMR in a GWAS involving 454,874 participants as instrumental variables to examine a potential causal relationship between BMR and MS. A genetically predicted higher BMR was associated with a greater risk of MS (odds ratio [OR]: 1.283 per one standard deviation increase in BMR, 95% confidence interval [CI]: 1.108-1.486, P = 0.001). Moreover, we used the lasso method to eliminate heterogeneity (Q statistic = 384.58, P = 0.370). There was no pleiotropy in our study and no bias was found in the sensitivity analysis using the leave-one-out test. We provide novel evidence that a higher BMR is an independent causal risk factor in the development of MS. Further work is warranted to elucidate the potential mechanisms.

Obesity history, physical exam, laboratory, body composition, and energy expenditure: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) on History, Physical Exam, Body Composition and Energy Expenditure is intended to provide clinicians an overview of the clinical and diagnostic evaluation of patients with pre-obesity/obesity.

Methods

The scientific information for this CPS is based upon published scientific citations, clinical perspectives of OMA authors, and peer review by the Obesity Medicine Association leadership.

Results

This CPS outlines important components of medical, dietary, and physical activity history as well as physical exams, with a focus on specific aspects unique to managing patients with pre-obesity or obesity. Patients with pre-obesity/obesity benefit from the same preventive care and general laboratory testing as those without an increase in body fat. In addition, patients with pre-obesity/obesity may benefit from adiposity-specific diagnostic testing - both generally and individually - according to patient presentation and clinical judgment. Body composition testing, such as dual energy x-ray absorptiometry, bioelectrical impedance, and other measures, each have their own advantages and disadvantages. Some patients in clinical research, and perhaps even clinical practice, may benefit from an assessment of energy expenditure. This can be achieved by several methods including direct calorimetry, indirect calorimetry, doubly labeled water, or estimated by equations. Finally, a unifying theme regarding the etiology of pre-obesity/obesity and effectiveness of treatments of obesity centers on the role of biologic and behavior efficiencies and inefficiencies, with efficiencies more often associated with increases in fat mass and inefficiencies more often associated with decreases in fat mass.

Conclusion

The Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) on History, Physical Exam, Body Composition and Energy Expenditure is one of a series of OMA CPSs designed to assist clinicians in the care of patients with the disease of pre-obesity/obesity.

Changes in Body Composition, Basal Metabolic Rate, and Blood Albumin during the First Year following Laparoscopic Mini-Gastric Bypass

Bariatric surgery is currently the only method that can significantly and continuously reduce weight and improve obesity-related comorbidities in morbidly obese patients. Significant weight loss through bariatric surgery can lead to changes in body composition. This study shows the changes in body composition, basal metabolic rate (BMR), and serum albumin in obese people following bariatric surgery. The study included 880 patients who underwent laparoscopic mini-gastric bypass surgery (LMGBP) between 2016 and 2020. The body mass index (BMI), bioelectrical impedance analysis (BIA), age, gender, blood albumin, WC (waist circumference), HC (hip circumference), BMR, and blood albumin were recorded at 0, 3, 6, and 12 months, postoperatively. The reduction in serum albumin concentration was not consistent with weight loss. Bariatric surgery promotes the breakdown of both fat and lean mass on the arms, torso, and thighs. This size reduction usually aggravates the concomitant skin redundancy in these areas which is a challenge for the plastic surgery team. Interestingly, the rate of lean mass reduction of the arms is faster than that of the torso and thighs. Excessive loss of lean body mass will also lower BMR and lead to subsequent weight gain. Despite the faster loss of proteins and lean mass in somatic areas, internal organs and viscera lose fats faster than proteins. According to this study, visceral proteins are the latest proteins to be affected by weight loss. This finding shows a different metabolic response of viscera comparing to somatic areas.

[Energy exchange: how we can personalize obesity therapy]

Obesity is a consequence of chronic energy imbalance when energy intake constantly exceeds expenditure, which leads to excess white adipose tissue accumulation. Effective treatment of obesity requires accurate measure of calories intake and expenditure, as well as related behavior to understand how energy homeostasis is regulated and evaluate the effectiveness of the measures taken. The greatest interest is to study features of energy metabolism in various forms of obesity. It is necessary to create an evidence-based, personalized approach to diet therapy and to increase the effectiveness of weight loss measures. Modern studies have shown that the use of indirect calorimetry in obesity treatment programs leads to greater weight loss compared to traditional diet therapy planning based on calculated formulas.

Unique Metabolic Features of Adults Discordant for Indices of Insulin Resistance

PURPOSE

Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and Adipose Insulin Resistance index (ADIPO-IR) values are often concordant. In this study we evaluated whether there are groups discordant for HOMA-IR and ADIPOpalmitate-IR and, if so, what are their defining characteristics.

METHODS

The body composition, basal metabolic rate (BMR), fasting plasma lipids, insulin, glucose, and free fatty acid (FFA) palmitate concentrations data of 466 volunteers from previous research studies were abstracted and analyzed. The middle 2 population quartiles for HOMA-IR and Adipose Insulin Resistance index palmitate concentration (ADIPOpalmitate-IR) defined medium HOMA-IR and ADIPOpalmitate-IR (MH and MA), the top and bottom quartiles were defined as high/low HOMA (HH/LH), and high/low ADIPOpalmitate as HA/LA. Because ADIPOpalmitate-IR was significantly greater in women than in men, we established sex-specific quartiles for each index. We identified groups discordant for HOMA-IR and ADIPO-IR (HHMA, LHMA, MHHA, and MHLA).

RESULTS

Body fat and fasting triglycerides (TGs) were significantly greater with higher indices in the concordant groups (HHHA > MHMA > LHLA). MHHA differed from MHLA by visceral fat (P < .01) and fasting TGs (P < .05), whereas HHMA differed (P < .01) from LHMA by BMR. By multivariate regression, the group factor contributed to BMR (P < .01) and visceral fat (P < .05).

CONCLUSIONS

Adults discordant for HOMA-IR and ADIPO-IR have unique features including differences in visceral fat, TGs, and BMR. This suggests different forms of insulin resistance are present, which should be considered when studying insulin resistance in the future.

Low basal metabolic rate as a risk factor for development of insulin resistance and type 2 diabetes

INTRODUCTION

Identification of physiological factors influencing susceptibility to insulin resistance and type 2 diabetes (T2D) remains an important challenge for biology and medicine. Numerous studies reported energy expenditures as one of those components directly linked to T2D, with noticeable increase of basal metabolic rate (BMR) associated with the progression of insulin resistance. Conversely, the putative link between genetic, rather than phenotypic, determination of BMR and predisposition to development of T2D remains little studied. In particular, low BMR may constitute a considerable risk factor predisposing to development of T2D.

RESEARCH DESIGN AND METHODS

We analyzed the development of insulin resistance and T2D in 20-week-old male laboratory mice originating from three independent genetic line types. Two of those lines were subjected to divergent, non-replicated selection towards high or low body mass-corrected BMR. The third line type was non-selected and consisted of randomly bred animals serving as an outgroup (reference) to the selected line types. To induce insulin resistance, mice were fed for 8 weeks with a high fat diet; the T2D was induced by injection with a single dose of streptozotocin and further promotion with high fat diet. As markers for insulin resistance and T2D advancement, we followed the changes in body mass, fasting blood glucose, insulin level, lipid profile and mTOR expression.

RESULTS

We found BMR-associated differentiation in standard diabetic indexes between studied metabolic lines. In particular, mice with low BMR were characterized by faster body mass gain, blood glucose gain and deterioration in lipid profile. In contrast, high BMR mice were characterized by markedly higher expression of the mTOR, which may be associated with much slower development of T2D.

CONCLUSIONS

Our study suggests that genetically determined low BMR makeup involves metabolism-specific pathways increasing the risk of development of insulin resistance and T2D.

Metabolic Factors Determining the Susceptibility to Weight Gain: Current Evidence

PURPOSE OF REVIEW

There is substantial inter-individual variability in body weight change, which is not fully accounted by differences in daily energy intake and physical activity levels. The metabolic responses to short-term perturbations in energy intake can explain part of this variability by quantifying the degree of metabolic "thriftiness" that confers more susceptibility to weight gain and more resistance to weight loss. It is unclear which metabolic factors and pathways determine this human "thrifty" phenotype. This review will investigate and summarize emerging research in the field of energy metabolism and highlight important metabolic mechanisms implicated in body weight regulation in humans.

RECENT FINDINGS

Dysfunctional adipose tissue lipolysis, reduced brown adipose tissue activity, blunted fibroblast growth factor 21 secretion in response to low-protein hypercaloric diets, and impaired sympathetic nervous system activity might constitute important metabolic factors characterizing "thriftiness" and favoring weight gain in humans. The individual propensity to weight gain in the current obesogenic environment could be ascertained by measuring specific metabolic factors which might open up new pathways to prevent and treat human obesity.

Basal metabolic rate and Charlson Comorbidity Index are independent predictors of metabolic syndrome in patients with rheumatoid arthritis

OBJECTIVE

To prospectively analyze predictors of metabolic syndrome (MetS) in patients with rheumatoid arthritis (RA) during 2years of follow-up.

METHODS

We recruited 319 consecutive patients with RA who did not have MetS. MetS was defined in accordance with the modified National Cholesterol Education Program/Adult Treatment Panel III 2005 for Asian populations. Sociodemographic data, laboratory findings, disease activity data, and medication history were collected during face-to-face interviews at baseline and follow-up. Independent predictors of MetS were assessed by univariate and multivariate logistic regression analyses.

RESULTS

Of the 247 patients with RA who completed the 2-year follow-up, 37 (15.0%) developed MetS. At baseline, these patients were older and had higher body mass index, waist circumference, waist-hip ratio, skeletal muscle mass, body fat mass, percent body fat, and Charlson Comorbidity Index scores, as well as lower basal metabolic rate (BMR). Moreover, these patients with MetS took less hydroxychloroquine and more oral hypoglycemic agents; they also had lower European Quality of Life Health-state Questionnaire scores. After exclusion of variables associated with MetS composition, multivariate analysis identified BMR (odds ratio [OR]=0.205, 95% confidence interval [CI]: 0.078-0.541, P=0.001) and Charlson Comorbidity Index score (OR=2.191, 95% CI: 1.280-3.751, P=0.004) as significant predictors of MetS.

CONCLUSIONS

Our study showed that the annual incidence rate of MetS was 11.5% in patients with RA. Moreover, the development of MetS was associated with BMR and Charlson Comorbidity Index score at baseline.

Metabolic Determinants of Weight Gain in Humans

One of the fundamental challenges in obesity research is to identify subjects prone to weight gain so that obesity and its comorbidities can be promptly prevented or treated. The principles of thermodynamics as applied to human body energetics demonstrate that susceptibility to weight gain varies among individuals as a result of interindividual differences in energy expenditure and energy intake, two factors that counterbalance one another and determine daily energy balance and, ultimately, body weight change. This review focuses on the variability among individuals in human metabolism that determines weight change. Conflicting results have been reported about the role of interindividual differences in energy metabolism during energy balance in relation to future weight change. However, recent studies have shown that metabolic responses to acute, short-term dietary interventions that create energy imbalance, such as low-protein overfeeding or fasting for 24 hours, may reveal the underlying metabolic phenotype that determines the degree of resistance to diet-induced weight loss or the propensity to spontaneous weight gain over time. Metabolically "thrifty" individuals, characterized by a predilection for saving energy in settings of undernutrition and dietary protein restriction, display a minimal increase in plasma fibroblast growth factor 21 concentrations in response to a low-protein overfeeding diet and tend to gain more weight over time compared with metabolically "spendthrift" individuals. Similarly, interindividual variability in the causal relationship between energy expenditure and energy intake ("energy sensing") and in the metabolic response to cold exposure (e.g., brown adipose tissue activation) seems, to some extent, to be indicative of individual propensity to weight gain. Thus, an increased understanding and the clinical characterization of phenotypic differences in energy metabolism among individuals (metabolic profile) may lead to new strategies to prevent weight gain or improve weight-loss interventions by targeted therapies on the basis of metabolic phenotype and susceptibility to obesity in individual persons.

Metabolic adaptations during negative energy balance and their potential impact on appetite and food intake

This review examines the metabolic adaptations that occur in response to negative energy balance and their potential putative or functional impact on appetite and food intake. Sustained negative energy balance will result in weight loss, with body composition changes similar for different dietary interventions if total energy and protein intake are equated. During periods of underfeeding, compensatory metabolic and behavioural responses occur that attenuate the prescribed energy deficit. While losses of metabolically active tissue during energy deficit result in reduced energy expenditure, an additional down-regulation in expenditure has been noted that cannot be explained by changes in body tissue (e.g. adaptive thermogenesis). Sustained negative energy balance is also associated with an increase in orexigenic drive and changes in appetite-related peptides during weight loss that may act as cues for increased hunger and food intake. It has also been suggested that losses of fat-free mass (FFM) could also act as an orexigenic signal during weight loss, but more data are needed to support these findings and the signalling pathways linking FFM and energy intake remain unclear. Taken together, these metabolic and behavioural responses to weight loss point to a highly complex and dynamic energy balance system in which perturbations to individual components can cause co-ordinated and inter-related compensatory responses elsewhere. The strength of these compensatory responses is individually subtle, and early identification of this variability may help identify individuals that respond well or poorly to an intervention.

Methodology of a multispecialty outpatient Obesity Treatment Research Program

Despite the large number of U.S. adults who overweight or obese, few providers have ready access to comprehensive lifestyle interventions, the cornerstone of medical obesity management. Our goal was to establish a research infrastructure embedded in a comprehensive lifestyle intervention treatment for obesity. The Obesity Treatment Research Program (OTRP) is a multi-specialty project at Mayo Clinic in Rochester, Minnesota designed to provide a high intensity, year-long, comprehensive lifestyle obesity treatment. The program includes a nutritional intervention designed to reduce energy intake, a physical activity program and a cognitive behavioral approach to increase the likelihood of long-term adherence. The behavioral intervention template incorporated the Diabetes Prevention Program and the Look AHEAD trial materials. The OTRP is consistent with national recommendations for the management of overweight and obesity in adults, but with embedded features designed to identify patient characteristics that might help predict outcomes, assure long-term follow up and support various research initiatives. Our goal was to develop approaches to understand whether there are patient characteristics that predict treatment outcomes.

Energy expenditure in the etiology of human obesity: spendthrift and thrifty metabolic phenotypes and energy-sensing mechanisms

The pathogenesis of human obesity is the result of dysregulation of the reciprocal relationship between food intake and energy expenditure (EE), which influences daily energy balance and ultimately leads to weight gain. According to principles of energy homeostasis, a relatively lower EE in a setting of energy balance may lead to weight gain; however, results from different study groups are contradictory and indicate a complex interaction between EE and food intake which may differentially influence weight change in humans. Recently, studies evaluating the adaptive response of one component to perturbations of the other component of energy balance have revealed both the existence of differing metabolic phenotypes ("spendthrift" and "thrifty") resulting from overeating or underfeeding, as well as energy-sensing mechanisms linking EE to food intake, which might explain the propensity of an individual to weight gain. The purpose of this review is to debate the role that human EE plays on body weight regulation and to discuss the physiologic mechanisms linking EE and food intake. An increased understanding of the complex interplay between human metabolism and food consumption may provide insight into pathophysiologic mechanisms underlying weight gain, which may eventually lead to prevention and better treatment of human obesity.

A Genome-Wide Association Study Using a Custom Genotyping Array Identifies Variants in GPR158 Associated With Reduced Energy Expenditure in American Indians

Pima Indians living in Arizona have a high prevalence of obesity, and we have previously shown that a relatively lower energy expenditure (EE) predicts weight and fat mass gain in this population. EE is a familial trait (heritability = 0.52); therefore, in the current study, we aimed to identify genetic variants that affect EE and thereby influence BMI and body fatness in Pima Indians. Genotypic data from 491,265 variants were analyzed for association with resting metabolic rate (RMR) and 24-h EE assessed in a whole-room calorimeter in 507 and 419 Pima Indians, respectively. Variants associated with both measures of EE were analyzed for association with maximum BMI and percent body fat (PFAT) in 5,870 and 912 Pima Indians, respectively. rs11014566 nominally associated with both measures of EE and both measures of adiposity in Pima Indians, where the G allele (frequency: Pima Indians = 0.60, Europeans <0.01) associated with lower 24-h EE (β = -33 kcal/day per copy), lower RMR (β = -31 kcal/day), higher BMI (β = +0.6 kg/m2), and higher PFAT (β = +0.9%). However, the association of rs11014566 with BMI did not directionally replicate when assessed in other ethnic groups. rs11014566 tags rs144895904, which affected promoter function in an in vitro luciferase assay. These variants map to GPR158, which is highly expressed in the brain and interacts with two other genes (RGS7 and CACNA1B) known to affect obesity in knockout mice. Our results suggest that common ethnic-specific variation in GPR158 may influence EE; however, its role in weight gain remains controversial, as it either had no association with BMI or associated with BMI but in the opposite direction in other ethnic groups.

Family history of type 2 diabetes, abdominal adipocyte size and markers of the metabolic syndrome

Background/Objectives

A major risk factor of type 2 diabetes mellitus (T2DM) is a positive family history of diabetes. First degree relatives (FDR) of patients with T2DM are more insulin resistant and are reported to have larger abdominal subcutaneous adipocytes than adults without a family history. Our objectives were to assess whether a family history of T2DM is associated with larger abdominal adipocytes independent of age, sex, and abdominal subcutaneous fat and to assess whether FDR of T2DM is also independently related to femoral adipocyte size, as well as visceral fat and fasting plasma triglyceride (TG) concentrations.

Methods

We extracted adipocyte size, body composition, plasma TG and demographic data of non-diabetic research participants of previous studies conducted in our laboratory. We ascertained the family history of T2DM from the electronic medical records. Multivariate regression analysis was used to assess whether FDR of T2DM are more likely to have other risk factors after adjusting for known covariates.

Results

Of 604 participants, 148 were a FDR of T2DM. Although abdominal and femoral adipocyte size was greater in FDR of T2DM than those without a family history (0.74 ± 0.33 vs 0.63 ± 0.33 µg lipid/cell, P < 0.001; 0.81 ± 0.29 vs 0.72 ± 0.33 µg lipid/cell, P=0.01, respectively), this was confounded by FDR of T2DM being older, having greater BMI’s and percent body fat. A family history of T2DM was a significant predictor of abdominal adipocyte size after adjustment for age and body fat distribution parameters in females (total R²=0.5, p < 0.0001), but not in males. A family history of T2DM was not independently predictive of femoral adipocyte size, visceral fat area or TG.

Conclusions

FDR of T2DM females have larger abdominal, but not femoral, adipocytes, even after accounting for age and body fat distribution.

Expression of Metabolic Syndrome in Women with Severe Obesity

BACKGROUND

The prevalence of metabolic syndrome (MetS) generally rises with increasing adiposity, but tends to plateau at the highest levels of body mass index (BMI) with some individuals, even with severe obesity, expressing few or no components of MetS. We examined factors associated with the expression of MetS in severely obese women participating in a large observational study.

METHODS

Anthropometrics, including Heath equation-adjusted bioimpedance-determined fat-free mass (FFM) and fat mass (FM), lipids and related laboratory measurements, resting energy expenditure (REE), and respiratory quotient (RQ), were studied in 949 women with severe obesity.

RESULTS

Even though the mean BMI was 45.7 kg/m² and all participants met MetS criteria for increased waist circumference, 30% of subjects did not have MetS. Unadjusted FM (P = 0.0011), FFM (P < 0.0001), and REE (P < 0.0001) were greater in the women with MetS. Surprisingly, in multivariate logistic regression FFM was positively associated with MetS (P = 0.0002), while FM was not (P = 0.89). Moreover, FFM, not FM, was significantly associated with all five components of MetS except for triglyceride levels. REE and RQ were higher in those with MetS, and REE was strongly associated with multiple components of MetS.

CONCLUSIONS

In women with severe obesity, higher FFM and REE were paradoxically associated with increased rather than decreased risk of MetS, while FFM-adjusted FM was unrelated to MetS.

Impact of 4 weeks of interval training on resting metabolic rate, fitness, and health-related outcomes

Resting metabolic rate (RMR) and substrate oxidation (respiratory exchange ratio; RER) are important indicators of health. The effects of interval training on RMR have not been thoroughly investigated, which was the purpose of the present study. Thirty men and women (mean ± SD age and maximal oxygen uptake: 28.8 ± 7.6 years and 33.0 ± 8.3 mL·kg^-1·min^-1) completed 4 weeks of Wingate-based sprint interval training (SIT), repeated 1-min high-intensity intervals (HIIT), or served as controls. Before and after training, RMR, resting RER, maximal oxygen uptake, body composition, physical activity, and energy intake were recorded. Data were analyzed using a repeated-measures ANOVA. RMR increased in response to 4 weeks of SIT training (1789 ± 293 to 1855 ± 320 kcal·day^-1; p = 0.003) but did not increase after HIIT (1670 ± 324 to 1704 ± 329 kcal·day^-1; p = 0.06). While SIT increased RMR by ∼2× the magnitude of HIIT, the difference was not significant (p = 0.5). Fasting substrate oxidation and RER did not change (p > 0.05). Maximal oxygen uptake increased, and small changes were also observed in percent body fat and fat mass (p < 0.05 for all). In conclusion, SIT provided a time-efficient stimulus to increase RMR after 4 weeks in healthy adults. However, the clinical relevance of the changes observed in this study remains to be determined. Further studies should be conducted in obese individuals and those with diabetes or insulin resistance to examine if interval training (≥4 weeks) influences resting metabolic rate in magnitudes similar to that reported here.