Twenty-four-hour energy expenditure in Pima Indians with type 2 (non-insulin-dependent) diabetes mellitus

An E115A Missense Variant in CERS2 Is Associated With Increased Sleeping Energy Expenditure and Hepatic Insulin Resistance in American Indians

Genetic determinants of interindividual differences in energy expenditure (EE) are largely unknown. Sphingolipids, such as ceramides, have been implicated in the regulation of human EE via mitochondrial uncoupling. In this study, we investigated whether genetic variants within enzymes involved in sphingolipid synthesis and degradation affect EE and insulin-related traits in a cohort of American Indians informative for 24-h EE and glucose disposal rates during a hyperinsulinemic-euglycemic clamp. Association analysis of 10,084 genetic variants within 28 genes involved in sphingolipid pathways identified a missense variant (rs267738, A>C, E115A) in exon 4 of CERS2 that was associated with higher sleeping EE (116 kcal/day) and increased rates of endogenous glucose production during basal (5%) and insulin-stimulated (43%) conditions, both indicators of hepatic insulin resistance. The rs267738 variant did not affect ceramide synthesis in HepG2 cells but resulted in a 30% decrease in basal mitochondrial respiration. In conclusion, we provide evidence that the CERS2 rs267738 missense variant may influence hepatic glucose production and postabsorptive sleeping metabolic rate.

ARTICLE HIGHLIGHTS

Factors related to hypermetabolism in individuals with type 2 diabetes mellitus and non-alcoholic fatty liver disease

Considering the progressive prevalence and co-occurrence of type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD), as well as the current evidence suggesting the elevated levels of basal metabolic rate (BMR) among these individuals, the present study aimed to identify factors determining hypermetabolism in such subjects. This cross sectional study was conducted in 30 to 53-year-old individuals with concurrent T2DM and NAFLD (controlled attenuation parameter score ≥ 260 dB/m). Resting energy expenditure (REE) was determined by an indirect calorimetry device. Hypermetabolism was defined as an elevated measured REE > 110% of the predicted REE. The multivariate logistic regression test was used for detecting factors associated with hypermetabolism. Between September, 2017, and March, 2018, a total of 95 eligible participants (64.40% male) with both T2DM and NAFLD were included, while 32.63% of them were classified as hypermetabolic. Overall, the mean recruitment age ± standard deviation and median (interquartile range) body mass index were 44.69 ± 5.47 years and 30.20 (27.80-33.30) kg/m², respectively. Demographic, anthropometric and biochemical variables did not vary significantly across two groups except for total body water, low-density lipoprotein cholesterol and dipeptidyl peptidase 4 (DPP-4) inhibitors (p < 0.05). According to the results of multivariable logistic regression analyses, hypermetabolism had a positive association with adiponectin (odds ratio [OR] 1.167, 95% confidence interval [CI] 1.015-1.342, p = 0.030), physical activity (OR 1.134, 95% CI 1.002-1.284, p = 0.046), alanine transaminase (OR 1.062, 95% CI 1.006-1.122, p = 0.031) and diastolic blood pressure (OR 1.067, 95% CI 1.010-1.127, p = 0.021). However, fat free mass was inversely related to hypermetabolism (OR 0.935, 95% CI 0.883-0.991, p = 0.023). Adiponectin, alanine transaminase, physical activity, diastolic blood pressure and fat free mass were independently associated with hypermetabolism in subjects with NAFLD and T2DM.

The Association Between Low Carbohydrate Diet and Resting Metabolic Rate in Overweight and Obese Women: A Cross-Sectional Study

Resting metabolic rate (RMR) accounts for most daily energy expenditure. The low carbohydrate diet (LCD) attenuates decreases in RMR. This study aims to investigate the relationship between an LCD and RMR status among overweight and obese women. We enrolled 291 overweight and obese women in this cross-sectional study. Body mass index (BMI), fat mass, fat-free mass, visceral fat, and insulin level were assessed. RMR was measured using indirect calorimetry. LCD score (LCDS) was measured using a validated semi-quantitative food frequency questionnaire. Analysis of variance, independent sample t-test, and Multinomial logistic regression tests were used. Results showed no relationship between LCDS and deviation of normal RMR (DNR) even after adjust for confounders (increased [Inc.] RMR: odds ratio [OR], 0.97; 95% confidence interval [CI], 0.92–1.01; p = 0.20; decreased [Dec.] RMR: OR, 0.97; 95% CI, 0.94–1.00; p = 0.14). Some components of LCDS had no significant association with DNR, such as carbohydrate and Dec. RMR in adjusted model (OR, 1.62; 95% CI, 0.98–1.37; p = 0.08) and monounsaturated fatty acids and Dec. RMR in adjusted model (OR, 0.48; 95% CI, 0.21–1.10, p = 0.08). However, refined grains had a significant association with Inc. RMR in crude model (OR, 0.87; 95% CI, 0.77–0.99, p = 0.04). There is no association between LCDS and RMR status.

Energy Requirements for Older Patients with Type 2 Diabetes: A Narrative Review of the Current Findings and Future Tasks

Aging patients with type 2 diabetes (T2DM) have a high risk of frailty and disability. This narrative review summarizes the current findings and future tasks regarding the following issues: (1) the optimum body mass index (BMI) target for patients with T2DM, (2) energy expenditure and requirements of patients with T2DM, and (3) the significance of low-intensity physical activity (LPA) as a key determinant of total energy expenditure (TEE) among the older population. While weight reduction is associated with an improvement in glycemic control, an increased risk of mortality as well as disability related to unhealthy BMI must also be considered, especially in older patients. The optimum BMI range and energy requirements for maintaining a healthy BMI should be identified. Limited evidence has shown that the TEE measured using the doubly labeled water method in patients with T2DM did not differ from that of subjects without diabetes, suggesting that the energy requirement data of subjects without diabetes may be applicable to these populations. LPA is one of the determinants of variability in the energy requirements of older patients with T2DM, and the beneficial effects of increasing LPA on nutritional intake and frailty prevention should be investigated further.

Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?

BACKGROUND

Evaluation of the resting energy expenditure (REE) is essential to ensure an appropriate dietary prescription for patients with type 2 diabetes. The aim of this record was to evaluate the accuracy of predictive equations for REE estimation in patients with type 2 diabetes, considering indirect calorimetry (IC) as the reference method.

METHODS

A cross-sectional study was performed in outpatients with type 2 diabetes. Clinical, body composition by electrical bioimpedance and laboratory variables were evaluated. The REE was measured by IC (QUARK RMR, Cosmed, Rome, Italy) and estimated by eleven predictive equations. Data were analyzed using Bland-Altman plots, paired t-tests, and Pearson's correlation coefficients.

RESULTS

Sixty-two patients were evaluated [50% female; mean age 63.1 ± 5.2 years; diabetes duration of 11 (1-36) years, and mean A1C of 7.6 ± 1.2%]. There was a wide variation in the accuracy of REE values predicted by equations when compared to IC REE measurement. In all patients, Ikeda and Mifflin St-Jeor equations were that most underestimated REE. And, the equations that overestimated the REE were proposed by Dietary Reference Intakes and Huang. The most accurate equations were FAO/WHO/UNO in women (- 1.8% difference) and Oxford in men (- 1.3% difference).

CONCLUSION

In patients with type 2 diabetes, in the absence of IC, FAO/WHO/UNO and Oxford equations provide the best REE prediction in comparison to measured REE for women and men, respectively.

Basal metabolic rate in Brazilian patients with type 2 diabetes: comparison between measured and estimated values

OBJECTIVES

The aims of this study are to investigate which of the seven selected predictive equation for estimating basal metabolic rate (BMR) is the best alternative to indirect calorimetry (IC) and to evaluate the dietary energy intake in patients with type 2 diabetes.

SUBJECTS AND METHODS

Twenty-one patients with type 2 diabetes participated in this diagnostic test study. Clinical and laboratorial variables were evaluated as well as body composition by absorptiometry dual X-ray emission (DXA) and BMR measured by IC and estimated by prediction equations. Dietary intake was evaluated by a quantitative food frequency questionnaire. Data were analyzed using Bland-Altman plots, paired t-tests, and Pearson's correlation coefficients.

RESULTS

Patients were 62 (48-70) years old, have had diabetes for 8 (2-36) yeas, and 52.4% were females. The mean body composition comprised a fat-free mass of 49.8 ± 9.4 kg and a fat mass of 28.3 ± 7.2 kg. The energy intake was 2134.3 ± 730.2 kcal/day and the BMR by IC was 1745 ± 315 kcal/day. There was a wide variation in the accuracy of BMR values predicted by equations when compared to IC BMR measurement. Harris-Benedict, Oxford, FAO/WHO/UNO equations produced the smallest differences to IC, with a general bias of < 8%. The FAO/WHO/UNO equation provided the best BMR prediction in comparison to measured BMR.

CONCLUSION

In patients with type 2 diabetes, the equation of the FAO/WHO/UNO was the one closest to the BMR values as measured by IC.

Assessment of energy expenditure using doubly labeled water, physical activity by accelerometer and reported dietary intake in Japanese men with type 2 diabetes: A preliminary study

The aim of the present study was to determine the total energy expenditure, physical activity and dietary intake of men with type 2 diabetes mellitus and control participants without type 2 diabetes mellitus who were matched for age and body mass index. The participants in the present study were 12 well-controlled type 2 diabetes mellitus patients and 10 controls, aged 40-75 years, with a body mass index <30 kg/m2 . Total energy expenditure under free-living conditions was assessed using the doubly labeled water method, and physical activity was measured using a triaxial accelerometer. Dietary intake was assessed using a self-recorded food intake diary during the measurement period. Participants were instructed to record their dietary intake over 3 days, including 2 weekdays. Total energy expenditure was not significantly different between the groups (P = 0.153), nor were energy (P = 0.969) or macronutrient intakes. In conclusion, when age and body mass index are matched, total energy expenditure and self-reported energy intake are not significantly different between type 2 diabetes mellitus patients and healthy controls.

Screening for basal metabolic rate and visceral fat among postmenopausal osteoporosis with type 2 diabetes mellitus

BACKGROUND

In people with type 2 diabetes mellitus, there is an increase in basal metabolic rate (BMR) which is associated with level of glycaemic control. Women with postmenopausal osteoporosis have decreased BMR. The aim of the present study is to find the BMR using Meffin-St Jeor predictive equation in women with type 2 diabetes mellitus (T2DM) who have attained menopause with osteoporosis.

MATERIALS & METHODS

100 women who have attained menopause, who were diagnosed to have osteoporosis with type 2 diabetes mellitus were assessed for BMR using Meffin-St Jeor predictive equation. Detailed history of diabetes and menopause were obtained. Blood glucose value was measured using standard glucometers. Body composition for visceral fat (VF) was measured using bioelectrical impedance analysis. Level of physical activity of the participants was measured using global physical activity questionnaire (GPAQ).

RESULTS

The median BMR of the participants was 1.075 (714, 1483.25). Statistically significant correlation was found between BMR and GPAQ (r_s = 0.731), BMR and VF (r_s = 0.678). However BMR was not correlated with FBS (r_s = 0.083) duration of diabetes (r_s = -0.046).

CONCLUSION

There is a decrease in BMR in women with T2DM with postmenopausal osteoporosis. BMR was significantly correlated with level of physical activity and visceral fat.

Resting Energy Expenditure, Insulin Resistance and UCP1 Expression in Human Subcutaneous and Visceral Adipose Tissue of Patients With Obesity

Determinants of resting energy expenditure (REE) in humans are still under investigation, especially the association with insulin resistance. Brown adipose tissue (AT) regulates energy expenditure through the activity of the uncoupling protein 1 (UCP1). White AT browning is the process by which some adipocytes within AT depots acquire properties of brown adipocytes ("brite" adipocytes) and it correlates with metabolic improvement. We analyzed determinants of REE in patients with obesity and assessed UCP1 expression as a "brite" marker in abdominal subcutaneous AT (SAT) and visceral omental AT (VAT). Clinical data, REE, free fat mass (FFM), and fat mass (FM) were determined in 209 patients with obesity. UCP1, PPARG coactivator 1 alpha (PPARGC1A), transcription factor A, mitochondrial (TFAM), T-box transcription factor 1 (TBX1), and solute carrier family 27 member 1 (SLC27A1) expression was assayed in SAT and VAT samples, obtained during sleeve gastrectomy from 62 patients with obesity. REE and body composition data were also available for a subgroup of 35 of whom. In 209 patients with obesity a multiple regression model was computed with REE as the dependent variable and sex, waist, FFM, FM, homeostasis model assessment-insulin resistance (HOMA), interleukin-6 and High Density Lipoprotein-cholesterol as the independent variables. Only FFM, FM and HOMA were independently correlated with REE (r = 0.787, AdjRsqr = 0.602). In each patient VAT displayed a higher UCP1, PPARGC1A, TFAM, TBX1, and SLC27A1 expression than SAT and UCP1 expression in VAT (UCP1-VAT) correlated with Body Mass Index (BMI) (r = 0.287, p < 0.05). Introducing UCP1-VAT in the multivariate model, we showed that FFM, HOMA, interleukin-6, High Density Lipoprotein-cholesterol, and UCP1-VAT were independent factors correlated with REE (r = 0.736, AdjRsqr = 0.612). We confirmed that REE correlates with FFM, FM and HOMA in a large cohort of patients. Our results clearly showed that UCP1-VAT expression was significantly increased in severe human obesity (BMI > 50 kg/m²) and that it behaved as an independent predictor of REE. Lastly, we suggest that an increased REE and browning in metabolically complicated severe obesity could represent an effort to counteract further weight gain.

Correlation between basal metabolic rate, visceral fat and insulin resistance among type 2 diabetes mellitus with peripheral neuropathy

BACKGROUND

Basal Metabolic Rate (BMR) means the amount of energy utilized by body in physical and psychological resting rate, after a night sleep, awake without any previous physical activity post meal (10 h after last meal) & neutral environment. In people with type 2 diabetes mellitus (T2DM) there is an increase in BMR which is said to be associated with the level of glycaemic control. So, the objective of the study was to find out the correlation between BMR, Insulin resistance and Visceral fat in T2DM with peripheral neuropathy.

MATERIALS & METHODS

A total of 50 participants with T2DM with peripheral neuropathy were included. Age group of 30-75 years were selected for the study. Participants with a known history of neurological disease, locomotor disability, and pregnancy were excluded from the study. Demographic details of the participants like duration of diabetes mellitus, age, Fasting Blood Glucose, Fasting Insulin, HOMA-IR, Glycated Haemoglobin (HBA1c), Neuropathy and Blood pressure values were noted. We measured Basal Metabolic Rate (BMR) by using Mifflin-St Jeor predictive equation in T2DM with peripheral neuropathy.

RESULTS

The mean age of the participants is 60.16 ± 10.62. The mean duration of T2DM 13.44 ± 11.92. In the present study we found a statistical significant correlation between BMR and HOMA IR (r = 0.913*; p = 0.000), BMR & Fasting blood sugar (FBS) (r = 0.281*; p = 0.048), BMR and Visceral fat (VF) (r = 0.332*; p = 0.018).

CONCLUSION

Basal metabolic rate is correlated to Homa-IR, visceral fat, fasting blood sugar and musculoskeletal mass among T2DM with peripheral neuropathy.

Total energy expenditure is comparable between patients with and without diabetes mellitus: Clinical Evaluation of Energy Requirements in Patients with Diabetes Mellitus (CLEVER-DM) Study

OBJECTIVE

Assessment of total energy expenditure (TEE) is essential for appropriate recommendations regarding dietary intake and physical activity in patients with and without diabetes mellitus (DM). However, few reports have focused on TEE in patients with DM, particularly in Asian countries. Therefore, we evaluated TEE in Japanese patients with DM using the doubly labeled water (DLW) method and physical activity level (PAL).

RESEARCH DESIGN AND METHODS

In this cross-sectional observational study, we evaluated 52 patients with type 2 DM and 15 patients without DM. Free-living TEE was measured over 12-16 days by the DLW method, and PAL was calculated as TEE divided by the basal metabolic rate (BMR) as assessed by indirect calorimetry. The equivalence margin was defined as 5 kcal/kg/day.

RESULTS

The numbers of patients with DM treated with insulin, oral antidiabetic drugs, and diet were 18 (34.6%), 20 (38.5%), and 14 (26.9%), respectively. The mean±SD level of glycated hemoglobin was 6.9%±0.8% and 5.5%±0.3% in the DM and non-DM group, respectively (p<0.001). The mean body mass index was 23.3±3.0 and 22.7±2.1 kg/m² in the DM and non-DM group, respectively. The mean TEE per kilogram body weight adjusted for sex and age was 36.5 kcal/kg/day and 37.5 kcal/kg/day in the DM and non-DM group, respectively, with no significant difference (mean difference, -1.0 kcal/kg/day; 95% CI -4.2 to 2.3 kcal/kg/day). The BMR tended to be higher in the DM than in the non-DM group (mean difference, 33 kcal/day; 95% CI, -15 to 80 kcal/day). The mean PAL adjusted for sex and age was 1.71 and 1.81 in the DM and non-DM group, respectively, without a significant difference (mean difference, -0.10; 95% CI -0.21 to 0.01).

CONCLUSION

TEE was comparable between Japanese patients with and without DM.

TRIAL REGISTRATION NUMBER

UMIN000023051.

Mealtime dosing of a rapid-acting insulin analog reduces glucose variability and suppresses daytime cardiac sympathetic activity: a randomized controlled study in hospitalized patients with type 2 diabetes

OBJECTIVE

Glucose variability induces endothelial dysfunction and cardiac autonomic nerve abnormality. Here we compared the effects of mealtime insulin aspart and bedtime insulin detemir on glucose variability, endothelial function, and cardiac autonomic nerve activity among Japanese patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Forty hospitalized patients received either mealtime insulin aspart or bedtime insulin detemir treatment for 2 weeks. We assessed glucose variability indices, including M-value, SD of blood glucose level, and mean blood glucose (MBG) level. Flow-mediated dilation (FMD) of the brachial artery was measured as an index of endothelial function. Low-frequency power, high-frequency power, and the low-frequency to high-frequency power ratio (LF:HF ratio) derived via heart rate variability analysis using a Holter ECG were employed as indices of cardiac autonomic nerve function.

RESULTS

M-values and MBG levels showed a considerably greater decrease in the insulin aspart group than in the insulin detemir group (p=0.006  vs p=0.001); no change in FMD was observed in either group. Daytime LF:HF ratio significantly decreased in the insulin aspart group but not in the insulin detemir group. Total insulin dose at endpoint in the insulin aspart group was significantly higher than that in the insulin detemir group (p<0.001).

CONCLUSIONS

Mealtime insulin aspart reduced glucose variability to a greater extent than bedtime insulin detemir in patients with type 2 diabetes. Despite the need for higher insulin doses, insulin aspart decreased daytime cardiac sympathetic nerve activity. These properties may subsequently help reduce cardiovascular risks.

TRIAL REGISTRATION NUMBER

UMIN000008369.

Associations Between C-Reactive Protein, Insulin Sensitivity, and Resting Metabolic Rate in Adults: A Mediator Analysis

Objective: Long-term positive energy balance promotes the development of obesity, a main risk factor for type 2 diabetes mellitus (T2DM). While an association between increased resting metabolic rate (RMR) and insulin sensitivity (IS) was shown previously, the underlying mechanisms remain unclear. Aim of the mediator analysis was to investigate the role of inflammation within the association between RMR and IS. Methods: Anthropometric, clinical, and lifestyle data were collected according to standard operating procedures. RMR was measured using indirect calorimetry. Homeostasis model assessment for insulin resistance (HOMA-IR) was used as an IS parameter and C-reactive protein (CRP) was measured to represent the inflammatory status. Statistical analyses were performed using SPSS. Results: The analysis included 782 adults (517 females) with a mean age of 32.4 ± 12.0 years and a mean body mass index (BMI) of 24.6 ± 5.2 kg/m². Regression analysis indicated a significant evidence for associations between RMR and HOMA-IR (ß = 39.3 ± 7.3 kcal/d; p ≤ 0.001) and CRP and HOMA-IR (ß = 0.5 ± 0.1; p ≤ 0.001) after adjustment for fat-free mass, sex, age, and study site. Results of the mediator analysis did not support the hypothesis that CRP is a mediator for the association between RMR and HOMA-IR. These results did not change after participant stratification according to sex or BMI. Conclusion: A significant evidence for an association between RMR and IS was shown in a large cohort. However, the inflammatory status, determined via CRP levels, was not a mediator within this association.

Parental history of type 2 diabetes is associated with lower resting energy expenditure in normoglycemic subjects

OBJECTIVE

Resting energy expenditure (REE) is linked to obesity, insulin resistance and type 2 diabetes (T2DM). REE and T2DM are inherited traits. Therefore, we investigated the effect of parental T2DM on REE in normoglycemic subjects.

METHODS

Eighty-seven subjects with parental T2DM and 83 subjects without parental T2DM were matched in age, gender, race, BMI, weight and waist circumference. Subjects underwent a 75 g oral glucose tolerance test; REE was determined by indirect calorimetry and body composition was assessed by dual energy X-ray absorptiometry. Statistical analysis was performed using Student's t-test, analysis of variance and regression analysis.

RESULTS

The mean age was 38.8±11.3 years, 57% were females and 53% were African-Americans. The mean BMI was 28.5±6.1 kg/m², waist circumference 91.8±15.1 cm, weight 83.9±20.3 kg, fat mass 31.0%±10.0%, mean fat-free mass (FFM) 54.4±12.9 kg. REE was significantly lower in subjects with parental diabetes, normalized REE 1364.4±263.4Kcal/day vs 1489.4±323.2 Kcal/day, p=0.006 and 29.2±5.3Kcal/kg FFM/day vs 31.9±6.0 Kcal/kg FFM/day, p=0.002. African-Americans had a lower REE compared with Caucasians 28.6±5.4Kcal/kg FFM/day vs 32.6±5.5 Kcal/kg FFM/day, p<0.0001. In a multiple regression model, ethnicity (p<0.0001), parental history of T2DM (p=0.006) and FFM (p=0.021) were independent predictors of REE.

CONCLUSION

Compared with subjects without parental diabetes, offspring with parental T2DM had lower REE, which was more pronounced in African-Americans. This metabolic alteration could increase the risk of obesity, insulin resistance and dysglycemia.

Postprandial Increase in Energy Expenditure Correlates with Body Weight Reduction in Patients with Type 2 Diabetes Receiving Diet Therapy

AIM

The clinical significance of energy expenditure (EE) in the treatment of type 2 diabetes has not been fully elucidated. Here we analyzed the relationships between EE and clinical measurements in patients with type 2 diabetes receiving diet therapy.

METHODS

A total of 100 patients (34 women and 66 men) with type 2 diabetes admitted to our hospital for glycemic control were enrolled. The participants received an energy-restricted diet during their hospitalization (median, 15 days). EE was measured in the fasted (FEE) and postprandial (PPEE) states using indirect calorimetry. The postprandial increment of EE (ΔEE) was calculated from the FEE and PPEE (ΔEE=PPEE-FEE).

RESULTS

FEE, PPEE, and ΔEE were 0.997±0.203, 1.104±0.213, and 0.107±0.134 kcal/min, respectively. Body weight decreased from 68.7±16.6 to 66.8±16.0 kg (p＜0.0001) during hospitalization. FEE and PPEE showed positive correlations with height, body weight, body mass index, and abdominal circumference at admission, but ΔEE was not correlated with these anthropometric measurements. On the other hand, ΔEE was inversely correlated with the body weight change. The association between ΔEE and the body weight change was independent of age, sex, and HbA1c.

CONCLUSIONS

Postprandial increase in energy expenditure may be a determinant of individual differences in weight reduction in patients with type 2 diabetes on diet therapy. As a simple surrogate for diet-induced thermogenesis, ΔEE may serve as a useful predictive marker for the efficacy of diet therapy.

Energy Expenditure in People with Diabetes Mellitus: A Review

Physical activity (PA) is an important non-therapeutic tool in primary prevention and treatment of diabetes mellitus (DM). To improve activity-based health management, patients need to quantify activity-related energy expenditure and the other components of total daily energy expenditure. This review explores differences between the components of total energy expenditure in patients with DM and healthy people and presents various tools for assessing the energy expenditure in subjects with DM. From this review, it appears that patients with uncontrolled DM have a higher basal energy expenditure (BEE) than healthy people which must be considered in the establishment of new BEE estimate equations. Moreover, studies showed a lower activity energy expenditure in patients with DM than in healthy ones. This difference may be partially explained by patient with DMs poor compliance with exercise recommendations and their greater participation in lower intensity activities. These specificities of PA need to be taken into account in the development of adapted tools to assess activity energy expenditure and daily energy expenditure in people with DM. Few estimation tools are tested in subjects with DM and this results in a lack of accuracy especially for their particular patterns of activity. Thus, future studies should examine sensors coupling different technologies or method that is specifically designed to accurately assess energy expenditure in patients with diabetes in daily life.

Relationship between myostatin and irisin in type 2 diabetes mellitus: a compensatory mechanism to an unfavourable metabolic state?

Myostatin and irisin are two myokines related to energy metabolism, acting on skeletal muscle and recently suggested on adipose tissue in mice. However, the exact role of these myokines in humans has not been fully established. Our aim was to evaluate the relationship between serum levels of myostatin and irisin in type 2 diabetes mellitus patients and non-diabetic controls and to explore its links with metabolic parameters. Case-control study including 73 type 2 diabetes mellitus patients and 55 non-diabetic subjects as control group. Circulating myostatin and irisin levels were measured by enzyme-linked immunosorbent assays. Type 2 diabetes mellitus patients showed significantly lower myostatin levels (p = 0.001) and higher irisin levels (p = 0.036) than controls. An inverse relationship was observed between myostatin and irisin levels (p = 0.002). Moreover, in type 2 diabetes mellitus patients, after adjusting by confounder factors, myostatin was negatively related to fasting plasma glucose (p = 0.005) and to triglyceride levels (p = 0.028) while irisin showed a positive association with these variables (p = 0.017 and p = 0.006 respectively). A linear regression analysis showed that irisin and fasting plasma glucose levels were independently associated to myostatin levels and that myostatin and triglyceride levels were independently associated to irisin concentrations in type 2 diabetes mellitus patients. Our results suggest that serum levels of myostatin and irisin are related in patients with type 2 diabetes. Triglyceride and glucose levels could modulate myostatin and irisin concentrations as a compensatory mechanism to improve the metabolic state in these patients although further studies are needed to elucidate whether the action of these myokines represents an adaptative response.

Increased BMR in overweight and obese patients with type 2 diabetes may result from an increased fat-free mass

The study aimed to determine the relationships between the basal metabolic rate (BMR) and body composition of overweight and obese Chinese adults with type 2 diabetes mellitus (DM). This cross-sectional clinical study enrolled 193 Chinese adults with type 2 DM who were overweight (24 kg/m(2)=BMI≤28 kg/m(2), n=99), or obese (BMI ≥28 kg/m(2), n=94). Ninety-seven adults with normal BMIs, including 50 DM patients and 47 healthy adults, were recruited as a control group. BMR was measured by indirect calorimetry; predicted BMR was calculated according to the Schofield equation; and the relationships between BMR, body composition, and biochemical results were determined by the Pearson correlation. The results showed that obese DM patients had significantly higher BMRs than both overweight patients (P<0.05) and patients with normal BMI did (P<0.05). The measured BMR was significantly lower than the predicted BMR (P<0.05) in all groups. Obese and overweight DM patients had significantly greater weight, waist circumference, hip circumference, BMI, body surface area, body fat percentage, fat mass, and fat-free mass than patients with normal BMI. Except for waist circumference, these body composition measurements were significantly increased in obese DM patients when compared with those in overweight DM patients (P<0.05). Fat-free mass was closely correlated with BMR in both DM patients (r=0.874, P<0.01) and in healthy controls (r=0.902, P<0.01). It was concluded that overweight and obese Chinese adults with type 2 DM had increased BMRs compared with normal-weight controls, which may result from the difference in fat-free mass.

Fasting hyperglycemia predicts lower rates of weight gain by increased energy expenditure and fat oxidation rate

CONTEXT

Increased adiposity and insulin resistance are associated with hyperglycemia and previous studies have reported that higher glucoses are associated with lower rates of weight gain. One possible mechanism is via increased energy expenditure (EE).

OBJECTIVE

To assess the relationships between changes in EE during spontaneous weight gain and concomitant changes in glucose levels.

DESIGN AND PARTICIPANTS

Body composition, metabolic, and glycemic data were available from nondiabetic Native Americans who underwent two measurements of 24-h EE during eucaloric feeding in a metabolic chamber (N = 144; time between measurements: 5.0 ± 3.3 years) or resting EE by ventilated hood system during the euglycemic-hyperinsulinemic clamp (N = 261; 4.5 ± 3.2 years). Long-term follow-up data (8.3 ± 4.3 years) for weight and body composition were available in 131 and 122 subjects, respectively.

MAIN OUTCOME MEASURES

Twenty four hour EE and respiratory quotient (RQ), resting (RMR), and sleeping (SMR) metabolic rates, glucose, and insulin levels, basal glucose output (BGO).

RESULTS

Weight gain-associated increase in fasting plasma glucose (FPG) levels was accompanied with decreased 24-h RQ (partial R = -0.24, P = .002) and increased 24-h EE, RMR, SMR, and fat oxidation after accounting for changes in body composition (partial R: 0.12 to 0.19, all P ≤ .05). Upon weight gain, BGO tended to increase (P = .07), while insulin infusion induced a decrease in EE (P = .04). Higher baseline FPG predicted lower rates of future weight gain (partial R = -0.18, P = .04).

CONCLUSIONS

Higher FPG after weight gain was associated with greater-than-expected increase in EE. The rise in BGO and the insulin-induced EE suppression at follow-up indicate that increased hepatic gluconeogenesis may be an important mediator of EE changes associated with weight gain.

Mathematical model for the contribution of individual organs to non-zero y-intercepts in single and multi-compartment linear models of whole-body energy expenditure

Mathematical models for the dependence of energy expenditure (EE) on body mass and composition are essential tools in metabolic phenotyping. EE scales over broad ranges of body mass as a non-linear allometric function. When considered within restricted ranges of body mass, however, allometric EE curves exhibit ‘local linearity.’ Indeed, modern EE analysis makes extensive use of linear models. Such models typically involve one or two body mass compartments (e.g., fat free mass and fat mass). Importantly, linear EE models typically involve a non-zero (usually positive) y-intercept term of uncertain origin, a recurring theme in discussions of EE analysis and a source of confounding in traditional ratio-based EE normalization. Emerging linear model approaches quantify whole-body resting EE (REE) in terms of individual organ masses (e.g., liver, kidneys, heart, brain). Proponents of individual organ REE modeling hypothesize that multi-organ linear models may eliminate non-zero y-intercepts. This could have advantages in adjusting REE for body mass and composition. Studies reveal that individual organ REE is an allometric function of total body mass. I exploit first-order Taylor linearization of individual organ REEs to model the manner in which individual organs contribute to whole-body REE and to the non-zero y-intercept in linear REE models. The model predicts that REE analysis at the individual organ-tissue level will not eliminate intercept terms. I demonstrate that the parameters of a linear EE equation can be transformed into the parameters of the underlying ‘latent’ allometric equation. This permits estimates of the allometric scaling of EE in a diverse variety of physiological states that are not represented in the allometric EE literature but are well represented by published linear EE analyses.

Skeletal muscle and organ masses differ in overweight adults with type 2 diabetes

Whether lean body mass (LBM) composition, especially skeletal muscle and abdominal organs, differs in adults with type 2 diabetes (T2DM) compared with nondiabetic healthy controls has not been investigated. A subset of African-American and Caucasian participants with T2DM from the Look AHEAD (Action for Health in Diabetes) trial had body composition assessed and compared with a sample of healthy controls. Skeletal muscle mass (SMM), liver, kidneys, and spleen mass were quantified using a contiguous slice magnetic resonance imaging (MRI) protocol. Cardiac mass was quantified by either a cardiac gated MRI protocol or by echocardiography. MRI volumes were converted to mass using assumed densities. Dual-energy X-ray absorptiometry assessed LBM. Using general linear models adjusted for height, weight, sex, age, race, and interactions of diabetes status with race or sex, persons with T2DM (n = 95) had less LBM (49.7 vs. 51.6 kg) and SMM (24.1 vs. 25.4 kg) and larger kidneys (0.40 vs. 0.36 kg) than controls (n = 76) (all P < 0.01). Caucasians with T2DM had larger livers (1.90 vs. 1.60 kg, P < 0.0001) and spleens (0.29 vs. 0.22 kg, P < 0.01), and T2DM men had less cardiac mass than controls (0.25 vs. 0.30 kg, P < 0.001). In this sample, T2DM is characterized by less relative skeletal muscle and cardiac mass in conjunction with larger kidneys, liver, and spleen. Further investigation is needed to establish the causes and metabolic consequences of these race- and sex-specific organ mass differences in T2DM.

On the evolutionary origins of obesity: a new hypothesis

Obesity is an escalating threat of pandemic proportions, currently affecting billions of people worldwide and exerting a devastating socioeconomic influence in industrialized countries. Despite intensive efforts to curtail obesity, results have proved disappointing. Although it is well recognized that obesity is a result of gene-environment interactions and that predisposition to obesity lies predominantly in our evolutionary past, there is much debate as to the precise nature of how our evolutionary past contributed to obesity. The "thrifty genotype" hypothesis suggests that obesity in industrialized countries is a throwback to our ancestors having undergone positive selection for genes that favored energy storage as a consequence of the cyclical episodes of famine and surplus after the advent of farming 10 000 years ago. Conversely, the "drifty genotype" hypothesis contends that the prevalence of thrifty genes is not a result of positive selection for energy-storage genes but attributable to genetic drift resulting from the removal of predative selection pressures. Both theories, however, assume that selection pressures the ancestors of modern humans living in western societies faced were the same. Moreover, neither theory adequately explains the impact of globalization and changing population demographics on the genetic basis for obesity in developed countries, despite clear evidence for ethnic variation in obesity susceptibility and related metabolic disorders. In this article, we propose that the modern obesity pandemic in industrialized countries is a result of the differential exposure of the ancestors of modern humans to environmental factors that began when modern humans left Africa around 70 000 years ago and migrated through the globe, reaching the Americas around 20 000 years ago. This article serves to elucidate how an understanding of ethnic differences in genetic susceptibility to obesity and the metabolic syndrome, in the context of historic human population redistribution, could be used in the treatment of obesity in industrialized countries.

Resting metabolic rate in obese diabetic and obese non-diabetic subjects and its relation to glycaemic control

Background

Both obesity and type II diabetes mellitus are associated with insulin resistance and abnormal metabolic reactions. This study was conducted to evaluate resting metabolic rate in obese diabetic patients and to assess its relation to glycaemic control.

Results

This is a case control study conducted in Gabir AbuEliz centre in Khartoum, Sudan. A random sample of 40 obese diabetic patients (cases) and 40 obese non-diabetic subjects (controls) were interviewed and examined clinically to exclude presence of acute or chronic medical illness. Haemoglobin A1c was measured for each participant using the “NycoCard Haemoglobin A1c test” (Axis -Shield/ Norway). Fasting blood sugar was measured using one touch(R) glucometer (LifeScan Canada Ltd). The PowerLab 8/35 with a gas analyzer (AD Instruments, Castle Hill Australia) was used for measurement of VO2, VCO2 and Respiratory exchange ratio (RER). Resting metabolic rate was calculated using the Weir equation. VO2 (mean+/-SD) ml/min was significantly higher among cases (209.9+/-42.7) compared to the controls (192.4+/-28.1), (P = 0.034). Similarly, VCO2 (mean+/-SD) ml/min was higher among cases (191.4+/-35.0) than controls (178.3+/-22.5), (P = 0.05). Resting metabolic rate “RMR” (mean+/-SD) kcal/day was higher in obese diabetic patients (1480.7 +/- 274.2) than obese non-diabetic subjects (1362.4+/- 184.8), (P = 0.027). Participants with high glycated haemoglobin had higher RMR than those with normal glycated haemoglobin (P = 0.016).

Conclusion

It is concluded that resting metabolic rate is significantly higher in obese diabetic patients compared to obese non-diabetics, especially in those with poor glycaemic control.

Dietary fiber, carbohydrate quality and quantity, and mortality risk of individuals with diabetes mellitus

BACKGROUND

Dietary fiber, carbohydrate quality and quantity are associated with mortality risk in the general population. Whether this is also the case among diabetes patients is unknown.

OBJECTIVE

To assess the associations of dietary fiber, glycemic load, glycemic index, carbohydrate, sugar, and starch intake with mortality risk in individuals with diabetes.

METHODS

This study was a prospective cohort study among 6,192 individuals with confirmed diabetes mellitus (mean age of 57.4 years, and median diabetes duration of 4.4 years at baseline) from the European Prospective Investigation into Cancer and Nutrition (EPIC). Dietary intake was assessed at baseline (1992-2000) with validated dietary questionnaires. Cox proportional hazards analysis was performed to estimate hazard ratios (HRs) for all-cause and cardiovascular mortality, while adjusting for CVD-related, diabetes-related, and nutritional factors.

RESULTS

During a median follow-up of 9.2 y, 791 deaths were recorded, 306 due to CVD. Dietary fiber was inversely associated with all-cause mortality risk (adjusted HR per SD increase, 0.83 [95% CI, 0.75-0.91]) and CVD mortality risk (0.76[0.64-0.89]). No significant associations were observed for glycemic load, glycemic index, carbohydrate, sugar, or starch. Glycemic load (1.42[1.07-1.88]), carbohydrate (1.67[1.18-2.37]) and sugar intake (1.53[1.12-2.09]) were associated with an increased total mortality risk among normal weight individuals (BMI≤25 kg/m(2); 22% of study population) but not among overweight individuals (P interaction≤0.04). These associations became stronger after exclusion of energy misreporters.

CONCLUSIONS

High fiber intake was associated with a decreased mortality risk. High glycemic load, carbohydrate and sugar intake were associated with an increased mortality risk in normal weight individuals with diabetes.

Genes and biochemical pathways in human skeletal muscle affecting resting energy expenditure and fuel partitioning

Genes influencing resting energy expenditure (REE) and respiratory quotient (RQ) represent candidate genes for obesity and the metabolic syndrome because of the involvement of these traits in energy balance and substrate oxidation. We aim to explore the molecular basis for individual variation in REE and fuel partitioning as reflected by RQ. We performed microarray studies in human vastus lateralis muscle biopsies from 40 healthy subjects with measured REE and RQ values. We identified 2,392 and 1,115 genes significantly correlated with REE and RQ, respectively. Genes correlated with REE and RQ encompass a broad array of functions, including carbohydrate and lipid metabolism, gene expression, mitochondrial processes, and membrane transport. Microarray pathway analysis revealed that REE was positively correlated with upregulation of G protein-coupled receptor signaling (meet criteria/total genes: 65 of 283) involved in autonomic nervous system functions, including those receptors mediating adrenergic, dopamine, γ-aminobutyric acid (GABA), neuropeptide Y (NPY), and serotonin action (meet criteria/total genes: 46 of 176). Reduced REE was associated with an increase in genes participating in ubiquitin-proteasome-dependent proteolytic pathways (58 of 232). Serine-type peptidase activity (9 of 76) was positively correlated with RQ, while genes involved in the protein phosphatase type 2A complex (4 of 9), mitochondrial function and cellular respiration (38 of 315), and unfolded protein binding (19 of 97) were associated with reduced RQ values and a preference for lipid fuel metabolism. Individual variations in whole body REE and RQ are regulated by differential expressions of specific genes and pathways intrinsic to skeletal muscle.

No evidence of mass dependency of specific organ metabolic rate in healthy humans

BACKGROUND

In humans, resting energy expenditure (REE) can be calculated from organ and tissue masses using constant specific organ metabolic rates. However, interspecies data suggest allometric relations between body mass and organ metabolic rate with higher specific metabolic rates in mammals with a smaller body mass.

OBJECTIVE

The objective was to compare the accuracy of REE prediction with the use of either constant or body mass-dependent specific organ metabolic rates.

DESIGN

Healthy subjects (79 women, 75 men) within the normal range of fat mass (FM) expected for a healthy body mass index and aged 18-78 y were stratified into tertiles of body mass. Fifty subjects were grouped as tertile 1 (<66.3 kg), 52 as tertile 2 (> or =66.3 to < or =77.2 kg), and 52 as tertile 3 (>77.2 kg). Magnetic resonance imaging was used to assess the volume of 4 internal organs (brain, heart, liver, and kidneys). REE was measured by indirect calorimetry (REE(m)) and compared with REE calculated from previously published constant (REE(c1)) and body mass-dependent organ metabolic rates (REE(c2)).

RESULTS

REE(m) increased significantly with weight tertile (tertile 1: 5536 +/- 529 kJ/d; tertile 2: 6389 +/- 672 kJ/d; tertile 3: 7467 +/- 745 kJ/d; P < 0.01). The deviation REE(m)-REE(c1) did not differ between weight tertiles (tertile 1: 66 +/- 382 kJ/d; tertile 2: 167 +/- 507 kJ/d; tertile 3: 86 +/- 480 kJ/d; NS) and showed no relation with body mass (r = -0.05, NS). By contrast, REE(m)-REE(c2) increased with increasing weight tertile (tertile 1: -45 +/- 369 kJ/d; tertile 2: 150 +/- 503 kJ/d; tertile 3: 193 +/- 482 kJ/d; P < 0.05) and correlated significantly with body mass (r = 0.16, P < 0.05).

CONCLUSION

Our data do not support a lower specific organ metabolic rate in humans with a larger body mass than in those with a smaller body mass.

Familial influences and obesity-associated metabolic risk factors contribute to the variation in resting energy expenditure: the Kiel Obesity Prevention Study

BACKGROUND

A low metabolic rate may be inherited and predispose to obesity, whereas a higher metabolic rate in obesity may be acquired by obesity-associated cardiometabolic risk.

OBJECTIVE

We aimed to explain the interindividual variation in resting energy expenditure (REE) by assessing 1) the association between REE and body composition, thyroid hormones, and obesity-related cardiometabolic risk factors, and 2) the familial (genetic and environmental) contribution to REE.

DESIGN

REE and metabolic risk factors (ie, blood pressure and plasma insulin, glucose, and C-reactive protein concentrations) were assessed in 149 two- or three-generation families, including at least one overweight or obese member. Heritability of REE, respiratory quotient (RQ), thyroid hormones [thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4)], and body composition (fat-free mass and fat mass) were estimated by using variance components-based quantitative genetic models.

RESULTS

REE adjusted for body composition, sex, and age (REEadj) significantly correlated with systolic and diastolic blood pressure, plasma insulin and glucose concentrations, and the homeostasis model assessment (HOMA) (r = 0.14-0.31, P < 0.05). Thyroid hormones had a modest influence on REE variance only. Heritability was 0.30 +/- 0.07 for REEadj and 0.29 +/- 0.08 for REE after additional adjustment for thyroid hormones and metabolic risk. Furthermore, heritability was estimated to be 0.22 +/- 0.08 for RQ, 0.37 +/- 0.08 for TSH, 0.68 +/- 0.06 for FT4, and 0.69 +/- 0.05 for FT3 (all significantly larger than zero).

CONCLUSIONS

Obesity-related cardiometabolic risk factors contribute to interindividual variation in REE, with hypertension and insulin resistance being associated with a higher REE. REE was moderately heritable, independent of body composition, sex, age, thyroid function, and cardiometabolic risk.

Reporting quality of randomized trials in the diet and exercise literature for weight loss

Background

To adequately assess individual studies and synthesize quantitative research on weight loss studies, transparent reporting of data is required. The authors examined the reporting quality of randomized trials in the weight loss literature, focusing exclusively on subject characteristics as they relate to enrollment, allocation, and follow-up.

Methods

An extensive literature review, which included a computerized search of the MEDLINE database, manual searches of bibliographic references, and cross-referencing of 92 review articles was conducted. A checklist, based on CONSORT recommendations, was used to collect information on whether or not authors reported age, gender, co-morbid disease, medication use, race/ethnicity, and postmenopausal status. Also tracked was whether or not initial and final sample size was reported and stratified by gender.

Results

Of 604 possible articles, 231 articles met eligibility criteria. Important subject characteristics were not reported as the following breakdown indicates: age (11%), gender (4%), race/ethnicity (86%), co-morbid disease states (34%), and medication use (92%). Additionally, 21% of articles failed to report initial sample size by gender while 69% neglected to report final sample size by gender.

Conclusion

Inadequate reporting can create difficulties with interpretation and can lead to biased results receiving false credibility. The quality of reporting for weight loss studies needs considerable improvement.

Increased 24-h energy expenditure in type 2 diabetes

OBJECTIVE

The aim of this study was to determine whether overweight and obese individuals with type 2 diabetes have higher basal and 24-h energy expenditure compared with healthy control subjects before and after adjustment for body composition, spontaneous physical activity (SPA), sex, and age.

RESEARCH DESIGN AND METHODS

Data from 31 subjects with type 2 diabetes and 61 nondiabetic control subjects were analyzed. The 24-h energy expenditure, basal metabolic rate (BMR), and sleeping energy expenditure (EEsleep) between 1:00 a.m. and 6:00 a.m. were measured in whole-body respiratory chambers. Body composition was assessed by dual-energy X-ray absorptiometry (DXA).

RESULTS

No significant differences in unadjusted EEsleep, BMR, and 24-h energy expenditure were observed between the type 2 diabetic group and the control group. After adjustment for fat-free mass (FFM), fat mass, SPA, sex, and age, EEsleep and BMR were, respectively, 7.7 and 6.9% higher in the type 2 diabetic group compared with the control group. This was equivalent to 144 +/- 40 kcal/day (P = 0.001) and 139 +/- 61 kcal/day (P = 0.026), respectively. Adjusted 24-h energy expenditure was 6.5% higher in the type 2 diabetic group compared with the nondiabetic control subjects (2,679 +/- 37 vs. 2,515 +/- 23 kcal/day, P = 0.002). In multiple regression analyses, FFM, fat mass, SPA, and diabetes status were all significant determinants of EEsleep and 24-h energy expenditure, explaining 83 and 81% of the variation, respectively.

CONCLUSIONS

This study confirms reports in Pima Indians that basal and 24-h energy expenditure adjusted for body composition, SPA, sex, and age are higher in individuals with type 2 diabetes compared with nondiabetic control subjects and may be even more pronounced in Caucasians.

Estimation of resting energy expenditure considering effects of race and diabetes status

OBJECTIVE

To evaluate the impact of diabetes status and race, in addition to other covariables, on the estimation of resting energy expenditure (REE).

RESEARCH DESIGN AND METHODS

Demographic, anthropometric, and clinical parameters were assessed in 166 adults of varying weights. Subjects were categorized by race (white versus black) and into three subgroups based on glucose tolerance (normoglycemia, impaired glucose tolerance, and type 2 diabetes), termed the diabetes status index (DSI). REE was measured by indirect calorimetry. A multiple regression model was established for optimal prediction of REE based on covariables.

RESULTS

An average decrease in REE of 135 kcal/day independent of all other variables was observed in blacks (P < 0.001). DSI was found to be a significant covariable (P = 0.002) in predicting REE, which was observed to be higher in diabetic women. Therefore, race and DSI entered the multiple regression equation to predict REE as significant independent variables, together with lean body mass (LBM) and age x BMI interaction (P < 0.001). Overall, REE prediction resulted in an R(2) of 0.79 and a root mean square error of 136 kcal/day. These values indicate that the resultant equations could offer advantages over other key published prediction equations. The equations are: 1) REE(female) = 803.8 + 0.3505 x age x (BMI - 34.524) - 135.0 x race + 15.866 x LBM + 50.90 x DSI; and 2) REE(male) = 909.4 + 0.3505 x age x (BMI -34.524) -135.0 x race + 15.866 x LBM -9.10 x DSI. The predictive value of the equations did not diminish substantially when fat-free mass estimated by skinfold calipers was substituted for dual-energy X-ray absorptiometry scan measurements of LBM.

CONCLUSIONS

Race and diabetes status are important when predicting REE, coupled with LBM, age, BMI, and sex. Race and DSI have not been considered in equations commonly used to predict REE. Their inclusion could improve individualization of dietary prescriptions for type 2 diabetic subjects and heterogeneous populations.

Reduced sympathetic nervous activity. A potential mechanism predisposing to body weight gain

The sympathetic nervous system is recognized to play a role in the etiology of animal and possibly human obesity through its impact on energy expenditure and/or food intake. We, therefore, measured fasting muscle sympathetic nerve activity (MSNA) in the peroneal nerve and its relationship with energy expenditure and body composition in 25 relatively lean Pima Indian males (means +/- SD; 26 +/- 6 yr, 82 +/- 19 kg, 28 +/- 10% body fat) and 19 Caucasian males (29 +/- 5 yr, 81 +/- 13 kg, 24 +/- 9% body fat). 24-h energy expenditure, sleeping metabolic rate, and resting metabolic rate were measured in a respiratory chamber, whereas body composition was estimated by hydrodensitometry. Pima Indians had lower MSNA than Caucasians (23 +/- 6 vs 33 +/- 10 bursts/min, P = 0.0007). MSNA was significantly related to percent body fat in Caucasians (r = 0.55, P = 0.01) but not in Pimas. MSNA also correlated with energy expenditure adjusted for fat-free mass, fat mass, and age in Caucasians (r = 0.51, P = 0.03; r = 0.54, P = 0.02; and r = 0.53, P = 0.02 for adjusted 24-h energy expenditure, sleeping metabolic rate, and resting metabolic rate, respectively) but not in Pima Indians. In conclusion, the activity of the sympathetic nervous system is a determinant of energy expenditure in Caucasians. Individuals with low resting MSNA may be at risk for body weight gain resulting from a lower metabolic rate. A low resting MSNA and the lack of impact of MSNA on metabolic rate might play a role in the etiology of obesity in Pima Indians.