Weight change adjusted equations for assessing resting metabolic rate in overweight and obese adults

The association between gut microbiota and resting metabolic rate in overweight/obese women: a case-control study

Purpose

When examining the underlying processes of obesity, evaluation of gut flora and energy homeostasis can be crucial since disruption of the normal gut microbiota community and energy imbalances are significant factors in the development of obesity. Therefore, this study aimed to compare the relative abundance of important obesity modulator gut microbiota (including Firmicutes, Bacteroidetes, Bifidobactrium spp., Lactobacillus spp., Bacteroides fragilis, Faecalibacterium prausnitzii, Akkermansia muciniphila, and Escherichia coli) in fecal samples of normometabilic and hypometabolic overweight/obese individuals.

Methods

This matched case-control study conducted on 36 healthy women aged 18-50 years old. An indirect calorimeter and impedance body analyzer were used to assess resting metabolic rate (RMR) and body composition, respectively. Dietary intake and physical activity were assessed using questionnaires. To determine the abundance of the abovementioned gut microbiota, quantitative polymerase chain reaction (qPCR) method was performed. Moreover, ELISA kits were used to assess leptin, ghrelin, and insulin hormones.

Results

The results highlighted higher load of Firmicutes (p = 0.02), F. prausnitzii (p < 0.001), and B. fragilis (p = 0.02) in the normometabolic individuals compared to the hypometabolic ones. Besides, the positive correlation between the abundance of Firmicutes (β = 7.76 × 10-1, p = 0.01), F. prausnitzii (β = 1.29 × 10-5, p = 0.01), and B. fragilis (β = 4.13 × 10-6, p = 0.04) with the RMR have been shown. Whereas the abundance of Bacteroidetes, A. muciniphila, Lactobacillus spp., Bifidobactrium spp., and E. coli showed no significant difference (p > 0.05) and no significant correlation with the RMR except Lactobacillus spp. (β = 1.73 × 10-4, p = 0.01).

Conclusion

It seems that gut microbiota can be a potential target for refining host energy homeostasis and treating obesity and its consequences.

Basal metabolic rate using indirect calorimetry among individuals living with overweight or obesity: The accuracy of predictive equations for basal metabolic rate

BACKGROUND & AIMS

Weight reduction programs in people with overweight or obesity can be informed by indirect calorimetry (IC) which is the gold standard to measure basal metabolic rate (BMR). Since IC is labor intensive and expensive, predictive equations are often used as an alternative. In this study the accuracy rate was assessed and bias statistics of predictive equations were compared to IC among subjects with overweight or obesity. Secondly, differences in clinical features between individuals with over-, accurate or underestimation of their BMR were evaluated.

METHODS

This cross sectional study included 731 subjects from the outpatient obesity clinic of the Antwerp University Hospital, Belgium. Fourteen equations were evaluated. Overestimation and underestimation was defined as >10 % and <10 % of measured BMR.

RESULTS

In the total population, mean age was 43 ± 13 years, mean BMI 35.6 ± 5.8 kg/m2 and 79.5 % were female. The highest accuracy rates were reached by the Henry (73 %), Ravussin (73 %) and Mifflin St. Jeor (73 %) equations. In the total population, the Mifflin St. Jeor and Henry equation were unbiased. The Akern, Livingston and Ravussin equations were biased to underestimation. All other equations were biased to overestimation. Subjects with an underestimation of BMR had significantly higher waist-hip ratio (1.02 ± 0.13 vs 0.91 ± 0.11; P < 0.001), higher visceral adipose tissue (239 ± 96 vs 162 ± 93; P < 0.001), lower fat free mass (kg) (67.6 (45.4-95.9) vs 54.0 (39.6-95.5); P < 0.001) and a higher prevalence of the Metabolic Syndrome (24 (77.4) vs 112 (37.5); P < 0.001). Individuals with an overestimation of BMR had significantly higher subcutaneous adipose tissue (545 ± 149 vs 612 ± 149; P < 0.05), lower fasting plasma insulin (81 (10-2019) vs 67 (27-253); P < 0.001) and lower 2-h plasma glucose (132 (30-430) vs 116 (43-193); P < 0.001) during OGTT.

CONCLUSIONS

In this study, the Henry and Mifflin St. Jeor equations have the highest accuracy and lowest bias to estimate the basal metabolic rate in a Caucasian, predominantly female, population living with overweight or obesity. Visceral and subcutaneous adipose tissue and presence of metabolic syndrome were significantly different in individuals with over- or underestimation of BMR.

Longitudinal estimates of resting energy expenditure using predictive equations in individuals with excess weight after weight loss: A systematic review with meta-analysis

BACKGROUND & AIMS

To determine which resting energy expenditure (REE) predictive equation has the lowest bias in the aggregate level in individuals with excess weight during weight loss interventions.

METHODS

Searches were performed in MEDLINE, Web of Science, Scopus, CENTRAL and gray literature databases. Longitudinal studies on weight loss interventions which evaluated REE by predictive equations compared to that measured by indirect calorimetry in adults with excess weight at different follow-up times were included. Meta-analyses were performed with the differences between biases of predictive equations of the REE at the different follow-up times of weight loss.

RESULTS

Of the total of 2178 occurrences found in the databases, only eight studies were included. The Harris-Benedict (1919) equation showed the smallest differences between bias up to the third month (MD = 103.33 kcal; 95%CI = -39.01; 245.67), in the sixth month (MD = 59.16 kcal; 95%CI = 8.74; 109.57) and at the 12th month (MD = -71.41 kcal; 95%CI = -150.38; 7.55) of weight loss follow-up. Weight loss does not seem to have an effect on bias at different follow-up times.

CONCLUSION

Harris-Benedict (1919) equation seems to be the most adequate to assess the REE of individuals with excess weight during weight loss. However, the finding of large estimated predictive intervals may indicate that predictive equations may not be handy tools for individuals losing and regaining weight due to changes other than body weight.

Comparison of Equations Estimating Resting Metabolic Rate in Older Adults with Type 2 Diabetes

Measuring resting metabolic rate (RMR) is time-consuming and expensive, and thus various equations for estimating RMR have been developed. This study's objective was to compare five equations in elderly people with type 2 diabetes (T2DM). RMR was measured in 90 older adults (≥65 years) with T2DM (mean body mass index (BMI) of 31.5 kg/m2), using indirect calorimetry. Results were compared to four frequently used equations (those of Cunningham, Harris and Benedict, and Gougeon developed for young adults with T2DM, and that of Lührmann, which was developed for the elderly), in addition to a new equation developed recently at the Academic College at Wingate (Nachmani) for overweight individuals. Estimation accuracy was defined as the percentage of subjects with calculated RMR within ±10% of measured RMR. Measured RMR was significantly underestimated by all equations. The equations of Nachmani and Lührmann had the best estimation accuracy: 71.4% in males and 50.9% in females. Skeletal muscle mass, fat mass, hemoglobin A1c (HbA1c), and the use of insulin explained 70.6% of the variability in measured RMR. RMR in elderly participants with T2DM was higher than that calculated using existing equations. The most accurate equations for this specific population were those developed for obesity or the elderly. Unbalanced T2DM may increase caloric demands in the elderly. It is recommended to adjust the RMR equations used for the target population.