Routine resting energy expenditure measurement increases effectiveness of dietary intervention in obesity

Evolution of the basal metabolic rate after Roux-en-Y gastric bypass: a systematic review and meta-analysis

Patients who undergo Roux-en-Y gastric bypass (RYGB) exhibit a reduction in total basal metabolic rate (BMR) after surgery, which seems to be intimately related to the amount of postoperative weight loss. The objective was to perform a systematic review and meta-analysis of the literature to determine and evaluate BMR changes after RYGB. The search was performed in certified databases, and the strategy was structured according to the PRISMA ScR. The quality evaluation of the articles included in this review was assessed with two different bias risk tools (ROBINS-I and NIH) according to each study design. Two meta-analyses were elaborated based on the results. 163 articles were selected (from 2016 to 2020), and 9 articles met the inclusion criteria. All of the selected studies evaluated only adult patients, mostly women. Postoperative BMR diminished in all of the included studies after surgery compared to preoperative values. The follow-up periods were 6, 12, 24 and 36 months. Eight articles were used for the meta-analysis after the quality assessment, a total of 434 participants. Compared to baseline values, mean postoperative reductions of 356.66 kcal/d after 6 months (p < 0.001) and 432.89 kcal/d (p < 0.001) after 1 year were observed. The BMR decreases during the first years after Roux-en-Y gastric bypass, especially during the first year postsurgery.

[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.

Telemedicine and urban diabetes during COVID-19 pandemic in Milano, Italy during lock-down: epidemiological and sociodemographic picture

BACKGROUND

Since 2010, more than half of World population lives in Urban Environments. Urban Diabetes has arisen as a novel nosological entity in Medicine. Urbanization leads to the accrual of a number of factors increasing the vulnerability to diabetes mellitus and related diseases. Herein we report clinical-epidemiological data of the Milano Metropolitan Area in the contest of the Cities Changing Diabetes Program. Since the epidemiological picture was taken in January 2020, on the edge of COVID-19 outbreak in the Milano Metropolitan Area, a perspective addressing potential interactions between diabetes and obesity prevalence and COVID-19 outbreak, morbidity and mortality will be presented. To counteract lock-down isolation and, in general, social distancing a pilot study was conducted to assess the feasibility and efficacy of tele-monitoring via Flash Glucose control in a cohort of diabetic patients in ASST North Milano.

METHODS

Data presented derive from 1. ISTAT (National Institute of Statistics of Italy), 2. Milano ATS web site (Health Agency of Metropolitan Milano Area), which entails five ASST (Health Agencies in the Territories). A pilot study was conducted in 65 screened diabetic patients (only 40 were enrolled in the study of those 36 were affected by type 2 diabetes and 4 were affected by type 1 diabetes) of ASST North Milano utilizing Flash Glucose Monitoring for 3 months (mean age 65 years, HbA1c 7,9%. Patients were subdivided in 3 groups using glycemic Variability Coefficient (VC): a. High risk, VC > 36, n. 8 patients; Intermediate risk 20 < VC < 36, n. 26 patients; Low risk VC < 20, n. 4 patients. The control group was constituted by 26 diabetic patients non utilizing Flash Glucose monitoring.

RESULTS

In a total population of 3.227.264 (23% is over 65 y) there is an overall prevalence of 5.65% with a significant difference between Downtown ASST (5.31%) and peripheral ASST (ASST North Milano, 6.8%). Obesity and overweight account for a prevalence of 7.8% and 27.7%, respectively, in Milano Metropolitan Area. We found a linear relationship (R = 0.36) between prevalence of diabetes and aging index. Similarly, correlations between diabetes prevalence and both older people depending index and structural dependence index (R = 0.75 and R = 0.93, respectively), were found. A positive correlation (R = 0.46) with percent of unoccupied people and diabetes prevalence was also found. A reverse relationship between diabetes prevalence and University level instruction rate was finally identified (R = - 0.82). Our preliminary study demonstrated a reduction of Glycated Hemoglobin (p = 0.047) at 3 months follow-up during the lock-down period, indicating Flash Glucose Monitoring and remote control as a potential methodology for diabetes management during COVID-19 lock-down.

HYPOTHESIS AND DISCUSSION

The increase in diabetes and obesity prevalence in Milano Metropolitan Area, which took place over 30 years, is related to several environmental factors. We hypothesize that some of those factors may have also determined the high incidence and virulence of COVID-19 in the Milano area. Health Agencies of Milano Metropolitan Area are presently taking care of diabetic patients facing the new challenge of maintaining sustainable diabetes care costs in light of an increase in urban population and of the new life-style. The COVID-19 pandemic will modify the management of diabetic and obese patients permanently, via the implementation of approaches that entail telemedicine technology. The pilot study conducted during the lock-down period indicates an improvement of glucose control utilizing a remote glucose control system in the Milano Metropolitan Area, suggesting a wider utilization of similar methodologies during the present "second wave" lock-down.

Deep Transcranial Magnetic Stimulation Affects Gut Microbiota Composition in Obesity: Results of Randomized Clinical Trial

Growing evidence highlights the crucial role of gut microbiota in affecting different aspects of obesity. Considering the ability of deep transcranial magnetic stimulation (dTMS) to modulate the cortical excitability, the reward system, and, indirectly, the autonomic nervous system (ANS), we hypothesized a potential role of dTMS in affecting the brain-gut communication pathways, and the gut microbiota composition in obesity. In a hospital setting, 22 subjects with obesity (5 M, 17 F; 44.9 ± 2.2 years; BMI 37.5 ± 1.0 kg/m²) were randomized into three groups receiving 15 sessions (3 per week for 5 weeks) of high frequency (HF), low frequency (LF) dTMS, or sham stimulation. Fecal samples were collected at baseline and after 5 weeks of treatment. Total bacterial DNA was extracted from fecal samples using the QIAamp DNA Stool Mini Kit (Qiagen, Italy) and analyzed by a metagenomics approach (Ion Torrent Personal Genome Machine). After 5 weeks, a significant weight loss was found in HF (HF: −4.1 ± 0.8%, LF: −1.9 ± 0.8%, sham: −1.3 ± 0.6%, p = 0.042) compared to LF and sham groups, associated with a decrease in norepinephrine compared to baseline (HF: −61.5 ± 15.2%, p < 0.01; LF: −31.8 ± 17.1%, p < 0.05; sham: −35.8 ± 21.0%, p > 0.05). Furthermore, an increase in Faecalibacterium (+154.3% vs. baseline, p < 0.05) and Alistipes (+153.4% vs. baseline, p < 0.05) genera, and a significant decrease in Lactobacillus (−77.1% vs. baseline, p < 0.05) were found in HF. Faecalibacterium variations were not significant compared to baseline in the other two groups (LF: +106.6%, sham: +27.6%; p > 0.05) as well as Alistipes (LF: −54.9%, sham: −15.1%; p > 0.05) and Lactobacillus (LF: −26.0%, sham: +228.3%; p > 0.05) variations. Norepinephrine change significantly correlated with Bacteroides (r² = 0.734; p < 0.05), Eubacterium (r² = 0.734; p < 0.05), and Parasutterella (r² = 0.618; p < 0.05) abundance variations in HF. In conclusion, HF dTMS treatment revealed to be effective in modulating gut microbiota composition in subjects with obesity, reversing obesity-associated microbiota variations, and promoting bacterial species representative of healthy subjects with anti-inflammatory properties.

Evaluation of Measured Resting Metabolic Rate for Dietary Prescription in Ageing Adults with Overweight and Adiposity-Based Chronic Disease

The primary objective of this study was to compare weight changes in two groups of ageing Irish adults with overweight and adiposity-based chronic disease: participants who had dietary energy requirements prescribed on the base of measured RMR and participants whose RMR was estimated by a prediction equation. Fifty-four Caucasian adults (male n = 25; female n = 29, age 57.5 ± 6.3 years, weight 90.3 ± 15.1 kg, height 171.5 ± 9.5 cm, BMI 30.7 ± 4.6 kg/m²) were randomly assigned to a dietary intervention with energy prescription based on either measured RMR or estimated RMR. RMR was measured by indirect calorimetry after an overnight fast and predicted values were determined by the Mifflin et al. (1990) prediction equation. All participants received individual nutritional counselling, motivational interviewing and educational material. Anthropometric variables, blood pressure, blood glucose and blood lipid profile were assessed over 12 weeks. Body weight at week 12 was significantly lower (p < 0.05) for both groups following dietary interventions, mRMR: −4.2%; eRMR: −3.2% of initial body weight. There was no significant difference in weight loss between groups. Overall, 20.8% mRMR and 17.4% of eRMR participants experienced clinically meaningful (i.e., ≥5% of initial weight) weight reduction. Weight reduction in adults aged ≥50 years over the short term (12 weeks) favoured a reduction in blood pressure, triglycerides and glucose, thus reducing cardiovascular disease risk factors. This research indicates that employing a reduced-calorie diet using indirect calorimetry to determine energy needs when improving weight outcomes in adults (>50 years) with overweight and adiposity-based chronic disease is equal to employing a reduced-calorie diet based on the Mifflin et al. (1990) prediction equation. A reduced-energy diet based on mRMR or eRMR facilitates clinically meaningful weight reduction in adults (≥50 years) over the short term (12 weeks) and favours a reduction in blood pressure, triglycerides and glucose, thus reducing cardiovascular disease risk factors. Moreover, the addition of motivational interviewing and behaviour change techniques that support and encourage small behaviour changes is effective in short-term weight management.

Accuracy of the Resting Energy Expenditure Estimation Equations for Healthy Women

Background: There exist several prediction equations for the estimation of resting energy expenditure (REE). However, none of these equations have been validated in the Chilean female population yet. The aims of this study are (1) to determine the accuracy of existing equations for prediction of REE and (2) to develop new equations in a sample of healthy Chilean women. Methods: A cross-sectional descriptive study was carried out on 620 Chilean women. The sample showed an age range between 18 and 73 years, a body mass index average of 28.5 ± 5.2 kg/m², and a prevalence of overweight and obesity of 41% and 33.2%, respectively. REE was measured by indirect calorimetry (REE_IC), which was used as the gold standard to determine the accuracy of twelve available REE prediction equations and to calculate alternative formulas for estimation of REE. Paired t-tests and Bland–Altman plots were used to know the accuracy of the estimation equations with REE_IC. At the same time, multiple linear regressions were performed to propose possible alternative equations. The analyses were carried out by age groups and according to nutritional status. Results: All the equations showed a tendency to overestimate REE, regardless of age or nutritional status. Overall, the Ireton-Jones equation achieved the highest mean percentage difference from REE_IC at 67.1 ± 31%. The alternative new equations, containing variables of body composition, reached a higher percentage of classification within ±10% of REE_IC. Conclusions: The available equations do not adequately estimate REE in this sample of Chilean women. Although they must be validated, the new formulas proposed show better adaptation to this Chilean sample.

The use of a portable metabolic monitoring device for measuring RMR in healthy adults

Objective measurement of RMR may be important for optimal nutritional care but is hindered by the price and practicality of the metabolic monitoring device. This study compared two metabolic monitoring devices for measuring RMR and VO2 and compared the measured RMR with the predicted RMR calculated from equations. RMR was measured using QUARK RMR (reference device) and Fitmate GS (COSMED) in a random order for 30 min, each on fasted participants. In total, sixty-eight adults participated (median age 22 years, interquartile range 21-32). Pearson correlation showed that RMR (r 0·86) and VO2 (r 0·86) were highly correlated between the two devices (P < 0·05). Intraclass correlation coefficients (ICC) showed good relative agreements regarding RMR (ICC = 0·84) and VO2 (ICC = 0·84) (P < 0·05). RMR measured by QUARK RMR was significantly higher (649 (sd 753) kJ/d) than Fitmate GS. Equations significantly overpredicted RMR. Accurate RMR (i.e. within ±10 % of the RMR measured by QUARK RMR) was found among 38 % of the participants for Fitmate GS and among 46-68 % depending on the equations. Bland-Altman analysis showed a low absolute agreement with QUARK RMR at an individual level for both Fitmate GS (limits of agreement (LOA): -828 to +2125 kJ/d) and equations (LOA ranged from -1979 to +1879 kJ/d). In conclusion, both Fitmate GS and predictive equations had low absolute agreements with QUARK RMR at an individual level. Therefore, these limitations should be considered when determining RMR using Fitmate GS or equations.

Weight loss induced by deep transcranial magnetic stimulation in obesity: A randomized, double-blind, sham-controlled study

AIM

To test the hypothesis that deep transcranial magnetic stimulation (dTMS) reduces food craving and causes weight loss via neuromodulation.

MATERIALS AND METHODS

This pilot study was designed as a randomized, double-blind, sham-controlled study. A total of 33 obese people (nine men, 24 women, mean age 48.1 ± 10.6 years, body mass index [BMI] 36.9 ± 4.7 kg/m² ) were randomized and completed the study: 13 participants underwent a 5-week treatment with high-frequency (HF) dTMS (18 Hz; HF group), 10 were treated with low-frequency (LF) dTMS (1 Hz; LF group), and 10 were sham-treated (sham group). Food craving, and metabolic and neuro-endocrine variables were evaluated at baseline, after the 5-week treatment, and at follow-up visits (1 month, 6 months, 1 year after the end of treatment).

RESULTS

The mixed-model analysis for repeated measures showed a significant interaction of time and groups for body weight (P = 0.001) and BMI (P = 0.001), with a significant body weight (-7.83 ± 2.28 kg; P = 0.0009) and BMI (-2.83 ± 0.83, P = 0.0009) decrease in the HF versus the sham group. A decreasing trend in food craving in the HF versus the LF and sham groups (P = 0.073) was observed. A significant improvement of metabolic and physical activity variables was found (P < 0.05) in the HF group.

CONCLUSIONS

We demonstrated the safety and efficacy of dTMS, in addition to physical exercise and a hypocaloric diet, in reducing body weight for up to 1 year in obese people. We hypothesize that a possible mechanism of HF dTMS treatment is modulation of the dopaminergic pathway and stimulation of physical activity.

Analysis of Predictive Equations for Estimating Resting Energy Expenditure in a Large Cohort of Morbidly Obese Patients

The treatment of obesity requires creating an energy deficit through caloric restriction and physical activity. Energy needs are estimated assessing the resting energy expenditure (REE) that in the clinical practice is estimated using predictive equations. In the present cross sectional study, we compared, in a large cohort of morbidly obese patients, the accuracy of REE predictive equations recommended by current obesity guidelines [Harris-Benedict, WHO/FAO/ONU and Mifflin-St Jeor (MJ)] and/or developed for obese patients (Muller, Muller BC, Lazzer, Lazzer BC), focusing on the effect of comorbidities on the accuracy of the equations. Data on REE measured by indirect calorimetry and body composition were collected in 4,247 obese patients (69% women, mean age 48 ± 19 years, mean BMI 44 ± 7 Kg/m²) admitted to the Istituto Auxologico Italiano from 1999 to 2014. The performance of the equations was assessed in the whole cohort, in 4 groups with 0, 1, 2, or ≥ 3 comorbidities and in a subgroup of 1,598 patients with 1 comorbidity (47.1% hypertension, 16.7% psychiatric disorders, 13.3% binge eating disorders, 6.1% endocrine disorders, 6.4% type 2 diabetes, 3.5% sleep apnoea, 3.1% dyslipidemia, 2.5% coronary disease). In the whole cohort of obese patients, as well as in each stratum of comorbidity number, the MJ equation had the highest performance for agreement measures and bias. The MJ equation had the best performance in obese patients with ≥3 comorbidities (accuracy of 61.1%, bias of -89.87) and in patients with type 2 diabetes and sleep apnoea (accuracy/bias 69%/-19.17 and 66%/-21.67 respectively), who also have the highest levels of measured REE. In conclusion, MJ equation should be preferred to other equations to estimate the energy needs of Caucasian morbidly obese patients when measurement of the REE cannot be performed. As even MJ equation does not precisely predict REE, it should be better to plan the diet intervention by measuring rather than estimating REE. Future studies focusing on the clinical differences that determine the high inter-individual variability of the precision of the REE predictive equations (e.g., on the organ-tissue metabolic rate), could help to develop predictive equations with a better performance.