Asian Low-Carbohydrate Diet with Increased Whole Egg Consumption Improves Metabolic Outcomes in Metabolic Syndrome: A 52-Week Intervention Study

BACKGROUND

The low-carbohydrate-ketogenic diet, an effective strategy to address metabolic syndrome (MetS) and obesity has raised concerns about high fat consumption on atherogenic lipoproteins.

OBJECTIVE

The aim of this study was to compare the Asian ketogenic diet (AKD), which incorporates balanced protein and fat intake from Asian foods, with a balanced low-caloric diet (BLC) in individuals diagnosed with MetS.

METHODS

A 52-week randomized clinical trial included three parallel groups: AKD with increased whole egg intake (Yolk-AKD, n = 28), yolk-free ketogenic diet with egg white supplementation (White-AKD, n = 26), and BLC (n = 22). Primary outcomes were anthropometric and metabolic changes.

RESULTS

The AKD groups achieved significant reductions in weight and waist circumference (P < 0.05). Compared to the BLC group, the AKD groups demonstrated significant improvements in insulin resistance at week 6 and in triglyceride levels at weeks 12 (Yolk-AKD) and 35 (White-AKD) (P < 0.05). The AKD groups experienced improvements in hormones associated with insulin sensitivity and appetite, while only the Yolk-AKD group had a significant decrease in inflammation-related hormones (P < 0.05). From weeks 35 to 52, the AKD maintained reductions in anthropometric measurements, blood pressure, improved glucose tolerance, enhanced lipid profiles, and better liver function compared to the BLC.

CONCLUSIONS

The AKD proved safe and effective, yielding various metabolic improvements in individuals with Mets compared to the BLC. Emphasizing a low-saturated fat diet while disregarding dietary cholesterol, this approach holds promise for MetS and obesity management. The inclusion of both White-AKD and Yolk-AKD groups allowed for a comprehensive assessment of the AKD's impact, elucidating the differential effects of whole egg consumption on metabolic outcomes. Further studies are warranted. This trial was registered at clinicaltrials.gov as NCT04608136.

Comparative analysis of basal metabolic rate measurement methods in overweight and obese individuals: A retrospective study

The global prevalence of overweight and obesity is on the rise, presenting significant health challenges worldwide. Obesity is associated with various chronic diseases and imposes substantial economic burdens on society. Accurate assessment of basal metabolic rate (BMR) is crucial for effective weight management strategies. This retrospective study, conducted at Baskent University Hospital between October 2019 and October 2023, analyzed data from 133 overweight and obese individuals. Various methods including indirect calorimetry (IC), predictive equations (Harris-Benedict and Mifflin-St Jeor), and bioelectrical impedance analysis (BIA) were used to estimate BMR. Additionally, demographic, clinical, and body composition data were recorded. The mean BMR measured using IC was 1581 ± 322 kcal/day, which was significantly lower than the BMR estimated by other methods such as BIA (1765.8 ± 344.09 kcal/day), Harris-Benedict (1787.64 ± 341.4 kcal/day), and Mifflin-St Jeor equations (1690.08 ± 296.36 kcal/day) (P < .001). Among the predictive equations, the Mifflin-St Jeor method provided BMR estimates closest to the gold standard IC. When BMR measurement methods were compared to IC, 36.8% of measurements with the Harris-Benedict equation method, 50.4% with the Mifflin-St Jeor equation method, and 36.1% with the BIA method were within ± 10% agreement with IC measurements. Significant correlations were found between BMR and body composition parameters such as fat-free mass, muscle mass, and fat mass (R = 0.681, P < .001; R = 0.699, P < .001; R = 0.595, P < .001, respectively). Regression analysis identified that variables such as weight, height, body mass index, and muscle mass significantly predicted BMR measured by IC, accounting for 69.1% of the variance. This study underscores the challenges in assessing BMR in overweight and obese individuals. While IC remains the gold standard, predictive equations and BIA offer alternative methods. The Mifflin-St Jeor equation emerged as a practical option, closely aligning with IC results. However, discrepancies between methods and the influence of body composition highlight the importance of individualized approaches to BMR assessment in weight management strategies.

Components of Total Energy Expenditure in Healthy and Critically Ill Children: A Comprehensive Review

BACKGROUND

Total energy expenditure (TEE) is the total energy expended by an individual to sustain life, activities, and growth. TEE is formed by four components: resting energy expenditure (REE), activity energy expenditure (AEE), growth-related energy expenditure (GEE), and the thermic effect of feeding (TEF). Some energy expenditure (EE) components may change throughout childhood and cannot be reliably estimated using prediction formulae.

OBJECTIVE

To summarize measured TEE components as reported in the literature in healthy and critically ill children.

METHODS

We searched MEDLINE, EMBASE, and CINAHL for studies published between 1946 and 7 September 2023. The primary outcome was energy expenditure. Included studies were published in English and measured one or more of TEE, AEE, GEE, and TEF with Indirect Calorimetry or Doubly Labeled Water in participants between 1 month and 18 years of age. We excluded studies reporting only REE or using predictive equations. Following abstraction, reported values were converted into kcal/kg/day or kcal/day as possible. Weighted mean values were calculated using median or means of EE measurements.

RESULTS

We found 138 studies, 8163 patients, and 16,636 eligible measurements. The median (IQR) study sample size was 20 (12, 35) patients. TEE was the most evaluated component. The median (IQR) TEE in infants was 73.1 (67.0, 76.5), in children 78.0 (66.0, 81.3), and in adolescents was 44.2 (41.8, 51.9) kcal/kg/day. Very few studies reported on GEE and TEF.

CONCLUSIONS

This is one of the first studies that summarizes components of total energy expenditure in different pediatric age groups in healthy and critically ill children. Growth- and feeding-associated energy expenditure are poorly reported in healthy children, while all components of TEE (except REE) are poorly reported in critically ill children.

Validity of predictive equations for total energy expenditure against doubly labeled water

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Adequacy of basal metabolic rate prediction equations in individuals with severe obesity: A systematic review and meta-analysis

The determination of energy requirements in clinical practice is based on basal metabolic rate (BMR), frequently predicted by equations that may not be suitable for individuals with severe obesity. This systematic review and meta-analysis examined the accuracy and precision of BMR prediction equations in adults with severe obesity. Four databases were searched in March 2021 and updated in May 2023. Eligible studies compared BMR prediction equations with BMR measured by indirect calorimetry. Forty studies (age: 28-55 years, BMI: 40.0-62.4 kg/m2) were included, most of them with a high risk of bias. Studies reporting bias (difference between estimated and measured BMR) were included in the meta-analysis (n = 20). Six equations were meta-analyzed: Harris & Benedict (1919); WHO (weight) (1985); Owen (1986); Mifflin (1990); Bernstein (1983); and Cunningham (1980). The most accurate and precise equations in the overall analysis were WHO (-12.44 kcal/d; 95%CI: -81.4; 56.5 kcal/d) and Harris & Benedict (-18.9 kcal/d; 95%CI -73.2; 35.2 kcal/d). All the other equations tended to underestimate BMR. Harris & Benedict and WHO were the equations with higher accuracy and precision in predicting BMR in individuals with severe obesity. Additional analyses suggested that equations may perform differently according to obesity BMI ranges, which warrants further investigation.

Nutrition Assessment and Management in Celiac Disease

Celiac disease (CeD) is the most common immune condition affecting the gastrointestinal tract; it is triggered by gluten and the only available treatment is a strict gluten-free diet (GFD). Therefore, for patients with CeD, adopting a GFD is not a lifestyle choice. The major problem is that a GFD is restrictive and, like all restrictive diets, it has the potential for adverse nutritional outcomes, especially if adopted for a long term. It is well known that GFD can be nutritionally inadequate and is frequently associated with vitamin and mineral deficiencies; it is also associated with excessive sugar and fat intake, particularly when gluten-free substitutes are consumed. Consequently, people with CeD are affected by higher rates of overweight and obesity and metabolic complications, such as fatty liver and cardiovascular disease. Therefore, assessment of nutritional status and diet quality at diagnosis and while on a long-term GFD is key in the management of CeD. This narrative review addresses nutritional considerations in CeD and management of common challenges associated with a GFD.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

BACKGROUND

Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

OBJECTIVE

The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

DATA SOURCES

A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

ELIGIBILITY CRITERIA

Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

ANALYSIS

A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

RESULTS

Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

CONCLUSION

Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

PROSPERO REGISTRATION

CRD42020218212.

Adequacy of the "pocket formulas" for measuring total energy expenditure in healthy women compared with the new DRI equation: A doubly labeled water study

OBJECTIVES

"Pocket formulas" are practical alternatives for calculating an individual's total energy expenditure (TEE). Typically, more sophisticated predictive equations are used, such as the new equations proposed in the Dietary Reference Intakes (DRI). Nevertheless, these new equations necessitate estimating physical activity levels (PALs). The aim of this study was to compare the use of pocket formulas (kcal/kg of body weight) with the new predictive equations for energy expenditure proposed by the DRI (2023) in healthy women and with the doubly labeled water (DLW) method to predict TEE.

METHODS

The TEEs of healthy adult women were measured by DLW and calculated using the pocket formulas (× 20, × 25, × 30, and × 35 kcal/kg of body weight) and the new DRI equation. PALs by triaxial accelerometers were also collected.

RESULTS

The study included 55 women. For the entire sample, the × 30 pocket formula had the lowest bias (-6%; limits of agreement [LOAs]: -39.8; 27.5; root mean square error: 373.4) and the highest precision (42%). The pocket formulas showed reasonable agreement in the different body mass index categories compared with the results found by the 2023 DRI proposal. For individuals with normal weight, the agreement was × 35 kcal/kg: bias (%) = -4.8; LoA = -41.5; 31.8, with overweight, it was × 30 kcal/kg: bias (%) = -2.2; LoA = -25.1; 20.6, and with obesity, it was × 30 kcal/kg: bias (%) = 4.2; LoA = -21.1; 29.4.

CONCLUSION

Pocket formulas provide a reasonable agreement with TEE in healthy, sedentary, or low-active adult women, which may be a more simplistic strategy when there is no PAL data for calculating the DRI equations.

Estimates of Resting Energy Expenditure and Total Energy Expenditure Using Predictive Equations for Individuals After Bariatric Surgery: a Systematic Review with Meta-analysis

PURPOSE

Patients after metabolic bariatric surgery (MBS) require attention to maintain energy balance and avoid weight regain. Predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) are needed since gold standard methods like calorimetry and doubly labeled water are rarely available in routine clinical practice. This study aimed to determine which predictive equation for REE and TEE has the lowest bias in subjects after MBS.

METHODS

MEDLINE, Embase, Web of Science, and CENTRAL searches were performed. Meta-analyses were performed with the data calculated by the predictive equations and measured by the gold standard methods for those equations that had at least two studies with these data. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist.

RESULTS

Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference =  - 39.71 kcal [95%CI =  - 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference =  - 54.60 kcal [95%CI =  - 87.92; - 21.28]).

CONCLUSION

The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS.

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.

Use of the activPAL® triaxial accelerometer to estimate total energy expenditure in low-income women: differences between body mass index classifications

Objective

This study aimed to assess the agreement between the total energy expenditure (TEE) estimated by the activPAL® triaxial accelerometers (ACC) and the TEE measured by the doubly labeled water method (DLW), as well as to assess if these values differ between the classifications of body mass index (BMI).

Materials and methods

This is a cross-sectional study. Low-income adult women (19-45y) with BMI ≥ 18.5 kg/m2 were included. Accelerometry data (activPAL® ) were collected over 7 consecutive days, which were used to calculate TEE-ACC and compared with DLW data. The Bland-Altman method, concordance correlation coefficient and root mean square error were used to assess agreement between methods.

Results

The sample consisted of 55 women with a mean age of 31 ± 5 years. The agreement between TEE-ACC and TEE-DLW showed a bias of -142.5 kcal (-7.1%). Among the BMI classifications, participants with normal weight show a bias of -417.1 kcal (-21.0%), participants with overweight, -87.5 kcal (-3.9%) and participants with obesity, 97.5 kcal (4.3%). Furthermore, the bias between the methods showed a significant and positive correlation with the body weight (r = 0.49; p < 0.01).

Conclusion

The TEE-ACC estimates from activPAL® were reasonably accurate when compared to the TEE-DLW, especially in women with overweight and obesity, being much less accurate in individuals with normal weight.

Hypoxia as a Double-Edged Sword to Combat Obesity and Comorbidities

The global epidemic of obesity is tightly associated with numerous comorbidities, such as type II diabetes, cardiovascular diseases and the metabolic syndrome. Among the key features of obesity, some studies have suggested the abnormal expansion of adipose-tissue-induced local endogenous hypoxic, while other studies indicated endogenous hyperoxia as the opposite trend. Endogenous hypoxic aggravates dysfunction in adipose tissue and stimulates secretion of inflammatory molecules, which contribute to obesity. In contrast, hypoxic exposure combined with training effectively generate exogenous hypoxic to reduce body weight and downregulate metabolic risks. The (patho)physiological effects in adipose tissue are distinct from those of endogenous hypoxic. We critically assess the latest advances on the molecular mediators of endogenous hypoxic that regulate the dysfunction in adipose tissue. Subsequently we propose potential therapeutic targets in adipose tissues and the small molecules that may reverse the detrimental effect of local endogenous hypoxic. More importantly, we discuss alterations of metabolic pathways in adipose tissue and the metabolic benefits brought by hypoxic exercise. In terms of therapeutic intervention, numerous approaches have been developed to treat obesity, nevertheless durability and safety remain the major concern. Thus, a combination of the therapies that suppress endogenous hypoxic with exercise plans that augment exogenous hypoxic may accelerate the development of more effective and durable medications to treat obesity and comorbidities.

Italian Ketogenic Mediterranean Diet in Overweight and Obese Patients with Prediabetes or Type 2 Diabetes

Obesity is a multifactorial disease strongly associated with insulin resistance and/or type 2 diabetes mellitus. Correct nutrition represents a valid strategy to fight these dysmetabolic pathologies responsible for numerous diseases, including inflammatory and cardiovascular ones. Medical nutrition therapy, including a Mediterranean diet (MD) and a very low-calorie ketogenic diet (VLKCD), is the first-line treatment for prediabetes/diabetes and overweight/obesity. Eighty patients (forty women and forty men) affected by overweight/obesity and type 2 diabetes mellitus or impaired glucose tolerance or impaired fasting glucose (51 (ys) ± 1.75; BMI (kg/m2) 33.08 ± 1.93; HA1c (%): 6.8% ± 0.25) were enrolled at the University Service of Diet Therapy, Diabetology and Metabolic Diseases, Policlinico Riuniti Hospital of Foggia, and subjected to a very-low-calorie Mediterranean diet and a very-low-calorie ketogenic Mediterranean diet for thirty days. Both diets result in a marked decrease in body weight (kg) and BMI (kg/m2). At the same time, only the very-low-calories ketogenic Mediterranean diet reduced waist and hip circumferences. Both diets helped reduce fat mass, but a major loss was achieved in a very low-calorie ketogenic Mediterranean diet. Among gluco-metabolic parameters, only the very-low-calorie ketogenic Mediterranean diet group showed a significant decrease in fasting blood glucose and HbA1c, insulin, C-peptide total cholesterol, LDL, and triglycerides. The results of our study seem to show that the very-low-calorie ketogenic Mediterranean diet is a good strategy to improve rapidly metabolic, anthropometric, and body composition parameters in patients with prediabetes or diabetes and overweight/obesity.