Effect of Carbohydrate-Restricted Diets and Intermittent Fasting on Obesity, Type 2 Diabetes Mellitus, and Hypertension Management: Consensus Statement of the Korean Society for the Study of Obesity, Korean Diabetes Association, and Korean Society of Hypertension

Discrepancies Between Recommendations in Evidence-Based Guidelines for the Management of Obesity in Adolescents and Adults: An Evidence Map

BACKGROUND/AIM

The aim of this evidence map is to provide a systematic overview of evidence-based guidelines for the management of obesity and to highlight discrepancies between recommendations for the pediatric and adult populations. Knowing about these discrepancies may be relevant during the transition from pediatrics to adult healthcare to enable a structured, successful transfer.

METHODS

We used two already existing guideline synopses of the Institute for Quality and Efficiency in Health Care as a basis and additionally searched Embase and Medline as well as guideline portals for creating a systematic overview of evidence-based pediatric and adult guidelines on obesity management. The quality of the identified guidelines was assessed using AGREE II.

RESULTS

Thirty-nine guidelines (published 08/2017 to 08/2023), containing 1248 recommendations, were included in the review and allocated to nine different healthcare categories. Discrepancies between the pediatric and adult populations were mainly identified in diagnostics and health behavior lifestyle treatment. Gaps were found in recommendations for bariatric surgery, pharmacotherapy, and emergency (-contraception).

CONCLUSION

The unique characteristics of adolescence are insufficiently addressed in current evidence-based guidelines. Therefore, healthcare professionals may benefit from considering the discrepancies and gaps identified in this review when providing recommendations during the transition of healthcare for this age group.

Quantitative analysis of the caloric restriction versus isocaloric diets models based on macronutrients composition: impacts on body weight regulation, anthropometric, and bioimpedance parameters in women with obesity

Introduction

Obesity is a growing public health issue, especially among young adults, with long-term management strategies still under debate. This prospective study compares the effects of caloric restriction and isocaloric diets with different macronutrient distributions on body composition and anthropometric parameters in obese women during a 12-week weight loss program, aiming to identify the most effective dietary strategies for managing obesity-related health outcomes.

Methods

A certified clinical nutritionist assigned specific diets over a 12-week period to 150 participants, distributed as follows: hypocaloric diets-low-energy diet (LED, 31 subjects) and very low-energy diet (VLED, 13 subjects); isocaloric diets with macronutrient distribution-low-carbohydrate diet (LCD, 48 subjects), ketogenic diet (KD, 23 subjects), and high-protein diet (HPD, 24 subjects); and isocaloric diet without macronutrient distribution-time-restricted eating (TRE, 11 subjects). Participants were dynamically monitored using anthropometric parameters: body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR) and bioelectrical impedance analysis (BIA) using the TANITA Body Composition Analyzer BC-418 MA III (T5896, Tokyo, Japan) at three key intervals-baseline, 6 weeks, and 12 weeks. The following parameters were evaluated: body weight, basal metabolic rate (BMR), percentage of total body fat, trunk fat, muscle mass, fat-free mass, and hydration status.

Results

All diets led to weight loss, but differences emerged over time. The TRE model resulted in significantly less weight loss compared to LED at the final follow-up (6.30 kg, p < 0.001), similar to the VLED (4.69 kg, p < 0.001). Isocaloric diets with varied macronutrient distributions showed significant weight loss compared to LED (p < 0.001). The KD reduced waist circumference at both 6 and 12 weeks (-4.08 cm, p < 0.001), while significant differences in waist-to-hip ratio reduction were observed across diet groups at 12 weeks (p = 0.01). Post-hoc analysis revealed significant fat mass differences at 12 weeks, with HPD outperforming IF (p = 0.01) and VLED (p = 0.003). LCD reduced trunk fat at 6 weeks (-2.36%, p = 0.001) and 12 weeks (-3.79%, p < 0.001). HPD increased muscle mass at 12 weeks (2.95%, p = 0.001), while VLED decreased it (-2.02%, p = 0.031). TRE showed a smaller BMR reduction at 12 weeks compared to LED.

Conclusion

This study highlights the superior long-term benefits of isocaloric diets with macronutrients distribution over calorie-restrictive diets in optimizing weight, BMI, body composition, and central adiposity.

Intermittent fasting for the prevention of cardiovascular disease: implications for clinical practice

Cardiovascular disease remains one of the most prevalent and preventable chronic conditions worldwide. Nutrition plays an important role in reducing several risk factors associated with cardiovascular disease. Intermittent fasting has been rapidly gaining interest among patients with cardiometabolic disease as a nutritional strategy for improving cardiovascular outcomes. However, research had yet to determine whether intermittent fasting provides greater cardiometabolic benefits compared to continuous daily caloric restriction. A recent Cochrane review has synthesised the benefits of intermittent fasting for the prevention of cardiovascular disease but is limited by its interpretation of the findings for clinical practice. This commentary aims to critically appraise the methods used within the review by Allaf et al, 2021 and expand upon the findings to determine its implications for clinical practice.

Low-Carbohydrate Diet and Human Health

Low-carbohydrate diets were initially recommended as a therapeutic dietary scheme for epilepsy, while increasing evidence suggests their potential application in the management of several other pathologies, such as diabetes, neoplasms, gastrointestinal and lung diseases, diseases of the cardiovascular system, as well as obesity [...].

Updated Meta-Analysis of Studies from 2011 to 2021 Comparing the Effectiveness of Intermittent Energy Restriction and Continuous Energy Restriction

Background

Despite the considerable number of trials and meta-analyses of studies on intermittent energy restriction (IER), it is not preferred to continuous energy restriction (CER) by the majority of obesity specialists. In this meta-analysis, we compare the effects of IER and CER on obesity using evidence from randomized controlled trials (RCTs).

Methods

A systematic electronic literature search was conducted to find RCTs published between January 1, 2011, and December 31, 2021 that directly compared IER and CER for an active weight loss period of at least 12 weeks and reported obesity indices or metabolic markers in adults with overweight or obesity. Finally, 16 RCTs from 25 articles with 1,438 participants were included.

Results

The attrition rates were 26.6% and 24.1% in the IER and CER groups, respectively, with no significant differences in changes in body weight, waist circumference, or body fat composition. CER changed blood glucose levels more than IER, but there was no significant difference in glycated hemoglobin levels. Systolic blood pressure was significantly lower in the CER group than the IER group, but diastolic blood pressure did not differ significantly between the groups. Changes in blood lipids did not differ significantly between the interventions. No differences between IER and CER were observed in the sensitivity analyses.

Conclusion

IER can be an alternative to CER because it induces comparable weight reduction and metabolic improvement. However, the effect of IER was not superior to that of CER, and its attrition rate was not lower than that of CER.

Non-pharmacologic treatment for obesity

Background: The prevalence of obesity has been rapidly increasing in the Korean population. Obesity is a well-known risk factor for various chronic diseases, including diabetes mellitus, hypertension, dyslipidemia, atherosclerosis, chronic kidney disease, degenerative arthritis, and autoimmune diseases. It also increases the risks of different malignancies, gall bladder disease, and pancreatitis.Current Concepts: Lifestyle intervention assisted by frequent behavioral therapy is crucial despite the modest amount of weight loss achieved. Energy intake restriction combined with increased physical activity can not only facilitate weight loss but also improve metabolic health. Furthermore, this combination can help maintain weight reduction during and after lifestyle interventions. Energy intake restriction with a daily deficit of 500–1,000 kcal and physical activity including aerobic exercise for 150 minutes or more per week and resistance training 2–4 times a week are generally recommended for obesity management.Discussion and Conclusion: Comprehensive lifestyle intervention should be individualized and supported by a multidisciplinary team. A long-term behavioral intervention is necessary for success in obesity treatment.

Current status of obesity treatment in Korea: based on the 2020 Korean Society for the Study of Obesity guidelines for obesity management

Background: Obesity is a complex disease, and its prevalence is gradually increasing globally. The increasing prevalence of obesity is shown to significantly affect the development of obesity-related comorbidities. This paper describes the recent updates on obesity treatment in Korea based on the 2020 Korean Society for the Study of Obesity guidelines for obesity management.Current Concepts: Accurate evaluation of obesity is important before treatment initiation. Obesity in Korean adults is defined as body mass index ≥25 kg/m2, and abdominal obesity is defined as waist circumference ≥90 cm for men and ≥85 cm for women. It is recommended that energy intake be reduced and that the degree of energy restriction be individualized based on patient characteristics and medical conditions. Guidelines recommend assessment of the exercise participation and health status before prescribing exercise therapy; aerobic exercise for at least 150 min/week or 3–5 times/week is advised to facilitate weight loss. Although nutrition and behavior therapy and increased physical activity constitute the mainstay of obesity treatment, pharmacotherapy is recommended concomitant with comprehensive lifestyle modification. Bariatric surgery should be actively considered in patients with severe obesity and in those with obesity-related comorbidities.Discussion and Conclusion: Obesity and the associated comorbidities result in an increased socioeconomic and medical burden. Multifactorial intervention using a team approach is warranted for optimal management of obesity. This guideline will benefit primary care physicians for safe and effective decision-making regarding obesity management and treatment.

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes

OBJECTIVE

For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurement serves as a good source of data to train machine learning models for prediction purposes. This study aimed at using machine learning methods to predict blood glucose for type 2 diabetic patients. We investigated various parameters influencing blood glucose, as well as determined the most effective machine learning algorithm in predicting blood glucose.

PATIENTS AND METHODS

273 patients were recruited in this research. Several parameters such as age, diet, family history, BMI, alcohol intake, smoking status et al were analyzed. Patients who had glycosylated hemoglobin less than 6.5% after 52 weeks were considered as having achieved glycemic control and the rest as not achieving it. Five machine learning methods (KNN algorithm, logistic regression algorithm, random forest algorithm, support vector machine, and XGBoost algorithm) were compared to evaluate their performances in prediction accuracy. R 3.6.3 and Python 3.12 were used in data analysis.

RESULTS

The statistical variables for which p< 0.05 was obtained were BMI, pulse, Na, Cl, AKP. Compared with the other four algorithms, XGBoost algorithm has the highest accuracy (Accuracy=99.54% in training set and 78.18% in testing set) and AUC values (1.0 in training set and 0.68 in testing set), thus it is recommended to be used for prediction in clinical practice.

CONCLUSION

When it comes to future blood glucose level prediction using machine learning methods, XGBoost algorithm scores the highest in effectiveness. This algorithm could be applied to assist clinical decision making, as well as guide the lifestyle of diabetic patients, in pursuit of minimizing risks of hyperglycemic or hypoglycemic events.