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      Impact of a Ketogenic Diet on Metabolic Parameters in Patients with Obesity or Overweight and with or without Type 2 Diabetes: A Meta-Analysis of Randomized Controlled Trials

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      1 , 2 , 3 , 2 , 3 , *
      Nutrients
      MDPI
      ketogenic diet, glycemic control, obesity, type 2 diabetes

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          Abstract

          The aim of this meta-analysis was to explore the efficacy of a ketogenic diet in metabolic control in patients with overweight or obesity and with or without type 2 diabetes. Embase, PubMed, and Cochrane Library were searched for randomized controlled trials that enrolled patients with overweight or obesity on a ketogenic diet for metabolic control. Fourteen studies were included in meta-analysis. The effects of ketogenic diets on glycemic control were greater for diabetic patients relative to those of low-fat diets, indicated by lower glycated hemoglobin (SMD, −0.62; p < 0.001) and homeostatic model assessment index (SMD, −0.29; p = 0.02), while comparable effects were observed for nondiabetic patients. Ketogenic diets led to substantial weight reduction (SMD, −0.46; p = 0.04) irrespective of patients’ diabetes status at baseline and improved lipid profiles in terms of lower triglyceride (SMD, −0.45; p = 0.01) and greater high-density lipoprotein (SMD, 0.31; p = 0.005) for diabetic patients. Other risk markers showed no substantial between-group difference post intervention. Our study findings confirmed that ketogenic diets were more effective in improving metabolic parameters associated with glycemic, weight, and lipid controls in patients with overweight or obesity, especially those with preexisting diabetes, as compared to low-fat diets. This effect may contribute to improvements in metabolic dysfunction-related morbidity and mortality in these patient populations.

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          Most cited references35

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          The medical risks of obesity.

          Obesity is at epidemic proportions in the United States and in other developed and developing countries. The prevalence of obesity is increasing not only in adults, but especially among children and adolescents. In the United States in 2003 to 2004, 17.1% of children and adolescents were overweight, and 32.2% of adults were obese. Obesity is a significant risk factor for and contributor to increased morbidity and mortality, most importantly from cardiovascular disease (CVD) and diabetes, but also from cancer and chronic diseases, including osteoarthritis, liver and kidney disease, sleep apnea, and depression. The prevalence of obesity has increased steadily over the past 5 decades, and obesity may have a significant impact on quality-adjusted life years. Obesity is also strongly associated with an increased risk of all-cause mortality as well as cardiovascular and cancer mortality. Despite the substantial effects of obesity, weight loss can result in a significant reduction in risk for the majority of these comorbid conditions. Those comorbidities most closely linked to obesity must be identified to increase awareness of potential adverse outcomes. This will allow health care professionals to identify and implement appropriate interventions to reduce patient risk and mortality. A systematic search strategy was used to identify published literature between 1995 and 2008 that reported data from prospective longitudinal studies of obesity and comorbid medical conditions. This article will review evidence for significant associations of obesity with comorbidities to provide information useful for optimal patient management.
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            European Guidelines for Obesity Management in Adults

            Obesity is a chronic metabolic disease characterised by an increase of body fat stores. It is a gateway to ill health, and it has become one of the leading causes of disability and death, affecting not only adults but also children and adolescents worldwide. In clinical practice, the body fatness is estimated by BMI, and the accumulation of intra-abdominal fat (marker for higher metabolic and cardiovascular disease risk) can be assessed by waist circumference. Complex interactions between biological, behavioural, social and environmental factors are involved in regulation of energy balance and fat stores. A comprehensive history, physical examination and laboratory assessment relevant to the patient's obesity should be obtained. Appropriate goals of weight management emphasise realistic weight loss to achieve a reduction in health risks and should include promotion of weight loss, maintenance and prevention of weight regain. Management of co-morbidities and improving quality of life of obese patients are also included in treatment aims. Balanced hypocaloric diets result in clinically meaningful weight loss regardless of which macronutrients they emphasise. Aerobic training is the optimal mode of exercise for reducing fat mass while a programme including resistance training is needed for increasing lean mass in middle-aged and overweight/obese individuals. Cognitive behavioural therapy directly addresses behaviours that require change for successful weight loss and weight loss maintenance. Pharmacotherapy can help patients to maintain compliance and ameliorate obesity-related health risks. Surgery is the most effective treatment for morbid obesity in terms of long-term weight loss. A comprehensive obesity management can only be accomplished by a multidisciplinary obesity management team. We conclude that physicians have a responsibility to recognise obesity as a disease and help obese patients with appropriate prevention and treatment. Treatment should be based on good clinical care, and evidence-based interventions; should focus on realistic goals and lifelong multidisciplinary management.
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              Use of metabolic markers to identify overweight individuals who are insulin resistant.

              Insulin resistance is more common in overweight individuals and is associated with increased risk for type 2 diabetes mellitus and cardiovascular disease. Given the current epidemic of obesity and the fact that lifestyle interventions, such as weight loss and exercise, decrease insulin resistance, a relatively simple means to identify overweight individuals who are insulin resistant would be clinically useful. To evaluate the ability of metabolic markers associated with insulin resistance and increased risk for cardiovascular disease to identify the subset of overweight individuals who are insulin resistant. Cross-sectional study. General clinical research center. 258 nondiabetic, overweight volunteers. Body mass index; fasting glucose, insulin, lipid and lipoprotein concentrations; and insulin-mediated glucose disposal as quantified by the steady-state plasma glucose concentration during the insulin suppression test. Overweight was defined as body mass index of 25 kg/m2 or greater, and insulin resistance was defined as being in the top tertile of steady-state plasma glucose concentrations. Receiver-operating characteristic curve analysis was used to identify the best markers of insulin resistance; optimal cut-points were identified and analyzed for predictive power. Plasma triglyceride concentration, ratio of triglyceride to high-density lipoprotein cholesterol concentrations, and insulin concentration were the most useful metabolic markers in identifying insulin-resistant individuals. The optimal cut-points were 1.47 mmol/L (130 mg/dL) for triglyceride, 1.8 in SI units (3.0 in traditional units) for the triglyceride-high-density lipoprotein cholesterol ratio, and 109 pmol/L for insulin. Respective sensitivity and specificity for these cut-points were 67%, 64%, and 57% and 71%, 68%, and 85%. Their ability to identify insulin-resistant individuals was similar to the ability of the criteria proposed by the Adult Treatment Panel III to diagnose the metabolic syndrome (sensitivity, 52%, and specificity, 85%). Three relatively simple metabolic markers can help identify overweight individuals who are sufficiently insulin resistant to be at increased risk for various adverse outcomes. In the absence of a standardized insulin assay, we suggest that the most practical approach to identify overweight individuals who are insulin resistant is to use the cut-points for either triglyceride concentration or the triglyceride-high-density lipoprotein cholesterol concentration ratio.
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                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                06 July 2020
                July 2020
                : 12
                : 7
                Affiliations
                [1 ]Clinical Trial Center, Hallym University Hospital, Anyang 14066, Korea; utds811@ 123456hotmail.com
                [2 ]College of Pharmacy, Ajou University, Suwon 16499, Korea; smjeon@ 123456ajou.ac.kr
                [3 ]Research Institute of Pharmaceutical Science and Technology (RIPST), Ajou University, Suwon 16499, Korea
                Author notes
                [* ]Correspondence: syshin@ 123456ajou.ac.kr ; Tel.: +82-31-219-3456
                [†]

                The authors contributed equally to this study.

                Article
                nutrients-12-02005
                10.3390/nu12072005
                7400909
                32640608
                afe3e996-5a5c-44a5-8749-4c911f05305e
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Nutrition & Dietetics
                ketogenic diet,glycemic control,obesity,type 2 diabetes
                Nutrition & Dietetics
                ketogenic diet, glycemic control, obesity, type 2 diabetes

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