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Jiang YC, Lai K, Muirhead RP, Chung LH, Huang Y, James E, Liu XT, Wu J, Atkinson FS, Yan S, Fogelholm M, Raben A, Don AS, Sun J, Brand-Miller JC, Qi Y. Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy. Am J Clin Nutr 2024; 120:864-878. [PMID: 39182617 DOI: 10.1016/j.ajcnut.2024.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 07/12/2024] [Accepted: 08/19/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, >50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impacts and predicting diabetes risk. OBJECTIVES This study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss. METHODS We studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over 8 wk. High-coverage lipidomics was conducted in serum samples before and after the dietary intervention. The lipidomic recalibration was assessed using differential lipid abundance comparisons and partial least squares discriminant analyses. Associations between lipid changes and clinical characteristics were determined by Spearman correlation and Bootstrap Forest of ensemble machine learning model. Baseline lipids, predictive of glycemic parameters changes postweight loss, were assessed using Bootstrap Forest analyses. RESULTS We quantified 439 serum lipid species and 9 related organic acids. Dietary intervention significantly reduced diacylglycerols, ceramides, lysophospholipids, and ether-linked phosphatidylethanolamine. In contrast, acylcarnitines, short-chain fatty acids, organic acids, and ether-linked phosphatidylcholine increased significantly. Changes in certain lipid species (e.g., saturated and monounsaturated fatty acid-containing glycerolipids, sphingadienine-based very long-chain sphingolipids, and organic acids) were closely associated with clinical glycemic parameters. Six baseline bioactive sphingolipids primarily predicted changes in fasting plasma glucose. In addition, a number of baseline lipid species, mainly diacylglycerols and triglycerides, were predictive of clinical changes in hemoglobin A1c, insulin and homeostasis model assessment of insulin resistance. CONCLUSIONS Newly discovered serum lipidomic alterations and the associated changes in lipid-clinical variables suggest broad metabolic reprogramming related to diet-mediated glycemic control. Novel lipid predictors of glycemic outcomes could facilitate early stratification of individuals with prediabetes who are metabolically less responsive to weight loss, enabling more tailored intervention strategies beyond 1-size-fits-all lifestyle modification advice. The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).
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Affiliation(s)
- Yingxin Celia Jiang
- Centenary Institute, The University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Kaitao Lai
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia; ANZAC Research Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Roslyn Patricia Muirhead
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Long Hoa Chung
- Centenary Institute, The University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Yu Huang
- Centenary Institute, The University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Elizaveta James
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Xin Tracy Liu
- Centenary Institute, The University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Julian Wu
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; Barker College, Hornsby, New South Wales, Australia
| | - Fiona S Atkinson
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Shuang Yan
- Department of Endocrinology and Metabolism Diseases, The 4th Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Mikael Fogelholm
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Anne Raben
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark; Clinical Research, Copenhagen University Hospital-Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Anthony Simon Don
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Jing Sun
- Rural Health Research Institute, Charles Sturt University, Leeds Parade, New South Wales, Australia; School of Medicine and Dentistry, Menzies Health Institute Queensland, Institute for Integrated Intelligence and Systems, Griffith University, Southport, Queensland, Australia.
| | - Jennie Cecile Brand-Miller
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia.
| | - Yanfei Qi
- Centenary Institute, The University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
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van Baak MA, Mariman ECM. Obesity-induced and weight-loss-induced physiological factors affecting weight regain. Nat Rev Endocrinol 2023; 19:655-670. [PMID: 37696920 DOI: 10.1038/s41574-023-00887-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/27/2023] [Indexed: 09/13/2023]
Abstract
Weight regain after successful weight loss resulting from lifestyle interventions is a major challenge in the management of overweight and obesity. Knowledge of the causal mechanisms for weight regain can help researchers and clinicians to find effective strategies to tackle weight regain and reduce obesity-associated metabolic and cardiovascular complications. This Review summarizes the current understanding of a number of potential physiological mechanisms underlying weight regain after weight loss, including: the role of adipose tissue immune cells; hormonal and neuronal factors affecting hunger, satiety and reward; resting energy expenditure and adaptive thermogenesis; and lipid metabolism (lipolysis and lipid oxidation). We describe and discuss obesity-associated changes in these mechanisms, their persistence during weight loss and weight regain and their association with weight regain. Interventions to prevent or limit weight regain based on these factors, such as diet, exercise, pharmacotherapy and biomedical strategies, and current knowledge on the effectiveness of these interventions are also reviewed.
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Affiliation(s)
- Marleen A van Baak
- NUTRIM School for Nutrition and Translational Research in Metabolism, Department of Human Biology, Maastricht University, Maastricht, Netherlands.
| | - Edwin C M Mariman
- NUTRIM School for Nutrition and Translational Research in Metabolism, Department of Human Biology, Maastricht University, Maastricht, Netherlands
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Jing T, Zhang S, Bai M, Chen Z, Gao S, Li S, Zhang J. Effect of Dietary Approaches on Glycemic Control in Patients with Type 2 Diabetes: A Systematic Review with Network Meta-Analysis of Randomized Trials. Nutrients 2023; 15:3156. [PMID: 37513574 PMCID: PMC10384204 DOI: 10.3390/nu15143156] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/07/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Dietary patterns play a critical role in diabetes management, while the best dietary pattern for Type 2 diabetes (T2DM) patients is still unclear. The aim of this network meta-analysis was to compare the impacts of various dietary approaches on the glycemic control of T2DM patients. METHODS Relevant studies were retrieved from PubMed, Embase, Web of Knowledge, Cochrane Central Register of Controlled Trials (CENTRAL), and other additional records (1949 to 31 July 2022). Eligible RCTs were those comparing different dietary approaches against each other or a control diet in individuals with T2DM for at least 6 months. We assessed the risk of bias of included studies with the Cochrane risk of bias tool and confidence of estimates with the Grading of Recommendations Assessment, Development, and Evaluation approach for network meta-analyses. In order to determine the pooled effect of each dietary approach relative to each other, we performed a network meta-analysis (NMA) for interventions for both HbA1c and fasting glucose, which enabled us to estimate the relative intervention effects by combing both direct and indirect trial evidence. RESULTS Forty-two RCTs comprising 4809 patients with T2DM were included in the NMA, comparing 10 dietary approaches (low-carbohydrate, moderate-carbohydrate, ketogenic, low-fat, high-protein, Mediterranean, Vegetarian/Vegan, low glycemic index, recommended, and control diets). In total, 83.3% of the studies were at a lower risk of bias or had some concerns. Findings of the NMA revealed that the ketogenic, low-carbohydrate, and low-fat diets were significantly effective in reducing HbA1c (viz., -0.73 (-1.19, -0.28), -0.69 (-1.32, -0.06), and -1.82 (-2.93, -0.71)), while moderate-carbohydrate, low glycemic index, Mediterranean, high-protein, and low-fat diets were significantly effective in reducing fasting glucose (viz., -1.30 (-1.92, -0.67), -1.26 (-2.26, -0.27), -0.95 (-1.51, -0.38), -0.89 (-1.60, -0.18) and -0.75 (-1.24, -0.27)) compared to a control diet. The clustered ranking plot for combined outcomes indicated the ketogenic, Mediterranean, moderate-carbohydrate, and low glycemic index diets had promising effects for controlling HbA1c and fasting glucose. The univariate meta-regressions showed that the mean reductions of HbA1c and fasting glucose were only significantly related to the mean weight change of the subjects. CONCLUSIONS For glycemic control in T2DM patients, the ketogenic diet, Mediterranean diet, moderate-carbohydrate diet, and low glycemic index diet were effective options. Although this study found the ketogenic diet superior, further high-quality and long-term studies are needed to strengthen its credibility.
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Affiliation(s)
- Tiantian Jing
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (T.J.)
| | - Shunxing Zhang
- Department of Global Public Health/Media, Culture, and Communication, Steinhardt School of Culture, Education, and Human Development, New York University, New York, NY 10016, USA
| | - Mayangzong Bai
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (T.J.)
| | - Zhongwan Chen
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (T.J.)
| | - Sihan Gao
- School of Public Health, University of Washington Seattle Campus, Seattle, WA 98105, USA
| | - Sisi Li
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (T.J.)
| | - Jing Zhang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (T.J.)
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Zhu R, Fogelholm M, Larsen TM, Poppitt SD, Silvestre MP, Vestentoft PS, Jalo E, Navas-Carretero S, Huttunen-Lenz M, Taylor MA, Stratton G, Swindell N, Kaartinen NE, Lam T, Handjieva-Darlenska T, Handjiev S, Schlicht W, Martinez JA, Seimon RV, Sainsbury A, Macdonald IA, Westerterp-Plantenga MS, Brand-Miller J, Raben A. A High-Protein, Low Glycemic Index Diet Suppresses Hunger but Not Weight Regain After Weight Loss: Results From a Large, 3-Years Randomized Trial (PREVIEW). Front Nutr 2021; 8:685648. [PMID: 34141717 PMCID: PMC8203925 DOI: 10.3389/fnut.2021.685648] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Previous studies have shown an increase in hunger during weight-loss maintenance (WLM) after diet-induced weight loss. Whether a combination of a higher protein, lower glycemic index (GI) diet and physical activity (PA) can counteract this change remains unclear. Aim: To compare the long-term effects of two diets [high protein (HP)-low GI vs. moderate protein (MP)-moderate GI] and two PA programs [high intensity (HI) vs. moderate intensity (MI)] on subjective appetite sensations during WLM after ≥8% weight loss (WL). Methods: Data derived from the 3-years PREVIEW randomized intervention study. An 8-weeks WL phase using a low-energy diet was followed by a 148-weeks randomized WLM phase. For the WLM phase, participants were assigned to one of the four groups: HP-MI, HP-HI, MP-MI, and MP-HI. Available data from 2,223 participants with overweight or obesity (68% women; BMI ≥ 25 kg/m2). Appetite sensations including satiety, hunger, desire to eat, and desire to eat something sweet during the two phases (at 0, 8 weeks and 26, 52, 104, and 156 weeks) were assessed based on the recall of feelings during the previous week using visual analogue scales. Differences in changes in appetite sensations from baseline between the groups were determined using linear mixed models with repeated measures. Results: There was no significant diet × PA interaction. From 52 weeks onwards, decreases in hunger were significantly greater in HP-low GI than MP-moderate GI (P time × diet = 0.018, P dietgroup = 0.021). Although there was no difference in weight regain between the diet groups (P time × diet = 0.630), hunger and satiety ratings correlated with changes in body weight at most timepoints. There were no significant differences in appetite sensations between the two PA groups. Decreases in hunger ratings were greater at 52 and 104 weeks in HP-HI vs. MP-HI, and greater at 104 and 156 weeks in HP-HI vs. MP-MI. Conclusions: This is the first long-term, large-scale randomized intervention to report that a HP-low GI diet was superior in preventing an increase in hunger, but not weight regain, during 3-years WLM compared with a MP-moderate GI diet. Similarly, HP-HI outperformed MP-HI in suppressing hunger. The role of exercise intensity requires further investigation. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT01777893.
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Affiliation(s)
- Ruixin Zhu
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Mikael Fogelholm
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Thomas M Larsen
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Sally D Poppitt
- Human Nutrition Unit, School of Biological Sciences, Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Marta P Silvestre
- Human Nutrition Unit, School of Biological Sciences, Department of Medicine, University of Auckland, Auckland, New Zealand.,Center for Health Technology Services Research, NOVA Medical School, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Pia S Vestentoft
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Elli Jalo
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Santiago Navas-Carretero
- Department of Nutrition, University of Navarra, Pamplona, Spain.,CIBERobn, Instituto de Salud Carlos III, Madrid, Spain.,Precision Nutrition Program, IMDEA Food, Campus de Excelencia Internacional, Universidad Autónoma de Madrid + Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Maija Huttunen-Lenz
- Institute for Nursing Science, University of Education Schwäbisch Gmünd, Schwäbisch Gmünd, Germany
| | - Moira A Taylor
- Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, Queen's Medical Centre, Nottingham, United Kingdom.,National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Gareth Stratton
- Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, Swansea University, Swansea, United Kingdom
| | - Nils Swindell
- Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, Swansea University, Swansea, United Kingdom
| | - Niina E Kaartinen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Tony Lam
- NetUnion sarl, Lausanne, Switzerland
| | | | - Svetoslav Handjiev
- Department of Pharmacology and Toxicology, Medical University of Sofia, Sofia, Bulgaria
| | - Wolfgang Schlicht
- Exercise and Health Sciences, University of Stuttgart, Stuttgart, Germany
| | - J Alfredo Martinez
- Department of Nutrition, University of Navarra, Pamplona, Spain.,CIBERobn, Instituto de Salud Carlos III, Madrid, Spain.,Precision Nutrition Program, IMDEA Food, Campus de Excelencia Internacional, Universidad Autónoma de Madrid + Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Radhika V Seimon
- The Boden Collaboration for Obesity, Nutrition, Exercise, and Eating Disorders, Faculty of Medicine and Health, Charles Perkins Centre, The University of Sydney, Camperdown, NSW, Australia
| | - Amanda Sainsbury
- School of Human Sciences (Exercise and Sports Science), Faculty of Science, The University of Western Australia, Crawley, WA, Australia
| | - Ian A Macdonald
- Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, Queen's Medical Centre, MRC/ARUK Centre for Musculoskeletal Ageing Research, ARUK Centre for Sport, Exercise and Osteoarthritis, National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Margriet S Westerterp-Plantenga
- Department of Nutrition and Movement Sciences, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Jennie Brand-Miller
- School of Life and Environmental Sciences and Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Anne Raben
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark.,Steno Diabetes Center Copenhagen, Gentofte, Denmark
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