Xihua L, Hong L. Obesity: epidemiology, pathophysiology, and therapeutics. Front Endocrinol (Lausanne). 2021;12:1070.

Google Scholar 

Metrics Evaluation Institute for Health (IHME). Global Burden of Disease Compare | IHME Viz Hub. https://vizhub.healthdata.org/gbd-compare/

Jazayeri-Tehrani SA, Rezayat SM, Mansouri S, Qorbani M, Alavian SM, Daneshi-Maskooni M, et al. Nano-curcumin improves glucose indices, lipids, inflammation, and Nesfatin in overweight and obese patients with non-alcoholic fatty liver disease (NAFLD): a double-blind randomized placebo-controlled clinical trial. Nutr Metab (Lond). 2019;16(1):1–13.

Article  Google Scholar 

Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):1–14.

Article  Google Scholar 

Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347–51.

Article  CAS  PubMed  Google Scholar 

Khan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F. Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr. 2018;10(1):74.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vidal-Ostos F, Ramos-Lopez O, Blaak E, Astrup A, Martinez J. The Triglyceride-glucose index as an adiposity marker and a predictor of fat loss induced by a low-calorie diet. Eur J Clin Invest. 2021;52:e13674.

PubMed  Google Scholar 

Unger G, Benozzi S, Perruzza F, Pennacchiotti G. Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr. 2014;61(10):533–40.

Article  PubMed  Google Scholar 

Locateli J, Lopes W, Simões C, De Oliveira G, Oltramari K, Bim R, et al. Triglyceride/glucose index is a reliable alternative marker for insulin resistance in South American overweight and obese children and adolescents. J Pediatr Endocrinol Metab. 2019;32(10):1163–70.

Article  CAS  PubMed  Google Scholar 

Foster-Powell K, Holt SHA, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr. 2002;76(1):5–56.

Article  CAS  PubMed  Google Scholar 

Sánchez-Íñigo L, Navarro-González D, Pastrana-Delgado J, Fernández-Montero A, Martínez JA. Association of triglycerides and new lipid markers with the incidence of hypertension in a Spanish cohort. J Hypertens. 2016;34(7):1257–65.

Article  PubMed  Google Scholar 

Da Silva A, Caldas APS, Rocha DMUP, Bressan J. Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: a systematic review and meta-analysis of cohort studies. Prim Care Diabetes. 2020;14(6):584–93.

Article  PubMed  Google Scholar 

Zhang S, Du T, Zhang J, Lu H, Lin X, Xie J, et al. The triglyceride and glucose index (TyG) is an effective biomarker to identify nonalcoholic fatty liver disease. Lipids Health Dis. 2017. https://doi.org/10.1186/s12944-017-0409-6.

Article  PubMed  PubMed Central  Google Scholar 

Sánchez-Íñigo L, Navarro-González D, Fernández-Montero A, Pastrana-Delgado J, Martínez JA. The TyG index may predict the development of cardiovascular events. Eur J Clin Invest. 2016;46(2):189–97.

Article  PubMed  Google Scholar 

Wang X, Liu J, Cheng Z, Zhong Y, Chen X, Song W. Triglyceride glucose-body mass index and the risk of diabetes: a general population-based cohort study. Lipids Heal Dis. 2021;20(1):1–10.

Google Scholar 

Van Namen M, Prendergast L, Peiris C. Supervised lifestyle intervention for people with metabolic syndrome improves outcomes and reduces individual risk factors of metabolic syndrome: a systematic review and meta-analysis. Metabolism. 2019;101:153988.

Article  PubMed  Google Scholar 

Huttunen-Lenz M, Hansen S, Christensen P, Larsen TM, Sandø-Pedersen F, Drummen M, et al. PREVIEW study—Influence of a behavior modification intervention (PREMIT) in over 2300 people with pre-diabetes: intention, self-efficacy and outcome expectancies during the early phase of a lifestyle intervention. Psychol Res Behav Manag. 2018;11:383–94.

Article  PubMed  PubMed Central  Google Scholar 

Verduci E, Banderali G, Di Profio E, Vizzuso S, Zuccotti G, Radaelli G. Effect of individual- versus collective-based nutritional-lifestyle intervention on the atherogenic index of plasma in children with obesity: a randomized trial. Nutr Metab (Lond). 2021. https://doi.org/10.1186/s12986-020-00537-w.

Article  Google Scholar 

Ramos-Lopez O, Milagro FI, Riezu-Boj JI, Martinez JA. Epigenetic signatures underlying inflammation: an interplay of nutrition, physical activity, metabolic diseases, and environmental factors for personalized nutrition. Inflamm Res. 2021;70(1):29–49.

Article  CAS  PubMed  Google Scholar 

Martinez JA, Navas-Carretero S, Saris WHM, Astrup A. Personalized weight loss strategies—the role of macronutrient distribution. Nat Rev Endocrinol. 2014;10(12):749–60.

Article  CAS  PubMed  Google Scholar 

González-Muniesa P, Mártinez-González M-A, Hu FB, Després J-P, Matsuzawa Y, Loos RJF, et al. Obesity. Nat Rev Dis Prim. 2017;3(1):17034.

Article  PubMed  Google Scholar 

Gregg E, Jakicic J, Blackburn G, Bloomquist P, Bray G, Clark J, et al. Association of the magnitude of weight loss and changes in physical fitness with long-term cardiovascular disease outcomes in overweight or obese people with type 2 diabetes: a post-hoc analysis of the Look AHEAD randomised clinical trial. Lancet Diabetes Endocrinol. 2016;4(11):913–21.

Article  PubMed  PubMed Central  Google Scholar 

Verdich C, Barbe P, Petersen M, Grau K, Ward L, Macdonald I, et al. Changes in body composition during weight loss in obese subjects in the NUGENOB study: comparison of bioelectrical impedance vs. dual-energy X-ray absorptiometry. Diabetes Metab. 2011;37(3):222–9.

Article  CAS  PubMed  Google Scholar 

Miketinas DC, Bray GA, Beyl RA, Ryan DH, Sacks FM, Champagne CM. Fiber intake predicts weight loss and dietary adherence in adults consuming calorie-restricted diets: the POUNDS Lost (Preventing Overweight Using Novel Dietary Strategies) Study. J Nutr. 2019;149(10):1742–8.

Article  PubMed  PubMed Central  Google Scholar 

Larsen TM, Dalskov S-M, van Baak M, Jebb SA, Papadaki A, Pfeiffer AFH, et al. Diets with high or low protein content and glycemic index for weight-loss maintenance. N Engl J Med. 2010;363(22):2102–13.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lombardo M, Bellia C, Moletto C, Aulisa G, Padua E, Della-Morte D, et al. Effects of quality and quantity of protein intake for type 2 diabetes mellitus prevention and metabolic control. Curr Nutr Rep. 2020;9(4):329–37.

Article  CAS  PubMed  Google Scholar 

Simopoulos AP. The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases. Exp Biol Med. 2008;233(6):674–88.

Article  CAS  Google Scholar 

Vogtschmidt YD, Raben A, Faber I, de Wilde C, Lovegrove JA, Givens DI, et al. Is protein the forgotten ingredient: effects of higher compared to lower protein diets on cardiometabolic risk factors. A systematic review and meta-analysis of randomised controlled trials. Atherosclerosis. 2021;328:124–35.

Article  CAS  PubMed  Google Scholar 

Ramos-Lopez O, Milagro FI, Allayee H, Chmurzynska A, Choi MS, Curi R, et al. Guide for current nutrigenetic, nutrigenomic, and nutriepigenetic approaches for precision nutrition involving the prevention and management of chronic diseases associated with obesity. J Nutrigenet Nutrigenomics. 2017;10(1–2):43–62.

CAS  PubMed  Google Scholar 

Liu X, He G, Lo K, Huang Y, Feng Y. The triglyceride-glucose index, an insulin resistance marker, was non-linear associated with all-cause and cardiovascular mortality in the general population. Front Cardiovasc Med. 2021;14:7.

CAS  Google Scholar 

Moore CS, Lindroos AK, Kreutzer M, Larsen TM, Astrup A, Van Baak MA, et al. Dietary strategy to manipulate ad libitum macronutrient intake, and glycaemic index, across eight European countries in the Diogenes Study. Obes Rev. 2010;11(1):67–75.

Article  CAS  PubMed  Google Scholar 

Dehghan M, Merchant AT. Is bioelectrical impedance accurate for use in large epidemiological studies? Nutr J. 2008;7(1):26.

Article  PubMed  PubMed Central  Google Scholar 

Kutáč P, Bunc V, Sigmund M. Whole-body dual-energy X-ray absorptiometry demonstrates better reliability than segmental body composition analysis in college-aged students. PLoS ONE. 2019;14(4):e0215599.

Article  PubMed  PubMed Central  Google Scholar 

VanItallie T, Yang M, Heymsfield S, Funk R, Boileau R. Height-normalized indices of the body’s fat-free mass and fat mass: potentially useful indicators of nutritional status. Am J Clin Nutr. 1990;52(6):953–9.

Article  CAS  PubMed  Google Scholar 

Wells J. Toward body composition reference data for infants, children, and adolescents. Adv Nutr. 2014;5(3):320S-329S.

Article  PubMed  PubMed Central  Google Scholar 

Valsesia A, Chakrabarti A, Hager J, Langin D, Saris W, Astrup A, et al. Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics. Sci Rep. 2020. https://doi.org/10.1038/s41598-020-65936-8.

Article  PubMed  PubMed Central  Google Scholar 

Bautista FP, Jasul G, Dampil OA. Insulin resistance and β-cell function of lean versus overweight or obese filipino patients with newly diagnosed type 2 diabetes mellitus. J ASEAN Fed Endocr Soc. 2019;34(2):164–70.

PubMed  PubMed Central  Google Scholar 

Hall K. What is the required energy deficit per unit weight loss? Int J Obes (Lond). 2008;32(3):573–6.

Article  CAS