Dietary protein and the glycemic index handle insulin resistance within a nutritional program for avoiding weight regain after energy-restricted induced weight loss

Fernando Vidal-Ostos, Omar Ramos-Lopez, Susan A Jebb, Angeliki Papadaki, Andreas F H Pfeiffer, Teodora Handjieva-Darlenska, Marie Kunešová, Ellen E Blaak, Arne Astrup, J Alfredo Martinez, Diet, Obesity, and Genes (Diogenes) Project, Fernando Vidal-Ostos, Omar Ramos-Lopez, Susan A Jebb, Angeliki Papadaki, Andreas F H Pfeiffer, Teodora Handjieva-Darlenska, Marie Kunešová, Ellen E Blaak, Arne Astrup, J Alfredo Martinez, Diet, Obesity, and Genes (Diogenes) Project

Abstract

Background and aim: The role of dietary protein and glycemic index on insulin resistance (based on TyG index) within a nutritional program for weight loss and weight maintenance was examined.

Methods: This study analyzed 744 adults with overweight/obesity within the DIOGenes project. Patients who lost at least 8% of their initial weight (0-8 weeks) after a low-calorie diet (LCD) were randomly assigned to one of five ad libitum diets designed for weight maintenance (8-34 weeks): high/low protein (HP/LP) and high/low glycemic index (HGI/LGI), plus a control. The complete nutritional program (0-34 weeks) included both LCD plus the randomized diets intervention. The TyG index was tested as marker of body mass composition and insulin resistance.

Results: In comparison with the LP/HGI diet, the HP/LGI diet induced a greater BMI loss (p < 0.05). ∆TyG was positively associated with resistance to BMI loss (β = 0.343, p = 0.042) during the weight maintenance stage. In patients who followed the HP/LGI diet, TyG (after LCD) correlated with greater BMI loss in the 8-34 weeks period (r = -0.256; p < 0.05) and during the 0-34 weeks intervention (r = -0.222, p < 0.05) periods. ΔTyG1 value was associated with ΔBMI2 (β = 0.932; p = 0.045) concerning the HP/LGI diet.

Conclusions: A HP/LGI diet is beneficial not only for weight maintenance after a LCD, but is also related to IR amelioration as assessed by TyG index changes. Registration Clinical Trials NCT00390637.

Keywords: Glycemic index; Insulin resistance; Metabolic improvement; Precision nutrition; Protein diet; TyG index.

Conflict of interest statement

The authors have nothing to disclose.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Flow chart and design concerning the participants enrolled in the current nutritional intervention
Fig. 2
Fig. 2
Change in TyG index after LCD and modifications in BMI (kg/m2) for each type of randomized diet, A Change in BMI (kg/m2) corresponding to the complete nutritional intervention, (period 3, 0–34 weeks); B Change in BMI (kg/m2) concerning the maintenance nutritional intervention, (period 2, 8–34 weeks). Results shown are based on ITT analysis. Types of diets: Control (healthy diet), LP/LGI (low protein, low glycemic index diet), LP/HGI (low protein, high glycemic index diet), HP/LGI (high protein, low glycemic index diet), HP/HGI (high protein, high glycemic index diet). Only significant differences were obtained between Low Protein/High Glycemic Index and High Protein/Low Glycemic Index (HP/LGI) diet (p = 0.015). Period 3: Corresponds to the differences between baseline and final parameters, encompassing the complete nutritional period (during 34 weeks); Period 2: Corresponds to the differences between the parameters after the low-calorie diet intervention (8 weeks) and after the nutritional treatment focused on weight maintenance for each type of randomized diet (8–34 weeks)

References

    1. Xihua L, Hong L. Obesity: epidemiology, pathophysiology, and therapeutics. Front Endocrinol (Lausanne). 2021;12:1070.
    1. Metrics Evaluation Institute for Health (IHME). Global Burden of Disease Compare | IHME Viz Hub.
    1. 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. doi: 10.1186/s12986-019-0331-1.
    1. 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. doi: 10.1186/s12933-018-0762-4.
    1. 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):3347–3351. doi: 10.1210/jc.2010-0288.
    1. 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. doi: 10.1186/s13098-018-0376-8.
    1. 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.
    1. Unger G, Benozzi S, Perruzza F, Pennacchiotti G. Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr. 2014;61(10):533–540. doi: 10.1016/j.endonu.2014.06.009.
    1. 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):1163–1170. doi: 10.1515/jpem-2019-0037.
    1. 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. doi: 10.1093/ajcn/76.1.5.
    1. 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):1257–1265. doi: 10.1097/HJH.0000000000000941.
    1. 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):584–593. doi: 10.1016/j.pcd.2020.09.001.
    1. 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 doi: 10.1186/s12944-017-0409-6.
    1. 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):189–197. doi: 10.1111/eci.12583.
    1. 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.
    1. 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. doi: 10.1016/j.metabol.2019.153988.
    1. 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;11:383–394. doi: 10.2147/PRBM.S160355.
    1. 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 doi: 10.1186/s12986-020-00537-w.
    1. 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. doi: 10.1007/s00011-020-01425-y.
    1. Martinez JA, Navas-Carretero S, Saris WHM, Astrup A. Personalized weight loss strategies—the role of macronutrient distribution. Nat Rev Endocrinol. 2014;10(12):749–760. doi: 10.1038/nrendo.2014.175.
    1. 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. doi: 10.1038/nrdp.2017.34.
    1. 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):913–921. doi: 10.1016/S2213-8587(16)30162-0.
    1. 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):222–229. doi: 10.1016/j.diabet.2010.10.007.
    1. 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):1742–1748. doi: 10.1093/jn/nxz117.
    1. 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):2102–2113. doi: 10.1056/NEJMoa1007137.
    1. 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):329–337. doi: 10.1007/s13668-020-00324-2.
    1. 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):674–688. doi: 10.3181/0711-MR-311.
    1. 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;328:124–135. doi: 10.1016/j.atherosclerosis.2021.05.011.
    1. 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.
    1. 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.
    1. 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. doi: 10.1111/j.1467-789X.2009.00602.x.
    1. Dehghan M, Merchant AT. Is bioelectrical impedance accurate for use in large epidemiological studies? Nutr J. 2008;7(1):26. doi: 10.1186/1475-2891-7-26.
    1. 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. doi: 10.1371/journal.pone.0215599.
    1. 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):953–959. doi: 10.1093/ajcn/52.6.953.
    1. Wells J. Toward body composition reference data for infants, children, and adolescents. Adv Nutr. 2014;5(3):320S–329S. doi: 10.3945/an.113.005371.
    1. 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 doi: 10.1038/s41598-020-65936-8.
    1. 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):164–170.
    1. Hall K. What is the required energy deficit per unit weight loss? Int J Obes (Lond) 2008;32(3):573–576. doi: 10.1038/sj.ijo.0803720.
    1. Tripepi G, Chesnaye N, Dekker F, Zoccali C, Jager K. Intention to treat and per protocol analysis in clinical trials. Nephrology (Carlton) 2020;25(7):513–517. doi: 10.1111/nep.13709.
    1. Rotella C, Dicembrini I. Measurement of body composition as a surrogate evaluation of energy balance in obese patients. World J Methodol. 2015;5(1):1. doi: 10.5662/wjm.v5.i1.1.
    1. Martins C, Roekenes J, Gower BA, Hunter GR. Metabolic adaptation is associated with less weight and fat mass loss in response to low-energy diets. Nutr Metab (Lond) 2021;18(1):1–7. doi: 10.1186/s12986-021-00587-8.
    1. Sacks FM, Carey VJ, Anderson CAM, Miller ER, Copeland T, Charleston J, et al. Effects of high vs low glycemic index of dietary carbohydrate on cardiovascular disease risk factors and insulin sensitivity: the OmniCarb randomized clinical trial. JAMA. 2014;312(23):2531–2541. doi: 10.1001/jama.2014.16658.
    1. Vega-López S, Venn BJ, Slavin JL. Relevance of the glycemic index and glycemic load for body weight, diabetes, and cardiovascular disease. Nutrients. 2018;10(10):1361. doi: 10.3390/nu10101361.
    1. Pesta DH, Samuel VT. A high-protein diet for reducing body fat: mechanisms and possible caveats. Nutr Metab (Lond) 2014;11(1):1–8. doi: 10.1186/1743-7075-11-53.
    1. Handjieva-Darlenska T, Handjiev S, Larsen TM, Van Baak MA, Jebb S, Papadaki A, et al. Initial weight loss on an 800-kcal diet as a predictor of weight loss success after 8 weeks: the Diogenes study. Eur J Clin Nutr. 2010;64(9):994–999. doi: 10.1038/ejcn.2010.110.
    1. Gögebakan Ö, Kohl A, Osterhoff MA, Van Baak MA, Jebb SA, Papadaki A, et al. Effects of weight loss and long-term weight maintenance with diets varying in protein and glycemic index on cardiovascular risk factors: the diet, obesity, and genes (diogenes) study: a randomized, controlled trial. Circulation. 2011;124(25):2829–2838. doi: 10.1161/CIRCULATIONAHA.111.033274.
    1. Jin J-L, Cao Y-X, Wu L-G, You X-D, Guo Y-L, Wu N-Q, et al. Triglyceride glucose index for predicting cardiovascular outcomes in patients with coronary artery disease. J Thorac Dis. 2018;10(11):6137–6146. doi: 10.21037/jtd.2018.10.79.
    1. Moon J, Koh G. Clinical evidence and mechanisms of high-protein diet-induced weight loss. J Obes Metab Syndr. 2020;29(3):166–173. doi: 10.7570/jomes20028.
    1. Santesso N, Akl EA, Bianchi M, Mente A, Mustafa R, Heels-Ansdell D, et al. Effects of higher-versus lower-protein diets on health outcomes: a systematic review and meta-analysis. Eur J Clin Nutr. 2012;66(7):780–788. doi: 10.1038/ejcn.2012.37.
    1. Drummen M, Tischmann L, Gatta-Cherifi B, Adam T, Westerterp-Plantenga M. Dietary protein and energy balance in relation to obesity and co-morbidities. Front Endocrinol. 2018;9:443. doi: 10.3389/fendo.2018.00443.
    1. Westerterp-Plantenga MS, Rolland V, Wilson SAJ, Westerterp KR. Satiety related to 24 h diet-induced thermogenesis during high protein/carbohydrate vs high fat diets measured in a respiration chamber. Eur J Clin Nutr. 1999;53(6):495–502. doi: 10.1038/sj.ejcn.1600782.
    1. Andriessen C, Christensen P, Vestergaard Nielsen L, Ritz C, Astrup A, Larsen TM, et al. Weight loss decreases self-reported appetite and alters food preferences in overweight and obese adults: observational data from the DiOGenes study. Appetite. 2018;125:314–322. doi: 10.1016/j.appet.2018.02.016.
    1. Rietman A, Schwarz J, Tomé D, Kok FJ, Mensink M. High dietary protein intake, reducing or eliciting insulin resistance? Eur J Clin Nutr. 2014;68(9):973–979. doi: 10.1038/ejcn.2014.123.
    1. Gardner CD, Trepanowski JF, Gobbo LCD, Hauser ME, Rigdon J, Ioannidis JPA, et al. Effect of low-fat VS low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion the DIETFITS randomized clinical trial. JAMA. 2018;319(7):667–679. doi: 10.1001/jama.2018.0245.
    1. Solomon TPJ, Haus JM, Kelly KR, Cook MD, Filion J, Rocco M, et al. A low-glycemic index diet combined with exercise reduces insulin resistance, postprandial hyperinsulinemia, and glucose-dependent insulinotropic polypeptide responses in obese, prediabetic humans. Am J Clin Nutr. 2010;92(6):1359–1368. doi: 10.3945/ajcn.2010.29771.
    1. Ndumele CE, Pradhan AD, Ridker PM. Interrelationships between inflammation, C-reactive protein, and insulin resistance. J Cardiometab Syndr. 2006;1(3):107–196. doi: 10.1111/j.1559-4564.2006.05538.x.
    1. Saisho Y. β-cell dysfunction: its critical role in prevention and management of type 2 diabetes. World J Diabetes. 2015;6(1):109. doi: 10.4239/wjd.v6.i1.109.
    1. Goyenechea E, Holst C, van Baak MA, Saris WHM, Jebb S, Kafatos A, et al. Effects of different protein content and glycaemic index of ad libitum diets on diabetes risk factors in overweight adults: the DIOGenes multicentre, randomized, dietary intervention trial. Diabetes Metab Res Rev. 2011;27(7):705–716. doi: 10.1002/dmrr.1218.
    1. Minh HV, Tien HA, Sinh CT, Thang DC, Chen CH, Tay JC, et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens. 2021;23:529–537. doi: 10.1111/jch.14155.
    1. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304. doi: 10.1089/met.2008.0034.
    1. Dagpo TD, Nolan CJ, Delghingaro-Augusto V. Exploring therapeutic targets to reverse or prevent the transition from metabolically healthy to unhealthy obesity. Cells. 2020;9(7):1596. doi: 10.3390/cells9071596.
    1. Ramos-Lopez O, Riezu-Boj J, Milagro F, Cuervo M, Goni L, Martinez J. Genetic and nongenetic factors explaining metabolically healthy and unhealthy phenotypes in participants with excessive adiposity: relevance for personalized nutrition. Ther Adv Endocrinol Metab. 2019;10:2042018819877303. doi: 10.1177/2042018819877303.
    1. Kusters Y, Schalkwijk C, Houben A, Kooi M, Lindeboom L, Op ’t Roodt J, et al. Independent tissue contributors to obesity-associated insulin resistance. JCI insight. 2017;2(13).
    1. Galarregui C, Navas-Carretero S, González-Navarro CJ, Martínez JA, Zulet MA, Abete I. Both macronutrient food composition and fasting insulin resistance affect postprandial glycemic responses in senior subjects. Food Funct. 2021;12:6540–6548. doi: 10.1039/D1FO00731A.
    1. Gołąbek KD, Regulska-Ilow B. Dietary support in insulin resistance: an overview of current scientific reports. Adv Clin Exp Med. 2019;28(11):1577–1585. doi: 10.17219/acem/109976.
    1. Reynolds AN, Akerman AP, Mann J. Dietary fibre and whole grains in diabetes management: systematic review and meta-analyses. PLoS Med. 2020;17(3):e1003053. doi: 10.1371/journal.pmed.1003053.
    1. Visuthranukul C, Sirimongkol P, Prachansuwan A, Pruksananonda C, Chomtho S. Low-glycemic index diet may improve insulin sensitivity in obese children. Pediatr Res. 2015;78(5):567–573. doi: 10.1038/pr.2015.142.
    1. Heymsfield S, Harp J, Reitman M, Beetsch W, Schoeller D, Erondu N, et al. Why do obese patients not lose more weight when treated with low-calorie diets? A mechanistic perspective. Am J Clin Nutr. 2007;85(2):346–354. doi: 10.1093/ajcn/85.2.346.
    1. Chao A, Quigley K, Wadden T. Dietary interventions for obesity: clinical and mechanistic findings. J Clin Invest. 2021;131(1):e140065. doi: 10.1172/JCI140065.
    1. Racette S, Das S, Bhapkar M, Hadley E, Roberts S, Ravussin E, et al. Approaches for quantifying energy intake and %calorie restriction during calorie restriction interventions in humans: the multicenter CALERIE study. Am J Physiol Endocrinol Metab. 2012;302(4):441–448. doi: 10.1152/ajpendo.00290.2011.
    1. Hall K, Sacks G, Chandramohan D, Chow C, Wang Y, Gortmaker S, et al. Quantification of the effect of energy imbalance on bodyweight. Lancet (London, England) 2011;378(9793):826–837. doi: 10.1016/S0140-6736(11)60812-X.
    1. Joffe Y, Houghton C. A Novel Approach to the Nutrigenetics and Nutrigenomics of Obesity and Weight Management. Curr Oncol Rep. 2016;18(7):1–7. doi: 10.1007/s11912-016-0529-6.
    1. Christiansen M, Ureña M, Borisevich D, Grarup N, Martínez J, Oppert J, et al. Abdominal and gluteofemoral fat depots show opposing associations with postprandial lipemia. Am J Clin Nutr. 2021;114:1467–1475. doi: 10.1093/ajcn/nqab219.
    1. Haghighatdoost F, Amini M, Aminorroaya A, Abyar M, Feizi A. Different metabolic/obesity phenotypes are differentially associated with development of prediabetes in adults: results from a 14-year cohort study. World J Diabetes. 2019;10(6):350–361. doi: 10.4239/wjd.v10.i6.350.

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