Associations of changes in reported and estimated protein and energy intake with changes in insulin resistance, glycated hemoglobin, and BMI during the PREVIEW lifestyle intervention study

Mathijs Drummen, Tanja C Adam, Ian A Macdonald, Elli Jalo, Thomas M Larssen, J Alfredo Martinez, Teodora Handjiev-Darlenska, Jennie Brand-Miller, Sally D Poppitt, Gareth Stratton, Kirsi H Pietiläinen, Moira A Taylor, Santiago Navas-Carretero, Svetoslav Handjiev, Roslyn Muirhead, Marta P Silvestre, Nils Swindell, Maija Huttunen-Lenz, Wolfgang Schlicht, Tony Lam, Jouko Sundvall, Laura Raman, Edith Feskens, Angelo Tremblay, Anne Raben, Margriet S Westerterp-Plantenga, Mathijs Drummen, Tanja C Adam, Ian A Macdonald, Elli Jalo, Thomas M Larssen, J Alfredo Martinez, Teodora Handjiev-Darlenska, Jennie Brand-Miller, Sally D Poppitt, Gareth Stratton, Kirsi H Pietiläinen, Moira A Taylor, Santiago Navas-Carretero, Svetoslav Handjiev, Roslyn Muirhead, Marta P Silvestre, Nils Swindell, Maija Huttunen-Lenz, Wolfgang Schlicht, Tony Lam, Jouko Sundvall, Laura Raman, Edith Feskens, Angelo Tremblay, Anne Raben, Margriet S Westerterp-Plantenga

Abstract

Background: Observed associations of high-protein diets with changes in insulin resistance are inconclusive.

Objectives: We aimed to assess associations of changes in both reported and estimated protein (PRep; PEst) and energy intake (EIRep; EIEst) with changes in HOMA-IR, glycated hemoglobin (HbA1c), and BMI (in kg/m2), in 1822 decreasing to 833 adults (week 156) with overweight and prediabetes, during the 3-y PREVIEW (PREVention of diabetes through lifestyle intervention and population studies In Europe and around the World) study on weight-loss maintenance. Eating behavior and measurement errors (MEs) of dietary intake were assessed. Thus, observational post hoc analyses were applied.

Methods: Associations of changes in EIEst, EIRep, PEst, and PRep with changes in HOMA-IR, HbA1c, and BMI were determined by linear mixed-model analysis in 2 arms [high-protein-low-glycemic-index (GI) diet and moderate-protein-moderate-GI diet] of the PREVIEW study. EIEst was derived from energy requirement: total energy expenditure = basal metabolic rate × physical activity level; PEst from urinary nitrogen, and urea. MEs were calculated as [(EIEst - EIRep)/EIEst] × 100% and [(PRep - PEst)/PEst] × 100%. Eating behavior was determined using the Three Factor Eating Questionnaire, examining cognitive dietary restraint, disinhibition, and hunger.

Results: Increases in PEst and PRep and decreases in EIEst and EIRep were associated with decreases in BMI, but not independently with decreases in HOMA-IR. Increases in PEst and PRep were associated with decreases in HbA1c. PRep and EIRep showed larger changes and stronger associations than PEst and EIEst. Mean ± SD MEs of EIRep and PRep were 38% ± 9% and 14% ± 4%, respectively; ME changes in EIRep and En% PRep were positively associated with changes in BMI and cognitive dietary restraint and inversely with disinhibition and hunger.

Conclusions: During weight-loss maintenance in adults with prediabetes, increase in protein intake and decrease in energy intake were not associated with decrease in HOMA-IR beyond associations with decrease in BMI. Increases in PEst and PRep were associated with decrease in HbA1c.This trial was registered at clinicaltrials.gov as NCT01777893.

Keywords: basal metabolic rate; measurement error of dietary intake reporting; obesity; physical activity level; prediabetes; urinary nitrogen as biomarker.

© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.

Figures

FIGURE 1
FIGURE 1
Participant flowchart. Numbers of participants in the HP and MP groups. Data used for the present analysis are indicated by “sufficient data,” including complete data on BMI, body composition, HOMA-IR, glycated hemoglobin, 4-d food diaries, urinary nitrogen, and accelerometry. HI, high-intensity; HP, high-protein, low-glycemic-index diet; LED, low-energy diet; MI, moderate-intensity; MP, moderate-protein, moderate-glycemic-index diet.
FIGURE 2
FIGURE 2
Mean reported minus estimated EI, P intake, and intake of CHO, F, and ethanol combined, as a percentage of estimated intake in the HP and MP groups. Data are presented as means ± SDs. Linear mixed-model analyses adjusting for age, sex, study center, and BMI were used to assess differences between the intervention groups. Significance levels of pairwise comparisons are indicated if the overall group effect was significant. *Significantly different from MP group: *P < 0.05; **P < 0.01; ***P < 0.001. n (female/male), weeks 0, 26, 52, 104, and 156: HP: 923 (618/305); 644 (429/215); 528 (352/176); 450 (288/162); and 413 (271/142), respectively; MP: 899 (594/305); 648 (428/220); 551 (354/197); 439 (273/166); and 420 (271/149), respectively. CHO, carbohydrate; EI, energy intake; F, fat; HP, high-protein, low-glycemic-index diet; MP, moderate-protein, moderate-glycemic-index diet; P, protein.
FIGURE 3
FIGURE 3
Frequency distributions of reported minus estimated EI and P intake as percentages of estimated EI and P intake in both groups together, at the different time points. n (female/male), weeks 0, 26, 52, 104, and 156: HP: 923 (618/305); 644 (429/215); 528 (352/176); 450 (288/162); and 413 (271/142), respectively; MP: 899 (594/305); 648 (428/220); 551 (354/197); 439 (273/166); and 420 (271/149), respectively. EI, energy intake; P, protein.

References

    1. WHO. Global report on diabetes. [Internet]. Geneva, Switzerland: World Health Organization; 2016; [accessed 27 November, 2018]. Available from: .
    1. Lean MEJ, Leslie WS, Alison CB, Brosnahan N, Thom G, McCombie L, Peters C, Zhyzhneuskaya S, Al-Mrabeh A, Hollingsworth KGet al. . Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet. 2018;391(10120):541–51.
    1. Lean MEJ, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L, Peters C, Zhyzhneuskaya S, Al-Mrabeh A, Hollingsworth KGet al. . Durability of a primary care-led weight-management intervention for remission of type 2 diabetes: 2-year results of the DiRECT open-label, cluster-randomised trial. Lancet Diabetes Endocrinol. 2019;7(5):344–55.
    1. Raben A, Vestentoft PS, Brand-Miller J, Jalo E, Drummen M, Simpson L, Martinez JA, Handjieva-Darlenska T, Stratton G, Huttunen-Lenz Met al. . The PREVIEW intervention study: results from a 3-year randomized 2×2 factorial multinational trial investigating the role of protein, glycaemic index and physical activity for prevention of type 2 diabetes. Diabetes Obes Metab. 2021;23(2):324–37.
    1. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM, Diabetes Prevention Program Research Group . Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.
    1. Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, Keinänen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas Met al. . Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343–50.
    1. Pan X-R, Li G-W, Hu Y-H, Wang J-X, Yang W-Y, An Z-X, Hu Z-X, Lin J, Xiao J-Z, Cao H-Bet al. . Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20(4):537–44.
    1. Fogelholm M, Larsen T, Westerterp-Plantenga M, Macdonald I, Martinez JA, Boyadjieva N, Poppitt S, Schlicht W, Stratton G, Sundvall Jet al. . PREVIEW: Prevention of diabetes through lifestyle intervention and population studies In Europe and around the World. Design, methods, and baseline participant description of an adult cohort enrolled into a three-year randomised clinical trial. Nutrients. 2017;9(6):632.
    1. Christensen P, Larsen TM, Westerterp-Plantenga M, Macdonald I, Martinez JA, Handjiev S, Poppitt S, Hansen S, Ritz C, Astrup Aet al. . Men and women respond differently to rapid weight loss: metabolic outcomes of a multi-centre intervention study after a low-energy diet in 2500 overweight, individuals with pre-diabetes (PREVIEW). Diabetes Obes Metab. 2018;20(12):2840–51.
    1. Westerterp-Plantenga MS, Lejeune MP, Nijs I, van Ooijen M, Kovacs EM. High protein intake sustains weight maintenance after body weight loss in humans. Int J Obes. 2004;28(1):57–64.
    1. Lejeune MP, Kovacs EM, Westerterp-Plantenga MS. Additional protein intake limits weight regain after weight loss in humans. Br J Nutr. 2005;93(2):281–9.
    1. Soenen S, Bonomi AG, Lemmens SG, Scholte J, Thijssen M, Frank van Berkum F, Westerterp-Plantenga MS. Relatively high-protein or “low-carb” energy-restricted diets for body weight loss and body weight maintenance?. Physiol Behav. 2012;107(3):374–80.
    1. Soenen S, Martens EA, Hochstenbach-Waelen A, Lemmens SGT, Westerterp-Plantenga MS. Normal protein intake is required for body weight loss and weight maintenance, and elevated protein intake for additional preservation of resting energy expenditure and fat free mass. J Nutr. 2013;143(5):591–6.
    1. Larsen TM, Dalskov S-M, van Baak M, Jebb SA, Papadaki A, Pfeiffer AFH, Martinez JA, Handjieva-Darlenska T, Kunešová M, Pihlsgård Met al. . Diets with high or low protein content and glycemic index for weight-loss maintenance. N Engl J Med. 2010;363(22):2102–13.
    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.
    1. Møller G, Sluik D, Ritz C, Mikkilä V, Raitakari OT, Hutri-Kähönen N, Dragsted LO, Larsen TM, Poppitt SD, Silvestre MPet al. . A protein diet score, including plant and animal protein, investigating the association with HbA1c and eGFR—the PREVIEW Project. Nutrients. 2017;9(7):763.
    1. Virtanen HEK, Koskinen TT, Voutilainen S, Mursu J, Tuomainen T-P, Kokko P, Virtanen JK. Intake of different dietary proteins and risk of type 2 diabetes in men: the Kuopio Ischaemic Heart Disease Risk Factor Study. Br J Nutr. 2017;117(6):882–93.
    1. Sluijs I, Beulens JWJ, van der A DL, Spijkerman AMW, Grobbee DE, van der Schouw YT. Dietary intake of total, animal, and vegetable protein and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-NL study. Diabetes Care. 2010;33(1):43–8.
    1. Zhao L-G, Zhang Q-L, Liu X-L, Wu H, Zheng J-L, Xiang Y-B. Dietary protein intake and risk of type 2 diabetes: a dose-response meta-analysis of prospective studies. Eur J Nutr. 2019;58(4):1351–67.
    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):973–9.
    1. Dhurandhar NV, Schoeller D, Brown AW, Heymsfield SB, Thomas D, Sørensen TI, Speakman JR, Jeansonne M, Allison DB, Energy Balance Measurement Working Group . Energy balance measurement: when something is not better than nothing. Int J Obes. 2015;39(7):1109–13.
    1. Murakami K, Livingstone MB. Prevalence and characteristics of misreporting energy intake in US adults: NHANES 2003–2012. Br J Nutr. 2015;114(8):1294–303.
    1. Castro-Quezada I, Ruano-Rodríguez C, Ribas-Barba L, Serra-Majem L. Misreporting in nutritional surveys: methodological implications. Nutr Hosp. 2015;31(Suppl 3):119–27.
    1. Heitmann BL, Lissner L. Can adverse effects of dietary fat intake be overestimated as a consequence of dietary fat underreporting?. Public Health Nutr. 2005;8(8):1322–7.
    1. Samuel-Hodge CD, Fernandez LM, Henriquez-Roldan CF, Johnston LF, Keyserling TC. A comparison of self-reported energy intake with total energy expenditure estimated by accelerometer and basal metabolic rate in African-American women with type 2 diabetes. Diabetes Care. 2004;27(3):663–9.
    1. Goris AH, Meijer EP, Westerterp KR. Repeated measurement of habitual food intake increases under-reporting and induces selective under-reporting. Br J Nutr. 2001;85(5):629–34.
    1. Goris AH, Westerterp-Plantenga MS, Westerterp KR. Undereating and underrecording of habitual food intake in obese men: selective underreporting of fat intake. Am J Clin Nutr. 2000;71(1):130–4.
    1. Goris AH, Meijer EP, Kester A, Westerterp KR. Use of a triaxial accelerometer to validate reported food intakes. Am J Clin Nutr. 2001;73(3):549–53.
    1. Westerterp KR, Goris AH. Validity of the assessment of dietary intake: problems of misreporting. Curr Opin Clin Nutr Metab Care. 2002;5(5):489–93.
    1. Bingham SA, Cummings JH. Urine nitrogen as an independent validatory measure of dietary intake: a study of nitrogen balance in individuals consuming their normal diet. Am J Clin Nutr. 1985;42(6):1276–89.
    1. Bingham SA. Urine nitrogen as a biomarker for the validation of dietary protein intake. J Nutr. 2003;133(3):921S–4S.
    1. Westerterp KR, Donkers J, Fredrix EW, Boekhoudt P. Energy intake, physical activity and body weight; a simulation model. Br J Nutr. 1995;73(3):337–47.
    1. Stunkard AJ, Messick S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J Psychosom Res. 1985;29(1):71–83.
    1. Bohrer BK, Forbush KT, Hunt TK. Are common measures of dietary restraint and disinhibited eating reliable and valid in obese persons?. Appetite. 2015;87;344–51.
    1. Lejeune MP, Hukshorn CJ, Saris WH, Westerterp-Plantenga MS. Effect of dietary restraint during and following pegylated recombinant leptin (PEG-OB) treatment of overweight men. Int J Obes. 2003;27(12):1494–9.
    1. Wolever TMS, Yang M, Zeng XY, Brand-Miller JC. Food glycemic index, as given in glycemic index tables, is a significant determinant of glycemic responses elicited by composite breakfast meals. Am J Clin Nutr. 2006;83(6):1306–12.
    1. Westerterp KR. Reliable assessment of physical activity in disease: an update on activity monitors. Curr Opin Clin Nutr Metab Care. 2014;17(5):401–6.
    1. Plasqui G, Westerterp KR. Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity. 2007;15(10):2371–9.
    1. Ekelund U, Yngve A, Brage S, Westerterp K, Sjöström M. Body movement and physical activity energy expenditure in children and adolescents: how to adjust for differences in body size. Am J Clin Nutr. 2004;79(5):851–6.
    1. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30(5):777–81.
    1. Sluik D, Brouwer-Brolsma EM, Berendsen AAM, Mikkilä V, Poppitt SD, Silvestre MP, Tremblay A, Pérusse L, Bouchard C, Raben Aet al. . Protein intake and the incidence of pre-diabetes and diabetes in 4 population-based studies: the PREVIEW project. Am J Clin Nutr. 2019;109(5):1310–18.
    1. Schoeller DA, Westerterp-Plantenga MS. Advances in the assessment of dietary intake. Boca Raton, FL: CRC Press; 2017.
    1. Drummen M, Tischmann L, Gatta-Cherifi B, Fogelholm M, Raben A, Adam TC, Westerterp-Plantenga MS. High versus moderate protein intake reduces adaptive thermogenesis and induces a negative energy balance during long-term weight loss maintenance in participants with pre-diabetes in the post-obese state –a PREVIEW study. J Nutr. 2020;150:458–63.

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