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The effect of inulin and resistant maltodextrin on weight loss during energy restriction: a randomised, placebo-controlled, double-blinded intervention

European Journal of Nutrition Aims and scope Submit manuscript



The objective of this study was to investigate the additive effects of combining energy restriction with dietary fibres on change in body weight and gut microbiota composition.


The study was a 12-week randomised, placebo-controlled, double-blinded, parallel intervention trial. A total of 116 overweight or obese participants were assigned randomly either to 10 g inulin plus 10 g resistant maltodextrin or to 20 g of placebo supplementation through 400 mL of milk a day, while on a − 500 kcal/day energy restricted diet.


Altogether, 86 participants completed the intervention. There were no significant differences in weight loss or body composition between the groups. The fibre supplement reduced systolic (5.35 ± 2.4 mmHg, p = 0.043) and diastolic (2.82 ± 1.3 mmHg, p = 0.047) blood pressure to a larger extent than placebo. Furthermore, a larger decrease in serum insulin was observed in the placebo group compared to the fibre group (− 26.0 ± 9.2 pmol/L, p = 0.006). The intake of fibre induced changes in the composition of gut microbiota resulting in higher abundances of Parabacteroides and Bifidobacteria, compared to placebo. The effects on blood pressure and glucose metabolism were mainly observed in women, and could be attributed to a higher gut microbiota diversity after intervention. Finally, the fibre group experienced a higher degree of gastrointestinal symptoms, which attenuated over time.


Supplementation of inulin and resistant maltodextrin did not provide an additional weight loss during an energy-restricted diet, but reduced both systolic and diastolic blood pressure. Furthermore, the fibre supplement did stimulate the growth of potentially beneficial bacteria genera.

Clinical trial registry

The study was registered at, NCT03135041.

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Fig. 1
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Alanine aminotransferase


Aspartate aminotransferase


Counts per minute


Energy percent


False discovery rate


Free fatty acids


Glycosylated haemoglobin A1c




Homeostatic model assessment of insulin resistance


High-sensitive C reactive protein


Intention to treat






Linear mixed model


Operational taxonomic unit


Principal coordinates


Principal coordinate analysis


Polymerase chain reaction


Per protocol


Parts per minute


Ribosomal Database Project


Ribosomal ribonucleic acid


Short chain fatty acid


Standard deviation


Standard error


Visual analogue scale


White blood cells


  1. Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A, Wilkening J, Edwards RA (2008) The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform 9:386.

    Article  CAS  Google Scholar 

  2. Smith SC Jr (2007) Multiple risk factors for cardiovascular disease and diabetes mellitus. Am J Med 120(3 Suppl 1):S3–S11.

    Article  PubMed  Google Scholar 

  3. World Health Organization (2018) Obesity and overweight. Fact sheet. World Health Organization. Accessed 10 Jan 2019

  4. World Health Organization (2009) Global health risks. Accessed 10 Jan 2019

  5. Alberti KG, Zimmet P, Shaw J (2006) Metabolic syndrome—a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabet Med 23(5):469–480.

    Article  CAS  PubMed  Google Scholar 

  6. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI (2006) An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444(7122):1027–1031.

    Article  PubMed  Google Scholar 

  7. Qin JJ, Li YR, Cai ZM, Li SH, Zhu JF, Zhang F, Liang SS, Zhang WW, Guan YL, Shen DQ, Peng YQ, Zhang DY, Jie ZY, Wu WX, Qin YW, Xue WB, Li JH, Han LC, Lu DH, Wu PX, Dai YL, Sun XJ, Li ZS, Tang AF, Zhong SL, Li XP, Chen WN, Xu R, Wang MB, Feng Q, Gong MH, Yu J, Zhang YY, Zhang M, Hansen T, Sanchez G, Raes J, Falony G, Okuda S, Almeida M, LeChatelier E, Renault P, Pons N, Batto JM, Zhang ZX, Chen H, Yang RF, Zheng WM, Li SG, Yang HM, Wang J, Ehrlich SD, Nielsen R, Pedersen O, Kristiansen K, Wang J (2012) A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490(7418):55–60.

    Article  CAS  PubMed  Google Scholar 

  8. Battson ML, Lee DM, Weir TL, Gentile CL (2018) The gut microbiota as a novel regulator of cardiovascular function and disease. J Nutr Biochem 56:1–15.

    Article  CAS  PubMed  Google Scholar 

  9. Maukonen J, Saarela M (2015) Human gut microbiota: does diet matter? Proc Nutr Soc 74(1):23–36.

    Article  CAS  PubMed  Google Scholar 

  10. Rothe M, Blaut M (2013) Evolution of the gut microbiota and the influence of diet. Benef Microbes 4(1):31–37.

    Article  CAS  PubMed  Google Scholar 

  11. Vandeputte D, Falony G, Vieira-Silva S, Wang J, Sailer M, Theis S, Verbeke K, Raes J (2017) Prebiotic inulin-type fructans induce specific changes in the human gut microbiota. Gut 66(11):1968–1974.

    Article  CAS  PubMed  Google Scholar 

  12. Reimer RA, Willis HJ, Tunnicliffe JM, Park H, Madsen KL, Soto-Vaca A (2017) Inulin-type fructans and whey protein both modulate appetite but only fructans alter gut microbiota in adults with overweight/obesity: a randomized controlled trial. Mol Nutr Food Res.

    Article  PubMed  Google Scholar 

  13. Baer DJ, Stote KS, Henderson T, Paul DR, Okuma K, Tagami H, Kanahori S, Gordon DT, Rumpler WV, Ukhanova M, Culpepper T, Wang X, Mai V (2014) The metabolizable energy of dietary resistant maltodextrin is variable and alters fecal microbiota composition in adult men. J Nutr 144(7):1023–1029.

    Article  CAS  PubMed  Google Scholar 

  14. Solah VA, Kerr DA, Hunt WJ, Johnson SK, Boushey CJ, Delp EJ, Meng X, Gahler RJ, James AP, Mukhtar AS, Fenton HK, Wood S (2017) Effect of fibre supplementation on body weight and composition, frequency of eating and dietary choice in overweight individuals. Nutrients.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Birketvedt GS, Shimshi M, Erling T, Florholmen J (2005) Experiences with three different fiber supplements in weight reduction. Med Sci Monit 11(1):PI5–PI8

    PubMed  Google Scholar 

  16. Li S, Guerin-Deremaux L, Pochat M, Wils D, Reifer C, Miller LE (2010) NUTRIOSE dietary fiber supplementation improves insulin resistance and determinants of metabolic syndrome in overweight men: a double-blind, randomized, placebo-controlled study. Appl Physiol Nutr Metab 35(6):773–782.

    Article  CAS  PubMed  Google Scholar 

  17. Dehghan P, Gargari BP, Jafar-Abadi MA, Aliasgharzadeh A (2014) Inulin controls inflammation and metabolic endotoxemia in women with type 2 diabetes mellitus: a randomized-controlled clinical trial. Int J Food Sci Nutr 65(1):117–123.

    Article  CAS  PubMed  Google Scholar 

  18. Dehghan P, Pourghassem Gargari B, Asghari Jafar-abadi M (2014) Oligofructose-enriched inulin improves some inflammatory markers and metabolic endotoxemia in women with type 2 diabetes mellitus: a randomized controlled clinical trial. Nutrition 30(4):418–423.

    Article  CAS  PubMed  Google Scholar 

  19. Nicolucci AC, Hume MP, Martinez I, Mayengbam S, Walter J, Reimer RA (2017) Prebiotics reduce body fat and alter intestinal microbiota in children who are overweight or with obesity. Gastroenterology 153(3):711–722.

    Article  PubMed  Google Scholar 

  20. Guess ND, Dornhorst A, Oliver N, Bell JD, Thomas EL, Frost GS (2015) A randomized controlled trial: the effect of inulin on weight management and ectopic fat in subjects with prediabetes. Nutr Metab (Lond) 12:36.

    Article  CAS  Google Scholar 

  21. Markowiak P, Slizewska K (2017) Effects of probiotics, prebiotics, and synbiotics on human health. Nutrients.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kellow NJ, Coughlan MT, Reid CM (2014) Metabolic benefits of dietary prebiotics in human subjects: a systematic review of randomised controlled trials. Br J Nutr 111(7):1147–1161.

    Article  CAS  PubMed  Google Scholar 

  23. Roberfroid M, Gibson GR, Hoyles L, McCartney AL, Rastall R, Rowland I, Wolvers D, Watzl B, Szajewska H, Stahl B, Guarner F, Respondek F, Whelan K, Coxam V, Davicco MJ, Leotoing L, Wittrant Y, Delzenne NM, Cani PD, Neyrinck AM, Meheust A (2010) Prebiotic effects: metabolic and health benefits. Br J Nutr 104(Suppl 2):S1–63.

    Article  CAS  PubMed  Google Scholar 

  24. Yoo JY, Kim SS (2016) Probiotics and prebiotics: present status and future perspectives on metabolic disorders. Nutrients 8(3):173.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. R Core Team (2018) R: a language and environment for statistical computing. Accessed 3 Sept 2018

  26. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO (1990) A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 51(2):241–247.

    Article  CAS  PubMed  Google Scholar 

  27. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, Glockner FO (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 41(1):e1.

    Article  CAS  PubMed  Google Scholar 

  28. Magoc T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27(21):2957–2963.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16):2194–2200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41(Database issue):D590–D596.

    Article  CAS  PubMed  Google Scholar 

  32. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335–336.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460–2461.

    Article  CAS  PubMed  Google Scholar 

  34. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16):5261–5267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Friedewald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18(6):499–502

    Article  CAS  PubMed  Google Scholar 

  36. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28(7):412–419

    Article  CAS  PubMed  Google Scholar 

  37. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M (2008) Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 40(1):181–188.

    Article  PubMed  Google Scholar 

  38. Lewis SJ, Heaton KW (1997) Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol 32(9):920–924.

    Article  CAS  PubMed  Google Scholar 

  39. Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon RO 3rd, Criqui M, Fadl YY, Fortmann SP, Hong Y, Myers GL, Rifai N, Smith SC Jr, Taubert K, Tracy RP, Vinicor F, Centers for Disease C, Prevention, American Heart A (2003) Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation 107(3):499–511

    Article  PubMed  Google Scholar 

  40. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA, Biddinger SB, Dutton RJ, Turnbaugh PJ (2014) Diet rapidly and reproducibly alters the human gut microbiome. Nature 505(7484):559–563.

    Article  CAS  PubMed  Google Scholar 

  41. Cotillard A, Kennedy SP, Kong LC, Prifti E, Pons N, Le Chatelier E, Almeida M, Quinquis B, Levenez F, Galleron N, Gougis S, Rizkalla S, Batto JM, Renault P, Consortium ANRM, Dore J, Zucker JD, Clement K, Ehrlich SD (2013) Dietary intervention impact on gut microbial gene richness. Nature 500(7464):585–588.

  42. So D, Whelan K, Rossi M, Morrison M, Holtmann G, Kelly JT, Shanahan ER, Staudacher HM, Campbell KL (2018) Dietary fiber intervention on gut microbiota composition in healthy adults: a systematic review and meta-analysis. Am J Clin Nutr 107(6):965–983.

    Article  PubMed  Google Scholar 

  43. Healey G, Murphy R, Butts C, Brough L, Whelan K, Coad J (2018) Habitual dietary fibre intake influences gut microbiota response to an inulin-type fructan prebiotic: a randomised, double-blind, placebo-controlled, cross-over, human intervention study. Br J Nutr 119(2):176–189.

    Article  CAS  PubMed  Google Scholar 

  44. Gibson GR, Beatty ER, Wang X, Cummings JH (1995) Selective stimulation of bifidobacteria in the human colon by oligofructose and inulin. Gastroenterology 108(4):975–982

    Article  CAS  PubMed  Google Scholar 

  45. Burns AM, Solch RJ, Dennis-Wall JC, Ukhanova M, Nieves C Jr, Mai V, Christman MC, Gordon DT, Langkamp-Henken B (2018) In healthy adults, resistant maltodextrin produces a greater change in fecal bifidobacteria counts and increases stool wet weight: a double-blind, randomized, controlled crossover study. Nutr Res 60:33–42.

    Article  CAS  PubMed  Google Scholar 

  46. Meyer D, Stasse-Wolthuis M (2009) The bifidogenic effect of inulin and oligofructose and its consequences for gut health. Eur J Clin Nutr 63(11):1277–1289.

    Article  CAS  PubMed  Google Scholar 

  47. Holscher HD (2017) Dietary fiber and prebiotics and the gastrointestinal microbiota. Gut Microbes 8(2):172–184.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Brahe LK, Le Chatelier E, Prifti E, Pons N, Kennedy S, Hansen T, Pedersen O, Astrup A, Ehrlich SD, Larsen LH (2015) Specific gut microbiota features and metabolic markers in postmenopausal women with obesity. Nutr Diabetes 5:e159.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Schneeberger M, Everard A, Gomez-Valades AG, Matamoros S, Ramirez S, Delzenne NM, Gomis R, Claret M, Cani PD (2015) Akkermansia muciniphila inversely correlates with the onset of inflammation, altered adipose tissue metabolism and metabolic disorders during obesity in mice. Sci Rep 5:16643.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Balkau B, Mhamdi L, Oppert JM, Nolan J, Golay A, Porcellati F, Laakso M, Ferrannini E, GroUP E-RS (2008) Physical activity and insulin sensitivity: the RISC study. Diabetes 57(10):2613–2618.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Canfora EE, van der Beek CM, Hermes GDA, Goossens GH, Jocken JWE, Holst JJ, van Eijk HM, Venema K, Smidt H, Zoetendal EG, Dejong CHC, Lenaerts K, Blaak EE (2017) Supplementation of diet with galacto-oligosaccharides increases bifidobacteria, but not insulin sensitivity, in obese prediabetic individuals. Gastroenterology 153(1):87–97.

    Article  CAS  PubMed  Google Scholar 

  52. Dewulf EM, Cani PD, Claus SP, Fuentes S, Puylaert PGB, Neyrinck AM, Bindels LB, de Vos WM, Gibson GR, Thissen JP, Delzenne NM (2013) Insight into the prebiotic concept: lessons from an exploratory, double blind intervention study with inulin-type fructans in obese women. Gut 62(8):1112–1121.

    Article  CAS  PubMed  Google Scholar 

  53. Li L, Guo WL, Zhang W, Xu JX, Qian M, Bai WD, Zhang YY, Rao PF, Ni L, Lv XC (2019) Grifola frondosa polysaccharides ameliorate lipid metabolic disorders and gut microbiota dysbiosis in high-fat diet fed rats. Food Funct 10(5):2560–2572.

    Article  CAS  PubMed  Google Scholar 

  54. Pan YY, Zeng F, Guo WL, Li TT, Jia RB, Huang ZR, Lv XC, Zhang J, Liu B (2018) Effect of Grifola frondosa 95% ethanol extract on lipid metabolism and gut microbiota composition in high-fat diet-fed rats. Food Funct 9(12):6268–6278.

    Article  CAS  PubMed  Google Scholar 

  55. den Besten G, van Eunen K, Groen AK, Venema K, Reijngoud DJ, Bakker BM (2013) The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res 54(9):2325–2340.

    Article  CAS  Google Scholar 

  56. Lee H, Lee Y, Kim J, An J, Lee S, Kong H, Song Y, Lee CK, Kim K (2018) Modulation of the gut microbiota by metformin improves metabolic profiles in aged obese mice. Gut Microbes 9(2):155–165.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gonzalez-Sarrias A, Romo-Vaquero M, Garcia-Villalba R, Cortes-Martin A, Selma MV, Espin JC (2018) The endotoxemia marker lipopolysaccharide-binding protein is reduced in overweight-obese subjects consuming pomegranate extract by modulating the gut microbiota: a randomized clinical trial. Mol Nutr Food Res 62(11):e1800160.

    Article  CAS  PubMed  Google Scholar 

  58. Nakayama J, Yamamoto A, Palermo-Conde LA, Higashi K, Sonomoto K, Tan J, Lee YK (2017) Impact of westernized diet on gut microbiota in children on Leyte island. Front Microbiol 8:197.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Franz MJ, Boucher JL, Rutten-Ramos S, VanWormer JJ (2015) Lifestyle weight-loss intervention outcomes in overweight and obese adults with type 2 diabetes: a systematic review and meta-analysis of randomized clinical trials. J Acad Nutr Diet 115(9):1447–1463.

    Article  PubMed  Google Scholar 

  60. Marques FZ, Mackay CR, Kaye DM (2018) Beyond gut feelings: how the gut microbiota regulates blood pressure. Nat Rev Cardiol 15(1):20–32.

    Article  PubMed  Google Scholar 

  61. Pluznick J (2014) A novel SCFA receptor, the microbiota, and blood pressure regulation. Gut Microbes 5(2):202–207.

    Article  PubMed  Google Scholar 

  62. Gomez-Arango LF, Barrett HL, McIntyre HD, Callaway LK, Morrison M, Dekker Nitert M, GroUP ST (2016) Increased systolic and diastolic blood pressure is associated with altered gut microbiota composition and butyrate production in early pregnancy. Hypertension 68(4):974–981.

    Article  CAS  PubMed  Google Scholar 

  63. Evans CE, Greenwood DC, Threapleton DE, Cleghorn CL, Nykjaer C, Woodhead CE, Gale CP, Burley VJ (2015) Effects of dietary fibre type on blood pressure: a systematic review and meta-analysis of randomized controlled trials of healthy individuals. J Hypertens 33(5):897–911.

    Article  CAS  PubMed  Google Scholar 

  64. Khan K, Jovanovski E, Ho HVT, Marques ACR, Zurbau A, Mejia SB, Sievenpiper JL, Vuksan V (2018) The effect of viscous soluble fiber on blood pressure: a systematic review and meta-analysis of randomized controlled trials. Nutr Metab Cardiovasc Dis 28(1):3–13.

    Article  CAS  PubMed  Google Scholar 

  65. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E (2015) Personalized nutrition by prediction of glycemic responses. Cell 163(5):1079–1094.

    Article  CAS  PubMed  Google Scholar 

  66. Hjorth MF, Blaedel T, Bendtsen LQ, Lorenzen JK, Holm JB, Kiilerich P, Roager HM, Kristiansen K, Larsen LH, Astrup A (2019) Prevotella-to-bacteroides ratio predicts body weight and fat loss success on 24-week diets varying in macronutrient composition and dietary fiber: results from a post hoc analysis. Int J Obes (Lond) 43(1):149–157.

    Article  CAS  Google Scholar 

  67. Kjølbæk L, Benítez-Páez A, Gómez del Pulgar E, Brahe L, Liebisch G, Matysik S, Rampelli S, Vermeiren J, Brigidi P, Larsen LH, Astrup A, Sanz Y (2019) Arabinoxylan oligosaccharides and polyunsaturated fatty acid effects on gut microbiota and metabolic markers in overweight individuals with signs of metabolic syndrome: a randomized cross-over trial. Clin Nutr.

    Article  PubMed  Google Scholar 

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The authors wish to thank the participants and the study staff (scientific employees, dieticians, kitchen staff, laboratory technicians, bachelor and master students) involved in the intervention study at the Department of Nutrition, Exercise and Sports, University of Copenhagen. The authors would also like to thank Christian Ritz for statistical advice on data analysis.


This work was supported by the European Union’s Seventh Framework Program, Grant agreement no. 613979 (MyNewGut).

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Authors and Affiliations




TB, LHL, YS and TML designed research; JRI and CM designed and provided the intervention products; ALH, ABP and TML conducted research; ALH and ABP analysed data; ALH and ABP drafted the paper and had primary responsibility for final content. All authors read and approved the final manuscript.

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Correspondence to Anne Lundby Hess or Alfonso Benítez-Páez.

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All the authors declare to have no conflict of interest.

Additional information

Data described in the manuscript, code book, etc. can be made available upon request pending on application and approval. The raw fasta sequences generated from the 16S amplicon sequencing of faecal DNA are publicly available at the MG-RAST server [1] upon the project accession number mgp88216.

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Hess, A.L., Benítez-Páez, A., Blædel, T. et al. The effect of inulin and resistant maltodextrin on weight loss during energy restriction: a randomised, placebo-controlled, double-blinded intervention. Eur J Nutr 59, 2507–2524 (2020).

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