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

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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 http://www.clinicaltrials.gov, NCT03135041.

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Abbreviations

ALAT:

Alanine aminotransferase

ASAT:

Aspartate aminotransferase

CPM:

Counts per minute

E%:

Energy percent

FDR:

False discovery rate

FFA:

Free fatty acids

HbA1c:

Glycosylated haemoglobin A1c

Hgb:

Haemoglobin

HOMA-IR:

Homeostatic model assessment of insulin resistance

hsCRP:

High-sensitive C reactive protein

ITT:

Intention to treat

kcal:

Calories

kJ:

Kilojoule

LMM:

Linear mixed model

OTU:

Operational taxonomic unit

PC:

Principal coordinates

PCoA:

Principal coordinate analysis

PCR:

Polymerase chain reaction

PP:

Per protocol

ppm:

Parts per minute

RDP:

Ribosomal Database Project

rRNA:

Ribosomal ribonucleic acid

SCFA:

Short chain fatty acid

SD:

Standard deviation

SE:

Standard error

VAS:

Visual analogue scale

WBC:

White blood cells

References

  1. 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. https://doi.org/10.1186/1471-2105-9-386

    CAS  Article  Google Scholar 

  2. 2.

    Smith SC Jr (2007) Multiple risk factors for cardiovascular disease and diabetes mellitus. Am J Med 120(3 Suppl 1):S3–S11. https://doi.org/10.1016/j.amjmed.2007.01.002

    Article  PubMed  Google Scholar 

  3. 3.

    World Health Organization (2018) Obesity and overweight. Fact sheet. World Health Organization. http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 10 Jan 2019

  4. 4.

    World Health Organization (2009) Global health risks. https://www.who.int/healthinfo/global_burden_disease/global_health_risks/en/. Accessed 10 Jan 2019

  5. 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. https://doi.org/10.1111/j.1464-5491.2006.01858.x

    CAS  Article  PubMed  Google Scholar 

  6. 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. https://doi.org/10.1038/nature05414

    Article  PubMed  Google Scholar 

  7. 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. https://doi.org/10.1038/nature11450

    CAS  Article  PubMed  Google Scholar 

  8. 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. https://doi.org/10.1016/j.jnutbio.2017.12.010

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Maukonen J, Saarela M (2015) Human gut microbiota: does diet matter? Proc Nutr Soc 74(1):23–36. https://doi.org/10.1017/S0029665114000688

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Rothe M, Blaut M (2013) Evolution of the gut microbiota and the influence of diet. Benef Microbes 4(1):31–37. https://doi.org/10.3920/BM2012.0029

    CAS  Article  PubMed  Google Scholar 

  11. 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. https://doi.org/10.1136/gutjnl-2016-313271

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 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. https://doi.org/10.1002/mnfr.201700484

    Article  PubMed  Google Scholar 

  13. 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. https://doi.org/10.3945/jn.113.185298

    CAS  Article  PubMed  Google Scholar 

  14. 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. https://doi.org/10.3390/nu9020149

    Article  PubMed  PubMed Central  Google Scholar 

  15. 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. 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. https://doi.org/10.1139/H10-074

    CAS  Article  PubMed  Google Scholar 

  17. 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. https://doi.org/10.3109/09637486.2013.836738

    CAS  Article  PubMed  Google Scholar 

  18. 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. https://doi.org/10.1016/j.nut.2013.09.005

    CAS  Article  PubMed  Google Scholar 

  19. 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. https://doi.org/10.1053/j.gastro.2017.05.055

    Article  PubMed  Google Scholar 

  20. 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. https://doi.org/10.1186/s12986-015-0033-2

    CAS  Article  Google Scholar 

  21. 21.

    Markowiak P, Slizewska K (2017) Effects of probiotics, prebiotics, and synbiotics on human health. Nutrients. https://doi.org/10.3390/nu9091021

    Article  PubMed  PubMed Central  Google Scholar 

  22. 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. https://doi.org/10.1017/S0007114513003607

    CAS  Article  PubMed  Google Scholar 

  23. 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. https://doi.org/10.1017/S0007114510003363

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Yoo JY, Kim SS (2016) Probiotics and prebiotics: present status and future perspectives on metabolic disorders. Nutrients 8(3):173. https://doi.org/10.3390/nu8030173

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    R Core Team (2018) R: a language and environment for statistical computing. https://www.R-project.org/. Accessed 3 Sept 2018

  26. 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. https://doi.org/10.1093/ajcn/51.2.241

    CAS  Article  PubMed  Google Scholar 

  27. 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. https://doi.org/10.1093/nar/gks808

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Magoc T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27(21):2957–2963. https://doi.org/10.1093/bioinformatics/btr507

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 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. https://doi.org/10.1128/AEM.01541-09

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 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. https://doi.org/10.1093/bioinformatics/btr381

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 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. https://doi.org/10.1093/nar/gks1219

    CAS  Article  Google Scholar 

  32. 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. https://doi.org/10.1038/nmeth.f.303

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460–2461. https://doi.org/10.1093/bioinformatics/btq461

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 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. https://doi.org/10.1128/AEM.00062-07

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 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

    CAS  Article  Google Scholar 

  36. 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

    CAS  Article  Google Scholar 

  37. 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. https://doi.org/10.1249/mss.0b013e31815a51b3

    Article  PubMed  Google Scholar 

  38. 38.

    Lewis SJ, Heaton KW (1997) Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol 32(9):920–924. https://doi.org/10.3109/00365529709011203

    CAS  Article  PubMed  Google Scholar 

  39. 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  Google Scholar 

  40. 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. https://doi.org/10.1038/nature12820

    CAS  Article  PubMed  Google Scholar 

  41. 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. https://doi.org/10.1038/nature12480

  42. 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. https://doi.org/10.1093/ajcn/nqy041

    Article  PubMed  Google Scholar 

  43. 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. https://doi.org/10.1017/S0007114517003440

    CAS  Article  PubMed  Google Scholar 

  44. 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

    CAS  Article  Google Scholar 

  45. 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. https://doi.org/10.1016/j.nutres.2018.09.007

    CAS  Article  PubMed  Google Scholar 

  46. 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. https://doi.org/10.1038/ejcn.2009.64

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Holscher HD (2017) Dietary fiber and prebiotics and the gastrointestinal microbiota. Gut Microbes 8(2):172–184. https://doi.org/10.1080/19490976.2017.1290756

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 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. https://doi.org/10.1038/nutd.2015.9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 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. https://doi.org/10.1038/srep16643

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 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. https://doi.org/10.2337/db07-1605

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 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. https://doi.org/10.1053/j.gastro.2017.03.051(e83)

    CAS  Article  PubMed  Google Scholar 

  52. 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. https://doi.org/10.1136/gutjnl-2012-303304

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. 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. https://doi.org/10.1039/c9fo00075e

    CAS  Article  PubMed  Google Scholar 

  54. 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. https://doi.org/10.1039/c8fo01116h

    CAS  Article  PubMed  Google Scholar 

  55. 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. https://doi.org/10.1194/jlr.R036012

    CAS  Article  Google Scholar 

  56. 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. https://doi.org/10.1080/19490976.2017.1405209

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 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. https://doi.org/10.1002/mnfr.201800160

    CAS  Article  PubMed  Google Scholar 

  58. 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. https://doi.org/10.3389/fmicb.2017.00197

    Article  PubMed  PubMed Central  Google Scholar 

  59. 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. https://doi.org/10.1016/j.jand.2015.02.031

    Article  PubMed  Google Scholar 

  60. 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. https://doi.org/10.1038/nrcardio.2017.120

    Article  PubMed  Google Scholar 

  61. 61.

    Pluznick J (2014) A novel SCFA receptor, the microbiota, and blood pressure regulation. Gut Microbes 5(2):202–207. https://doi.org/10.4161/gmic.27492

    Article  PubMed  Google Scholar 

  62. 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. https://doi.org/10.1161/hypertensionaha.116.07910

    CAS  Article  PubMed  Google Scholar 

  63. 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. https://doi.org/10.1097/HJH.0000000000000515

    CAS  Article  PubMed  Google Scholar 

  64. 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. https://doi.org/10.1016/j.numecd.2017.09.007

    CAS  Article  PubMed  Google Scholar 

  65. 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. https://doi.org/10.1016/j.cell.2015.11.001

    CAS  Article  PubMed  Google Scholar 

  66. 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. https://doi.org/10.1038/s41366-018-0093-2

    CAS  Article  Google Scholar 

  67. 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. https://doi.org/10.1016/j.clnu.2019.01.012

    Article  PubMed  Google Scholar 

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Acknowledgements

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.

Funding

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

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Contributions

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.

Corresponding authors

Correspondence to Anne Lundby Hess or Alfonso Benítez-Páez.

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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). https://doi.org/10.1007/s00394-019-02099-x

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Keywords

  • Dietary fibre
  • Gut microbiome
  • Inulin
  • Maltodextrin
  • Obesity
  • Weight loss