Prebiotic fiber has been examined as a way to foster gut bacteria less associated with obesity. Tests of prebiotic fiber in reducing obesity have occurred mainly in animals, adults, and Caucasians when the highest obesity rates are in African American and Latinx youth. Response to prebiotic fiber is determined by the pre-existing intestinal microbiota. The type of microbiota varies based on diet and physical activity (PA), so it is important to examine acceptability and response to prebiotic fiber in those most at risk for obesity.
Methods:
This cluster randomized controlled feasibility trial included an online program designed to improve diet and PA along with administration of prebiotic fiber for 12 weeks in 123 4th and 5th grade students where 98% were eligible for free or reduced fee lunch. Of these 56% were male; 71% Latinx; 15% African American; and 14% Other.
Results:
A decrease in body fat (BF) was associated with higher pre-test BF. Lower BMI was associated with a decrease in fecal Tenericutes and an increase in Actinobacteria.
Conclusion:
Prebiotic fiber was evaluated in additional studies. Determining those most responsive to prebiotic fiber can also permit individual recommendations for greater inclusion in usual diet choices.
Get full access to this article
View all access options for this article.
References
1.
Statistics NCfH. National Health and Nutrition Examination Survey 2017–march 2020 Prepandemic Data files Development of Files and Prevalence estimates for Selected Health Outcomes. 2021.
2.
BaumanA, RutterH, BaurL. Too little, too slowly: International perspectives on childhood obesity. Public Health Res Pract, 2019; 29(1).
3.
YusufZI, DongarwarD, YusufRA, et al.Social determinants of overweight and obesity among children in the United States. Int J MCH AIDS, 2020; 9(1):22–33.
4.
SantosJG, AlvesBC, HammesTO, et al.Dietary interventions, intestinal microenvironment, and obesity: A systematic review. Nutr Rev, 2019; 77(9):601–613.
5.
RinninellaEA-O, RaoulP, CintoniMA-O, et al.What is the healthy gut microbiota composition? A changing ecosystem across age, environment, diet, and diseases. Microorganisms, 2019; 7(1).
6.
NicolucciAC, HumeMP, MartínezI, et al.Prebiotics reduce body fat and alter intestinal microbiota in children who are overweight or with obesity. Gastroenterology, 2017; 153(3):711–722.
7.
LiberA, SzajewskaH. Effect of oligofructose supplementation on body weight in overweight and obese children: A randomised, double-blind, placebo-controlled trial. Br J Nutr, 2014; 112(12):2068–2074.
8.
VerduciE, CalcaterraV, Elisabetta DiP, et al.Brown adipose tissue: New challenges for prevention of childhood obesity. A narrative review. Nutrients, 2021; 13(5):1450.
9.
AbramsS, GriffinI, HawthorneK, et al.Effect of prebiotic supplementation and calcium intake on body mass index. J Pediatr, 2007; 151(3):293–298.
10.
ZhangF, YeJ, ZhuX, et al.Anti-obesity effects of dietary calcium: The evidence and possible mechanisms. Int J Mol Sci, 2019; 20(12).
11.
SantacruzA, MarcosA, WarnbergJ, et al.; EVASYON Study Group. Interplay between weight loss and gut microbiota composition in overweight adolescents. Obesity (Silver Spring), 2009; 17(10):1906–1915.
12.
BaiJ, HuY, BrunerDW. Composition of gut microbiota and its association with body mass index and lifestyle factors in a cohort of 7–18 years old children from the American Gut Project. Pediatr Obes, 2019; 14(4):e12480.
13.
BrowneNT, SnethenJA, GreenbergCS, et al.When pandemics collide: The impact of COVID-19 on childhood obesity. J Pediatr Nurs, 2021; 56:90–98.
14.
BrowneNT, HodgesEA, SmallL, et al.Childhood obesity within the lens of racism. Pediatr Obes, 2022; 17(5):e12878.
15.
FrennM, HeinrichA, DohmenC, et al.What can parents do to reduce youth obesity? An initial study with a diverse sample. J Pediatr Nurs, 2011; 26(5):428–434.
16.
FrennM, PruszynskiJE, FelzerH, et al.Authoritative feeding behaviors to reduce child bmi through online interventions. J Spec Pediatr Nurs, 2013; 18(1):65–77.
17.
FrennM, KaugarsA, GarciaJ, et al.Child body fat and body mass index: Which determinants are most important? West J Nurs Res, 2020; 42(8):593–602.
18.
PablosA, NebotV, Vañó-VicentV, et al.Effectiveness of a school-based program focusing on diet and health habits taught through physical exercise. Appl Physiol Nutr Metab, 2018; 43(4):331–337.
19.
WongSS. Community-based healthy living medicine, with a focus on K-12, physical education, and nutrition. Prog Cardiovasc Dis, 2017; 60(3):450–455.
20.
StrieterL, LadduDR, SainsburyJ, et al.The importance of school-based healthy living initiatives: Introducing the health and wellness academy concept. Prog Cardiovasc Dis, 2019; 62(1):68–73.
21.
CunninghamSA, HardyST, JonesR, et al.Changes in the incidence of childhood obesity. Pediatrics, 2022; 150(2).
LukaskiHC. Evolution of bioimpedance: A circuitous journey from estimation of physiological function to assessment of body composition and a return to clinical research. Eur J Clin Nutr, 2013; 67 (Suppl 1):S2–S9.
25.
BarreiraTV, StaianoAE, KatzmarzykPT. Validity assessment of a portable bioimpedance scale to estimate body fat percentage in white and african- american children and adolescents. Pediatr Obes, 2013; 8(2):e29–e32.
26.
ProchaskaJJ, SallisJF, RuppJ. Screening measure for assessing dietary fat intake among adolescents. Prev Med, 2001; 33(6):699–706.
27.
ProchaskaJJ, SallisJF, LongB. A physical activity screening measure for use with adolescents in primary care. Arch Pediatr Adolesc Med, 2001; 155(5):554–559.
28.
DeSantisTZJr, HugenholtzP, KellerK, et al.Nast: A multiple sequence alignment server for comparative analysis of 16s rrna genes. Nucleic Acids Research, 2006; 34(Web Server issue):W394–W9.
29.
ColeJR, WangQ, CardenasE, et al.The ribosomal database project: Improved alignments and new tools for rrna analysis. Nucleic Acids Research, 2009; 37(Database issue):D141–D5.
30.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria; 2018. Available from: https://www.R-project.org/
31.
Van BuurenS. Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res, 2007; 16(3):219–242.
32.
KlineRB. Principles and Practice of Structural Equation Modeling. 4th ed. Guilford Publications; 2016.
33.
KievitRA, BrandmaierAM, ZieglerG, et al.; NSPN Consortium. developmental cognitive neuroscience using latent change score models: A tutorial and applications. Dev Cogn Neurosci, 2018; 33:99–117.
34.
OksanenJ, Guillaume BlanchetF, et al.Vegan: Community Ecology Package. R package version 2.0-10. 2013.
35.
GosleeSC, UrbanDL. The ecodist package for dissimilarity-based analysis of ecological data. J Stat Soft, 2007; 22(7):1–19.
36.
McArdleBH, AndersonMJ. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology, 2001; 82(1):290–297.
37.
PolitDF, BeckCT. Nursing Research: Generating and Assessing Evidence for Nursing Practice. 11 ed. Lippincott: Philadelphia; 2020.
38.
BrownDR. Tenericutes. In: Bergey’s Manual of Systematics of Archaea and Bacteria. WILEY; 2018; pp. 1–3.
39.
BorgoF, VerduciE, RivaA, et al.Relative abundance in bacterial and fungal gut microbes in obese children: A case control study. Child Obes, 2017; 13(1):78–84.
40.
AguilarT, NavaGM, Olvera-RamírezAM, et al.Gut bacterial families are associated with body composition and metabolic risk markers in school-aged children in rural Mexico. Child Obes, 2020; 16(5):358–366.
41.
MurugesanS, Ulloa-MartínezM, Fau-Martínez-RojanoH, et al.Study of the diversity and short-chain fatty acids production by the bacterial community in overweight and obese Mexican children. Eur J Clin Microbiol Infect Dis, 2015; 34(7):1337–1346.
42.
HoutmanTA, EckermannHA, SmidtH, et al.Gut microbiota and BMI throughout childhood: The role of firmicutes, bacteroidetes, and short-chain fatty acid producers. Sci Rep, 2022; 12(1):3140.
43.
Vazquez‐MorenoM, Perez‐HerreraA, Locia‐MoralesD, et al.Association of gut microbiome with fasting triglycerides, fasting insulin and obesity status in mexican children. Pediatric Obesity, 2021; 16(5):1–8.
44.
ChoKY. Lifestyle modifications result in alterations in the gut microbiota in obese children. BMC Microbiol, 2021; 21(1):10.
45.
Betancourt-GarciaMM, ArguellesA, MontesJ, et al.Pediatric nonalcoholic fatty liver disease: The rise of a lethal disease among Mexican American Hispanic children. Obes Surg, 2017; 27(1):236–244.
46.
O’GradyJ, O’ConnorEM, ShanahanF. Review article: Dietary fibre in the era of microbiome science. Aliment Pharmacol Ther, 2019; 49(5):506–515.
47.
ParkY, GuldmannJ-M. Understanding disparities in community green accessibility under alternative green measures: A metropolitan-wide analysis of Columbus, Ohio, and Atlanta, Georgia. Landscape and Urban Planning, 2020; 200:103806.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
