Clinical trial NCT04642482: Synbiotic Therapy on Intestinal Microbiota and Insulin Resistance in Obesity
Official title: The Effect of Synbiotic on Intestinal Microbiota and HOMA-IR (Homeostatic Model Assessment of Insulin Resistance) on Obesity
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: PHASE4
Conditions: Obesity
Insulin Resistance
Keywords: Intestinal Microbiota
Probiotic
Insulin Resistance
Obesity
Lead sponsor: Hasanuddin University
Sponsor class: OTHER
First posted: 2020-11-24
Enrollment: 16
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Intervention model description: A multiple-arm study involves three different groups receiving synbiotic (probiotic + prebiotic) and placebo. Observation of intestinal microbiota and insulin resistance is observed in repeated measurement
Primary purpose: TREATMENT

Brief summary: Background :

There is a plausible relationship between microbial gut and insulin resistance. Intervention to prevent insulin resistance by modifying the microbial gut has been proposed but limited studies demonstrates the expected impact. One of the possible way to manipulate the microbial gut is the administration of synbiotic (prebiotic and probiotic).

Objective :

This study aim to address the impact of synbiotic administration to the microbial gut and insulin resistance.

Brief Methodology :

A Quasi Experimental study with multiple arms is conducted to healthy participants. All subjects will undergo a microbial gut taxonomic analysis using faecal sample and blood examination to determine the insulin resistance status (using Homeostatic Model Assessment for Insulin Resistance/HOMA-IR approach). Synbiotic will be given to intervention arm and active comparator will use maltodextrin. Repeated measurement will be conducted after 8 weeks and 12 weeks from the day of administration.

Hypothesis : A superiority trial hypothesis is applied, assuming that the synbiotic group will demonstrates higher variety of microbial gut and lower HOMA-IR level

Detailed description: Study Location :

This study will recruit the healthy participants from the university

Target Population:

Healthy Participants

General Study Design :

Quasi Experimental study with a comparator

Sample Size calculation :

Difference between two means of HOMA IR from pilot data (7) and standard deviation 2.9, with 5% Type I error, and 80% Power yielded a total of 16 participants for two arms

Management of Sample:

1. Faecal Sample handling

   1. Patient should undergo 8 hours of fasting prior to faecal examination
   2. DNA Extraction
   3. Lysate preparation and centrifuge faecal sample
   4. Mixing lysate with sample
   5. Column wash
   6. DNA elution
   7. DNA storing
   8. DNA sequencing and analysis
   9. taxonomical analysis
2. Fasting blood glucose

   1. Patient should undergo 8 hours of fasting prior to Fasting blood glucose
   2. Blood is taken from cubital vein
   3. Spectrometry is conducted based on the NADPH formation from the equation below

   D-glucose+ATP -----\> Glucose-6-phospate+ADP Glucose-6-phospate+NAD ---- G-6-PDH ---\> D-Gluconate-6 phospate+NADH+H
3. Insulin level

   1. Centrifuge blood to obtain the serum
   2. The monoclonal anti-insulin antibody is given to the serum
   3. detection is based on the anti-insulin antibody and insulin complex formation
4. Homeostatic Model Assessment for Insulin Resistance/ HOMA-IR value is calculated from glucose level multiply by insulin level and divided by 405.

Protection for adverse event

1. All subjects are given the consent regarding the potential harm of synbiotic administration
2. All subjects will follow the protocol of reporting the any adverse event (most likely, severe constipation)
3. All subjects will be treated accordingly and hospitalisation if needed.

Statistical Analysis

1. General Analysis : Intention To Treat (ITT)
2. Propensity Score Matching will be conducted prior to intervention
3. A repeated measure ANOVA will be performed, whereas Generalized Linear Mixed Model treating the intervention as dummy variable will be performed if ANOVA assumptions can not be fulfilled.

Eligibility criteria: Inclusion Criteria:

1. Age above 18 years old
2. Not receive antibiotic prescription within the last 6 months

Exclusion Criteria

1. Taking medication that alters the blood sugar
2. Taking probiotic or synbiotic product (such as yogurt)
3. Participant who do not take the synbiotic intervention for more than 3 days consecutively
4. incomplete follow up examination results
5. Develop adverse effect

Primary outcomes:
measure: Homeostatic Model Assessment for Insulin Resistance (HOMA-IR)
timeFrame: Changes of HOMA IR value from baseline to 8 weeks
Secondary outcomes:
Other outcomes:
measure: Abundance-based Coverage Estimator (ACE) Index of Faecal Sample
timeFrame: Prior to intervention (Time 0), 8 weeks after Time 0, and 12 weeks after Time 0
measure: Shannon Index of Faecal Sample
timeFrame: Prior to intervention (Time 0), 8 weeks after Time 0, and 12 weeks after Time 0
Locations:
city: Makassar
country: Indonesia
facility: Faculty of Medicine, Muhammadiyah University
state: South Sulawesi
Study officials:
affiliation: Hasanuddin University
name: Nasrum Massi, Prof.
role: PRINCIPAL_INVESTIGATOR
affiliation: Hasanuddin University
name: Andi Anggeraini
role: PRINCIPAL_INVESTIGATOR
References:
citation: Andreasen AS, Larsen N, Pedersen-Skovsgaard T, Berg RM, Moller K, Svendsen KD, Jakobsen M, Pedersen BK. Effects of Lactobacillus acidophilus NCFM on insulin sensitivity and the systemic inflammatory response in human subjects. Br J Nutr. 2010 Dec;104(12):1831-8. doi: 10.1017/S0007114510002874. Epub 2010 Sep 6.
citation: Hagerty SL, Hutchison KE, Lowry CA, Bryan AD. An empirically derived method for measuring human gut microbiome alpha diversity: Demonstrated utility in predicting health-related outcomes among a human clinical sample. PLoS One. 2020 Mar 2;15(3):e0229204. doi: 10.1371/journal.pone.0229204. eCollection 2020.
citation: Brahe LK, Astrup A, Larsen LH. Is butyrate the link between diet, intestinal microbiota and obesity-related metabolic diseases? Obes Rev. 2013 Dec;14(12):950-9. doi: 10.1111/obr.12068. Epub 2013 Aug 16.
citation: Bermudez-Brito M, Plaza-Diaz J, Munoz-Quezada S, Gomez-Llorente C, Gil A. Probiotic mechanisms of action. Ann Nutr Metab. 2012;61(2):160-74. doi: 10.1159/000342079. Epub 2012 Oct 2.
citation: Cani PD, Neyrinck AM, Fava F, Knauf C, Burcelin RG, Tuohy KM, Gibson GR, Delzenne NM. Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia. Diabetologia. 2007 Nov;50(11):2374-83. doi: 10.1007/s00125-007-0791-0. Epub 2007 Sep 6.
citation: Cani PD, Delzenne NM. The role of the gut microbiota in energy metabolism and metabolic disease. Curr Pharm Des. 2009;15(13):1546-58. doi: 10.2174/138161209788168164.
citation: Chakraborti CK. New-found link between microbiota and obesity. World J Gastrointest Pathophysiol. 2015 Nov 15;6(4):110-9. doi: 10.4291/wjgp.v6.i4.110.
citation: Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, Almeida M, Arumugam M, Batto JM, Kennedy S, Leonard P, Li J, Burgdorf K, Grarup N, Jorgensen T, Brandslund I, Nielsen HB, Juncker AS, Bertalan M, Levenez F, Pons N, Rasmussen S, Sunagawa S, Tap J, Tims S, Zoetendal EG, Brunak S, Clement K, Dore J, Kleerebezem M, Kristiansen K, Renault P, Sicheritz-Ponten T, de Vos WM, Zucker JD, Raes J, Hansen T; MetaHIT consortium; Bork P, Wang J, Ehrlich SD, Pedersen O. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013 Aug 29;500(7464):541-6. doi: 10.1038/nature12506.
citation: Delzenne NM, Neyrinck AM, Cani PD. Gut microbiota and metabolic disorders: How prebiotic can work? Br J Nutr. 2013 Jan;109 Suppl 2:S81-5. doi: 10.1017/S0007114512004047.
citation: Griffiths EA, Duffy LC, Schanbacher FL, Qiao H, Dryja D, Leavens A, Rossman J, Rich G, Dirienzo D, Ogra PL. In vivo effects of bifidobacteria and lactoferrin on gut endotoxin concentration and mucosal immunity in Balb/c mice. Dig Dis Sci. 2004 Apr;49(4):579-89. doi: 10.1023/b:ddas.0000026302.92898.ae.
citation: He C, Shan Y, Song W. Targeting gut microbiota as a possible therapy for diabetes. Nutr Res. 2015 May;35(5):361-7. doi: 10.1016/j.nutres.2015.03.002. Epub 2015 Mar 14.
citation: Kassaian N, Feizi A, Aminorroaya A, Jafari P, Ebrahimi MT, Amini M. The effects of probiotics and synbiotic supplementation on glucose and insulin metabolism in adults with prediabetes: a double-blind randomized clinical trial. Acta Diabetol. 2018 Oct;55(10):1019-1028. doi: 10.1007/s00592-018-1175-2. Epub 2018 Jun 22.
citation: Kim YA, Keogh JB, Clifton PM. Probiotics, prebiotics, synbiotics and insulin sensitivity. Nutr Res Rev. 2018 Jun;31(1):35-51. doi: 10.1017/S095442241700018X. Epub 2017 Oct 17.
citation: Kootte RS, Vrieze A, Holleman F, Dallinga-Thie GM, Zoetendal EG, de Vos WM, Groen AK, Hoekstra JB, Stroes ES, Nieuwdorp M. The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes Metab. 2012 Feb;14(2):112-20. doi: 10.1111/j.1463-1326.2011.01483.x. Epub 2011 Nov 22.
citation: Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK, Al-Soud WA, Sorensen SJ, Hansen LH, Jakobsen M. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One. 2010 Feb 5;5(2):e9085. doi: 10.1371/journal.pone.0009085.
citation: Naito E, Yoshida Y, Makino K, Kounoshi Y, Kunihiro S, Takahashi R, Matsuzaki T, Miyazaki K, Ishikawa F. Beneficial effect of oral administration of Lactobacillus casei strain Shirota on insulin resistance in diet-induced obesity mice. J Appl Microbiol. 2011 Mar;110(3):650-7. doi: 10.1111/j.1365-2672.2010.04922.x. Epub 2011 Feb 1.
citation: Saad MJ, Santos A, Prada PO. Linking Gut Microbiota and Inflammation to Obesity and Insulin Resistance. Physiology (Bethesda). 2016 Jul;31(4):283-93. doi: 10.1152/physiol.00041.2015.