J Neurogastroenterol Motil 2023; 29(2): 218-228  https://doi.org/10.5056/jnm22031
Total Transit Time and Probiotic Persistence in Healthy Adults: A Pilot Study
Annie Tremblay,1* Jeremie Auger,1 Zainab Alyousif,2 Sara E Caballero Calero,1 Olivier Mathieu,1 Daniela Rivero-Mendoza,2 Amal Elmaoui,1 Wendy J Dahl,2 and Thomas A Tompkins1
1Rosell Institute for Microbiome and Probiotics, Montreal, Quebec, Canada; and 2Department of Food Science and Human Nutrition, University of Florida, Gainesville, FL, USA
Correspondence to: *Annie Tremblay, PhD
Rosell Institute for Microbiome and Probiotics, 6100 Royalmount Avenue, Montreal, Quebec, H4P 2R2, Canada
Tel: +1-514-283-5428, E-mail: atremblay@lallemand.com
Annie Tremblay and Jeremie Auger contributed equally to this study.
Received: March 8, 2022; Revised: July 5, 2022; Accepted: July 13, 2022; Published online: April 30, 2023
© The Korean Society of Neurogastroenterology and Motility. All rights reserved.

cc This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Motility, stool characteristics, and microbiota composition are expected to modulate probiotics’ passage through the gut but their effects on persistence after intake cessation remain uncharacterized. This pilot, open-label study aims at characterizing probiotic fecal detection parameters (onset, persistence, and duration) and their relationship with whole gut transit time (WGTT). Correlations with fecal microbiota composition are also explored.
Thirty healthy adults (30.4 ± 13.3 years) received a probiotic (30 × 109 CFU/capsule/day, 2 weeks; containing Lactobacillus helveticus R0052, Lacticaseibacillus paracasei HA-108, Bifidobacterium breve HA-129, Bifidobacterium longum R0175, and Streptococcus thermophilus HA-110). Probiotic intake was flanked by 4-week washout periods, with 18 stool collections throughout the study. WGTT was measured using 80% recovery of radio-opaque markers.
Tested strains were detected in feces ~1-2 days after first intake and persistence after intake cessation was not significantly different for R0052, HA-108, and HA-129 (~3-6 days). We identified 3 WGTT subgroups within this population (named Fast, Intermediate, and Slow), which could be classified by machine learning with high accuracy based on differentially abundant taxa. On average, R0175 persisted significantly longer in the intermediate WGTT subgroup (~8.5 days), which was mainly due to 6 of the 13 Intermediate participants for whom R0175 persisted ≥ 15 days. Machine learning classified these 13 participants according to their WGTT cluster (≥ 15 days or < 5 days) with high accuracy, highlighting differentially abundant taxa potentially associated with R0175 persistence.
These results support the notion that host-specific parameters such as WGTT and microbiota composition should be considered when designing studies involving probiotics, especially for the optimization of washout duration in crossover studies but also for the definition of enrollment criteria or supplementation regimen in specific populations.
Keywords: Bifidobacterium; Gastrointestinal transit; Lactobacillus ; Microbiota; Probiotics

While a longer positive detection period of probiotics in feces may suggest a longer effective period in the gastrointestinal (GI) tract, long-term GI tract colonization by probiotics is not considered a requirement for their effect, nor is it expected in healthy adults with a stable and resilient microbiota. Recent studies have indicated that probiotic persistence after intake cessation is both strain-specific and dose-dependent,1-3 but the factors underlying the observed interindividual variability in short-term probiotic strain persistence are poorly defined. From a clinical trial standpoint, a better understanding of the host-specific determinants of persistence would strengthen the design of crossover studies where differences in persistence at both participant- and strain-specific levels may affect conclusions. Typically, crossover studies with probiotics require washout periods of variable duration, often 2-5 weeks between intervention periods,4-7 but the rationale behind washout duration selection is rarely described.

Gut transit time was shown to correlate with variations in fecal microbiota composition or diversity in both diseased and healthy populations, and was identified among covariates to consider for microbiome analyses.8-12 Recently, in a study enrolling 1102 healthy individuals, microbiota composition was found to effectively discriminate between WGTT categories measured using the blue dye method,13 although it remains unclear whether the differences in microbiota composition are caused by variability in transit time or vice-versa. Conceptually, host-specific intestinal function characteristics such as WGTT and microbiota composition are expected to affect the kinetics of probiotics’ passage through the GI tract. However, to our knowledge, no clinical studies have examined the impact of these factors on probiotic persistence after intake cessation. Hence, this pilot study investigated the relationships between strain-specific probiotic detection onset and persistence in feces with WGTT in a small cohort of healthy adults and explored the correlation between these factors and gut microbiota composition.

Materials and Methods

Study Design and Participants

This 10-week open-label pilot study, conducted in Florida, United States, between December 2019 and February 2020 (Fig. 1), enrolled 30 healthy adults aged 18-55 years having at least one bowel movement per day and agreeing to undertake study procedures. The trial was approved by the University of Florida Institutional Review Board 1 (IRB201902202). The protocol was prospectively registered in August 2019 on ClinicalTrials.gov (NCT04065503). All participants provided written informed consent and the study was conducted in accordance with the Declaration of Helsinki.

Figure 1. Schematic representation of the study design. Schematics of the 10-week study, including a 4-week baseline washout period (Day −28 to 0), followed by a 2-week intervention (daily multi-strain probiotic intake, Day 0 to 14), and a 4-week washout (Day 14 to 42). Daily diary and weekly questionnaire (Day −7 onwards) are represented by the green line, days of transit markers intake (Day 0 to 2) are marked by grey squares, and stool sample collection time points (Day −3 onwards) are identified by black squares. GSRS, Gastrointestinal Symptom Rating Scale.

Exclusion criteria were elite athletes or long-distance runners, any intestinal disease or condition, immune disorders, or possible immune-deficient status (eg, due to surgery), pregnancy or breastfeeding (or plans for the coming 2 months), currently consuming fermented foods, probiotics, or antibiotics, refusal to complete a 4-week washout period before starting the study intervention, using other investigational products within 3 months of the screening visit, and milk or soy allergy. Participants were also asked to refrain from consuming probiotics and fermented products (eg, yogurts, kombucha, fermented pickles, and other fermented foods with live, active cultures) for the duration of the study. Alcohol (wine, beer, and spirits) was permitted.

Study Intervention

The commercial multi-strain probiotic (30 billion CFU/capsule) manufactured by Lallemand Health Solutions Inc, was composed of Lactobacillus helveticus R0052 (45%), Lacticaseibacillus paracasei HA-108 (17%), Bifidobacterium breve HA-129 (16%), Bifidobacterium longum R0175 (6%), and Streptococcus thermophilus HA-110 (16%). Probiotic capsules (hydroxyl-propyl-methyl cellulose and titanium dioxide) also contained excipients (potato starch, ascorbic acid, and magnesium stearate). Probiotic intake (1 capsule/day, 14 days) was flanked by 4-week washout periods, with 18 stool collections (Fisher Scientific Commode Collection System; Cat. No. 02-544-208; Thermo Fisher Scientific Inc, Waltham, MA, USA) throughout the study. At the end of the intervention period, participants returned any remaining probiotic capsules.

Strain-specific Detection by Real-time Polymerase Chain Reaction

Stool samples were homogenized, aliquoted and stored at –80°C within 6 hours of defecation. Total DNA was extracted from each fecal sample in duplicates using the ZymoBIOMICS 96 MagBead DNA kit (Cat. No. D4308; Zymo Research, Irvine, CA, USA) according to the manufacturer’s instructions. Briefly, approximately 150 mg of feces were homogenized using a bead beater homogenizer (MP Bio FastPrep-24 5G) with 750 µL of ZymoBIOMICS lysis solution in BashingBead lysis tubes (0.1 mm and 0.5 mm). After centrifugation, DNA was extracted from the supernatants using the KingFisher Flex Purification System automated extraction instrument equipped with the BindIt software (Thermo Fisher Scientific Inc). Standard curves were generated by serially diluting the DNA extracted from a control clinical fecal matrix spiked with 109 bacteria of the target strain. Absolute quantification of L. helveticus R0052, L. paracasei HA-108, B. breve HA-129, and B. longum R0175 in fecal samples was performed by real-time polymerase chain reaction (qPCR) using strain-specific primers (Table 1) on the CFX384 Touch Real-Time PCR Detection System (Bio-Rad Laboratories, Inc, Hercules, CA, USA).

Table 1 . Strain-specific Primers Used for the Detection of Probiotic Strains

Bacterial strainPrimer namePrimer sequence (5’-3’)Amplicon size (bp)Amplification
L. helveticus R0052pIR52-1-orf5 F1AGAATCAAGCAGAGACTGGCTACG150Per reaction (10 µL):
pIR52-1-orf5 R1GGACCGGATTTGAGTAGAGGTAPrimers 300 nM each
1 × SYBR Select Master Mix
1 µL of DNA
L. paracasei HA-108R0215_SM_LP1_F1GAAAGCCCGAGATGTGTATCA134Cycling conditions:
1 × 2 min at 95°C
30 sec at 60°C
30 sec at 72°C
B. breve HA-129HA-129_225-F2CGACCCTAATGACGTGGAGG1951 × 60°C to 95°C

L. helveticus, Lactobacillus helveticus; L. paracasei, Lacticaseibacillus paracasei; B. longum, Bifidobacterium longum; B. breve, Bifidobacterium breve.

Whole Gut Transit Time

Participants consumed radio-opaque markers (ROM; Sitzmarks, 1 capsule/day, from day 0 to day 2) along with breakfast, and total fecal collections were made throughout intervention days 0-2 and the morning of day 3. WGTT was estimated using the ROM 80% recovery method.14-17 Each daily capsule contained a different shape of marker to allow to distinguish the intake day when recovering from the stools. Briefly, the 3 distinct shapes of markers excreted in total daily stool (Days 0-2, and morning of Day 3) were recovered and counted. The time of first appearance of the markers in stools was recorded, but WGTT was calculated from the time of marker ingestion to the time of the stool collection corresponding to the passage of 80% of the markers.14,16,17

16S Amplicon Sequencing and Microbiome Analysis

DNA extracted from baseline fecal samples (Day 0) was also used for microbiota profiling via 16S ribosomal RNA V3-V4 region amplicon sequencing according to the Illumina protocol (15044223 Rev. B). Briefly, libraries were prepared, assessed for DNA quantity and quality, and indexed in a second PCR for multiplexing before paired-end sequencing (2 × 300 cycles) on an Illumina MiSeq system with a MiSeq Reagent 600 cycles v3 Kit cartridge (Cat. No. MS-102-3003; Illumina, Inc, San Diego, CA, USA).

The 16S amplicon sequencing results were exported in fastq format from the MiSeq and imported into QIIME 2, which allows the tracking of every parameter used in the various modules and integrated software.18-20 After import, the reads were inspected for quality, then the forward reads trimmed both at 280 base pair and based on q-scores.21 The selected reads were clustered into amplicon sequence variant (ASV) abundance tables using the denoiser module deblur22 and the ASV tables used for alpha diversity, visualized using the QIIME 2 viewer server online (https://view.qiime2.org/).

The ASV tables were converted to genus-level taxonomic abundance tables using QIIME’s feature-classifier trained on the GreenGenes 16S database.23 These tables were used to determine the microbiome profiles associated with group characteristics, like the transit categories or the persistence status. The classification was done using QIIME’s sample-classifier tool24 and the machine learning algorithm “ExtraTrees.” The genus-level abundance of the important taxa (for the machine learning classifier algorithm) was plotted using R’s pirateplot library (a modern equivalent to boxplots).

Adverse Events and Gastrointestinal Tolerance

Adverse events were queried during visits and via daily online questionnaires. GI symptoms were monitored weekly using an online Gastrointestinal Symptom Rating Scale (GSRS) questionnaire, which utilizes a 7-point Likert scale, where 1 represents “no discomfort at all” and 7 represents “very severe discomfort.”25 Symptoms assessed by the GSRS evaluate 5 dimensions of gastrointestinal discomfort: reflux, abdominal pain, indigestion, diarrhea, and constipation.

Statistical Methods

The qPCR data for strain-specific abundance in the stool samples series were analyzed using ANOVA with Tukey’s multiple comparison test in GraphPad Prism version 9.3.0 (Boston, MA, USA).

Data Availability Statement

The data that support the findings of this study are openly available in the NCBI Sequence Read Archive at https://www.ncbi.nlm.nih.gov/sra, reference number PRJNA790866.


Participants’ Flow and Baseline Characteristics

Of the 32 healthy adults assessed for eligibility, 1 participant lost interest before the baseline period, and another withdrew before the start of the intervention (unrelated health issue); 30 participants completed the study and were included in the analyses. The baseline characteristics of the 30 study participants are given in Table 2. As expected from inclusion criteria, Bristol stool scale (BSS) and GSRS scores were in the normal ranges at baseline; these parameters remained unchanged throughout the study (data not shown).

Table 2 . Participant Demographics and Characteristics

VariableParticipants (n = 30)
Gender (M/F)12/18
Age (yr)30.4 ± 13.3 (range 19-55)
Normal (18.5 to 24.9)14 (46.7)
Overweight (25 to 29.9)5 (16.6)
Obese (> 30)11 (36.7)
BSS score at baseline3.8 ± 0.9
Bowel movement frequency (stools/day) at baseline1.4 ± 0.9
GSRS syndromes at baseline
Abdominal pain1.6 ± 0.7
Reflux1.3 ± 0.6
Indigestion1.7 ± 0.8
Constipation1.6 ± 0.9
Diarrhea1.3 ± 0.7

M, male; F, female; BMI, body mass index; BSS, Bristol stool scale; GSRS, Gastrointestinal Symptom Rating Scale, 7-point Likert scale, 1 represents “no discomfort at all” and 7 represents “very severe discomfort.”

Data are expressed as n, mean ± SD, or n (%).

Probiotics Detection Onset, Persistence, and Duration

Most of the participants (25/30; 83%) provided ≥ 89% of the required samples (ie, ≥ 16/18). The onset of fecal detection after first intake (ie, time to detection), the persistence of fecal detection after last intake (ie, time to non-detection), and total number of days between the first and last detection (ie, duration of detection) were calculated based on individual stool collection dates and strain-specific qPCR detection (Table 3). The average time to detection was not significantly different between strains; all strains were detected in the feces between 1-2 days after first intake. Both the time to non-detection and duration of detection were significantly different for R0175, which displayed a significantly longer persistence (P < 0.01, vs R0052).

Table 3 . Strain-specific Time to Detection, Time to Non-detection, and Duration of Detection

StrainNumber of days (mean ± SEM [n])
Time to detection
(P = 0.533)
Time to non-detection
(P = 0.012)
Duration of detection
(P = 0.011)
L. helveticus R00521.4 ± 0.2 (30)3.4 ± 1.1 (25)14.9 ± 1.1 (25)
B. longum R01751.3 ± 0.1 (29)8.5 ± 1.7 (27)a20.3 ± 1.7 (27)a
L. paracasei HA-1081.1 ± 0.1 (30)4.7 ± 0.5 (27)16.6 ± 0.4 (27)
B. breve HA-1291.2 ± 0.1 (28)5.8 ± 0.8 (27)17.3 ± 0.8 (28)

aP < 0.01 vs R0052.

L. helveticus, Lactobacillus helveticus; B. longum, Bifidobacterium longum; L. paracasei, Lacticaseibacillus paracasei; B. breve, Bifidobacterium breve.

One-way ANOVA with Tukey’s multiple comparisons.

Whole Gut Transit Time and Microbiota Analysis

The average WGTT of this healthy population was 54.5 hours (95% CI, 47.7-61.3). Observation of individual WGTT values in ascending order revealed the presence of 3 distinct subgroups with average transit times of 28.2 hour (95% CI, 2.1-35.2), 47.3 hours (95% CI, 44.4-49.2), and 75.6 hours (95% CI, 71.8-79.5), which were named Fast (n = 5), Intermediate (n = 14), and Slow (n = 11), respectively (Fig. 2). This subgrouping was not visible when the time to first detection of the markers in stools was analyzed (Supplementary Fig. 1).

Figure 2. Whole gut transit time subgroups. Participants’ whole gut transit time (WGTT) values (hr) graphed in ascending order, outlining 3 subgroups named Fast (X¯ = 28.2 hr), Intermediate (X¯ = 47.3 hr), and Slow (X¯ = 75.6 hr).

Considering that microbiota composition was identified as a strong predictor of WGTT,13 a machine learning algorithm was used to explore differences between the 3 subgroups and identify the bacterial taxa most strongly associated with each transit group. Average score and per-class receiver operating characteristic (ROC) curves (Fig. 3A) revealed a better performance of the algorithm at predicting the Intermediate and Slow subgroups based on microbiota profiles compared to chance. The accuracy of separation between the Fast (n = 5) and Intermediate (n = 14) transit subgroups was lower, most likely because of the smaller number of participants in the Fast subgroup. Nevertheless, overall accuracy was good (89.9%) (Fig. 3B), with an accuracy of 100% for the Intermediate and Slow subgroups alone (Fig. 3C).

Figure 3. Characterization of the population by transit time and microbiota profile. (A) Receiver operating characteristic curves show the performance of the machine learning classification model for the 3 whole gut transit time (WGTT) subgroups at all classification thresholds for average scores and per-class. Model optimization curves are the true positive rates over the range of false positive rates. (B) Confusion matrix representing model accuracy and overall and baseline accuracy for the Extra-Tree classification of the samples for the WGTT subgroups. AUC, area under the curve; Inter., Intermediate subgroup.

Alpha diversity was significantly higher in the Slow subgroup versus Intermediate and/or Fast with all metrics used (Supplementary Fig. 2). Among the top 42 most important taxa driving the subgroup classification (Supplementary Fig. 3), a subset of 12 taxa were identified visually on Pirate Plots for their marked subgroup-specific variations. Notably, Roseburia, Eggerthella, Acidaminococcus, Prevotella, Sutterella, and Bifidobacterium were less abundant in the Slow subgroup versus Intermediate and/or Fast (Fig. 4A), while Oscillospira, Lachnospira, Allistipes, Rikenellacea, Akkermansia, and Turicibacter were more abundant in the Slow subgroup versus Fast and/or Intermediate (Fig. 4B).

Figure 4. Selection of representative taxa discriminating between whole gut transit time subgroups. Pirate plots showing selected discriminant taxa with pronounced whole gut transit time (WGTT)-associated variation in abundance, with 6 taxa displaying (A) a lower abundance in Slow subgroup vs Intermediate (Inter.) and/or Fast, or (B) an increased abundance in the Slow subgroup vs Inter. and/or Fast.

We next explored the relationship between transit time subgroups and strain-specific kinetics of detection, which revealed that the longer persistence of the R0175 strain (Table 3) was due to participants from our Intermediate WGTT subgroup (Fig. 5A). There was no significant difference in the average time to detection of each strain, regardless of the WGTT subgroup (Fig. 5B).

Figure 5. Persistence of probiotic strains according to whole gut transit time (WGTT) subgroups. (A) Kinetics of the average onset and recovery of Lactobacillus helveticus R0052, Bifidobacterium longum R0175, Lacticaseibacillus paracasei HA-108, and Bifidobacterium breve HA-129 strains (from top, respectively) over the study duration in the 3 transit time categories (Fast, Intermediate [Inter.], and Slow). Mixed model ANOVA with Tukey’s multiple comparison test. *P < 0.05. (B) Time to detection (onset), (C) time to non-detection (persistence), and (D) duration of detection (bottom) in days for individual participants in each transit category for L. helveticus R0052, B. longum R0175, L. paracasei HA-108, and B. breve HA-129 strains (left to right). Ordinary one-way ANOVA with Tukey’s multiple comparisons test. *P < 0.05.

For R0052, HA-108, and HA-129, there was no difference in the average time to non-detection (Fig. 5C) and duration of detection (Fig. 5D), despite a visible trend towards a longer persistence in the Slow subgroup. In most participants, these 3 strains were no longer detected within approximately 5 days to 10 days. However, B. longum R0175 persisted on average significantly longer in the feces of participants from our Intermediate WGTT subgroup (Fig. 5A); this difference was driven by 46% of the participants (6/13) from this subgroup for both time to non-detection (Fig. 5C) and total duration of detection (Fig. 5D), suggesting that another factor in addition to WGTT may favor the persistence of B. longum R0175 in some individuals but not others.

Machine learning revealed marked differences between the baseline microbiota composition of participants from our intermediate WGTT subgroup; the algorithm could distinguish the “R0175-permissive” participants with 100% accuracy as shown by the average and per-class ROC curves (Fig. 6A) and confusion matrix (Fig. 6B), revealing 31 discriminating features (Supplementary Fig. 4) between participants in whom B. longum R0175 persisted for ≥ 15 days (n = 6) or for less than 5 days (n = 7). Notably, baseline abundance of the Bifidobacterium and Coprococcus genera, as well as the Erysipelotrichaceae and Ruminococcaceae families was reduced in participants with longer B. longum R0175 persistence (Fig. 6C), while abundance of the Blautia, Roseburia, Parabacteroides, and Eggerthella genera was increased (Fig. 6D).

Figure 6. Discriminating taxa associated with shorter or longer persistence of Bifidobacterium longum R0175. (A) Receiver operating characteristic curves measuring the performance of the machine learning classification model at all classification thresholds of average scores (left panel) and per-class (right panel). Model optimization curves are the true positive rates over the range of false positive rates. (B) Confusion matrix of the machine learning classification model. Pirate plots of a selection of 8 taxa displaying either (C) a lower abundance or (D) a higher abundance in the individuals with increased B. longum R0175 persistence. AUC, area under the curve; NP, non-persistent; P, persistent.

The results of the present investigation illustrate that probiotic persistence in the fecal microbiome after intake cessation was not significantly different for most probiotic strains tested (~3-6 days) except for B. longum R0175. The latter persisted for significantly longer (15-30 days) in a subset of individuals with an intermediate WGTT, which was associated with a specific fecal microbiota composition identified by machine learning.

We used a simplified method to assess WGTT based on the stool recovery of 80% of ingested ROM.14,17 This non-invasive method avoids the unnecessary use of repeated abdominal X-rays in healthy individuals.16 The 80% excretion cutoff over 3 days, as described for the mean transit time fecal marker recovery method,17,26,27 is representative of the non-linearity of gut transit (mixing of residues)28 and are less likely to be affected by probiotic intake timing (ie, intake at breakfast) in relation to sleeping schedule and daily bowel movement habits compared to methods using first stool appearance. Indeed, both the 80% marker recovery and the mean transit time methods, which were previously compared and deemed equivalent, yielded longer average values than the use of a blue dye capsule to assess transit time.15 The premise that WGTT is affected by the ongoing mixing of gut residue, as has previously been demonstrated,28 was confirmed in the present study; some stools on the fourth day contained all 3 marker shapes (taken for 3 consecutive mornings on study Days 0-2) irrespective of participants’ daily stool frequency. Consistent with these notions, when data were analyzed using the time of first appearance of the ROM markers, we observed a better correspondence with the averaged first detection (onset) of the probiotics in the stools, but no clear subgroups were observed as opposed to the 80% recovery method that delineated transit categories compatible with observations of others about the about the 24-hour periodicity of WGTT.29 Overall, using the 80% recovery method, our population displayed an average WGTT of 54.5 hours, with a range that is compatible with transit time values considered normal in healthy adults consuming a Western diet.13,29-33

We found that our population contained 3 subgroups with average WGTT corresponding partially to transit time categories observed in a recent study using the blue dye method (muffins) in a cohort of > 1000 individuals.13 In that study, the general population sample was clustered in 4 transit time categories; the faster transit category (≤ 12 hours) was not represented in our cohort most likely because of methodological differences between first appearance of the blue dye in stools and the 80% recovery of ROMs. The “normal” category described by Asnicar et al13 was bimodal, divided into a faster and slower subset (average 24 hours and 48 hours, respectively), and the average transit time in the slow transit category was approximately 96 hours. The latter 3 groups in the Asnicar et al13 study correspond relatively to the WGTT subgroups observed in our cohort using the 80% ROM method, namely subgroups clearly visible on the ascending WGTT curve (Fig. 2) with average transit times of 28.2, 47.3, and 75.6 hours. The cutoff of our Slow subgroup (> 60 hours) also corresponds to normative gut transit time values reported previously,29 stipulating that within the normal gut transit time range, a fast category was defined by a cutoff of < 14 hours, an intermediate category by a gut transit time between 14-58 hours, and a slow category by a cutoff of ≥ 59 hours.

Considering that our observed WGTT groups corresponded to cutoffs reported by others, we explored the microbiome profiles of our WGTT subgroups. In accordance with the results from other studies,8,13,34 we observed a higher abundance of Alistipes, Bacteroides, and Akkermansia in the slow transit subgroup. Oscillospira, which is elevated in individuals with constipation,35 was more abundant in the slow transit subgroup. Tian et al,36 reported that adults with slow transit constipation displayed higher diversity. In their study, in accordance with the present findings, Prevotella and Roseburia were lower and Oscillospira higher in adults with the slow transit constipation vs Intermediate transit controls; however, in contrast to the present findings, Bifidobacterium, Ruminococcus, and Parabacteroides were higher.36 Additionally, Mancabelli et al37 also reported that individuals with functional constipation had a lower relative abundance of Roseburia. However, participants in our Slow transit subgroup would not be considered constipated as defined by infrequent bowel movements or low BSS (Table 2), and thus may not be expected to fully share microbiota composition with those considered constipated.

Recent studies have demonstrated that higher dosage typically results in a higher proportion of individuals with positive detection during the supplementation period, regardless of the probiotic strain used.1-3 Even at higher dosages, probiotics are sometimes not recovered in 100% of the participants; this phenomenon was attributed to the fact that some healthy adults can be viewed as “non-responders” due to the “colonization resistance” of their microbiota.1 We observed that some participants exhibited a longer persistence for B. longum R0175 specifically, suggesting a more permissive microbiome composition. This seemingly permissive profile was characterized by an increased abundance of Blautia, Roseburia, Parabacteroides, and Eggerthella, and a lower abundance of Bifidobacterium, Coprococcus, Erysipelotrichaceae, and Ruminococcaceae. Thus, it could be speculated that the longer B. longum R0175 persistence could be related to the availability of a specific niche that is otherwise occupied by commensal Bifidobacteria or associated taxa (“bacterial communities”) in other participants.

This study has limitations. We assessed gut transit time during the first 3 days of probiotic intake but not during the washout periods, hence this study cannot answer the question of whether probiotic intake modulated WGTT. In addition, it is not possible to distinguish the most important contributor between the influence of WGTT or microbiota composition. While these strains are known to survive GI tract conditions, the measure of live bacteria instead of qPCRs could result in a shorter time to non-detection or duration of detection.38 The size of the cohort was small, but the observed subgroups are consistent with normative transit times cutoffs from other, larger studies.8,13,35 However, other potential covariates could not be taken into account; the diet of the participants was not controlled, and it cannot be excluded that differences in microbially available carbohydrate or protein intake could have influenced the data.

In conclusion, the onset of detection in feces was similar for the 4 probiotics tested regardless of the observed WGTT categories. However, although persistence after intake cessation was similar for R0052, HA-108, and HA-129, there was a clear difference in R0175 persistence related to intermediate WGTT and a specific microbiota composition. This study shows that, in addition to strain-specificity, host-specific parameters such as WGTT and microbiota composition may underlie the interindividual variability observed in probiotic persistence after intake cessation. This finding supports the notion that these parameters should be considered when designing studies involving probiotics, especially in terms of the definition of enrollment criteria, the choice of probiotic supplementation regimen, or for the optimization of washout duration in crossover studies.

Supplementary Materials

Note: To access the supplementary figures mentioned in this article, visit the online version of Journal of Neurogastroenterology and Motility at http://www.jnmjournal.org/, and at https://doi.org/10.5056/jnm22031.


The authors thank Varuni Nagulesapillai for help with study initiation, Megumi Randall and Ahmed Syed for assistance with qPCR experiments, and Sylvie Binda for reviewing the manuscript and providing valuable input.

Financial support

The study was funded by Lallemand Health Solutions Inc.

Conflicts of interest

Annie Tremblay, Jeremie Auger, Sara E Caballero Caler, Olivier Mathieu, Amal Elmaoui, and Thomas A Tompkins are employed by Lallemand Health Solutions Inc, a company that manufactures and markets the tested probiotics to business clients but not to consumers. Remaining authors report no conflict of interest.

Author contributions

Experimental design and protocol: Thomas A Tompkins and Wendy J Dahl; study conduct: Zainab Alyousif, Sara E Caballero Calero, Amal Elmaoui, Daniela Rivero-Mendoza, and Wendy J Dahl; data analysis: Annie Tremblay, Jeremie Auger, Zainab Alyousif, Sara E Caballero Calero, Olivier Mathieu, Daniela Rivero-Mendoza, and Amal Elmaoui; data interpretation: Annie Tremblay, Jeremie Auger, Sara E Caballero Calero, Thomas A Tompkins, and Wendy J Dahl; manuscript writing of first draft: Annie Tremblay, Jeremie Auger, Zainab Alyousif, Olivier Mathieu, Amal Elmaoui, and Wendy J Dahl; and manuscript editing of final version: Annie Tremblay, Jeremie Auger, Sara E Caballero Calero, Olivier Mathieu, Thomas A Tompkins, and Wendy J Dahl. All authors read and approved the final manuscript.

  1. Morelli L, Pellegrino P. A critical evaluation of the factors affecting the survival and persistence of beneficial bacteria in healthy adults. Benef Microbes 2021;12:15-25.
    Pubmed CrossRef
  2. Tremblay A, Fatani A, Ford AL, et al. Safety and effect of a low- and high-dose multi-strain probiotic supplement on microbiota in a general adult population: a randomized, double-blind, placebo-controlled study. J Diet Suppl 2021;18:227-247.
    Pubmed CrossRef
  3. Taverniti V, Koirala R, Dalla Via A, et al. Effect of cell concentration on the persistence in the human intestine of four probiotic strains administered through a multispecies formulation. Nutrients 2019;11:285.
    Pubmed KoreaMed CrossRef
  4. Macfarlane S, Cleary S, Bahrami B, Reynolds N, Macfarlane GT. Synbiotic consumption changes the metabolism and composition of the gut microbiota in older people and modifies inflammatory processes: a randomised, double-blind, placebo-controlled crossover study. Aliment Pharmacol Ther 2013;38:804-816.
    Pubmed CrossRef
  5. Asemi Z, Khorrami-Rad A, Alizadeh SA, Shakeri H, Esmaillzadeh A. Effects of synbiotic food consumption on metabolic status of diabetic patients: a double-blind randomized cross-over controlled clinical trial. Clin Nutr 2014;33:198-203.
    Pubmed CrossRef
  6. Murakami K, Habukawa C, Nobuta Y, Moriguchi N, Takemura T. The effect of Lactobacillus brevis KB290 against irritable bowel syndrome: a placebo-controlled double-blind crossover trial. Biopsychosoc Med 2012;6:16.
    Pubmed KoreaMed CrossRef
  7. Guandalini S, Magazzù G, Chiaro A, et al. VSL#3 improves symptoms in children with irritable bowel syndrome: a multicenter, randomized, placebo-controlled, double-blind, crossover study. J Pediatr Gastroenterol Nutr 2010;51:24-30.
    Pubmed CrossRef
  8. Vandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M, Raes J. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 2016;65:57-62.
    Pubmed KoreaMed CrossRef
  9. Tigchelaar EF, Bonder MJ, Jankipersadsing SA, Fu J, Wijmenga C, Zhernakova A. Gut microbiota composition associated with stool consistency. Gut 2016;65:540-542.
    Pubmed CrossRef
  10. Falony G, Joossens M, Vieira-Silva S, et al. Population-level analysis of gut microbiome variation. Science 2016;352:560-564.
    Pubmed CrossRef
  11. Hollister EB, Cain KC, Shulman RJ, et al. Relationships of microbiome markers with extraintestinal, psychological distress and gastrointestinal symptoms, and quality of life in women with irritable bowel syndrome. J Clin Gastroenterol 2020;54:175-183.
    Pubmed KoreaMed CrossRef
  12. Hugerth LW, Andreasson A, Talley NJ, et al. No distinct microbiome signature of irritable bowel syndrome found in a Swedish random population. Gut 2020;69:1076-1084.
    Pubmed KoreaMed CrossRef
  13. Asnicar F, Leeming ER, Dimidi E, et al. Blue poo: impact of gut transit time on the gut microbiome using a novel marker. Gut 2021;70:1665-1674.
    Pubmed KoreaMed CrossRef
  14. Hinton JM, Lennard-Jones JE, Young AC. A new method for studying gut transit times using radioopaque markers. Gut 1969;10:842-847.
    Pubmed KoreaMed CrossRef
  15. Marlett JA, Slavin JL, Brauer PM. Comparison of dye and pellet gastrointestinal transit time during controlled diets differing in protein and fiber levels. Dig Dis Sci 1981;26:208-213.
    Pubmed CrossRef
  16. Corazziari E, Cucchiara S, Staiano A, et al. Gastrointestinal transit time, frequency of defecation, and anorectal manometry in healthy and constipated children. J Pediatr 1985;106:379-382.
    Pubmed CrossRef
  17. Cummings JH, Jenkins DJ, Wiggins HS. Measurement of the mean transit time of dietary residue through the human gut. Gut 1976;17:210-218.
    Pubmed KoreaMed CrossRef
  18. Bolyen E, Rideout JR, Dillon MR, et al. QIIME 2: reproducible, interactive, scalable, and extensible microbiome data science. PeerJ Preprints 2018;6.
  19. Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019;37:852-857.
    Pubmed KoreaMed CrossRef
  20. McKinney W. Data structures for statistical computing in python. Proceedings of the 9th python in science conference (SciPy 2010), Austin, Texas: 56-61.
    Pubmed CrossRef
  21. Rivers AR, Weber KC, Gardner TG, Liu S, Armstrong SD. ITSxpress: software to rapidly trim internally transcribed spacer sequences with quality scores for marker gene analysis. F1000Research 2018;7:1418.
    Pubmed KoreaMed CrossRef
  22. Amir A, McDonald D, Navas-Molina JA, et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2017;2:e00191-16.
    Pubmed KoreaMed CrossRef
  23. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. JMLR 2011;12:2825-2830.
  24. Bokulich NA, Subramanian S, Faith JJ, et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods 2013;10:57-59.
    Pubmed KoreaMed CrossRef
  25. Revicki DA, Wood M, Wiklund I, Crawley J. Reliability and validity of the gastrointestinal symptom rating scale in patients with gastroesophageal reflux disease. Qual Life Res 1998;7:75-83.
    Pubmed CrossRef
  26. Stephen AM, Dahl WJ, Johns DM, Englyst HN. Effect of oat hull fiber on human colonic function and serum lipids. Cereal Chemistry 1997;74:379-383.
  27. Stephen AM, Dahl WJ, Sieber GM, van Blaricom JA, Morgan DR. Effect of green lentils on colonic function, nitrogen balance, and serum lipids in healthy human subjects. Am J Clin Nutr 1995;62:1261-1267.
    Pubmed CrossRef
  28. Wiggins HS, Cummings JH. Evidence for the mixing of residue in the human gut. Gut 1976;17:1007-1011.
    Pubmed KoreaMed CrossRef
  29. Nandhra GK, Mark EB, Di Tanna GL, et al. Normative values for region-specific colonic and gastrointestinal transit times in 111 healthy volunteers using the 3D-transit electromagnet tracking system: influence of age, gender, and body mass index. Neurogastroenterol Motil 2020;32:e13734.
    Pubmed CrossRef
  30. Rao SS, Camilleri M, Hasler WL, et al. Evaluation of gastrointestinal transit in clinical practice: position paper of the American and European neurogastroenterology and motility societies. Neurogastroenterol Motil 2011;23:8-23.
    Pubmed CrossRef
  31. Southwell BR, Clarke MC, Sutcliffe J, Hutson JM. Colonic transit studies: normal values for adults and children with comparison of radiological and scintigraphic methods. Pediatr Surg Int 2009;25:559-572.
    Pubmed CrossRef
  32. Ghoshal UC, Sengar V, Srivastava D. Colonic transit study technique and interpretation: can these be uniform globally in different populations with non-uniform colon transit time? J Neurogastroenterol Motil 2012;18:227-228.
    Pubmed KoreaMed CrossRef
  33. Kim ER, Rhee PL. How to interpret a functional or motility test - colon transit study. J Neurogastroenterol Motil 2012;18:94-99.
    Pubmed KoreaMed CrossRef
  34. Gobert AP, Sagrestani G, Delmas E, et al. The human intestinal microbiota of constipated-predominant irritable bowel syndrome patients exhibits anti-inflammatory properties. Sci Rep 2016;6:39399.
    Pubmed KoreaMed CrossRef
  35. Chen YR, Zheng HM, Zhang GX, Chen FL, Chen LD, Yang ZC. High oscillospira abundance indicates constipation and low BMI in the Guangdong gut microbiome project. Sci Rep 2020;10:9364.
    Pubmed KoreaMed CrossRef
  36. Tian H, Chen Q, Yang B, Qin H, Li N. Analysis of gut microbiome and metabolite characteristics in patients with slow transit constipation. Dig Dis Sci 2021;66:3026-3035.
    Pubmed CrossRef
  37. Mancabelli L, Milani C, Lugli GA, et al. Unveiling the gut microbiota composition and functionality associated with constipation through metagenomic analyses. Sci Rep 2017;7:9879.
    Pubmed KoreaMed CrossRef
  38. Arioli S, Koirala R, Taverniti V, Fiore W, Guglielmetti S. Quantitative recovery of viable Lactobacillus paracasei CNCM I-1572 (L. casei DG®) after gastrointestinal passage in healthy adults. Front Microbiol 2018;9:1720.
    Pubmed KoreaMed CrossRef

This Article



Aims and Scope