Abstract
Human gut microbiome is a promising target for managing type 2 diabetes
(T2D). Measures altering gut microbiota like oral intake of probiotics
or berberine (BBR), a bacteriostatic agent, merit metabolic
homoeostasis. We hence conducted a randomized, double-blind,
placebo-controlled trial with newly diagnosed T2D patients from 20
centres in China. Four-hundred-nine eligible participants were enroled,
randomly assigned (1:1:1:1) and completed a 12-week treatment of either
BBR-alone, probiotics+BBR, probiotics-alone, or placebo, after a
one-week run-in of gentamycin pretreatment. The changes in glycated
haemoglobin, as the primary outcome, in the probiotics+BBR
(least-squares mean [95% CI], −1.04[−1.19, −0.89]%) and BBR-alone group
(−0.99[−1.16, −0.83]%) were significantly greater than that in the
placebo and probiotics-alone groups (−0.59[−0.75, −0.44]%, −0.53[−0.68,
−0.37]%, P < 0.001). BBR treatment induced more gastrointestinal side
effects. Further metagenomics and metabolomic studies found that the
hypoglycaemic effect of BBR is mediated by the inhibition of DCA
biotransformation by Ruminococcus bromii. Therefore, our study reports
a human microbial related mechanism underlying the antidiabetic effect
of BBR on T2D. (Clinicaltrial.gov Identifier: [90]NCT02861261).
Subject terms: Metagenomics, Metagenomics, Type 2 diabetes, Type 2
diabetes
__________________________________________________________________
The gut microbiome affects systemic metabolism and is a therapeutic
target for type 2 diabetes. Here the authors demonstrate in a
randomized controlled trial that effects of berberine, a plant alkaloid
known to lower blood glucose, may be explained by the inhibition of
Ruminococcus bromii mediated biotransformation of the bile acid
deoxycholic acid.
Introduction
The complex pathophysiology of type 2 diabetes (T2D) has posed a major
challenge to the control of hyperglycaemia and diabetes-related
mortality and morbidity^[91]1–[92]3. In the past decade, the key role
of gut microbiota in regulating host metabolism and the associations of
gut microbial dysbiosis with the development of obesity and diabetes
has been extensively explored^[93]4–[94]10. Evidence from both human
and animal studies has suggested that the gut microbiome serves as the
common route to mediate the therapeutic effects of bariatric surgery,
diet control and antidiabetic medications^[95]4,[96]11–[97]15. Several
bacterial metabolic pathways regulating the production or transport of
amino acids (aromatic, branched-chain amino acids and intermediates of
histidine degradation)^[98]16,[99]17, short-chain fatty acids
(SCFAs)^[100]18–[101]20 and bile acids (BAs)^[102]16,[103]21,[104]22
have been implicated in mediating bacterial regulation of host
metabolic homoeostasis. Recent evidence has shown that both of the oral
antidiabetic medications, metformin^[105]15 and acarbose^[106]13, can
inhibit microbial BA metabolism by altering gut microbiome symbiosis
and block gut BA signalling, thereby partially exerting their metabolic
benefits. Interestingly, BA signalling has been proven to be required
for gut microbiome-induced obesity and mediates the therapeutic effect
of bariatric surgery^[107]22–[108]24. Thus, the gut microbiome and
microbial BA signalling, in particular, have become elusive targets for
treating T2D.
The hunt for the microbial targeted remedies for T2D or other metabolic
diseases has gained increasing attention. In records of Ayurvedic
medicine in India and traditional medicine in China, berberine (BBR), a
natural plant alkaloid extracted from Berberis aristata and Coptis
chinensis (Huanglian), as an ancient antidiarrhoeal medication, has
been reported to be an effective remedy for metabolic disorders,
including T2D, by promoting liver lipid metabolism or adipose
browning^[109]25–[110]27. However, similar to metformin, the specific
in vivo target of BBR has barely been clarified and its poor oral
bioavailability has suggested a potential effect on the gut microbiome.
16S rRNA gene-sequencing studies in rodents have shown significant gut
microbiota alterations induced by BBR and several microbial-related
mechanisms, including the potential to alter SCFA and BA metabolism,
have been found to underlie the metabolic benefits of
BBR^[111]28–[112]31. However, how the human gut microbiome responds to
BBR treatment and how the microbial alterations are related to the
metabolic benefits of BBR have not yet been investigated.
The potential for using probiotics to treat metabolic or other diseases
constitutes another heated topic in gut microbiome studies. The
inconsistent usage of strains and formulas, the heterogeneity of the
target population and various qualities and validities across the
studies might be the reasons for the controversial results of probiotic
intervention^[113]32–[114]34. Interestingly, studies, including ours,
have revealed that indigenous probiotics containing genera such as
Lactobacillus and Bifidobacterium are enriched in faeces from T2D
participants after antidiabetic treatment with a single use of
acarbose^[115]14 or metformin^[116]11,[117]12, which are associated
with an antidiabetic effect, but reported to be inhibited by BBR
administration^[118]31. Hence, it prompts a possibility whether the
application of probiotics together with a treatment such as BBR could
confer superior antidiabetic benefits than using probiotics or BBR
alone.
Therefore, aiming to find an effective strategy for treating T2D by
altering gut microbiome dysbiosis, we have designed and conducted the
Probiotics and BBR on the Efficacy and Change of Gut Microbiota in
Patients with Newly Diagnosed Type 2 Diabetes (PREMOTE) trial. The
primary objective of the trial is to determine and compare the efficacy
of probiotics + BBR (Prob + BBR), BBR + placebo (BBR) or
probiotics + placebo (Prob), to that of placebo (Plac) in reducing
glycaemic haemoglobin (HbA1c) among participants diagnosed with T2D.
The secondary outcomes, including clinical metabolic measurements, are
also evaluated and compared across the groups. Comprehensive
metagenomics and metabolomics analyses are employed to investigate the
potential for regulating the gut microbiome of BBR and/or probiotics
treatments, and how these gut microbial changes correlated with the
antidiabetic effect after a 7-day antibiotic pretreatment.
Results
Participants and clinical outcomes after intervention
A total of 566 participants were screened for eligibility from 18
August 2016 to 18 July 2017, of whom 409 eligible participants were
randomized with 106 in the Prob + BBR group, 102 in the Prob group, 98
in the BBR group and 103 in the Plac group (Fig. [119]1). The baseline
characteristics of the participants were similar among the four groups
(Table [120]1). By the end of the intervention, a total of 391
participants were included in the primary analysis. For the primary
outcome, the change in HbA1c showed a significant difference between
the four treatment groups (P < 0.001). The reduction in HbA1c at week
13 in the Prob + BBR group (least-squares mean [95% confidence
interval, 95% CI], −1.04 [−1.19, −0.89]%) or BBR group (−0.99 [−1.16,
−0.83]%) was significantly greater than that in the Plac group (−0.59
[−0.75, −0.44]%, both P < 0.001) and the Prob group (−0.53 [−0.68,
−0.37]%, both P < 0.001), but no difference was found between those of
the Prob + BBR and BBR groups (P = 0.70) or between the Prob and Plac
groups (P = 0.53) (Table [121]2). Generalized estimating equation (GEE)
analysis adjusted for confounding factors according to the protocol
yielded similar results (Supplementary Table [122]1). Thus, BBR and BBR
with probiotics were both superior to the Plac in lowering HbA1c, but
Prob was not.
Fig. 1.
[123]Fig. 1
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Flow diagram of participant enrolment in the PREMOTE Trial.
Table 1.
Demographics and baseline characteristics of the randomized
participants in Intervention Groups.
Characteristic Plac (n = 103) Prob (n = 102) BBR (n = 98) Prob + BBR
(n = 106)
Age (IQR), years 54 (46–61) 54 (45–59) 53 (42–61) 53.5 (47–60)
Male sex, no. (%) 61 (59.2) 65 (63.7) 59 (60.2) 60 (56.6)
Diabetes duration, months 5 (3–9) 5 (3–10) 6 (3–11) 5 (3–11)
Body weight, kg 72.1 ± 12.5 71.9 ± 11.8 71.1 ± 13.6 70.9 ± 11.1
Body mass index, kg m^−2a 26.2 ± 3.43 25.6 ± 2.96 25.7 ± 3.43
25.5 ± 2.86
Waist circumference, cm 91.9 ± 9.0 91.6 ± 8.5 90.7 ± 9.6 90.6 ± 8.2
Systolic blood pressure, mm Hg 129.0 ± 14.2 128.6 ± 13.8 127.9 ± 14.5
126.0 ± 12.7
Diastolic blood pressure, mm Hg 80.3 ± 8.9 80.5 ± 8.3 79.3 ± 9.3
78.7 ± 9.0
HbA1c, %^b 7.81 ± 0.81 7.78 ± 0.82 7.68 ± 0.76 7.66 ± 0.82
HbA1c, mmol/mol^c 61.86 ± 14.64 61.53 ± 14.54 60.44 ± 15.19
60.22 ± 14.54
Fasting plasma glucose, mmol L^−1 8.13 ± 1.48 8.39 ± 1.55 8.16 ± 1.55
8.07 ± 1.32
Post-load plasma glucose, mmol L^−1 15.04 ± 2.58 14.80 ± 3.29
14.22 ± 3.23 14.39 ± 3.17
Fasting serum insulin (IQR), μIU ml^−1 11.75 (8.00–17.43) 10.67
(8.55–15.16) 9.96 (7.00–16.03) 10.10 (7.54–14.00)
Post-load serum insulin (IQR), μIU ml^−1 48.12 (35.99–82.90) 45.27
(34.33–64.60) 54.63 (31.27–69.77) 44.40 (32.35–64.33)
Fasting serum C peptide (IQR), ng ml^−1 2.73 (2.08–3.38) 2.50
(2.08–3.29) 2.53 (1.99–3.08) 2.43 (2.11–3.25)
Post-load serum C peptide (IQR), ng ml^−1 7.65 (5.86–9.58) 7.06
(5.69–8.79) 7.52 (5.83–9.16) 7.11 (5.75–9.59)
Triglyceride (IQR), mmol L^−1 1.44 (1.06–1.91) 1.51 (1.02–2.40) 1.54
(1.09–2.26) 1.66 (1.19–2.35)
Total cholesterol, mmol L^−1 5.18 ± 0.97 5.24 ± 1.04 4.99 ± 1.06
5.25 ± 0.94
HDL cholesterol, mmol L^−1 1.25 ± 0.28 1.20 ± 0.27 1.22 ± 0.28
1.19 ± 0.23
LDL cholesterol, mmol L^−1 3.33 ± 0.84 3.42 ± 0.86 3.22 ± 0.89
3.39 ± 0.80
HOMA-IR (IQR)^d 4.45 (3.05–5.77) 4.14 (2.98–5.70) 3.74 (2.60–5.67) 3.51
(2.55–4.85)
HOMA-β (IQR)^e 53.46 (33.10–91.25) 44.07 (33.55–70.10) 52.09
(31.44–76.21) 49.62 (29.76–75.24)
[125]Open in a new tab
BBR berberine, IQR interquartile range, Prob Probiotics. No significant
differences were observed among the four groups in any of the baseline
characteristics. Data were presented as mean ± SD or median (IQR).
^aBody mass index (BMI) is the weight in kilograms divided by the
square of the height in metres.
^bHbA1c is glycated haemoglobin, shown as the DCCT (Diabetes Control
and Complications Trial) units.
^cHbA1c is glycated haemoglobin, shown as the IFCC (International
Federation of Clinical Chemistry) units.
^dHOMA-IR refers to (Fasting serum insulin (μIU ml^−1) × Fasting plasma
glucose (mmol L^−^1))/22.5, homoeostasis model assessment index for
assessing insulin resistance.
^eHOMA-β, refer to (20 × Fasting serum insulin (μIU ml^−1))/(Fasting
plasma glucose (mmol L^−1)− 3.5), homoeostasis model assessment index
for assessing β-cell function.
Table 2.
Primary outcomes in all and older participants (age ≥ 50 years).
HbA1c 0 w (%) HbA1c 13 w (%) Change in HbA1c (95% CI)^a Change in HbA1c
(95% CI)^b Model 1 Model 2
P-value^c P-value^d P-value^c P-value^d
All participants
Plac 7.81 ± 0.81 7.23 ± 0.97 −0.59 (−0.75, −0.44) / 5.99E − 04 /
6.31E − 04
Prob 7.78 ± 0.82 7.27 ± 0.90 −0.53 (−0.68, −0.37) 0.07 (−0.20, 0.34)
0.53 5.15E − 05 0.52 5.11E − 05
BBR 7.68 ± 0.76 6.71 ± 0.77 −0.99 (−1.16, −0.83) −0.40 (−0.67, −0.13)
5.99E − 04 / 6.31E − 04 /
Prob + BBR 7.66 ± 0.82 6.62 ± 0.66 −1.04 (−1.19, −0.89) −0.44 (−0.71,
−0.18) 8.41E − 05 0.70 6.99E − 05 0.66
Age ≥ 50 years
Plac 7.76 ± 0.77 7.24 ± 1.13 −0.59 (−0.78, −0.39) / 0.03 / /
Prob 7.64 ± 0.69 7.07 ± 0.61 −0.52 (−0.72, −0.33) 0.06 (−0.26, 0.39)
0.65 8.65 E − 03 / /
BBR 7.58 ± 0.69 6.82 ± 0.81 −0.90 (−1.10, −0.70) −0.31 (−0.65, 0.02)
0.03 / / /
Prob + BBR 7.61 ± 0.73 6.62±0.57 −0.99 (−1.17, −0.82) −0.41 (−0.72,
−0.09) 2.39E − 03 0.48 / /
[126]Open in a new tab
BBR berberine treatment, HbA1c glycated haemoglobin, Plac placebo, Prob
probiotics treatment, Prob + BBR berberine plus probiotics treatment.
Data were presented as mean ± SD. Model 1: analysis of variance (ANOVA)
were performed to compare the Change in HbA1c between groups. Model 2:
Multivariate ANOVA were performed to compare the Change in HbA1c
between groups adjust for age group (age group defined as <50 and ≥50
years).
All P-values reported were two-sided for multiple comparisons using
Bonferroni correction. A statistical significance level was set at
P < 0.008.
^aThe values are least-squares means.
^bPlacebo subtracted change in HbA1c, least-squares means.
^cP-values refer to comparison of change in HbA1c between Plac group
and the other groups using ANOVA on the basis of intention-to-treat
(ITT) analysis.
^dP-values refer to comparison of change in HbA1c between BBR group and
the other groups using ANOVA on the basis of ITT analysis.
Similar improvements were found in the other metabolic parameters
(secondary outcomes) by BBR containing treatments, such as fasting
plasma glucose (FPG), post-load plasma glucose (PPG), blood
triglycerides (TGs), total cholesterol (TC) and low-density lipoprotein
(LDL) cholesterol levels, except for homoeostasis model assessment
index for insulin resistance (HOMA-IR), which was significantly lowered
by Prob + BBR but not by BBR (Supplementary Table [127]2). More cases
of gastrointestinal adverse effect (AE) cases occurred in both BBR arms
and glycaemic control did not differ in participants with
gastrointestinal AEs. All other AEs were comparable between the
intervention and Plac groups (Supplementary Table [128]3) with normal
hepatic and renal function after treatment (Supplementary
Table [129]4). Subgroup analyses showed that diabetes duration and
gastrointestinal AEs did not affect the primary outcome in our study
(Supplementary Tables [130]5 and [131]6, post-hoc analysis).
Metagenomic analysis showed a significant impact of BBR on the human gut
microbiome
Metagenomic analysis with high throughput shotgun sequencing^[132]35
showed that the alterations in the gut microbiome after 1 week of
gentamycin treatment (Supplementary Fig. [133]1a–d) had recovered to
the baseline status after 13 weeks of Plac intervention, regarding to
the gene count and α-diversity (Supplementary Fig. [134]1e–f and
Supplementary Data [135]1, Wilcoxon signed-rank test, P > 0.05).
Consistently, principal coordinates analysis (PCoA) revealed that the
altered overall gut microbial composition in Plac arm at the species
and functional level based on Kyoto Encyclopedia of Genes and Genomes
Orthologue (KO) profiles (Supplementary Fig. [136]1c, d) were largely
recovered from gentamycin pretreatment (Supplementary Fig. [137]1g, h).
The reconstitution of the gut microbiome after probiotics treatment was
similar to that after Plac treatment (Fig. [138]2a, b, Supplementary
Fig. [139]1e–h and Supplementary Data [140]1, Wilcoxon signed-rank
test, P > 0.05), except for the enrichment of the ingested probiotics
species (Fig. [141]2d and Supplementary Data [142]2, Wilcoxon
matched-pairs signed-rank test, q < 0.05). Thus, Probiotics treatment
showed similar effects not only on glycaemic control but also on the
resilience of the gut microbiota after gentamycin pretreatment with
placebo.
Fig. 2. BBR significantly altered gut microbiome symbiosis after 13 weeks of
treatment.
[143]Fig. 2
[144]Open in a new tab
a Gene count (upper panel) and Shannon index (lower panel) of genes in
different arms, baseline and post treatment; Plac, Placebo, n = 96;
Prob, probiotics treatment, n = 98; BBR, berberine treatment, n = 85;
Prob + BBR: berberine plus probiotics treatment, n = 102; *P < 0.05,
**P < 0.01, ***P < 0.001, two-sided Kruskal–Wallis test. Dark lines in
the boxes indicate medians, the width of the notches is the IQR, the
lowest and highest values within 1.5 times the IQR from the first and
third quartiles. b Distance-based redundancy analysis (dbRDA) plot
based on Bray–Curtis distances of species in post-treatment samples was
performed to assess the difference between the four treatment arms
(Permanova P < 0.001). Projection of species-level gut microbiome
samples constrained by treatment methods. Marginal box plots show the
separation of the constrained projection coordinates (boxes show
medians and quartiles, error bars extend to most the extreme value
within 1.5 interquartile ranges), Plac, n = 96; Prob, n = 98; BBR,
n = 85; Prob + BBR, n = 102. c Venn diagram showing the overlapping of
microbial species among the four treatment arms that were altered from
baseline to post treatment, two-sided Wilcoxon matched-pairs
signed-rank test, q < 0.05. d Heatmap of gut microbial species that
showed significantly changed their relative abundances (RAs) post
treatment vs. baseline. Plac, n = 96; Prob, n = 98; BBR, n = 85;
Prob + BBR: n = 102. The changes in nine species in probiotics formula
ingested by participants were separately shown below. *q < 0.05,
two-sided Wilcoxon match-pairs signed-rank test. The colour key
represents the Z score. Bifidobacterium catenulatum–Bpc, B.
catenulatum–Bifidobacterium pseudocatenulatum complex. Source data and
exact P-value are provided in the Source Data file.
BBR (either alone or with probiotics) treatments, instead,
significantly altered the gut microbiome composition after 13 weeks of
intervention compared to the Plac treatment (Fig. [145]2) and to that
of the baseline and antibiotic treatment groups (Supplementary
Fig. [146]1e, h), but the BBR and Prob + BBR groups shared similar
changes in microbial composition and function (Supplementary
Fig. [147]1g, h and Fig. [148]2). A total of 78 species changed their
relative abundances (RAs) in BBR and Prob + BBR but not in the Plac and
Prob groups (Fig.[149]2c, d, baseline vs. post treatment, Wilcoxon
matched-pair signed-rank test, q < 0.05). Among the 78 BBR-induced
species, 36 were designated as the key BBR responsive taxa, the RAs of
which showed significant alterations in post-treatment faecal samples
of both BBR treatment groups compared to those in Plac or Prob group
(Supplementary Fig. [150]2 and Supplementary Data [151]3, Dunn’s
P < 0.05, vs. Plac, or vs. Prob, Kruskal–Wallis (KW) test). BBR
depleted the species that mainly produce single sugar or SCFAs from
fermenting polysaccharides or oligosaccharides, including Roseburia
spp., Ruminococcus bromii, Faecalibacterium prausnitzii and
Bifidobacterium spp., which were frequently reported to cross-feed with
the other saccharides degraders^[152]36–[153]39. The species enriched
by BBR included two Bacteroides spp. and multiple taxa of
γ-Proteobacteria, which were also induced by metformin
treatment^[154]11,[155]15. Probiotics supplementation did not affect
the global alterations in gut microbiome composition induced by BBR
(Supplementary Fig. [156]1g, h and Fig. [157]2), except for elevating
the RAs of probiotics species but not that of Bifidobacterium longum.
The pathway enrichment analysis (Supplementary Data [158]4) showed that
compared to the control groups, BBR significantly attenuated protein
translation, DNA replication, and fatty acid and amino acid
biosynthesis, which was attributed to the bacteriostatic
characteristics of BBR. BBR induced the degradation potential of
multiple xenobiotics and glycans. BBR also elevated the bacterial
response functions similar to metformin^[159]11, e.g., the bacterial
secretion system, the two-component system and the ABC (ATP-binding
cassette) transport were promoted. For the most part, Prob + BBR
affected similar functional pathways with the BBR group (Supplementary
Data [160]4).
BBR altered microbial BA metabolism and the blood BA pool
BAs are known to regulate host metabolic homoeostasis and the gut
microbiota plays key roles in modulating host BA pool composition, and
hence BA signalling^[161]21,[162]22. Different microbial BAs mediate
the therapeutic effects of either acarbose or metformin, the widely
prescribed antidiabetic medicines^[163]14,[164]15. We thus sought to
investigate whether microbial BA metabolism and host blood BA pool were
also affected by BBR treatment. In addition to depleting the Eggtherlla
lenta that harbours the complete BA-induced operon
(Bai)^[165]40,[166]41 (Fig. [167]2d and Supplementary Fig. [168]2), BBR
also decreased the total RAs of multiple genes involved in microbial BA
metabolism, including BaiI, BaiA, BaiN and particularly the BaiE that
encodes the rate-limiting enzyme of 7α/β dehydratases, whereas none of
these genes showed significant changes in abundance in the Plac or Prob
arm (Fig. [169]3a). Echoed with the changes of Bai genes in faeces, the
plasma BA profiling by liquid chromatography/mass spectrometry (LC/MS)
detected significant increases in glycochenodeoxycholic acid (GCDCA)
and decreases in deoxycholic acid species (DCAs), including DCA,
glycine and taurine-conjugated DCA (glycodeoxycholic acid and
taurodeoxycholic acid (TDCA)) after BBR treatment, contributing to the
decreased blood unconjugated/conjugated BA ratio (Uncon/con BA) and
secondary BA components (Fig. [170]3b and Supplementary Data [171]5).
Furthermore, the positive correlation between Bai genes RAs and blood
secondary BA (DCAs and lithocholic acids (LCAs)) levels were strong and
consistent in both baseline and post-treatment measurements, supporting
the microbial origins of circulating secondary BAs (Fig. [172]3c).
Thus, although the RAs of Bsh were not altered, changes in the blood BA
profile suggested that two key gut microbial transformation procedures,
the BA deconjugation and dehydroxylation, could be both inhibited by
BBR treatment. The GEE analysis in participants from both BBR arms
showed that the changes in blood DCAs were significantly correlated
with the HbA1c, FPG, PPG and TC improvements, which were the main
clinical outcomes of BBR treatment (Fig. [173]3d), and this
relationship was consistent when analysis was performed in the single
BBR containing arm (Supplementary Data [174]6). Of note, similar to the
metformin and acarbose results, the plasma FGF19 levels were also
reduced in both BBR treatment groups (Fig. [175]3e). The above results
suggested that BBR treatment reduced the gut microbial BA
transformation and hence lowered the gut FXR activity, which may
contribute to its antidiabetic effect.
Fig. 3. BBR altered microbial BA metabolism and correlated with blood BAs and
clinical outcomes.
[176]Fig. 3
[177]Open in a new tab
a Changes in RAs of bile acid-inducible (Bai) genes induced by the
treatments of four arms. hsdh, hydroxysteroid dehydrogenase; Bsh, gene
encoding bile salt hydrolase. The Z-score was calculated with the
two-sided Wilcoxon matched-pairs signed-rank test. A Z-score > 0
indicated an increase after treatment, while a z-score < 0 indicated a
decrease after treatment. *P < 0.01, **P < 0.001, ***P < 0.0001; Plac,
Placebo, n = 96; Prob, probiotics treatment, n = 98; BBR, berberine
treatment, n = 85; Prob + BBR, berberine plus probiotics treatment,
n = 102. b Comparisons of bile acid (BA) composition between baseline
and post treatment in the four arms. CA, cholic acid; CDCA,
chenodeoxycholic acid; DCA, deoxycholic acid; GCA, glycocholic acid;
GCDCA, glycochenodeoxycholic acid; GDCA, glycodeoxycholic acid; GLCA,
glycolithocholic acid; GUDCA, glycoursodeoxycholic acid; LCA,
lithocholic acid; TCA, taurocholic acid; TCDCA, taurocholic
chenodeoxycholic acid; TDCA, taurodeoxycholic acid; TLCA,
taurolithocholic acid; TUDCA, tauroursodeoxycholic acid; UDCA,
ursodeoxycholic acid. *q < 0.01, **q < 0.001, ***q < 0.0001, two-sided
Wilcoxon match-pairs signed-rank test. c Correlations between microbial
BA genes and blood BA compositions at the baseline (upper panel) vs.
post treatment (lower panel) for all participants, Spearman
correlation, colour key represented rho value, *q < 0.01. d Heatmap of
correlations between the blood BAs and clinical outcomes. Multivariate
GEE controlling for age, sex and BMI. The colour key represents the
β-value, *q < 0.01. e Plasma FGF19 levels pre and post treatment,
*P < 0.05, **P < 0.01, ***P < 0.001, two-sided Wilcoxon matched-pairs
signed-rank test, dark lines in the boxes indicate medians, the width
of the notches is the IQR, the lowest and highest values within 1.5
times the IQR from the first and third quartiles, Plac, n = 96; Prob,
n = 98; BBR, n = 85; Prob + BBR: n = 102. 12a/nonBA,
12a-hydroxylated/non–12a-hydroxylated bile acids; 2hPPG, post-load
plasma glucose; cp120, post-load serum C peptide; FPG, fasting plasma
glucose; HbA1c, glycated haemoglobin; HOMA-IR, homoeostasis model
assessment index for assessing insulin resistance; HOMA-β, homoeostasis
model assessment index for assessing β-cell function; ins120, post-load
serum insulin; TC, total cholesterol; Uncon/Con BA,
unconjugated/conjugated bile acids. Baseline, baseline levels; post,
post-treatment levels. Source data and exact P-value are provided in
the Source Data file.
BBR inhibited R. bromii to attenuate DCA transformation
To determine which commensal bacteria affected by BBR might mediate its
inhibitory effect on microbial BA metabolism, we further examined the
correlations of the post-treatment RAs of key BBR responsive species
(Supplementary Fig. [178]2) with the changes in clinical outcomes and
the changes in plasma BA levels. We found that most secondary BA
correlating species were those also associated with the changes of
HbA1c and other clinical outcomes, including mainly LDL-C, TC, and TG
(Fig. [179]4a, P < 0.05). The HbA1c-correlated taxa were dominated by
those that were depleted by BBR treatment including R. bromii.
Interestingly, most of these taxa are not BA converters, except for
Eggthella lenta, suggesting the existence of unknown BA metabolism
potential in these species. Strains of Ruminococcus have been reported
to regulate BA metabolism^[180]42,[181]43 and we thus performed in
vitro culture experiments on one strain of R. bromii, AF25-7, isolated
from a faecal sample of a Chinese woman^[182]44, to test whether the
strain could transform primary BAs. To our surprise, this AF25-7 strain
not only demonstrated a substantial DCA transformation ability
(Fig. [183]4b, P < 0.001) in vitro but also showed significant growth
inhibition in response to BBR at a concentration as low as 25 μg/ml in
vitro (Fig. [184]4c). Thus, R. bromii could be the target of BBR in the
gut microbiome to reduce the microbial production of secondary BA that
is associated with the effective glycaemic control achieved with BBR.
Fig. 4. R. bromii was inhibited by BBR to attenuate DCA transformation.
[185]Fig. 4
[186]Open in a new tab
a The two-panel heatmap on the left shows the correlations between the
key BBR responsive species and with major clinical outcomes and plasma
levels of bile acid. The colour key shows Rho calculated by partial
Spearman’s correlation with adjustment for age, sex and BMI. Δ of
clinical parameters or BAs = 100% × (baseline value − post treatment
value)/baseline value. Species in blue represent depleted species and
species in orange represent enriched species after BBR treatments.
*P < 0.05. b Bile acid transformation assay for R. bromii. The
percentage composition of deoxycholic acid (DCA) and lithocholic acid
(LCA) in the culture media with which R.bromii had grown for 24 h with
primary bile acid (CA and CDCA) treatment were measured by LC/MS.
n = 3, data are shown as the mean ± SD. c The growth curve of R. bromii
with different concentrations of BBR in the in vitro culture
experiment, demonstrated a significant inhibitory effect of BBR on R.
bromii starting at a concentration of 25 μg ml^−1, n = 3, P < 0.001,
determined by two-way repeated-measures ANOVA, data are shown as the
mean ± SD. Bifidobacterium catenulatum − Bpc, B.
catenulatum–Bifidobacterium pseudocatenulatum complex. Source data and
exact P-value are provided in the Source Data file.
Probiotics improved glycaemic control in older participants treated with BBR
Subgroup analysis in pre-stratified age (<50 and ≥50 years) groups
showed that probiotics, with comparable baseline values, marginally but
significantly improved the antidiabetic effect of BBR in participants
older than 50 years and exerted the extra benefit of improving HOMA-IR
(Supplementary Table [187]2 and Supplementary Tables [188]7 and
[189]8). Similar benefits from probiotic supplementation were shown in
participants older than 54 years of age (median age in this population)
(Supplementary Table [190]1). Probiotics containing species were
significantly more enriched after treatment in older participants than
in younger ones and the post-treatment RAs of Lactobacillus crispatus
and Lactobacillus salivarius were only significantly elevated in older
participants compared with their baseline RAs (P < 0.05, Wilcoxon
matched-pairs signed-rank test, Supplementary Fig. [191]3). Moreover,
probiotic containing species, except for B. longum, exhibited a
dose–response relationship with the improvement in HbA1c levels in the
older but not younger participants of the Prob + BBR arm (Supplementary
Fig. [192]4, Spearman correlation, P < 0.05). However, the differences
of both R. bromii and DCAs between the Prob + BBR and BBR arms shown in
the older participants were similar with those in the total population
(Supplementary Figs. [193]5 and [194]6), suggesting that the R.
bromii/DCAs might not be the cause of the extra benefit of probiotic
supplementation in older participants.
Discussion
In this multicentre, randomized, double-blind, Plac-controlled clinical
trial conducted in 409 drug-naive T2D patients, we confirmed the
hypoglycaemic effect of BBR in Chinese participants and demonstrated
the BBR-induced changes in the human gut microbiome and blood BA pool
composition in comparison with the Plac. The triple association between
the gut microbiome, blood BAs and clinical outcomes suggested a
potential microbial-related mechanism underlying the metabolic benefits
of BBR. R. bromii, identified as a DCA convertor in this study, might
serve as a microbial target of BBR. Our study failed to find
significant metabolic improvement with probiotic supplementation in T2D
patients, except when it was used in combination with BBR in the older
participants.
Probiotics have recently been suggested to delay the recovery of
microbiome symbiosis from baseline conditions in healthy volunteers
treated with antibiotics^[195]45. However, this observation implies
that interventions following antibiotics pretreatment might bear an
opportunity to reset the gut microbiome from the diseased status, such
as obesity or T2D-related microbial dysbiosis^[196]4,[197]17. Potential
approaches can include either treatment with beneficial bacteria
(replenishment, such as probiotic supplementation) or suppression of
the growth of unfavourable taxa with agents such as BBR (surveillance)
or both, following temporary antibiotic treatment. However, we did not
find a superior effect of Prob compared to that of Plac or Prob + BBR
to BBR in treating diabetes, nor were there different changes in gut
microbiome symbioses compared to those of Plac. Such findings could be
the result of strain-specific functional variation, suggesting the
requirement of a more precise strategy for probiotics treatment. This
was consistent with the conclusion from preponderant
literatures^[198]46–[199]48 that probiotics have limited effects in the
treatment of metabolic diseases. Therefore, the strategy of
surveillance, including, e.g., the use of BBR might be more effective
than the strategy of replenishment for treating hyperglycaemia in the
context that correcting gut microbiota dysbiosis is a feasible and
effective way to manage T2D.
Owing to the methodological or population differences, studies on human
samples from cohorts taking different antidiabetic medications
currently have identified few concordant taxa of so-called antidiabetic
bacteria. The microbial BA transformation pathway seems to be targeted
by diverse antidiabetic agents, either to decrease 7α/β dehydroxylation
or to alter bile salt deconjugation, which consequently modulate the
host BA pool and hence mediates the hypoglycaemics effect of
medications^[200]11,[201]14,[202]15. R. bromii has been previously
reported mainly to ferment dietary carbohydrates and produce single
sugar or SCFA, such as acetate, but not propriate or
butyrate^[203]39,[204]49 Consistently, none of the butyrate-producing
gene has been identified in multiple R. bromii strains isolated from
the Chinese population in our previous work^[205]44. The correlation
analysis together with the in vitro BA biotransformation experiment in
this study revealed the unprecedently reported DCA production capacity
of this species and supported the causal relationship between changes
in plasma DCA and the faecal R. bromii abundances after BBR treatment.
Thus, the R. bromii/DCA axis could be one of the gut microbial
effectors of BBR with regard to its antidiabetic effect. Regarding the
fact that the complete Bai operon is not known to be present in R.
bromii genomes, it is possible that other uncharacterized proteins that
regulate microbial BA metabolism might exist in this taxon to regulate
the DCA transformation. Further in vivo and in vitro studies are
required to delineate the molecular mechanism by which R. bromii to
produce DCA and how other key BBR responsive species may be involved in
the hypoglycaemic effect of BBR.
It is noteworthy that metformin has been reported to increase
conjugated UDCA levels, which further attenuated gut FXR activity by
inhibiting Bsh activity^[206]15. However, neither GUDCA nor TUDCA
levels were altered after treatment with BBR or Prob + BBR, nor was the
RA of Bsh. It is possible that the discrepancy between the effects of
these two medications on microbial BA metabolism might have resulted
from the different target taxa or other BA biotransformation enzymes
that were affected by treatments. For instance, the RA of BaiE, the
rate-limiting enzyme for bacterial secondary BA metabolism was
inhibited by BBR but not by metformin. Therefore, the DCA species
rather than its upstream UDCAs were altered by BBR. As the most
abundant BA component in faeces, the alterations in DCA should have
been the main contributor to the fluctuations of gut FXR activity. The
downregulated plasma levels of FGF19 further supported our hypothesis
that the decrease of DCA species by BBR could diminish the gut FXR. In
vivo studies should be employed in the future to confirm this
hypothesis.
Notably, the R. bromii/DCAs axis was unrelated to the additional
benefit of Prob+BBR in reducing HbA1c in older participants. The
beneficial effects of probiotics in aged host have been sporadically
reported^[207]50,[208]51. Probiotics exhibit metabolic benefits by
improving the gut barrier and alleviating inflammation^[209]52, which
are also key to the development of ageing-related diseases^[210]53. BBR
suppressed multiple Bifidobacterium spp. either in the human
participants in our study or in rodents^[211]31,[212]54.
Health-associated Bifidobacterium spp. have been shown to be depleted
along with ageing but enriched in extremely aged healthy
subjects^[213]34. Thus, our probiotics formula containing 2 strains of
Bifidobacterium might be of particular benefit for older T2D patients
treated with BBR. It thus might not be appropriate to connect the
potential benefits to the health of aged patients to the add-on
hypoglycaemic benefits of supplementing probiotics with BBR, but at
least possible that the effect of probiotic supplements on metabolic
disorders might be related with the recipient age and the medications.
This study has several limitations. First, as this trial was conducted
in Chinese people residing in China and had a relatively short duration
for randomized intervention, the findings derived from this
investigation may not be generalized to other racial/ethnic populations
without caution. Second, the participants enroled in our study were all
drug naive with relatively short duration of diabetes and records of
lifestyle interventions were not obtained. Our design of the
randomized, placebo-controlled, parallel four-arm trial has largely
reduced the potential study effects, which might be introduced by
unstable metabolic conditions, but future studies should enrol
participants with longer disease durations and record detailed
lifestyle changes. In addition, more participants experienced
gastrointestinal AEs in the BBR-treated groups than that in the Plac or
Prob groups, although the AEs did not affect the antidiabetic effect of
BBR or gut microbiome features in this study with a 3-month treatment,
again the concern needs to be addressed in trials with longer
intervention duration. Notwithstanding these limitations, our findings
may have important implications for managing T2D in patients by
treating microbiome dysbiosis.
Methods
Trial design and oversight
We conducted a randomized, double-blind, -placebo-controlled clinical
trial in 20 medical centres in China (ClinicalTrials.gov number,
[214]NCT02861261). Participants were enroled between 18 August 2016 and
18 July 2017. The trial conformed to the provisions of the Declaration
of Helsinki and was approved by the ethics committees at each
participating centre. All the participants provided written informed
consent.
Participants and intervention procedure
The eligible participants were those with newly diagnosed T2D according
to the World Health Organization criteria^[215]55 and were drug naive
for glycaemic control but with at least 2 months of stable lifestyle
intervention.
After completing the screening assessment (from −2 weeks to −3 days),
eligible participants were given an oral broad-spectrum antibiotic
(gentamicin sulfate 80 mg twice daily) for 7 days during the run-in
period, to improve probiotic colonization^[216]34. Then, the
participants were randomly assigned into one of the following four
groups in a 1 : 1 : 1 : 1 ratio as follows: BBR (0.6 g per 6 pills,
twice daily before meal) plus probiotics (4 g per 2 strips of powder,
once daily at bedtime) (Prob + BBR), probiotics plus Plac (Prob), BBR
plus Plac (BBR), or Plac plus Plac (Plac). Treatments were administered
for 12 weeks and patients visited the centre every 4 weeks until the
end of the study. The randomization procedure was stratified by age
group and utilized a block size of eight, and the random numbers were
generated by utilizing a validated interactive Web-based Response
System, which was maintained by an independent data manager. The study
personnel and participants were blinded to the assignment of treatment
arms.
The detailed inclusion criteria
Patients are eligible to be included in the study only if they meet all
of the following criteria
1. Newly diagnosed T2D according to the 1999 World Health Organization
criteria (Appendix 4). Both genders eligible.
2. Age: ≥20 and <70 years.
3. BMI: ≥19.0 and ≤35.0 kg m^−2.
4. Fully understand the study.
5. Give written informed consent.
6. Are drug naive (have been treated with healthy lifestyle
modification only) for management of hyperglycaemia (including oral
antidiabetic agents, GLP-1 agonists, or insulin).
7. Have at least 2 months of lifestyle intervention (diet and
exercise) for glycaemic control before screening.
8. HbA1c ≥ 6.5% and ≤10.0%, and FPG ≥ 7.0 and ≤13.3 mmol L^−1 at
screening.
The detailed exclusion criteria
Patients will be excluded from the study if they meet any of the
following criteria:
1. Severe liver dysfunction, defined as serum alanine aminotransferase
concentration more than 2.5 times above upper limit of normal
range. Impaired renal function (defined as
serum-creatinine > 132 μmol L^−1 or estimated glomerular filtration
rate (eGFR) < 60 mL (min × 1.73 × m^2)^−1); psychiatric disease,
severe infection, severe anaemia and neutropenia.
2. Severe organic heart diseases, including but not limited to
congenital heart disease, rheumatic heart disease, hypertrophic or
dilated cardiomyopathy. New York Heart Association class (NYHA)
grade of heart function ≥ III.
3. Allergic to gentamycin or other amino glycosides antibiotics.
4. Type 1 diabetes, monogenic diabetes, diabetes due to injury of the
pancreas or other secondary diabetes mellitus (due to such as
Cushing syndrome, thyroid abnormalities or acromegaly).
5. Is previously or currently treated with antidiabetic agents,
including oral antidiabetic agents, GLP-1 agonists or insulin.
6. Have taken BBR hydrochloride tablets in the past 1 year or
previously used BBR hydrochloride tablets for more than a week.
7. Taken other probiotics or probiotics product in the past 3 months.
8. History of acute diabetic complications including diabetic
ketoacidosis or hyperosmolar hyperglycaemic non-ketonic coma within
3 months.
9. Taken weight control drugs (including weight-loss drugs); oral,
intramuscular, intravenous, non-alimentary canal or intra-articular
administration of corticosteroid hormones in the past 3 months.
10. Pregnancy.
11. Participated in other clinical trials in the past 3 months.
12. Medical history of malignant tumour (except local skin basal cell
carcinoma) in the past 5 years, whatever with evidence of
recurrence or metastasis or not.
13. History of active substance and alcohol abuse. History of
alcohol-related diseases in the past 2 years.
14. Having digestive tract disease, which causes accurate and chronic
diarrhoea or severe constipation.
15. Medical history of intestine resection or other digestive tract
surgery (such as cholecystectomy) in the past 1 year, or other
non-gastrointestinal surgery in the past 6 months.
16. Any condition, which in the investigator’s opinion, could interfere
with the results of the trial.
The detailed special criteria for the study of gut microbiome
1. Keep light diet for 3 days before the screening and during the
whole study period, avoid fatty foods unless with special
requirements.
2. Do not eat fermented dairy products (such as yoghurt) and
probiotics for at least 7 days before the screening and during the
entire research.
3. Do not take antibiotics (such as penicillin, cephalosporins,
tetracycline, etc.) other than the study medication, or other
interventions that could affect the gastrointestinal tract for 2
months before the screening and during the whole study period. If
antibiotics must be taken for special reasons such as for the
patients’ safety consideration by the judgement of the
investigators, the use of antibiotic medications must be recorded
in detail in the Concomitant Medication Form.
4. Taking steroids, cyclosporine (immunosuppressive agent) or
antitumor agents 3 months before the screening and during the whole
study period are not permitted.
The list of institutional review boards
1. Ruijin Hospital Ethics Committee, Shanghai Jiao Tong University
School of Medicine, Shanghai, PR China.
2. Ren Ji Hospital Ethics Committee, Shanghai Jiao Tong University
School of Medicine, Shanghai, PR China.
3. Shanghai Tenth People’s Hospital Ethics Committee, Tongji
University, Shanghai, PR China.
4. Xin Hua Hospital Ethics Committee, Shanghai Jiao Tong University
School of Medicine, Shanghai, PR China.
5. Central Hospital Ethics Committee, Minhang district, Shanghai, PR
China.
6. Chang Hai Hospital Ethics Committee, Second Military Medical
University, Shanghai, PR China.
7. Tong Ren Hospital Ethics Committee, Shanghai Jiao Tong University
School of Medicine, Shanghai, PR China.
8. Shanghai First People’s Hospital Ethics Committee, Shanghai Jiao
Tong University School of Medicine, Shanghai, PR China.
9. The Second Affiliated Hospital Ethics Committee, Zhejiang
University School of Medicine, Zhejiang Province, PR China.
10. The First Affiliated Hospital Ethics Committee, Wenzhou Medical
University, Zhejiang Province, PR China.
11. Xuzhou Central Hospital Ethics Committee, Jiangsu Province, PR
China.
12. Nanjin Drum Tower Hospital Ethics Committee, Nanjing University
Medical School, Jiangsu Province, PR China.
13. Jiangsu Province Hospital Ethics Committee, The First Affiliated
Hospital of Nanjing University Medical School, Jiangsu Province, PR
China.
14. Qilu Hospital Ethics Committee, Shandong University, Shandong
Province, PR China.
15. Peking University Shenzhen Hospital Ethics Committee, Shenzhen, PR
China.
16. The First Affiliated Hospital Ethics Committee, Sun Yat-sen
University, Guangdong Province, PR China.
17. Sun Yat-sen Memory Hospital Ethics Committee, Sun Yat-sen
University, Guangdong Province, PR China.
18. Fujian Provincial Hospital Ethics Committee, Fujian Province, PR
China.
19. Wuhan Union Hospital Ethics Committee, Tongji Medical College,
Huazhong University of Science and Technology, Hubei Province, PR
China.
20. Nanfang Hospital Ethics Committee, Southern Medical University,
Guangdong Province, PR China.
21. Institutional Review Board of BGI-Shenzhen, Guangdong Province, PR
China.
At baseline and each visit thereafter, questionnaires were completed
about patient medical history, acceptability of the study medication,
adherence and adverse events. Blood for HbA1c, serum insulin and C
peptide levels were determined in a centralized assayed. Other
specimens were transported with dry ice to the centre laboratory and
stored at −80 °C thereafter.
The clinical outcomes included the improvement of glycaemic control,
defined as the changes in HbA1c levels, as the primary outcome and the
changes in fasting or post-load blood glucose, lipids, insulin, HOMA-IR
and HOMA-β for assessing β-cell function as the secondary outcomes,
from baseline to a 13-week follow-up.
BBR used in the present study was produced by industrialized synthesis.
The multi-strain probiotics products contained nine proprietary strains
of probiotics seen below. BBR, probiotics and their matching Plac were
the courtesies from Northeast Pharmaceutical Group Co., Ltd, Shenyang,
Liaoning, China, and Shanghai Jiaoda Onlly Co., Ltd, Shanghai, China,
respectively. The two companies had no role in the design and conduct
of the study; collection, management, analysis or interpretation of the
data; preparation, review or approval of the manuscript; or decision to
submit the manuscript for publication.
A CONSORT checklist of information reporting a randomized trial was
included (Supplementary Note [217]1).
Biochemical measures
HbA1c, serum insulin and C peptide were performed in central laboratory
in Ruijin Hospital. HbA1c was measured by high-performance liquid
chromatography using the VARIANT II Haemoglobin Testing System (Bio-Rad
Laboratories, Hercules, CA, USA). Serum insulin and C peptide were
measured by electrocheuminescence immunoassay “ECLIA” on cobase601
immunoassay analysers (Roche Diagnostic, Basel, Switzerland). The
sensitivity range, intra-assay coefficient of variability (CV) and
inter-assay CV for HbA1c were 3.5–19.0%, 0.39 and 0.45; for insulin was
0.2–1000 μIU mL^−1, 1.1 and 3.6; C-peptide was 0.01–40.0 ng mL^−1, 0.7
and 1.95, respectively.
Metagenomic analysis
For metagenomic library construction and sequencing, we used the
BGISEQ-500 platform as previously described^[218]35. In brief, DNA
samples were subjected to random fragmentation, end-repair and
subsequent adaptor ligation for DNA nanoball-based library construction
and combined primer anchor synthesis-based shotgun metagenomic
sequencing using a paired-end 100 bp mode. A total of 1192 faecal DNA
samples, from three time points (baseline, n = 405; after 1 week of
antibiotic treatment, n = 403; and after four-arm-based 3-month
interventions, n = 384) were sequenced and subjected to subsequent
metagenomic analysis. After removing low-quality and human-derived
sequences as described^[219]35, high-quality non-human reads
(9.98 ± 2.31 GB per sample) were aligned to the 9.9 M integrated gene
catalogue (IGC) by SOAP2.22 using the criterion of ≥90% identity.
Sequence-based gene abundance profiling was performed as
follow^[220]56.
Step 1: For any sample S, calculation of the copy number of each gene:
[MATH:
bi=xi<
/mrow>Li
:MATH]
Step 2: Calculation of the RA of gene i:
[MATH:
ri=bi<
/mrow>∑j
bj :MATH]
r[i]: the RA of gene i in sample S.
x[i]: the number of mapped reads.
L[i]: the length of gene i. The RAs of phyla, species and KOs were
calculated by the sum of the RAs of their annotated genes. The number
of genes, which represented gene richness was calculated for each
sample in accordance with a previous study^[221]56. Alpha diversity was
quantified by the Shannon index using RA profiles at the gene level. At
the species level, we further confined our analyses to species with at
least 100 annotated genes in each of at least 20% of samples, which
resulted in 131 species accounting for on average 99.56% of the
annotated microbial species composition. Except for B. longum, eight of
the nine probiotics containing species including Bifidobacterium breve,
Lactobacillus casei, L. crispatus, Lactobacillus fermentum,
Lactobacillus plantarum, Lactobacillus rhamnosus, L. salivarius and
Lactobacillus gasseri did not meet the above selection criteria, and
thus they were subjected to further analyses separately from the
profiling of the 131 species.
Gut microbial dissimilarities between groups at the species and KO
level were visualized by unconstrained PCoA, using Bray–Curtis
dissimilarities based on species and KO profiles (PCoA function, R
3.3.2, ape package). Distance-based redundancy analysis between four
treatment arms was also conducted using the RAs of species (capscale
function, R 3.3.2, vegan package).
Differentially enriched the Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathways (modules) between groups were identified according to
the reporter Z-scores of all detected KOs involved in the given pathway
(module)^[222]57. An absolute reporter score value ≥ 1.96 (95%
confidence according to normal distribution) was used as the detection
threshold for significance.
The institutional review board of BGI-Shenzhen approved the analyses of
faecal samples/meta data collected by all participating centres under
ethical clearance number BGI-R087-1-T1.
Profiling of microbial genes involved in BA biotransformation
The identification of microbial genes involved in BA biotransformation
was performed as previously described^[223]14. According to analysis
with the updated KEGG database (Version 87), in the secondary BA (SBA)
biosynthesis pathway (map00121), a baiN gene encoding enzymes (K07007,
3-dehydro-bile acid Delta4,6-reductase [[224]EC: 1.3.1.114]) involved
in the final steps of SBA biosynthesis was newly recruited in this
study. The RA of each BA gene was also calculated from the sum of their
annotated genes.
Metabolomic measures
A total of 746 plasma samples from baseline and post-treatment
collections (Plac n = 96, Prob n = 96, BBR n = 81 and Prob + BBR
n = 100) were subjected to the blood BA profile analysis, covering over
15 BA species. Sample preparation as described in previous
study^[225]14. An extraction solvent was made with methanol containing
0.1 μg mL^−^1 cholic acid (CA)-d4, 0.3 μg mL^−1 chenodeoxycholic acid
(CDCA)-d4, 0.2 μg mL^−1 glycocholic acid-d5, 0.2 μg mL^−1 GCDCA-d4,
0.1 μg mL^−1 taurocholic acid-d5 and 0.1 μg mL^−1 TDCA-d5.
Quality-control samples made from a mixture of equal volume of all
serum samples were prepared in the same method as the serum samples and
were analysed once after every ten real samples.
A Vanquish UPLC-Q Exactive (Thermo Fisher Scientific, Rockford, IL,
USA) and an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 μm,
Waters, Milford, MA, USA) were used for LC separation. The oven
temperature was 50 °C and the flow rate was 0.35 mL min^−1. A 7e4
resolution MS full scan mode with a scan range of m z^−1 80–1200 was
used in the analysis. The spray voltage was 3.5 kV for positive mode
and 3.00 kV for negative mode. The capillary temperature was 300 °C and
the auxiliary gas heater temperature was 350 °C. The sheath gas and
auxiliary gas were 45 and 10 (in arbitrary units), respectively.
Plasma FGF19 (R&D Systems, Minneapolis, MN, USA) was analysed using
commercially available enzyme-linked immunosorbent assay kits in
accordance with the manufacturer’s instructions.
Multi-strain probiotics composition
The multi-strain probiotics consists of nine proprietary strains of
lactic acid bacteria. Each sachet contains ≥50 billion colony forming
unit (CFU) of live, freeze-dried bacteria (Supplementary Table [226]9).
Growth experiment of R. bromii
R. bromii strain number AF25-7 was isolated from faecal sample of a
healthy Chinese adult^[227]44. It was cultured in MPYG medium
(Supplementary Table [228]10) and incubated in anaerobe chamber,
BACTRON600-28 (SHELLAB, Cornelius, OR, USA) with 5% hydrogen, 10%
carbon dioxide and 85% nitrogen at 37 °C. The 16S rRNA gene of R.
bromii was amplified by the PCR and sequenced, to ensure the successful
recovery of R. bromii from −70 °C. The primers used for 16S rRNA gene
were: 341 F: 5′-CCTACGGGAGGCAGCAG-3′, 926 R:
5′-CCGTCAATTCCTTTRAGTTT-3′. For growth curve experiment, we seeded R.
bromii at 10% in a volume of 1.5 ml media with different concentration
of BBR (0, 12.5, 25, 50, 100 μg ml^−1) and measured OD600 of the
bacterial culture every 1–2 h in a plate reader (CMax Plus, Molecular
Devices, San Jose, CA, USA). Six replicates were prepared in three
independent experiments. Growth curve and BA biotransformation of in
vitro culture experiment were assessed with two-way analysis of
variance (ANOVA) and unpaired Student’s t-test; P-values reported were
two-sided; statistical significance was defined as P < 0.05.
In vitro BA biotransformation of R. bromii
The BA transformation assay was performed at an independent batch of
culture. R. bromii was added into 1.5 ml of MPYG medium containing CDCA
and CA at an initial concentration of 100 μM and cultured overnight.
Vehicle controls were prepared as CDCA containing MPYG medium without
adding R. bromii. Three replicates were prepared in three independent
experiments. Cell-free supernatants were obtained by centrifugation at
12,000 × g for 5 min. Quantification of BA in R. bromii supernatants
was performed on an Acquity H-class UPLC system using a BEH C18 column
(Waters) coupled to QTRAP 5500 (SCIEX, Canada) in MRM
(multiple-reaction monitoring) mode^[229]58. BA standards CDCA, CA, DCA
and LCA (Sigma-Aldrich) were prepared with distilled water at a final
concentration of 100 μM. Stock solutions of the CDCA, CA, DCA and LCA
were further diluted with 50% methanol to give final concentrations of
2 to 2000 p.p.b. A mixed-standard solution containing each of the
D4-labelled BAs was used as the internal standard solution and added in
calibration curves and samples for normalization. SkylineV4.2^[230]59
was used for data analysis and sample quantification.
Statistical analyses for clinical parameters
For all studied participants, the aim of the study is a comparison of
slopes in repeated measurements with equal allocation among the four
treatment arms. Based on previous studies^[231]27,[232]60, with a
sample size of 360 studied participants, the power for the primary
outcome reaches 86% (two-sided test, α = 5%). We assumed that the
overall dropout rate during the study period would be 10%. To account
for follow-up losses, the power for the primary outcome was set to 86%
if 400 study subjects were recruited.
Statistical analyses of clinical data were performed using SAS version
9.4 (SAS Institute, Cary, NC, USA). All P-values reported were
two-sided. Data analyses were implemented using intention-to-treat
principles based on randomized treatment assignments in which all
available data were used and missing data were not imputed, because the
rate of participants lost to follow-up was <5% overall. Baseline
demographic and clinical characteristics were assessed and compared by
treatment group with the χ^2-tests for categorical variables and with
ANOVA. For the primary outcome, changes in HbA1c, an analysis of
covariance model adjusted for age group (<50 and ≥50 years) was used to
examine the difference between treatment groups. The overall difference
among the four treatment groups was compared with the use of a global
test of unordered groups. If the difference was significant at a
P-value of <0.05, then all (six) pairwise comparisons were made with
adjustments for multiplicity in which statistical significance was
defined as P < 0.008 after Bonferroni correction.
Multivariate GEE model was used to examine whether Prob or BBR
intervention lowered HbA1c levels, as well as other secondary outcomes
compared with Plac group and to examine whether Prob + BBR treatment
was associated with a significantly lower HbA1c level as compared with
Plac group, than Prob or BBR intervention compared with Plac group
after controlling for potential confounding factors, including baseline
HbA1c level, age, body mass index (BMI), total protein, aspartate
transaminase, LDL cholesterol and HOMA-IR.
Statistical analyses for metagenomics, BAs and their correlations with
clinical parameters
Wilcoxon signed-rank tests were applied to detect differences in the
gut microbial features (richness, diversity, RAs of species) and plasma
BAs levels between baseline and post treatment measurements in each
treatment arm. KW tests were applied to detect differences in the gut
microbial features (richness, diversity, RAs of species and KOs)
between the four groups. Dunn’s post hoc tests were further performed
to explore the differences between two groups. A Dunn’s P-value < 0.05
was considered significant. The Benjamini–Hochberg (BH) method was used
to correct the multiple comparisons of species, genes and blood BAs
(function p.adjust, package stats). A BH-adjusted P-value (q) < 0.05
was considered significant.
The correlations between the RAs of microbial genes involved in BA
biotransformation and plasma BA species were assessed by Spearman’s
correlation analysis. The correlations between RAs of microbial species
in BBR treatment arms post treatment and changes in (1) BA species and
in (2) clinical parameters were assessed by partial Spearman’s
correlation analysis after adjustment for age, sex and BMI, and a
P-value of <0.01 or <0.05 was considered significant, respectively.
GEE analysis was performed to assess the longitudinal associations
between changes in BA species and clinical parameters in four treatment
arms after adjustment for age, sex and BMI. The P-value of each
regression coefficient was calculated and a P-value of <0.01 was
considered significant.
Reporting summary
Further information on research design is available in the [233]Nature
Research Reporting Summary linked to this article.
Supplementary information
[234]Supplementary Information^ (2.2MB, docx)
[235]41467_2020_18414_MOESM2_ESM.docx^ (13.5KB, docx)
Description of Additional Supplementary Files
[236]Supplmentary Data 1^ (73.3KB, xlsx)
[237]Supplmentary Data 2^ (21.2KB, xlsx)
[238]Supplmentary Data 3^ (63KB, xlsx)
[239]Supplmentary Data 4^ (62.9KB, xlsx)
[240]Supplmentary Data 5^ (27.6KB, xlsx)
[241]Supplmentary Data 6^ (897.6KB, xlsx)
[242]Supplementary Data 7^ (281KB, xlsx)
[243]Reporting Summary^ (228.6KB, pdf)
Acknowledgements