INTESTINAL METAGENOMIC FEATURE AS SELECTION MARKER OF CURATIVE EFFECT OF ACARBOSE FOR TREATING TYPE 2 DIABETES

Information

  • Patent Application
  • 20180340225
  • Publication Number
    20180340225
  • Date Filed
    December 16, 2015
    8 years ago
  • Date Published
    November 29, 2018
    6 years ago
Abstract
An intestinal microorganism metagenomic feature is a bacteroid intestinal pattern. By using the intestinal metagenomic pattern, it was found that type 2 diabetic patients with different intestinal parasitic bacterial flora had remarkably different responses to treatment with the diabetic hypoglycemic drug acarbose. Therefore, before treatments, the intestinal patterns of the patients can be assessed to select those patients likely to experience optimal curative effects, and to determine whether acarbose is suitable for the treatment of individual patients with type 2 diabetes. In addition, while intestinal patterns are conventionally distinguished by DNA sequencing or PCT amplification of parasitic bacteria in excrements, the intestinal patterns can be well distinguished using a bile acid composition, especially secondary bile acids, under baseline conditions. The intestinal patterns can be identified through markers in blood, and then be used as markers for diagnosis.
Description
TECHNICAL FIELD

The present invention relates to use of the characteristics of gut microbiota metagenome as a screening marker for Acarbose efficacy in patients with Type 2 diabetes.


BACKGROUND ART

At present, drug efficacy assessment and pre-treatment classification diagnosis are not available in the treatment of Type 2 diabetes. The pathological and physiological mechanisms of Type 2 diabetes mainly include insulin resistance and insulin secretion deficiency. Although there are drugs for insulin resistance and insulin secretion deficiency in the treatment of Type 2 diabetes, no scientific and feasible method is available to classify patients into mainly insulin resistance or mainly insulin secretion deficiency.


Currently, the clinically feasible program is to measure the patient BMI, waist circumference and insulin level. According to the BMI, waist circumference that exceed Chinese standard, or the HOMAIR calculated by patient's fasting blood glucose or insulin level, the insulin resistance can be judged. There are no universal standard for the insulin level at home and abroad. Generally, it is represented by the HOMA β index calculated by patient's blood glucose and insulin, but it cannot be used as an index for determining the degree of insulin resistance and insulin secretion deficiency. Therefore, it is unable to meet the requirements for precision medical care.


Clinically, the glucose clamp test is used to accurately assess the insulin resistance and β cell functions. The glucose clamp test with positive-glucose high insulin level is used to assess the insulin resistance levels, while the clamp test with high glucose level is used to assess the β cell insulin secretion functions. The two methods take long time, and patients need to lie in bed for 4 to 5 hours. The operations must be completed by experienced nurses. Blood should be collected from multiple points for the real-time monitoring of blood glucose and the determination of insulin level. This method is expensive, with poor patient compliance, so it is difficult to carry out clinically.


The precision medicine raises the requirement of individualized diagnosis and treatment. The tumor-targeted drugs have been used clinically. However, no effective regimen for targeted therapy of Type 2 diabetes has been found so far. There are a number of therapeutic regimens for the Type 2 diabetes and patients have different responses, which lead to a low blood glucose control rate for patients with Type 2 diabetes. For the main pathogenesis of Type 2 diabetes, there are a variety of drugs for insulin secretion deficiency and insulin resistance, but no simple and exact clinical diagnosis method for insulin secretion deficiency or insulin resistance is available.


The existing studies use the liver, fat (insulin resistance) and islet β cells (islet function) as the main organs involved in Type 2 diabetes. Recently, the pathological and physiological functions of gut microbiota and intestinal mucosal epithelial absorption, barrier and endocrine are increasingly recognized for the pathogenesis of Type 2 diabetes and treatment strategy. For example, gut microbiota metagenome studied have shown that there was significant difference in the gut microbiota between patients with Type 2 diabetes and normal patients [Qin, J., et al., A metagenome-wide association study of gut microbiota in Type 2 diabetes. Nature, 2012.]. The gut-modified bariatric surgery could reduce the body weight of obese patients, and surprisingly, the blood glucose in obese patients with Type 2 diabetes was well controlled without medication after surgery, and even cured completely [Carlsson, L. M. S., et al., Bariatric Surgery and Prevention of Type 2 Diabetes in Swedish Obese Subjects. New England Journal of Medicine, 2012. 367(8): p. 695-704, Schauer, P. R., et al., Bariatric surgery versus intensive medical therapy for diabetes—3-year outcomes. N Engl J Med, 2014. 370(21): p. 2002-13]. The drugs that simulate intestinal hormones, such as GLP-1 agonists and DPPIV inhibitors, have become oral hypoglycemic agents with highest prescription dose in the world, and related cardiovascular benefits have been reported.


The concept of enterotype [Arumugam, M., et al., Enterotypes of the human gut microbiome. Nature, 2011. 473 (7346): p. 174-80] was first proposed by Peer Bork, which meant that the composition of intestinal parasites were relatively fixed in the populations. There are 2 to 3 kinds of enterotypes in the populations. With the increased sample size and the improved sequencing technique, especially the promotion of the second generation of sequencing, the enterotype can be classified into 2 types: one is the Prevotella-based Prevotella enterotype, and the other is Bacteroides-based Bacteroides enterotype. At present, no evidence has shown the direct association between enterotype and various medical health indexes of human body. The corresponding gene function studies suggest the metabolic ability of vitamins is varied for different enterotypes, which is associated with the meat-vegetable dietary habit of the host. However, although gut microbiota is also considered to be an important medium of metabolism in human body [Haiser, H. J. and P. J. Turnbaugh, Is it time for a metagenomic basis of therapeutics? Science, 2012. 336(6086): p. 1253-5], no clinical trial evidences can be available now.


SUMMARY OF INVENTION

An object of the present invention is to overcome the drawback of lack of directly relevant evidences between the enterotypes and health indicators of the human body in the prior art and provide characteristics of gut metagenome as a screening marker of Acarbose efficacy in patients with Type 2 diabetes; particularly provide an application of characteristics of gut microbiota metagenome as a screening marker of Acarbose efficacy in patients with Type 2 diabetes. In the present invention, it is discovered that patients with Type 2 diabetes with different gut microbiota showed significant difference in the therapeutic response to diabetic hypoglycemic agent-Acarbose. Therefore, the Bacteroides-based Bacteroides enterotype can be used as a screening marker of Acarbose efficacy in patients with Type 2 diabetes.


The object of the present invention is achieved through the following technical solutions:


The present invention relates to an application of characteristics of gut microbiota metagenome as a screening marker of Acarbose efficacy in patients with Type 2 diabetes, wherein the characteristics of gut microbiota metagenome is Bacteroides enterotype.


Preferably, the Bacteroides enterotype is determined by DNA sequencing or PCR amplification of parasites in feces in vitro.


Preferably, the PCR amplification specifically comprises: extract the DNA of parasites in feces in vitro and perform 16Sma PCR amplification for specific enrichment strains.


Preferably, the Bacteroides enterotype is determined by detecting secondary bile acid in the in vitro blood samples. The secondary bile acids include UDCA, TUDCA, GUDCA, DCA, TDCA, GDCA, LCA, TLCA, GLCA. In the present invention, two kinds of enterotypes are found, one is Prevotella-based Prevotella enterotype, and the other is Bacteroides-based Bacteroides enterotype. In the Bacteroides enterotype, the deoxycholic acid and lithocholic acid levels are significantly lower than those in Prevotella enterotype, while the ursodeoxycholic acid level with protective effect is higher than that in the Prevotella enterotype. The further gut metagenome analysis showed that, ursodesoxycholic acid is further decomposed into KO of lithocholic acid, which is apparently enriched in the Prevotella enterotype, suggesting that the metabolic ability of bile acids in gut microbiota was significantly different in patients with two enterotypes.


Preferably, the detection of secondary bile acid comprises the following steps:


S1. Sample pretreatment: Add 300 μL of internal standard methanol to every 75 μL of blood samples, to extract the target compound and precipitate the protein, vortex, centrifuge and draw the supernatant, then lyophilize, re-dissolve in 50 μL of acetonitrile solution (25%, volume), and wait for sample injection;


S2. Detection: conduct sample analysis using 1290 Infinity liquid phase and 6460A triple quadrupole mass spectrometry system;


Perform the liquid phase separation using 100 mm×2.1 mm ACQUITY UPLC C8 column having a particle size of 1.7 m, of which, phase A is 10 mM NH4HCO3 aqueous solution, phase B is pure acetonitrile; initially 25% phase B (by volume), retaining 0.5 min, followed by increased to 40% phase B (by volume) linearly within 12.5 min, then increased to 90% (by volume) within 1 min, flush the system for 3 min, recover to 25% phase B (by volume) in 0.5 min, after equilibrating 2.5 min, the flow rate is 0.35 ml/min, column temperature is 35° C. and the injection volume is 5 μL;


Mass spectrometry is performed by ESI source negative ion mode, with main parameters as follows: Gas Temp: 350° C.; Gas Flow: 8 l/min; Nebulizer: 40 psi; Sheath Gas Temp: 400° C.; Sheath Gas Flow: 8 l/min; Capillary: 3500 V; Nozzle voltage: 400 V.


Preferably, the efficacy of Acarbose in the patients with Type 2 diabetes and Bacteroides enterotype includes improving the insulin resistance, reducing the secondary bile acid, and promoting the reduction of cardiovascular risks in addition to glucose-lowering.


Preferably, the indicators for reducing the harmful secondary bile acid include GDCA, TDCA, TLCA, and the indicators for reducing the binding of taurine with bile acid include TCA, TDCA, TLCA, TUDCA.


Preferably, the indicators for improving insulin resistance include decreased fasting blood glucose, decreased fasting C peptide and insulin level, down-regulated waist-to-hip ratio, down-regulated HOMA insulin resistance index and up-regulated Adiponectin.


Preferably, the indicators that promote the reduction of cardiovascular risks include decreased PDGFAA, PDGFAABB, endothelin, and VegfC plasma factor.


The present invention further relates to a kit used for screening of Acarbose efficacy in patients with Type 2 diabetes, comprising:


A reagent used to collect in vitro stool samples or in vitro blood samples;


A reagent used to determine the enterotype by DNA sequencing or PCR amplification of the parasites in the in vitro stool samples, or a reagent used to determine the enterotype by detecting the secondary bile acid in the in vitro blood samples.


There are two kinds of enterotypes, one is Prevotella-based Prevotella enterotype, and the other is Bacteroides-based Bacteroides enterotype. Different enterotype can predict the benefits of patients for treatment of diabetes with Acarbose, especially the effect of improving insulin resistance, reducing secondary bile acid, and promoting the reduction of cardiovascular risks in addition to glucose-lowering. Specifically, Bacteroides enterotype has a better effect of improving insulin resistance, reducing secondary bile acid, and promoting the reduction of cardiovascular risks in addition to glucose-lowering.


Compared with prior art, the prevent invention can achieve the following beneficial effects:


1) It is discovered that patients with Type 2 diabetes with different gut microbiota showed significant difference in the therapeutic response to diabetic hypoglycemic agent-Acarbose by using the concept of enterotype firstly. Therefore, before medication, patients can be classified according to the enterotype, to select the populations with optimal efficacy and determine if an individual patient with Type 2 diabetes is applicable to Acarbose treatment.


2) The classification of enterotypes is generally based on DNA sequencing or PCR amplification of parasites in the feces; while in the baseline, the bile acid component, especially secondary bile acid, can be used for distinguishing the enterotypes; the typing of gut microbiota (i.e. enterotype) can be identified by the blood markers (i.e. secondary bile acid level in the plasma), to become a marker for diagnosis.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a schematic diagram of the typing of the enterotypes, of which, A. horizontal clustering and correlation of Bacteroides enterotype; B. horizontal clustering and correlation of Prevotella enterotype; C. Comparison of Bacteroides biological abundance in two enterotypes; D. Comparison of Prevotella biological abundance in two enterotypes. EntB: Bacteroides enterotype. EntP: Prevotella enterotype;



FIG. 2 shows the changes in curative effect indexes after treatment with Acarbose in two enterotypes, the black represents a significant decrease after treatment, white represents a significant increase after treatment, and gray represents no significant change after treatment, P<0.05;



FIG. 3 shows the difference of bile acid spectra and their changes after Acarbose treatment between the two enterotypes, of which, A. bile acids with difference in the baseline in two enterotypes; B. Difference of bile acid spectran between two enterotypes at baseline. C. Difference of two KO related to secondary bile acid metabolism between two enterotypes at baseline. D. The changed bile acid compositions and their signal maps between two enterotypes after Acarbose treatment. The black represents a significant decrease after treatment, white represents a significant increase after treatment, and gray represents no significant change after treatment, P<0.05.





DETAILED DESCRIPTIONS OF THE INVENTION

The present invention will be described in detail with reference to the following embodiments. The following embodiments can help technicians skilled in the art to further understand this invention without limiting the invention in any way. It should be noted that a plurality of modifications and improvements may be made by those skilled in the art without departing from the spirit of the invention, all of which will fall within the scope of protection of the invention.


Embodiment

For naïve patients with Type 2 diabetes, their liver and kidney functions, blood glucose and lipid levels, intestinal hormones, inflammatory factors, and cardiovascular risk-related factors are evaluated before medication. Their stool, urine, and blood samples are retained. After clear diagnosis and evaluation, patients are treated for 3 months at a daily dose of Acarbose 300 mg. The patients' blood glucose levels are followed up every month within 3 months, and the medication is adjusted according to the blood glucose level. Three months later, the pre-medication assessment is repeated, and the urine, stool and blood samples are retained.


Specific steps are as follows:


1. Collection of Clinical Samples


a) A randomized, opened, positive control method is adopted to collect the naïve patients with Type 2 diabetes and normal controls of their spouses. The clinical and biochemical data, gastrointestinal motility of diabetic patients before and after Acarbose treatment are compared and their blood and stool samples are collected. The newly diagnosed patients of Type 2 diabetes without medication receive routine examinations, including the retention of stool and blood samples.


Collection of Stool Samples


b) Stool


i. The instrument: Plastic basin (the diameter less than the caliber of household flush toilet)


ii. Freshness protection package, sterile small-handle spoon


iii. 50 ml sterile centrifuge tube


iv. Place the plastic basin (the diameter less than the caliber of household flush toilet) into a flush toilet, cover the freshness protection package (do not immerse the edges of the freshness protection package into the water of the toilet). If samples are taken in the hospital, cover a freshness protection package directly in the clean potty, to retain the stool samples;


v. Mix the upper layer of the fresh stool sample well with a small-handle spoon, pick up a small amount into a 50 ml sterile centrifuge tube. Take at least 10 g sample each tube, tighten the tube cover (indicate the sampling time, sampling group and number on the centrifuge tube wall). Retain stools for each subject each time, a total of 3 tubes of samples (RNAlater treatment tube, glycerine tube, and treatment-free tube).


vi. Immediately place the collected samples into −80° C. for cryopreservation.


c) Retention of Serum Samples


i. Draw venous blood 15 ml under fasting condition, of which, 1.5 ml is used for detection of plasma glucose, 6.5 ml is added to ordinary tubes (including aprotinin, DPPV inhibitor), and 6.5 ml is added to anticoagulant tubes (including heparin and RNA later)


ii. Centrifuge the blood in ordinary tube at 4° C., when serum is separated out, draw about 3 ml, and divide to three 1.5 ml Eppendorf on average, then tighten the tube caps;


iii. Centrifuge the anticoagulation blood immediately at 4° C., to separate out about 3 ml of plasma, then divide to three 1.5 ml imported RNAase free Eppendorf tubes on average,


iv. Indicate the sample name, center number and random number on the tube in details;


v. Cover the tubes for one week with plastic tapes;


vi. Keep them at −20° C. (placed at −80° C. if condition permitted), and subpackage the plasma and immediately place them at −20° C. or dry ice.


2. Determination of Bile Acid


The reagents Sodium taurochenodeoxycholate (TCDCA), Sodium glycocholate hydrate (GCA), Sodium taurodeoxycholate (TDCA), Chenodeoxycholic acid (CDCA), Ursodeoxycholic acid (UDCA), Taurocholic acid (TCA), Sodium glycodeoxycholate (GDCA), Glycoursodeoxycholic acid (GUDCA), Cholic acid (CA), Deoxycholic acid (DCA), Sodium glycochenodeoxycholate (GCDCA), Sodium tauroursodeoxycholate (TUDCA), Sodium taurolithocholate (TLCA), Lithocholic acid (LCA), NH4HCO3 are purchased from Sigma, USA; Glycochenodeoxycholic Acid 3-Sulfate Disodium Salt (GCDCS) are synthesized in the laboratory of Zhejiang University; Chenodeoxycholic Acid-d4 (CDCA-d4), Glycochenodeoxycholic Acid-d5 3-Sulfate Disodium Salt (GCDCS-d5), Taurochenodeoxycholic Acid-d5 (TCDCA-d5), Cholic Acid-d5(CA-d5), Glycocholic acid-d5(GCA-d5), Lithocholylglycine (GLCA) are purchased from TRC, Canada; and Taurodeoxycholic Acid-d5(TDCA-d5), Taurocholic acid-d5(TCA-d5) are purchased from CIL, USA. Acetonitrile and methanol are purchased from Merk, Germany.


Sample pretreatment: Take 75 μL of blood sample, add 300 μL of internal standard methanol, to extract the target compound and precipitate the protein, vortex 30s, centrifuge 10 min at the rate of 15000 rpm, draw 200 μL of the supernatant, then lyophilize, re-dissolve in 50 μL of 25% acetonitrile solution, and wait for sample injection. Instrument and method: conduct sample analysis using 1290 Infinity liquid phase (Agilent, USA) and 6460A triple quadrupole mass spectrometry system (Agilent, USA). Perform the liquid phase separation using 100 mm×2.1 mm ACQUITY UPLC C8 column having a particle size of 1.7 μm (Waters, USA), of which, phase A is 10 mM NH4HCO3 aqueous solution, phase B is pure acetonitrile; initially 25% phase B, retaining 0.5 min, followed by increased to 40% phase B linearly within 12.5 min, then increased to 90% within 1 min, flush the system for 3 min, recover to 25% phase B in 0.5 min, after equilibrating 2.5 min, the flow rate is 0.35 ml/min, column temperature is 35° C. and the injection volume is 5 μL; Mass spectrometry is performed by ESI source negative ion mode, with main parameters as follows: Gas Temp: 350° C.; Gas Flow: 8 l/min; Nebulizer: 40 psi; Sheath Gas Temp: 400° C.; Sheath Gas Flow: 8 l/min; Capillary: 3500 V; Nozzle voltage: 400 V. The bile acid is detected under a reaction monitoring mode (MRM). The concentration of the internal standard and the main mass spectrum parameters are shown in Table 1. The setting of mass spectrum parameters for bile acid analysis is shown in Table 2.









TABLE 1







Concentration of internal standard and main MS parameters













Con-



Collision



centration
parent
daughter

Energy



(mg/L)
ion
ion
Fragmentor
(eV)
















CA-d5
0.08
412.5
412.5
200
10


CDCA-d4
0.3
395.4
395.4
200
10


GCA-d5
0.2
469.2
74.1
200
35


GCDCA-d4
0.2
452.3
74.1
240
40


GCDCS-d5
0.2
533.3
453.3
200
35


TCA-d5
0.1
519.2
80.1
320
90


TCDCA-d5
0.1
503.3
80.2
300
70


TDCA-d5
0.1
503.3
80.2
300
70
















TABLE 2







MS parameters for bile acid analysis and internal standard for calibration














Compound
Internal standard
parent ion
daughter ion
Fragmentor
Collision Energy eV

















Lithocholic acid
LCA
CA-d5
375.3
375.3
200
2


Chenodesoxycholic acid
CDCA
CDCA-d4
391.4
391.4
200
10


Deoxycholic acid
DCA
CDCA-d4
391.4
391.4
200
10


Ursodeoxycholic acid
UDCA
CDCA-d4
391.4
391.4
200
5


Bile acid
CA
CA-d5
407.5
407.5
200
10


Glycine conjugated with lithocholic acid
GLCA
GCA-d5
432.3
74
200
35


Glycine conjugated with deoxycholic acid
GDCA
GCDCA-d4
448.2
74.1
200
40


Glycine conjugated with chenodesoxycholic acid
GCDCA
GCDCA-d4
448.3
74.1
240
42


Glycine conjugated with ursodeoxycholic acid
GUDCA
GCDCA-d4
448.3
74.1
200
40


Glycine conjugated with bile acid
GCA
GCA-d5
464.2
74.1
200
35


Taurine conjugated with lithocholic acid
TLCA
TCA-d5
482.1
80.1
300
70


Taurine conjugated with chenodesoxycholic acid
TCDCA
TCDCA-d5
498.3
80.2
300
70


Taurine conjugated with lithocholic acid
TDCA
TDCA-d5
498.3
80.2
300
70


Taurine conjugated with ursodeoxycholic acid
TUDCA
TCA-d5
498.3
80.1
320
70


Taurine conjugated with bile acid
TCA
TCA-d5
514.2
80.1
320
90









3. DNA Sequencing of Gut Microbiota


For HiSeq 2500 sequencing, the fragments at the length of 350 bp are used to establish the database and compare with 9.9M human intestinal gene set, to obtain the phylum, species and genus of IMG (70% coverage rate and 65% recognition rate at the phylum level, 85% recognition rate at the genus level, and 95% recognition rate at the species level). The clustering analysis of intestinal parasites is performed by principal component analysis (PCA).


In the present invention, the gut microbiota colony DNA extraction and second generation sequencing of metagenome are performed in patients' feces, then compared with the published 9.9M human gut metagenome gene set, with a matching rate about 77%. About 143 kinds of gut microbiota with annotation information and difference before and after medication are found by clustering analysis. The genus level analysis shows that, different clustering of gut microbiota is found at the baseline in patients with Type 2 diabetes. According to the characteristic accumulation of gut microbiota, the enterotype is obtained. In the present invention, there are mainly two enterotypes: one is the Prevotella-based Prevotella enterotype, and the other is Bacteroides-based Bacteroides enterotype (FIG. 1).


According to enterotype classification, there are no significant differences in the sex and age distribution between the two types of patients with Type 2 diabetes. There are no significant differences in the baseline blood glucose levels, body weights, liver and kidney functions and other health indicators between them. Only the levels of red blood cells, hemoglobin and interleukin 6 are slightly higher in the patients of Prevotella enterotype (P<0.05). (Table 3)


After treatment, the efficacy of this drug for treatment of diabetes in the two groups of patients at baseline is observed.


First, the most major efficacy of Acarbose is reflected by the reduction in 2h postprandial plasma glucose and HbA1c. But there are no differences in the two indexes between the two enterotypes (FIG. 2, Table 4).


Second, there is difference in control of fasting blood glucose level between the two enterotypes. The patients of Bacteroides enterotype show significant improvement and the degree of improvement after treatment; while the patients with Type 2 diabetes of the Prevotella enterotype do not show significant improvement in fasting blood glucose level. The other metabolic related indexes, including insulin, body weight, BMI, waist circumference, cardiovascular risk factors and intestinal hormones are significantly different between the two enterotypes.


The fasting C peptide and insulin levels are significantly decreased after treatment in the Bacteroides enterotype. After calculation, the HOMAIR index, which reflects insulin resistance, decreases after Acarbose treatment, but this benefit is significant only in the patients of Bacteroides enterotype, but not significant in patients of Prevotella enterotype, suggesting that patients with Type 2 diabetes of Bacteroides enterotype are more likely to improve their status of insulin resistance after taking Acarbose. The standard meal-induced insulin release curve, waist-to-hip ratio, and Adiponectin levels that are related to the insulin resistance show significant decrease after Acarbose treatment in T2DM patients of Bacteroides enterotype, but these indexes show no significant change in the patients of Prevotella enterotype.


Acarbose can cause a decrease in TG, APOA and DBP, and there are no significant differences between the two enterotypes after treatment; however, some plasma factors associated with diabetic vascular complications such as PDGFAA and PDGFAABB, endothelin, VegfC are significantly lower in the Bacteroides enterotype, suggesting that the Acarbose treatment can bring more benefits of reducing vascular complications in addition to lowering blood glucose level and risks of macrovascular diseases in the Bacteroides enterotype.


Gut hormone is a hot research target in the treatment of Type 2 diabetes, and its level is changed significantly in the Acarbose treatment. Among the several gut hormones detected, the elevated GLP1, glucagon, PYY, and ghrelin and GIP at each time point after medication are significant in the Bacteroides enterotype, but not significant in the Prevotella enterotype, suggesting that any metabolic benefit of Acarbose through gut hormones is more significant in the Bacteroides enterotype.


Third, there is a difference in bile acid spectrum between the two enterotypes at the baseline level (FIG. 3A, B). In the Bacteroides enterotype, the levels of deoxycholic acid and lithocholic acid in secondary bile acid are significantly lower than those in Prevotella enterotype, whereas the ursodeoxycholic acid level with protective effect is higher than that in Prevotella enterotype. The further gut metagenome analysis showed that, the ursodesoxycholic acid is further degraded into lithocholic acid KO, which is enriched in Prevotella enterotype apparently (FIG. 3C), suggesting that there is significant difference in the bile acid metabolic ability of gut microbiota between patients of two enterotypes. After treatment with Acarbose, the difference in bile acid composition is more pronounced between both enterotypes (FIG. 3D). The two kinds of primary bile acids are significantly increased in two enterotypes after treatment with Acarbose, but without enterotype-specific changes, suggesting that Acarbose may affect the whole reabsorption of bile acid in the small intestine.


Therefore, this study shows that, different enterotypes can predict patients' benefits from Acarbose treatment of diabetes, especially improving the insulin resistance, reducing the secondary bile acid, and promoting the reduction of cardiovascular risks in addition to glucose-lowering. The enterotype diagnosis can be completed by ordinary DNA PCR amplification of 16sRNA of characteristic bacteria genus, which is convenient and economical, making the precision medical care of Type 2 diabetes possible.









TABLE 3







Baseline clinical indexes of two enterotypes












Bacteroides
Prevotella


Clinical Index
P value
enterotype
enterotype













SBP
0.052305571
126.68 ± 15.68 
134.92 ± 19.75 


DBP
0.569158065
80.87 ± 8.49 
82.54 ± 11.12


Height
0.406982243
166.76 ± 7.27 
168.13 ± 7.97 


Body weight
0.632776326
72.76 ± 9.93 
74.44 ± 12.11


BMI
0.426864667
26.17 ± 3.48 
26.2 ± 2.73


Waist circumference
0.382242756
90.58 ± 7.7 
92.53 ± 10.05


Hip circumference
0.850454946
99.15 ± 7.58 
98.71 ± 6.25 


Waist-to-hip ratio
0.073022015
0.91 ± 0.05
0.94 ± 0.06


RBC count
0.008822773*
4.77 ± 0.45
5.01 ± 0.3 


Hemoglobin
0.015559629*
142.47 ± 20.66 
150.75 ± 11.96 


Hematocrit
0.110126957
 9.5 ± 22.96
 9.69 ± 22.65


WBC count
0.221434929
6.35 ± 1.46
6.67 ± 1.35


Granulocyte percentage
0.721728164
57.18 ± 12.59
54.07 ± 17.36


Percentage of
0.823390429
 31.2 ± 10.11
30.08 ± 12.05


lymphocytes


Percentage of
0.92702484
5.68 ± 2.12
5.64 ± 2.2 


monocytes


Platelet count
0.977269042
207.14 ± 53.01 
208.26 ± 53.28 


ALT
0.770933665
34.74 ± 20.18
39.33 ± 38.6 


AST
0.821446414
26.76 ± 12.39
32.45 ± 33.84


Alkaline phosphatase
0.003733751*
68.02 ± 17.83
 81.7 ± 21.67


Glutamyl transpeptidase
0.219732988
38.87 ± 37.46
48.13 ± 54.35


Total bilirubin
0.517075592
15.66 ± 5.85 
14.33 ± 4.54 


Direct bilirubin
0.930882347
3.17 ± 1.58
 3.3 ± 1.57


Total protein
0.555631501
72.16 ± 4.18 
72.99 ± 4.34 


Albumin
0.439028944
46.81 ± 26.74
44.38 ± 4.21 


Uric acid
0.389295382
5.01 ± 1.2 
5.24 ± 1.03


Creatinine
0.424300196
66.58 ± 14.76
69.16 ± 12.41


Urea nitrogen
0.93814784
301.17 ± 73.87 
301.29 ± 65.47 


Potassium
0.802728704
 6.46 ± 17.43
4.11 ± 0.51


Sodium
0.153693762
137.39 ± 17.91 
140.64 ± 3.13 


Chloride
0.45778794
102.73 ± 2.78 
102.32 ± 2.5  


Triglycerides
0.479383839
2.29 ± 1.56
2.28 ± 2.29


Total cholesterol
0.763815537
5.03 ± 0.91
5.05 ± 1.54


High density lipoprotein
0.974185078
3.05 ± 0.81
3.17 ± 1.05


Low density lipoprotein
0.341454604
 1.2 ± 0.45
1.18 ± 0.2 


Apolipoprotein A
0.99402696
1.34 ± 0.18
1.36 ± 0.17


Apolipoprotein B
0.923810467
1.03 ± 0.21
1.06 ± 0.29


Lipoprotein A
0.833966588
 62.95 ± 144.85
164.75 ± 516.3 


Blood glucose 0 min
0.091734172
7.91 ± 1.4 
7.29 ± 1.11


Blood glucose 30 min
0.130269742
10.91 ± 1.96 
10.27 ± 1.87 


Blood glucose 60 min
0.257347624
14.24 ± 2.43 
13.79 ± 1.84 


Blood glucose 120 min
0.243421632
14.78 ± 2.86 
  14 ± 2.36


Blood glucose 180 min
0.741659488
12.11 ± 3.21 
11.51 ± 3.04 


Serum insulin 0 min
0.509699318
9.54 ± 4.74
11.83 ± 11.34


Serum insulin 30 min
0.234997693
 19.7 ± 11.99
 22.7 ± 12.31


Serum insulin 60 min
0.469868561
36.99 ± 21.41
42.04 ± 27.34


Serum insulin 120 min
0.517877918
48.41 ± 26.27
55.15 ± 35.47


Serum insulin 180 min
0.889080689
38.78 ± 26.07
35.42 ± 22.9 


Serum C-peptide 0 min
0.979767063
 2.5 ± 0.74
2.67 ± 1.27


Serum C-peptide 30 min
0.318552538
3.42 ± 1.1 
 3.6 ± 1.17


Serum C-peptide 60 min
0.694278947
5.08 ± 1.77
5.24 ± 1.86


Serum C-peptide 120 min
0.809629053
7.59 ± 2.29
7.62 ± 2.45


Serum C-peptide 180 min
0.343793846
 7.3 ± 2.51
6.61 ± 1.97


Plasma insulin 0 min
0.469445501
478.88 ± 234.65
476.64 ± 338.58


Plasma insulin 30 min
0.810557675
791.99 ± 393.84
808.26 ± 506.26


Plasma insulin 60 min
0.838524751
1396.45 ± 752.85 
1467.09 ± 1061.37


Plasma insulin 120 min
0.741764202
1934.72 ± 997.05 
2110.35 ± 1443.51


Plasma insulin 180 min
0.754907581
1641.1 ± 947.84
1639.39 ± 1040.15


Plasma C-peptide 0 min
0.384853423
1175.79 ± 418.14 
1127.3 ± 498.93


Plasma C-peptide 30 min
0.813883421
1670.96 ± 718.73 
1669.2 ± 711.29


Plasma C-peptide 60 min
0.8047981
2578.14 ± 1156.59
  2530 ± 1212.03


Plasma C-peptide 120 min
0.852096376
3871.17 ± 1525.71
3800.54 ± 1372  


Plasma C-peptide 180 min
0.587827408
3626.21 ± 1473.21
3336.12 ± 1109.94


HbAlc
0.394975993
7.65 ± 0.91
7.45 ± 0.72


GHRP 0 min
0.647538837
47.52 ± 28.77
54.93 ± 51.33


GHRP 30 min
0.270618653
54.38 ± 33.75
 51.8 ± 43.65


GHRP 60 min
0.100675425
45.72 ± 25.99
39.13 ± 30.64


GHRP 120 min
0.420950166
35.62 ± 17.43
34.83 ± 24.87


GHRP 180 min
0.608065141
42.79 ± 21.75
47.11 ± 36.95


GLP-1 0 min
0.925944614
 6.46 ± 10.84
5.02 ± 5.17


GLP-1 30 min
0.874727025
19.49 ± 18.36
18.24 ± 15.26


GLP-1 60 min
0.891679993
16.79 ± 13.74
14.11 ± 8.46 


GLP-1 120 min
0.706003564
11.35 ± 12.71
8.58 ± 7.07


GLP-1 180 min
0.412865834
 9.46 ± 12.94
7.22 ± 6.79


Glu 0 min
0.976095762
29.11 ± 13.28
30.48 ± 17.95


Glu 30 min
0.738477919
37.97 ± 20.1 
 37.5 ± 23.81


Glu 60 min
0.605878342
35.31 ± 19.34
34.12 ± 23.53


Glu 120 min
0.343451344
25.45 ± 12.45
24.88 ± 16.59


Glu 180 min
0.911837296
22.34 ± 12.16
23.56 ± 14.08


PYY 0 min
0.902682115
53.23 ± 56.11
41.27 ± 30.44


PYY 30 min
0.826513742
67.21 ± 59.48
61.55 ± 43.07


PYY 60 min
0.984419278
 64.4 ± 55.36
55.23 ± 32.99


PYY 120 min
0.556885888
55.41 ± 50.64
51.09 ± 30.71


PYY 180 min
0.705995014
57.51 ± 56.59
43.62 ± 32.17


GIP 0 min
0.466825554
76.52 ± 47.45
 72.4 ± 53.13


GIP 30 min
0.959035633
 358.7 ± 222.63
357.55 ± 244.17


GIP 60 min
0.693172872
443.67 ± 220.91
417.88 ± 238.84


GIP 120 min
0.453200572
406.03 ± 192.67
388.55 ± 218.63


GIP 180 min
0.181743927
323.22 ± 179.85
289.97 ± 235.54


PP 0 min
0.318610459
93.28 ± 78.66
82.39 ± 78.19


PP 30 min
0.569166173
313.48 ± 220.69
258.05 ± 114.37


PP 60 min
0.133570837
284.41 ± 196.22
217.63 ± 115.08


PP 120 min
0.245241771
222.38 ± 163.09
180.86 ± 108.39


PP 180 min
0.106766755
180.24 ± 111.6 
143.88 ± 97.4 


IL 17M
0.962918479
138.85 ± 74.04 
155.04 ± 123.32


G-CSF
0.803059249
50.43 ± 21.81
49.25 ± 17.99


IFN r1
0.181566671
45.55 ± 42.56
34.79 ± 17.12


IL 10
0.861536003
1.68 ± 1.51
1.69 ± 1.65


MIP-3a
0.333709066
22.75 ± 19.47
25.06 ± 16.77


IL 12
0.993168795
8.76 ± 7.01
8.42 ± 5.75


IL 15
0.267007222
232.97 ± 132.57
258.05 ± 119.82


IL 17A
0.7415298
32.16 ± 9.43 
31.08 ± 9.36 


IL 22
0.13451041
42.34 ± 40.57
30.96 ± 10.48


IL 9
0.480277271
81.35 ± 54.9 
69.56 ± 35.58


IL 33
0.751136369
37.46 ± 13.13
35.71 ± 13.65


IL 2
0.472384797
58.54 ± 49.48
47.94 ± 29.24


IL 21
0.905751857
28.21 ± 11.92
31.75 ± 22.21


IL 4
0.927445079
20.29 ± 15.95
 19.9 ± 13.78


IL 23
0.902434691
37.04 ± 23.49
36.37 ± 20.38


IL 5
0.892399481
45.18 ± 36.22
38.48 ± 19.04


IL IL17E
0.608790357
25.15 ± 31.08
19.56 ± 10.22


IL 27
0.534356146
71.75 ± 56.63
 60.5 ± 37.44


IL 31
0.793285936
112.57 ± 57.24 
101.87 ± 34.87 


TNF B
0.885718446
45.25 ± 32.83
40.52 ± 18.34


IL 28a
0.591202712
20.21 ± 9.34 
19.56 ± 8.97 


FGF 19M
0.802617256
23.01 ± 4.13 
23.27 ± 3.78 


FGF 23M
0.132019218
45.88 ± 9.26 
41.96 ± 6.75 


Oncostatin
0.496200994
105.9 ± 75.52
93.87 ± 65.35


cTn
0.139027346
19.77 ± 18.76
 14.9 ± 10.08


ET
0.560717515
12.79 ± 2.7 
12.88 ± 3.41 


FGF 21
0.738341043
 0.3 ± 0.32
0.26 ± 0.26


NGF
0.100733692
2.41 ± 3.11
2.83 ± 2.61


HGF
0.271836439
 408.2 ± 258.41
465.78 ± 249  


MCP 1
0.454428023
244.24 ± 136.45
246.4 ± 77.85


TNF a
0.292633798
4.56 ± 2.19
5.67 ± 4.46


SICAM
0.684945545
161.33 ± 75.77 
188.08 ± 166.69


MPO
0.27368969
385.65 ± 351.89
464.48 ± 363.39


sP-selectin
0.165666585
141.01 ± 76.01 
270.22 ± 572.86


sVCAM
0.233334305
703.18 ± 244.75
820.42 ± 410.18


PDGF AA
0.945399512
4951.68 ± 1904.72
4902.38 ± 1831.41


PDGF AABB
0.490732791
26581.76 ± 14551.83
27347.81 ± 11562.13


RANTES
0.899107284
54893.62 ± 24860.48
52830.46 ± 19005.15


LEP
0.632956992
5307140.26 ± 3052569.78
6121581.85 ± 4297113.91


NGAL
0.43181483
136543.91 ± 75938.84 
158878.69 ± 102095.95


Resistin
0.051869812
16813.35 ± 9816.98 
21730.58 ± 11698.65


Adipokine
0.377073702
3322727.99 ± 1554037.78
3526181.62 ± 1630515.78


PAI 1
0.635961359
57833.47 ± 21242.95
  59240 ± 19939.5


CRP
0.310428088
11.92 ± 15.92
21.22 ± 49.48


FETU-A
0.790035938
333.82 ± 50.16 
332.62 ± 40.54 


L-selectin
0.973025194
1.54 ± 0.25
1.58 ± 0.32


FABP 3
0.706810959
 2282.3 ± 1074.01
2481.19 ± 1192.16


FABP 4
0.706016525
14084.38 ± 12064.5 
13559.88 ± 12861.95


ECGF
0.1033731
187.05 ± 127.4 
234.75 ± 131.23


ET
0.640239461
3.78 ± 1.99
4.08 ± 3.37


FGF 1
0.400574693
13.71 ± 10.83
12.86 ± 5.24 


VEGF c
0.614322703
113.88 ± 37.89 
113.02 ± 46.82 


VEGF d
0.115360982
163.15 ± 117.25
203.19 ± 123.58


FGF 2
0.685128912
37.29 ± 22.25
41.01 ± 26.94


VEGF a
0.416512674
499.98 ± 398.81
400.25 ± 267.47


LEP
0.732125461
6490.77 ± 6637.01
6502.46 ± 5785.56


IL 8
0.812673509
17.03 ± 26.21
18.69 ± 24.86


IL 1b
0.912521353
0.48 ± 0.79
0.59 ± 1.29


IL 13
0.442815325
237.23 ± 132.1 
254.36 ± 129.07


IL 6
0.044391053*
2.85 ± 5.27
 3.1 ± 3.63


LBP
0.185062045
20.61 ± 7.28 
18.66 ± 7.99 


HOMA-IR
0.895763831
3.33 ± 1.73
3.83 ± 3.38


Plasma HOMA-IR
0.206702804
3.76 ± 2.07
3.44 ± 2.46



















TABLE 4









P value
Mean












Bacterioide


Prevotella

Acarbose












enterotype
enterotype
Bacterioide enterotype

Prevotella enterotype















Acarbose
Acarbose
Baseline
After treatment
Baseline
After treatment

















Adipisin
0.735655998
0.678771973
3168874.417
3174901.441
3105379.8
3221619.467


Adpn*
0.009070051
0.072998047
4659142.417
5157339.824
6825515.733
10063264.07


AKP*
0.000298293
0.148678549
68.38235294
59.22857143
85.42666667
77.5


ALB
0.052938425
0.125506471
49.74545455
42.26
45.58571429
44.38571429


ALT
0.023923875
0.315055201
37.68
29.42857143
40.7
44.35714286


APOA*
0.011314861
0.125
1.328214286
1.2475
1.377142857
1.267272727


APOB
0.42397348
0.1875
1.029642857
0.963571429
1.131428571
0.927272727


AST*
0.009259021
0.183967151
28.20294118
22.45714286
35.91333333
29.35714286


BMI*
3.26E−05
0.003494192
26.42555556
25.51138889
26.06866667
25.11333333


bun
0.017056015
0.221189047
309.2823529
329.2
306.212
326.4615385


BW*
2.32E−05*
0.00367322*
74.45277778
71.91111111
75.38
72.63333333


cl
0.365775393
0.149551339
102.9375
103.4
101.6923077
103.3571429


CP0*
0.000751479
0.229309082
2.620833333
2.216388889
2.668666667
2.485333333


CP120*
5.92E−08
0.001159668
7.842777778
5.204166667
7.615333333
5.304666667


CP180*
1.25E−09
0.012036948
7.327777778
4.843333333
6.262666667
4.734666667


CP30*
3.59E−05
0.023068064
3.602222222
2.7525
3.502
2.802


CP60*
5.42E−06
6.10E−05
5.375
3.71
5.294
3.609333333


creatine
0.530974718
1
67.10882353
67.61428571
67.98666667
67.41538462


crp
0.852740904
0.229309082
13.5725
29.96117647
10.216
19.638


DBIL
0.42621214
0.833885439
2.755882353
2.965714286
3.542857143
6.292857143


DBP*
0.000195001
0.049089037
82.08333333
75.37142857
80.46666667
74.93333333


egf
0.458006509
0.267578125
193.7005882
183.3530303
206.3742857
267.604


ENDOM
0.030053592
0.207824804
12.36111111
13.43939394
13.73333333
14.26666667


Endothelin*
0.039909487
0.363606971
3.46
4.158571429
4.926428571
4.776


FABP3
0.903465115
0.890380859
2374.760833
2350.673429
2293.333333
2146.933333


FABP4
0.103153911
0.252380371
14146.31429
11504.54286
12673.33333
10510.4


fetuinA*
0.000150225
0.000610352
340.4180556
400.9188235
334.9993333
417.3386667


fgf1
0.327136563
0.777511253
11.08944444
11.65771429
14.00642857
13.78933333


FGF19M
0.000721153
0.006268599
22.44444444
19.73529412
22.6
19.3


FGF1M
0.327494344
0.62856768
21.94444444
22.57352941
24.4
24.33333333


fgf2
0.271079158
0.94425068
33.13027778
35.39542857
42.55357143
43.98266667


fgf21
0.502579383
0.779828433
0.317777778
0.37
0.339285714
0.296


fgf21M
0.000200362
0.002624512
143.0416667
100.0588235
136.4
97.93333333


FGF23M
5.55E−05
0.628914718
44.90277778
37.07352941
42.36666667
43.46666667


FGF2M
0.219893155
0.805893176
12.30555556
12.69117647
13.13333333
13.66666667


G120*
1.75E−07
6.10E−05
14.64277778
9.171388889
13.292
9.170666667


G180*
7.36E−09
0.000244141
11.68388889
7.941388889
10.65
7.852857143


G30*
4.07E−10
0.00012207
10.73055556
7.622777778
9.652
7.601428571


G60*
5.82E−11
0.00012207
13.99611111
9.074166667
13.12066667
9.055


GIP0
0.890667984
0.71484375
79.71542857
80.26388889
84.04533333
57.39785714


GIP120*
0.007692964
0.067626953
382.0711765
264.0158333
354.922
227.7985714


GIP180*
0.002999473
0.067626953
290.1938235
201.5647222
259.104
194.8235714


GIP30*
0.01013191
0.020263672
336.1434286
219.1305556
342.732
208.4314286


GIP60*
0.000305745
0.00402832
439.1731429
258.945
382.3233333
217.2485714


GMCSFM
0.400942689
0.798033799
49.375
64.61764706
55.3
67.96666667


GO*
2.34E−06
0.30279541
7.9075
6.606944444
7.002666667
6.712


HB
0.866145989
0.063711823
144.9714286
140.6431429
152.3333333
147.8333333


HBA1C*
3.54E−07
0.001087736
7.625
6.425
7.306666667
6.313333333


HDL
0.888463907
0.109863281
2.97
2.989411765
3.304615385
2.868571429


HGF
0.162910502
0.561401367
415.1552778
434.1691176
377.43
400.8593333


HIP
0.135245032
0.9372558
100.2028571
98.82777778
98.06666667
97.21428571


HOMA.IR
6.62E−05
0.638671875
3.585236667
2.487479506
3.79496563
3.326864889


IFNr1
0.259007015
0.609162891
38.76388889
42.10294118
37.6
38.8


IL10
0.241252899
0.635498047
1.525384615
2.300869565
1.787857143
1.797692308


IL12
0.444698256
0.524475098
7.881142857
10.11903226
9.138666667
9.944


IL13
0.394072711
0.735351563
231.2214286
311.6553846
251.2146667
279.3569231


IL15M
0.098387918
0.488708496
224.3034286
333.31875
269.8993333
291.8106667


IL17AM
0.823963715
0.726607536
32.86111111
33.73529412
33.2
32.03333333


IL17EM
0.870855906
0.949882816
21.51388889
22.23529412
21.36666667
21.23333333


IL17FM
0.055283514
0.30279541
124.9583333
181.3529412
183.1333333
232.6333333


IL1B
0.694494109
1
0.374722222
0.532352941
0.324285714
0.910666667


IL1bM
0.359000687
0.977243513
13.77777778
14.66176471
12.53333333
22.23333333


IL21
0.850725867
0.394055106
26.04166667
29.13235294
35.9
34.46666667


IL22M
0.175108876
0.191177717
36.47222222
42.5
32.83333333
30.5


IL23M
0.085674334
0.460211098
34.80555556
39.35294118
39.56666667
41.7


IL27
0.81743512
0.348590881
59.43055556
61.27941176
69.36666667
73.1


IL28a
0.80929836
0.18707508
20.02777778
20.04411765
20.9
18.36666667


IL2M
0.567407854
0.267970259
45.19444444
53.45588235
53.2
58.76666667


IL31M
0.270120255
0.719726563
98.86111111
112.9558824
105.7333333
112.3333333


IL33M
0.304945974
0.181618578
36.18055556
42.13235294
38.7
43.5


IL4M
0.104184122
0.120544434
17.69057143
30.73387097
22.642
31.074


IL5M
0.644309509
0.890380859
34.56944444
38.44117647
42.33333333
43.96666667


IL6
0.346414942
0.12890625
2.577586207
4.845555556
1.631111111
2.665


IL6M
0.724177037
0.04439591
42.08333333
55.85294118
31.06666667
40.53333333


IL8
0.648417992
0.463134766
15.06722222
11.92176471
9.640714286
15.43866667


IL9M
0.644339761
0.488708496
67.23611111
84.63235294
76.13333333
84.9


INS0
0.000815737
0.798233177
10.30666667
8.284444444
12.27
11.734


INS120
2.86E−06
0.003356934
50.66222222
30.33111111
59.02866667
27.83866667


INS180
1.30E−06
0.02557373
39.57222222
22.83277778
35.00266667
20.88266667


INS30
0.000414037
0.018066406
22.1
14.49833333
22.598
14.20533333


INS60
0.000114652
0.004272461
40.315
23.70222222
45.708
23.404


K
0.665771961
0.239135782
4.2628125
7.892571429
4.193846154
4.054285714


LBP
0.317407376
0.390991211
19.56027778
17.36264706
15.45214286
17.616


LDL
0.122171856
0.556146527
1.154285714
1.241764706
1.201538462
1.168571429


LEPTIN
0.157752971
0.006713867
6318.866667
6480.940882
7200.680714
4521.537333


LPA
0.433978558
0.625
66.63321429
32.841
303.14
214.7509091


Lselectin*
0.034002222
0.003438
1.522777778
1.686176471
1.591333333
2.047333333


Lymph
0.873712631
0.030175493
30.11470588
28.87428571
32.56785714
28.21428571


MCP1
0.069589183
0.006713867
245.4563889
256.6388235
211.976
252.7773333


MIP3A
0.000646785
0.100000618
20.46548387
43.76967742
20.34642857
29.93571429


monocyte
0.810992023
0.861220365
5.728125
5.480571429
5.481538462
5.828571429


mpo
0.542937988
1
366.3775
275.7567647
411.0126667
467.2593333


Na
0.097485128
0.064957971
139.40625
136.8574286
140.3076923
142.2142857


NEU
0.279685889
0.001656248
60.20588235
58.46857143
52.50571429
63.59285714


NGAL
0.787128593
0.276855469
129328.8056
125535.0294
137207.4667
174248.3333


NGF
0.78915379
0.887042291
1.8775
1.865
3.097333333
2.434


oscatinM
0.231878957
0.454284668
105.3888889
86.93939394
85.43333333
101.1


PAI1
0.7103313
0.252380371
56209.66667
52769.94118
54084.73333
61006.73333


pCP0
0.003281006
0.583007813
1264.89
1098.934444
1066.730667
1065.952857


pCP120
3.16E−05
0.00012207
3983.970588
2787.309722
3804.866667
2723.04


pCP180
1.50E−06
0.024536133
3659.411765
2451.553333
3335.466667
2526.179286


pCP30
4.56E−05
0.013427734
1799.170286
1394.301389
1597.980667
1362.325714


pCP60
0.000122547
0.000610352
2780.398571
1891.040833
2546.807333
1935.42


PDGFAA
0.046347912
0.488708496
5322.730571
4701.948824
4845.333333
4507.333333


PDGFAABB
0.004710246
0.561401367
29869.68571
24130.63636
26779.73333
24006.8


pgh0*
0.009070051
0.206054688
42.28705882
55.75416667
50.14833333
53.4025


pgh0180*
0.002696353
0.16015625
41.34516129
60.30111111
46.26363636
50.87333333


pgh120*
3.87E−06
0.07421875
34.24242424
58.55861111
35.18454545
42.5275


pgh30*
0.000781945
0.233398438
46.99676471
70.15138889
44.22846154
64.28307692


pgh60*
8.13E−05
0.041311226
43.87
66.50916667
33.40307692
56.35083333


pGLP10
0.31839608
0.577148438
4.098333333
4.564117647
5.125
3.643846154


pGLP1120
0.066918263
0.855224609
7.805294118
10.47416667
8.146666667
9.07


pGLP1180*
0.012499022
0.501586914
5.6478125
7.518333333
6.400666667
6.457857143


pGLP130
0.062569209
0.057373047
15.29647059
12.20628571
20.41714286
11.40714286


pGLP160
0.642244034
0.807739258
14.34941176
13.20333333
14.42066667
13.72


pgluc0
0.234643616
0.637695313
29.12342857
37.29794118
31.76666667
39.20090909


pgluc120*
0.00154797
0.02734375
24.03272727
35.94735294
23.82571429
29.62545455


pgluc180*
0.000424966
0.153576397
21.732
32.29147059
23.65714286
30.42818182


pgluc30
0.385465678
0.909667969
38.742
43.28277778
37.34333333
41.17166667


pgluc60
0.016413683
0.518554688
35.55685714
43.96361111
30.278
36.68


pHOMA.IR*
0.00102708
0.71484375
3.951110809
2.565193754
3.249134038
2.716974268


pins0*
0.009625357
0.71484375
494.0097143
391.0080556
469.0473333
416.3028571


pins120*
3.16E−05
0.057373047
2011.773529
1168.609722
2303.542
1373.493077


pins180*
7.24E−06
0.067626953
1613.704118
916.6516667
1780.701333
1152.767143


pins30*
0.003007197
0.172607422
817.7231429
561.7958333
780.2073333
639.8921429


pins60*
0.000283359
0.03527832
1469.366571
892.8875
1574.948667
968.9964286


platelet
0.964365002
0.670003472
212.1714286
213.9714286
208.8
206.2666667


PP0
0.727960433
0.049438477
85.98828571
80.31583333
107.5466667
69.67214286


PP120
0.050439826
0.03527832
231.4273529
258.8033333
203.484
260.1364286


PP180
0.446560021
0.501586914
182.5308824
173.9941667
171.2373333
204.6585714


PP30
0.703771093
0.03527832
319.3008571
331.6930556
279.108
376.6992857


PP60
0.045642266
0.016601563
305.7042857
330.7513889
237.3526667
318.92


ppyy0
0.539026541
0.91015625
42.186
49.602
51.0175
43.56636364


ppyy120*
0.038631681
0.921875
44.34516129
54.44848485
61.04916667
54.01538462


ppyy180*
0.033681393
0.431640625
45.31344828
56.15371429
54.65916667
51.59230769


ppyy30
0.707845747
0.359375
56.97933333
55.73625
77.28307692
64.33909091


ppyy60
0.33367527
0.764648438
56.174375
59.37029412
69.93692308
56.77923077


RANTES
0.179124002
0.524475098
58731.5
50811.91176
50815.2
47562.53333


RBC
0.321296464
0.306429005
4.783428571
4.832
5.008
4.945333333


RCv

0.942925705
9.936176471
2.580285714
9.274
0.442142857


resistin
0.624272656
0.276855469
15960.5
14639.64706
21493.73333
25013.66667


rGT*
5.29E−05
0.020263672
42.75714286
24.82285714
60.07333333
43.12142857


SBP
0.122812744
0.064531987
127.0277778
120.8611111
127.8
119.8


sicam1*
2.21E−06
0.018066406
167.5533333
112.2894118
163.1493333
122.9993333


Spselectin*
0.001039933
0.120544434
142.3733333
92.83058824
139.506
102.7933333


svcam1*
0.000430483
0.041259766
674.3169444
484.8473529
738.3453333
545.498


TBIL
0.316992017
0.216308594
15.36176471
16.35714286
13.74285714
14.9


TC
0.059412126
0.026855469
4.968857143
4.735588235
5.514615385
4.882857143


TG*
0.000665817
0.01668677
2.482857143
1.525
2.532307692
1.693571429


TNFa
0.877708881
0.07823218
4.660833333
4.772647059
3.962666667
4.572


TNFBM
0.572530012
0.900061308
35.625
38.44117647
45.33333333
44.7


TPRO*
0.031620353
0.57587042
72.07941176
70.11428571
74.08571429
73.55


Trophonin M
0.796523868
0.167835191
17.48611111
16.15151515
16.53333333
17.9


urea
0.205820381
0.700616425
4.894848485
4.731428571
5.081333333
4.953846154


vegfa
0.253948028
0.172607422
516.5967647
475.2033333
308.4314286
340.1686667


vegfc
0.025448661
0.414306641
121.5308824
106.3214706
113.8453846
128.6914286


vegfd
9.05E−06
0.010742188
193.5427778
145.3523529
249.5521429
195.0753333


WAIST*
0.004813593
0.029960994
91.11428571
88.45277778
92.26666667
88.92857143


WBC
0.387914524
0.334179383
6.413428571
6.912571429
6.38
6.146


WHR*
0.036332936
0.068270928
0.909714286
0.895555556
0.94
0.913571429








Claims
  • 1. An application of characteristics of gut microbiota metagenome as a as a screening marker of Acarbose efficacy in patients with Type 2 diabetes, wherein the characteristic of gut microbiota metagenome is Bacteroides enterotype.
  • 2. The application according to claim 1, wherein the Bacteroides enterotype is determined by DNA sequencing or PCR amplification of parasites in feces in vitro.
  • 3. The application according to claim 2, wherein the PCR amplification specifically comprises: extract the DNA of parasites in feces in vitro and perform 16Srna PCR amplification for specific enrichment strains.
  • 4. The application according to claim 1, wherein the Bacteroides enterotype is determined by detecting secondary bile acid in the in vitro blood samples.
  • 5. The application according to claim 4, wherein the detection of secondary bile acid comprises the following steps: S1. Sample pretreatment: Add 300 μL of internal standard methanol to every 75 μL of blood samples, to extract the target compound and precipitate the protein, vortex, centrifuge and draw the supernatant, then lyophilize, re-dissolve in 50 μL of acetonitrile solution (25%, volume), and wait for sample injection;S2. Detection: conduct sample analysis using 1290 Infinity liquid phase and 6460A triple quadrupole mass spectrometry system;Perform the liquid phase separation using 100 mm×2.1 mm ACQUITY UPLC C8 column having a particle size of 1.7 μm, of which, phase A is 10 mM NH4HCO3 aqueous solution, phase B is pure acetonitrile; initially 25% phase B (by volume), retaining 0.5 min, followed by increased to 40% phase B (by volume) linearly within 12.5 min, then increased to 90% (by volume) within 1 min, flush the system for 3 min, recover to 25% phase B (by volume) in 0.5 min, after equilibrating 2.5 min, the flow rate is 0.35 ml/min, column temperature is 35° C. and the injection volume is 5 μL;Mass spectrometry is performed by ESI source negative ion mode, with main parameters as follows: Gas Temp: 350° C.; Gas Flow: 8 l/min; Nebulizer: 40 psi; Sheath Gas Temp: 400° C.; Sheath Gas Flow: 8 l/min; Capillary: 3500 V; Nozzle voltage: 400 V.
  • 6. The application according to claim 1, wherein the efficacy of Acarbose in the patients with Type 2 diabetes and Bacteroides enterotype includes improving the insulin resistance, reducing the secondary bile acid, and promoting the reduction of cardiovascular risks in addition to glucose-lowering.
  • 7. The application according to claim 6, wherein the indicators for reducing the secondary bile acid include GDCA, TDCA, TLCA, and the indicators for reducing the binding of taurine with bile acid include TCA, TDCA, TLCA, TUDCA.
  • 8. The application according to claim 6, wherein the indicators for improving insulin resistance include decreased fasting blood glucose, decreased fasting C peptide and insulin level, down-regulated waist-to-hip ratio, down-regulated HOMA insulin resistance index and up-regulated Adiponectin.
  • 9. The application according to claim 6, wherein the indicators that promote the reduction of cardiovascular risks include decreased PDGFAA, PDGFAABB, endothelin, and VegfC plasma factor.
  • 10. A kit used for screening of Acarbose efficacy in patients with Type 2 diabetes, comprising: a reagent used to collect in vitro stool samples or in vitro blood samples; anda reagent used to determine the enterotype by DNA sequencing or PCR amplification of the parasites in the in vitro stool samples, or a reagent used to determine the enterotype by detecting the secondary bile acid in the in vitro blood samples.
Priority Claims (1)
Number Date Country Kind
201510703463.9 Oct 2015 CN national
PCT Information
Filing Document Filing Date Country Kind
PCT/CN2015/097566 12/16/2015 WO 00