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WO2022266266A1 - Méthodes et compositions pour évaluer et pour traiter une dérégulation de la glycémie - Google Patents

Méthodes et compositions pour évaluer et pour traiter une dérégulation de la glycémie Download PDF

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WO2022266266A1
WO2022266266A1 PCT/US2022/033693 US2022033693W WO2022266266A1 WO 2022266266 A1 WO2022266266 A1 WO 2022266266A1 US 2022033693 W US2022033693 W US 2022033693W WO 2022266266 A1 WO2022266266 A1 WO 2022266266A1
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subject
risk
individual
dysregulation
progressing
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Nan Shen
Nevenka Dimitrova
Han-Chen Cleo HO
Ying Cai
Momchilo VUYISICH
Damon TANTON
Guruduth S. Banavar
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Viome Life Sciences Inc
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Viome Life Sciences Inc
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material

Definitions

  • Type 2 diabetes is the most prevalent endocrine disease, affecting more than 400 million people worldwide, and this number is expected to rise to 700 million by the year 2025 (Zhou et al., 2016) (Chatterjee et al., 2017).
  • the increased prevalence of T2D is a major challenge for healthcare systems globally and novel tools are needed to reduce the burden of T2D. Recognizing and treating the early stages (and even predisease) is the most cost effective way to treat T2D, before end-organ damage, such as heart disease, stroke, renal disease, blindness and limb amputation, ensues. (Bailes, 2002).
  • Gut microbiome research has primarily focused on genomic DNA. Multiple studies have shown that the gut microbiome is altered in individuals with metabolic disorders, such as obesity (Tumbaugh et al., 2009) (Ley et al., 2006), T2D (Larsen et al., 2010) (Gurung et al., 2020) and progression of glucose intolerance (Zhang et al., 2013), and that the gut microbiome may be a causal factor in the development of these disease processes. (Wang et al., 2020).
  • Figure 2 Progression of disease: differentially expressed features in a color-bar scatter plot (KW statistics, FDR p-value ⁇ 0.05). Panels present scatter plots of resulting patient features which include (a) differentially expressed species shown as circles between non- medicated T2D vs non-medicated non-T2D and (b) differentially expressed KOs shown as triangles between non-medicated T2D vs non-medicated non-T2D. Filled markers represent obesity related features, and hollow markers represent T2D-unique features.
  • Figure 3 a) Differential prevalence of species: left panel represents the differential prevalence between pre-T2D vs non-T2D, and the right panel represents the differential prevalence between T2D vs non-T2D b) Differential expression of species between pre-T2D vs non-T2D c) Differential prevalence of KOs, grouped by KEGG category: left panel represents the differential prevalence between non-medicated pre-T2D vs non-medicated non-T2D, and the right side represents the differential prevalence between non-medicated non T2D vs -medicated d) Differential expression of KOs, grouped by KEGG category: left panel represents the differential expression between non-medicated pre-T2D vs non-medicated non-T2D, and the right side represents the differential expression between non-medicated T2D vs non-medicated non-T2D .
  • FIG. 4 Important features in T2D disease progression classification model distinguishing T2D patients and prediabetic patients from presumed healthy using SVM (species with differential expression > 0.1; top 300 KOs with the highest coefficients and differential expression > 0.2)
  • a) Important species in T2D classifiers left side shows non-medicated pre-T2D vs non-T2D important species; right side shows non-medicated T2D vs non-T2D important species Important KOs in classifiers grouped by KEGG L3 category
  • Important KOs in classifiers grouped by KEEG L3 category Important KOs in classifiers grouped by KEEG L3 category.
  • Figure 5 Bar plots and probability density distribution plots of Ruminococcus and Blautia features (a) Prevalence bar plot (top) and expression violin plots (bottom) of the species in the Ruminococcus genus (b) Prevalence bar plot (top) and expression violin plots (bottom) of the species in the Blautia genus
  • Figure 6 Progression of disease: non-diabetic, pre-diabetic and T2D diversity and richness a) Taxa and KO richness; b) Taxa and KO Shannon diversity [0013]
  • Figure 7 Statistically significant richness, diversity (a-b) and feature scatter plots distinguishing T2D vs. general population using descriptive statistics (species FDR p value ⁇ 0.05; KOs FDR p value ⁇ 0.00001) (c-1).
  • Venn diagrams representing overlapping features shared between the T2D progression and the diagnostic statistical analysis (g-j) (a) taxa and KO richness; (b) taxa and KO Shannon diversity; (c) differential prevalence of species; (d) differential expression of species; (e) differential prevalence of KOs; (1) differential expression of KOs; (g) sharing significantly prevalent species between progression and diagnostic descriptive analysis; (h) sharing significantly prevalent KOs between progression and diagnostic descriptive analysis; (i) sharing significantly differentially expressed species between progression and diagnostic descriptive analysis; (j) sharing significantly differentially expressed KOs between progression and diagnostic descriptive analysis.
  • Figure 8 Type 2 diabetes diagnostic analysis and support vector machine approach a) ROC curves of diagnostic classifiers b) Risk scores of the validation cohort.
  • FIG. 9 Important diagnostic model features using support vector machine approach (species with differential expression > 0.1; top 300 KOs with the highest coefficients and differential expression > 0.2) (a-b).
  • c) correlation between T2D risk score and BMI in the validation cohort (n 2406)
  • Important KOs in T2D vs. non-diabetic classifier
  • Figure 12 Biological interpretation of the type 2 diabetes metatranscriptomic analysis and derived features. Orange and green circles represent features enriched in the diseased (preT2D or T2D) and non-diabetic cohorts, respectively.
  • T2D Type 2 diabetes
  • pre-diabetes or even stages prior to pre-diabetes, based at least in part on gut metatranscriptomic information.
  • likely control or no control of blood glucose by metformin is determined and/or modified based on metatranscriptomic evidence. While previous metagenomic studies have revealed compelling insights into the role that microorganisms play in the development of T2D, these approaches have described the presence of organisms and genes, rather than activity of these microbiome elements, as is used here in a metatranscriptomic analysis.
  • Hatch et ak 2019
  • the current standard of care for diagnosing and treating Type 2 diabetes is based largely on HbAlc values, with aHbAlc of less than 5.7% considered non-diabetic, 5.7-6.4% considered prediabetic, and 6.5% or higher diabetic. Prediabetic and diabetic individuals are often treated with lifestyle modification, medication, or both. Metformin is the most common diabetes medication used.
  • the individual is prediabetic and the assessment is risk for diabetes.
  • a risk score for the individual for progressing to prediabetes or diabetes can be based on an algorithm that includes metatranscriptomic data.
  • Such data may show that an individual who would be considered non-diabetic by conventional methods, e.g., an individual with aHbAlc value of less than 5.7%, may be at elevated risk for development of prediabetes and/or diabetes, and treatment may be initiated to slow or prevent development of prediabetes and/or diabetes in the individual.
  • treatment which can include lifestyle modification and/or medication, can be initiated and/or monitored based on the information.
  • methods for determining whether or not an individual who is prediabetic for example as determined by HbAlc, may have elevated risk for progressing to diabetes, based at least in part on metatranscriptomic information from the individual.
  • a risk score for the individual for progressing to diabetes can be based on an algorithm that includes metatranscriptomic data.
  • Treatment which can include lifestyle modification and/or medication, can be initiated and/or monitored based on the information. Further provided herein are methods for determining whether or not an individual will experience control of blood glucose with treatment with metformin based at least in part on metatranscriptomic information from the individual; in certain embodiments, treatment initiation or modulation may be based on such information.
  • Methods and compositions are based on collection of information from one or more samples from an individual that allow evaluation of gut microbiome; typically such samples will be stool samples.
  • Sample collection, ambient temperature sample preservation, total RNA extraction, physical removal of ribosomal RNAs (rRNAs), preparation of directional libraries, e.g., Illumina libraries, and sequencing, e.g., Illumina sequencing, may be performed by any suitable method, for example, as describe in Hatch et al. (2019), A robust metatranscriptomic technology for population-scale studies of diet, gut microbiome, and human health. Inti. J. Genom. https://doi.org/10.1155/2019/1718741, and in Patent Publication Nos.
  • RNA stabilizer can be added to the sample to preserve RNA during sampling and shipping.
  • Phenotypic data may also be collected from the individual, and can include any data relevant to assessing risk of diabetes and/or to microbiome modification, such as age, height, weight, BMI (can be calculated from weight and height), gender, ethnicity, use of medications, especially those that may affect the microbiome, such as antibiotics, proton pump inhibitors or acid suppressants, health history, which can include, e.g., abdominal surgery and/or diseases that may affect gut microbiome, such as IBS, IBD, colon cancer; HbAlc values, blood glucose values, blood insulin values, glucose tolerance test values (blood glucose and, optionally, blood insulin), and other suitable characteristics as will be apparent to one of skill in the art.
  • phenotypic data is also used in the evaluation of risk of glucose dysregulation.
  • a method of determining risk for developing diabetes or prediabetes in an individual for example an individual who is nondiabetic by conventional measure, e.g., an HbAlc of less than 5.7%, , or determining risk of developing or progressing blood glucose dysregulation, by evaluating mRNA in a stool sample from the individual, and, in certain embodiments, treating the individual based on their level of risk, for example, through administration of medication, dietary changes, supplements, such as pre-biotic or pro-biotic supplements, or other lifestyle changes (e.g., exercise). Dosage of each of the treatments can be based on features of the risk assessment.
  • a method of determining risk for developing diabetes in an individual for example an individual who is prediabetic by conventional measure, e.g., an HbAlc of 5.7 to 6.4%, or determining risk of developing or progressing blood glucose dysregulation, by evaluating mRNA in a stool sample from the individual, and, in certain embodiments, treating the individual based on their level of risk, for example, through administration of medication, dietary changes, supplements, such as pre-biotic or pro-biotic supplements, or other lifestyle changes (e.g., exercise). Dosage of each of the treatments can be based on features of the risk assessment.
  • a method of determination of risk for developing diabetes or prediabetes, or determining risk of developing or progressing blood glucose dysregulation involves providing a biological sample from the individual that comprises a gut microbiome, for example a stool sample; sequencing nucleic acids from the sample to provide sequence information, where the nucleic acids comprise RNA, e.g., mRNA; determining from the sequence information one or more features of the nucleic acid sequences; and executing a classification model, for example, by computer, that infers, from the one or more feature of in the feature set, a measure of risk for development of a prediabetic state, a diabetic state, or development or progression of glycemic dysregulation in the individual.
  • RNA e.g., mRNA
  • the sample can be analyzed for, e.g., transcriptional information, such as taxonomic information and KO classification.
  • the one or more features comprise (1) determinations of KEGG-Orthologs (KOs) for gene-level activity within the sample the determinations of KOs comprise at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 150, 170, or 200 different KOs and/or not more than 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 150, 170, 200 or 300 KOs.
  • the one or more features comprise (2) measures of active microbial species within the sample, wherein the one or more measures are included in a feature set.
  • the measures of active microbial species can include measures of species from at least 1, 2,
  • the biological sample comprises a stool sample.
  • the individual is a human.
  • the individual has an HbAlc of less than 5.7% and is conventionally classified as nondiabetic.
  • the measure of risk for the individual indicates that the individual is at risk of developing glycemic dysregulation.
  • the individual has an HbAlc of 5.7-6.4% and is conventionally classified as prediabetic.
  • the measure of risk for the individual indicates that the individual is at risk of progressing glycemic dysregulation.
  • the individual has an HbAlc of 6.5% or greater and is conventionally classified as diabetic.
  • the measure of risk for the individual indicates that the individual is at risk of progressing glycemic dysregulation.
  • the sequence information comprises metatranscriptomic information.
  • the feature set used by the classification algorithm includes at least measures of activity of one or more microbial taxa.
  • the feature set used by the classification algorithm includes at least measures of taxa involved in one or more metabolic pathways.
  • the classification model uses expression levels for one or more species of one or more genus shown in Figure 9a.
  • the classification model uses expression levels for species of at least, exactly, and/or no more than any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 of the genuses shown in Figure 9a.
  • a higher risk of developing or progressing glycemic dysregulation is determined, at least in part, from relatively lower expression of Ruminococus species, e.g., as compared to expression levels found in nondiabetic individuals.
  • the Ruminococus species comprise Ruminococcus bicirculans, R. callidus, and/or R. champallensis .
  • a higher risk of developing or progressing glycemic dysregulation is determined, at least in part, from relatively lower expression of Blautia species in the sample, e.g., as compared to expression levels found in nondiabetic individuals.
  • the Blautia species comprise Blautia massiliensis and/or Blautia Mar seille-P 3087.
  • the classification model uses at least, exactly, and/or no more than any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
  • the method further comprises outputting the inference to a user interface device or to a computer-readable memory.
  • an individual is found to be at risk for developing or progressing glycemic dysregulation, for example by the method of the above paragraph, and a method comprises delivering and/or administering to the individual one or more therapeutic interventions to decrease glycemic dysregulation or the development of glycemic dysregulation.
  • the choice of therapy, including dosing regimen, can be based at least in part on the determination of risk and/or on metatranscriptomic analysis.
  • the therapeutic intervention comprises administering one or more pharmaceutical agents to the individual.
  • the one or more pharmaceutical agents comprise metformin, sodium glucose co-tranporter 2 inhibitor (SGLT2i), a glucagon-like peptide 1 receptor agonist (GLP-RA), or a combination thereof.
  • the pharmaceutical agent comprises metformin.
  • 500 mg of metformin is taken daily.
  • 1000 mg of metformin is taken daily.
  • 1500 mg of metformin is taken daily.
  • 2000 mg of metformin is taken daily.
  • 2500 mg of metformin is taken daily.
  • one or more therapeutic interventions is used to modulate the gut microbiome to improve responsiveness to metformin.
  • one or more pro-biotic or pre-biotic supplements, or a combination of both is given to improve response to metformin.
  • one or more dietary modifications such as one or more of those modifications described elsewhere herein, is given to improve response to metformin.
  • the pharmaceutical agent comprises a SGLT2i selected from the group consisting of canagliflozin, dapagliflozin, and empagliflozin.
  • the pharmaceutical agent comprises a GLP-RA selected from the group consisting of Dulaglutide, liraglutide, lixisenatide, Exenatide, Exenatide extended release, Semaglutide, injection, and Semaglutide, oral.
  • the therapy comprises a dietary therapy.
  • the dietary therapy comprises eating a whole foods plant-based diet and reducing or eliminating meat; reducing or eliminating cheese consumption, eating the largest meal of the day at midday, or a combination thereof.
  • the dietary therapy comprises eating a diet of an average of less than 200, 150, 100, or 50 grams of carbohydrate daily, periodically fasting for at least 16 hours, or a combination thereof.
  • the therapy comprises exercise, for example resistance training, endurance training, high intensity training, or a combination thereof.
  • therapy comprises administration of one or more supplements.
  • the supplements comprise one or more pre-biotics, one or more probiotics, or a combination thereof.
  • the one or more prebiotics and/or probiotics are chosen to modulate the gut microbiome of the individual toward a biome that favors decreased glucose dysregulation.
  • a method for determining whether or an individual is able to control blood glucose with metformin comprising a) providing a biological sample from the individual; b) sequencing nucleic acids from the sample to provide sequence information, wherein the nucleic acids comprise RNA, e.g., mRNA; c) determining, from the sequence information one or more features of the nucleic acid sequences; d) executing by computer a classification model that infers, from one or more features in the feature set, a measure of likelihood of control of blood glucose with metformin for the individual.
  • Control of blood glucose may be determined in any suitable manner; in certain embodiments, control of blood glucose is defined as reducing HbAlc to 6.5% or lower.
  • the one or more features comprise (1) determinations of KEGG-Orthologs (KOs) for gene-level activity within the sample. In certain embodiments the determinations of KOs comprise at least 5, 10, 15, 20,
  • the one or more features comprise (2) measures of active microbial species within the sample, wherein the one or more measures are included in a feature set.
  • measures of active microbial species include measures of species from at least 1, 2, 5, 7, 10, 12, 15, 17, 20, 22, 25, 30, 40, or 50 genuses and/or not more than 2, 5, 7, 10, 12, 15, 17, 20, 22, 25, 30, 40, 50 or 70 genuses.
  • the biological sample comprises a stool sample.
  • the individual is a human.
  • the individual has an HbAlc of less than 5.7% and is conventionally classified as nondiabetic.
  • the individual has an HbAlc of 5.7-6.4% and is conventionally classified as prediabetic.
  • the individual has an HbAlc of 6.5% or greater and is conventionally classified as diabetic.
  • the sequence information comprises metatranscriptomic information.
  • the feature set used by the classification algorithm includes at least measures of activity of one or more microbial taxa. In certain embodiements the feature set used by the classification algorithm includes at least measures of taxa involved in one or more metabolic pathways.
  • the classification model uses expression levels for one or more species of one or more genuses shown in Figure 10c. In certain embodiments the classification model uses expression levels for species of at least, exactly, and/or no more than any of 1, 2, 3, 4, or 5, of the genuses shown in Figure 10c. In certain embodiments the classification model uses at least, exactly, and/or no more than any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
  • an individual is treated with metformin based on the determination of the above paragraph.
  • 500 mg of metformin is taken daily.
  • 1000 mg of metformin is taken daily.
  • 1500 mg of metformin is taken daily.
  • 2000 mg of metformin is taken daily.
  • 2500 mg of metformin is taken.
  • one or more therapeutic interventions is used to modulate the gut microbiome to improve responsiveness to metformin.
  • one or more pro-biotic or pre-biotic supplements, or a combination of both is given to improve response to metformin.
  • one or more dietary modifications such as one or more of those modifications described elsewhere herein, is given to improve response to metformin.
  • Also provided herein is a method comprising: a) providing biological samples from each of a first set of subjects, a second set of subjects, and a third set of subjects, wherein the biological samples comprise a stool samples, and wherein the first set of subjects have been diagnosed with Type 2 diabetes, the second set of subjects have been diagnosed as prediabetic, and the third set of subjects have been diagnosed as nondiabetic; b) sequencing nucleic acids in the biological samples to provide sequence information; and c) performing a statistical analysis on the sequence information to produce a model that predicts a risk of developing or progressing glucose dysregulation in an individual.
  • the statistical analysis comprise a model developed by machine learning.
  • the statistical analysis comprises an analysis selected from correlational, Pearson correlation, Spearman correlation, chi-square, comparison of means (e.g., paired T-test, independent T-test, ANOVA) regression analysis (e.g., simple regression, multiple regression, linear regression, non-linear regression, logistic regression, polynomial regression stepwise regression, ridge regression, lasso regression, elasticnet regression) and non-parametric analysis (e.g., Wilcoxon rank-sum test, Wilcoxon sign- rank test, sign test).
  • means e.g., paired T-test, independent T-test, ANOVA
  • regression analysis e.g., simple regression, multiple regression, linear regression, non-linear regression, logistic regression, polynomial regression stepwise regression, ridge regression, lasso regression, elasticnet regression
  • non-parametric analysis e.g., Wilcoxon rank-sum test, Wilcoxon sign- rank test, sign test.
  • a system comprising: (i) a computer comprising: (a) a processor; and (b) a memory storage module, coupled to the processor, the memory storing a module comprising: (1) nucleic acid sequence information from a biological sample from a subject comprising a gut microbiome; (2) a classification model which, based on values including the measurements, classifies the subject as at risk for developing or progressing glucose dysregulation, wherein the classification model is selected to have a sensitivity of at least 75%, at least 85% or at least 95%; and (3) computer executable instructions for implementing the classification model on the test data.
  • Also provided herein is a method for developing a computer model for inferring, from feature data, a risk of developing or progressing glucose dysregulation in a subject comprising: a) training a machine learning algorithm on a training data set, wherein the training data set comprises, for each of a plurality of subjects, (1) a class label classifying a subject as nondiabetic, prediabetic, or diabetic; and (2) feature data comprising quantitative measures for each of a plurality of features selected from gut microbiome transcriptome expression, and wherein the machine learning algorithm develops a model that infers a class label for a subject based on the feature data.
  • Also provided herein is a method that infers a risk of developing or progressing glucose dysregulation in a subject, the method comprising: (a) providing a data set comprising, for the subject, feature data for each of a plurality of features selected from gut microbiome transcriptome gene expression data and taxa activity data; and (b) executing a computer model on the data set to infer the risk of developing or progressing glucose dysregulation in the subject.
  • a software product comprising a computer readable medium in tangible form comprising machine executable code, which, when executed by a computer processor, infers a risk of developing or progressing glucose dysregulation in a subject by: (a) accessing a data set comprising, for a subject, feature data for each of a plurality of features selected from gut microbiome transcriptome gene expression data and taxa activity data; and (b) executing a computer model on the data set to infer the risk of developing or progressing glucose dysregulation in the subject.
  • Also provided herein is a method of providing therapy to decrease a risk of developing or progressing glucose dysregulation or to normalize glucose regulation in a subject comprising: (a) inferring the presence of a risk of developing or progressing glucose dysregulation in a subject according to a method as described herein; and (b) administering a therapeutic intervention to the subject effective to decrease the risk of developing or progressing glucose dysregulation or to normalize glucose regulation in the subject.
  • Also provided herein is a method for diagnosing and treating developing or progressing glucose dysregulation in a subject, the method comprising: (a) receiving from a subject a sample comprising a gut microbiome; (b) determining nucleic acid sequences of a microorganism component of the sample; (c) determining alignments of the nucleic acid sequence to reference nucleic acid sequences associated with developing or progressing glucose dysregulation; (d) generating a microbiome feature dataset for the subject based upon the alignments; (e) generating an inference of the risk of developing or progressing glucose dysregulation in the subject upon processing the microbiome feature dataset with an inference model derived from a population of subjects; and (1) at an output device associated with the subject, providing a therapy to the subject with the risk of developing or progressing glucose dysregulation upon processing the inference with a therapy model designed to treat the glucose dysregulation.
  • Also provided herein is a method comprising: (a) measuring, in a sample from a subject comprising an gut microbiome, activity of one or more biomarkers selected from Figure 9; (b) inferring, from the measurements, a risk of developing or progressing glucose dysregulation in the subject; and (c) delivering to the subject a therapeutic intervention effective to decrease the risk of developing or progressing glucose dysregulation or to normalize glucose regulation in the subject.
  • Also provided herein is a method comprising: a) providing biological samples from each of a first set of subjects and a second set of subjects having glucose dysregulation and having been subject to a therapeutic intervention, wherein the biological samples comprise a gut microbiome and wherein the first set of subjects responded positively to the therapeutic intervention and the second set of subjects did not respond positively to the therapeutic intervention; b) sequencing nucleic acids in the biological samples to provide sequence information; and c) performing a statistical analysis on the sequence information to produce a model that infers having a positive response or lack of positive response to the therapeutic intervention in a subject.
  • Also provided herein is a method of treating a subject with glucose dysregulation comprising: (a) inferring that the subject will respond positively to each of one or more therapeutic interventions by executing a model on nucleic acid information from a biological sample from the subject comprising a gut microbiom; and (b) administering to the subject one or more therapeutic interventions to treat the glucose dysregulation;
  • the therapeutic intervention comprises administration of metformin.
  • embodiment 1 provided is a method for determining whether an individual is at risk for development or progression of glycemic dysregulation comprising a) providing a biological sample from the individual; b) sequencing nucleic acids from the sample to provide sequence information, wherein the nucleic acids comprise mRNA; c) determining, from the sequence information one or more features of the nucleic acid sequences; d) executing by computer a classification model that infers, from one or more features in the feature set, a measure of risk for development or progression of glycemic dysregulation in the individual.
  • the one or more features comprise (1) determinations of KEGG-Orthologs (KOs) for gene-level activity within the sample.
  • KOs comprise at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 150, 170, or 200 different KOs and/or not more than 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 150, 170, 200 or 300 KOs.
  • the one or more features comprise (2) measures of active microbial species within the sample, wherein the one or more measures are included in a feature set.
  • the measures of active microbial species include measures of species from at least 1, 2, 5, 7, 10, 12, 15, 17, 20, 22, 25, 30, 40, or 50 genuses and/or not more than 2, 5, 7, 10, 12, 15, 17, 20, 22, 25, 30, 40, 50 or 70 genuses.
  • the biological sample comprises a stool sample.
  • the individual is a human.
  • the individual has an HbAlc of less than 5.7% and is conventionally classified as nondiabetic.
  • any preceding embodiment wherein the measure of risk for the individual indicates that the individual is at risk of developing glycemic dysregulation.
  • the individual has an HbAlc of 5.7-6.4% and is conventionally classified as prediabetic.
  • the measure of risk for the individual indicates that the individual is at risk of progressing glycemic dysregulation.
  • the individual has an HbAlc of 6.5% or greater and is conventionally classified as diabetic.
  • the measure of risk for the individual indicates that the individual is at risk of progressing glycemic dysregulation.
  • the sequence information comprises metatranscriptomic information.
  • the feature set used by the classification algorithm includes at least measures of activity of one or more microbial taxa.
  • the feature set used by the classification algorithm includes at least measures of taxa involved in one or more metabolic pathways.
  • embodiment 17 provided is the method of any preceding embodiment wherein the classification model uses expression levels for one or more species of one or more genus shown in Figure 9a.
  • embodiment 18 provided is the method of any preceding embodiment wherein the classification model uses expression levels for species of at least, exactly, and/or no more than any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 of the genuses shown in Figure 9a.
  • embodiment 19 provided is the method of any preceding embodiment wherein a higher risk of developing or progressing glycemic dysregulation is determined, at least in part, from relatively lower expression of Ruminococus species compared to nondiabetic individuals.
  • embodiment 20 provided is the method of any preceding embodiment wherein the Ruminococus species comprise Ruminococcus bicirculans,
  • R. callidus, and/or R. champallensis are provided in embodiment 21 provided is the method of any preceding embodiment wherein a higher risk of developing or progressing glycemic dysregulation is determined, at least in part, from relatively lower expression of Blautia species in the sample compared to nondiabetic controls.
  • the Blautia species comprise Blautia massiliensis and/or Blautia Mar seille- P3087.
  • the classification model uses at least, exactly, and/or no more than any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
  • any preceding embodiment further comprising outputting the inference to a user interface device or to a computer-readable memory.
  • a method comprising delivering and/or administering to an individual one or more therapeutic interventions to decrease glycemic dysregulation or the development of glycemic dysregulation, wherein the individual has been found to be at risk for developing or progressing glycemic dysregulation by the method of any preceding embodiment.
  • the therapeutic intervention comprises administering one or more pharmaceutical agents to the individual.
  • embodiment 27 provided is the method of embodiment 25 or 26 wherein the one or more pharmaceutical agents comprise metformin, a sodium glucose co-tranporter 2 inhibitor (SGLT2i), a glucagon-like peptide 1 receptor agonist (GLP-RA), or a combination thereof.
  • the pharmaceutical agent comprises metformin.
  • embodiment 29 provided is the method of embodiment 28 wherein 500 mg of metformin is taken daily.
  • embodiment 30 provided is the method of any preceding embodiment wherein 1000 mg of metformin is taken daily.
  • embodiment 31 provided is the method of embodiment 28 wherein 1500 mg of metformin is taken daily.
  • embodiment 32 provided is the method of embodiment 28 wherein 2000 mg of metformin is taken daily.
  • embodiment 33 provided is the method of embodiment 28 wherein 2500 mg of metformin is taken daily.
  • the pharmaceutical agent comprises a SGLT2i selected from the group consisting of canagliflozin, dapagliflozin, and empagliflozin.
  • the pharmaceutical agent comprises a GLP-RA selected from the group consisting of Dulaglutide, liraglutide, lixisenatide, Exenatide, Exenatide extended release, Semaglutide, injection, and Semaglutide, oral.
  • the therapy comprises a dietary therapy.
  • the dietary therapy comprises eating a whole foods plant- based diet and reducing or eliminating meat; reducing or eliminating cheese consumption, eating the largest meal of the day at midday, or a combination thereof.
  • the dietary therapy comprises eating a diet of an average of less than 200, 150, 100, or 50 grams of carbohydrate daily, periodically fasting for at least 16 hours, or a combination thereof.
  • the therapy comprises exercise.
  • the therapy comprises administration of one or more supplements.
  • embodiment 41 provided is the method of embodiment 40 wherein the supplements comprise one or more pre-biotics, one or more probiotics, or a combination thereof.
  • embodiment 42 provided is the method of embodiment 41 wherein the one or more prebiotics and/or probiotics are chosen to modulate the gut microbiome of the individual toward a biome that favors decreased glucose dysregulation.
  • embodiment 43 provided is a method for determining whether an individual is able to control blood glucose with metformin comprising a) providing a biological sample from the individual; b) sequencing nucleic acids from the sample to provide sequence information, wherein the nucleic acids comprise mRNA; c) determining, from the sequence information one or more features of the nucleic acid sequences; d) executing by computer a classification model that infers, from one or more features in the feature set, a measure of likelihood of control of blood glucose with metformin for the individual.
  • the one or more features comprise (1) determinations of KEGG- Orthologs (KOs) for gene-level activity within the sample.
  • embodiment 45 provided is the method of embodiment 43 or 44 wherein the determinations of KOs comprise at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 150, 170, or 200 different KOs and/or not more than 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 150, 170, 200 or 300 KOs.
  • embodiment 46 provided is the method of any of embodiments 43-45 wherein the one or more features comprise (2) measures of active microbial species within the sample, wherein the one or more measures are included in a feature set.
  • the measures of active microbial species include measures of species from at least 1, 2, 5, 7, 10, 12, 15, 17, 20, 22, 25, 30, 40, or 50 genuses and/or not more than 2, 5, 7, 10, 12, 15, 17, 20, 22, 25, 30, 40, 50 or 70 genuses.
  • the biological sample comprises a stool sample.
  • the individual is a human.
  • the individual has an HbAlc of less than 5.7% and is conventionally classified as nondiabetic.
  • embodiment 51 provided is the method of any of embodiments 43-50 wherein the individual has an HbAlc of 5.7-6.4% and is conventionally classified as prediabetic.
  • embodiment 52 provided is the method of any of embodiments 43-51 wherein the individual has an HbAlc of 6. 5% or greater and is conventionally classified as diabetic.
  • the sequence information comprises metatranscriptomic information.
  • embodiment 54 provided is the method of any of embodiments 43-53 wherein the feature set used by the classification algorithm includes at least measures of activity of one or more microbial taxa.
  • embodiment 55 provided is the method of any of embodiments 43-54 wherein the feature set used by the classification algorithm includes at least measures of taxa involved in one or more metabolic pathways.
  • embodiment 56 provided is the method of any of embodiments 43-55 wherein the classification model uses expression levels for one or more species of one or more genuses shown in Figure 10c.
  • embodiment 57 provided is the method of any of embodiments 43-56 wherein the classification model uses expression levels for species of at least, exactly, and/or no more than any of 1, 2, 3, 4, or 5, of the genuses shown in Figure 10c.
  • embodiment 58 provided is the method of any of embodiments 43-57 wherein the classification model uses at least, exactly, and/or no more than any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
  • KOs selected from those of Figure lOd.
  • embodiment 59 provided is the method of any of embodiments 43- 58 wherein 500 mg of metformin is taken daily.
  • the method of any of embodiments 43-59 wherein 1000 mg of metformin is taken daily.
  • embodiment 61 provided is the method of any of embodiments 43-60 wherein 1500 mg of metformin is taken daily.
  • embodiment 62 provided is the method of any of embodiments 43-61 wherein 2000 mg of metformin is taken daily.
  • 2500 mg of metformin is taken daily.
  • a method comprising: a) providing biological samples from each of a first set of subjects, a second set of subjects, and a third set of subjects, wherein the biological samples comprise a stool samples, and wherein the first set of subjects have been diagnosed with type 2 diabetes, the second set of subjects have been diagnosed as prediabetic, and the third set of subjects have been diagnosed as nondiabetic; b) sequencing nucleic acids in the biological samples to provide sequence information; and c) performing a statistical analysis on the sequence information to produce a model that predicts a risk of developing or progressing glucose dysregulation in an individual.
  • embodiment 65 provided is the method of embodiment 64 wherein the statistical analysis comprises an analysis selected from correlational, Pearson correlation, Spearman correlation, chi-square, comparison of means (e.g., paired t-test, independent t-test, ANOVA) regression analysis (e.g., simple regression, multiple regression, linear regression, non-linear regression, logistic regression, polynomial regression stepwise regression, ridge regression, lasso regression, elasticnet regression) and non-parametric analysis (e.g., Wilcoxon rank-sum test, Wilcoxon sign-rank test, sign test).
  • a therapy comprises any of the therapies described for embodiment 1.
  • a system comprising: (i) a computer comprising: (a) a processor; and (b) a memory storage module, coupled to the processor, the memory storing a module comprising: (1) nucleic acid sequence information from a biological sample from a subject comprising a gut microbiome;(2) a classification model which, based on values including the measurements, classifies the subject as at risk for developing or progressing glucose dysregulation, wherein the classification model is selected to have a sensitivity of at least 75%, at least 85% or at least 95%; and(3) computer executable instructions for implementing the classification model on the test data.
  • a method for developing a computer model for inferring, from feature data, a risk of developing or progressing glucose dysregulation in a subject comprising: a) training a machine learning algorithm on a training data set, wherein the training data set comprises, for each of a plurality of subjects, (1) a class label classifying a subject as nondiabetic, prediabetic, or diabetic; and (2) feature data comprising quantitative measures for each of a plurality of features selected from gut microbiome transcriptome expression, and wherein the machine learning algorithm develops a model that infers a class label for a subject based on the feature data.
  • embodiment 70 provided is a method that infers a risk of developing or progressing glucose dysregulation in a subject, the method comprising: (a) providing a data set comprising, for the subject, feature data for each of a plurality of features selected from gut microbiome transcriptome gene expression data and taxa activity data; and(b) executing a computer model on the data set to infer the risk of developing or progressing glucose dysregulation in the subject.
  • a software product comprising a computer readable medium in tangible form comprising machine executable code, which, when executed by a computer processor, infers a risk of developing or progressing glucose dysregulation in a subject by:(a) accessing a data set comprising, for a subject, feature data for each of a plurality of features selected from gut microbiome transcriptome gene expression data and taxa activity data; and(b) executing a computer model on the data set to infer the risk of developing or progressing glucose dysregulation in the subject.
  • embodiment 72 provided is a method of providing therapy to decrease a risk of developing or progressing glucose dysregulation or to normalize glucose regulation in a subject comprising: (a) inferring the presence of a risk of developing or progressing glucose dysregulation in a subject according to a method as described herein; and(b) administering a therapeutic intervention to the subject effective to decrease the risk of developing or progressing glucose dysregulation or to normalize glucose regulation in the subject.
  • a method for diagnosing and treating developing or progressing glucose dysregulation in a subject comprising: (a) receiving from a subject a sample comprising a gut microbiome; (b) determining nucleic acid sequences of a microorganism component of the sample; (c) determining alignments of the nucleic acid sequence to reference nucleic acid sequences associated with developing or progressing glucose dysregulation; (d) generating a microbiome feature dataset for the subject based upon the alignments; (e) generating an inference of the risk of developing or progressing glucose dysregulation in the subject upon processing the microbiome feature dataset with an inference model derived from a population of subjects; and (1) at an output device associated with the subject, providing a therapy to the subject with the risk of developing or progressing glucose dysregulation upon processing the inference with a therapy model designed to treat the glucose dysregulation.
  • a method comprising: (a) measuring, in a sample from a subject comprising an gut microbiome, activity of one or more biomarkers selected from Figure 9;(b) inferring, from the measurements, a risk of developing or progressing glucose dysregulation in the subject; and(c) delivering to the subject a therapeutic intervention effective to decrease the risk of developing or progressing glucose dysregulation or to normalize glucose regulation in the subject.
  • a method comprising: a) providing biological samples from each of a first set of subjects and a second set of subjects having glucose dysregulation and having been subject to a therapeutic intervention, wherein the biological samples comprise a gut microbiome and wherein the first set of subjects responded positively to the therapeutic intervention and the second set of subjects did not respond positively to the therapeutic intervention; b) sequencing nucleic acids in the biological samples to provide sequence information; and c) performing a statistical analysis on the sequence information to produce a model that infers having a positive response or lack of positive response to the therapeutic intervention in a subject.
  • embodiment 76 provided is a method of treating a subject with glucose dysregulation comprising: (a) inferring that the subject will respond positively to each of one or more therapeutic interventions by executing a model on nucleic acid information from a biological sample from the subject comprising a gut microbiome; and(b) administering to the subject one or more therapeutic interventions to treat the glucose dysregulation.
  • the therapeutic intervention comprises administration of metformin.
  • a method comprising at a computer system comprising at least one processor and a memory, storing at least one program for execution by the at least one processor: a) obtaining nucleic acid sequence data in electronic form for a first plurality of nucleic acids from a biological sample of the subject, wherein the nucleic acid sequence data comprises sequences for 500, 1000, 2000, 5000, 7000, or 10,000 or more nucleic acids, preferably 1000 or more, more preferably 5000 or more, even more preferably 10,000 or more nucleic acids, in the plurality of nucleic acids; b) obtaining phenotypic data about the subject in electronic form, the phenotypic data comprising a plurality of responses; c) identifying, from the nucleic acid sequence data, a corresponding presence or absence of each respective functional activity condition in a plurality of functional activity conditions in the subject by: 1) determining a corresponding measure of transcriptional activity for each respective KEGG ortholog designation in a plurality of KE
  • the data analyzed for the purpose of this Example was obtained from Viome customers, who either completed a research Informed Consent Form (approved by a federally -accredited Institutional Review Board), or agreed to have their data analyzed in the terms and conditions, during the purchase of a gut microbiome test. All study data are de-identified; the laboratory, bioinformatics, and data science team members never had access to any personally identifiable information.
  • RNA sample collection and laboratory analysis The metatranscriptomic method that we use is designed for large-scale population analysis of stool samples as described previously (Hatch et al., 2019), and included sample collection, ambient temperature sample preservation, total RNA extraction, physical removal of ribosomal RNAs (rRNAs), preparation of directional Illumina libraries, and Illumina sequencing. The stability of the RNA stabilizer was tested for up to 28 days at ambient temperature, including shipping.
  • Phenotype data collection The clinical phenotypes and medication status were labeled based on the answers to the questionnaire.
  • the non-diabetic group includes presumed healthy individuals who are not reporting medical conditions as well as individuals who have reported comorbidities, while excluding anyone taking antibiotics within one month, proton pump inhibitor and/or acid suppressants, had abdominal surgery or with diseases that may affect gut microbiome (e.g. IBS, IBD, colon cancer).
  • IBS intestinal microbiome
  • IBD intestinal cancer
  • Bioinformatics processing Paired-end reads were mapped to a catalog of 53,660 microbial genome assemblies spanning archaea, bacteria, fungi, protozoa, and viruses (the complete genomes available in NCBI Reference Sequence Database were downloaded, and the GenBank sequence database was used for viral genomes.) Strain-level relative activities were computed from mapped reads via the expectation-maximization (EM) algorithm (Dempster et al., 1977). Relative activities at other levels of the taxonomic tree were then computed by aggregation according to the taxonomic rank.
  • EM expectation-maximization
  • Relative activities for biological functions were computed by mapping paired-end reads to a catalog of 52,324,420 microbial genes, quantifying gene-level relative activities with the EM algorithm, and then aggregating gene-level activity by KEGG Ortholog (KO) annotation (Kanehisa & Goto, 2000). The identified and quantified active microbial species and KOs for each sample were then provided to the T2D classifier.
  • KOs Mapping KOs to functional categories for presentation.
  • the Python module “Bio.KEGG” was used to take as input the KO name and return KO hierarchy at three different levels (level-2 corresponds to level B, level-3 corresponds to level C, and level-4 corresponds to level D in KEGG).
  • Level 2 and level 3 annotation were assigned using EM algorithms (Dempster et al., 1977) with KOs weighted by absolute values coefficients or reversed p values in log scale.
  • Richness and diversity Richness and diversity. Richness was estimated as the number of present species or KOs in each sample. Shannon-diversity was computed to evaluate the alpha diversity of samples. All comparisons between richness or Shannon-diversity were done with independent t-test.
  • Machine learning The microbiome data was transformed using the centered log ratio transformation (CLR) (Aitchison, 1982) after imputation of zero values using multiplicative replacement (Marti n-Femandez et al., 2003).
  • CLR centered log ratio transformation
  • Machine-learned models using stool microbiome are Support Vector Machines.
  • Other models were also tried, such as logistic regression and random forest, and the model giving the highest AUC was adopted.
  • Hyperparameter optimization was done using a nested 5-fold cross-validation on the discovery cohort.
  • the inner layer was used for feature selection, training and tuning. The top 20% features with the highest KW test effect size were selected.
  • the outer layer was used for estimation of model performance.
  • the hyperparameters were scored based on balanced accuracy and the final models were evaluated based on balanced accuracy and AUC.
  • the non-diabetic controls were subsampled with matched age, sex and BMI to samples with T2D or pre-diabetics.
  • the number of subsampled non-diabetic controls was three times that of samples with conditions.
  • the two classes were not matched because of the small sample size.
  • the machine learning models take in multiple features at the same time.
  • the contribution of each feature is more complex than that of descriptive analysis which takes one feature at a time.
  • some models will pick more features enriched in non-diabetic samples. It does not necessarily mean that the model’s only predicting non-diabetic samples. Instead, these features are enriched in non-diabetic samples and were also features with lower expression in diabetic samples.
  • Phenotype stratification Figure describes the clinical phenotypes based on the self-reported labels in this Example.
  • the non-diabetic group includes presumed healthy individuals who are not reporting medical or mental conditions and are not actively taking medications, as well as individuals who have reported comorbidities or are taking medications.
  • the non-diabetic group excludes anyone taking antibiotics within one month of sample collection, proton pump inhibitors and/or acid suppressants, had abdominal surgery or report diseases that may affect gut microbiome significantly (e.g. IBS, IBD, colon cancer).
  • IBS body mass index
  • T2D body mass index
  • the stratification in Figure 1 was used to analyze disease progression, diagnosis and medication control, using multivariate statistical analysis and machine learning.
  • Table 2 presents a summary of the comparison cohorts and relevant analyses related to disease progression and T2D risk score.
  • the first row represents the main cohort comparison used as the basis for the T2D risk score, to be described in more detail below.
  • the next three rows represent the analyses for disease progression.
  • the last row presents the analysis for the obesity /BMI related microbial features.
  • T2D type 2 diabetes
  • Comparison cohorts Cohort Diff. Prev. Diff. Expr. ; Classification j Mean ; Mean Classifi ; matchin si feats si feats ⁇ method ROC- balanced er e
  • DSM 100440 has higher prevalence and expression in T2D patients (see Table 2A and Figure 5a).
  • Blautia is positively associated with T2D.
  • Blautia massiliensis and Blautia Marseille-P 3087 have lower prevalence and expression in T2D as opposed to the non-T2D while there are six other species that have indeed higher prevalence and expression in Type 2 Diabetes patients (see Table 3 and Figure 5b).
  • Table 2A Table 3. Prevalence and expression of the Blautia species along stages of disease progression [0072] Diagnostic Model and Risk Score. To pick up early signals of metabolic disease, statistical analysis was performed to derive features that are associated with all the subjects with T2D vs. the general population who may have other comorbidities and may be subjected to different types of treatments, and separately performed ML analysis to derive a risk score for T2D. As shown in Figure 6b, taxa and KO diversity decrease in the T2D patients (KO diversity is significant p 3.27e-07). The taxonomic composition using the differential prevalence (based on Chi-square method) is presented (see Figure 7c) and KOs (see Figure 7e) of the microbiomes of Type 2 Diabetes patients vs.
  • T2D as well as in the general population vs. T2D cohorts (taxa and KO overlaps are presented in Figure 7g-j).
  • a general classifier was developed with 922 KOs and species which is informative to provide a risk score that can predict whether an individual has a high risk of being diagnosed with T2D using the gut microbiome (see Figure 8a and Figure 9a-b).
  • Figure 11 presents results in richness (see Figure 11a) and diversity (see Figure lib) between Metformin-treated-HBAlc controlled and the non-controlled groups, and higher and statistically significant differences in KO richness and species richness in the non-controlled group were observed.
  • the scatter plots are presented in Figure 10.
  • T2D Metatranscriptome The association between type 2 diabetes and an imbalanced gut microbial composition has been reported in the past many years, and studies have suggested that gut dysbiosis is a factor in the development of insulin resistance (Allin et al., 2015; Sircana et al., 2018). Dysbiosis in T2D has been characterized by a reduction of Firmicutes species and an increase in the typically low-abundance organisms such as Proteobacteria (Larsen et al., 2010).
  • the statistical model used in this Example reveals several enriched Firmicutes features on both the prediabetes and T2D sides when compared to the presumed healthy population ( Figure 2a, Figure 3a and 3b).
  • Firmicutes enriched in the T2D samples include Erysipelatoclostridium ramosum, Blautia producta, and uncharacterized species from Anaerococcus, Blautia, Clostridia, Clostridiales, Coprobacillus, and Streptococcus.
  • Blautia and Streptococcus have been positively linked to T2D-associated gut microbiota (Candela et al.,
  • E. lenta and R. pickettii are also indicated by the statistical model in the pre-T2D and T2D samples ( Figure 2a, Figure 3a and 3b).
  • E. lenta is another opportunistic pathogen reported in the Chinese metagenome-wide study to be T2D-enriched
  • R. pickettii a pathogen capable of causing bacteremia and hospital infection outbreaks, has been suggested as an aggravator of glucose intolerance in obesity (Udayappan et al., 2017).
  • these results underscore the prominence of opportunistic pathogens in a disrupted microbiome and their potentials as T2D progression markers.
  • succinate While the linkage between succinate, dysbiosis, and inflammatory diseases such as IBD is recognized, succinate has also been suggested to have beneficial metabolic effects and play a role in glucose tolerance and insulin sensitivity (De Vadder et al., 2016; Femandez- Veledo & Vendrell, 2019). Microbiota-derived succinate has been shown to promote glucose homeostasis by serving as a substrate of intestinal gluconeogenesis (IGN), leading to the inhibition of hepatic glucose production (De Vadder et al., 2016). Additionally, esters of succinic acid have been shown to be metabolized into succinate intracellularly and exert insulinotropic effects (MacDonald & Fahien, 1988).
  • IGN intestinal gluconeogenesis
  • Bile-Resistant Bacteria Associated with Presumed Healthy Individuals are capable of modifying and deconjugating primary bile acids synthesized in hepatocytes into secondary bile acids, resulting in a pool of diversified bile forms in the intestines.
  • the relationship between the chemical diversity of the bile acids, their signaling properties, and bile homeostasis in T2D patients has been demonstrated increasingly. From the descriptive analysis herein, the differential expression of bile-resistant microbes, such as Alistipes, Bilophila, Bacteroides, and Sutterella is observed in the presumed healthy cohort ( Figure 2a & Figure 3a and 3b).
  • Bilophila wadsworthia utilizes taurine as an electron acceptor and its growth has been shown to be promoted by high-fat diets due to the increased availability of taurine-conjugated bile acids synthesized to facilitate lipid digestion (Devkota et al., 2012).
  • the machine learning model identified bile salt hydrolase (K01442) to be an important feature for the prediction of preT2D and T2D microbiome.
  • K01442 bile salt hydrolase
  • the enrichment of bile-resistant bacteria may be the result of unrestricted diet in the presumed healthy cohort, altered bile acid homeostasis in T2D patients, or other feedback mechanisms underlying the T2D microbiome.
  • Bacteroides thetaiotaomicron an opportunistic pathogen and common inhabitant of a normal gut microbiome, is capable of fermenting a broad range of polysaccharides and considered by the model to be important in predicting those who cannot control their HbAlc levels.
  • thetaiotaomicron has been shown to modulate inflammation by activating the Treg pathways, which play a role in reducing inflammatory response and improving insulin resistance (Hoffmann et al., 2016), but its ability to enhance the energy harvest and nutrient absorption of the host has also been demonstrated (Hooper et al., 2001; Samuel & Gordon, 2006).
  • B. eggerthii is identified as a potential predictor of non-responders. This microbe has been shown to increase in abundance in T2D microbiome and to aggravate colitis in mice models (Dziarski et al., 2016; Medina-Vera et al., 2019). Overall, we find that more research is needed to understand the taxa in both the responder and non-responder groups, as the features seem to present a complex picture of metformin efficacy.
  • ramosum a feature observed in diseased cohorts herein, is an opportunistic bacterium that may promote intestinal absorption of glucose and fat in obesity (Mandic et al., 2019; Woting et al., 2014), has also been shown to be T2D-enriched in a metagenomic study of a Chinese cohort (Qin et al., 2012). Higher prevalence and abundance of Eggerthella and Ralstonia features, including E. lenta and R. pickettii, are also observed in the data herein. The opportunistic E.
  • picketti has been shown to increase in obese patients with impaired glucose tolerance and type 2 diabetes, and the organism has been suggested to play a role in the development of glucose intolerance and increased inflammatory markers (Udayappan et al., 2017).
  • E. albertii is an enteropathogen increasingly associated with diarrhea and gastroenteritis.
  • the abundance of Escherichia spp. has been shown to increase as a result of metformin treatment (Forslund et al., 2015).
  • the model also identifies KOs involved in the biosynthesis of O-antigen, the exterior constituent of LPS and often contributing to virulence, and the biosynthesis, regulation, and export of LPS, particularly in the prediabetes population.
  • Elevated plasma tonicity is thought to be a risk factor for diabetes progression in patients with hyperglycemia (Stookey et al., 2004), while hyperosmotic stress has been shown in adipocytes to suppress insulin action through the serine phosphorylation of insulin receptor substrate-1 (IRS1), leading to cellular insulin resistance (Gual et al., 2003). It is therefore surmised that the prediabetic gut microbiome experiences local osmotic stress, and by actively uptaking and accumulating osmoprotective compounds such as glycine betaine in the cells, bacteria adapt to osmotic stress in response to osmolarity changes in the environment (Sleator & Hill, 2002).
  • osmoprotective compounds such as glycine betaine
  • the active functions of the preT2D cohort largely mirror that of the T2D microbiome in terms of carbohydrate metabolism, lipid metabolism, and transporters. Additionally functions were identified associated with oxidative stress, including KOs involved in sequestering oxidative damage and glutathione- dependent redox chemistry, to have higher differential expression in preT2D. Oxidative stress can induce inflammation and is known to play a significant role in the progression of type 2 diabetes (Folli et al., 2011). Metabolic endotoxemia has also been shown to promote oxidative stress, while antibiotic treatment reduces its occurrence (P. D. Cani et al., 2008).
  • the model further identifies features in cysteine and methionine metabolism in predicting control over HbAlc. Many of such features are involved in the metabolic reactions surrounding cysteine, a precursor to glutathione. Glutathione is one of the most critical molecules in the defense against oxidative stress. Low plasma cysteine level and the resultant decreased glutathione synthesis have been associated with inflammation in IBD patients (Sido et al., 1998). Perhaps in line with the importance of cysteine, several KOs surrounding the serine and glycine nodes are also suggested by the model. These amino acids serve as the precursors not only to each other but also to cysteine.
  • glycine is tied to increased insulin sensitivity (Adeva-Andany et al., 2018), and earlier studies have shown that glycine degradation activities are enriched in metformin-untreated T2D patients and in prediabetic exercise non-responders (Forslund et al., 2015; Liu et al., 2020).
  • the treatment model provided herein also identifies some features in lysine biosynthesis to be important. The butyrogenic property of lysine has been demonstrated by a human gut commensal (Bui et al., 2015), but more information is required to clarify how microbially produced lysine is linked to T2D.
  • the richness of the metatranscriptomic data also enabled development and validation of a T2D risk model, with a score that can distinguish individuals with glycemic dysregulation (prediabetes and diabetes) from those with normal glucose metabolism.
  • the methodologies include clinical grade laboratory analyses and bioinformatics, machine learning, and advanced statistical approaches leveraging data from 53,947 individuals. While some o findings are consistent with the published literature, surprisingly, novel associations were observed, as the metatranscriptome approach allows for more direct functional observations of the human gut microbiome.
  • the metatranscriptomic analysis illuminates the significance of both the taxa and microbial pathways pertaining to opportunistic pathogens, lipopolysaccharides (LPS), lipid metabolism, and ion transporters, as well as implications for inflammation, oxidative stress, and osmotic stress in the diseased cohorts.
  • LPS lipopolysaccharides
  • ion transporters lipid metabolism
  • ion transporters lipid metabolism
  • inflammation, oxidative stress, and osmotic stress in the diseased cohorts In the non-diabetic, presumed-healthy population, the analysis highlights the elevated activity of features associated with bile resistance, short chain fatty acids (SCFAs) production, cell motility, and succinate production that have not been reported before at the metatranscriptomic level.
  • SCFAs short chain fatty acids
  • the machine learning model was able to distinguish novel metatranscriptomic features that segregate patients who receive metformin and are able to control their HbAlc from those who do not.
  • the model points to complex changes in the amino acid metabolism, pyruvate metabolism, TCA cycle, and oxidative phosphorylation, as well as proteins related to genetic information processing important in determining treatment response.
  • Microbiota-Produced Succinate Improves Glucose Flomeostasis via Intestinal Gluconeogenesis.
  • Cell Metabolism 24(1), 151-157. https://doi.Org/10.1016/j.cmet.2016.06.013 De Vadder, F., Kovatcheva-Datchary, P., Goncalves, D., Vinera, J., Zitoun, C., Duchampt, A., Backhed, F., &
  • Gut microbiota-derived succinate Friend or foe in human metabolic diseases? Reviews in Endocrine & Metabolic Disorders, 20(4), 439-447. https://doi.org/10.1007/sl 1154- 019-09513 -z

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Abstract

La présente invention concerne des méthodes destinées à déterminer le risque de développement ou de progression d'une dérégulation de la glycémie chez un sujet, à déterminer la probabilité de répondre à la metformine chez un sujet souffrant d'une dérégulation du glucose, et à administrer un traitement à un sujet en fonction de la détermination.
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