Methods for determining the status of the gut microbiome

Abstract

The present invention provides methods for determining the state of a subject's gut microbiome given one or more CAZyme abundances provided from the subject's gut metagenomic data. The present invention also provides methods for maintaining or improving the state of a subject's gut microbiome.

Description

[Technical field]

[0001] The present invention relates to a method for determining the status of the gut microbiome of a subject. The present invention also relates to a method for maintaining or improving the status of the gut microbiome of a subject.

[0002] [Background technology] The microbiome can refer to both the composition of microorganisms within an ecosystem and their "theatre of activity," which can include microbial structural elements (nucleic acids, proteins, lipids, polysaccharides), metabolic products (signaling molecules, toxins, organic molecules, inorganic molecules), and molecules produced by the coexisting host and structured by the surrounding environmental conditions (e.g., Berg, G., et al., 2020. Microbiome, 8(1), pp.1-22).

[0003] The gut microbiome changes rapidly and dramatically in the first few years of life (Yatsunenko, T., et al., 2012. Nature, 486 (7402), pp. 222-227). In infants, certain factors such as mode of delivery, antibiotic use, and duration of exclusive breastfeeding affect the gut microbiome. Several other factors, such as living area, siblings, and furry pets, can also affect the infant gut microbiome (Dogra SK, et al. Microorganisms 2021). Importantly, the establishment of an infant's gut microbiome has long-term effects on subsequent health (Dogra SK, et al. Gut Microbes. 2015; 6 (5): 321-5). Therefore, it is important to promote age-appropriate gut microbiome maturation in infants.

[0004] Various approaches have been used to study the maturation of microbiome in early life. However, these approaches do not investigate the functional maturation of gut microbiome ecosystem. Known approaches are usually based on bacterial composition, and show changes in bacterial taxa, but do not provide any functional information. It is difficult to use such approaches to provide personalized recommendations and nutritional interventions to promote age-appropriate gut microbiome maturation.

[0005] [Summary of the invention] We developed an approach to use functional data to provide trajectories of change in early-life microbiome (ELM) development. We showed that carbohydrate-associated enzyme (CAZyme) abundance in gut metagenomic data can be used to generate ELM trajectories of change. We identified key CAZymes in the trajectories of change in the reference ELM and showed how these key CAZymes change across the early life span in infants.

[0006] Furthermore, the inventors describe how CAZyme abundance can be adjusted to maintain or return a subject to the ELM change trajectory. Personalized recommendations and dietary advice can also be provided to the infant's caregiver(s), such as adjusting the amount of CAZyme-associated carbohydrates in the infant's diet, to maintain or return the infant to the ELM change trajectory.

[0007] In one aspect, the invention provides a method for providing a trained regression model for determining a state of a subject's gut microbiome, the method comprising: (a) providing gut metagenomic data from a population of healthy subjects; and (b) training a regression model on the gut metagenomic data, wherein age of the healthy subjects at the time of data collection is regressed against abundance of one or more carbohydrate associated enzymes (CAZymes) provided from the gut metagenomic data.

[0008] The age of the healthy subject at the time of data collection may be regressed against a plurality of CAZyme abundances provided from the gut metagenomic data. In some embodiments, the age of the healthy subject at the time of data collection is regressed against a CAZyme abundance of 5 or more, a CAZyme abundance of 10 or more, a CAZyme abundance of 20 or more, a CAZyme abundance of 50 or more, or a CAZyme abundance of 100 or more. In some embodiments, the method further comprises determining the CAZyme abundances from the gut metagenomic data. In some embodiments, the CAZyme abundances are relative CAZyme abundances, suitably the CAZyme abundances are relative to the total bacterial gene.

[0009] The CAZyme may be any microbial CAZyme. The CAZyme may be selected from glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE), and their associated carbohydrate-binding modules (CBM). Suitably, the CAZymes are classified by clan, family, and / or subfamily. Suitably, the CAZymes are classified by family. Suitably, CAZyme is selected from the group consisting of GH1, GH2, GH3, GH4, GH5, GH6, GH7, GH8, GH9, GH10, GH11, GH12, GH13, GH14, GH15, GH16, GH17, GH18, GH19, GH20, GH21, GH22, GH23, GH24, GH25, GH26, GH27, GH28, GH29, GH30, GH31, GH32, GH33, GH34, GH35, GH36, GH37, GH38, GH39, GH40, GH41, GH42, GH43, GH44, GH45, GH46, GH47, GH48, GH49, GH50, GH51, GH52, GH53, GH54, GH55, GH56, GH57, GH58, GH59, GH60, GH61, GH62, GH63, GH64, GH65, GH66, GH67, GH68, GH69, GH70, GH71, GH72, GH73, GH74, GH75, GH76, GH77, GH78, GH79, GH80, GH81, GH82, GH83, GH84, GH85, GH86, GH87, GH88, GH89, GH90, GH91, GH92, GH93, GH94, GH95, GH96, GH97, GH98, GH99, GH100, GH101, GH102, GH103, GH104, GH105, GH106, GH107, GH108, GH109, ​​GH110, GH111, GH112, GH113, GH114, GH115, GH11 6, GH117, GH118, GH119, GH120, GH121, GH122, GH123, GH124, GH125, GH126, GH127, GH128, GH129, GH130, GH131, GH132, GH 133, GH134, GH135, GH136, GH137, GH138, GH139, GH140, GH141, GH142, GH143, GH144, GH145, GH146, GH147, GH148, GH149,GH150、GH151、GH152、GH153、GH154、GH155、GH156、GH157、GH158、GH159、GH160、GH161、GH162、GH163、GH164、GH165、GH166、GH167、GH168、GH169、GH170、GH171、GH172、GT1、GT2、GT3、GT4、GT5、GT6、GT7、GT8、GT9、GT10、GT11、GT12、GT13、GT14、GT15、GT16、GT17、GT18、GT19、GT20、GT21、GT22、GT23、GT24、GT25、GT26、GT27、GT28、GT29、GT30、GT31、GT32、GT33、GT34、GT35、GT36、GT37、GT38、GT39、GT40、GT41、GT42、GT43、GT44、GT45、GT46、GT47、GT48、GT49、GT50、GT51、GT52、GT53、GT54、GT55、GT56、GT57、GT58、GT59、GT60、GT61、GT62、GT63、GT64、GT65、GT66、GT67、GT68、GT69、GT70、GT71、GT72、GT73、GT74、GT75、GT76、GT77、GT78、GT79、GT80、GT81、GT82、GT83、GT84、GT85、GT86、GT87、GT88、GT89、GT90、GT91、GT92、GT93、GT94、GT95、GT96、GT97、GT98、GT99、GT100、GT101、GT102、GT103、GT104、GT105、GT106、GT107、GT108、GT109、GT110、GT111、GT112、GT113、GT114、PL1、PL2、PL3、PL4、PL5、PL6、PL7、PL8、PL9、PL10、PL11、PL12、PL13、PL14、PL15、PL16、PL17、PL18、PL19、PL20、PL21、PL22、PL23、PL24、PL25、PL26、PL27、PL28、PL29、PL30、PL31、PL32、PL33、PL34、PL35、PL36、PL37、PL38、PL39、PL40、PL41、PL42、CE1、CE2、CE3、CE4、CE5、CE6、CE7、CE8、CE9、CE10、CE11、CE12、CE13、CE14、CE15、CE16、CE17、CE18、CE19、CBM1, CBM2, CBM3, CBM4, CBM5, CBM6, CBM7, CBM8, CBM9, CBM10, CBM11, CBM12, CBM13, CBM14, CBM15, CBM16, CBM17, CBM18, CBM19, CBM20, CBM21, CBM22, CBM23, CBM2 4, CBM25, CBM26, CBM27, CBM28, CBM29, CBM30, CBM31, CBM32, CBM33, CBM34, CBM35, CBM36, CBM37, CBM38, CBM39, CBM40, CBM41, CBM42, CBM43, CBM44, CBM45, CBM46 , CBM47, CBM48, CBM49, CBM50, CBM51, CBM52, CBM53, CBM54, CBM55, CBM56, CBM57, CBM58, CBM59, CBM60, CBM61, CBM62, CBM63, CBM64, CBM65, CBM66, CBM67, CBM68, CBM69, CBM70, CBM71, CBM72, CBM73, CBM74, CBM75, CBM76, CBM77, CBM78, CBM79, CBM80, CBM81, CBM82, CBM83, CBM84, CBM85, CBM86, CBM87, and CBM88.

[0010] The trained regression model may be a trajectory of early life microbiome (ELM) change. The trained regression model may predict the age of a subject given gut metagenomic data. Suitably, the trained regression model relates the age of a healthy subject to its microbiome age, microbiome maturation index, and / or microbiome maturation age.

[0011] The trained regression models can be for infancy and / or childhood. In some embodiments, the trained regression models are for ages 0-5, 0-3, 0-2. In some embodiments, the trained regression models are for ages 0-24 months.

[0012] In some embodiments, the method further comprises obtaining gut metagenomic data from a population of healthy subjects. Suitably, the gut metagenomic data is obtained or can be obtained from a faecal sample. Suitably, the gut metagenomic data is obtained or can be obtained by next generation sequencing.

[0013] The healthy subject may be an infant and / or a child. In some embodiments, the healthy subject may be 0-5 years old, or 0-3 years old, or 0-2 years old. In some embodiments, the healthy subject is 0-24 months old. Suitably, the population of healthy subjects comprises at least 20 healthy subjects, at least 40 healthy subjects, at least 60 healthy subjects, at least 80 healthy subjects, or at least 100 healthy subjects. Suitably, the gut metagenomic data from the population of healthy subjects comprises at least 50 samples, at least 100 samples, at least 200 samples, at least 300 samples, at least 400 samples, or at least 500 samples.

[0014] The regression model may be a tree-based regression model, such as a random forest regression model. In some embodiments, the age of the healthy subject at the time of sample collection is also regressed against one or more additional features provided from the gut metagenomic data.

[0015] In one aspect, the present invention provides a trained regression model obtained or obtainable by a method according to the present invention.

[0016] In one aspect, the invention provides a trained regression model for determining the state of a subject's gut microbiome given one or more CAZyme abundances provided from the subject's gut metagenomic data. Suitably, the trained regression model is obtained or obtainable by a method according to the invention.

[0017] In one aspect, the invention provides a method for predicting age of a subject, the method comprising: (a) providing a regression model trained by a method according to the invention, or providing a trained regression model according to the invention; (b) providing gut metagenomic data from a subject; and (c) predicting age of the subject given the subject's gut metagenomic data and the trained regression model.

[0018] In one aspect, the invention provides a method for determining a gut microbiome status of a subject, the method comprising: (a) providing a regression model trained by a method according to the invention, or providing a trained regression model according to the invention; (b) providing gut metagenomic data from the subject; and (c) determining whether the subject is an outlier in the trained regression model, wherein if the subject is not an outlier in the trained regression model, the subject's gut microbiome status is healthy and / or if the subject is an outlier in the trained regression model, the subject's gut microbiome status is unhealthy.

[0019] In some embodiments, a subject is an outlier based on the standard error (SE), confidence interval, prediction interval, and / or standard deviation in the trained regression model. In some embodiments, a subject is an outlier if the subject's gut metagenomic data is less than or equal to -2SE or more than 2SE from the trained regression line, if the subject's gut metagenomic data is outside the 95% confidence interval in the trained regression model, if the subject's gut metagenomic data is outside the 95% prediction interval in the trained regression model, and / or if the subject has a Z-score less than or equal to -2 or more than 2 in the trained regression model. In some embodiments, a subject is an outlier if the subject's gut metagenomic data is outside the 95% prediction interval in the trained regression model.

[0020] In one aspect, the invention provides a method for determining a gut microbiome status of a subject, comprising: (a) providing a regression model trained by a method according to the invention, or a trained regression model according to the invention, wherein the trained regression model is a trajectory of ELM changes; (b) providing gut metagenomic data from the subject; and (c) determining whether the subject is on a trajectory of ELM changes, wherein if the subject is on a trajectory of ELM changes, the subject's gut microbiome status is healthy, and / or if the subject is off a trajectory of ELM changes, the subject's gut microbiome status is not healthy. Suitably, if the subject's gut metagenomic data is not significantly different from the trajectory of ELM changes, the subject is on a trajectory of ELM changes, and / or if the subject's gut metagenomic data is significantly different from the trajectory of ELM changes, the subject is off a trajectory of ELM changes.

[0021] In some embodiments, a subject is determined to be off the ELM change trajectory based on the standard error (SE), confidence interval, prediction interval, and / or standard deviation of the ELM change trajectory. In some embodiments, a subject is determined to be off the ELM change trajectory if the subject's gut metagenomic data is less than or equal to -2SE or more than 2SE from the ELM change trajectory, if the subject's gut metagenomic data is outside the 95% confidence interval of the ELM change trajectory, if the subject's gut metagenomic data is outside the 95% prediction interval of the ELM change trajectory, and / or if the subject has a Z-score less than or equal to -2 or more than 2. In some embodiments, a subject is determined to be off the ELM change trajectory if the subject's gut metagenomic data is outside the 95% prediction interval of the ELM change trajectory.

[0022] In one aspect the invention provides a method for determining a status of the gut microbiome of a subject, comprising predicting the age of the subject by a method according to the invention, wherein if the predicted age of the subject does not significantly differ from the subject's actual age, the status of the subject's gut microbiome is healthy and / or if the predicted age of the subject is significantly different from the subject's actual age, the status of the subject's gut microbiome is unhealthy.

[0023] In some embodiments, the predicted age of a subject significantly differs from the subject's actual age if the predicted age of the subject differs by about 0.5 or more, about 0.6 or more, about 0.7 or more, about 0.8 or more, about 0.9 or more, or about 1 year or more.

[0024] Suitably, in the method for predicting age or determining gut microbiome status of a subject according to the present invention, the subject's gut metagenomic data is obtained or can be obtained from a fecal sample and / or by next generation sequencing. In some embodiments, the subject's gut metagenomic data may provide a CAZyme abundance of 5 or more, a CAZyme abundance of 10 or more, a CAZyme abundance of 20 or more, a CAZyme abundance of 50 or more, or a CAZyme abundance of 100 or more.

[0025] In one aspect, the invention provides a method for maintaining or improving a gut microbiome status of a subject, the method comprising: (a) determining the gut microbiome status of the subject by a method according to the invention; and (b) adjusting the subject's diet, nutrient intake, and / or lifestyle to maintain or improve the gut microbiome status of the subject. After adjusting the subject's diet, nutrient intake, and / or lifestyle, the subject's gut microbiome status may be healthy.

[0026] Adjusted diet, nutrient intake, and / or lifestyle of a subject may increase the abundance of favorable CAZymes and / or decrease the abundance of unfavorable CAZymes.

[0027] In one aspect, the invention provides a method for determining the diet and / or ingested nutrients of a subject, the method comprising: (a) determining the status of the subject's gut microbiome by a method according to the invention; and (b) determining the diet and / or ingested nutrients required to maintain or improve the status of the subject's gut microbiome.

[0028] Suitably, the subject is administered a food and / or a supplement to increase the abundance of a preferred CAZyme and / or decrease the abundance of a non-preferred CAZyme. The food and / or supplement may comprise one or more carbohydrates. Suitably, the carbohydrate is selected from the group consisting of 4-methylumbelliferyl 6-azido-6-deoxy-β-D-galactoside, N-acetyl-D-galactosamine, N-acetyl-D-glucosamine, N-acetylglucosamine, N-glycan, O-antigen, O-glycan, acarbose, acetylated glucuronoxylan, agar, aldouronate, alginate, α-galactoside, α-glucan, α-mannan, α-mannoside, arabinan, arabinoxylooligosaccharide, arabinofuranoside, alpha-galactosides, alpha-glucans, alpha-mannans, alpha-mannosides, arabinan, arabinoxylooligosaccharides ...alpha-galactosides, alpha-glucans, alpha-mannans, alpha-mannosides, arabinan, alpha-galactosides, alpha-glucans, Nooligosaccharide, arabinofuranose, arabinogalactan, arabinose, arabinoxylan, β-galactooligosaccharide, β-galactoside, β-glucan, β-glucoside, β-mannan, capsule polysaccharide, carboxymethylcellulose, carrageenan, cellobiose, cellodextrin, cellooligosaccharide, cellotriose, cellulose, chitin, chitobiose, chitooligosaccharide, chondroitin disaccharide, chondroitin sulfate, chondroitin sulfate, cyclomaltodextrin, d-galactosamine, dextran Tran, Emulsan, Extracellular polysaccharide, Fructan, Fructooligosaccharide, Fucose, Fucosyllactose, Galactan, Galactomannan, Galactooligosaccharide, Galactose, Galacturonic acid, Gentiobiose, Glucan, Glucomannan, Glucosamine, Glucose, Glycogen, Glycosaminoglycan, Hemicellulose, Heparosan, Homogalacturonan, Host glycan, Human milk oligosaccharide, Inulin, Isomaltose, Isomaltotriose, Kestose, Lact-n-triose, Lact ose, laminaribiose, laminarin, levan, lichenan, lignocellulose, lipopolysaccharides, maltodextrin, maltooligosaccharides, maltose, maltotriose, mannan, mannooligosaccharides, mannose, melezitose, melibiose, methylglucuronoarabinoxylan, mucin, oligogalacturonide, outer shell polysaccharide, panose, pectin, plant polysaccharides, polygalacturonic acid, polysialic acid, porphyran, pullulan, raffinose oligosaccharide, raffinose, rhamnogalacturonan,The sugar may be selected from one or more of rhamnose, ribose, sialic acid, sialoglycoconjugates, sophorose, sorbitol, stachyose, starch, sucrose, trehalose, ulvan, unsaturated hyaluronic acid disaccharides, xanthan, xylan, xylobiose, xylodextrin, xyloglucan, xylooligosaccharides, xylose, and xylotriose.

[0029] In one aspect, the present invention provides a data processing system comprising means for carrying out the method according to the present invention.

[0030] In one aspect, the present invention provides a data processing system including a processor configured to perform a method according to the present invention.

[0031] In one aspect, the invention provides a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out a method according to the invention.

[0032] In one aspect, the present invention provides a computer program comprising instructions that, when executed by a computer, cause the computer to determine a trained regression model for determining a state of a subject's gut microbiome from a population of healthy subjects, given the age of the healthy subject at the time of data collection and one or more CAZyme abundances provided from gut metagenomic data of the healthy subjects.

[0033] In one aspect, the invention provides a computer program comprising instructions that, when executed by a computer, cause a computer to predict the age of a subject or determine the status of a subject's gut microbiome given one or more CAZyme abundances provided from the subject's gut metagenomic data.

[0034] In one aspect, the invention provides a computer program comprising instructions that, when executed by a computer, causes a computer to predict the age of a subject or determine the state of a subject's gut microbiome given gut metagenomic data from a population of healthy subjects and a regression model trained on the gut metagenomic data of the subject, the regression model being trained by regressing the age of the healthy subject at the time of data collection against one or more CAZyme abundances provided from the gut metagenomic data of the healthy subjects. In some embodiments, the trained regression model is a trained regression model according to the invention.

[0035] In one aspect, the invention provides a computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform a method according to the invention.

[0036] In one aspect, the invention provides a computer readable medium comprising instructions that, when executed by a computer, cause the computer to determine a trained regression model for determining a state of a subject's gut microbiome from a population of healthy subjects, given the age of the healthy subject at the time of data collection and one or more CAZyme abundances provided from gut metagenomic data of the healthy subjects.

[0037] In one aspect, the invention provides a computer readable medium comprising instructions that, when executed by a computer, cause a computer to predict the age of a subject or determine the status of a subject's gut microbiome given one or more CAZyme abundances provided from the subject's gut metagenomic data.

[0038] In one aspect, the invention provides a computer readable medium comprising instructions that, when executed by a computer, cause a computer to predict the age of a subject or determine the state of a subject's gut microbiome given gut metagenomic data from a population of healthy subjects and a regression model trained on the gut metagenomic data of the subject, the regression model being trained by regressing the age of the healthy subject at the time of data collection against one or more CAZyme abundances provided from the gut metagenomic data of the healthy subjects. In some embodiments, the trained regression model is a trained regression model according to the invention.

[0039] In one aspect, the invention provides a computer readable data carrier having stored thereon a computer program according to the invention.

[0040] In one aspect, the invention provides a data carrier signal carrying a computer program according to the invention.

[0041] In one aspect, the invention provides the use of one or more CAZyme abundances provided from a subject's gut metagenomic data to predict the age of a subject or to determine the status of the subject's gut microbiome.

[0042] In one aspect, the invention provides for the use of one or more CAZyme abundances provided from gut metagenomic data from a population of healthy subjects to train a regression model.

[0043] In one aspect, the invention provides the use of a trained regression model according to the invention for predicting the age of a subject or for determining the status of the gut microbiome of a subject.

[0044] Suitably the subject is an infant or a child. Suitably the subject is aged 0 to 5 years, or 0 to 3 years, or 0 to 2 years. Suitably the subject is aged 0 to 24 months. [Brief description of the drawings]

[0045] [Figure 1] Trajectories of change in the early-life microbiome (ELM) derived using CAZyme abundances. Trajectories of change in the early-life microbiome (ELM) derived using CAZyme abundances provided by the reference subject gut metagenomic data. [Diagram 2] ELM change trajectories showing the abundance of the major CAZymes per time range. The same ELM change trajectories are shown in Fig. 1, but in Fig. 2 we show the abundance of the major CAZymes per different time range. [Diagram 3] Returning outliers to CAZyme-based ELM change trajectories. Outliers are returned to ELM change trajectories by adjusting the abundance of CAZymes in the subject metagenomic data. [Figure 4] Trajectories of change in ELM compared with reference and non-reference CAZyme-based. Clear differences in the trajectories of change in CAZyme-based ELM are seen when comparing referent and non-reference subjects.

[0046] [Mode for carrying out the invention] Various preferred features and embodiments of the present invention will now be described by way of non-limiting examples, it being understood that a person skilled in the art can combine all features of the invention disclosed herein without departing from the scope of the invention disclosed.

[0047] It should be noted that as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0048] As used herein, the terms "comprising," "comprises," and "comprised of" are synonymous with "including," "includes," or "containing," "contains," are inclusive or open-ended, and do not exclude additional, unspecified members, elements, or method steps. The terms "comprising," "comprises," and "comprised of" also include the term "consisting of."

[0049] Numeric ranges are inclusive of the numbers defining the range.

[0050] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Any publications cited herein should not be construed as an admission that they constitute prior art to the claims appended hereto.

[0051] The methods and systems disclosed herein may be used by physicians, medical professionals, laboratory technicians, infant care providers, and the like. How to serve a trained regression model The present invention provides a method for providing a trained regression model for determining the status of the gut microbiome of a subject. The present invention also provides a trained regression model obtained or obtainable by such a method.

[0052] Microbiota and Microbiome "Gut microbiota" may refer to the composition of microorganisms (including bacteria, archaea, and fungi) that inhabit the digestive tract.

[0053] The term "gut microbiome" may encompass both the "gut microbiota" and their "theatre of activity", which may include their structural elements (nucleic acids, proteins, lipids, polysaccharides), metabolic products (signaling molecules, toxins, organic and inorganic molecules), and molecules produced by the coexisting host and structured by the surrounding environmental conditions (e.g., Berg, G., et al., 2020. Microbiome, 8(1), pp.1-22).

[0054] Therefore, in the present invention, the term "gut microbiome" may be used interchangeably with the term "gut microbiota."

[0055] The "gut microbiome status" of a subject may also refer to the state, condition, or development of the subject's gut microbiome at a particular time. The term "gut microbiome status" may refer to the age of the subject's microbiome, the maturation index of the microbiome, and / or the trajectory of change of the early life microbiome (ELM).

[0056] The "microbiome age" of a subject may refer to the predicted age of the subject based on the subject's gut microbiome data. For example, the subject's actual / chronological age may be predicted from gut metagenomic data using a machine learning-based artificial intelligence approach. The subject's "microbiome maturity index" or "microbiome maturity age" may also be obtained as described above.

[0057] A subject's "ELM change trajectory" may refer to a fitted curve obtained to describe the relationship between "microbiome age" or "microbiome maturation index" and actual age. The curve may be fitted by methods such as LOESS or smooth spline using another cohort or subset of the data (for external validity purposes).

[0058] Carbohydrate-associated enzymes (CAZymes) The present invention uses the abundance of one or more CAZymes to train a regression model and determine the state of a subject's gut microbiome.

[0059] Carbohydrate-associated enzymes (CAZymes) may refer to enzymes involved in the synthesis, metabolism, and transport of carbohydrates. CAZymes may include glycoside hydrolases (GHs), glycosyltransferases (GtSs), polysaccharide lyases (pLSs), carbohydrate esterases (CEs), and carbohydrate-binding modules (CBMs). The CAZy database (http: / / www.cazy.org) is a proprietary CAZyme classification system that is widely used by the scientific community. CAZy represents close to 300 families of catalytic and accessory modules, representing more than 100,000 non-redundant entries.

[0060] Suitably, the CAZyme is a microbial CAZyme. The CAZyme can be classified according to any suitable classification system (see, for example, Lombard, V., et al., 2014. Nucleic acids research, 42(D1), pp.D490-D495). The CAZymes can be classified by the same classification system(s) or by one or more different classification systems. Suitably, the CAZymes are classified by clan, family, and / or subfamily. Suitably, the CAZymes are classified by family.

[0061] Appropriately、CAZymeは、GH1、GH2、GH3、GH4、GH5、GH6、GH7、GH8、GH9、GH10、GH11、GH12、GH13、GH14、GH15、GH16、GH17、GH18、GH19、GH20、GH21、GH22、GH23、GH24、 GH25、GH26、GH27、GH28、GH29、GH30、GH31、GH32、GH33、GH34、GH35、GH36、GH37、GH38、GH39、GH40、GH41、GH42、GH43、GH44、GH45、GH46、GH47、GH48、GH49、 GH50、GH51、GH52、GH53、GH54、GH55、GH56、GH57、GH58、GH59、GH60、GH61、GH62、GH63、GH64、GH65、GH66、GH67、GH68、GH69、GH70、GH71、GH72、GH73、GH74、 GH75、GH76、GH77、GH78、GH79、GH80、GH81、GH82、GH83、GH84、GH85、GH86、GH87、GH88、GH89、GH90、GH91、GH92、GH93、GH94、GH95、GH96、GH97、GH98、GH99、 GH100、GH101、GH102、GH103、GH104、GH105、GH106、GH107、GH108、GH109、GH110、GH111、GH112、GH113、GH114、GH115、GH116、GH117、GH118、GH119、GH12 0、GH121、GH122、GH123、GH124、GH125、GH126、GH127、GH128、GH129、GH130、GH131、GH132、GH133、GH134、GH135、GH136、GH137、GH138、GH139、GH140、GH1 41、GH142、GH143、GH144、GH145、GH146、GH147、GH148、GH149、GH150、GH151、GH152、GH153、GH154、GH155、GH156、GH157、GH158、GH159、GH160、GH161 162、GH163、GH164、GH165、GH166、GH167、GH168、GH169、GH170、GH171、GH172、GT1、GT2、GT3、GT4、GT5、GT6、GT7、GT8、GT9、GT10、GT11、GT12、GT13、GT14GT15、GT16、GT17、GT18、GT19、GT20、GT21、GT22、GT23、GT24、GT25、GT26、GT27、GT28、GT29、GT30、GT31、GT32、GT33、GT34、GT35、GT36、GT37、GT38、GT39、GT40、GT41、GT42、GT43、GT44、GT45、GT46、GT47、GT48、GT49、GT50、GT51、GT52、GT53、GT54、GT55、GT56、GT57、GT58、GT59、GT60、GT61、GT62、GT63、GT64、GT65、GT66、GT67、GT68、GT69、GT70、GT71、GT72、GT73、GT74、GT75、GT76、GT77、GT78、GT79、GT80、GT81、GT82、GT83、GT84、GT85、GT86、GT87、GT88、GT89、GT90、GT91、GT92、GT93、GT94、GT95、GT96、GT97、GT98、GT99、GT100、GT101、GT102、GT103、GT104、GT105、GT106、GT107、GT108、GT109、GT110、GT111、GT112、GT113、GT114、PL1、PL2、PL3、PL4、PL5、PL6、PL7、PL8、PL9、PL10、PL11、PL12、PL13、PL14、PL15、PL16、PL17、PL18、PL19、PL20、PL21、PL22、PL23、PL24、PL25、PL26、PL27、PL28、PL29、PL30、PL31、PL32、PL33、PL34、PL35、PL36、PL37、PL38、PL39、PL40、PL41、PL42、CE1、CE2、CE3、CE4、CE5、CE6、CE7、CE8、CE9、CE10、CE11、CE12、CE13、CE14、CE15、CE16、CE17、CE18、CE19、CBM1、CBM2、CBM3、CBM4、CBM5、CBM6、CBM7、CBM8、CBM9、CBM10、CBM11、CBM12、CBM13、CBM14、CBM15、CBM16、CBM17、CBM18、CBM19、CBM20、CBM21、CBM22、CBM23、CBM24、CBM25、CBM26、CBM27、CBM28、CBM29、CBM30、CBM31、CBM32、CBM33、CBM34、CBM35, CBM36, CBM37, CBM38, CBM39, CBM40, CBM41, CBM42, CBM43, CBM44, CBM45, CBM46, CBM47, CBM48, CBM49, CBM50, CB M51, CBM52, CBM53, CBM54, CBM55, CBM56, CBM57, CBM58, CBM59, CBM60, CBM61, CBM62, CBM63, CBM64, CBM65, CBM66, CBM67 , comprising, consisting essentially of, or consisting of one or more (e.g., 5 or more, 10 or more, 20 or more, 50 or more, or 100 or more) of CBM68, CBM69, CBM70, CBM71, CBM72, CBM73, CBM74, CBM75, CBM76, CBM77, CBM78, CBM79, CBM80, CBM81, CBM82, CBM83, CBM84, CBM85, CBM86, CBM87, and CBM88.

[0062] Appropriately、CAZymeは、GH1、GH2、GH3、GH4、GH5、GH6、GH7、GH8、GH9、GH10、GH11、GH12、GH13、GH14、GH15、GH16、GH17、GH18、GH19、GH20、GH21、GH22、GH23、GH24、 GH25、GH26、GH27、GH28、GH29、GH30、GH31、GH32、GH33、GH34、GH35、GH36、GH37、GH38、GH39、GH40、GH41、GH42、GH43、GH44、GH45、GH46、GH47、GH48、GH49、 GH50、GH51、GH52、GH53、GH54、GH55、GH56、GH57、GH58、GH59、GH60、GH61、GH62、GH63、GH64、GH65、GH66、GH67、GH68、GH69、GH70、GH71、GH72、GH73、GH74、 GH75、GH76、GH77、GH78、GH79、GH80、GH81、GH82、GH83、GH84、GH85、GH86、GH87、GH88、GH89、GH90、GH91、GH92、GH93、GH94、GH95、GH96、GH97、GH98、GH99、 GH100、GH101、GH102、GH103、GH104、GH105、GH106、GH107、GH108、GH109、GH110、GH111、GH112、GH113、GH114、GH115、GH116、GH117、GH118、GH119、GH12 0、GH121、GH122、GH123、GH124、GH125、GH126、GH127、GH128、GH129、GH130、GH131、GH132、GH133、GH134、GH135、GH136、GH137、GH138、GH139、GH140、GH1 41、GH142、GH143、GH144、GH145、GH146、GH147、GH148、GH149、GH150、GH151、GH152、GH153、GH154、GH155、GH156、GH157、GH158、GH159、GH160、GH161 162、GH163、GH164、GH165、GH166、GH167、GH168、GH169、GH170、GH171、GH172、GT1、GT2、GT3、GT4、GT5、GT6、GT7、GT8、GT9、GT10、GT11、GT12、GT13、GT14GT15、GT16、GT17、GT18、GT19、GT20、GT21、GT22、GT23、GT24、GT25、GT26、GT27、GT28、GT29、GT30、GT31、GT32、GT33、GT34、GT35、GT36、GT37、GT38、GT39、GT40、GT41、GT42、GT43、GT44、GT45、GT46、GT47、GT48、GT49、GT50、GT51、GT52、GT53、GT54、GT55、GT56、GT57、GT58、GT59、GT60、GT61、GT62、GT63、GT64、GT65、GT66、GT67、GT68、GT69、GT70、GT71、GT72、GT73、GT74、GT75、GT76、GT77、GT78、GT79、GT80、GT81、GT82、GT83、GT84、GT85、GT86、GT87、GT88、GT89、GT90、GT91、GT92、GT93、GT94、GT95、GT96、GT97、GT98、GT99、GT100、GT101、GT102、GT103、GT104、GT105、GT106、GT107、GT108、GT109、GT110、GT111、GT112、GT113、GT114、PL1、PL2、PL3、PL4、PL5、PL6、PL7、PL8、PL9、PL10、PL11、PL12、PL13、PL14、PL15、PL16、PL17、PL18、PL19、PL20、PL21、PL22、PL23、PL24、PL25、PL26、PL27、PL28、PL29、PL30、PL31、PL32、PL33、PL34、PL35、PL36、PL37、PL38、PL39、PL40、PL41、PL42、CE1、CE2、CE3、CE4、CE5、CE6、CE7、CE8、CE9、CE10、CE11、CE12、CE13、CE14、CE15、CE16、CE17、CE18、CE19、CBM1、CBM2、CBM3、CBM4、CBM5、CBM6、CBM7、CBM8、CBM9、CBM10、CBM11、CBM12、CBM13、CBM14、CBM15、CBM16、CBM17、CBM18、CBM19、CBM20、CBM21、CBM22、CBM23、CBM24、CBM25、CBM26、CBM27、CBM28、CBM29、CBM30、CBM31、CBM32、CBM33、CBM34、CBM35, CBM36, CBM37, CBM38, CBM39, CBM40, CBM41, CBM42, CBM43, CBM44, CBM45, CBM46, CBM47, CBM48, CBM49, CBM50, CB M51, CBM52, CBM53, CBM54, CBM55, CBM56, CBM57, CBM58, CBM59, CBM60, CBM61, CBM62, CBM63, CBM64, CBM65, CBM66, CBM67 , comprising, consisting essentially of, or consisting of one or more (e.g., 5 or more, 10 or more, 20 or more, 50 or more, or 100 or more) of CBM68, CBM69, CBM70, CBM71, CBM72, CBM73, CBM74, CBM75, CBM76, CBM77, CBM78, CBM79, CBM80, CBM81, CBM82, CBM83, CBM84, CBM85, CBM86, CBM87, and CBM88.

[0063] As used herein, "CAZyme abundance" may refer to the abundance of CAZyme genes in metagenomic data. Abundance may be relative abundance and / or absolute abundance. Suitably, abundance is relative abundance, for example abundance may be calculated relative to total bacterial genes (see, for example, Kaur, K., et al., 2020. PloS one, 15(4), p.e0231197) or relative to total CAZyme abundance. Suitably, abundance is calculated relative to total bacterial genes.

[0064] Gut metagenomic data from a population of healthy subjects The method for providing a trained regression model may comprise providing gut metagenomic data from a population of healthy subjects. Suitably, the method further comprises obtaining gut metagenomic data from a population of healthy subjects.

[0065] Gut metagenomic data A subject's "gut metagenomic data" or "gut metagenomic data" may refer to the entire genetic content of the subject's gut, including all genomes and genes from the gut microbiota (see, e.g., Berg, G., et al., 2020 Microbiome, 8(1), pp. 1-22; Pasolli, E., et al., 2019. Cell, 176(3), pp. 649-662, and Qin, J., et al., 2010. Nature, 464(7285), pp. 59-65).

[0066] The gut metagenomic data may provide one or more CAZyme abundances. Suitably, the gut metagenomic data may provide 2 or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10) CAZyme abundances, 3 or more CAZyme abundances, 4 or more CAZyme abundances, 5 or more CAZyme abundances, 6 or more CAZyme abundances, 7 or more CAZyme abundances, 8 or more CAZyme abundances, 9 or more CAZyme abundances, 10 or more CAZyme abundances, 20 or more CAZyme abundances, 30 or more CAZyme abundances, 50 or more CAZyme abundances, 50 or more CAZyme abundances, or 100 or more CAZyme abundances. Suitably, the gut metagenomic data may provide 500 or less CAZyme abundances, 400 or less CAZyme abundances, 200 or less CAZyme abundances, or 100 or less CAZyme abundances. Suitably, the gut metagenomic data may provide between 2 and 500 CAZyme abundances, between 5 and 200 CAZyme abundances, or between 10 and 100 CAZyme abundances.

[0067] The gut metagenomic data may be obtained or can be obtained by any suitable sampling method. For example, the gut metagenomic data may be obtained or can be obtained by any technique described in Tang, Q., et al., 2020 Frontiers in cellular and infection microbiology, 10, p.151. The gut metagenomic data may be obtained or can be obtained from fecal samples, endoscopic samples (e.g., biopsy samples, luminal brush samples, laser capture microdissection samples), aspirated intestinal fluid samples, or surgical samples, or by in vivo models or intelligent capsules.

[0068] Suitably, gut metagenomic data may be or can be obtained from fecal samples. Fecal samples are naturally collected, non-invasive, and can be sampled repeatedly. Fecal material flash frozen at -80°C can maintain a consistent state in terms of microorganisms without preservatives and has been widely recognized as the gold standard for gut metagenomics, although other preservation methods, with or without preservatives, can also be utilized to obtain metagenomic data similar to that of fresh samples.

[0069] Gut metagenomic data may be obtained or can be obtained from a sample by any suitable method. For example, gut metagenomic data may be obtained or can be obtained from a sample by sequencing (e.g., next generation sequencing (NGS)). NGS allows profiling of the genomic DNA of all microorganisms present in a sample. NGS methods may include, for example, shotgun sequencing approaches as described in Poussin, C., et al., 2018. Drug discovery today, 23(9), pp.1644-1657.

[0070] Healthy subjects A "population of healthy subjects" may refer to a population of subjects that have no known underlying health conditions. In some embodiments, the population of subjects has no underlying health conditions. The healthy subjects may be human subjects. The subjects may be male and / or female.

[0071] A healthy subject may be of any age. Suitably, a healthy subject may be an infant, a toddler, and / or a young child. The term "infant" may refer to a subject aged 0 to 1 year, or 0 months to less than 1 year. The term "infant" may refer to a subject aged 1 to 3 years, or 1 to less than 3 years. The term "child" may refer to a subject under 18 years of age. A healthy subject may be an infant, a toddler, and / or a young child. The term "young children" may refer to a subject aged from about 3 years to about 5 years.

[0072] Suitably, the healthy subject is 5 years of age or less, 4 years of age or less, 3 years of age or less, 2 years of age or less, 1 year of age or less, or 0.5 years of age or less. Suitably, the healthy subject is 60 months of age or less, 48 ​​months of age or less, 36 months of age or less, 24 months of age or less, 12 months of age or less, or 6 months of age or less.

[0073] Suitably, the healthy subject is aged 0 years or older, 0.5 years or older, or 1 year or older. Suitably, the healthy subject is aged 0 months or older, 6 months or older, or 12 months or older.

[0074] Suitably, the healthy subject is 0 to 5 years old, 0 to 4 years old, 0 to 3 years old, 0 to 2 years old, or 0 to 1 years old. In some embodiments, the healthy subject is 0 to 2 years old. Suitably, the healthy subject is 0 months to 60 months old, 0 months to 48 months old, 0 months to 36 months old, 0 months to 24 months old, or 0 months to 12 months old. In some embodiments, the healthy subject is 0 months to 24 months old.

[0075] Suitably the healthy subject is aged between 0 and 1 year, 0.5 and 1 year, or 1 and 2 years. Suitably the healthy subject is aged between 0 and 12 months, 6 and 12 months, or 12 and 24 months.

[0076] The population of healthy subjects may include any number of subjects suitable for training a regression model. The population of healthy subjects may include at least 10 subjects, at least 20 subjects, at least 30 subjects, at least 40 subjects, at least 50 subjects, at least 60 healthy subjects, at least 80 healthy subjects, or at least 100 healthy subjects. Suitably, the population of healthy subjects may include 500 subjects or less, 100 subjects or less, or 50 subjects or less. Suitably, the population of healthy subjects may include between 10 and 500 subjects.

[0077] The gut metagenomic data from a population of healthy subjects may include any number of samples suitable for training a regression model. Suitably, the gut metagenomic data includes at least 50 samples, at least 100 samples, at least 200 samples, at least 300 samples, at least 400 samples, at least 500 samples, or at least 1000 samples.

[0078] The gut metagenomic data from a population of healthy subjects may include any number of samples from any number of subjects suitable for training a regression model. Suitably, the gut metagenomic data from a population of healthy subjects may include at least 50 samples from at least 10 subjects.

[0079] Regression analysis The present invention may use regression analysis to relate the age of a population of healthy subjects at the time of metagenomic data collection to the abundance of one or more CAZymes provided by the subjects' metagenomic data.

[0080] Regression analysis is a set of statistical processes for estimating the relationship between a dependent variable (e.g., the age of a population of healthy subjects at the time of data collection) and one or more independent variables (e.g., one or more CAZyme abundances from gut metagenomic data). Regression analysis can be used to provide a trained (or fitted) regression model.

[0081] The regression analysis can be performed using any suitable regression model. Suitable regression models are well known to those skilled in the art. Exemplary regression models include decision tree regression, linear regression, polynomial regression, quantile regression, ridge regression, lasso regression, elastic net regression, and support vector regression.

[0082] Suitably, the regression analysis is performed using machine learning techniques, examples of which include tree-based regression models (e.g. random forest regression models), recursive partitioning, regularization and shrinkage methods, boosting and gradient descent methods, and Bayesian methods.

[0083] Suitably, the regression model is a tree-based regression model (e.g. a random forest regression model). In some embodiments, the regression model is a random forest regression model.

[0084] The regression analysis can be performed by training a regression model on the gut metagenomic data. For example, the regression analysis can be performed by training a regression model using the age of the healthy subject at the time of data collection and one or more CAZyme abundances provided from the gut metagenomic data.

[0085] As used herein, "training" with respect to "fitting" a regression model may mean determining the function of the trajectory of change that most closely fits the data according to appropriate statistical criteria. For example, ordinary least squares may be used to calculate the function that minimizes the sum of the squares of the difference between the true data and the function.

[0086] Other additional features The present invention may use one or more additional features (in addition to the abundance of one or more CAZymes) to determine the state of a subject's gut microbiome.

[0087] The term "features" includes responses derived from metagenomic data, other microbiome data, and / or general metadata. Additional features may include, for example, the abundance of one or more microbial taxa.

[0088] For example, a method for providing a trained regression model may include training a regression model on gut metagenomic data from a population of healthy subjects, where the age of the healthy subjects at the time of data collection is regressed against one or more CAZyme abundances provided from the gut metagenomic data and one or more additional features.

[0089] Trained regression model The present invention provides a trained regression model for determining the state of a subject's gut microbiome given one or more CAZyme abundances provided from the subject's gut metagenomic data. The trained regression model may be obtained or can be obtained by any of the methods described herein.

[0090] The "trained regression model" (also known as a "fitted regression model") can relate the age of a healthy subject to the subject's metagenomic data. The trained regression model can provide a "trained regression function" or "trained regression line" that relates the age of a healthy subject to one or more CAZyme abundances described herein, as well as other statistics such as the standard error of the regression, the confidence interval, the prediction interval, and / or the standard deviation of the regression.

[0091] The trained regression model can also predict the age of a subject given gut metagenomic data.For example, the trained regression model can also predict the age of a subject given one or more CAZyme abundances described herein.Prediction can be based on the assumption that the subject is healthy.

[0092] The trained regression model may also relate the age of a healthy subject to its microbiome age and / or microbiome maturation index. The trained regression model may be the trajectory of change in the early life microbiome (ELM).

[0093] The trained regression models may be for any age. For example, the trained regression models may be for infants, toddlers, and / or children. The term "infancy" may refer to ages 0-1 year, or 0 months-1 year. The term "toddlerhood" may refer to ages 1-3 years, or 1-3 years. The term "childhood" may refer to ages up to 18 years. The trained regression models may be for infancy, toddlerhood, and / or childhood. The term "early childhood" may refer to ages 3-5 years, or 3-5 years.

[0094] Suitably the trained regression models are for ages 5 or less, 4 or less, 3 or less, 2 or less, 1 or less or 0.5 or less. Suitably the trained regression models are for ages 60 or less, 48 ​​or less, 36 or less, 24 or less, 12 or less or 6 or less.

[0095] Suitably the trained regression models are for ages 0 and up, 0.5 and up or 1 year and up. Suitably the trained regression models are for ages 0 months and up, 6 months and up or 12 months and up.

[0096] Suitably, the trained regression model is for ages 0-5 years, 0-4 years, 0-3 years, 0-2 years, or 0-1 years. In some embodiments, the trained regression model is for ages 0-2 years. Suitably, the trained regression model is for ages 0-60 months, 0-48 months, 0-36 months, 0-24 months, or 0-12 months. In some embodiments, the trained regression model is for ages 0-24 months.

[0097] Suitably the trained regression models are for ages 0 to 1 year, 0.5 to 1 year, or 1 to 2 years. Suitably the trained regression models are for ages 0 to 12 months, 6 to 12 months, or 12 to 24 months.

[0098] The trained regression model depends on the population of healthy subjects selected and may vary, for example, according to the age, diet, medication, mode of delivery, duration of exclusive breastfeeding, region of residence, ethnicity, siblings, and pets of the healthy population.

[0099] Method for determining the status of the gut microbiome of a subject The present invention provides a method for determining the state of a test subject's gut microbiome given the subject's gut metagenomic data, which may use any of the trained regression models described herein.

[0100] The present invention also provides the use of one or more CAZyme abundances provided from a subject's gut metagenomic data to determine the state of the gut microbiome of a subject.The present invention also provides the use of a trained regression model according to the present invention to determine the state of the gut microbiome of a subject.

[0101] Subject of interest The test subject may be any suitable subject. For example, the test subject may be a subject that has the same characteristics as the population of healthy subjects on which the regression model was trained (except that the subject may or may not be healthy). For example, the test subject may have an age that falls within the age range of the healthy population at the time of data collection. The test subject may be a human. The test subject may be male or female.

[0102] The test subjects may be of any age. For example, the test subjects may be infants, toddlers, and / or children. The test subjects may be infants, toddlers, and / or young children.

[0103] Suitably the test subject is 5 years of age or less, 4 years of age or less, 3 years of age or less, 2 years of age or less, 1 year of age or less, or 0.5 years of age or less. Suitably the test subject is 60 months of age or less, 48 ​​months of age or less, 36 months of age or less, 24 months of age or less, 12 months of age or less, or 6 months of age or less.

[0104] Suitably, the test subject is aged 0 years or older, 0.5 years or older, or 1 year or older. Suitably, the test subject is aged 0 months or older, 6 months or older, or 12 months or older.

[0105] Suitably, the test subject is 0 to 5 years old, 0 to 4 years old, 0 to 3 years old, 0 to 2 years old, or 0 to 1 years old. In some embodiments, the test subject is 0 to 2 years old. Suitably, the test subject is 0 months to 60 months old, 0 months to 48 months old, 0 months to 36 months old, 0 months to 24 months old, or 0 months to 12 months old. In some embodiments, the test subject is 0 months to 24 months old.

[0106] Suitably, the test subject is aged 0 to 1 year, 0.5 to 1 year, or 1 to 2 years. Suitably, the test subject is aged 0 to 12 months, 6 to 12 months, or 12 to 24 months.

[0107] Gut metagenomic data from subjects The subject's gut metagenomic data may be obtained or can be obtained by any suitable sampling method described herein. The subject's gut metagenomic data may be obtained or can be obtained by the same method as the gut metagenomic data from a population of healthy subjects, or by a different method.

[0108] The subject's gut metagenomic data may be or can be obtained from a fecal sample, an endoscopic sample (e.g., a biopsy sample, a luminal brush sample, a laser capture microdissection sample, etc.), an aspirated intestinal fluid sample, a surgical sample, or an in vivo model or intelligent capsule. Preferably, the subject's gut metagenomic data is obtained from a fecal sample.

[0109] The gut metagenomic data may be obtained or can be obtained by any suitable method. For example, the gut metagenomic data may be obtained or can be obtained by sequencing (e.g., next-generation sequencing (NGS)), or any other suitable method. One advantage of the present invention is that a next-generation sequencing method may not be required to determine the detailed gut microbiome state of a subject, since the trained regression model can determine several important CAZyme abundances that have the greatest impact on the gut microbiome state of a subject.

[0110] In some embodiments, the subject's gut metagenomic data is obtained by NGS methods.

[0111] The gut metagenomic data may provide one or more CAZyme abundances. Suitably, the gut metagenomic data may provide 2 or more (e.g. 2, 3, 4, 5, 6, 7, 8, 9, 10) CAZyme abundances, 3 or more CAZyme abundances, 4 or more CAZyme abundances, 5 or more CAZyme abundances, 6 or more CAZyme abundances, 7 or more CAZyme abundances, 8 or more CAZyme abundances, 9 or more CAZyme abundances, 10 or more CAZyme abundances, 20 or more CAZyme abundances, 30 or more CAZyme abundances, 50 or more CAZyme abundances, 50 or more CAZyme abundances, or 100 or more CAZyme abundances. Suitably, the gut metagenomic data may provide a CAZyme abundance of 500 or less, a CAZyme abundance of 400 or less, a CAZyme abundance of 200 or less, a CAZyme abundance of 100 or less, a CAZyme abundance of 50 or less, a CAZyme abundance of 40 or less, a CAZyme abundance of 30 or less, a CAZyme abundance of 20 or less, a CAZyme abundance of 10 or less, a CAZyme abundance of 9 or less, a CAZyme abundance of 8 or less, a CAZyme abundance of 7 or less, a CAZyme abundance of 6 or less, a CAZyme abundance of 5 or less, a CAZyme abundance of 4 or less. Suitably, the gut metagenomic data may provide a CAZyme abundance of 2-500, a CAZyme abundance of 5-200, or a CAZyme abundance of 10-100. Suitably, the gut metagenomic data may provide an abundance of between 2 and 50 CAZymes, or an abundance of between 5 and 20 CAZymes. The subject's gut metagenomic data may provide abundances for the same CAZymes as the gut metagenomic data from a population of healthy subjects, or for different CAZymes.

[0112] Outliers in trained regression models In one aspect, the present invention provides a method for determining a gut microbiome status of a subject, comprising determining whether the subject is an outlier in a trained regression model. The method may use any of the trained regression models described herein.

[0113] Suitably, if the subject is not an outlier in the trained regression model, then the subject's gut microbiome status is healthy, and / or if the subject is an outlier in the trained regression model, then the subject's gut microbiome status is unhealthy.

[0114] In some embodiments, a subject's gut microbiome status is healthy if the subject is not an outlier in the trained regression model. In this context, a "healthy" gut microbiome status means that the subject has a gut metagenome that is not significantly different from the gut metagenome of a population of healthy subjects.

[0115] A "healthy" gut microbiome status can mean that a subject is at a proper state of gut maturation, at a proper state of gut progression, and / or at a proper stage of gut continuity.

[0116] In one embodiment, a healthy gut microbiome status means that the subject is in a proper gut maturation state. "Proper gut maturation state" can mean that the subject's gut microbiome is normal or properly mature.

[0117] In one embodiment, a healthy gut microbiome status means that the subject is in a proper gut progression state. "Proper gut progression state" can mean that the subject's gut microbiome is progressing or evolving in a timely manner.

[0118] In one embodiment, a healthy gut microbiome status means that a subject has proper gut continuity. "Proper gut continuity" can mean that a subject's gut microbiome is in good time.

[0119] In some embodiments, if a subject is an outlier, the subject's gut microbiome status is not healthy. In this context, an "unhealthy" gut microbiome status means that the subject has a gut metagenome that is significantly different from the gut metagenome of a population of healthy subjects.

[0120] An "unhealthy" gut microbiome state can mean that the subject is not in a proper gut maturation state, is not in a proper gut progression state, and / or is not in a proper gut continuum. In one embodiment, an unhealthy gut microbiome state means that the subject is not in a proper gut maturation state. In one embodiment, an unhealthy gut microbiome state means that the subject is not in a proper gut progression state. In one embodiment, an unhealthy gut microbiome state means that the subject is not in a proper gut continuum state.

[0121] Any suitable statistical method may be used to determine whether a subject is an outlier in the trained regression model (see, for example, Hodge, V. and Austin, J., 2004. Artificial intelligence review, 22(2), pp.85-126). For example, a subject may be determined to be an outlier based on the standard error, confidence interval, prediction interval, and / or standard deviation in the trained regression model. Suitably, a subject may be determined to be an outlier if the subject's gut metagenomic data is significantly different from the trained regression line based on the standard error, confidence interval, prediction interval, and / or standard deviation of the trained regression line.

[0122] Appropriate cutoffs are well known to those skilled in the art, for example, 3 standard deviations from the mean is a common cutoff in practice for identifying outliers in a Gaussian or Gaussian-like distribution.

[0123] In some embodiments, a subject is determined to be an outlier based on the standard error of the trained regression model. The standard error (SE) of the regression represents the average distance that observed values ​​deviate from the regression line. Suitably, a subject is an outlier if the subject's gut metagenomic data is less than or equal to -2SE or more than 2SE, less than or equal to -3SE or more than 3SE, or less than or equal to -4SE or more than 4SE from the trained regression line. Suitably, a subject is an outlier if the subject's gut metagenomic data is less than or equal to -2SE or more than 2SE from the trained regression line.

[0124] In some embodiments, a subject is determined to be an outlier based on the confidence interval of the trained regression model. The confidence interval may be determined by any suitable method, for example using a resampling technique (e.g. bootstrap resampling). Suitably, a subject is an outlier if the subject's gut metagenomic data is outside the 90% confidence interval, 95% confidence interval, 98% confidence interval, or 99% confidence interval in the trained regression model. Suitably, a subject is an outlier if the subject's gut metagenomic data is outside the 95% confidence interval in the trained regression model.

[0125] In some embodiments, a subject is determined to be an outlier based on the prediction interval of the trained regression model. Suitably, a subject is an outlier if the subject's gut metagenomic data is outside the 90% prediction interval, the 95% prediction interval, the 98% prediction interval, or the 99% prediction interval in the trained regression model. Suitably, a subject is an outlier if the subject's gut metagenomic data is outside the 95% prediction interval in the trained regression model.

[0126] In some embodiments, a subject is determined to be an outlier based on the standard deviation of the trained regression model.For example, Z-score can be used to determine whether a subject is an outlier.Z-score is the number of standard deviations above and below the mean.Compatibly, a subject is an outlier if it has a Z-score of -2 or less or 2 or more, a Z-score of -3 or less or 3 or more, or a Z-score of -4 or less or 4 or more in the trained regression model.Compatibly, a subject is an outlier if it has a Z-score of -2 or less or 2 or more in the trained regression model.

[0127] In some embodiments, a subject is determined to be an outlier if the subject's gut metagenomic data is less than or equal to -2SE or more than 2SE from the trained regression line, if the subject's gut metagenomic data is outside the 95% confidence interval in the trained regression model, if the subject's gut metagenomic data is outside the 95% prediction interval in the trained regression model, and / or if the subject has a Z-score less than or equal to -2 or more than 2 in the trained regression model.

[0128] In some embodiments, a subject is determined to be an outlier if the subject's gut metagenomic data falls outside the 95% confidence interval in the trained regression model and / or if the subject's gut metagenomic data falls outside the 95% prediction interval in the trained regression model.

[0129] In some embodiments, a subject is determined to be an outlier if the subject's gut metagenomic data falls outside the 95% prediction interval in the trained regression model.

[0130] Are we on the ELM change trajectory? In one aspect, the present invention provides a method for determining the state of the gut microbiome of a subject, comprising determining whether the subject is on a trajectory of ELM change.The method may use any of the trained regression models described herein.

[0131] Suitably, if the subject is on a trajectory of change in ELM, the subject's gut microbiome status is healthy, and / or if the subject is off a trajectory of change in ELM, the subject's gut microbiome status is unhealthy.

[0132] In some embodiments, if the subject is on a trajectory of ELM changes, the subject's gut microbiome status is healthy. In some embodiments, if the subject is off a trajectory of ELM changes, the subject's gut microbiome status is not healthy.

[0133] Suitably, a subject is on a trajectory of ELM change if the subject's gut metagenomic data is not significantly different from the trajectory of ELM change, and / or a subject is off a trajectory of ELM change if the subject's gut metagenomic data is significantly different from the trajectory of ELM change.

[0134] Any suitable method may be used to determine whether a subject is on a trajectory of change in ELM. For example, a subject may be determined to be on a trajectory of change in ELM based on the standard error, confidence interval, prediction interval, and / or standard deviation of the trajectory of change in ELM.

[0135] In some embodiments, a subject is determined to be off the ELM change trajectory based on the standard error (SE) of the ELM change trajectory. Suitably, a subject is off the ELM change trajectory when the subject's gut metagenomic data is less than or equal to -2SE or more than 2SE, less than or equal to -3SE or more than 3SE, or less than or equal to -4SE or more than 4SE from the ELM change trajectory. Suitably, a subject is off the ELM change trajectory when the subject's gut metagenomic data is less than or equal to -2SE or more than 2SE away from the ELM change trajectory.

[0136] In some embodiments, a subject is determined to be off the ELM change trajectory based on the confidence interval of the ELM change trajectory. Suitably, a subject is off the ELM change trajectory if the subject's gut metagenomic data is outside the 90% confidence interval, 95% confidence interval, 98% confidence interval, or 99% confidence interval of the ELM change trajectory. Suitably, a subject is off the ELM change trajectory if the subject's gut metagenomic data is outside the 95% confidence interval of the ELM change trajectory.

[0137] In some embodiments, a subject is determined to be off the ELM change trajectory based on a prediction interval of the ELM change trajectory. Suitably, a subject is off the ELM change trajectory if the subject's gut metagenomic data is outside the 90% prediction interval, 95% prediction interval, 98% prediction interval, or 99% prediction interval of the ELM change trajectory. Suitably, a subject is off the ELM change trajectory if the subject's gut metagenomic data is outside the 95% prediction interval of the ELM change trajectory.

[0138] In some embodiments, a subject is determined to be out of the ELM change trajectory based on the standard deviation of the ELM change trajectory.For example, Z score can be used to determine whether a subject is out of the ELM change trajectory.Compatibly, a subject is an outlier if it has a Z score of -2 or less or 2 or more, a Z score of -3 or less or 3 or more, or a Z score of -4 or less or 4 or more.Compatibly, a subject is out of the ELM change trajectory if it has a Z score of -2 or less or 2 or more.

[0139] In some embodiments, a subject is determined to be off the ELM change trajectory if the subject's gut metagenomic data is below -2SE or above 2SE from the ELM change trajectory, if the subject's gut metagenomic data is outside the 95% confidence interval of the ELM change trajectory, if the subject's gut metagenomic data is outside the 95% prediction interval of the ELM change trajectory, and / or if the subject has a Z-score below -2 or above 2.

[0140] In some embodiments, a subject is determined to be off the ELM change trajectory if the subject's gut metagenomic data is outside the 95% confidence interval of the ELM change trajectory model and / or if the subject's gut metagenomic data is outside the 95% prediction interval of the ELM change trajectory.

[0141] In some embodiments, a subject is determined to be off the ELM change trajectory if the subject's gut metagenomic data falls outside the 95% prediction interval of the ELM change trajectory.

[0142] Predicting the age of the subject The present invention provides a method for predicting the age of a subject given gut metagenomic data. The prediction may be based on the assumption that the subject is healthy.

[0143] The method can include predicting the age of the subject given the subject's gut metagenomic data and a trained regression model. The trained regression model can be any trained regression model described herein and / or can be obtained or obtained by any method described herein.

[0144] The method may include: (a) providing gut metagenomic data from a population of healthy subjects; (b) training a regression model on the gut metagenomic data, where the age of the healthy subjects at the time of data collection is regressed against one or more CAZyme abundances provided from the gut metagenomic data; (c) providing gut metagenomic data from a test subject; and (d) predicting the age of the test subject given the gut metagenomic data of the test subject and the trained regression model.

[0145] The present invention provides a method for determining a subject's gut microbiome status, wherein if the subject's predicted age does not significantly differ from the subject's actual age, the subject's gut microbiome status is healthy, and / or if the subject's predicted age significantly differs from the subject's actual age, the subject's gut microbiome status is unhealthy.

[0146] Any suitable method may be used to determine whether a subject's predicted age significantly differs from the subject's actual age, for example based on the standard error, confidence interval, prediction interval, and / or standard deviation of a trained regression model (as described in more detail above).

[0147] Suitably, a subject's predicted age significantly differs from the subject's actual age if the predicted age is -2SE or less or 2SE or more, -3SE or less or 3SE or more, or -4SE or less or 4SE or more from the subject's actual age. Suitably, a subject's predicted age significantly differs from the subject's actual age if the predicted age is -2SE or less or 2SE or more from the subject's actual age.

[0148] Suitably, the predicted age of a subject differs significantly from the actual age of a subject having an age Z-score of -2 or less or equal to 2, an age Z-score of -3 or less or equal to 3, or an age Z-score of -4 or less or equal to 4. Suitably, the predicted age of a subject differs significantly from the actual age of a subject when the subject has an age Z-score of -2 or less or equal to 2 or more.

[0149] Suitably, the predicted age of the subject is significantly different from the actual age of the subject when the predicted age of the subject differs by about 1 month or more (e.g., 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, or 12 months), about 2 months or more, about 3 months or more, about 4 months or more, about 5 months or more, about 6 months or more, about 7 months or more, about 8 months or more, about 9 months or more, about 10 months or more, about 11 months or more, or about 12 months or more.Suitably, the predicted age of the subject is significantly different from the actual age of the subject when the predicted age of the subject differs by about 0.5 years or more (e.g., 0.5 years, 0.6 years, 0.7 years, 0.8 years, 0.9 years, or 1 year), about 0.6 years or more, about 0.7 years or more, about 0.8 years or more, about 0.9 years or more, or about 1 year or more.

[0150] The present invention also provides the use of one or more CAZyme abundances provided from a subject's gut metagenomic data to predict the age of a subject.The present invention also provides the use of a trained regression model according to the present invention to predict the age of a subject.

[0151] Methods for maintaining or improving the state of the gut microbiome in a subject The present invention provides methods for maintaining or improving the status of the gut microbiome in a subject.

[0152] The method may include determining the status of the subject's gut microbiome using any of the methods described herein and adjusting the subject's diet, nutrient intake, and / or lifestyle to maintain or improve the subject's gut microbiome status.

[0153] After adjusting the subject's diet, nutrient intake, and / or lifestyle, the subject's gut microbiome status can be healthy. After adjusting the subject's diet, nutrient intake, and / or lifestyle, the subject can be in a proper gut maturation state, a proper gut progression state, and / or a proper gut continuation state.

[0154] The subject's adjusted diet, nutrient intake, and / or lifestyle can change one or more CAZyme abundances. Suitably, the CAZyme abundances have a high impact on the trained regression model and / or are significantly different from the median CAZyme abundance in the trained regression model. In some embodiments, the CAZyme abundances have a high impact on the trained regression model and are significantly different from the median CAZyme abundance in the trained regression model can be changed by the subject's adjusted diet, nutrient intake, and / or lifestyle. Any suitable statistical method can be used to identify the CAZyme abundances that have a high impact on the trained regression model, for example, based on feature importance. Feature importance can be determined using any suitable statistical method, for example, based on SHaPley Additive Planation (SHAPS) value (Lundberg SM, et al. Nat Mach Intell. 2020).

[0155] Any suitable statistical method can also be used to identify CAZyme abundances that are significantly different from the median CAZyme abundance in the trained regression model, for example based on standard error, confidence intervals, prediction intervals, and / or standard deviations (as described in more detail above).

[0156] A subject's adjusted diet, nutrient intake, and / or lifestyle can increase the abundance of favorable CAZymes and / or decrease the abundance of unfavorable CAZymes.

[0157] Suitably, the subject's adjusted diet, nutrient intake, and / or lifestyle may increase the abundance of one or more preferred CAZymes. In this context, a "preferred CAZyme" may be a CAZyme that is less abundant in the subject's gut metagenomic data than in a population of healthy subjects. For example, a preferred CAZyme may be a CAZyme that has a lower abundance in the subject's gut metagenomic data compared to the median CAZyme abundance in the trained regression model.

[0158] Suitably, the subject's adjusted diet, nutrient intake, and / or lifestyle can reduce the abundance of one or more unfavorable CAZymes. In this context, an "unfavorable CAZyme" can be a CAZyme that is more abundant in the subject's gut metagenomic data than in a population of healthy subjects. For example, an unfavorable CAZyme can be a CAZyme that has a higher abundance in the subject's gut metagenomic data compared to the median CAZyme abundance in the trained regression model.

[0159] Suitably, after adjusting the subject's diet, ingested nutrients, and / or lifestyle, the state of the subject's gut microbiome is healthy. The state of the gut microbiome can be determined using any method described herein (e.g., the same method used to determine the gut microbiome state before adjusting the subject's diet, ingested nutrients, and / or lifestyle).

[0160] The present invention also provides a method for determining the diet and / or nutrient intake of a subject, which may comprise determining the diet and / or nutrient intake necessary to maintain or improve the state of the gut microbiome of the subject.

[0161] These personalized recommendations may change over time as the state of the subject's gut microbiome changes over time. The method of the present invention may be used one or more times (e.g., one, two, three, four, five, six, seven, eight, nine, ten times) or two or more times when the test subject is 0 to 1 year old, 0.5 to 1 year old, or 1 to 2 years old. The method of the present invention may be used one or more times (e.g., one, two, three, four, five, six, seven, eight, nine, ten times) or two or more times when the test subject is 0 to 12 months old, 6 to 12 months old, or 12 to 24 months old. The method of the present invention may be used two or more times (e.g., two, three, four, five, six, seven, eight, nine, ten times), three or more times, four or more times, or five or more times during the first five years of the subject's life. For example, the methods of the invention may be used two or more times (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10 times), three or more times, four or more times, or five or more times within the first two years of life.

[0162] meal In one aspect, the invention provides a method of maintaining or improving the state of the gut microbiome comprising adjusting the diet of a subject. For example, the subject can be provided with dietary recommendations.

[0163] In one aspect, the invention provides a method of determining a diet for a subject, the method may comprise determining a diet necessary to maintain or improve the state of the gut microbiome of the subject.

[0164] The methods of the invention may involve defining a diet to increase the abundance of one or more preferred CAZymes and / or to decrease the abundance of one or more unfavourable CAZymes.

[0165] The methods of the invention may include implementing a diet to increase the abundance of one or more preferred CAZymes and / or to decrease the abundance of one or more unfavourable CAZymes.

[0166] A subject's "diet" may include all foods ingested by the subject. Diet is known to have a significant impact on the gut microbiome.

[0167] A subject's diet may provide multiple food groups. The term "food group" may refer to a collection of foods that share similar nutritional properties or biological classification. Nutritional guides typically divide foods into food groups, and Recommended Dietary Allowances recommend the daily intake of each group for a healthy diet. Examples of food groups include fruits, vegetables, legumes, nuts or seeds, meats, starches or grains, dairy products, and oils and fats.

[0168] The subject's diet may also provide multiple food types. The term "food type" may refer to a collection of foods from the same food group that share more similar nutritional properties or biological classification. Each food group may be further grouped into multiple food types. Examples of food types for the fruit food group may include apples, bananas, citrus fruits, berries, other fruits (e.g., pears, peaches, pineapples), and dried fruits. Suitable food groups and food types can be easily identified by any suitable method known in the art. For example, suitable food groups and food types may be based on published observations (e.g., Dwyer JT. The Journal of Nutrition. 2018; 148 (suppl 3): 1575S-80S).

[0169] Suitably, the subject's diet may be adjusted by altering the amounts of one or more food groups and / or one or more food types in the subject's diet. Suitably, the subject is recommended and / or administered foods to adjust their diet.

[0170] Suitable CAZymes can be identified based on known CAZyme activity and / or CAZyme substrate specificity. For example, to increase the abundance of a preferred CAZyme, the subject's diet can be adjusted to increase the amount of one or more substrates for the CAZyme. CAZyme activity and substrate specificity are available from the CAZy database (http: / / www.cazy.org) and dbCAN-PUL, a database of experimentally characterized CAZyme gene clusters and their substrates (Ausland, C., et al., 2021. Nucleic Acids Research, 49(D1), pp. D523-D528).

[0171] The abundance of bacteria in the gut may be related to the ability of gut bacteria to degrade various carbohydrates by CAZymes. (El Kaoutari A, et al. Nat Rev Microbiol. 2013). Thus, the abundance of CAZymes may vary based on various factors, including dietary habits (Bhattacharya T, et al. PLoS One. 2015). In infants, the transition from milk to solid food affects CAZymes (Ye L, et al. FEMS Microbiol Ecol. 2019). A high-fiber diet increased the abundance of 11 microbiome-encoded glycan-degrading CAZymes (Wastyk HC, et al. Cell. 2021). Several other nutritional strategies for CAZyme modulation are discussed in Belzer C, Trends Microbiol. 2022. The experimentally known CAYzmes regulatory substrates can be classified into the categories of β-glucans and hemicelluloses, α-glucans, algal glycans, animal and fungal glycans, and other glycans, details of which can be found in specialized databases (Ausland C, et al. Nucleic Acids Res. 2021).

[0172] In some embodiments, the food comprises one or more CAZyme substrates. In some embodiments, the food comprises one or more carbohydrates. In some embodiments, the food comprises dietary fiber (e.g., carbohydrate polymers, oligomers, and lignin that escape digestion in the small intestine and reach the colon intact). In some embodiments, the food comprises one or more CAZyme-associated carbohydrates and / or one or more CAZyme-associated dietary fibers. As used herein, "CAZyme-associated" carbohydrates and dietary fibers can refer to carbohydrates and dietary fibers that can be substrates for one or more CAZymes.

[0173] Suitably the carbohydrate and / or dietary fibre is selected from the group consisting of 4-methylumbelliferyl 6-azido-6-deoxy-β-D-galactoside, N-acetyl-D-galactosamine, N-acetyl-D-glucosamine, N-acetylglucosamine, N-glycan, O-antigen, O-glycan, acarbose, acetylated glucuronoxylan, agar, alduronate, alginate, α-galactoside, α-glucan, α-mannan, α-mannoside, arabinan, arabinoxylooligosaccharides, arabinofuranooligosaccharides, arabinofuranose, arabinogalactan, arabinofuranoglyc ... arabinoxylan, β-galactooligosaccharide, β-galactosides, β-glucans, β-glucosides, β-mannans, capsule polysaccharides, carboxymethylcellulose, carrageenan, cellobiose, cellodextrin, cellooligosaccharide, cellotriose, cellulose, chitin, chitobiose, chitooligosaccharide, chondroitin disaccharide, chondroitin sulfate, chondroitin sulfate, cyclomaltodextrin, d-galactosamine, dextran, emulsan, extracellular polysaccharide, fructan, fructooligosaccharide, fucose, fucosyllactose, galactan, galactoma mannan, galactooligosaccharide, galactose, galacturonic acid, gentiobiose, glucan, glucomannan, glucosamine, glucose, glycogen, glycosaminoglycan, hemicellulose, heparosan, homogalacturonan, host glycan, human milk oligosaccharide, inulin, isomaltose, isomaltotriose, kestose, lacto-n-triose, lactose, laminaribiose, laminarin, levan, lichenan, lignocellulose, lipopolysaccharide, maltodextrin, maltooligosaccharide, maltose, maltotriose, mannan, manno Oligosaccharides, mannose, melezitose, melibiose, methylglucuronoarabinoxylan, mucin, oligogalacturonide, outer shell polysaccharides, panose, pectin, plant polysaccharides, polygalacturonic acid, polysialic acid, porphyran, pullulan, raffinose oligosaccharides, raffinose, rhamnogalacturonan, rhamnose, ribose, sialic acid, sialo-sugar complexes, sophorose, sorbitol, stachyose, starch, sucrose, trehalose, ulvan, unsaturated hyaluronic acid disaccharides, xanthan, xylan, xylobiose, xylodextrin,The xyloglucan may be selected from one or more of xylooligosaccharides, xylose, and xylotriose.

[0174] Nutrients and supplements In one aspect, the invention provides a method of maintaining or improving the state of the gut microbiome comprising adjusting the nutrients ingested by a subject. For example, a subject can be provided with nutrients or supplement recommendations (e.g., meal plans or recipes).

[0175] In one aspect, the invention provides a method of determining the nutritional intake of a subject, which may comprise determining the intake of nutrients required to maintain or improve the state of the gut microbiome of the subject.

[0176] The methods of the invention may include determining ingested nutrients to increase the abundance of favorable CAZymes and / or decrease the abundance of unfavorable CAZymes.

[0177] The methods of the invention may include administering nutrients or supplements to increase the abundance of favorable CAZymes and / or decrease the abundance of unfavorable CAZymes.

[0178] The term "nutrients" may refer to any substance that is essential for the development and health of a subject. The term nutrients encompasses "macronutrients" such as carbohydrates, fats and fatty acids, and proteins, and "micronutrients" such as vitamins and minerals. The "ingested nutrients" of a subject may include all nutrients ingested by the subject.

[0179] Examples of macronutrients include carbohydrates (including fiber and sugars), proteins, and lipids (including long chain polyunsaturated fatty acids). Examples of micronutrients include vitamins (including vitamin A, vitamin D, vitamin C, folic acid, vitamin B6, vitamin B12, and vitamin E) and minerals (including sodium, potassium, calcium, iron, zinc, magnesium, and phosphorus).

[0180] As used herein, a "supplement" or "dietary supplement" may be used to supplement a subject's nutrition (and is typically used as such, but dietary supplements may also be added to any type of composition that is intended for ingestion). The supplement may be in any form suitable for ingestion by a subject and may contain any suitable nutrients.

[0181] Suitably, a subject's ingested nutrients may be adjusted by varying the amount of one or more nutrients in the subject's diet and / or by providing dietary supplements. In some embodiments, the one or more nutrients comprise or consist of one or more carbohydrates. In some embodiments, the one or more nutrients comprise or consist of dietary fiber (e.g., carbohydrate polymers, oligomers, and lignins that escape digestion in the small intestine and reach the colon intact). In some embodiments, the one or more nutrients comprise one or more CAZyme-associated carbohydrates and / or one or more CAZyme-associated dietary fibers.

[0182] Suitably the carbohydrate and / or dietary fibre is selected from the group consisting of 4-methylumbelliferyl 6-azido-6-deoxy-β-D-galactoside, N-acetyl-D-galactosamine, N-acetyl-D-glucosamine, N-acetylglucosamine, N-glycan, O-antigen, O-glycan, acarbose, acetylated glucuronoxylan, agar, alduronate, alginate, α-galactoside, α-glucan, α-mannan, α-mannoside, arabinan, arabinoxylooligosaccharides, arabinofuranooligosaccharides, arabinofuranose, arabinogalactan, arabinofuranoglyc ... arabinoxylan, β-galactooligosaccharide, β-galactosides, β-glucans, β-glucosides, β-mannans, capsule polysaccharides, carboxymethylcellulose, carrageenan, cellobiose, cellodextrin, cellooligosaccharide, cellotriose, cellulose, chitin, chitobiose, chitooligosaccharide, chondroitin disaccharide, chondroitin sulfate, chondroitin sulfate, cyclomaltodextrin, d-galactosamine, dextran, emulsan, extracellular polysaccharide, fructan, fructooligosaccharide, fucose, fucosyllactose, galactan, galactoma mannan, galactooligosaccharide, galactose, galacturonic acid, gentiobiose, glucan, glucomannan, glucosamine, glucose, glycogen, glycosaminoglycan, hemicellulose, heparosan, homogalacturonan, host glycan, human milk oligosaccharide, inulin, isomaltose, isomaltotriose, kestose, lacto-n-triose, lactose, laminaribiose, laminarin, levan, lichenan, lignocellulose, lipopolysaccharide, maltodextrin, maltooligosaccharide, maltose, maltotriose, mannan, manno Oligosaccharides, mannose, melezitose, melibiose, methylglucuronoarabinoxylan, mucin, oligogalacturonide, outer shell polysaccharides, panose, pectin, plant polysaccharides, polygalacturonic acid, polysialic acid, porphyran, pullulan, raffinose oligosaccharides, raffinose, rhamnogalacturonan, rhamnose, ribose, sialic acid, sialo-sugar complexes, sophorose, sorbitol, stachyose, starch, sucrose, trehalose, ulvan, unsaturated hyaluronic acid disaccharides, xanthan, xylan, xylobiose, xylodextrin,The xyloglucan may be selected from one or more of xylooligosaccharides, xylose, and xylotriose.

[0183] lifestyle habits In one aspect, the invention provides a method of maintaining or improving the state of the gut microbiome comprising adjusting the lifestyle of a subject. For example, the subject can be provided with lifestyle recommendations.

[0184] The methods of the invention may involve adjusting the lifestyle of a subject to increase the abundance of favorable CAZymes and / or decrease the abundance of unfavorable CAZymes.

[0185] The term "lifestyle" refers to any lifestyle choice made by a subject (or the subject's caregiver / carer) and may include dietary intake data, data from lifestyle questionnaires, motivations or preferences. For example, lifestyle characteristics may be whether a subject is vegan or omnivorous, whether a subject is lactose intolerant, frequency of physical activity, and / or frequency of sedentary activity. Suitably, a subject's lifestyle may be adjusted by modifying the subject's meal frequency and timing and / or frequency of physical activity.

[0186] Microbial taxon abundance Suitably, the abundance of a CAZyme may be adjusted by increasing the abundance and / or function of one or more favorable microbial taxa and / or decreasing the abundance and / or function of one or more unfavorable microbial taxa.

[0187] CAZymes are associated with different microbial taxa (see, e.g., the CAZy database), and therefore, by adjusting the level of a microbial taxon, the abundance of a CAZyme can also be adjusted. In this context, a "preferred microbial taxon" can be a microbial taxon associated with a preferred CAZyme, and an "unfavorable microbial taxon" can be a microorganism associated with an unfavorable CAZyme.

[0188] Suitable microbial taxa are known in the art. For example, Bacteroides thetaiotaomicron contains numerous glycoside hydrolases (GH CAZymes) for glycan utilization such as fructans, mannans and starches (Sonnenburg ED, et al. Proc Natl Acad Sci USA. 2006). Bacteroides contains most CAZyme families, including 80 GH / PL and 13 CBM families (Ye L, et al. FEMS Microbiol Ecol. 2019). Some of the other bacterial genera that can be adjusted to achieve a CAZyme profile are Bifidobacterium, Lactobacillus, Eubacterium, Roseburia, and Ruminococcus (Bhattacharya T, et al. PLoS One. 2015; Flint HJ, et al. Gut Microbes. 2012).

[0189] The method of the present invention may include determining and / or implementing a diet to increase the abundance and / or function of one or more favorable microbial taxa and / or to decrease the abundance and / or function of one or more unfavorable microbial taxa. It is known that there is a correlation between diet and microbial taxa (see, for example, Wu, GD, et al., 2011. Science, 334(6052), pp.105-108). For example, diets with increased fiber can stimulate the growth of Bifidobacterium and Lactobacillus (see, for example, Wegh, CA, et al., 2017. Expert review of gastroenterology & hepatology, 11(11), pp.1031-1045). Suitably, the subject is recommended and / or administered a food to adjust the diet. In some embodiments, the food comprises dietary fiber (e.g., carbohydrate polymers, oligomers, and lignin that escape digestion in the small intestine and reach the colon intact). In some embodiments, the food contains vitamins such as riboflavin (vitamin B2), retinol (vitamin A), and calciferol (vitamin D), and / or minerals such as manganese, zinc, and potassium.

[0190] The method of the present invention may include determining ingested nutrients and / or administering nutrients or supplements to increase the abundance and / or function of one or more favorable microbial taxa and / or decrease the abundance and / or function of one or more unfavorable microbial taxa. Suitably, the ingested nutrients of a subject may be adjusted by changing the amount of one or more nutrients in the subject's diet and / or by providing a dietary supplement. It is known that there is a correlation between nutrients and microbial taxa (see, for example, Wu, GD, et al., 2011. Science, 334(6052), pp. 105-108). For example, it is known that human milk oligosaccharides can regulate the gut microbiota (see, for example, Sela, DA and Mills, DA, 2010. Trends in microbiology, 18(7), pp. 298-307). Suitably, the subject is administered food and / or supplements to adjust the ingested nutrients. In some embodiments, the foods and / or supplements include prebiotics (e.g., human milk oligosaccharides and / or inulin), probiotics, synbiotics, vitamins (e.g., riboflavin (vitamin B2), retinol (vitamin A), and / or calciferol (vitamin D)), and / or minerals (e.g., manganese, zinc, and / or potassium).

[0191] In some embodiments, the food and / or supplements include prebiotics, probiotics, and / or synbiotics. Any suitable prebiotics, probiotics, and / or synbiotics may be used (see, for example, Thomas, DW and Greer, FR, 2010. Pediatrics, 126(6), pp. 1217-1231).

[0192] The term "prebiotic" may refer to indigestible components that selectively stimulate the favorable growth and / or activity of one or more microbial taxa, thereby providing benefits to a subject. Examples of prebiotics include human milk oligosaccharides. Examples of prebiotic oligosaccharides include galacto-oligosaccharides (GOS), fructo-oligosaccharides (FOS), 2'-fucosyllactose, lacto-N-neo-tetraose, and inulin.

[0193] The term "probiotic" may refer to ingredients that contain a sufficient number of viable microorganisms to alter the gut microbiota of a subject (see, for example, Hill, C., et al., 2014. Nature reviews Gastroenterology & hepatology, 11(8), p.506). Examples of probiotic microorganisms include Escherichia, Bacteroides, Roseburia, Faecalibacterium, Sutterella, SMB53, Collinsella, Ruminococcus, Akkermansia, Veillonella, Parabacteroides, Clostridium, Oscillospira, Megasphingida, and others. Mention may be made of the following genus of bacteria: Megasphaera, Fusobacterium, Citrobacter, Neisseria, Bifidobacterium, Lachnospira, Dialister, Ruminococcus, Blautia, Streptococcus, Eggerthella, Enterococcus, Paraprevotella.

[0194] Suitably, the probiotic comprises commercially available probiotic strains and / or strains that have been shown to have health benefits (see, for example, Fijan, S., 2014. International journal of environmental research and public health, 11(5), pp. 4745-4767). In some embodiments, the probiotic comprises one or more strains selected from E. coli Nissle 1917, B. infantis, B. animalis subsp. lactis, B. bifidum, B. longum, B. breve, S. thermophilus, E. durans, and E. faecium. In some embodiments, the probiotic comprises Escherichia, Bifidobacterium, Streptococcus, and / or Enterococcus. In some embodiments, the probiotic comprises one or more strains selected from E. coli Nissle 1917, B. infantis, B. animalis subsp. lactis, B. bifidum, B. longum, B. breve, S. thermophilus, E. durans, and E. faecium.

[0195] The term "synbiotic" can refer to a component that contains both probiotics and prebiotics (see, e.g., Swanson, KS, et al., 2020. Nature Reviews Gastroenterology & Hepatology, 17(11), pp. 687-701).

[0196] In some embodiments, the foods and / or supplements include vitamins and / or minerals. Dietary guidelines have been established for certain vitamins and minerals.

[0197] Examples of vitamins include vitamin A, vitamin D, vitamin C, folic acid, vitamin B2, vitamin B6, vitamin B12, and vitamin E. For example, the vitamins may include or consist of riboflavin (vitamin B2), retinol (vitamin A), and / or calciferol (vitamin D).

[0198] Examples of minerals include sodium, potassium, calcium, iron, zinc, magnesium, and phosphorus. For example, the minerals may comprise or consist of manganese, zinc, and / or potassium. Minerals are typically used in their salt form.

[0199] The method of the present invention may include adjusting the lifestyle of a subject to increase the abundance and / or function of one or more favorable microbial taxa and / or decrease the abundance and / or function of one or more unfavorable microbial taxa. Suitably, the lifestyle of a subject may be adjusted by changing the frequency and timing of the subject's meals and / or the frequency of physical activity. For example, it is known that regular meal patterns can regulate gut microbiota (see, for example, Paoli, A., et al., 2019. Nutrients, 11(4), p.719), and that exercise can affect gut microbiota (see, for example, O'Sullivan, O., et al., 2015. Gut microbes, 6(2), pp.131-136).

[0200] Computer Programs and Computer Readable Media The methods described may be computer-implemented methods.

[0201] In one aspect, the present invention provides a data processing system comprising means for carrying out the method of the present invention.

[0202] In one aspect, the present invention provides a data processing apparatus including a processor configured to perform the method of the present invention.

[0203] In one aspect, the invention provides a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the invention.

[0204] In one aspect, the invention provides a computer readable medium comprising instructions that, when executed by a computer, cause the computer to perform the method of the invention.

[0205] In one aspect, the invention provides a computer readable data carrier having stored thereon the computer program of the invention.

[0206] In one aspect, the invention provides a data carrier signal carrying the computer program of the invention.

[0207] The systems described herein may display to the user a dashboard or other suitable user interface that is customized based on the subject of the test, for example, the subject's gut metagenomic sample, the subject's determined gut microbiome status, and the subject's personalized advice and recommendations, such as nutritional solutions to maintain or improve the subject's gut microbiome status.

[0208] Serving a trained regression model The methods described herein for determining a trained regression model may be computer-implemented methods.

[0209] For example, in one aspect, the invention provides a computer-implemented method for providing a trained regression model, the method comprising: (a) providing gut metagenomic data from a population of healthy subjects; and (b) training a regression model on the gut metagenomic data, wherein age of the healthy subjects at the time of data collection is regressed against abundance of one or more carbohydrate-associated enzymes (CAZymes) provided from the gut metagenomic data.

[0210] In one aspect, the invention provides a data processing system comprising means for determining a trained regression model for one or more CAZyme abundances given gut metagenomic data from a population of healthy subjects providing the ages of the healthy subjects at the time of data collection, as described herein.

[0211] In one aspect, the present invention provides a data processing device comprising a processor configured to determine a trained regression model for one or more CAZyme abundances given gut metagenomic data from a population of healthy subjects providing the ages of the healthy subjects at the time of data collection, as described herein.

[0212] In one aspect, the invention provides a computer program comprising instructions which, when executed by a computer, cause the computer to determine a trained regression model for one or more CAZyme abundances given gut metagenomic data from a population of healthy subjects providing the ages of the healthy subjects at the time of data collection, as described herein.

[0213] In one aspect, the invention provides a computer readable medium comprising instructions that, when executed by a computer, cause the computer to determine a trained regression model for one or more CAZyme abundances given gut metagenomic data from a population of healthy subjects providing the ages of the healthy subjects at the time of data collection, as described herein.

[0214] Predicting the age of the subject The methods described herein for predicting the age of a subject may be computer-implemented methods.

[0215] For example, in one aspect, the invention provides a computer-implemented method for predicting age of a subject, the method comprising: (a) providing a trained regression model according to the invention; (b) providing gut metagenomic data from a subject; and (c) predicting age of the subject given the subject's gut metagenomic data and the trained regression model.

[0216] In one aspect, the invention provides a data processing system comprising means for predicting the age of a subject given a trained regression model according to the invention and their gut metagenomic data as described herein.

[0217] In one aspect, the present invention provides a data processing apparatus comprising a processor configured to predict the age of a subject given a trained regression model according to the present invention and their gut metagenomic data as described herein.

[0218] In one aspect, the invention provides a computer program comprising instructions which, when executed by a computer, cause the computer to predict the age of a subject given a trained regression model according to the invention and the subject's gut metagenomic data as described herein.

[0219] In one aspect, the invention provides a computer readable medium comprising instructions that when executed by a computer cause the computer to predict the age of a subject given a trained regression model according to the invention described herein and their gut metagenomic data.

[0220] Determining the status of the gut microbiome in a subject The methods for determining the status of a subject's gut microbiome described herein may be computer-implemented methods.

[0221] Outliers in trained regression models In one aspect, the invention provides a computer-implemented method for determining a subject's gut microbiome status, the method comprising: (a) providing a trained regression according to the invention; (b) providing gut metagenomic data from the subject; and (c) determining whether the subject is an outlier in the trained regression model; where if the subject is not an outlier in the trained regression model, the subject's gut microbiome status is healthy, and / or if the subject is an outlier, the subject's gut microbiome status is unhealthy.

[0222] In one aspect, the invention provides a data processing system comprising means for determining the state of a subject's gut microbiome given a trained regression according to the invention and their gut metagenomic data as described herein.

[0223] In one aspect, the present invention provides a data processing apparatus comprising a processor configured to determine a state of a subject's gut microbiome given a trained regression according to the present invention and their gut metagenomic data as described herein.

[0224] In one aspect, the invention provides a computer program comprising instructions that, when the program is executed by a computer, cause the computer to determine the state of a subject's gut microbiome given a trained regression according to the invention and their gut metagenomic data, as described herein.

[0225] In one aspect, the invention provides a computer readable medium comprising instructions that when executed by a computer cause the computer to determine the state of a subject's gut microbiome given a trained regression according to the invention and their gut metagenomic data, as described herein.

[0226] Are we on the ELM change trajectory? In one aspect, the invention provides a computer-implemented method for determining a subject's gut microbiome status, comprising: (a) providing a trained regression model according to the invention, wherein the trained regression model is a trajectory of ELM change; (b) providing gut metagenomic data from the subject; and (c) determining whether the subject is on a trajectory of ELM change, wherein the subject is on a trajectory of ELM change if the subject's gut metagenomic data is not significantly different from the trajectory of ELM change, and / or the subject is off a trajectory of ELM change if the subject's gut metagenomic data is significantly different from the trajectory of ELM change.

[0227] In one aspect, the present invention provides a data processing system comprising means for determining the state of a subject's gut microbiome given a trajectory of ELM changes according to the present invention and their gut metagenomic data as described herein.

[0228] In one aspect, the present invention provides a data processing device comprising a processor configured to determine the state of a subject's gut microbiome given a trajectory of ELM changes according to the present invention and the subject's gut metagenomic data as described herein.

[0229] In one aspect, the invention provides a computer program comprising instructions that, when executed by a computer, cause the computer to determine the state of a subject's gut microbiome given a trajectory of ELM changes according to the invention and their gut metagenomic data, as described herein.

[0230] In one aspect, the invention provides a computer readable medium comprising instructions that, when executed by a computer, cause the computer to determine the state of a subject's gut microbiome given a trajectory of ELM changes according to the invention and their gut metagenomic data, as described herein.

[0231] When predicted ages are significantly different In one aspect, the invention provides a computer implemented method for determining a state of a subject's gut microbiome, comprising predicting the age of the subject by a method according to the invention, wherein if the predicted age of the subject does not significantly differ from the subject's actual age, the state of the subject's gut microbiome is healthy, and / or if the predicted age of the subject is significantly different from the subject's actual age, the state of the subject's gut microbiome is unhealthy.

[0232] In one aspect, the present invention provides a data processing system comprising means for determining a status of a subject's gut microbiome given the subject's age predicted according to the present invention and the subject's actual age.

[0233] In one aspect, the present invention provides a data processing apparatus comprising a processor configured to determine a state of a subject's gut microbiome given the subject's age predicted according to the present invention and the subject's actual age.

[0234] In one aspect, the invention provides a computer program comprising instructions which, when the program is executed by a computer, cause the computer to determine a state of the subject's gut microbiome given the subject's age predicted according to the invention and the subject's actual age.

[0235] In one aspect, the invention provides a computer readable medium comprising instructions that, when executed by a computer, cause the computer to determine a state of a subject's gut microbiome given the subject's age predicted according to the invention and the subject's actual age.

[0236] [Example] The present invention will now be further described by way of examples, which are meant to be provided to aid those skilled in the art in practicing the invention, and are not intended to limit the scope of the invention in any way.

[0237] Example 1 Test Description We obtained metagenomic sequencing data from fecal samples from infants in the BCP Enhanced Study (Howell BR, et al. Neuroimage. 2019), a subsidiary study of the BCP study that expanded its scope to investigate the effects of nutrients (https: / / www.med.unc.edu / neurology / unc-launches-new-study-to-uncover-nutritional-impacts-on-early-brain-development / ).

[0238] Example 2 Generation of CAZyme Profiles To determine the relative abundance of CAZymes in each sample, a workflow was applied to generate metagenomic assembled genomes (MAGs) from metagenomic sequencing data. More specifically, the ATLAS pipeline (Kieser, S., et al., 2020. BMC bioinformatics, 21(1), pp. 1-8) was used, where translated gene products (predicted using Prodigal, Hyatt, D., et al., 2010. BMC bioinformatics, 11(1), pp. 1-11) were clustered to generate a catalog of non-redundant genes and proteins, which were mapped to the eggNOG catalog v5 (Huerta-Cepas, J., et al., 2019. Nucleic acids research, 47(D1), pp. D309-D314). The relative abundance of CAZymes was determined for each sample.

[0239] Example 3. Determination of the reference set Similar to the selection of the infant reference set described in the literature (Subramanian S, Huq S, et al. Nature. 2014; Stewart CJ, et al. Nature 2018), the inclusion criteria for our reference set were vaginal delivery and full-term birth (gestational age: 259-293 days), and the exclusion criteria was antibiotic use. Thus, in this reference set, we obtained 209 samples from 66 infants (out of a total of 662 samples from 215 infants). The remaining samples were the non-reference set.

[0240] Example 4 Derivation of trajectories of change in the early life microbiome (ELM) Knowledge and concepts of early-life microbiome changes and maturation are discussed in Dogra SK, et al. Microorganisms. 2021. Methods to test these early-life microbiome changes and maturation are discussed in Banjac J, et al. Bioinformatics. 2023. Details of many other techniques can be found at: https: / / github.com / JelenaBanjac / microbiome-toolbox. The inventors performed analysis of the results and figures presented herein using the Microbiome Toolbox dashboard available at https: / / microbiome-toolbox.azurewebsites.net / .

[0241] The results obtained for the ELM change trajectory are shown in Figure 1. The model uses the top 22 features and has a mean absolute error of prediction (MAE): 46.362 and a percent of variance captured R^2: 0.941. These CAZyme features and their relative importance are shown in Table 1.

[0242] [Table 1]

[0243] The abundance of the main CAZymes that are significant in the trajectories of reference changes for different time ranges are shown in Figure 2. For visualization purposes, the time ranges shown in Figure 2 are 90 days, each corresponding to approximately 3 months. Also, for visualization purposes, we only show the top 5 significant bacterial CAZyme features per time range. Additionally, for visualization purposes, we switch off the display of the actual samples and the 95% prediction intervals around them.

[0244] Example 5 Identification and correction of samples that deviate from the ELM change trajectory Some samples (infants at a given time point) deviate from the change trajectory. Examples of samples that deviate from the change trajectory are shown in Figure 3. For visualization purposes, the time ranges shown in Figure 3 are 90 days, corresponding to approximately 3 months each. Also, for visualization purposes, we only show the top 5 significant bacterial CAZyme features per time range. Additionally, for visualization purposes, we switch off the display of the 95% confidence intervals and 95% prediction intervals around the samples.

[0245] Samples that fall outside the ELM trajectory do not have the major CAZymes in the amounts and ranges found in healthy reference infants of the same age group. Interventions can be used to restore these CAZymes to their normal amounts, and the sample can be brought back onto the ELM trajectory.

[0246] This is confirmed by simulations in which the major CAZyme abundances of outlier samples are checked against reference samples in the same age group (time domain). These major CAZyme abundances are then altered to be the same as the median abundances of reference samples in the same age group (time domain). As labeled "Intervention" in the legend on the right side of the figure and indicated by the line and circle near the center of the figure, predictions using this model showed that the alterations could bring the outlier samples back onto the ELM change trajectory (see Figure 3).

[0247] Infants who have strayed from the change trajectory can be brought back on track through personalized food and dietary advice, or through the administration of nutritional supplements such as vitamins and minerals or prebiotics, probiotics or synbiotics.

[0248] Example 6 Comparison of differences in early life microbiome (ELM) change trajectories between reference and non-reference For the change trajectory of the reference set of 209 samples (see Example 3), the MAE was 46.362 and the R^2 was 0.941. For the change trajectory of the non-reference set of 453 samples, the MAE was 119.442 and the R^2 was 0.599. The comparison of the reference statistical p-value with the non-reference statistical p-value was significant by spline-based test (Shields-Cutler RR, et al. Front Microbiol. 2018): 0.002. A comparison of the change trajectory of the reference with the non-reference change trajectory is shown in Figure 4. For visualization purposes, the display of the actual samples and the 95% prediction intervals around them is switched off.

[0249] Example 7 Statistical Methods To define the fit of the change trajectory and determine whether a sample is on the ELM change trajectory, the following equation was used:

[0250] Least Squares Polynomial Fitting

[0251]

number

[0252] n is the number of samples (x i ,y i ), where i∈[0,n] in the dataset. x i is the independent variable (i.e., age at the time of data collection). y iis the dependent variable that is fitted by the model function. y has three parameters and degree 2 (i.e., the Microbiome Maturity Index (MMI)). β0,β1,β2 are the parameters of the model that are optimized to minimize E.

[0253] Chi-square First, y i is the standard deviation σ i Second, we consider the residual r=y(x|β0,β1,β2)-y i Next, consider the following function:

[0254]

number

[0255] Finding the best parameters for optimal fitting, χ 2 The probability distribution of is χ with n-2 degrees of freedom. 2 When there is no uncertainty in y1, the distribution is σ i = 1. This is only used to assess the goodness of fit. Furthermore, we use the reduced chi-square

[0256]

number

number

[0257] Confidence interval The 95% probability interval around the fitting line contains the mean of the new value for a particular age for the collected values. The residuals were iteratively resampled 500 times, and the darker the overlap, the higher the confidence.

[0258]

number

number

number

number

[0259]

number

[0260] Prediction intervals This shaded interval around the fitting line is the 95% probability that it contains a future new observation at that particular age collection.

number

number

number

number

number

[0261] Embodiment Various preferred features and embodiments of the present invention are described with reference to the following numbered paragraphs. 1. A method for providing a trained regression model for determining a state of a subject's gut microbiome, comprising: (a) providing gut metagenomic data from a population of healthy subjects; (b) training a regression model on the gut metagenomic data, wherein the age of the healthy subject at the time of data collection is regressed against the abundance of one or more carbohydrate-associated enzymes (CAZymes) provided by the gut metagenomic data; method. 2. The method of paragraph 1, wherein the age of the healthy subject at the time of data collection is regressed against a plurality of CAZyme abundances provided from the gut metagenomic data. 3. The method of paragraph 1 or 2, wherein the age of the healthy subject at the time of data collection is regressed against the abundance of 5 or more CAZymes, the abundance of 10 or more CAZymes, the abundance of 20 or more CAZymes, the abundance of 50 or more CAZymes, or the abundance of 100 or more CAZymes. 4. The method of any one of paragraphs 1 to 3, further comprising determining the CAZyme abundance from the gut metagenomic data. 5. The method of any one of paragraphs 1 to 4, wherein the CAZyme abundance is a relative CAZyme abundance, preferably the CAZyme abundance is with respect to the total bacterial gene. 6. The method of any one of paragraphs 1 to 5, wherein the CAZyme is a microbial CAZyme. 7. The method of any one of paragraphs 1 to 6, wherein the CAZyme is selected from glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE), and their associated carbohydrate-binding modules (CBMs). 8. The method of any one of paragraphs 1 to 7, wherein the CAZymes are classified by clan, family and / or subfamily, preferably the CAZymes are classified by family. 9. The above CAZyme, GH1, GH2, GH3, GH4, GH5, GH6, GH7, GH8, GH9, GH10, GH11, GH12, GH13, GH14, GH15, GH16, GH17, GH18, GH19, GH20, GH21, GH22, GH23, GH24, GH25, GH26, GH27, GH28, GH29, GH30, GH31, GH32, GH33, GH34, GH35, GH36, GH37, GH38, GH39, GH40, GH41, GH42, GH43, GH44, GH45, GH46, GH47, GH48, GH49, G H50、GH51、GH52、GH53、GH54、GH55、GH56、GH57、GH58、GH59、GH60、GH61、GH62、GH63、GH64、GH65、GH66、GH67、GH68、GH69、GH70、GH71、GH72、GH73、GH74、 GH75、GH76、GH77、GH78、GH79、GH80、GH81、GH82、GH83、GH84、GH85、GH86、GH87、GH88、GH89、GH90、GH91、GH92、GH93、GH94、GH95、GH96、GH97、GH98、GH99、 GH100、GH101、GH102、GH103、GH104、GH105、GH106、GH107、GH108、GH109、GH110、GH111、GH112、GH113、GH114、GH115、GH116、GH117、GH118、GH119、GH12 0、GH121、GH122、GH123、GH124、GH125、GH126、GH127、GH128、GH129、GH130、GH131、GH132、GH133、GH134、GH135、GH136、GH137、GH138、GH139、GH140、GH1 41、GH142、GH143、GH144、GH145、GH146、GH147、GH148、GH149、GH150、GH151、GH152、GH153、GH154、GH155、GH156、GH157、GH158、GH159、GH160、GH161 162、GH163、GH164、GH165、GH166、GH167、GH168、GH169、GH170、GH171、GH172、GT1、GT2、GT3、GT4、GT5、GT6、GT7、GT8、GT9、GT10、GT11、GT12、GT13、GT14GT15、GT16、GT17、GT18、GT19、GT20、GT21、GT22、GT23、GT24、GT25、GT26、GT27、GT28、GT29、GT30、GT31、GT32、GT33、GT34、GT35、GT36、GT37、GT38、GT39、GT40、GT41、GT42、GT43、GT44、GT45、GT46、GT47、GT48、GT49、GT50、GT51、GT52、GT53、GT54、GT55、GT56、GT57、GT58、GT59、GT60、GT61、GT62、GT63、GT64、GT65、GT66、GT67、GT68、GT69、GT70、GT71、GT72、GT73、GT74、GT75、GT76、GT77、GT78、GT79、GT80、GT81、GT82、GT83、GT84、GT85、GT86、GT87、GT88、GT89、GT90、GT91、GT92、GT93、GT94、GT95、GT96、GT97、GT98、GT99、GT100、GT101、GT102、GT103、GT104、GT105、GT106、GT107、GT108、GT109、GT110、GT111、GT112、GT113、GT114、PL1、PL2、PL3、PL4、PL5、PL6、PL7、PL8、PL9、PL10、PL11、PL12、PL13、PL14、PL15、PL16、PL17、PL18、PL19、PL20、PL21、PL22、PL23、PL24、PL25、PL26、PL27、PL28、PL29、PL30、PL31、PL32、PL33、PL34、PL35、PL36、PL37、PL38、PL39、PL40、PL41、PL42、CE1、CE2、CE3、CE4、CE5、CE6、CE7、CE8、CE9、CE10、CE11、CE12、CE13、CE14、CE15、CE16、CE17、CE18、CE19、CBM1、CBM2、CBM3、CBM4、CBM5、CBM6、CBM7、CBM8、CBM9、CBM10、CBM11、CBM12、CBM13、CBM14、CBM15、CBM16、CBM17、CBM18、CBM19、CBM20、CBM21、CBM22、CBM23、CBM24、CBM25、CBM26、CBM27、CBM28、CBM29、CBM30、CBM31、CBM32、CBM33、CBM34、CBM35, CBM36, CBM37, CBM38, CBM39, CBM40, CBM41, CBM42, CBM43, CBM44, CBM45, CBM46, CBM47, CBM48, CBM49, CBM50, CBM51, CBM52, CBM53, CBM54, CBM55, CBM56, CBM57, CBM58, CBM59, CBM60, CBM61, CBM62, CBM63, CBM64, The method of any of paragraphs 1-8, wherein the compound is selected from one or more of CBM65, CBM66, CBM67, CBM68, CBM69, CBM70, CBM71, CBM72, CBM73, CBM74, CBM75, CBM76, CBM77, CBM78, CBM79, CBM80, CBM81, CBM82, CBM83, CBM84, CBM85, CBM86, CBM87, and CBM88. 10. The method of any one of paragraphs 1 to 9, wherein the trained regression model predicts the age of a subject given the subject's gut metagenomic data. 11. The method of any one of paragraphs 1 to 10, wherein the trained regression model relates the age of a healthy subject to the subject's microbiome age, microbiome maturation index, and / or microbiome maturation age. 12. The method of any one of paragraphs 1 to 11, wherein the trained regression model is a trajectory of change in the early life microbiome (ELM). 13. The method of any one of paragraphs 1 to 12, wherein the trained regression model is for infancy and / or childhood, preferably, the trained regression model is for ages 0-5 years, 0-3 years, or 0-2 years, more preferably, the trained regression model is for ages 0-24 months. 14. The method of any one of paragraphs 1 to 13, further comprising obtaining the gut metagenomic data from a population of healthy subjects. 15. The method of any one of paragraphs 1 to 14, wherein the gut metagenomic data is obtained or obtainable from a fecal sample and / or by next generation sequencing. 16. The method of any one of paragraphs 1 to 15, wherein the gut metagenomic data from the population of healthy subjects comprises at least 50 samples, at least 100 samples, at least 200 samples, at least 300 samples, at least 400 samples, or at least 500 samples. 17. The method according to any one of paragraphs 1 to 4, wherein the healthy subject is an infant and / or a child, preferably the healthy subject is 0 to 5 years old, or 0 to 3 years old, or 0 to 2 years old, more preferably the healthy subject is 0 to 24 months old. 18. The method of any one of paragraphs 1 to 17, wherein the population of healthy subjects comprises at least 20 healthy subjects, at least 40 healthy subjects, at least 60 healthy subjects, at least 80 healthy subjects, or at least 100 healthy subjects. 19. The method of any one of paragraphs 1 to 18, wherein the regression model is a tree-based regression model, preferably a random forest regression model. 20. The method of any one of paragraphs 1 to 19, wherein the age of the healthy subject at the time of sample collection is also regressed against one or more additional features provided from the gut metagenomic data. 21. A trained regression model obtained or obtainable by a method according to any one of paragraphs 1 to 20. 22. A trained regression model for determining the state of the gut microbiome of a subject given one or more CAZyme abundances provided from the subject's gut metagenomic data. 23. A trained regression model according to paragraph 22, obtained or obtainable by a method according to any one of paragraphs 1 to 20. 24. A method for predicting the age of a subject, comprising: (a) providing a regression model trained by the method according to any of paragraphs 1 to 20 or a trained regression model according to any of paragraphs 21 to 23; (b) providing gut metagenomic data from the subject; and (c) predicting the age of the subject given the gut metagenomic data of the subject and the trained regression model. 25. A method for determining the status of a subject's gut microbiome, comprising: (a) providing a regression model trained by the method according to any of paragraphs 1 to 20 or a trained regression model according to any of paragraphs 21 to 23; (b) providing gut metagenomic data from the subject; and (c) determining whether the subject is an outlier in the trained regression model; The subject's gut microbiome status is healthy if the subject is not an outlier in the trained regression model, and / or the subject's gut microbiome status is unhealthy if the subject is an outlier in the trained regression model. 26. The method of paragraph 25, wherein the subject is an outlier based on the standard error (SE), confidence interval, prediction interval, and / or standard deviation in the trained regression model, preferably, the subject is an outlier if the gut metagenomic data of the subject is less than or equal to -2SE or more than 2SE from the trained regression line, if the gut metagenomic data of the subject is outside a 95% confidence interval in the trained regression model, if the gut metagenomic data of the subject is outside a 95% prediction interval in the trained regression model, and / or if the gut metagenomic data of the subject has a Z-score less than or equal to -2 or more than 2 in the learned regression model, more preferably, the subject is an outlier if the gut metagenomic data of the subject is outside a 95% prediction interval in the learned regression model. 27. A method for determining the status of a subject's gut microbiome, comprising: (a) providing a regression model trained by the method of any of paragraphs 1 to 20, or the trained regression model of any of paragraphs 21 to 23, wherein the trained regression model is a trajectory of change in ELM; (b) providing gut metagenomic data from the subject; and (c) determining whether the subject is on a trajectory of change in ELM; The method, wherein the subject's gut microbiome status is healthy if the subject is on a trajectory of change in the ELM, and / or the subject's gut microbiome status is unhealthy if the subject is off a trajectory of change in the ELM. 28. The method of paragraph 27, wherein the subject is on a trajectory of change in the ELM if the gut metagenomic data of the subject is not significantly different from the trajectory of change in the ELM, and / or the subject is off a trajectory of change in the ELM if the gut metagenomic data of the subject is significantly different from the trajectory of change in the ELM. 29. The method of paragraph 27 or 28, wherein the subject is determined to be off the ELM change trajectory based on the standard error (SE), confidence interval, prediction interval, and / or standard deviation of the ELM change trajectory, preferably the subject is determined to be off the ELM change trajectory if the gut metagenomic data of the subject is below -2SE or above 2SE from the ELM change trajectory, if the gut metagenomic data of the subject is outside a 95% confidence interval of the ELM change trajectory, if the gut metagenomic data of the subject is outside a 95% prediction interval of the ELM change trajectory, and / or if the subject's Z-score is below -2 or above 2, more preferably the subject is determined to be off the ELM change trajectory if the gut metagenomic data of the subject is outside a 95% prediction interval of the ELM change trajectory. 30. A method for determining a state of the gut microbiome of a subject, comprising predicting the age of the subject by the method described in paragraph 24, wherein if the predicted age of the subject does not significantly differ from the actual age of the subject, the state of the gut microbiome of the subject is healthy, and / or if the predicted age of the subject is significantly different from the actual age of the subject, the state of the gut microbiome of the subject is not healthy. 31. The method of paragraph 30, wherein the predicted age of the subject significantly differs from the actual age of the subject if the predicted age of the subject differs significantly from the actual age of the subject by more than about 0.1 year, more than about 0.2 years, more than about 0.3 years, more than about 0.4 years, more than about 0.5 years, more than about 0.6 years, more than about 0.7 years, more than about 0.8 years, more than about 0.9 years, or more than about 1 year. 32. The method of any one of paragraphs 24 to 31, wherein the gut metagenomic data of the subject is obtained or can be obtained from a fecal sample. 33. The method of any one of paragraphs 24 to 32, wherein the gut metagenomic data of the subject has been obtained or can be obtained by next generation sequencing. 34. The method of any one of paragraphs 24 to 33, wherein the gut metagenome of the subject provides a CAZyme abundance of 5 or more, a CAZyme abundance of 10 or more, a CAZyme abundance of 20 or more, a CAZyme abundance of 50 or more, or a CAZyme abundance of 100 or more. 35. A method for maintaining or improving the status of the gut microbiome in a subject, comprising: (a) determining the status of the gut microbiome of the subject by a method according to any one of paragraphs 25 to 34; (b) adjusting the subject's diet, nutrient intake, and / or lifestyle to maintain or improve the state of the gut microbiome in the subject. 36. The method of paragraph 35, wherein the gut microbiome status of the subject is healthy after adjusting the subject's diet, nutrient intake, and / or lifestyle. 37. The method of paragraph 35 or 36, wherein the adjusted diet, nutrient intake, and / or lifestyle of the subject increases the abundance of preferred CAZymes and / or decreases the abundance of unpreferred CAZymes. 38. A method for determining a subject's diet and / or nutrient intake, comprising: (a) determining the status of the gut microbiome of the subject by a method according to any one of paragraphs 25 to 34; (b) determining the diet and / or ingested nutrients required to maintain or improve the status of the gut microbiome in the subject. 39. The method of any one of paragraphs 35 to 38, wherein food and / or supplements are administered to the subject to increase the abundance of a preferred CAZyme and / or decrease the abundance of an unpreferred CAZyme. 40. The method of paragraph 39, wherein the food and / or supplement comprises one or more carbohydrates. 41. The carbohydrate is 4-methylumbelliferyl 6-azido-6-deoxy-β-D-galactoside, N-acetyl-D-galactosamine, N-acetyl-D-glucosamine, N-acetylglucosamine, N-glycan, O-antigen, O-glycan, acarbose, acetylated glucuronoxylan, agar, alduronate, alginate, α-galactoside, α-glucan, α-mannan, α-mannoside, arabinan, arabinoxylooligosaccharide, arabinofuranooligosaccharide, arabinofuranose, arabinogalactan, arabinose, arabinoxy orchid, β-galactooligosaccharide, β-galactosides, β-glucans, β-glucosides, β-mannans, capsule polysaccharides, carboxymethylcellulose, carrageenan, cellobiose, cellodextrin, cellooligosaccharide, cellotriose, cellulose, chitin, chitobiose, chitooligosaccharide, chondroitin disaccharide, chondroitin sulfate, chondroitin sulfate, cyclomaltodextrin, d-galactosamine, dextran, emulsan, extracellular polysaccharide, fructan, fructooligosaccharide, fucose, fucosyllactose, galactan, galactomannan, galact Oligosaccharide, galactose, galacturonic acid, gentiobiose, glucan, glucomannan, glucosamine, glucose, glycogen, glycosaminoglycan, hemicellulose, heparosan, homogalacturonan, host glycan, human milk oligosaccharide, inulin, isomaltose, isomaltotriose, kestose, lacto-n-triose, lactose, laminaribiose, laminarin, levan, lichenan, lignocellulose, lipopolysaccharide, maltodextrin, maltooligosaccharide, maltose, maltotriose, mannan, mannooligosaccharide, manno , melezitose, melibiose, methylglucuronoarabinoxylan, mucin, oligogalacturonide, outer shell polysaccharide, panose, pectin, plant polysaccharide, polygalacturonic acid, polysialic acid, porphyran, pullulan, raffinose oligosaccharide, raffinose, rhamnogalacturonan, rhamnose, ribose, sialic acid, sialoglucoconjugate, sophorose, sorbitol, stachyose, starch, sucrose, trehalose, ulvan, unsaturated hyaluronic acid disaccharide, xanthan, xylan, xylobiose, xylodextrin, xyloglucan,41. The method of paragraph 40, wherein the xylooligosaccharides are selected from one or more of xylooligosaccharides, xylose, and xylotriose. 42. A data processing system comprising means for carrying out the method according to any one of paragraphs 1 to 20 or 25 to 38. 43. A data processing system comprising a processor configured to carry out the method described in any one of paragraphs 1-20 or 25-38. 44. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out a method as described in any one of paragraphs 1 to 20 or 25 to 38. 45. A computer program comprising instructions that, when executed by a computer, cause the computer to determine a trained regression model for determining the state of the gut microbiome of a healthy subject from a population of healthy subjects, given the age of the healthy subject at the time of data collection and one or more CAZyme abundances provided from gut metagenomic data of the healthy subject. 46. ​​A computer program comprising instructions that, when executed by a computer, cause the computer to predict the age of a subject or determine the status of the gut microbiome of the subject given one or more CAZyme abundances provided from the subject's gut metagenomic data. 47. A computer program comprising instructions that, when executed by a computer, cause the computer to predict the age of a subject or determine the status of the gut microbiome of a subject given gut metagenomic data from a population of healthy subjects and a regression model trained on the gut metagenomic data of the subject, wherein the regression model has been trained by regressing the age of the subject at the time of data collection against the abundance of one or more CAZymes provided from the gut metagenomic data of the healthy subjects. 48. The computer program of paragraph 47, wherein the trained regression model is the trained regression model of any one of paragraphs 21 to 23. 49. A computer readable medium comprising instructions that, when executed by a computer, cause the computer to perform the method described in any one of paragraphs 1-20 or 25-38. 50. A computer-readable medium comprising instructions that, when executed by a computer, cause the computer to determine a trained regression model to determine the state of the gut microbiome of a healthy subject from a population of healthy subjects, given the age of the healthy subject at the time of data collection and one or more CAZyme abundances provided from gut metagenomic data of the healthy subject. 51. A computer-readable medium comprising instructions that, when executed by a computer, cause the computer to predict the age of a subject or determine the status of the gut microbiome of the subject given one or more CAZyme abundances provided from the subject's gut metagenomic data. 52. A computer readable medium comprising instructions that, when executed by a computer, cause the computer to predict the age of the subject or determine the status of the gut microbiome of the subject given gut metagenomic data from a population of healthy subjects and a regression model trained on the gut metagenomic data of the healthy subjects, wherein the regression model has been trained by regressing the age of the healthy subject at the time of data collection against one or more CAZyme abundances provided from the gut metagenomic data of the healthy subjects. 53. A computer-readable medium according to paragraph 52, wherein the trained regression model is a trained regression model according to any of paragraphs 21 to 23. 54. A computer-readable data carrier storing a computer program according to any one of paragraphs 44 to 48. 55. A data carrier signal carrying a computer program according to any one of paragraphs 44 to 48. 56. Use of one or more CAZyme abundances provided from a subject's gut metagenomic data to predict the age of a subject or to determine the status of the subject's gut microbiome. 57. Use of one or more CAZyme abundances provided from gut metagenomic data from a population of healthy subjects to train a regression model. 58. Use of a trained regression model according to any of paragraphs 21 to 23 for predicting the age of a subject or for determining the status of the gut microbiome of said subject. 59. The method according to any of paragraphs 35 to 41, the data processing system according to paragraph 42 or 43, the computer program according to any of paragraphs 44 to 48, the computer readable medium according to any of paragraphs 49 to 53, the computer readable data carrier according to paragraph 54, the data carrier signal according to paragraph 55 or the use according to any of paragraphs 56 to 58, wherein the subject is an infant or child, preferably wherein the subject is 0 to 5 years old, or 0 to 3 years old, or 0 to 2 years old, more preferably wherein the subject is 0 to 24 months old.

[0262] All publications mentioned in the above specification are incorporated herein by reference. Various modifications and alterations of the disclosed methods, compositions and uses of the present invention will be apparent to those skilled in the art without departing from the scope and spirit of the present invention. Although the present invention has been disclosed in connection with certain preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the disclosed modes for carrying out the invention that are obvious to those skilled in the art are intended to be within the scope of the following claims.

Claims

1. 1. A method for providing a trained regression model for determining a gut microbiome status of a subject, comprising: (a) providing gut metagenomic data from a population of healthy subjects; (b) training a regression model on the gut metagenomic data, wherein the age of the healthy subject at the time of data collection is regressed against the abundance of one or more carbohydrate-associated enzymes (CAZymes) provided from the gut metagenomic data.

2. The CAZyme is selected from glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), carbohydrate esterases (CE), and their associated carbohydrate-binding modules (CBMs), and preferably the CAZyme is selected from GH1, GH2, GH3, GH4, GH5, GH6, GH7, GH8, GH9, GH10, GH11, GH12, GH13, GH14, GH15, GH16, GH17, GH18, GH19, GH20, GH21, GH22, GH23, GH24, GH25, GH26, GH27, GH28, GH29, GH30, GH31, GH32, GH33, GH34, GH35, GH36, GH37, GH38, GH39, GH40, GH41, GH42, GH43, GH44, GH45, GH46, GH47, GH48, GH49, GH50, GH51, GH52, GH53, GH54, GH55, GH56, GH57, GH58, GH59, GH60, GH61, GH62, GH63, GH64, GH65, GH66, GH67, GH68, GH69, GH70, GH71, GH72, GH73, GH74, GH75, GH76, GH77, GH78, GH79, GH80, GH81, GH82, GH83, GH84, GH85, GH86, GH87, GH88, GH89, GH90, GH91, 9, GH30, GH31, GH32, GH33, GH34, GH35, GH36, GH37, GH38, GH39, GH40, GH41, GH42, GH43, GH44, GH45, GH46, GH47, GH48, GH49, GH50, GH51, GH52, GH53, GH 54, GH55, GH56, GH57, GH58, GH59, GH60, GH61, GH62, GH63, GH64, GH65, GH66 , GH67, GH68, GH69, GH70, GH71, GH72, GH73, GH74, GH75, GH76, GH77, GH78, GH 79, GH80, GH81, GH82, GH83, GH84, GH85, GH86, GH87, GH88, GH89, GH90, GH91 , GH92, GH93, GH94, GH95, GH96, GH97, GH98, GH99, GH100, GH101, GH102, GH1 03, GH104, GH105, GH106, GH107, GH108, GH109, ​​GH110, GH111, GH112, GH113 , GH114, GH115, GH116, GH117, GH118, GH119, GH120, GH121, GH122, GH123, GH 124, GH125, GH126, GH127, GH128, GH129, GH130, GH131, GH132, GH133, GH13 4, GH135, GH136, GH137, GH138, GH139, GH140, GH141, GH142, GH143, GH144, G H145, GH146, GH147, GH148, GH149, GH150, GH151, GH152, GH153, GH154, GH1 55, GH156, GH157, GH158, GH159, GH160, GH161, GH162, GH163, GH164, GH165,GH166、GH167、GH168、GH169、GH170、GH171、GH172、GT1、GT2、GT3、GT4、GT5、GT6、GT7、GT8、GT9、GT10、GT11、GT12、GT13、GT14、GT15、GT16、GT17、GT18、GT19、GT20、GT21、GT22、GT23、GT24、GT25、GT26、GT27、GT28、GT29、GT30、GT31、GT32、GT33、GT34、GT35、GT36、GT37、GT38、GT39、GT40、GT41、GT42、GT43、GT44、GT45、GT46、GT47、GT48、GT49、GT50、GT51、GT52、GT53、GT54、GT55、GT56、GT57、GT58、GT59、GT60、GT61、GT62、GT63、GT64、GT65、GT66、GT67、GT68、GT69、GT70、GT71、GT72、GT73、GT74、GT75、GT76、GT77、GT78、GT79、GT80、GT81、GT82、GT83、GT84、GT85、GT86、GT87、GT88、GT89、GT90、GT91、GT92、GT93、GT94、GT95、GT96、GT97、GT98、GT99、GT100、GT101、GT102、GT103、GT104、GT105、GT106、GT107、GT108、GT109、GT110、GT111、GT112、GT113、GT114、PL1、PL2、PL3、PL4、PL5、PL6、PL7、PL8、PL9、PL10、PL11、PL12、PL13、PL14、PL15、PL16、PL17、PL18、PL19、PL20、PL21、PL22、PL23、PL24、PL25、PL26、PL27、PL28、PL29、PL30、PL31、PL32、PL33、PL34、PL35、PL36、PL37、PL38、PL39、PL40、PL41、PL42、CE1、CE2、CE3、CE4、CE5、CE6、CE7、CE8、CE9、CE10、CE11、CE12、CE13、CE14、CE15、CE16、CE17、CE18、CE19、CBM1、CBM2、CBM3、CBM4、CBM5、CBM6、CBM7、CBM8、CBM9、CBM10、CBM11、CBM12、CBM13、CBM14、CBM15、CBM16、CBM17、CBM18, CBM19, CBM20, CBM21, CBM22, CBM23, CBM24, CBM25, CBM26, CBM27, CBM28, CBM29, CBM30, CBM31, CBM32, CBM33, CBM34, CBM35, CBM36 , CBM37, CBM38, CBM39, CBM40, CBM41, CBM42, CBM43, CBM44, CBM45, CBM46, CBM47, CBM48, CBM49, CBM50, CBM51, CBM52, CBM53, CBM54, CBM55 , CBM56, CBM57, CBM58, CBM59, CBM60, CBM61, CBM62, CBM63, CBM64, CBM65, CBM66, CBM67, CBM68, CBM69, CBM70, CBM71, CBM72, CBM73, CBM74, CBM75, CBM76, CBM77, CBM78, CBM79, CBM80, CBM81, CBM82, CBM83, CBM84, CBM85, CBM86, CBM87, and CBM88.

3. 3. The method of claim 1 or 2, further comprising obtaining said gut metagenomic data from a population of healthy subjects, preferably wherein said gut metagenomic data has been obtained or is obtainable from a fecal sample and / or by next generation sequencing.

4. 4. The method according to any one of claims 1 to 3, wherein the healthy subject is an infant and / or a child, preferably the healthy subject is aged 0 to 5 years, or 0 to 3 years, or 0 to 2 years, more preferably the healthy subject is aged 0 to 24 months.

5. The method according to any one of claims 1 to 4, wherein the regression model is a tree-based regression model, preferably a random forest regression model.

6. A trained regression model obtained or obtainable by the method according to any one of claims 1 to 5.

7. A trained regression model for determining the state of a subject's gut microbiome given one or more CAZyme abundances provided from the subject's gut metagenomic data.

8. 1. A method for determining the status of a subject's gut microbiome, comprising: (a) providing a regression model trained by the method of any one of claims 1 to 5 or a trained regression model of claim 6 or 7; (b) providing gut metagenomic data from the subject; and (c) determining whether the subject is an outlier in the trained regression model; The method of claim 1, wherein the subject's gut microbiome status is healthy if the subject is not an outlier in the trained regression model, and / or the subject's gut microbiome status is unhealthy if the subject is an outlier in the trained regression model.

9. 1. A method for maintaining or improving the status of the gut microbiome in a subject, comprising: (a) determining the status of the gut microbiome of the subject by the method of claim 8; (b) adjusting the subject's diet, nutrient intake, and / or lifestyle to maintain or improve the state of the gut microbiome in the subject.

10. The subject is administered a food and / or supplement to increase the abundance of favorable CAZymes and / or decrease the abundance of unfavorable CAZymes, preferably the food and / or supplement comprises one or more carbohydrates, more preferably the carbohydrate is selected from the group consisting of 4-methylumbelliferyl 6-azido-6-deoxy-β-D-galactoside, N-acetyl-D-galactosamine, N-acetyl-D-glucosamine, N-acetylglucosamine, N-glycans, O-antigens, O-glycans, acarbose, acetylated glucuronoxy Orchid, agar, alduronate, alginate, α-galactosides, α-glucans, α-mannans, α-mannosides, arabinans, arabinoxylooligosaccharides, arabinofuranooligosaccharides, arabinofuranose, arabinogalactans, arabinose, arabinoxylans, β-galactooligosaccharides, β-galactosides, β-glucans, β-glucosides, β-mannans, capsule polysaccharides, carboxymethylcellulose, carrageenans, cellobiose, cellodextrin, cellooligosaccharides, cellotriose, cellulose, chitin, chitobiose, chitooligosaccharides, chondroitin, cellulose ... isotonic acid, chondroitin disaccharide, chondroitin sulfate, cyclomaltodextrin, d-galactosamine, dextran, emulsan, extracellular polysaccharide, fructan, fructooligosaccharide, fucose, fucosyllactose, galactan, galactomannan, galactooligosaccharide, galactose, galacturonic acid, gentiobiose, glucan, glucomannan, glucosamine, glucose, glycogen, glycosaminoglycan, hemicellulose, heparosan, homogalacturonan, host glycan, human milk oligosaccharide, inulin, isomaltose, isomaltose lyose, kestose, lacto-n-triose, lactose, laminaribiose, laminarin, levan, lichenan, lignocellulose, lipopolysaccharides, maltodextrin, maltooligosaccharides, maltose, maltotriose, mannan, mannooligosaccharides, mannose, melezitose, melibiose, methylglucuronoarabinoxylan, mucin, oligogalacturonide, outer shell capsule polysaccharides, panose, pectin, plant polysaccharides, polygalacturonic acid, polysialic acid, porphyran, pullulan, raffinose oligosaccharides, raffinose, rhamnogalacturonan,10. The method of claim 9, wherein the sugar is selected from one or more of rhamnose, ribose, sialic acid, sialoglycoconjugates, sophorose, sorbitol, stachyose, starch, sucrose, trehalose, ulvan, unsaturated hyaluronic acid disaccharides, xanthan, xylan, xylobiose, xylodextrin, xyloglucan, xylooligosaccharides, xylose, and xylotriose.

11. A data processing system comprising means for carrying out the method according to any one of claims 1 to 5 or 8.

12. A computer program comprising instructions which, when said program is executed by a computer, cause said computer to carry out a method according to any one of claims 1 to 5 or 8.

13. 20. Use of one or more CAZyme abundances provided from a subject's gut metagenomic data to predict the age of said subject or to determine the status of said subject's gut microbiome.

14. Use of one or more CAZyme abundances provided from gut metagenomic data from a population of healthy subjects to train a regression model.

15. 8. Use of a trained regression model according to claim 6 or 7 for predicting the age of a subject or for determining the status of the gut microbiome of a subject.

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