CN106202989A - A kind of method obtaining child's individuality biological age based on oral microbial community - Google Patents
A kind of method obtaining child's individuality biological age based on oral microbial community Download PDFInfo
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Abstract
The present invention provides a kind of method obtaining child's individuality biological age based on oral microbial community, and described method includes obtaining the sample containing described child individuality oral microorganism；Extract the DNA of oral microorganism；Described DNA information is converted into microbiologic population's information, utilizes random forests algorithm, oral microbial community information and age are carried out regression analysis, build regression model, it is thus achieved that described Chinese population child's Individual Age.The scheme that the present invention provides can obtain the biological age that Chinese population child is individual exactly, can be without invasive, obtain saliva of buccal cavity or dental plaque sample simply, efficiently, child's Individual Age is detected for a long time, this is beneficial to quickly judge host's now physiological health state, give a clue for health monitoring, improve disease early diagnosis speed simultaneously.
Description
Technical field
The present invention relates to microorganism detection model field, a kind of method obtaining child's individuality biological age based on oral microbial community.
Background technology
The mankind are not lonely in generation, and everyone is internal all carries billions of microorganisms, and the mankind collectively constitute one " superior biological body " with the microorganism of its parachorium.Not having microorganism in uterus, what the mankind contacted with microorganism for the first time is birth canal.After birth, by having milk and contacting with external environment, more microorganism migrates in mankind's body.Human microbial group has an age characteristics, and mankind internal microorganism group gradually builds up with age, and all one's life changes along with physiogenesis and constantly evolves.Those enter after birth human body and to health produce material impact microorganism be the natural endowment day after tomorrow important carrying those of, another genome being equivalent to also exist in mankind's body in addition to the genome of the mankind itself life and health by expression regulation human body, it is now recognized that symbiotic microorganism can be as the second genome of human body, the summation of its hereditary information is referred to as microorganism group (microbiome), the complicated personal feature that the imparting mankind do not rely on its own evolution and obtain.Therefore, full appreciation human body symbiosis flora can the degree of depth disclose its on health or the impact of morbid state, thus build microbiologic population and exist and contacting between situation of change and host's physiological status.
Oral system is the transport hub inside and outside connection human body, for the very important site of perching of human body fungal component group, maintains the state in a healthy and balanced way of oral cavity flora 26S Proteasome Structure and Function, has important meaning that is deep and that can not be ignored for health.With blood test and stone age as compared with medical diagnosis on disease medium, the sampling of site, oral cavity has the advantages such as low invasive, low cost, sample collecting and process be simple, quick.
The growth promoter of people can represent with two " ages ": i.e. biological age (biological age) and life age (chronological age).Biological age refers to individual in the potential vital stage, and the position at current place, is the aggregative index of human health status, is the objective statement of organism aging process degree.The life age refers to the individual actual age counted day from birth, is as the criterion with elapsed time on calendar.Impact due to factors such as nutrition, disease, heredity, environment, some life ages are not consistent with development degree (biological age), so living, the age can not truly reflect physical development, a maturity, and biological age has close relationship with individual physiological health.
Skeletal age (stone age is widely used at present；Skeletal age, SA；Bone age, BA) evaluate biological age or ripe situation.Bone-age determination includes the body parts such as wrist portion, elbow joint, knee joint and foot, and wrist portion is because it is sensitive and it is convenient and conventional clinically use X-ray film to carry out wrist portion to be measured child to take the photograph sheet.But wrist portion skeleton number is many, there are carpal bone 8 pieces, metacarpal bone 5 pieces, phalanges 14 pieces, add chi, radius totally 29 pieces, additionally, plant the important symbol that sesamoid bone is also skeleton development inside thumb.Assessment method mainly includes Atlas Method and scoring method, and wherein Atlas Method is simple, directly perceived, but assessor or compares and interpretation with whole X-ray film in practical operation, still there is the problems such as subjective interpretation is strong, osseous maturation combination is many；Though scoring method is relatively objective, but bone development grade classification is meticulous, and it is big that standard grasps difficulty, thus reduces the reliability of Assessing Standards For Skeletal, and the foundation of Skeletal Age Standards spectrum library and computer read tablet systematic research are urgently to be resolved hurrily.In addition, though human body is almost harmless by X-ray, but long-term tracking for upgrowth and development of children still needs to take safeguard procedures when processing, rather than lonizing radiation bone age evaluates the exploitation with method still in initial stage, the interpretation precision used such as ultrasound detection is relatively low, and methodology still suffers from problem.And in addition to the stone age, measure also tooth maturation degree and secondary sex characteristics development degree that the method for biological age is conventional, but these methods generally assess many dependence assessor's subjective judgment, result is all value range, more difficult calculating individual biological age accurately, and evaluation index is heterogeneous relatively large between individuality.It would therefore be highly desirable to develop operation objective, accurate, easy, without invasive, high-throughout biological age appraisal procedure.
Summary of the invention
For above-mentioned weak point present in prior art, the technical problem to be solved in the present invention is to provide a kind of method obtaining child's individuality biological age based on oral microbial community.
A kind of the technical scheme is that method obtaining child's individuality biological age based on oral microbial community of the present invention, comprises the following steps:
Data collection: collect child's individuality oral cavity sample of multiple time point；
Data convert: extract the DNA information obtaining oral cavity sample, utilize bioinformatics method that described DNA information is converted into oral microbial community information；
The Primary Construction of data model: using the oral microbial community information of acquisition as input variable, utilize random forest method, age information is returned by it, Primary Construction preliminary mathematical model based on oral microbial community infomation detection biological age；
The optimization of mathematical model and determining: sorting at the importance degree of model according to variable, the combination of simplified model variable, finally determines the model that child's Individual Age detects under not affecting model performance premise；
The detection of child's individuality biological age: using desired microorganisms group information as input variable, utilizes the mathematical model set up to carry out regression analysis, it is thus achieved that the child's individuality now biological age detected.
Described oral cavity sample is dental plaque sample on saliva or gum.
The described oral microbial community information that is converted into by DNA information comprises the following steps:
16s RNA or the full-length genome information of DNA information is obtained by high-flux sequence means；
Carry out horizontal bacterial strains information from door to kind for 16s RNA or full-length genome information to incorporate into；
Add up each sample sequence number of each species in kind of level respectively, and the sequence number obtained with this population of samples calculates its ratio, thus obtain the relative abundance of each species.
The Primary Construction of described data model, comprises the following steps:
1) using the composition of whole carefully species level of oral microorganism obtained and relative abundance thereof as input variable；
2) utilize random forest method, the age information that child is individual is returned by input variable, Primary Construction preliminary mathematical model based on oral microbial community infomation detection biological age.
The optimization of described data model and determining, comprises the following steps:
1) each variable of the kind representing bacterium in preliminary mathematical model importance degree to model performance is obtained；
2) according to variable, model importance degree is sorted from small to large, gradually reduce variable quantity, utilize random forest method, carry out the regression analysis to the age, it is thus achieved that the model of different variable combinations；
3) evaluate the combination of simplification variable under not reducing model performance premise, be defined as age correlated variables, so that it is determined that final optimization pass model.
The detection of described child's individuality biological age, comprises the following steps:
1) DNA of child's individuality oral cavity sample is obtained；
2) utilize bioinformatics method that DNA information is converted to oral microbial community information；
3) relative abundance of the individual age correlated variables of child is obtained；
4) utilize random forest method, using the composition of age correlated variables and abundance thereof as variable, the age detection model set up is carried out regression analysis, it is thus achieved that the individual biological age now of child.
Also include: the biological age that the child obtained is individual is contrasted with its actual age, knows the now growth promoter situation of child, if i.e. biological age is less than actual age, then point out this child to have owing to the factors such as disease cause the possibility of growth retardation；If biological age is equal to actual age, then point out this upgrowth and development of children situation normal；If biological age is higher than actual age, then prompting child has the possibility of precocity.
The present invention has the following advantages and beneficial effect:
1. object collection and the process of the present invention are simple, without invasive, low cost；
2. the model of the present invention is set up and is optimized easily operated, data and processes efficiently；
3. the assessment of the present invention is objective, automatization, it is possible to provide exact numerical；
4. the present invention is widely used: its application is applicable not only to large-scale crowd assessment, it is possible to realize long term monitoring for individuality；Its application form not only can detect child's individuality now biological age, it is possible to as assessment ontogeny growth and the householder method of health condition.
Accompanying drawing explanation
Fig. 1 implements the design of experiment provided for the present invention；
Fig. 2 implements the oral microbial community architectural feature figure provided for the present invention；
Fig. 3 filters out age-related oral microorganism by random forest homing method form and to model performance percentage contribution figure for what the present invention implemented to provide；
After Fig. 4 implements, for the present invention, the optimization provided, model is applied to healthy group and dental caries group result figure.
Detailed description of the invention
Below in conjunction with the accompanying drawings and embodiment the present invention is described in further detail.
The present invention can detect the biological age of Pediatric Oral Emergency as embodiment (Fig. 1), including following content to utilize oral plaque and saliva microbiologic population to build and to optimize:
(1) Pediatric Oral Emergency health status clinical information (table 1) is collected:
The oral health of south, the Guangzhou full-time child of Chinese and English kindergarten is tracked investigation, every half a year checks once, continue 1 year three times to check, it is spaced the most again 1 year and checks, child's dmfs (dental caries according to investigation records, lose, dental filling number) index, select to have the child of following three class oral health variation characteristics according to this research purpose and include this subject study in: 1. healthy group (H2H group): oral cavity dental caries situation remains 17 healthy children；2. dental caries group, including dental caries generation group (H2C group): dental caries situation experience in oral cavity newly sends out 21 children of process, and dental caries progression group (C2C group) from health to dental caries: oral cavity dental caries situation experiences from having suffered from the dental caries 12 children to dental caries evolution.Inclusion criteria includes: about 4 years old age, 20 whole eruptions of deciduous teeth, and exclusion standard includes: has whole body system disease and the oral disease such as periodontal, halitosis, within three months, takes antibiotic.Obtain volunteer guardian with regard to matters such as the every details of whole experiment flow and later data announcements to agree to, and sign Informed Consent Form.On the gum taken when choosing the examination of mouth of all selected children, dental plaque and saliva sample amount to 284.
Investigation method: checked that examination apparatus autoclave sterilization removes soft dirt by cotton swab if desired in the way of inspection combination is visited and examined by two tooth body dental pulp specialists.Unified understanding, method and standard before checking, the Kappa value of standard consistency inspection is all higher than 0.92.Use the World Health Organization (WHO) " oral health surveys basic skills " (1997) diagnostic criteria to dental caries.Hat dental caries diagnostic criteria: the nest ditch point gap of tooth or shiny surface have obvious cavity or substantially destroy under enamel or clearly can visit and soften at the bottom of hole or the disease damage of hole wall is designated as dental caries, includes charges or the fissure blockade person that simultaneously has dental caries.Following performance is had when lacking other positive symptom, not list dental caries recording interval in: 1. white or Chalk mottle point；2. the coloring examined without softening or rough spot are visited；3. glaze particle gap or the coloring of nest ditch, but without destruction of moving under water under obvious enamel；4. to severe dental fluorosis in, glossy, matter hard, has dolly dimple；5. according to distribution or medical history, observe cause disease damage dental caries because of abrasion in conjunction with palpation, inspection.
The sample clinical data that table 1 present example provides
(2) children's salivary and supragingival plaque sample are collected:
Sampling previous hour experimenter and avoid feed and drinking-water, every sub-sampling 9:100-12:00 the most in the morning, during sampling, child keeps gently facing upward head, eye closing, upright seat.Collect child's nonirritant saliva about 3-5ml in 50ml sterile centrifugation tube, and every 1ml is sub-packed in 1.5ml centrifuge tube；Re-use aseptic toothbrush and gather bacterial plaque on whole eruption deciduous teeth gums 1 minute, the bacterial plaque adhered on toothbrush is transferred to fill the 50ml centrifuge tube of 10ml distilled water, avoids during sampling touching other sites, oral cavity such as mucosa.Sample is numbered respectively and is placed in-80 DEG C of preservation DNA to be extracted.
(3) extracting genome DNA and PCR expand 16S rRNA genetic fragment
Use high salt DNA extraction method.Centrifuge tube 13, the 000rpm/min centrifugation 15min respectively of bacterial plaque and saliva will be filled, and abandon supernatant, be separately added into 1ml lysate, lysate mixture will add 30 μ L E.C. 3.4.21.64s and 150 μ L 10%SDS, 53 DEG C of water-bath concussion incubated overnight.Adding 400 μ L 5M NaCl and cultivate 10min on ice, 13,000rpm/min are centrifuged 10min.Add isopyknic saturated phenol solution, be mixed into emulsion form to aqueous phase phenol, with 13,000rpm/min centrifugation 15min, draws the sticky aqueous phase in upper strata and extracts once to new pipe, repetition phenol.Add isopyknic chloroform isoamyl alcohol mixed liquor (24:1), rotate mixing, with 13,000rpm/min centrifugation 15min, take upper strata sticky aqueous phase transfer.Add 800 μ L isopropanols, incubated at room temperature 1min, with 13,000rpm/min centrifugation 15min.Abandoning supernatant, 70% ethanol is washed twice, is dissolved in 50 μ L TE solution after drying.
Use the Qubit quantitative DNA concentration of ultramicron spectrophotometer, electrophoresis detection DNA integrity.DNA after extraction is stored in-20 DEG C.About 15ng DNA is used for building 16S and expands library.
For obtaining relatively accurate germline development information, choose V1-V3 hypervariable region in 16S rRNA fragment (Escherichia coli positions 5-534) and expand target fragment as PCR.Determine PCR forward primer (5 '-NNNNNNN-TGGAGAGTTTGATCCTGGCTCAG-3 ') and downstream primer (5 '-NNNNNNN-TACCGCGGCTGCTGGCAC-3 '), NNNNNNN i.e. IDtag, it is seven bases of the random combine designed for the different sample source of difference, it is separately added into 5 ' ends of upstream and downstream primer, utilizes this Multi-example parallel signature technology to complete multiple sample and check order on sequenator simultaneously.
Each sample carries out three PCR amplifications, PCR reaction system (25 μ L) comprises the Gotag Hotstart polymerase of 12.5 μ L, each 1 μ L upstream and downstream primer (concentration 5pM), 1 μ L genomic DNA (5ng μ L-1), 9.5 μ L PCR rank sterilized water, react at Thermocycler PCR system.Reaction condition is set as: 95 DEG C of denaturations 2min, 94 DEG C of degeneration 30s, and anneal 56 DEG C of 25s, and 72 DEG C extend 25s, totally 25 circulations, and last 72 DEG C extend 5min.Gel electrophoresis (1.2%Q agarose is all carried out after PCR primer mixing, 5V cm-1,40min), confirm expanding effect, agarose gel is placed under uviol lamp, extracting the DNA band of about 500bp length, the operating process provided according to Qiagen MiniElute test kit carries out reclaiming, purification purpose sheet segment DNA, washs with 20 μ L.
(4) 454GS FLX Titanium order-checking
Main flow is as follows: 1. prepared by library, use Agilent BioAnalyzer 2100 biological analyser and the associating of PicoGreen ultramicron spectrophotometer quantitatively, three parts of DNA library are built altogether after being mixed with equimolar by difference sample, be connected modification with specific linkers, degenerative treatments reclaims single stranded DNA；2. emulsifying PCR, is fixed on magnetic bead by DNA library, through amplification emulsifying, forms oil water mixture, and each DNA segment carries out independent parallel amplification at microreactor, produces millions of meter identical copies.Break emulsified state, reclaim the DNA fragmentation that purification is incorporated on magnetic bead；3. sequencing reaction, the magnetic bead carrying DNA is mixed with other reactants, putting into and be placed in PTP plate in 454GS FLX Titanium machine, the interpolation of each nucleotide complementary with template strand can produce fluorescence signal and be captured by CCD camera, is gradually completing order-checking；Sequencing reaction data are carried out base parsing by system information instrument by 4. data collection.
(5) the high flux data of acquisition are converted into concrete microbiologic population's data
Sequence quality controls: 454 high-quality sequence analysis flow processs are based primarily upon MOTHUR platform, sets quality control specifications, and standard compliant sequence fragment is considered high-quality sequence, is retained.The most at least one end primer can be matched, it is allowed to editing distance (insert, delete, lack, the base quantity of mispairing) less than 2；2. sequence length is more than 150bp；3. arranging the base reading frame of a 50bp, start base one by one from the first of every sequence base and be moved rearwards by, often move a base, calculate once the mass fraction average in this reading frame, this performance figure average need to be more than 35；4. fuzzy base is not contained；5. allow sequence label mispairing quantity less than 1.After preliminary filtration, need further sequence to be carried out the examination of order-checking mistake, including the step such as " preclustering " and chimera (Chimera) sequence lookup.Select UCHIME program looks and delete these sequences.
Germline development information analysis based on 16S data base: use MOTHUR sorting technique to carry out horizontal bacterial strains information from door to kind for Human Oral Cavity core microorganism 16S data base (CORE) and incorporate into, add up each sample sequence number of each species in each categorization levels respectively, and the sequence number obtained with this population of samples calculates its ratio, thus obtain the relative abundance of each species of each class.
(6) impact (Fig. 2) that different factors are distributed for oral cavity flora:
Structure of community computational methods based on Jie Sen-aromatic (Jensen-Shannon) matrix: it is in addition to the evolutionary distance of sample room, also can investigate the difference of abundance in sample bacterium kind level.Antibacterial wealth of species distribution in sample can be regarded as the probability distribution of species, it is possible to use the Mutual information entropy (Jensen-Shannon divergence, JSD) of this probability distribution of sample room measures the difference of the microorganism group of sample room.Distance D of sample room (a, computing formula b) is as follows:
PaAnd PbAbundance distribution in representative sample a and sample b respectively.JSD (X, Y) defines the Mutual information entropy (Jensen-Shannon divergence) in two samples between different probability distribution X and Y.
KLD is the Kullback-Leibler dispersion between X and Y, and concrete computational methods are as follows:
nullNon-supervisory principal coordinate analysis: Jensen-Shannon matrix is carried out principal coordinate analysis (PCoA:Principal Coordinates Analysis) to show oral microbial community architectural feature between different sample，Each species information is considered as the most independent variable not associated by PCoA，It is analyzed with the matrix of sample × variable relative abundance，With on the premise of not considering Environmental Factors，Without prejudice、The overall inherent flora result observing sample，Find one or more potential variable (principal coordinate，Principal coordinate，PC) with farthest in relatively low dimensional best explain in sample variation，Each principal coordinate represents explainable overall structure degree of variation under this dimension，Thus reach the purpose that Data Dimensionality Reduction processes and sorts sample，Wherein the score (Score) of sample is the linear combination of species score.
Displacement multi-variate statistical analysis result display oral microbial community has obvious age characteristics, these age characteristicss are relevant with ontogeny Maturity and health status, support to set up the method (Fig. 1, Fig. 2) assessing child's individuality biological age according to oral microbial community:
1. in each ecological site, time/age factor is the most important factor determining Flora distribution.
2. in each ecological site, other key factors affecting Flora distribution according to its importance rank order are: the packet of health/morbid state, sample, individual heterogeneity.
3. (including H2H, H2C, C2C group) in different grouping, in healthy group, time factor is on its flora impact maximum, and time factor receives morbid state impact and suppressed to flora influence in dental caries group.
Result above is pointed out: oral cavity flora as the medium of Individual Age detection, and can react host buccal health status.
(7) mathematical model (Fig. 1) of oral status detection is tentatively set up
Depending in machine learning, random forest method is a model comprising multiple decision tree, and the classification of its output is the mode by indivedual classifications setting output, this model is widely used in excavating the incidence relation between target variable and numerous explanatory variable.The method not only can set up classification or regression model, can determine that differentiation particular state or the variable of label simultaneously, and can be by its importance values to judge the size of its separating capacity.In this example, random forest method utilizes the randomForest software kit of R to realize, and sets up 5000 trees, and other are default setting.Using the 2/3 of input data as training dataset, to input the 1/3 of data as test data set, carry out 100 experiments at random to reduce error.
Using the oral microbial community antibacterial kind data in H2H group as input variable, using actual monthly age corresponding to each sample as sample information, it is revert to discrete output variable (monthly age of prediction), the preliminary mathematical model setting up detection child's individuality biological age.
Random forest machine learning (Random Forests, RF) it is a kind of machine learning based on classifier algorithm, proposed by LeoBreiman, by bootstrap resampling technique, have from training set (data set) n and repeat with putting back to randomly draw k new training sample (train set) set of sample generation, then k classification tree composition random forest is generated according to self-service sample set, depending on the classification results of new data presses the how many mark formed of classification tree ballot, error in classification depends on the classification capacity of every one tree and the dependency between them.The classification capacity of single tree may be the least, but after randomly generating substantial amounts of decision tree, a test sample can select most probable classification by the classification results of every one tree after statistics.It improves the precision of prediction of model by collecting a large amount of classification trees, there is not overfitting, precision of prediction height due to it, and this model is widely used in excavating the incidence relation between target variable and numerous explanatory variable.
(8) model (Fig. 3) of the detection child's individuality biological age set up is optimized
Except setting up detection model and prediction, random forest method can be used for the importance of evaluation variable, and feature selection uses random method to remove to divide each node, then compares the error produced under different situations.Evaluation criterion is that this variable is the most important intuitively, and the impact on forecast result is the biggest.The Assessment of Important of Random Forest model explanatory variable uses similar standard: the value of a certain for all inspection specimens explanatory variable upset at random, use former Random Forest model that test samples is forecast again, the outer error of fitting increase of bag is the most, and this explanatory variable is the most important.The outer error of fitting increments of bag can be used for quantitative assessment explanatory variable importance.This patent uses ten times of cross validations (Ten-Fold Cross Validation) to include the minimum number of variable needed for evaluating structure model in.Random repetition 100 times, using average as the estimation to algorithm accuracy.Cross validation (Cross-Validation, CV) it is a kind of statistical analysis technique for verifying classifier performance, it is mainly used in modelling evaluation obtaining reliable and stable model, i.e. under certain meaning, initial data (dataset) is grouped, a part is as training set (train set), another part is as checking collection (validation set), first by training set, grader is trained, the model that obtains of training tested by recycling checking collection, by each error in classification as the index of classification of assessment device performance.And data set is divided into very by ten times of cross validations, in turn by wherein 9 parts as training data, as test data, test for 1 part.Test all can draw corresponding error in classification every time, and 10 result averages are as the estimation to arithmetic accuracy.
Variable is returned importance ranking according to it to the age, by along with variable reduce and random forest regression model to distinguish the set of variables cooperation that do not significantly changes of age ability be final age related microorganisms label.nullWherein，The label deriving from dental plaque includes the general Salmonella of Rockwell (Prevotella loescheii)，Denitrification Kingella (Kingella denitrificans)，Leptothrix BU064 (Leptotrichia BU064)，Multiform Fusobacterium nucleatum subspecies (Fusobacterium nucleatum subsp.polymorphum)，The outstanding bacterium 602D02 (Bergeyella602D02) of uncle，Oral cavity core bar bacterium (Cardiobacterium valvarum)，Streptococcus mitis/streptococcus pneumoniae/baby streptococcus/Streptococcus oralis (Streptococcus mitis/Streptococcus pneumonia/Streptococcus infantis/Streptococcus oralis)，Yellow Neisseria/neisseria mucosa/neisseria pharyngis (Neisseria flava/Neisseria mucosa/Neisseria pharyngis)，Very thin Campylobacter spp (Campylobacter gracilis)，Golden yellow Neisseria (Neisseria flavescens)，15 labels deriving from saliva include Detection of Porphyromonas CW034 (Porphyromonas CW034)，Lattice step on streptococcus (Streptococcus gordonii)，Veillonella atypica/different Veillonella/veillonella parvula (Veillonella atypical/Veillonella dispar/Veillonella parvula),Oral digestion streptococcus (Peptostreptococcus stomatis)，Secondary Streptococcus sanguis/Streptococcus oralis (Streptococcus parasanguinis/Streptococcus oralis)，Cilium bacterium BU064 (Leptotrichia BU064)，(Porphyromonas catoniae)，TM7 oral cavity taxon 352 (TM7oral taxon 352)，General Salmonella oral cavity taxon 299 (Prevotella oral taxon 299)，Produce black general Salmonella (Prevotella melaninogenica)，Ditch Eubacterium/small and weak Eubacterium (Eubacterium sulci/Eubacterium infirmum)，The outstanding bacterium 602D02 (Bergeyella 602D02) of uncle，Golden yellow Neisseria (Neisseria flavescens)，Neisseria meningitidis/neisseria polysaccharea (Neisseria meningitides/Neisseria polysaccharea)，Severe foster particle chain bacterium (Granulicatella elegans) (Fig. 3).
Utilize random forest method, with age related microorganisms label as input variable, using actual monthly age corresponding to each sample as sample information, it is revert to discrete output variable (monthly age of prediction), the model of the final detection child's individuality biological age set up after optimizing.
(9) application of model and performance (Fig. 4) thereof after optimizing
Model after optimization is applied in different group, will the composition of age related microorganisms label of each sample and abundance thereof as input variable, age detection model is utilized to carry out regression analysis, draw the biological age of now this sample, result shows: in healthy group, it is seen that substantially keep consistent with the life age by the biological age of oral microbial community detection gained；In dental caries group, substantially less than being lived the age by the biological age of oral microbial community detection gained, (t checks, p < 0.05), the potential Maturity inhibiting flora of generation of prompting oral disease, thus cause the reduction at oral cavity flora age, the biological age that child is individual can preferably be evaluated by the model that the above results explanation is set up, and points out the oral cavity flora age can react child's individuality oral health state.
Regression analysis based on random forest of the present invention can be found in Breiman L (2001) Random forests.Mach Learn 45:5 32.) and (Knights D, Costello EK, Knight R.Supervised classification of human microbiota.FEMS Microbiol Rev.2011Mar；35(2):343-59.doi:10.1111/j.1574-6976.2010.00251.x.Epub 2010Oct 7.Review.PubMed PMID:21039646..
Certainly, described above is not limitation of the present invention, and the present invention is also not limited to the example above, those skilled in the art, in the practical range of the present invention, the change made, retrofits, adds or replaces, all should belong to protection scope of the present invention.
Claims (7)
1. the method obtaining child's individuality biological age based on oral microbial community, it is characterised in that
Comprise the following steps:
Data collection: collect child's individuality oral cavity sample of multiple time point；
Data convert: extract the DNA information obtaining oral cavity sample, utilize bioinformatics method by described
DNA information is converted into oral microbial community information；
The Primary Construction of data model: using the oral microbial community information of acquisition as input variable, utilize
Random forest method, returns it age information, and Primary Construction is based on oral microbial community information
The preliminary mathematical model of detection biological age；
The optimization of mathematical model and determining: sort at the importance degree of model according to variable, do not affecting mould
Under type performance premise, the combination of simplified model variable, finally determines the model that child's Individual Age detects；
The detection of child's individuality biological age: using desired microorganisms group information as input variable, has utilized
The mathematical model set up carries out regression analysis, it is thus achieved that the child's individuality now biological age detected.
One the most according to claim 1 obtains child's individuality biological age based on oral microbial community
Method, it is characterised in that described oral cavity sample is dental plaque sample on saliva or gum.
One the most according to claim 1 obtains child's individuality biological age based on oral microbial community
Method, it is characterised in that the described oral microbial community information that is converted into by DNA information includes following step
Rapid:
16s RNA or the full-length genome information of DNA information is obtained by high-flux sequence means；
Carry out horizontal bacterial strains information from door to kind for 16s RNA or full-length genome information to incorporate into；
Add up each sample sequence number of each species in kind of level, and the sequence obtained with this population of samples respectively
Columns calculates its ratio, thus obtains the relative abundance of each each species.
One the most according to claim 1 obtains child's individuality biological age based on oral microbial community
Method, it is characterised in that the Primary Construction of described data model, comprise the following steps:
1) composition and the relative abundance thereof of the whole thin species level of the oral microorganism obtained are become as input
Amount；
2) utilize random forest method, the age information that child is individual is returned, tentatively by input variable
Build preliminary mathematical model based on oral microbial community infomation detection biological age.
One the most according to claim 1 obtains child's individuality biological age based on oral microbial community
Method, it is characterised in that the optimization of described data model and determining, comprise the following steps:
1) each variable importance journey to model performance of the kind representing bacterium in preliminary mathematical model is obtained
Degree；
2) according to variable, model importance degree is sorted from small to large, gradually reduces variable quantity, utilize with
Machine forest method, carries out the regression analysis to the age, it is thus achieved that the model of different variable combinations；
3) evaluate the combination of simplification variable under not reducing model performance premise, be defined as age correlated variables,
So that it is determined that final optimization pass model.
One the most according to claim 1 obtains child's individuality biological age based on oral microbial community
Method, it is characterised in that the detection of described child's individuality biological age, comprise the following steps:
1) DNA of child's individuality oral cavity sample is obtained；
2) utilize bioinformatics method that DNA information is converted to oral microbial community information；
3) relative abundance of the individual age correlated variables of child is obtained；
4) random forest method is utilized, using the composition of age correlated variables and abundance thereof as variable, to foundation
Age detection model carry out regression analysis, it is thus achieved that the individual biological age now of child.
One the most according to claim 1 obtains child's individuality biological age based on oral microbial community
Method, it is characterised in that also include: biological age individual for the child obtained is entered with its actual age
Row contrast, knows the now growth promoter situation of child, if i.e. biological age is less than actual age, then carries
Show that this child has owing to the factors such as disease cause the possibility of growth retardation；If biological age is equal to reality
Age, then point out this upgrowth and development of children situation normal；If biological age is higher than actual age, then point out
Child has the possibility of precocity.
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