Method, system and application for assessing the development status of infants based on gut microbiota

Abstract

The application discloses a kind of method, system and application based on intestinal flora to assess the growth and development state of children of 0-6 years old, belong to the development assessment field of children of 0-6 years old, and the development state to be assessed includes one or several of neuropsychological development, skin, stool, crying, night sleep;The method comprises the following steps: obtaining the intestinal flora information of the target population to be predicted;The intestinal flora information includes the relative abundance information of the flora significantly related to the corresponding development state;Intestinal flora information is input into the prediction evaluation model constructed in advance, and the evaluation result output by the prediction evaluation model is obtained, and the quantitative result corresponds to different development states.The present application predicts the neuropsychological development, skin, stool, crying condition, night sleep index of children in the short and long term through the intestinal flora of children, provides target points that can be referred to for improvement, can timely find the adverse development results that may exist in the future, and timely prevent and improve.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of 0-6 year-old children development evaluation, and in particular to a method and system for evaluating the growth and development state of 0-6 year-old children based on intestinal flora and application. BACKGROUND

[0002] There are about 10 14 microorganisms in the human body, most of which parasitize in the human intestinal tract, and the microorganisms in the human intestinal tract play an important role in individual nutritional metabolism, immunity and physiological function regulation. The period of 0-6 years old is an important window period for the establishment of intestinal flora, which has a great influence on the establishment and maturation of intestinal flora in the future. The biggest feature of the intestinal flora of 0-6 year-old children is instability, great individual difference and easy to be affected by external factors.

[0003] Although the intestinal flora of 0-6 year-old children has great difference, the intestinal flora of infants after birth has certain regularity from the initial colonization to the subsequent evolution process. At present, the generally accepted development mode of infant intestinal microorganisms is that facultative anaerobes colonize in the early stage, and continuously consume the oxygen in the intestine, and when the intestine becomes an anaerobic environment, strict anaerobes begin to colonize in the intestine of infants. With the gradual growth of infants, the intestinal flora develops from low diversity and low complexity to high diversity and complexity, which usually takes three to four years. The intestine is the digestive and absorptive organ of the human body and is also the largest immune organ of the human body. During the colonization process of intestinal flora in early life, it affects the development of individuals in early life to some extent.

[0004] Feeding mode affects intestinal flora and short-term and long-term health, the World Health Organization and the United Nations Children's Fund recommend that mothers should insist on exclusive breastfeeding for the first 6 months after the birth of infants, and breast milk has rich nutrients that can meet the energy and nutrients necessary for early growth and development of children, and also has various oligosaccharides and prebiotics that can regulate the composition of infant intestinal flora, improve the immunity of children and reduce the risk of infectious diseases.

[0005] Neuropsychological development, skin, stool, crying, night sleep and other indicators are used to evaluate the healthy development of children from various dimensions, and the influencing factors of the indicators are diversified. At present, the observation of these indicators is mainly through in vitro discovery, which ignores the relationship between the changes in children's body and clinical manifestations.

[0006] Therefore, the present application is proposed. SUMMARY

[0007] OBJECTIVE

[0008] In order to overcome the above-mentioned defects, the purpose of the present application is to provide a method, system and application for evaluating the growth and development state of children aged 0-6 years old. The present application increases the angle of health evaluation of the development state of children aged 0-6 years old by establishing the intestinal flora change curve and reference range of children aged 0-6 years old.

[0009] Solution

[0010] In order to achieve the purpose of the present application, the technical solution adopted by the present application is as follows:

[0011] In the first aspect, the present application provides a method for evaluating the growth and development state of children aged 0-6 years old, the development state to be evaluated including one or more of neuropsychological development, skin, stool, crying, and night sleep; the method comprising the following steps:

[0012] Obtaining intestinal flora information of the target population to be predicted; the intestinal flora information including relative abundance information of flora significantly related to the corresponding development state;

[0013] Inputting the intestinal flora information into a pre-constructed prediction and evaluation model to obtain an evaluation result output by the prediction and evaluation model, the evaluation result including a quantitative result of the corresponding development state, the quantitative result corresponding to different development states;

[0014] Wherein, the prediction and evaluation model is obtained by multiple regression calculation according to the corresponding intestinal flora information significantly related to the development state to be evaluated.

[0015] Further, the prediction and evaluation model includes several prediction and evaluation sub-models distinguished according to age and the development state to be evaluated; the selection of the prediction and evaluation sub-model is based on age and the development state to be evaluated, and the corresponding intestinal flora information significantly related to the corresponding age range and the development state to be evaluated is input for prediction and evaluation; and / or,

[0016] The intestinal flora information is obtained by detection in the fecal sample of the target population to be predicted.

[0017] Further, the construction method of the prediction and evaluation model includes:

[0018] Obtaining intestinal flora information of several samples in multiple regions, combining the intestinal flora information and clinical information to establish an intestinal flora database of children aged 0-6 years old, the intestinal flora information including relative abundance information of flora at the level of door, department and genus, and the clinical information including age and development state information; the development state information including one or more of neuropsychological development, skin, stool, crying and night sleep; the development state information is quantitatively processed, including: scoring the questionnaire for neuropsychological development, assigning points according to whether or not having skin diseases, assigning points according to the stool shape, assigning points according to the crying condition, and scoring according to the number of awakenings during night sleep;

[0019] Grouping the data in the database by age, and grouping again according to the relative abundance of the flora in the group, dividing into high abundance group and low abundance group with the average value of the relative abundance of each bacterium as the boundary, calculating the average value of the quantified development state information of the high abundance group and the low abundance group respectively, comparing the average values of the two groups to obtain the difference fold, and performing significance analysis;

[0020] Selecting the bacteria significantly related to the clinical information as the independent variable, the score or the assigned score of the clinical information of the children aged 0-6 years as the dependent variable, setting a random seed for each age group, randomly dividing the data into a training set and a validation set in proportion, performing linear regression calculation on the training set to obtain a linear prediction model of the flora and the clinical information of the children aged 0-6 years, and testing by the validation set;

[0021] Obtaining multiple linear regression models according to the number of random seeds, inducing the model coefficient range according to the multiple models obtained, and using the average value as the calculation model coefficient to obtain a prediction evaluation model, and evaluating the development state of the children aged 0-6 years according to the predicted clinical information of the children aged 0-6 years.

[0022] Further, the construction of the intestinal flora database of children aged 0-6 years includes: collecting the feces of children aged 0-6 years, sequencing and analyzing by 16S amplicon sequencing technology to obtain sequencing data corresponding to the intestinal flora of children aged 0-6 years, denoising and aggregating the sequencing data to obtain basic classification units OTUs, annotating the classification units to obtain the composition of the intestinal flora of children aged 0-6 years, and merging the intestinal flora information and the clinical information according to the sample number to establish the intestinal flora database of children aged 0-6 years.

[0023] Further, the denoising includes: Reads splicing filtering, and OTUs clustering.

[0024] Further, when the development state to be evaluated is neuro-psychological development, the flora significantly related to neuro-psychological development includes a number of bacteria collections related to age as follows (1)-(3):

[0025] (1) Staphylococcus (Staphylococcus), Ligilactobacillus (Ligilactobacillus), Ruminococcus. Torques group (Ruminococcus. Torques); optionally, the child is aged 0 days-4 months;

[0026] (2) Veillonella (Veillonella), Prevotella (Prevotella); optionally, the child is aged 5 months-12 months;

[0027] (3) TM7x (TM7x genus), Weissella, Devosia, Bradyrhizobium, Atopobium, Glutamicibacter, Cupriavidus, Lachnospiraceae_FCS020_group, RB41; optionally, the child is aged between 13 months and 6 years;

[0028] Alternatively, where the developmental state to be assessed is skin, the microbiota significantly associated with skin includes several bacterial collections associated with age as follows (i) - (ii):

[0029] (i) Clostridium_sensu_stricto_1, Brevundimonas, Subdoligranulum, Faecalibacterium; optionally, the child is aged between 0 days and 4 months;

[0030] (ii) Bifidobacterium, Bacillus;

[0031] Alternatively, where the developmental state to be assessed is stool properties, the microbiota significantly associated with stool properties includes several bacterial collections associated with age as follows i) - iv):

[0032] i) Staphylococcus, Veillonella, Parabacteroides, Dialister, Haemophilus; optionally, the child is aged between 0 and 15 days;

[0033] ii) Lachnospira, Flavonifractor, Agathobacter, Phascolarctobacterium; optionally, the child is aged between 16 days and 30 days;

[0034] iii) Clostridium sensu stricto 1, Bacteroides, Parabacteroides, Akkermansia, Citrobacter, Bilophila, Epulopiscium, Phascolarctobacterium, Collinsella, Sarcina; optionally, the child is aged between 31 days and 4 months;

[0035] iv) Escherichia. Shigella, Clostridium sensu stricto 1, Staphylococcus, Bacteroides, Bacillus, Erysipelatoclostridium, Subdoligranulum, Dialister, Epulopiscium, Ruminococcus. torques group, CAG.56, Sellimonas; optionally, the child is aged between 5 months and 6 years;

[0036] Alternatively, the microbiota significantly associated with crying when the developmental state to be assessed is crying comprises, in relation to age, several collections of bacteria as follows 1) to 3):

[0037] 1) Erysipelatoclostridium, Klebsiella, Citrobacter; optionally, the child is aged between 8 days and 15 days;

[0038] 2) Staphylococcus, Rothia, Phascolarctobacterium, Ruminococcus; optionally, the child is aged between 16 days and 30 days;

[0039] 3) Acinetobacter, Enhydrobacter, Aeromonas, Ligilactobacillus, Veillonella, Megasphaera, Prevotella, Fusobacterium, Lactobacillus, Flavonifractor, Lachnospiraceae_NK4A136_group, Fusicatenibacter, Ruminococcus, Eubacterium. hallii group, Actinomyces, Atopobium; optionally, the child is aged between 31 days and 4 months;

[0040] Alternatively, when the developmental state to be assessed is night sleep, the microbiota significantly associated with night sleep includes several bacterial collections associated with age as follows a) to d):

[0041] a) Parabacteroides;

[0042] b) Lactobacillus, Phascolarctobacterium;

[0043] c) Stenotrophomonas, Enterococcus, Prevotella, Lactobacillus, Bilophila, Flavonifractor, Sutterella, Ruminococcus. torques group, unidentified Chloroplast, Fusicatenibacter, Dorea, Proteus, Myroides;

[0044] d) Staphylococcus, Enterobacter, Prevotella-9, Erysipelatoclostridium, Coprococcus, Fusobacterium, Flavonifractor, Sutterella, Lachnoclostridium, Actinomyces, Anaerostipes, Eubacterium fissicatena group, Negativicoccus, and Allisonella.

[0045] Furthermore, the age grouping method includes: 0-7 days, 8-15 days, 16-30 days, 31 days to 4 months, 5 months to 12 months, 13 months to 24 months, 25 months to 36 months, and 37 months to 6 years old.

[0046] Furthermore, clinical information included neuropsychological development, skin, bowel movements, crying conditions, or nighttime sleep;

[0047] The prediction model formulas for intestinal flora information and neuropsychological development include one or more of the following formulas (1) to (3):

[0048] (1) When children are between 0 days and 4 months old, the formula for predicting neuropsychological development is:

[0049] h=(51.0705~52.7283)+(-540.3316~54.9656)×Staphylococcus-(3599.8924~2076.5719)×Ligilactobacillus+(127.5668~3359.586)×Ruminococcus._torques_group;

[0050] Alternatively h = 51.9239-249.4833×Staphylococcus-2448.8087×Ligilactobacillus+1394.1283×Ruminococcus._torques_group;

[0051] Wherein, Staphylococcus is the relative abundance of Staphylococcus, Ligilactobacillus is the relative abundance of Ligilactobacillus, Ruminococcus._torques_group is the relative abundance of Ruminococcus. Torques group;

[0052] (2) When the child is 5 months to 12 months old, the formula for predicting neuropsychological development is:

[0053] h = (55.2745-56.409) - (34.2045-26.1645) x Veillonella + (794.7139-2787.5344) x Prevotella;

[0054] Optionally, h = 55.8377 - 30.5745 x Veillonella + 1188.1085 x Prevotella;

[0055] Wherein, Veillonella is the relative abundance of Veillonella, and Prevotella is the relative abundance of Prevotella;

[0056] (3) When the child is 13 months to 6 years old, the formula for predicting neuropsychological development is:

[0057] h = (49.9123-52.6285) + (2343.7701-4663.8371) x TM7x + (-60.5126-3457.3805) x Weissella + (3641.8351-4855.5426) x Devosia + (1378.1606-2869.502) x Bradyrhizobium +

[0058] (866.0322-3817.5774) x Atopobium + (1370.5024-4909.5865) x Glutamicibacter -

[0059] (11328.4011-6658.3515) x Cupriavidus + (1408.4641-2853.6566) x

[0060] Lachnospiraceae_FCS020_group + (2431.4707-4358.3283) x RB41;

[0061] Optionally h = 51.0569 + 3311.0714 x TM7x + 857.1083 x Weissella + 4384.629 x Devosia + 2224.3299 x Bradyrhizobium + 1831.3744 x Atopobium + 2466.8918 x Glutamicibacter - 9012.034 x

[0062] Cupriavidus + 2119.8855 x Lachnospiraceae_FCS020_group + 3757.0831 x RB41;

[0063] wherein TM7x is the relative abundance of the genus TM7x, Weissella is the relative abundance of the genus Weissella, Devosia is the relative abundance of the genus Devosia, Bradyrhizobium is the relative abundance of the genus Bradyrhizobium, Atopobium is the relative abundance of the genus Atopobium, Glutamicibacter is the relative abundance of the genus Glutamicibacter, Cupriavidus is the relative abundance of the genus Cupriavidus, Lachnospiraceae_FCS020_group is the relative abundance of the group Lachnospiraceae_FCS020, and RB41 is the relative abundance of the genus RB41;

[0064] and / or the intestinal microbiota information and the predictive model formula of the skin comprise one or several of the following formulas (i) to (ii):

[0065] (i) when the child is aged from 0 day to 4 months, the formula for predicting the skin is:

[0066] h = (0.0659-0.0894) + (0.2965-0.4306) x Clostridium_sensu_stricto_1 + (298.8977-432.2361) x Brevundimonas + (1.6479-2.5646) x Subdoligranulum - (21.6623-5.945) x Faecalibacterium;

[0067] Optionally h = 0.0794 + 0.3781 x Clostridium_sensu_stricto_1 + 354.1341 x Brevundimonas + 2.0446 x Subdoligranulum - 12.0461 x Faecalibacterium;

[0068] Wherein Clostridium_sensu_stricto_1 is the relative abundance of strict Clostridium, Brevundimonas is the relative abundance of Brevundimonas, Subdoligranulum is the relative abundance of Subdoligranulum, and Faecalibacterium is the relative abundance of Faecalibacterium;

[0069] (ii) When the child is aged from 5 months to 6 years old, the formula for predicting the skin is:

[0070] h = (0.1305-0.1715) - (0.2813-0.2075) x Bifidobacterium + (135.426-160.0824) x Bacillus;

[0071] Alternatively, h = 0.148 - 0.2464 x Bifidobacterium + 150.5487 x Bacillus;

[0072] Wherein Bifidobacterium is the relative abundance of Bifidobacterium, and Bacillus is the relative abundance of Bacillus;

[0073] And / or, the prediction model formula of the intestinal flora information and the stool character includes one or several of the following formulas i) to iv):

[0074] i) When the child is aged from 0 to 15 days old, the formula for predicting the stool character is:

[0075] h = (2.5624-2.6469) - (2.6623-0.8585) x Staphylococcus - (4.0757-3.3154) x Veillonella - (2.3037-1.444) x Parabacteroides + (8.4342-535.3406) x Dialister - (3.8718-3.0043) x Haemophilus;

[0076] Alternatively, h = 2.6152 - 1.1958 x Staphylococcus - 3.6283 x Veillonella - 1.8468 x

[0077] Parabacteroides + 152.3533 x Dialister - 3.5718 x Haemophilus;

[0078] Wherein Staphylococcus is the relative abundance of Staphylococcus, Veillonella is the relative abundance of Veillonella, Parabacteroides is the relative abundance of Parabacteroides, Dialister is the relative abundance of Dialister, Haemophilus is the relative abundance of Haemophilus;

[0079] ii) When the child is 16 days to 30 days old, the formula for predicting the stool form is:

[0080] h = (2.3114-2.4241) + (2.4508-683.2421) x Lachnospira + (2.3546-12.7348) x Flavonifractor - (405.4702-62.5708) x Agathobacter - (13.1682-9.6822) x Phascolarctobacterium;

[0081] Optionally h = 2.3783 + 182.608 x Lachnospira + 8.5137 x Flavonifractor - 122.2349 x

[0082] Agathobacter - 11.4688 x Phascolarctobacterium;

[0083] Wherein Lachnospira is the relative abundance of Lachnospira, Flavonifractor is the relative abundance of Flavonifractor, Agathobacter is the relative abundance of Agathobacter, and Phascolarctobacterium is the relative abundance of Phascolarctobacterium;

[0084] iii) When the child is 31 days to 4 months old, the formula for predicting the stool form is:

[0085] h = (2.3441-2.441) + (-0.1699-0.0419) x Clostridium_sensu_stricto_1 + (0.0728-0.2473) x Bacteroides + (1.7218-2.1934) x Parabacteroides + (1.2942-3.6235) x Akkermansia +

[0086] (-0.5946-2.7963) x Citrobacter + (-11.0145-21.6328) x Bilophila - (1.8488-1.354) x

[0087] Epulopiscium + (6.8497~11.2683) x Phascolarctobacterium + (2.3211~5.0496) x Collinsella -

[0088] (221.8181~121.729) x Sarcina;

[0089] Alternatively h = 2.3775 - 0.0542 x Clostridium_sensu_stricto_1 + 0.1665 x Bacteroides + 1.9577 x Parabacteroides + 2.7415 x Akkermansia + 1.8706 x Citrobacter + 1.9948 x Bilophila - 1.6425 x

[0090] Epulopiscium + 8.7979 x Phascolarctobacterium + 3.2822 x Collinsella - 165.6212 x Sarcina;

[0091] Where Clostridium_sensu_stricto_1 is the relative abundance of Clostridium sensu stricto 1, Bacteroides is the relative abundance of Bacteroides, Parabacteroides is the relative abundance of Parabacteroides, Akkermansia is the relative abundance of Akkermansia, Citrobacter is the relative abundance of Citrobacter, Bilophila is the relative abundance of Bilophila, Epulopiscium is the relative abundance of Epulopiscium, Phascolarctobacterium is the relative abundance of Phascolarctobacterium, Collinsella is the relative abundance of Collinsella, Sarcina is the relative abundance of Sarcina;

[0092] iv) For children aged 5 months to 6 years, the formula to predict stool form is:

[0093] h = (2.9053-3.0947) - (1.0973-0.7369) x Escherichia. Shigella - (0.4223-0.3035) x Clostridium_sensu_stricto_1 - (15.7456-14.3356) x Staphylococcus + (0.3984-0.5591) x Bacteroides - (399.5418-60.4927) x Bacillus - (1.4938-0.4026) x Erysipelatoclostridium

[0094] (12.6503-8.9228) x Subdoligranulum + (22.3406-24.9229) x Dialister - (104.8873-77.8148) x Epulopiscium + (-0.347-7.5903) x Ruminococcus. torques_group - (458.5554-407.3394) x CAG.56 + (218.2145-397.8098) x Sellimonas;

[0095] Alternatively h = 2.9704 - 0.8727 x Escherichia. Shigella - 0.3608 x Clostridium_sensu_stricto_1 - 14.9315 x Staphylococcus + 0.4969 x Bacteroides - 203.9802 x Bacillus - 0.9976 x

[0096] Erysipelatoclostridium - 10.9023 x Subdoligranulum + 24.0384 x Dialister - 93.5327 x

[0097] Epulopiscium + 2.4099 x Ruminococcus. torques_group - 427.6705 x CAG.56 + 333.3722 x Sellimonas;

[0098] Wherein, Escherichia.Shigella is the relative abundance of Escherichia-Shigella, Clostridium_sensu_stricto_1 is the relative abundance of Clostridium_sensu_stricto_1, Staphylococcus is the relative abundance of Staphylococcus, Bacteroides is the relative abundance of Bacteroides, Bacillus is the relative abundance of Bacillus, Erysipelatoclostridium is the relative abundance of Erysipelatoclostridium, Subdoligranulum is the relative abundance of Subdoligranulum, Dialister is the relative abundance of Dialister, Epulopiscium is the relative abundance of Epulopiscium, Ruminococcus._torques_group is the relative abundance of Ruminococcus._torques_group, CAG.56 is the relative abundance of CAG.56, Sellimonas is the relative abundance of Sellimonas;

[0099] And / or, the prediction model formula of the intestinal flora information and the crying condition includes one or several of the following formulas 1) ~ 3):

[0100] 1) When the child is 8 days to 15 days old, the formula for predicting the crying condition is:

[0101] h=(1.6702~1.8308)+(1.5851~2.4425)xErysipelatoclostridium-(1.5395~1.1238)Klebsiella-(20.1973~8.7767)Citrobacter;

[0102] Optionally, h=1.7244+1.8593xErysipelatoclostridium-1.2836xKlebsiella-12.9963xCitrobacter;

[0103] Wherein, Erysipelatoclostridium is the relative abundance of Erysipelatoclostridium, Klebsiella is the relative abundance of Klebsiella, and Citrobacter is the relative abundance of Citrobacter;

[0104] 2) When the child is 16 days to 30 days old, the formula for predicting the crying condition is:

[0105] h = (1.5075~1.5779) + (-0.9539~38.2314) x Staphylococcus - (111.3334~14.6111) x Rothia - (15.6307~9.5096) x Phascolarctobacterium - (7.9704~5.5118) x Ruminococcus;

[0106] h = 1.5331 + 20.0659 x Staphylococcus - 42.3019 x Rothia - 13.2372 x

[0107] Phascolarctobacterium - 6.9779 x Ruminococcus;

[0108] Wherein, Staphylococcus is the relative abundance of Staphylococcus, Rothia is the relative abundance of Rothia, Phascolarctobacterium is the relative abundance of Phascolarctobacterium, and Ruminococcus is the relative abundance of Ruminococcus;

[0109] 3) When the child is 31 days to 4 months old, the formula for predicting crying is:

[0110] h = (1.4678~1.5216) + (-1.2963~3.8134) x Acinetobacter - (147.9583~82.5599) x

[0111] Enhydrobacter + (214.5577~611.1134) x Aeromonas - (61.6441~45.7525) x Ligilactobacillus -

[0112] (1.5492-1.1626) x Veillonella + (0.3757-2.8055) x Megasphaera + (-26.169-226.5171) x Prevotella - (31.1451-28.1141) x Fusobacterium - (1.1191-0.486) x Lactobacillus + (-1.2058-0.9898) x Flavonifractor - (215.1284-112.9927) x Lachnospiraceae_NK4A136_group + (-2.2375-8.3244) x Fusicatenibacter - (179.0185-49.5399) x Ruminococcus + (-477.6953-262.9145) x

[0113] Eubacterium. hallii group - (88.7991-73.9559) x Actinomyces + (160.468-224.39) x Atopobium;

[0114] Optionally h = 1.4959 + 1.279 x Acinetobacter - 117.4994 x Enhydrobacter + 394.8747 x

[0115] Aeromonas - 54.0312 x Ligilactobacillus - 1.3707 x Veillonella + 1.3511 x Megasphaera + 62.6096 x Prevotella - 30.0045 x Fusobacterium - 0.7988 x Lactobacillus + 0.6225 x Flavonifractor - 167.8314 x Lachnospiraceae_NK4A136_group + 3.1171 x Fusicatenibacter - 109.4375 x Ruminococcus - 89.6414 x Eubacterium. hallii group - 82.0443 x Actinomyces + 199.7983 x Atopobium;

[0116] Wherein, Acinetobacter is the relative abundance of Acinetobacter, Enhydrobacter is the relative abundance of Enhydrobacter, Aeromonas is the relative abundance of Aeromonas, Ligilactobacillus is the relative abundance of Ligilactobacillus, Veillonella is the relative abundance of Veillonella, Megasphaera is the relative abundance of Megasphaera, Prevotella is the relative abundance of Prevotella, Fusobacterium is the relative abundance of Fusobacterium, Lactobacillus is the relative abundance of Lactobacillus, Flavonifractor is the relative abundance of Flavonifractor, Lachnospiraceae_NK4A136_group is the relative abundance of Lachnospiraceae NK4A136 group, Fusicatenibacter is the relative abundance of Fusicatenibacter, Ruminococcus is the relative abundance of Ruminococcus, Eubacterium._hallii_group is the relative abundance of Eubacterium. hallii group, Actinomyces is the relative abundance of Actinomyces, Atopobium is the relative abundance of Atopobium;

[0117] And / or, the prediction model formula of the intestinal flora information and the night sleep condition includes one or more of the following formulas a) to d):

[0118] a) When the child's age is 0 days to 15 days, the formula of the night sleep condition is:

[0119] h = (1.667-1.7575) + (2.0551-3.3078) x Parabacteroides;

[0120] Optionally, h = 1.7084 + 2.5764 x Parabacteroides;

[0121] Wherein, Parabacteroides is the relative abundance of Parabacteroides;

[0122] b) When the child's age is 16 days to 30 days, the formula of the night sleep condition is:

[0123] h = (1.9871-2.1135) - (36.0173-5.136) x Lactobacillus + (19.9734-35.2859) x

[0124] Phascolarctobacterium;

[0125] Alternatively, h = 2.0218 - 12.0345 × Lactobacillus + 26.0639 × Phascolarctobacterium;

[0126] Among them, Lactobacillus is the relative abundance of Lactobacillus, and Phascolarctobacterium is the relative abundance of Phascolarctobacterium;

[0127] c) For children aged 31 days to 4 months, the formula for nighttime sleep is:

[0128] h=(2.1063~2.153)+(485.8899~703.2182)×Stenotrophomonas-(1.1099~0.9931)×Enterococcus+(45.4132~142.3935)×Prevotella-(3.0807~2.7328)×Lactobacillus+

[0129] (6.9523~27.6025)×Bilophila+(-0.4976~4.8429)×Flavonifractor+(3.4943~6.1713)×Sutterella+(7.1024~12.505)×Ruminococcus._torques_group-(405.374~222.0575)×

[0130] unidentified_Chloroplast+(0.0185~4.2892)×Fusicatenibacter+(232.9549~397.3759)×Dorea-(3.843~2.5359)×Proteus-(126.278~76.0815)×Myroides;

[0131] Alternatively h = 2.1267 + 580.5244 × Stenotrophomonas - 1.0646 × Enterococcus + 76.9896 ×

[0132] Prevotella-2.9066×Lactobacillus+16.0708×Bilophila+0.7412×Flavonifractor+4.4946×

[0133] Sutterella + 8.8728 x Ruminococcus. torques group - 280.1981 x unidentified_Chloroplast + 1.3007 x Fusicatenibacter + 310.8906 x Dorea - 3.3466 x Proteus - 98.8985 x Myroides;

[0134] wherein Stenotrophomonas is the relative abundance of Stenotrophomonas, Enterococcus is the relative abundance of Enterococcus, Prevotella is the relative abundance of Prevotella, Lactobacillus is the relative abundance of Lactobacillus, Bilophila is the relative abundance of Bilophila, Flavonifractor is the relative abundance of Flavonifractor, Sutterella is the relative abundance of Sutterella, Ruminococcus. torques group is the relative abundance of Ruminococcus. torques group, unidentified_Chloroplast is the relative abundance of unidentified_Chloroplast, Fusicatenibacter is the relative abundance of Fusicatenibacter, Dorea is the relative abundance of Dorea, Proteus is the relative abundance of Proteus, Myroides is the relative abundance of Myroides;

[0135] d) for children aged 5 months to 6 years, the formula for night sleep is:

[0136] h = (1.8273-1.8898) + (5.8052-7.3144) x Staphylococcus - (5.3856-1.5202) x Enterobacter + (1.744-9.8407) x Prevotella_9 + (-1.5188-0.2748) x Erysipelatoclostridium + (-2.1975-30.4482) x Coprococcus - (66.6501-15.2271) x Fusobacterium + (-0.8691-4.0197) x Flavonifractor

[0137] (5.4952-9.5591) x Sutterella + (3.2347-5.8771) x Lachnoclostridium + (53.2421-66.6003) x Actinomyces + (124.4522-194.2467) x Anaerostipes + (6.1294-71.2023) x

[0138] Eubacterium. fissicatena group (158.923 ~ 76.3152) x Negativicoccus + (112.5601 ~ 779.4042) x Allisonella;

[0139] Optionally h = 1.8537 + 6.6401 x Staphylococcus - 2.9561 x Enterobacter + 4.1107 x

[0140] Prevotella_9 - 0.5677 x Erysipelatoclostridium + 8.9717 x Coprococcus - 29.4941 x

[0141] Fusobacterium + 1.7762 x Flavonifractor + 7.6957 x Sutterella + 4.7329 x Lachnoclostridium + 61.5478 x Actinomyces + 164.25 x Anaerostipes + 37.7456 x Eubacterium. fissicatena group - 121.676 x Negativicoccus + 403.4906 x Allisonella;

[0142] Wherein, Staphylococcus is the relative abundance of Staphylococcus, Enterobacter is the relative abundance of Enterobacter, Prevotella_9 is the relative abundance of Prevotella_9, Erysipelatoclostridium is the relative abundance of Erysipelatoclostridium, Coprococcus is the relative abundance of Coprococcus, Fusobacterium is the relative abundance of Fusobacterium, Flavonifractor is the relative abundance of Flavonifractor, Sutterella is the relative abundance of Sutterella, Lachnoclostridium is the relative abundance of Lachnoclostridium, Actinomyces is the relative abundance of Actinomyces, Anaerostipes is the relative abundance of Anaerostipes, Eubacterium. fissicatena group is the relative abundance of Eubacterium. fissicatena group, Negativicoccus is the relative abundance of Negativicoccus, Allisonella is the relative abundance of Allisonella.

[0143] In a second aspect, a system for evaluating the growth and development state of a child aged 0-6 years is provided, which is applied to the method of any one of the first aspect, and comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.

[0144] In a third aspect, the method of the first aspect or the system of the second aspect is applied to the preparation of a product for evaluating the growth and development state of a child aged 0-6 years.

[0145] Advantages

[0146] By evaluating the development level of the child through the information of neuropsychological development, skin, stool, crying, and night sleep, the development result indicators of the child are monitored, that is, there are differences in the clinical manifestations of infant health caused by the internal development of the intestinal flora of the child, and the present application predicts the indicators of neuropsychological development, skin, stool, crying, and night sleep of the child in the short and long term through the intestinal flora of the child, provides a target point that can be referred to for improvement, can timely find the adverse development results that may exist in the future, and timely prevent and improve. DETAILED DESCRIPTION

[0147] So that the objects, technical solutions and advantages of the embodiments of the present application are more apparent, the technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0148] In addition, in order to better illustrate the present application, a large number of specific details are given in the specific embodiments below. Those skilled in the art should understand that the present application can also be implemented without some specific details. In some embodiments, the raw materials, elements, methods, means, and the like that are well known to those skilled in the art are not described in detail, so as to highlight the main idea of the present application.

[0149] Unless otherwise explicitly indicated, throughout the specification and claims, the term "comprise" or its variants such as "comprises" or "comprising" will be understood to include the stated element or component, but not to exclude other elements or components.

[0150] The application launched a mother and infant nutrition cohort study project in China in 2014, which is a long-term tracking project in multiple representative areas. The project established a multi-point multi-center and prospective mother and infant nutrition cohort in 7 provinces and cities, 8 diet circles and 15 points. 3766 mothers and infants were enrolled. At the time points of the seventh day, the fifteenth day, the first month, the second month, the third month, the fourth month, the fifth month, the sixth month, the eighteenth month, the twenty-fourth month, the thirtieth month, the thirty-sixth month, the fourth year, the fifth year and the sixth year, 28354 biological samples such as breast milk, feces and urine were collected. Among them, 737 fecal samples of children aged 0-6 years old who are breastfed were collected, and a 0-6 intestinal microbiome database of children aged 0-6 years old who are breastfed was established.

[0151] For the research method of intestinal flora, sequencing technology is mainly used to sequence the intestinal flora marker gene (V3V4 region of 16S rRNA), bioinformatics technology is used to process the sequencing results, and relevant theories are used to summarize the data to realize the observation of intestinal flora.

[0152] The relative abundance of the application represents the relative proportion of a certain microbial classification unit in the whole microbial community. It is usually expressed in percentage form, and the calculation method is to divide the relative abundance of a certain classification unit (its relative number in a sample) by the total number of microorganisms and multiply by 100%. This is a common way to describe the composition of microorganisms. For example, the relative abundance of a bacterium is the proportion of the bacterium in the total bacterial abundance detected in the sample.

[0153] The application first establishes the 0-6 intestinal flora change trend according to the collected 0-6 year old child data, plans the 0-6 year old child intestinal flora change range of Chinese children at different time nodes of 0-6 years old after birth, and establishes the standardized 0-6 year old child intestinal flora development state evaluation index. Further explore the correlation between intestinal flora and development index, select a group of marker flora, evaluate the physical development state of 0-6 year old children by evaluating the relative abundance of each bacterium in the marker flora group, and realize the purpose of predicting the development state of children by using intestinal flora.

[0154] In the first aspect, the application provides a method for evaluating the growth and development state of children aged 0-6 years old, and the development state to be evaluated includes one or more of neuropsychological development, skin, stool, crying and night sleep.

[0155] Obtain intestinal flora information of the target population to be predicted; the intestinal flora information includes relative abundance information of the flora significantly related to the corresponding development state;

[0156] inputting the intestinal flora information into a pre-constructed prediction evaluation model to obtain an evaluation result output by the prediction evaluation model, the evaluation result including: a quantification result of the corresponding development state, the quantification result corresponding to different development states;

[0157] The prediction evaluation model is obtained by multiple regression calculation according to the screened intestinal flora information significantly related to the development state to be evaluated.

[0158] Further, the prediction evaluation model includes a plurality of prediction evaluation sub-models distinguished according to age and the development state to be evaluated; the selection of the prediction evaluation sub-model is based on age and the development state to be evaluated, and the prediction evaluation is performed by inputting the intestinal flora information significantly related to the corresponding age range and the development state to be evaluated; and / or,

[0159] The intestinal flora information is obtained by detection in a fecal sample of a target population to be predicted.

[0160] Further, the construction method of the prediction evaluation model includes:

[0161] Obtaining intestinal flora information of a plurality of samples in multiple regions, combining the intestinal flora information and clinical information to establish an intestinal flora database of children aged 0-6 years old, the intestinal flora information including relative abundance information of flora at the level of phylum, family and genus, and the clinical information including age and development state information; the development state information including one or more of neuropsychological development, skin, stool, crying condition and night sleep; quantitatively processing the development state information, including: scoring the questionnaire for neuropsychological development, assigning points according to whether the child has skin disease, assigning points according to stool shape, assigning points according to crying condition, and scoring according to the number of awakenings during night sleep;

[0162] Grouping the data in the database by age, and further grouping according to the relative abundance of flora in each group, dividing into a high-abundance group and a low-abundance group with the average value of the relative abundance of each bacterium as the boundary, calculating the mean value of the quantified development state information of the high-abundance group and the low-abundance group respectively, comparing the mean values of the two groups to obtain the difference fold, and performing significance analysis;

[0163] Selecting bacteria significantly related to clinical information as independent variables, and the score or points of 0-6-year-old children clinical information as dependent variables, setting a random seed for each age group, randomly dividing the data into a training set and a validation set in proportion, performing linear regression calculation on the training set to obtain a linear prediction model of flora and 0-6-year-old children clinical information, and testing by the validation set;

[0164] According to the random seed number, a plurality of linear regression models are obtained, according to the plurality of obtained models, the model coefficient range is induced, and the average value is used as the calculation model coefficient to obtain a prediction evaluation model, and according to the predicted 0-6-year-old child clinical information, the development state of the 0-6-year-old child is evaluated.

[0165] Among them, the score of the neuropsychological development questionnaire is the score obtained according to the Ages&Stages Questionnaires (ASQ) test questionnaire for the physical development, communication ability, limb movement, problem solving, personal and social interaction ability of children of different ages, reflecting the ability and willingness of children to understand language signals and express orally. The data is obtained by professional personnel using ASQ questionnaires of different ages to evaluate children of corresponding ages, then calculating the total score according to the ASQ questionnaire scoring principle through the corresponding software, and then all the children's score results are summarized according to different ASQ categories. This time, the ASQ communication score is mainly used as the evaluation data, and the children's own flora data are matched one by one. According to the high and low of the relative abundance of flora, the ASQ score is divided into two groups, the difference fold is calculated and t test is carried out, the difference fold and p value of the high and low two groups are obtained, the statistically significant flora is selected as the independent variable, the ASQ communication score is the dependent variable, and the multiple linear regression calculation is carried out. The sample data is brought into the regression model to obtain the estimated value, which can be considered as a kind of prediction of the ASQ score.

[0166] Among them, scoring according to whether or not suffering from skin disease refers to whether the child suffers from skin diseases such as dermatitis, urticaria, etc. According to whether the skin is diseased, registration is made, and the skin disease score is set to 1, and the non-skin disease score is set to 0. This evaluation mainly focuses on the possibility of children suffering from skin diseases (dermatitis, urticaria), and the intestinal flora is affected by external factors. When children suffer from skin diseases, the intestinal flora of children will correspondingly change. We establish the relationship between intestinal flora and children's skin diseases, use intestinal flora to predict the possibility of children suffering from skin diseases, and prevent the occurrence of skin diseases in time. The estimated score calculated by the model can be understood as a risk assessment score, and the closer the score is to 1, the higher the risk of disease. The way to obtain data is to consult the child's mother at different ages of the child, the child's recent disease condition, mainly about whether the child suffers from skin diseases such as dermatitis, urticaria, etc. According to whether or not the disease, registration is made, and the disease is set to 1, and the non-disease is set to 0. The disease condition and the intestinal flora information of children are combined, and the two groups are grouped according to the abundance of the flora. The t-test is performed on the index values of the two groups, and the p-value less than 0.05 is calculated. The difference bacteria are used as the independent variable, and the disease or not is used as the dependent variable. Multiple regression is calculated, and the calculation result according to the formula is a numerical result. The estimated score can be understood as a risk assessment score, and the closer the score is to 1, the higher the risk of disease.

[0167] The scores of stool characteristics, night sleep, and crying: The establishment process of these three detection indicators is similar. The indicators are divided according to different types, and scores are given. The scoring principles are shown in the table. Through questionnaire survey of the stool characteristics, night sleep, and crying of children at different ages of the child, the collected scales are summarized, and the summarized data are combined with the flora data. According to the relative abundance of each bacterium in the flora being higher / lower than the average relative abundance, the bacteria are divided into high and low groups. According to the high and low groups, the difference multiples and t-test of the summarized scores are obtained. The p-value less than 0.05 is selected as the independent variable, and the index score is used as the dependent variable. Multiple regression is calculated, and the sample information is put into the model for calculation to obtain a prediction score. The score can be considered as a tendency. For example, if the prediction score of a is 1.7, it is considered that he is more inclined to the result of 2. The scoring tendency relationship is shown in the table below.

[0168] The scoring principles are shown in Table 1:

[0169] Table 1: Scoring principles of stool characteristics, night sleep, and crying

[0170]

[0171] The actual trait tendency relationship corresponding to the score estimated by the model is:

[0172] When the estimated score is 0≤x<1.5, the corresponding trait is a trait with a score of 1;

[0173] When the estimated score is 1.5≤x<2.5, the corresponding trait is a trait with a score of 2;

[0174] When the estimated score is 2.5≤x<3.5, the corresponding trait is a trait with a score of 3;

[0175] When the estimated score is 3.5≤x<4.5, the corresponding trait is a trait with a score of 4;

[0176] When the estimated score is 4.5≤x, the corresponding trait is a trait with a score of 5.

[0177] Further, the database of intestinal flora of children aged 0-6 years old is constructed by collecting the feces of children aged 0-6 years old, sequencing and analyzing by 16S amplicon sequencing technology, obtaining the sequencing data corresponding to the intestinal flora of children aged 0-6 years old, denoising and aggregating the sequencing data to obtain basic classification units OTUs, annotating the classification units to obtain the composition of the intestinal flora of children aged 0-6 years old, and merging the intestinal flora information and clinical information according to the sample number to establish the database of intestinal flora of children aged 0-6 years old.

[0178] Further, the denoising includes: Reads splicing filtering and OTUs clustering.

[0179] A feasible implementation is as follows:

[0180] (1) The collected samples are processed, 16s amplicon sequencing technology is used, double-end sequencing of the library is performed based on the illumina NovaSeq sequencing platform, sequencing data corresponding to the intestinal flora of children aged 0-6 years old is obtained, the data is denoised and aggregated in the qiime 2 platform to obtain basic classification units OTUs, the classification units are annotated according to the existing biological level annotator to obtain the composition of the intestinal flora of children aged 0-6 years old, and the composition of the flora is presented in the format of relative abundance;

[0181] (2) Using R studio software to read in all breastfed sample data (including 0-6 year-old children's intestinal flora composition and clinical information table), merging according to sample unique number, merging clinical information and intestinal flora composition information together, and establishing a pure breastfed 0-6 year-old children database. According to different time points, the samples are divided into eight categories: 0-7 days (stage 1), 8-15 days (stage 2), 16-30 days (stage 3), 31 days-4 months (stage 4), 5 months-12 months (stage 5), 13 months-24 months (stage 6), 25 months-36 months (stage 7), and 37 months-6 years (stage 8).

[0182] (2) Based on the database obtained in (1), according to the above time classification mode, each group is operated as follows: the relative abundance of flora in the group is re-grouped according to being higher than the average and lower than the average, and the height and weight information of the high abundance group and the low abundance group is compared to obtain the difference multiple; in addition, t-test is performed to obtain p value. It is found that part of the flora is associated with the clinical performance.

[0183] (3) Further screening of the results calculated in (2), selecting bacteria with p value less than 0.05, defined as having significant correlation with 0-6 year-old children's height and weight information. According to the grouping method in (1), set a random seed, randomly select data for each time grouping, use 80% of the samples in each group as the prediction set, use the flora to perform linear regression calculation on 0-6 year-old children's height and weight, obtain the linear model of flora and clinical performance, use the remaining 20% of the samples as the test set, calculate the root mean square error, R 2 , mean absolute error of the model by using ten-fold cross-validation method. According to the number of random seeds, a plurality of linear regression models are obtained, according to the plurality of models, the model coefficient range is induced, and the average value is used as the calculation model coefficient, and the calculation model, model evaluation results and value range are calculated.

[0184] Further, when the development state to be evaluated is neuro-psychological development, the flora significantly related to neuro-psychological development includes the following (1)-(3) related to age:

[0185] (1) Staphylococcus (Staphylococcus), Ligilactobacillus (Ligilactobacillus), Ruminococcus. Torques group (Ruminococcus. Torques); optionally, the child's age is 0 days-4 months;

[0186] (2) Veillonella, Prevotella; optionally, the child is aged between 5 months and 12 months;

[0187] (3) TM7x, Weissella, Devosia, Bradyrhizobium, Atopobium, Glutamicibacter, Cupriavidus, Lachnospiraceae_FCS020_group, RB41; optionally, the child is aged between 13 months and 6 years;

[0188] Alternatively, where the developmental state to be assessed is skin, the microbiota significantly associated with skin comprises, in relation to age, several collections of bacteria as follows (i) - (ii):

[0189] (i) Clostridium_sensu_stricto_1, Brevundimonas, Subdoligranulum, Faecalibacterium; the child is aged between 0 days and 4 months;

[0190] (ii) Bifidobacterium, Bacillus;

[0191] Alternatively, where the developmental state to be assessed is stool form, the microbiota significantly associated with stool form comprises, in relation to age, several collections of bacteria as follows i) - iv):

[0192] i) Staphylococcus, Veillonella, Parabacteroides, Dialister, Haemophilus; optionally, the child is aged between 0 and 15 days;

[0193] ii) Lachnospira, Flavonifractor, Agathobacter, Phascolarctobacterium; optionally, the child is aged between 16 days and 30 days;

[0194] iii) Clostridium sensu stricto 1, Bacteroides, Parabacteroides, Akkermansia, Citrobacter, Bilophila, Epulopiscium, Phascolarctobacterium, Collinsella, Sarcina; optionally, the child is aged between 31 days and 4 months;

[0195] iv) Escherichia. Shigella, Clostridium sensu stricto 1, Staphylococcus, Bacteroides, Bacillus, Erysipelatoclostridium, Subdoligranulum, Dialister, Epulopiscium, Ruminococcus. torques group, CAG.56, Sellimonas; optionally, the child is aged between 5 months and 6 years;

[0196] Alternatively, the microbiota significantly associated with crying when the developmental state to be assessed is crying comprises, in relation to age, several collections of bacteria as follows 1) to 3):

[0197] 1) Erysipelatoclostridium, Klebsiella, Citrobacter; optionally, the child is aged between 8 days and 15 days;

[0198] 2) Staphylococcus, Rothia, Phascolarctobacterium, Ruminococcus; optionally, the child is aged between 16 days and 30 days;

[0199] 3) Acinetobacter, Enhydrobacter, Aeromonas, Ligilactobacillus, Veillonella, Megasphaera, Prevotella, Fusobacterium, Lactobacillus, Flavonifractor, Lachnospiraceae_NK4A136_group, Fusicatenibacter, Ruminococcus, Eubacterium. hallii group, Actinomyces, Atopobium; optionally, the child is aged between 31 days and 4 months;

[0200] Alternatively, when the developmental state to be assessed is night sleep, the microbiota significantly associated with night sleep includes several bacterial collections associated with age as follows a) to d):

[0201] a) Parabacteroides;

[0202] b) Lactobacillus, Phascolarctobacterium;

[0203] c) Stenotrophomonas, Enterococcus, Prevotella, Lactobacillus, Bilophila, Flavonifractor, Sutterella, Ruminococcus. torques group, unidentified Chloroplast, Fusicatenibacter, Dorea, Proteus, Myroides;

[0204] d) Staphylococcus, Enterobacter, Prevotella_9, Erysipelatoclostridium, Coprococcus, Fusobacterium, Flavonifractor, Sutterella, Lachnoclostridium, Actinomyces, Anaerostipes, Eubacterium. fissicatena group, Negativicoccus, Allisonella.

[0205] Further, the clinical information includes neurodevelopment, skin, stool, crying, or night sleep;

[0206] The prediction model formula of the intestinal flora information and neurodevelopment includes one or more of the following formulas (1) to (3):

[0207] (1) When the child is 0 days to 4 months old, the prediction formula of neurodevelopment is:

[0208] h = (51.0705-52.7283) + (-540.3316-54.9656) x Staphylococcus - (3599.8924-2076.5719) x Ligilactobacillus + (127.5668-3359.586) x Ruminococcus. torques group;

[0209] Wherein, Staphylococcus is the relative abundance of Staphylococcus, Ligilactobacillus is the relative abundance of Ligilactobacillus, and Ruminococcus. torques group is the relative abundance of Ruminococcus. torques group;

[0210] (2) When the child is 5 months to 12 months old, the prediction formula of neurodevelopment is:

[0211] h = (55.2745-56.409) - (34.2045-26.1645) x Veillonella + (794.7139-2787.5344) x Prevotella;

[0212] Wherein, Veillonella is the relative abundance of Veillonella genus, and Prevotella is the relative abundance of Prevotella genus;

[0213] (3) When the child is 13 months to 6 years old, the predicted neuropsychological development formula is:

[0214] h = (49.9123-52.6285) + (2343.7701-4663.8371) x TM7x + (-60.5126-3457.3805) x Weissella + (3641.8351-4855.5426) x Devosia + (1378.1606-2869.502) x Bradyrhizobium

[0215] (866.0322-3817.5774) x Atopobium + (1370.5024-4909.5865) x Glutamicibacter

[0216] (11328.4011-6658.3515) x Cupriavidus + (1408.4641-2853.6566) x

[0217] Lachnospiraceae_FCS020_group + (2431.4707-4358.3283) x RB41;

[0218] Wherein, TM7x is the relative abundance of TM7x genus, Weissella is the relative abundance of Weissella genus, Devosia is the relative abundance of Devosia genus, Bradyrhizobium is the relative abundance of Bradyrhizobium genus, Atopobium is the relative abundance of Atopobium genus, Glutamicibacter is the relative abundance of Glutamicibacter genus, Cupriavidus is the relative abundance of Cupriavidus genus, and Lachnospiraceae_FCS020_group is the relative abundance of Lachnospiraceae_FCS020_group;

[0219] Optionally, the intestinal flora information and the predicted model formula of neuropsychological development include one or more of the following formulas (1)-(3):

[0220] (1) When the child is 0 days to 4 months old, the predicted neuropsychological development formula is:

[0221] h = 51.9239-249.4833 x Staphylococcus-2448.8087 x Ligilactobacillus + 1394.1283 x

[0222] Ruminococcus. torques group;

[0223] (2) For children aged 5 months to 12 months, the formula for predicting neuropsychological development is:

[0224] h = 55.8377 - 30.5745 x Veillonella + 1188.1085 x Prevotella;

[0225] (3) For children aged 13 months to 6 years, the formula for predicting neuropsychological development is:

[0226] h = 51.0569 + 3311.0714 x TM7x + 857.1083 x Weissella + 4384.629 x Devosia + 2224.3299 x Bradyrhizobium + 1831.3744 x Atopobium + 2466.8918 x Glutamicibacter - 9012.034 x

[0227] Cupriavidus + 2119.8855 x Lachnospiraceae_FCS020_group + 3757.0831 x RB41;

[0228] The root mean square error, R 2 , and the mean absolute error of the prediction model formula of the intestinal flora information and neuropsychological development are shown in Table 2.

[0229] Table 2, Root mean square error, R 2 , and mean absolute error of the prediction model formula of the intestinal flora information and neuropsychological development

[0230]

[0231] Further, the prediction model formula of the intestinal flora information and the skin includes one or several of the following formulas (i) to (ii):

[0232] (i) For children aged 0 days to 4 months, the formula for predicting the skin is:

[0233] h = (0.0659-0.0894) + (0.2965-0.4306) x Clostridium_sensu_stricto_1 + (298.8977-432.2361) x Brevundimonas + (1.6479-2.5646) x Subdoligranulum - (21.6623-5.945) x Faecalibacterium;

[0234] wherein Clostridium_sensu_stricto_1 is the relative abundance of Clostridium sensu stricto, Brevundimonas is the relative abundance of Brevundimonas, Subdoligranulum is the relative abundance of Subdoligranulum, and Faecalibacterium is the relative abundance of Faecalibacterium;

[0235] (ii) for children aged 5 months to 6 years, the formula for predicting the skin is:

[0236] h = (0.1305-0.1715) - (0.2813-0.2075) x Bifidobacterium + (135.426-160.0824) x Bacillus;

[0237] wherein Bifidobacterium is the relative abundance of Bifidobacterium, and Bacillus is the relative abundance of Bacillus;

[0238] Optionally, the gut microbiota information and the predictive model formula for the skin include one or several of the following formulas (i)-(ii):

[0239] (i) for children aged 0 days to 4 months, the formula for predicting the skin is:

[0240] h = 0.0794 + 0.3781 x Clostridium_sensu_stricto_1 + 354.1341 x Brevundimonas + 2.0446 x Subdoligranulum - 12.0461 x Faecalibacterium;

[0241] (ii) for children aged 5 months to 6 years, the formula for predicting the skin is:

[0242] h = 0.148 - 0.2464 x Bifidobacterium + 150.5487 x Bacillus;

[0243] The root mean square error, R 2, mean absolute error as shown in Table 3.

[0244] Table 3, root mean square error, R2, and mean absolute error of prediction model formulae of gut microbiota information and skin 2 , mean absolute error

[0245]

[0246] Further, the prediction model formulae of gut microbiota information and stool properties include one or several of the following formulae i) to iv):

[0247] i) When the age of the child is 0 to 15 days, the formula for predicting stool properties is:

[0248] h = (2.5624-2.6469) - (2.6623-0.8585) x Staphylococcus - (4.0757-3.3154) x Veillonella - (2.3037-1.444) x Parabacteroides + (8.4342-535.3406) x Dialister - (3.8718-3.0043) x Haemophilus;

[0249] wherein Staphylococcus is the relative abundance of Staphylococcus, Veillonella is the relative abundance of Veillonella, Parabacteroides is the relative abundance of Parabacteroides, Dialister is the relative abundance of Dialister, and Haemophilus is the relative abundance of Haemophilus;

[0250] ii) When the age of the child is 16 to 30 days, the formula for predicting stool properties is:

[0251] h = (2.3114-2.4241) + (2.4508-683.2421) x Lachnospira + (2.3546-12.7348) x Flavonifractor - (405.4702-62.5708) x Agathobacter - (13.1682-9.6822) x Phascolarctobacterium;

[0252] wherein Lachnospira is the relative abundance of Lachnospira, Flavonifractor is the relative abundance of Flavonifractor, Agathobacter is the relative abundance of Agathobacter, and Phascolarctobacterium is the relative abundance of Phascolarctobacterium;

[0253] iii) For children aged 31 days to 4 months, the formula to predict stool form is:

[0254] h = (2.3441-2.441) + (-0.1699-0.0419) x Clostridium_sensu_stricto_1 + (0.0728-0.2473) x Bacteroides + (1.7218-2.1934) x Parabacteroides + (1.2942-3.6235) x Akkermansia + (-0.5946-2.7963) x Citrobacter + (-11.0145-21.6328) x Bilophila + (-1.8488-1.354) x Epulopiscium + (6.8497-11.2683) x Phascolarctobacterium + (2.3211-5.0496) x Collinsella + (221.8181-121.729) x Sarcina.

[0255]

[0256]

[0257]

[0258] wherein Clostridium_sensu_stricto_1 is the relative abundance of Clostridium_sensu_stricto_1, Bacteroides is the relative abundance of Bacteroides, Parabacteroides is the relative abundance of Parabacteroides, Akkermansia is the relative abundance of Akkermansia, Citrobacter is the relative abundance of Citrobacter, Bilophila is the relative abundance of Bilophila, Epulopiscium is the relative abundance of Epulopiscium, Phascolarctobacterium is the relative abundance of Phascolarctobacterium, Collinsella is the relative abundance of Collinsella, Sarcina is the relative abundance of Sarcina.

[0259] iv) For children aged 5 months to 6 years, the formula to predict stool form is:

[0260] ​​​h = (2.9053~3.0947) - (1.0973~0.7369) x Escherichia.Shigella - (0.4223~0.3035) x Clostridium_sensu_stricto_1 - (15.7456~14.3356) x Staphylococcus + (0.3984~0.5591) x Bacteroides - (399.5418~60.4927) x Bacillus - (1.4938~0.4026) x Erysipelatoclostridium

[0261] (12.6503~8.9228) x Subdoligranulum + (22.3406~24.9229) x Dialister - (104.8873~77.8148) x Epulopiscium + (-0.347~7.5903) x Ruminococcus._torques_group - (458.5554~407.3394) x CAG.56 + (218.2145~397.8098) x Sellimonas

[0262] Wherein, Escherichia.Shigella is the relative abundance of Escherichia-Shigella, Clostridium_sensu_stricto_1 is the relative abundance of Clostridium_sensu_stricto_1, Staphylococcus is the relative abundance of Staphylococcus, Bacteroides is the relative abundance of Bacteroides, Bacillus is the relative abundance of Bacillus, Erysipelatoclostridium is the relative abundance of Erysipelatoclostridium, Subdoligranulum is the relative abundance of Subdoligranulum, Dialister is the relative abundance of Dialister, Epulopiscium is the relative abundance of Epulopiscium, Ruminococcus._torques_group is the relative abundance of Ruminococcus._torques_group, CAG.56 is the relative abundance of CAG.56, and Sellimonas is the relative abundance of Sellimonas.

[0263] Optionally, the prediction model formula of the intestinal flora information and the stool character includes one or several of the following formulas i)~iv):

[0264] i) When the age of the child is 0~15 days, the formula for predicting the stool character is:

[0265] h = 2.6152 - 1.1958 x Staphylococcus - 3.6283 x Veillonella - 1.8468 x Parabacteroides + 152.3533 x Dialister - 3.5718 x Haemophilus;

[0266] ii) for children aged 16 days to 30 days, the formula to predict stool form is:

[0267] h = 2.3783 + 182.608 x Lachnospira + 8.5137 x Flavonifractor - 122.2349 x Agathobacter - 11.4688 x Phascolarctobacterium;

[0268] iii) for children aged 31 days to 4 months, the formula to predict stool form is:

[0269] h = 2.3775 - 0.0542 x Clostridium_sensu_stricto_1 + 0.1665 x Bacteroides + 1.9577 x

[0270] Parabacteroides + 2.7415 x Akkermansia + 1.8706 x Citrobacter + 1.9948 x Bilophila - 1.6425 x

[0271] Epulopiscium + 8.7979 x Phascolarctobacterium + 3.2822 x Collinsella - 165.6212 x Sarcina;

[0272] iv) for children aged 5 months to 6 years, the formula to predict stool form is:

[0273] h = 2.9704 - 0.8727 x Escherichia.Shigella - 0.3608 x Clostridium_sensu_stricto_1 - 14.9315 x Staphylococcus + 0.4969 x Bacteroides - 203.9802 x Bacillus - 0.9976 x Erysipelatoclostridium - 10.9023 x Subdoligranulum + 24.0384 x Dialister - 93.5327 x Epulopiscium + 2.4099 x

[0274] Ruminococcus. torques group - 427.6705 x CAG.56 + 333.3722 x Sellimonas.

[0275] The root mean square error, R 2 , and mean absolute error of the prediction model formula of the intestinal flora information and stool properties are shown in Table 4.

[0276] Table 4, root mean square error, R 2 , and mean absolute error of the prediction model formula of the intestinal flora information and stool properties

[0277]

[0278] Further, the prediction model formula of the intestinal flora information and the crying condition includes one or several of the following formulas 1) to 3):

[0279] 1) When the child is 8 days to 15 days old, the formula for predicting the crying condition is:

[0280] h = (1.6702-1.8308) + (1.5851-2.4425) x Erysipelatoclostridium - (1.5395-1.1238) x Klebsiella - (20.1973-8.7767) x Citrobacter;

[0281] Wherein, Erysipelatoclostridium is the relative abundance of Erysipelatoclostridium, Klebsiella is the relative abundance of Klebsiella, and Citrobacter is the relative abundance of Citrobacter;

[0282] 2) When the child is 16 days to 30 days old, the formula for predicting the crying condition is:

[0283] h = (1.5075-1.5779) + (-0.9539-38.2314) x Staphylococcus - (111.3334-14.6111) x Rothia - (15.6307-9.5096) x Phascolarctobacterium - (7.9704-5.5118) x Ruminococcus;

[0284] Wherein, Staphylococcus is the relative abundance of Staphylococcus, Rothia is the relative abundance of Rothia, Phascolarctobacterium is the relative abundance of Phascolarctobacterium, and Ruminococcus is the relative abundance of Ruminococcus;

[0285] 3) For children aged 31 days to 4 months, the formula to predict the crying situation is:

[0286] h = (1.4678 ~ 1.5216) + (-1.2963 ~ 3.8134) x Acinetobacter - (147.9583 ~ 82.5599) x Enhydrobacter + (214.5577 ~ 611.1134) x Aeromonas - (61.6441 ~ 45.7525) x Ligilactobacillus

[0287]

[0288] (1.5492 ~ 1.1626) x Veillonella + (0.3757 ~ 2.8055) x Megasphaera + (-26.169 ~ 226.5171) x Prevotella - (31.1451 ~ 28.1141) x Fusobacterium - (1.1191 ~ 0.486) x Lactobacillus + (-1.2058 ~ 0.9898) x Flavonifractor - (215.1284 ~ 112.9927) x Lachnospiraceae_NK4A136_group + (-2.2375 ~ 8.3244) x Fusicatenibacter - (179.0185 ~ 49.5399) x Ruminococcus + (-477.6953 ~ 262.9145) x

[0289] Eubacterium._hallii_group - (88.7991 ~ 73.9559) x Actinomyces + (160.468 ~ 224.39) x Atopobium

[0290] ​Wherein, Acinetobacter is the relative abundance of Acinetobacter, Enhydrobacter is the relative abundance of Enhydrobacter, Aeromonas is the relative abundance of Aeromonas, Ligilactobacillus is the relative abundance of Ligilactobacillus, Veillonella is the relative abundance of Veillonella, Megasphaera is the relative abundance of Megasphaera, Prevotella is the relative abundance of Prevotella, Fusobacterium is the relative abundance of Fusobacterium, Lactobacillus is the relative abundance of Lactobacillus, Flavonifractor is the relative abundance of Flavonifractor, Lachnospiraceae_NK4A136_group is the relative abundance of Lachnospiraceae NK4A136 group, Fusicatenibacter is the relative abundance of Fusicatenibacter, Ruminococcus is the relative abundance of Ruminococcus, Eubacterium._hallii_group is the relative abundance of Eubacterium. hallii group, Actinomyces is the relative abundance of Actinomyces, Atopobium is the relative abundance of Atopobium;

[0291] Preferably, the prediction model formula of the intestinal flora information and the crying condition comprises one or several of the following formulas 1) to 3):

[0292] 1) When the child is 8 days to 15 days old, the formula for predicting the crying condition is:

[0293] h = 1.7244 + 1.8593 x Erysipelatoclostridium - 1.2836 x Klebsiella - 12.9963 x Citrobacter;

[0294] 2) When the child is 16 days to 30 days old, the formula for predicting the crying condition is:

[0295] h = 1.5331 + 20.0659 x Staphylococcus - 42.3019 x Rothia - 13.2372 x

[0296] Phascolarctobacterium - 6.9779 x Ruminococcus;

[0297] 3) When the child is 31 days to 4 months old, the formula for predicting the crying condition is:

[0298] h = 1.4959 + 1.279 x Acinetobacter - 117.4994 x Enhydrobacter + 394.8747 x Aeromonas - 54.0312 x Ligilactobacillus - 1.3707 x Veillonella + 1.3511 x Megasphaera + 62.6096 x Prevotella - 30.0045 x Fusobacterium - 0.7988 x Lactobacillus + 0.6225 x Flavonifractor - 167.8314 x

[0299] Lachnospiraceae_NK4A136_group + 3.1171 x Fusicatenibacter - 109.4375 x Ruminococcus - 89.6414 x Eubacterium. hallii group - 82.0443 x Actinomyces + 199.7983 x Atopobium;

[0300] The root mean square error, R 2 , mean absolute error of the prediction model formula of the intestinal flora information and the crying situation are as shown in Table 5.

[0301] Table 5, the root mean square error, R 2 , mean absolute error of the prediction model formula of the intestinal flora information and the crying situation

[0302]

[0303] Further, the prediction model formula of the intestinal flora information and the night sleep situation includes one or several of the following formulas a) ~ d):

[0304] a) When the child is 0 days to 15 days old, the formula of the night sleep situation is:

[0305] h = (1.667 ~ 1.7575) + (2.0551 ~ 3.3078) x Parabacteroides;

[0306] Wherein, Parabacteroides is the relative abundance of Parabacteroides;

[0307] b) When the child is 16 days to 30 days old, the formula of the night sleep situation is:

[0308] h = (1.9871 ~ 2.1135) - (36.0173 ~ 5.136) x Lactobacillus + (19.9734 ~ 35.2859) x

[0309] Phascolarctobacterium;

[0310] Among them, Lactobacillus is the relative abundance of Lactobacillus, and Phascolarctobacterium is the relative abundance of Phascolarctobacterium;

[0311] c) For children aged 31 days to 4 months, the formula for nighttime sleep is:

[0312] h=(2.1063~2.153)+(485.8899~703.2182)×Stenotrophomonas-(1.1099~0.9931)×Enterococcus+(45.4132~142.3935)×Prevotella-(3.0807~2.7328)×Lactobacillus+

[0313] (6.9523~27.6025)×Bilophila+(-0.4976~4.8429)×Flavonifractor+(3.4943~6.1713)×Sutterella+(7.1024~12.505)×Ruminococcus._torques_group-(405.374~222.0575)×

[0314] unidentified_Chloroplast+(0.0185~4.2892)×Fusicatenibacter+(232.9549~397.3759)×Dorea-(3.843~2.5359)×Proteus-(126.278~76.0815)×Myroides;

[0315] Stenotrophomonas, Enterococcus, Prevotella, Lactobacillus, Bilophila, Flavonifractor, Sutterella, Ruminococcus. torques group, unidentified Chloroplast, Fusicatenibacter, Dorea, Proteus, Myroides;

[0316] d) The formula for the sleep condition at night when the child is aged from 5 months to 6 years old is:

[0317] h = (1.8273-1.8898) + (5.8052-7.3144) x Staphylococcus - (5.3856-1.5202) x Enterobacter + (1.744-9.8407) x Prevotella_9 + (-1.5188-0.2748) x Erysipelatoclostridium + (-2.1975-30.4482) x Coprococcus - (66.6501-15.2271) x Fusobacterium + (-0.8691-4.0197) x Flavonifractor

[0318] + (5.4952-9.5591) x Sutterella + (3.2347-5.8771) x Lachnoclostridium + (53.2421-66.6003) x Actinomyces + (124.4522-194.2467) x Anaerostipes + (6.1294-71.2023) x

[0319] Eubacterium. fissicatena group - (158.923-76.3152) x Negativicoccus + (112.5601-779.4042) x Allisonella;

[0320] Wherein, Staphylococcus is the relative abundance of Staphylococcus, Enterobacter is the relative abundance of Enterobacter, Prevotella_9 is the relative abundance of Prevotella_9, Erysipelatoclostridium is the relative abundance of Erysipelatoclostridium, Coprococcus is the relative abundance of Coprococcus, Fusobacterium is the relative abundance of Fusobacterium, Flavonifractor is the relative abundance of Flavonifractor, Sutterella is the relative abundance of Sutterella, Lachnoclostridium is the relative abundance of Lachnoclostridium, Actinomyces is the relative abundance of Actinomyces, Anaerostipes is the relative abundance of Anaerostipes, Eubacterium._fissicatena_group is the relative abundance of Eubacterium._fissicatena_group, Negativicoccus is the relative abundance of Negativicoccus, and Allisonella is the relative abundance of Allisonella.

[0321] Preferably, the intestinal flora information and the prediction model formula of the night sleep condition include one or several of the following formulas a) to d):

[0322] a) When the age of the child is 0 to 15 days, the formula of the night sleep condition is:

[0323] h = 1.7084 + 2.5764 x Parabacteroides;

[0324] b) When the age of the child is 16 to 30 days, the formula of the night sleep condition is:

[0325] h = 2.0218 - 12.0345 x Lactobacillus + 26.0639 x Phascolarctobacterium;

[0326] c) When the age of the child is 31 days to 4 months, the formula of the night sleep condition is:

[0327] h = 2.1267 + 580.5244 x Stenotrophomonas - 1.0646 x Enterococcus + 76.9896 x Prevotella - 2.9066 x Lactobacillus + 16.0708 x Bilophila + 0.7412 x Flavonifractor + 4.4946 x Sutterella + 8.8728 x

[0328] Ruminococcus. torques group - 280.1981 x unidentified Chloroplast + 1.3007 x

[0329] Fusicatenibacter + 310.8906 x Dorea - 3.3466 x Proteus - 98.8985 x Myroides;

[0330] d) The formula for the night sleep condition of children aged 5 months to 6 years is:

[0331] h = 1.8537 + 6.6401 x Staphylococcus - 2.9561 x Enterobacter + 4.1107 x Prevotella_9 - 0.5677 x Erysipelatoclostridium + 8.9717 x Coprococcus - 29.4941 x Fusobacterium + 1.7762 x

[0332] Flavonifractor + 7.6957 x Sutterella + 4.7329 x Lachnoclostridium + 61.5478 x Actinomyces + 164.25 x Anaerostipes + 37.7456 x Eubacterium. fissicatena group - 121.676 x Negativicoccus + 403.4906 x Allisonella;

[0333] The root mean square error, R 2 , and the mean absolute error of the prediction model formula of the intestinal flora information and the night sleep condition are shown in Table 6.

[0334] Table 6, the root mean square error, R 2 , and the mean absolute error of the prediction model formula of the intestinal flora information and the night sleep condition

[0335]

[0336]

[0337] The linear model establishment effect calculated by the training set and the validation set is evaluated by the root mean square error, R 2 , the mean absolute error, and the R 2Indicates the ability of the independent variable to explain the dependent variable. Values ​​closer to 1 indicate a stronger explanatory power of the model. Mean absolute error measures the average absolute error between the model's predicted values ​​and the actual values. Smaller values ​​indicate a better model.

[0338] The above results show that the model formula obtained by the present invention has good prediction performance.

[0339] The specific test examples are as follows:

[0340] Test Case 1: Neuropsychological Prediction

[0341] Three samples aged 31 days to 4 months were randomly selected, and the relative abundance of bacterial genera related to neuropsychology in this age group was substituted into the corresponding formula:

[0342] h=51.9239-249.4833×Staphylococcus-2448.8087×Ligilactobacillus+1394.1283×

[0343] Ruminococcus._torques_group;

[0344] The model prediction value was calculated and evaluated according to the prediction value specification. The results are shown in Table 7. It was found that the model evaluation status was close to the child's actual status, which proved that the model fit was good and the ASQ3 questionnaire score could be predicted using the microbiome.

[0345] Table 7. Comparison of neuropsychological questionnaire scores and model prediction results

[0346] Indicator Sample 1 Sample 2 Sample 3 Questionnaire score 50 45 45 Staphylococcus 0.00135318 0.00405954 0 Ligilactobacillus 0 0.00270636 0.00270636 [Ruminococcus]_torques_group 0 0 0 Model prediction value 51.5863 44.2838 45.2965 Difference (questionnaire score - model prediction value) -1.5863 0.7162 -0.2965 .

[0347] Test Example 2: Crying State Prediction

[0348] Three samples between 16 and 30 days old were randomly selected, and the relative abundance of bacterial genera associated with crying in this age group was substituted into the corresponding formula:

[0349] h=1.5331+20.0659×Staphylococcus-42.3019×Rothia-13.2372×

[0350] Phascolarctobacterium-6.9779×Ruminococcus;

[0351] The model prediction value was calculated and evaluated according to the prediction value specification. The results are shown in Table 8. It was found that the model evaluation status was the same as the child's actual status, which proved that the model fit was good and the microbiome could be used to evaluate the crying status.

[0352] Table 8, comparison of actual state and prediction results of crying situation

[0353] Indicator Sample 1 Sample 2 Sample 3 Actual score of crying state 1 2 2 Staphylococcus 0 0 0.00270636 Rothia 0 0 0 Phascolarctobacterium 0.00135318 0 0 Ruminococcus 0.073071719 0 0 Model prediction value 1.005300538 1.5331 1.587405549 Judgment result value (tendency result value obtained from prediction value) 1 2 2 .

[0354] Test Example 3: sleep state prediction

[0355] Three samples of 31 days to 4 months were randomly selected, and the specific relative abundance of genera related to sleep state at this age was brought into the corresponding formula:

[0356] h = 2.1267 + 580.5244 x Stenotrophomonas - 1.0646 x Enterococcus + 76.9896 x Prevotella - 2.9066 x Lactobacillus + 16.0708 x Bilophila + 0.7412 x Flavonifractor + 4.4946 x Sutterella + 8.8728 x Ruminococcus. torques group - 280.1981 x unidentified Chloroplast + 1.3007 x

[0357] Fusicatenibacter + 310.8906 x Dorea - 3.3466 x Proteus - 98.8985 x Myroides

[0358] The model prediction value was calculated, and the prediction value was evaluated according to the prediction value specification, and the results are shown in Table 9. It was found that the model evaluation state and the actual state of the child were the same, proving that the model fitting was good, and the evaluation of sleep state using the bacterial flora could be realized.

[0359] Table 9, comparison of actual state and prediction results of sleep situation

[0360]

[0361]

[0362] Test Example 4: stool shape prediction

[0363] Three samples of 8 to 15 days were randomly selected, and the specific relative abundance of genera related to stool shape at this age was brought into the corresponding formula:

[0364] h = 2.6152 - 1.1958*Staphylococcus - 3.6283*Veillonella - 1.8468*Parabacteroides + 152.3533*Dialister - 3.5718*Haemophilus;

[0365] The model prediction value is calculated, and the prediction value is evaluated according to the prediction value specification, and the results are shown in Table 10, and it is found that the model evaluation state is the same as the actual state of the child, proving that the model fitting is good, and the evaluation of sleep state by using the flora can be realized.

[0366] Table 10, Comparison of actual state and prediction results of stool characteristics

[0367] Indicator Sample 1 Sample 2 Sample 3 Actual score of stool form 2 3 2 Staphylococcus 0.09472 0 0.00406 Veillonella 0 0 0.00135 Parabacteroides 0 0 0.39513 Dialister 0 0 0 Haemophilus 0.16915 0.00271 0 Model prediction value 1.89777 2.60553 1.87571 Judgment result value (tendency result value obtained from prediction value) 2 3 2 .

[0368] Test Example 5: Skin prediction

[0369] Three samples of 31 days to 4 months were randomly selected, and the specific skin-related genus relative abundance of this age group was brought into the corresponding formula:

[0370] h = (0.0659-0.0894) + (0.2965-0.4306)*Clostridium_sensu_stricto_1 + (298.8977-432.2361)*Brevundimonas + (1.6479-2.5646)*Subdoligranulum - (21.6623-5.945)*Faecalibacterium;

[0371] The model prediction value is calculated, and the prediction value is evaluated according to the prediction value specification, and the results are shown in Table 11, and it is found that the model evaluation state is the same as the actual state of the child, proving that the model fitting is good, and the evaluation of sleep state by using the flora can be realized.

[0372] Table 11, Comparison of actual state and prediction results of skin

[0373] Indicator Sample 1 Sample 2 Sample 3 Skin problem 0 1 1 Clostridium_sensu_stricto_1 0.34776725 0.29364005 0.34641407 Brevundimonas 0 0 0 Subdoligranulum 0.0121786 0.25981056 0.26792964 Faecalibacterium 0 0 0 Model prediction value 0.23579121 0.72163397 0.75818809 Judgment result value (tendency result value obtained from prediction value) 0 1 1 .

[0374] The above results show that the prediction and evaluation formula of the present application has good model fitting effect, and can realize accurate prediction of neuro-psychological development, skin, stool, crying condition, night sleep and the like, and has good evaluation value.

[0375] It has been verified that the fitting effect of other model formulas is also good.

[0376] In a second aspect, there is provided a system for evaluating the growth and development state of a child aged 0-6 years, applied to the method of any one of the first aspect, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.

[0377] In a third aspect, there is provided use of the method of the first aspect or the system of the second aspect in the preparation of a product for evaluating the growth and development state of a child aged 0-6 years.

[0378] The beneficial effects of the present application include:

[0379] 1. The data used in the embodiments of the present application are all from children aged 0-6 years in China, and all belong to breast-feeding. The clinical cohort covers multiple regions in China and has a long tracking time, and the data content is rich, which is more suitable for determining the indicators of children aged 0-6 years in China.

[0380] 2. The current evaluation of the health level of infants is mainly through the development indicators of neuropsychological development, skin, stool, crying, night sleep, and the neuropsychological development indicators of language, action, problem-solving ability, whether the child is sick and the type of disease, and other health status indicators, but it is difficult to evaluate the health level of the internal development of the infant. The present application increases the angle of health evaluation of the internal development state of children aged 0-6 years by establishing the change curve and reference range of the intestinal flora of children aged 0-6 years.

[0381] 3. Evaluating the development level of children through neuropsychological development, skin, stool, crying, night sleep and other information is a monitoring of the development result indicators of children, i.e., the differences in the clinical manifestations of infant health caused by the internal development of the intestinal flora of children, and predicting the near and long-term neuropsychological development, skin, stool, crying, night sleep indicators of children through the intestinal flora of children provides a target that can be referenced for improvement, which can find possible adverse development results in the future and prevent and improve in time.

[0382] 4. The intestinal flora is in a state of continuous dynamic change and maintenance, and external intervention has an impact on the composition of the flora. The intestinal flora of children aged 0-6 years is more susceptible to external influences. It is difficult to determine the final outcome of child development caused by the influence of different factors on the intestinal flora of children. According to the reference standard of the flora provided by the present application, the possible adverse development of the intestinal flora can be found in time, and the adverse outcome can be changed.

[0383] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the growth and development status of children aged 0 to 6 years old based on intestinal flora, characterized in that: The developmental state to be assessed is the skin developmental state, and the age of the children aged 0 to 6 years to be assessed is specifically 0 days to 4 months. The method comprises the following steps: Obtaining intestinal flora information of the target population to be predicted; the intestinal flora information includes relative abundance information of flora significantly associated with the corresponding developmental state; Inputting intestinal flora information into a pre-built prediction and evaluation model to obtain an evaluation result output by the prediction and evaluation model, the evaluation result including: a quantitative result of the corresponding developmental state, wherein the quantitative result corresponds to different developmental states; The prediction and evaluation model is obtained by multivariate regression calculation based on the corresponding intestinal flora information that is significantly correlated with the developmental status to be evaluated; Significant skin-associated bacterial communities included the following age-related bacterial collections: Clostridium_sensu_stricto_1, Brevundimonas, Subdoligranulum, and Faecalibacterium in children aged 0 days to 4 months.

2. The method according to claim 1, characterized in that The predictive evaluation model includes several predictive evaluation sub-models differentiated according to age and developmental status to be evaluated; the prediction evaluation sub-model is selected based on age and developmental status to be evaluated, and the corresponding intestinal flora information significantly correlated with the corresponding age group and developmental status to be evaluated is input for predictive evaluation.

3. The method according to claim 1, characterized in that The intestinal flora information is obtained by detecting fecal samples of the target population to be predicted.

4. The method according to claim 1, wherein The methods for constructing the prediction and evaluation model include: Obtain intestinal flora information from multiple samples across multiple regions, and combine this intestinal flora information with clinical information to establish a intestinal flora database for children aged 0-6 years. The intestinal flora information includes the relative abundance of flora at the phylum, family, and genus levels, while the clinical information includes age and developmental status information; developmental status information refers to skin information; developmental status information is quantified, including assigning scores based on whether or not the child has skin diseases. The data in the database were grouped by age, and then further grouped according to the relative abundance of the bacterial communities within the groups. The average relative abundance of each bacteria was used as the boundary to divide the data into high-abundance and low-abundance groups. The means of the quantitative developmental status information of the high-abundance and low-abundance groups were calculated respectively. The means of the two groups were compared to obtain the difference fold and perform significance analysis. Bacteria significantly associated with clinical information were selected as independent variables, and the scores or assignments of clinical information of children aged 0-6 years were selected as dependent variables. A random seed was set for each age group, and the data were randomly divided into training and validation sets in proportion. The training set was subjected to multivariate linear regression to obtain a linear prediction model of the bacterial flora and clinical information of children aged 0-6 years, which was then tested on the validation set. Multiple linear regression models were obtained based on the number of random seeds. The range of model coefficients was summarized based on the obtained multiple models, and the average value was used as the calculation model coefficient to obtain a predictive evaluation model. The developmental status of children aged 0-6 years was evaluated based on the predicted clinical information of children aged 0-6 years.

5. The method according to claim 4, characterized in that The construction of the intestinal flora database of children aged 0-6 years includes: collecting feces of children aged 0-6 years, sequencing and analyzing them through 16S amplicon sequencing technology to obtain sequencing data corresponding to the intestinal flora of children aged 0-6 years, denoising and aggregating the sequencing data to obtain basic taxonomic units (OTUs), annotating the taxonomic units to species, and obtaining the composition of the intestinal flora of children aged 0-6 years. The intestinal flora information and clinical information are merged according to the sample number to establish the intestinal flora database of children aged 0-6 years.

6. The method according to claim 5, characterized in that Noise reduction includes: Reads were spliced ​​and filtered, and then OTUs were clustered.

7. The method according to claim 1, characterized in that The prediction model formulas for intestinal flora information and skin development status include the following formula (i): (i) For children aged 0 days to 4 months, the formula for predicting skin development is: h=(0.0659~0.0894)+(0.2965~0.4306)×Clostridium_sensu_stricto_1+(298.8977~432.2361 )×Brevundimonas+(1.6479~2.5646)×Subdoligranulum-(21.6623~5.945)×Faecalibacterium; Among them, Clostridium_sensu_stricto_1 is the relative abundance of Clostridium stricto, Brevundimonas is the relative abundance of Brevundimonas, Subdoligranulum is the relative abundance of Subdoligranulum, and Faecalibacterium is the relative abundance of Faecalibacterium.

8. A system for evaluating the growth and development status of children aged 0 to 6 years old, applied to the method according to any one of claims 1 to 7, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. Use of the system according to claim 8 in preparing a product for evaluating the growth and development status of children aged 0 to 6 years, wherein the development status is skin development status, and the age of the children aged 0 to 6 years to be evaluated is specifically 0 days to 4 months.

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