CN111961712A - Molecular marker for diagnosing attention deficit hyperactivity disorder syndrome - Google Patents
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Abstract
The invention belongs to the technical field of biomedical engineering, and relates to a molecular marker for diagnosing attention deficit hyperactivity disorder syndrome. Based on the fact that Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental nervous system diseases of children and teenagers, the main clinical characteristics of the ADHD are melt or difficulty in concentrating attention, excessive activity or weakness in self-control, and the etiology and pathogenesis of the ADHD are unknown, a group of miRNAs with different expression differences in ADHD child serum samples is provided and verified, namely compared with a healthy child group, miR-4466 is increased in ADHD child serum, and miR-4516, miR-6090, miR-3960 and miR-4281 are decreased in ADHD child serum; and the miRNA with the expression difference and the established analysis model can be used as molecular diagnostic markers of ADHD and for predicting the occurrence probability of ADHD, and have certain clinical reference value in the aspect of diagnosing ADHD.
Description
Technical Field
The invention belongs to the technical field of biomedical engineering, and relates to a diagnostic molecular marker for Attention Deficit Hyperactivity Disorder (ADHD).
Background
The prior art discloses that Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental and neurological diseases in children and adolescents,
experts at home and abroad consider attention deficit hyperactivity disorder as a developmental disorder. ADHD is characterized clinically by inattention, hyperactivity, or impaired continence. Studies have shown that ADHD has three subtypes, including the attention deficit subtype, the impulse hyperactivity subtype and the mixed subtype. Compared with healthy children, the cognitive function, learning ability and future occupational ability of the children with hyperactivity are often poor, control over self emotion of the children and certain obstruction on social contact of the children are not facilitated, even the incidence rate of safety accidents, accidental injuries and drug abuse is increased, and certain economic and psychological burdens can be caused for families. Currently, the prevalence of ADHD in children and adolescents worldwide is estimated to be about 3.4%, with boys having a significantly higher prevalence of ADHD than girls, with a male to female ratio of about 3: 1-10: 1. The prevalence of ADHD in children and adolescents in our country is about 5.7%, about 80% of ADHD children will continue to the adolescent stage, 40% of children will continue to adults, 30% of children will continue to be lifelong, and some patients will be accompanied by other mental disorders such as depression, anxiety, autism, and the like; it is believed that infants with persistent ADHD symptoms need to be diagnosed and intervened as early as possible. The current Diagnostic standard for ADHD relies mainly on The "Diagnostic and Statistical Manual of Mental Disorders" 5 th edition (DSM-5) set by The American society of psychiatry of agriculture, APA as a Diagnostic standard, and The core symptoms and features of ADHD in The DSM-5 standard mainly include The following: (1) symptom severity, symptom duration, and patient age and development level (i.e., age before 12 years, symptom duration >6 months) (2) symptoms are associated with impaired learning performance, peer-to-peer and family relationships, and adaptability, and frequently occur to have an impact on living ability; (3) type of ADHD Performance according to DSM-5: attention defect type (attention defect symptom accords with diagnosis standard), multi-impulse type (multi-impulse symptom accords with diagnosis standard), mixed type (attention defect symptom and multi-impulse symptom accord with diagnosis standard); because the diagnosis of ADHD mainly adopts a form of a scale to perform questionnaire survey, the diagnosis is easily influenced by dual subjective factors of an interviewer (medical staff) and an interviewee (generally, family members of children patients), and a certain proportion of over-diagnosis phenomenon can occur; the questionnaire survey steps are more, the time spent is longer, medical staff may miss some scale problems in use, and the obtained questionnaire result is influenced by factors such as the growth environment and culture level of the interviewee, so that the diagnosis result has certain bias. Currently there is a clinical lack of molecular markers associated with ADHD diagnosis, and researchers in the industry are concerned with ADHD-associated marker molecules that will provide clinical reference values for the clinical diagnosis of ADHD.
Micrornas (mirnas) are known to be highly conserved, small non-coding RNAs of typically 18-25 nucleotides in length, which cause termination of transmission of genetic information or genetic variation by targeting a 3 'untranslated region (3' UTR) that recognizes the target messenger RNA (mRNA) to degrade the mRNA or inhibit expression of translation. miRNA participates in a wide range of biological processes and plays a role in growth, development, aging and apoptosis. MicroRNA exists in tissue cells and body fluid, and is researched to detect microRNA in various body fluids, including plasma, serum, saliva, cerebrospinal fluid and the like, can stably exist in various body fluids, is not easy to degrade, and has remarkable differential expression in diseases and normal physiological states. Research shows that the nerve development process is precisely regulated and controlled by various factors in a specific time and space sequence, and if the regulating and controlling factors are abnormal, neuropsychiatric disorder may occur. miRNA is used as a gene post-transcriptional regulatory factor and is proved to be involved in development and regulation of a nervous system, abnormal expression of microRNA is possibly related to certain nervous system diseases, compared with healthy people, miR-124 is highly expressed in forehead and hippocampus of the brain of a depression patient, and miR-7 is remarkably reduced in brain areas of dopaminergic neurons in Parkinson's Disease (PD) patients and Parkinson's disease animal models.
It is reported that the study of using the cDNA chip, protein chip and human miRNA chip of ADHD animal model SHR and control group WKY rat to find the genes related to ADHD has been greatly improved. In the early stage experiment of the application, animal model research, cell experiments and clinical samples prove that the ADHD rat model SHR and the normal control model WKY forehead region of brain have the differential expression of Galectin-3 and miRNA-let-7d, and miRNA-let-7d directly acts on the 3 'untranslated region (3' UTR) of Galectin-3 (Galectin-3) to negatively regulate expression of Galectin-3, meanwhile, the decrease of galectin-3 may inhibit the expression of Tyrosine hydroxylase by inhibiting the interaction of cAMP response element binding protein and Tyrosine Hydroxylase (TH) gene, the tyrosine hydroxylase is a key enzyme in the dopamine metabolic process, the reduction of the tyrosine hydroxylase can reduce the synthesis of dopamine in the central nervous system, and researches prove that the tyrosine hydroxylase expression is reduced in the prefrontal cortex area of the brain of an SHR rat. But at present, no efficient early diagnosis index exists.
Based on the current state and the basis of the prior art, the inventors of the present application intend to provide a diagnostic molecular marker of attention deficit hyperactivity disorder syndrome (ADHD). The diagnostic molecular marker can provide a high-efficiency early diagnosis index for Attention Deficit Hyperactivity Disorder (ADHD).
References relevant to the present application are:
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Disclosure of Invention
The present invention aims to provide a diagnostic molecular marker for Attention Deficit Hyperactivity Disorder (ADHD) based on the current state of the art. The diagnostic molecular marker can provide a high-efficiency early diagnosis index for Attention Deficit Hyperactivity Disorder (ADHD).
Based on the research team preliminary experiments, on the basis that miR-let-7d expression is increased in an ADHD rat model, a group of stable microRNAs exist in the blood serum of ADHD children patients obtained through further experimental researches, and can be used as molecular diagnosis markers of ADHD.
The invention adopts the following technical scheme:
1. screening a group of microRNAs with different expressions in the blood serum of the ADHD children compared with the children in a control group, and verifying;
2. on the basis of verifying a group of ADHD children serum differential expression microRNAs, establishing a mathematical model, and further verifying the differential microRNAs as ADHD diagnosis and typing biological markers;
3. evaluating the change of microRNAs with different expression in drug intervention serum of ADHD children within 3 months, and monitoring the treatment condition of ADHD diseases by the screened microRNAs;
obtaining a group of stable microRNAs which exist in the ADHD infant serum and can be used as ADHD molecular diagnostic markers, wherein the stable microRNAs are miR-4466, miR-4516, miR-6090, miR-4763-3p and miR-4281; the AUC of the established subset attention-deficient regression analysis model ADHD was 0.915 (sensitivity: 84.00%; specificity: 86.90%), the AUC of the hyperactive impulsive model was 0.847 (sensitivity: 76.32%; specificity: 81.58%), and the AUC of the mixed model was 0.949 (sensitivity: 96.52%; specificity: 78.26%).
More specifically, the following experimental analysis and verification are carried out in the invention:
1) collecting serum samples of 160 study subjects as a screening group from 1 month to 12 months 2014, wherein 40 ADHD groups and 120 children in a control group are selected; adopting a Human miRNA chip V21.0(Agilent Human MiRNA Microarray V21.0) of Agilent corporation containing 2549 Human miRNA probes to obtain miRNA spectrums of ADHD children and normal children, screening out microRNAs with statistically different expressions, and carrying out RT-qPCR verification;
2) 160 children were collected as test groups from 1 month to 7 months 2015, wherein 80 ADHD group children, 80 control group children, 30 pediatric pneumonia group and 30 vitamin D deficiency disease were used for independent RT-qPCR verification of the selected micrornas.
3) The 320 subjects summarized according to steps 1 and 2: establishing a logistic regression model based on RT-qPCR data of 120 ADHD children and 200 microRNAs screened by the children in the control group;
4) 362 children in Hangzhou children hospital are collected as a verification group, wherein 132 children in ADHD group, 230 children in control group are verified to be a logistic regression model, and the sensitivity and specificity of diagnosis ADHD of the logistic regression model are evaluated by using a receiver operating characteristic curve (ROC);
5) collecting 37 of 252 ADHD children with 3-month follow-up sample information, and observing the change trend of screened microRNA after ADHD children are treated;
the result shows that 5 miRNAs are screened and verified as potential ADHD biomarkers, namely miR-4466, miR-4516, miR-6090, miR-4763-3P and miR-4281, compared with a control group, the expression of miR-4466 is up-regulated, the expression of the other 4 miRNAs is down-regulated, P is less than 0.001, and the difference has statistical significance;
the AUC of the regression model constructed by summarizing the data of the screening group and the data of the testing group is 0.940, the sensitivity is 86.67%, the specificity is 90.00%, the AUC of the regression model of the verification group is 0.927, and the sensitivity and the specificity are 83.33% and 86.36% respectively. The AUC for the attention-deficient subtype of ADHD was 0.915 (sensitivity: 84.00%; specificity: 86.90%), the AUC for the hyperactivity impulse was 0.847 (sensitivity: 76.32%; specificity: 81.58%), and the AUC for the mixed type was 0.949 (sensitivity: 96.52%; specificity: 78.26%);
after three-month standard treatment, compared with the first-diagnosis infant patients, the miR-4466 expression level in the serum sample of the follow-up children is reduced, and the miR-4516, miR-6090, miR-4763-3p and miR-4281 expression levels are increased;
after ADHD is treated, the miRNA expression level with differential expression in the blood serum of ADHD children gradually approaches that of healthy children along with the improvement of symptoms.
The application provides and verifies a group of miRNAs with expression differences in ADHD child serum samples, namely compared with a healthy child group, miR-4466 is increased in ADHD child serum, and miR-4516, miR-6090, miR-3960 and miR-4281 are decreased in ADHD child serum; and a Logistic regression analysis model established on the basis of the group of miRNAs can be used as a molecular diagnostic marker of ADHD and for predicting the occurrence probability of ADHD, and has a certain clinical reference value in the aspect of diagnosing ADHD.
Drawings
Fig. 1 shows the age distribution profile of ADHD children, where a: age distribution of ADHD children; b: population distribution of ADHD subtype.
FIG. 2 shows the gender distribution of children for different ADHD subtypes, where A is ADHD-AI: attention deficit is dominant; ADHD-HI: the multi-action impulse is a main type; ADHD-C mixed type.
FIG. 3 shows the height and weight of ADHD children, wherein: a: height status of three ADHD subtype children; b: body weight status of three ADHD subtypes.
Fig. 4 shows ADHD children intelligence, SNAP score, wherein: a: C-WISC (Chinese Wechsler intellectual Scale for Children) is adopted, namely Chinese revision Weldman Intelligence Scale, FIQ (full intellness quott) is a total Intelligence quotient; VIQ (verbal intelligence quotient), namely a speech intelligent quotient; PIQ (performance intelligence quotient) namely an operation intelligence quotient, B: the SNAP Scale (Swanson, Nolan, and Pelham Rating Scale).
Fig. 5 is a technical route diagram of the main idea of the invention.
FIG. 6 is a heat map display of ADHD children's serum differential microRNA, wherein,
blue indicates that the value is small in the chip, namely the expression level of microRNA in a serum sample is low, and red indicates that the value is large in the chip, namely the expression level of microRNA in the serum sample is high.
FIG. 7 RT-qPCR validation of the screened set of microRNAs with P < 0.001.
FIG. 8: the expression of microRNA profiles of the training set was performed.
Fig. 9 is a specificity validation of microRNA expression profiles, PM 2.5: particles having an aerodynamic equivalent diameter of less than or equal to 2.5 microns in the atmosphere (also referred to as accessible lung particles).
FIG. 10 is the establishment of an analytical model showing ROC (receiver operating characteristic curve) analysis; AUC (area Under the dark): area under the curve; sensitivity: sensitivity; specificity: specificity; the area under the ROC curve is between 1.0 and 0.5; when AUC >0.5, the closer the AUC is to 1, the better the diagnostic effect.
FIG. 11: verification of the model, wherein, a: verifying the relative expression quantity of 5 microRNAs in the group; b, carrying out ROC curve analysis on the verification group data in a model; c: introducing the ADHD-AI group data into ROC curve analysis of the model; d: introducing the ADHD-HI group data into ROC curve analysis of the model; e: the ADHD-C data was included in ROC curve analysis of the model.
Fig. 12 expression of ADHD children microRNA profile over three months of follow-up, where a: comparison of SNAP scores in first-visit children and treated follow-up children (3 months); B-F: comparing the relative expression quantity of 5 microRNAs of a first-diagnosis child, a follow-up child (3 months) and a control group child (healthy child); g: the ROC curves of the first-visit children and the follow-up children were compared.
FIG. 13 is a comparison of relative expression levels of microRNAs at first visit, three months and six months of follow-up visits of nine ADHD children, wherein A is miR-4466; b: miR-4516; c, miR-6090; d, miR-4763-3 p; e, miR-4281: p is less than 0.05; p < 0.01; ***: p is less than 0.001.
Detailed Description
Example 1
Based on previous research bases, including: compared with PC12H cells, the microRNA let-7d in PC12L cells is low in expression, the tyrosine hydroxylase is high in expression, and when the microRNA let-7d expression plasmid and a control plasmid thereof are transfected in PC12L cells, the messenger RNA level and the protein level of TH are obviously reduced after the microRNA let-7d is over-expressed; further collecting blood plasma samples of patients for testing, preliminarily finding that the blood plasma expression level of microRNA let-7d has significant difference between 35 children suffering from ADHD and 35 normal control groups in a preliminary experiment, and detecting that 35 children suffering from ADHD and normal control blood plasma thereof find that: the expression level of plasma microRNA let-7d in the ADHD group is obviously higher than that in the control group; in an experiment of an ADHD rat model, miR-138-1,34c,296 and 494 has reduced expression in the prefrontal cortex of the brain, and transcription factors Bhlhb2 and glucocorticoid receptor Nr3c1 are abnormally increased; experiments demonstrated that MicroRNA-138, 34c,296, and 494 acted on the 3' -untranslated region of the Bhlhb2 messenger RNA to inhibit Bhlhb2 expression, and after the Bhlhb2 was knocked out, SHRs hyperactivity became worse, indicating that the dysregulation of the Nr3c1-Bhlhb2 axis was often associated with ADHD; in this example, the sample size of the ADHD infant and its control group was further expanded, and a group of micrornas with expression differences in ADHD infant were further screened: 742 serum samples are collected and divided into a screening group (40 ADHD children and 120 healthy children), an exercise group (80 ADHD children, 80 healthy children, 30 infantile pneumonia and 30 vitamin D deficiency children) and a verification group (132 ADHD children and 230 healthy children), the screening group preliminarily screens a group of miRNA with statistical differences by using an Agilent miRNA expression profile chip detection method and verifies the miRNA by using reverse transcription-real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), the exercise group independently verifies the results of the screening group again, logistic regression analysis is established on sample data of the screening group samples and the exercise group sample data to predict the probability of ADHD, and the sample data of the verification group is used for verifying the established logistic regression analysis model.
The present embodiment includes:
determination of the study subjects: 742 serum samples were collected, wherein 160 samples were collected from the subsidiary british children hospital at the university of medical science in wenzhou at two time periods, namely, 1 month 2014 to 12 months 2014 and 2016 8 months to 2017 month 8, and are respectively 40 ADHD children, 120 control children, 80 ADHD children and 80 control children; 36 samples (18 ADHD children, 18 healthy children) were collected in the national hospital of zhejiang province from 8 months to 12 months in 2017; 406 samples (154 ADHD children, 252 healthy children) were collected at the hangzhou city children hospital from 2016 to 2017 at 12; 60 samples (30 children patients with pneumonia, 30 children with vitamin D deficiency) were collected in Jiangsu Shuyang protocol and hospital from 9 months 2017 to 12 months 2017; the selected study subjects obtain written informed consent of the parents or legal guardians and pass the approval by the local hospital ethics committee, children aged 12 years and older need to be approved by the patients in addition to the parents or legal guardians, and the study subjects can participate in the study; subjects were divided into ADHD group and control group, pediatric pneumonia group, and vitamin deficiency child group. The ADHD group and the control group are mainly matched according to the age, the sex, the height and the weight;
group ADHD: two assistant chief and medical doctors and more medical staff confirm that the children are ADHD children, and the selection criteria are as follows:
(1) children 6 to 14 years old;
(2) intelligence Quotients (IQ) of the selected children are more than or equal to 70;
(3) according to the fourth or fifth edition (DSM-IV/V) of the handbook for diagnosing and counting mental disorders of the American psychiatric society ADHD clinical diagnosis standard, questionnaires for various scales are carried out;
(4) the physical examination, the nervous system examination, the mental condition examination and the related laboratory auxiliary examination are carried out in detail, and the somatic diseases and the nervous system diseases are excluded;
(5) excluding children with nerve development retardation, conduct disorder, mood disorder, learning disorder and neuropsychiatric diseases;
(6) the comprehensive assessment of the class officer and the task deems that no obvious learning difficulty exists in the aspects of Chinese and mathematics, and the end-of-term examination score is reduced by more than 1 standard deviation from the average score in the whole class;
(7) no psychotropic medication was used for nearly 1 month.
And (3) diagnosis procedure: taking Chinese edition ' Conners ' Teacher Rating Scale (TRS) and ' Conners parent assessment questionnaire (PSQ) as screening tools, a child health care physician with unified training investigates acquaintances (parents and teachers) of ADHD children and normal children to respectively complete TRS and PSQ. After TRS and PSQ screening, the suspected ADHD person, the parents and the responsible teacher are consulted separately by the trained specialist, the diagnosis is confirmed after 6 months of follow-up visit, and false positives are eliminated as far as possible. In the follow-up visit process, the suspicious person carries out the intellectual quotient determination. The ADHD diagnosis criteria were DSM-IV compliant, and all diagnostician parents completed the risk factor questionnaire and the accompanying behavioral and sleep question questionnaire.
The diagnostically relevant assessments include:
(1) questionnaire survey
Investigating the acquaintances (such as parents and teachers) of ADHD children and normal children, and knowing the general condition of the children, the pregnancy condition of mothers, the growth and development of the children, the living habit condition, the family condition, the environmental condition and the social condition;
(2) physical evaluation
The growth and nutrition conditions of children are evaluated by three indexes of age-specific weight (W/A), age-specific height (H/A) and height-specific weight (W/H) according to the median M +/-Standard Deviation (SD) of the World Health Organization (WHO) standard. Low body weight: the age-based weight of the child is lower than the median M-2SD of the reference population value of the same age and the same sex; growth retardation: the age and height of the children are lower than the median M-2SD of the reference population value of the same age and the same sex; emaciation: the height and the weight of the children are lower than the median M-2SD of the reference population value of the same age and the same sex. Moderate [ M- (-2 SD-3 SD) ], severe < M-3 SD);
(3) intellectual quotient assessment
The Wechsler children intelligence diagnostic scale (C-WISC) is adopted to evaluate the general intelligence level, the speech intelligence level, the operation intelligence level and various specific abilities of children. Intelligence Quotient (IQ) average level: 100 +/-15; high intelligence quotient: IQ > 115; the intelligence is low: IQ < 70;
(4) grade of learning achievement
The learning achievement refers to the examination achievement of the child at the end of the latest period before the evaluation of the child health care department in the Wenzhou medical school affiliated Ying child hospital, wherein the grade evaluation of the learning achievement is as follows: "excellent" means that the language or mathematics score is above 90 points; "good" means that the language or mathematics score is between 80 and 90 minutes; "passing" means that the Chinese or mathematics score is between 60-79 minutes; "failing" means that the Chinese or mathematical performance is below 60 points.
Control group of children: the control group consisted of outpatient physical examination children, and the inclusion criteria were as follows:
(1) children 6 to 14 years old;
(2) children with attention deficit hyperactivity disorder, no mental retardation, conduct disorder, mood disorder, autism, learning disorder, and various neuropsychiatric diseases;
(3) children with other systemic diseases excluded;
(4) no psychotropic medication was used for nearly 1 month.
The pediatric pneumonia group: children diagnosed with pediatric pneumonia in the hospital department had the following inclusion criteria:
(1) the clinical characteristics are as follows: has symptoms of fever (axillary temperature is more than 38.5 ℃), cough, wheeze, rapid respiration (30 times/min), dyspnea, etc.;
(2) lung auscultation smells middle and fine humdrum and/or chest imaging changes with pneumonia;
(3) other auxiliary checks: detection of etiology; peripheral blood leukocytes; acute phase reaction indexes (C-reactive protein, procalcitonin, erythrocyte sedimentation rate, etc.).
Vitamin D deficiency group: the composition of children diagnosed with vitamin D deficiency in the outpatient department was as follows:
(1) high risk factors: lack of solar radiation; vitamin D is not supplied prophylactically;
(2) the clinical manifestations are as follows: some children develop non-specific neuropsychiatric symptoms: irritability, crying, and the like;
(3) laboratory examination (primary diagnostic basis): serum 25- (OH) vitamin D is less than or equal to 37.5 nmol/L.
The main experimental materials and instruments used include:
reagents and instruments required for extraction of total RNA from serum, wherein,
reagent: MiRcute serum/plasma miRNA extraction separation kit (DP503 Tiangen biochemical technology)
2 major instruments required for RNA extraction
Reagents and main instruments required for RT-qPCR:
RT: HiFiScript reverse transcription kit (CW2569M kang century)
Reagents required for qPCR
RT-qPCR main instrument
| Name of instrument | Company name |
| Mini palm type centrifuge LX-100 type | Shanghai Yuan electronics technology Co Ltd |
| PCR instrument | Dongsheng Innovation Biotech. Co., Ltd (China) |
| XW-80A vortex mixer | Shanghai Medical University Instrument Factory |
| 96-hole fluorescent quantitative PCR instrument | Biorad CFX Connect |
The experimental method comprises the following steps:
collecting samples:
ADHD children, children with infantile pneumonia, children with vitamin D deficiency and healthy children for clinical examination in the study all have a rest of 30min before blood collection, blood is collected from the elbow vein by 3-5ml, the children are kept still for 30min at room temperature, the temperature is 4 ℃ and 15000rpm, the centrifugation is carried out for 10min, a micropipettor is used for sucking the supernatant (serum) into a sterile EP tube of 1.5ml, and the sterile EP tube is frozen and stored in a refrigerator at the temperature of-80 ℃ for later use; and (3) RNA extraction:
(1) sample treatment: adding 900 mul of lysis solution MZA into every 200 mul of serum, oscillating and mixing uniformly for 30sec by a vortex oscillator until complete homogenization, and reversing and mixing uniformly;
(2) standing at room temperature for 5min to completely separate nucleic acid protein complex;
(3) adding 200 μ l chloroform, covering the tube cover, shaking vigorously for 15sec, and standing at room temperature for 5 min;
(4)12000rpm, 4 ℃, centrifuging for 15min, and dividing the sample into three layers: a yellow organic phase, a white intermediate layer and a colorless aqueous phase, wherein RNA is mainly in the aqueous phase, and water is transferred to a new tube for the next operation;
(5) the volume of the transfer solution is measured, and 2 times of the volume of the absolute ethyl alcohol is slowly added into the transfer solution and mixed evenly (precipitation may occur). Transferring the obtained solution and precipitate into adsorption column MiRelute, standing at room temperature for 2min, centrifuging at 12000rpm at room temperature for 30sec, discarding eluate, and keeping adsorption column;
(6) adding 700 μ l deproteinized solution MRD (checking whether ethanol is added or not) into adsorption column, standing at room temperature for 2min, centrifuging at 12000rpm for 30sec, and discarding waste liquid;
(7) adding 500 μ l of rinsing solution RW (checking whether ethanol is added or not) into the adsorption column, standing at room temperature for 2min, centrifuging at 12000rpm at room temperature for 30sec, and discarding the waste liquid;
(8) repeating the step 7 once;
(9) centrifuge at 12000rpm for 2min at room temperature, discard the collection tube. (the purpose of this step is to remove the residual rinsing liquid in the adsorption column, after centrifugation, the adsorption column is left at room temperature for a moment to be fully dried;
(10) transferring the adsorption column into a new RNase-Free 1.5ml centrifuge tube, and adding 30 μ l RNase-Free ddH to the center of the adsorption membrane 20, standing for 2min at room temperature, and centrifuging for 2min at the room temperature of 12000 rpm;
(11) the step can be repeated for 10 times, so that the yield of RNA is improved;
(12) detecting the RNA concentration by using an ultraviolet spectrophotometer: mu.l of the RNA solution was diluted with 99. mu.l of DEPC water, and the purity of the extracted RNA was judged by measuring the RNA concentration and purity (OD260 and OD260/OD280 ratio) with a spectrophotometer.
Reverse transcription of RT:
(1) dissolving an RNA template, a primer, dNTP Mix, DTT, RT Buffer, HiFiScript and RNase-Free Water and placing on ice for later use;
(2) the reaction system was configured according to the following table, with a total volume of 20. mu.l;
(3) RT primer
(4) Mixing uniformly by vortex oscillation, and centrifuging for a short time to collect the solution on the tube wall to the tube bottom;
(5) cDNA Synthesis conditions: incubating at 42 deg.C for 15min, and incubating at 85 deg.C for 5 min;
(6) after the reaction is finished, centrifuging for a short time, and cooling on ice;
(7) the reverse transcription product can be directly used for fluorescence quantitative PCR reaction or stored for a long time at the temperature of minus 20 ℃.
Real-time fluorescent quantitative PCR:
(1) cDNA template, primers, Universal probes, Taqman mix, ddH 20, dissolving on ice, slightly and uniformly mixing the mixture upside down to avoid foaming as much as possible, centrifuging for a short time, and then placing on ice for later use;
(2) the reaction system was configured according to the following table, with a total volume of 10. mu.l
(3) PCR primer
(4) PCR reaction procedure
Two-part PCR reaction conditions,
Statistical method
(1) Descriptive analysis
First, the data rate and composition ratio are counted.
Second, measure the average and standard deviation of the data.
(2) One factor analysis
The pairing of the counting data2And (6) checking.
Measuring the matching of the data and t test.
In the whole statistical analysis process, Excel 2003 is adopted to establish a database, and software such as SPSS13.0 is adopted to carry out statistical analysis.
Bias control:
(1) design phase
Control of Confounding bias (Conyielding bias) using a variety of methods including pairing and limiting inclusion criteria
(2) Stage of study
In the data collection process, the child health-care doctors and clinical examination doctors who are trained uniformly investigate ADHD children and normal children, complete a questionnaire of influence factors of attention deficit hyperactivity disorder of children, and carry out consistency, correctness and completeness examination to reduce Information bias (Information bias). The qualification rate is more than 95 percent;
(3) analysis phase
And controlling the hybrid bias by adopting various methods such as pairing, conditional Logistic regression and the like.
The results show that:
general conditions for four groups of children: 742 samples were collected from 1 month 2014 to 12 months 2017, with 252 ADHD groups with an average age of 8.16 + -1.81 years, 208 boys accounting for 82.54%, an average height of 128.96 + -10.73 cm, and an average weight of 29.01 + -6.99 kg. 430 healthy children, average age 8.04 + -1.71 years, and boys 350, 81.40%, average height 127.13 + -9.83 cm, and average weight 29.13 + -6.78 kg. The pneumonia group comprises 30 patients with average age of 7.97 + -1.64 years and 22 boys with average height of 128.04 + -11.22 cm and average weight of 29.52 + -5.90 kg. The vitamin D deficiency group comprises 30 cases, the average age is 8.60 +/-2.07 years, the number of the boys is 25, the average height is 131.18 +/-9.95 cm, and the average weight is 26.56 +/-6.58 kg; through multi-sample variance analysis, the four groups have no statistical difference in age, height and weight, and P & gt is 0.05; to exclude the influence of heavy metals in the environment, the blood lead content of four groups of children is collected, which is 33.58 + -8.01 mug/L, 37.72 + -10.84 mug/L, 31.60 + -11.17 mug/L and 37.18 + -12.02 mug/L respectively, the difference has no statistical significance, and P & gt is 0.05.
Table 1:Clinical characteristics of the study population in four groups
Wherein: data form: mean. + -. standard deviation or n [% ]. ADHD: attention deficit hyperactivity disorder; healthy children: a healthy child; pneumonia: infantile pneumonia; vitamin D defiiency: vitamin D deficiency, blood lead levels ≥ 100(μ g/L) is diagnosed as lead poisoning (standard from Chinese centers for disease prevention and control).
The specific analysis result of the ADHD children clinical characteristics,
ADHD child age distribution: of the 252 ADHD children, 151 out of 6 to 8 years of age accounted for 59.92 of the ADHD children, with the proportion of ADHD children decreasing with age. The number of children diagnosed with ADHD-C was the greatest, 115 (45.63%), next to ADHD-AI, 99 (39.29%), the minimum number of ADHD-HI children, 38 (15.08%), as shown in FIG. 1,
ADHD child gender distribution: in three different subtypes of ADHD-AI, ADHD-HI and ADHD-C, the proportion of boys is 85.86%, 78.95% and 80.87%, respectively, and there is no statistical difference, and the proportion of boys and girls is about 3.7-6.1: 1, as shown in FIG. 2:
height and weight of ADHD children: the height of the children in the ADHD-AI group is 131.15 +/-11.63 centimeters, and the weight of the children is 30.57 +/-7.45 kilograms; the children in the ADHD-HI group had a height of 126.09 + -11.48 cm and a weight of 28.07 + -7.12 kg; the children in ADHD-C group had a height of 128.02 + -9.31 cm and a weight of 17.98 + -6.31 kg. Through analysis of variance, the comparison difference between three groups has no statistical significance, and P > 0.05 (shown in figure 3).
ADHD Children intelligence and SNAP scoring conditions Weibull intelligence scores were performed on 252 ADHD children, with the FIQ, VIQ and PIQ scores of ADHD-AI being 85.90 + -14.23, 84.92 + -14.61 and 87.79 + -11.92 respectively. The FIQ, VIQ and PIQ of the ADHD-HI group were found to be 89.63. + -. 16.10 points, 90.37. + -. 10.14 points and 96.27. + -. 14.63 points, respectively. The FIQ, VIQ and PIQ of the ADHD-C group are respectively 87.48 + -12.36 points, 86.30 + -13.66 points and 91.13 + -13.55 points, and through analysis of variance, the FIQ, VIQ and PIQ have no statistical difference among three subtype groups, and P > 0.05, as shown in FIG. 4A; on SNAP scores, the ADHD-AI group and ADHD-HI group were not statistically different compared; through t-test analysis, the SNAP scores of the ADHD-C group, the ADHD-AI group and the ADHD-HI group are higher and have significant difference, and P is less than 0.001, as shown in FIG. 4B; the clinical manifestations of children diagnosed with mixed ADHD are shown to be prominent.
ADHD children serum difference microRNA screening:
in this example, follow (1) three separate sets of samples were collected, time-wise, as a screening set (ADHD, n-40; healthy child, n-120), exercise set (ADHD, n-80; healthy child, n-80), verification set (ADHD, n-132; healthy child, n-230); (2) screening samples of the screening group by adopting an Agilent miRNA expression profile chip technology to screen m and croRNA which have differences in ADHD child serum samples compared with a healthy child group, analyzing microRNA with statistical differences through a paired sample T test, and carrying out RT-qPCR verification on the samples of the screening group; (3) collecting practice group samples to perform independent RT-qPCR verification on the differential microRNAs obtained by screening in the step (2); (4) the specificity of the screened microRNA is verified by using 30 collected serum samples of the infantile pneumonia and 30 serum samples of the vitamin D deficiency children (compared with a healthy group, whether the screened microRNA has difference in other disease types or not); (5) summarizing the microRNA data books of the screening group and the microRNA data of the practice group, and establishing a logistic regression analysis model; after the establishment is finished, whether the ROC curve analysis model has the reference significance for assisting in diagnosing ADHD is judged; (6) collecting a verification group sample, independently verifying the screened microRNA again, and verifying the model established in the step (4) after the verification is finished; (7) collecting ADHD child serum samples with follow-up information in 3-6 months, observing the change trend of microRNA, bringing follow-up data into a model, and detecting whether the model has an effect of evaluating the treatment effect of diseases; a specific technical roadmap is shown in fig. 5:
differential micrornas screened in this example: as shown in table 2, there are 9 micrornas with statistical differences, P < 0.05, where miR-4466 is expressed in ADHD children serum in elevated (fold change 1.22, P0.045), miR-4516(fold change 0.61, P < 0.001), miR-6090(fold change 0.84, P0.015), miR-3960(fold change 0.84, P0.020), miR-4281(fold change 0.77, P0.003), miR-6869-5P (fold change 0.80, P0.009), miR-4763-3P (fold change 0.92, P0.016, P046, P040.0210, P < 5), and miR-14-3P (fold change 0.320, P < 0.320).
Table 2:Screening results on circulating miRNAs
In order to more visually express the expression trend of the screened micrornas in ADHD children, statistically different microRNA data were presented in the form of heat maps, as shown in fig. 6.
And (3) verifying microRNA of a screening group: RT-qPCR experiments are carried out to verify that the statistically different microRNAs screened by the chip detect 9 different microRNAs, wherein miR-4466, miR-4516, miR-6090, miR-4763-3P and miR-4821 are consistent with the screening result of the chip, namely the expression of miR-4466 in ADHD children serum is up-regulated, the expression of miR-4516, miR-6090, miR-4763-3P and miR-4821 in ADHD children serum is down-regulated, P is less than 0.001, and the differences have statistical significance. The expression trend of miR-320c is consistent with the chip result, but P is more than 0.05, and the statistical difference is not generated. miR-16-5p was not detected when RT-qPCR was performed. The verification result of the miR-6869-5P and miR-3960 is opposite to the chip screening result, namely compared with the serum sample of a healthy child, the expression of the miR-6869-5P and miR-3960 in the ADHD child serum sample is increased, the expression trend of the miR-6869-5P is not statistically different (P is more than 0.05), the expression trend of the miR-3960 is statistically different, and P is less than 0.001; in this example, 5 microRNAs (miR-4466, miR-4516, miR-6090, miR-4763-3p and miR-4821) with consistent verification results and chip results and statistical significance are selected from 9 microRNAs to perform further research, as shown in FIG. 7.
And (3) carrying out microRNA (ribonucleic acid) profile expression and specificity verification on the microRNA:
and (3) practice group microRNA profile expression: in order to further independently verify 5 microRNAs obtained by the screening group, serum samples of 80 ADHD children and 80 healthy children are collected for RT-qPCR verification, and the verification results are shown in figure 8, wherein miR-4466 is obviously increased in expression in ADHD child serum samples, miR-4516, miR-6090, miR-4763-3P and miR-4281 are obviously reduced in expression in ADHD child serum samples, the differences have statistical significance, and P is less than 0.001, as shown in figure 8.
Differential microRNA specificity validation in ADHD children: further verifying the specificity of the screened microRNAs, 30 serum samples of children pneumonia patients were collected at the child hospital department, and 30 serum samples of children with vitamin D deficiency were collected at the child clinic department, as shown in FIG. 9A: miR-4466, miR-4516, miR-6090, miR-4763-3p and miR-4281 are verified in the infantile pneumonia group and the vitamin D deficiency group. Compared with healthy children (including healthy children in the screening group, n is 120, healthy children in the practice group, n is 80), P is more than 0.05, and the difference has no statistical significance; compared with ADHD children (including ADHD children in the screening group, n is 40; ADHD children in the exercise group, n is 80), miR-4466 expression is down-regulated in serum samples of the pediatric pneumonia group and the vitamin D deficiency group; miR-4516, miR-6090, miR-4763-3P and miR-4281 are up-regulated in expression, P is less than 0.001, and the difference has statistical significance, which indicates that compared with healthy children, children with pulmonary inflammation and children with vitamin D deficiency, miR-4466, miR-4516, miR-6090 and miR-4763-3p and miR-4281 are specific for differences in ADHD children in serum expression; in order to eliminate the influence of environmental pollution on the research object, the invention retrospectively analyzes the daily average concentration value of PM2.5 from 2013 to 2016 in hangzhou city, wenzhou city and taizhou city in zhejiang province, as shown in fig. 9B: for four years, the PM2.5 of three cities does not basically exceed 60 mu g/m3(ii) a As shown in fig. 9C: the average concentration of PM2.5 in days of the warm state and the Hangzhou of the residential areas of the ADHD children group (sample collection time: 2014, 2016 and 2017), the healthy children group (sample collection time: 2014, 2016 and 2017), the pediatric pneumonia group (sample collection time: 2017) and the vitamin D deficiency children group (sample collection time: 2017) is lower than 50 mu g/m3The study subject inhabits the environment well (the PM2.5 concentration is less than 75 mug/m 3 represents the good inhabited environment, the data and the standard are from environmental protection agency of Zhejiang province), and the influence on the expression quantity of miR-4516 in the serum due to environmental pollution is eliminated.
Establishing an analysis model:
summarizing data sets of a screening group (ADHD, n is 40; healthy children, n is 120) and a practice group (ADHD, n is 80; healthy children, n is 80), establishing a stepwise Logistic regression model (Logistic regression model) for estimating the probability that children are likely to be diagnosed as ADHD, firstly carrying out Logistic regression analysis on 5 screened miRNAs, analyzing the sensitivity and specificity by ROC (figure 10A), wherein the AUC of miR-4466 is observed to be high-expressed in serum compared with the healthy children group, the AUC of MiR-4466 is 0.756 (95% CI, 0.696-0.809), miR-4516, miR-6090, miR-4763-3p and miR-4281 is 0.879 (95% CI, 0.831-0.918), 0.580 (95% CI, 0.705), 0.824-0.705 (0.824), 0.714-0.95% CI, 0.797-0.714 and 0.714), the effect of auxiliary diagnosis of ADHD by using single differential microRNA from ADHD children serum is not obvious, and then 5 microRNA data obtained by screening are integrated and gathered to establish a logic model for diagnosing ADHD, wherein the logic (P ═ ADHD) ═ 0.303 ═ miR-4466-1.216 ═ miR-4516-0.225 ═ miR-6090-0.320 ═ miR-4763-0.465 ═ miR-4281-7.559[ logic P ═ ln P/(1-P) ], and then the diagnostic performance of the model is evaluated by using ROC analysis, as shown in fig. 10B: the AUC of the microRNA group was 0.940 (95% CI, 0.901 to 0.966, sensitivity 86.67%, specificity 90.00%, P <0.0001), and to test whether the model was specific for diagnosing ADHD, data from children with pneumonia and vitamin D deficiency were entered into the model, respectively, as shown in fig. 10C, the AUC of the pneumonia group was 0.603 (95% CI, 0.520 to 0.682), and the AUC of the vitamin D deficient group was 0.566 (95% CI, 0.482 to 0.646), indicating that neither sensitivity nor specificity was evident in the pediatric diagnosis of pneumonia and vitamin D deficiency for this group of micrornas.
Verification of the analytical model: to verify the reliability of the above-established model formula parameters, the logical model was tested by collecting again an independent verification group (ADHD n: 132, healthy child n: 230) consisting of 362 serum samples; similarly, relative expression levels of miR-4466, miR-4516, miR-6090, miR-4763-3P and miR-4281 were measured by qPCR from children in the validation cohort, and the expression trend of circulating miR-4466 in ADHD children serum was found to be consistent with that of screening cohort and exercise cohort ADHD children, being significantly elevated in ADHD subjects, but decreased in miR-4516, miR-6090, miR-4763-3P and miR-4281 (fig. 10A), all results were in line with entering the model, and further demonstrated by ROC curves that AUC was 0.927 (95% CI, 0.889 to 0.956, sensitivity 83.33%, specificity 86.36%, P <0.0001, fig. 10B), further investigating whether the model was effective in diagnosing all three subtypes of ADHD, total collection of 252 cases of ADHD children serum samples, these included 99 children of ADHD-AI, 38 children of ADHD-HI and 115 children of ADHD-C, each group of data was entered into a model and ROC analysis was performed, and the results showed that the AUC of ADHD-AI was 0.915 (95% CI, 0.867 to 0.950, sensitivity 84.00%, specificity 86.90%, P <0.0001, fig. 10C) and ADHD-HI 0.847 (95% CI, 0.764 to 0.919, sensitivity 76.32%, specificity 81.58%, P <0.0001), and the AUC of ADHD-C was 0.949 (95% CI, 0.917 to 0.973, sensitivity 96.52%, specificity 78.26%, P <0.0001, indicating that the model is not restricted to a certain subtype d, and that the diagnostic performance of the miRNA profile is effective.
Use of the model in follow-up children:
three months follow-up children: further validation of miRNA panel and diagnostic mode for the course of treatment of ADHD children; of the 742 children in this study, 37 children with ADHD had a 3-month follow-up. Treatment of children with ADHD was for 10 weeks, the first 6 weeks, an initial methylphenidate (ritalin) dose of 0.3mg/kg/d, an increase of 0.3mg/kg/d weekly, a target dose of 1.2mg/kg/d (excluding significant side effects or space for no further improvement), and an increase to a maximum of 1.8mg/kg/d depending on the actual clinical response dose. Maintaining the optimal dosage during the 7 th to 10 th periods; to assess the efficacy of treatment, at week 12, the test children were again scored on the SNAP scale and the results showed that the scores decreased compared to the first visit indicating effective treatment (figure 12A), and that the expression of miR-4516, miR-6090, miR-4763-3p and miR-4281 increased in the follow-up samples compared to the first visit, while the trend of miR-4466 reversed (figure 10B), indicating that the patient's serum microRNA expression levels gradually became normal with treatment; ROC curve analysis was performed in 37 patients by substituting the first visit children's miRNA panel data into the model with AUC of 0.813 (95% CI, 0.764 to 0.919, sensitivity 81.08%, specificity 70.27%), followed by a visit children (3 months) with AUC of 0.714 (95% CI, 0.598 to 0.813, sensitivity 54.05%, specificity 86.49%), with statistical differences between the two sets of ROC curves (P0.017, fig. 12G);
follow-up children for six months: of 37 children with follow-up 3-month record of ADHD, 9 patients were recorded by 6-month follow-up, and as shown in FIG. 13A, miR-4466 was gradually reduced in ADHD children serum with the increase of treatment time, tending to the expression of miR-4466 in healthy children serum; the expression trends of miR-4516, miR-6090, miR-4763-3P and miR-4281 are gradually increased, as shown in FIGS. 13B-E, compared with ADHD children and healthy children who visit for 6 months, P is greater than 0.05, the difference is not statistically significant, and the difference indicates that the differential microRNA expression level tends to be normal after the ADHD children are treated; the first visit data of 9 ADHD children, the three-month follow-up data, the six-month follow-up data and healthy children group data serving as a control are brought into a model, and through ROC curve analysis, the statistical difference (P is 0.037) exists between the AUC (0.852, 95% CI, 0.608-0.972) of the first-visit ADHD children and the AUC (0.617, 95% CI, 0.363-0.832) of 3-month follow-up; however, the AUC (0.617, 95% CI, 0.363 to 0.832) for the 3-month followed patients compared to the AUC (0.543, 95% CI, 0.297 to 0.775) for the 6-month follow-up, the results showed no statistical difference (P ═ 0.797).
Experiments prove that a group of miRNAs with expression differences in ADHD child serum samples are provided and verified, namely compared with a healthy child group, miR-4466 is increased in ADHD child serum, and miR-4516, miR-6090, miR-3960 and miR-4281 are decreased in ADHD child serum; after three months of routine treatment, compared with the first-diagnosis infant patients, the miR-4466 expression level in the serum sample of the follow-up children is reduced,
the expression level of miR-4516, miR-6090, miR-4763-3p and miR-4281 is increased;
after ADHD is treated, the miRNA expression level with differential expression in the blood serum of ADHD children gradually approaches that of healthy children along with the improvement of symptoms. The Logistic regression analysis model established on the basis of the group of miRNAs can be used as a molecular diagnostic marker of ADHD and for predicting the occurrence probability of ADHD, and has a certain clinical reference value in the aspect of diagnosing ADHD.
Claims (6)
1. A diagnosis molecular marker for attention deficit hyperactivity disorder syndrome is characterized by being a group of stable microRNAs which are miR-4466, miR-4516, miR-6090, miR-4763-3p and miR-4281 respectively.
2. The diagnostic molecular marker for attention deficit hyperactivity disorder according to claim 1, wherein the stable microRNAs are screened for differential expression in ADHD children's serum.
3. A regression analysis model of attention deficit hyperactivity disorder syndrome is characterized by being established based on a group of stable microRNAs with differential expression in ADHD children serum, wherein the microRNAs are miR-4466, miR-4516, miR-6090, miR-4763-3p and miR-4281 respectively.
4. The regression analysis model for attention deficit hyperactivity disorder according to claim 3, wherein said regression analysis model provides an ADHD subtype attention deficit AUC of 0.915 and sensitivity of 84.00%; the specificity is 86.90%, the AUC of the multi-action impulse type is 0.847, and the sensitivity is 76.32%; specificity 81.58%), AUC of mixed type 0.949, sensitivity 96.52%; specificity was 78.26%.
5. Use of the diagnostic molecular marker for attention deficit hyperactivity disorder syndrome according to claim 1 for the preparation of a formulation for the diagnosis of attention deficit hyperactivity disorder syndrome, wherein the diagnostic molecular markers are miR-4466, miR-4516, miR-6090, miR-4763-3p, miR-4281; miR-4466 expression was up-regulated, and the expression of the remaining 4 miRNAs was down-regulated.
6. Use of the regression analysis model for attention deficit hyperactivity disorder syndrome according to claim 3, wherein the AUC of ADHD subtype attention deficit hyperactivity disorder is 0.915 and the sensitivity is 84.00% for preparing a formulation for diagnosing attention deficit hyperactivity disorder syndrome; the specificity is 86.90%, the AUC of the multi-action impulse type is 0.847, and the sensitivity is 76.32%; specificity 81.58%), AUC of mixed type 0.949, sensitivity 96.52%; specificity was 78.26%.
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