Multicenter Analysis of Genomic and Metabolic Data of Neonatal Genetic Diseases
Conditions: Hereditary Diseases
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University

 
Clinical research protocol 
 
Project name: Multicenter analysis of genomic and metabolic 
data of neonatal genetic diseases 
Leading unit: The Sixth Affiliated Hospital, Sun Yat-sen 
University 
Project leader: Hu Hao 
Department: Pediatrics  
Research period: September 1, 2022 to December 31, 2025 
Project number: EK-YCDX-20220803 
NCT No.: none. 
Version No.: V1.0  
Version Date: August 03, 2022 

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Catalogue 
Abstract ............................................................................................................................ 
1. project basis ................................................................................................................. 
1.1. Research background ......................................................................................... 
1.2. creativity ............................................................................................................ 
2. goal of study ................................................................................................................. 
3. Research Design and Statistical processing ................................................................. 
3.1. Research Design................................................................................................. 
3.2. sample size ......................................................................................................... 
3.3. data collection .................................................................................................... 
3.4. Inclusion criteria ................................................................................................ 
3.5. Excluded criteria ................................................................................................ 
3.6. Observation items .............................................................................................. 
3.7. Statistical ............................................................................................................ 
4. Ethics in clinical research ............................................................................................ 
4.1. Data source......................................................................................................... 
4.2. Informed consent ............................................................................................... 
4.3. Benefits of participating in research .................................................................. 
4.4. Privacy protection measures .............................................................................. 
4.5. Data Recording .................................................................................................. 
5. Research progress ........................................................................................................ 
6. References .................................................................................................................... 
 
 
 
 
 
 
 

                                       
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Abstract 
object name 
Multicenter analysis of genomic and metabolic data of neonatal 
genetic diseases 
goal of study 
( 1 ) Gene sequencing data ( 138 genes related to 133 common 
genetic diseases ) and tandem mass spectrometry metabolomics data 
( 11 amino acids and 28 acylcarnitines ) of about 40,000 newborns 
from the South China Neonatal Genetic Screening Alliance 
participating units were collected and collated to complete the 
database construction of genes and mass spectrometry.  
( 2 ) A retrospective analysis of the incidence, pathogenic mutations, 
suspected pathogenic mutations, and the carrying rate of unexplained 
mutations of 133 common neonatal genetic diseases in 40,000 cases 
was conducted, and the distribution of high-frequency variation sites 
in the population was counted, providing a scientific basis for the 
selection of mutation sites for large-scale genetic screening of 
neonatal genetic diseases.  
( 3 ) Protein function prediction and enzyme catalytic activity 
analysis were performed on high-frequency mutation sites of genetic 
metabolic diseases ( IEM ). Characteristic metabolites were analyzed 
and verified for mutation sites with low enzyme catalytic activity. 
The feasibility of using protein function artificial intelligence analysis 
platform and tandem mass spectrometry metabolite data to accurately 
predict the pathogenicity of genetic metabolic lesions was explored.  
( 4 ) Explore the use of genome and metabolome big data and 
machine learning algorithms such as Random forest, Support Vector 
Machine, Elastic net, Multilayer Perceptron to construct prediction 
models for common genetic diseases, and strive to achieve accurate 
diagnosis and prediction of common genetic diseases using simple 
tandem mass spectrometry metabolome data, and expand the 
application range of tandem mass spectrometry technology for 
disease detection. 

                                       
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research design 
Cross-Sectional observational study 
Research period 
September 2022 to December 2025 
Leading unit 
The Sixth Affiliated Hospital, Sun Yat-sen University 
Participating 
units 
South China Neonatal genetic screening Alliance (including 
cooperation units of 123 hospitals) 
Research 
Consultant 
Xin Xiao、Wenhao Zhou 
research object 
Gene screening data of 40,000 newborns ( 138 genes related to 133 
common genetic diseases ) and tandem mass spectrometry data ( 11 
amino acids and 28 acylcarnitines ). 
Inclusion criteria ( 1 ) Newborns who underwent genetic screening and tandem mass 
spectrometry at the same time.  
( 2 ) Age : 0-28 days, gestational age 37-42 weeks. 
Excluded 
criteria 
Data that meets any of the following conditions need to be 
eliminated :  
( 1 ) Neonatal data with unclear clinical basic information ;  
( 2 ) Lack of traceability core information data ;  
( 3 ) The data that the test results cannot be analyzed and interpreted. 
sample size 
Gene screening and tandem mass spectrometry data of 40,000 
newborns. 
data collection 
( 1 ) Basic information : gender, age, sample type, subject traceability 
number / ID number, etc.  
( 2 ) Clinical symptoms, biochemical and imaging data of positive 
samples.  
( 3 ) Gene detection results and tandem mass spectrometry results.  

                                       
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( 4 ) Date of test data, instrument model, reagent type, etc. 
Statistical 
processing 
IBM-SPSS 26.0 statistical software was used to analyze the test 
results. Logistic regression analysis was used to evaluate the 
correlation between genotype and clinical phenotype. Generalized 
multifactor dimensionality reduction method was used to analyze 
gene-gene interaction and genotype-phenotype correlation. Based on 
tandem mass spectrometry data, machine learning algorithms such as 
random forest, support vector machine, elastic network, and 
multi-layer perceptron were used to construct a genotype prediction 
model. The prediction effect of the model was evaluated by accuracy, 
balance accuracy, sensitivity, and specificity. 
abbreviation 
Principal Investigator, PI; tandem mass spectrometry, MS/MS; Inborn 
errors of metabolism, IEM 
 

                                       
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1. project basis 
1.1 Research background 
With the development of China 's social economy, the incidence of infectious 
diseases and malnutrition diseases has decreased significantly. Birth defects have 
gradually become a major factor threatening the health of infants and young children, 
leading to serious disabilities and even death of newborns and infants, such as 
structural malformations, intellectual development disorders, and language 
development disorders. It has seriously affected the survival and quality of life of 
children, brought serious mental and economic burdens to families, and has become a 
serious public health problem. Preventing and reducing birth defects is one of the 
important measures to promote the construction of healthy China. The " National 
Birth Defects Comprehensive Prevention and Control Program " issued by the 
National Health and Health in 2018 and the " Healthy China Action ( 2019-2030 ) " 
issued by the State Council in 2019 all proposed to comprehensively strengthen the 
comprehensive prevention and control of birth defects, improve the birth defects 
prevention and control network, improve the accessibility of comprehensive 
prevention and control services for birth defects, and carry out in-depth tertiary 
prevention. Among them, the " Healthy China Initiative ( 2019-2030 ) " specifically 
mentioned the need to pay attention to and strengthen the screening of genetic 
metabolic diseases and other related birth defects in maternal and child health 
screening. 
Genetic factors are an important cause of birth defects. At present, there are more 
than 8000 kinds of known single-gene genetic diseases. The overall incidence of 
genetic diseases is high, the incidence is hidden, and it is easy to cause disability and 
death. Neonatal disease screening is the last line of life defense for the prevention of 
birth defects. It is necessary to strengthen the group screening of congenital genetic 
diseases that seriously endanger the health of newborns. Clinical studies have shown 
that some birth defects are preventable and treatable, and early diagnosis is the top 
priority, especially birth defects caused by genetic factors. The clinical phenotypes are 

                                       
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complex and diverse and lack specificity. If they are not diagnosed and treated in time, 
they often cause disability and death. Through the screening of neonatal genetic 
diseases, early diagnosis and early intervention of genetic diseases can be achieved, 
which can avoid or reduce serious complications and nervous system damage. It is a 
very effective preventive measure to reduce the harm of birth defects. 
At present, the commonly used methods of neonatal screening technology 
include fluorescence immunoassay, enzyme immunoassay and MS / MS technology, 
but these technologies are all biochemical indicators, and there are some deficiencies : 
1 Screening results are affected by gestational age, birth weight, nutrition, medication, 
environmental differences and other factors, which are prone to false positive results, 
resulting in maternal and family anxiety ; 2 Screening results are affected by 
gestational age, birth weight, nutrition, medication, environmental differences and 
other factors, which are prone to false positive results, resulting in maternal and 
family anxiety. The false negative rate of some diseases screening was high, such as 
neonatal intrahepatic cholestasis caused by citrin protein deficiency, polyacyl-CoA 
dehydrogenase deficiency, ornithine carbamyl transferase deficiency, etc., and their 
metabolites were usually normal in the neonatal period or when they were not ill, 
resulting in missed diagnosis. 3 Metabolite detection cannot determine the disease 
classification of genetic diseases, such as methylmalonic acidemia. There are five 
common types of methylmalonic acidemia. The severity of different types of 
symptoms, clinical medication and prognosis are different. ( 4 ) Screening diseases 
can not meet the clinical needs, can not cover some of the higher incidence of 
metabolic diseases, such as mucopolysaccharidosis, hepatolenticular degeneration and 
glycogen storage disease. With the improvement of the goal of birth defect prevention 
in China, neonatal disease screening technology also needs continuous improvement 
and development to meet the needs of society. 
In recent years, gene detection technology has become the gold standard for the 
diagnosis of most genetic diseases. After more than ten years of iterative updating, the 
detection accuracy of gene detection technology is getting higher and higher, the 
detection flux is getting larger and larger, and the detection cost is getting lower and 

                                       
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lower, which makes it possible for gene detection technology to screen newborns. In 
the past ten years, genetic screening technology has been widely used in many 
countries such as Europe and the United States, such as preimplantation genetic 
screening, prenatal screening, neonatal screening, hypertension gene screening, 
familial hypercholesterolemia gene screening, etc. The results of screening are 
satisfactory. In 2016, the Chinese Neonatal Genome Project was initiated by the 
Genetic Counseling Branch of the Chinese Genetic Society and the Children 's 
Hospital of Fudan University in Shanghai. The program will carry out 100,000 
samples of neonatal genetic testing within 5 years, aiming to build a Chinese neonatal 
genome database, establish neonatal genetic testing standards, promote the 
industrialization of neonatal genetic testing, and improve the genetic counseling 
training system. Since the launch of the program, the results have been remarkable. In 
2018, Professor Zhou Wenhao 's team of Children 's Hospital Affiliated to Fudan 
University, head of the Neonatology Group of the Pediatrics Branch of the Chinese 
Medical Association, took the lead in carrying out a clinical-related whole exome 
sequencing gene screening research project for newborns. The project has achieved 
significant results. The data show that genetic testing technology can greatly improve 
the positive rate of genetic disease screening. In 2020, Professor Zhao Zhengyan, 
former director of the Pediatrics Branch of the Chinese Medical Association, also 
conducted neonatal genetic screening research. More than 2500 known pathogenic 
mutation sites of 135 genes in 75 common genetic diseases were used as detection 
targets. The data of samples from North China, Northwest, Southwest, Central South 
and East China showed that the positive detection rate of pathogenic mutations was 
2.3 %, and the carrying rate was 26.6 %. Professor Zhao Zhengyan and his team 
explored a large-scale and large-scale gene screening model based on hot spot 
mutation detection. In the same year, the ' Beijing neonatal disease screening 
management method ' clearly included deafness gene screening into the screening 
content of the neonatal disease screening management method. In early 2021, led by 
Professor Han Lianshu of Xinhua Hospital Affiliated to Shanghai Jiao Tong 
University and the head of the new screening center of Shanghai Institute of 

                                       
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Pediatrics, a multi-center neonatal genetic screening research project was launched in 
China with 8 provincial and municipal tertiary hospitals. 159 genes of 156 common 
genetic diseases were selected for sequencing screening. At present, nearly 30,000 
samples have been analyzed, the positive detection rate is close to 4.0 %, and the 
carrying rate is 51.98 %. The results show that neonatal genetic screening combined 
with existing biochemical screening shows obvious advantages, which can detect 
disease risk earlier, reduce missed diagnosis of conventional new screening, provide 
detection rate, assist rapid diagnosis of diseases, early intervention, and reduce family 
and social harm. 
In Guangdong Province, our research team took the lead in carrying out a 
preliminary study of neonatal genetic screening. From June 2019 to April 2020, our 
team also completed a study of neonatal genetic screening in 1739 cases of 
Guangzhou Science and Technology Plan Project in Guangdong Province.In this study, 
high-throughput sequencing technology based on target gene capture was used to 
detect common genetic disease genes in heel blood samples. The results showed that a 
total of 158 positive cases were screened out, with a positive rate of 8.81 %. A total of 
972 cases carried one or more gene pathogenic variation sites, with a carrying rate of 
54.21 %, suggesting that the pathogenic variation of common neonatal genetic 
diseases has a high incidence and carrying rate in Guangdong, but the study has the 
problem of insufficient sample size. Compared with the results of traditional neonatal 
mass spectrometry screening, we found that gene screening has obvious advantages, 
which effectively reduces the false positive rate and false negative rate of screening, 
significantly expands the diseases of neonatal genetic disease screening, makes 
disease diagnosis and typing faster and more accurate, and can specifically design the 
diseases and genes of gene screening according to regional or ethnic differences.  
In recent years, the application of ' artificial intelligence + ' in the medical field 
has become a hot spot of modern science and technology. The medical industry is 
highly complex and professional. Artificial intelligence can play a role in many 
aspects, including medical image recognition, auxiliary diagnosis, drug research and 
development and other fields. Recently, an artificial intelligence-assisted diagnostic 

                                       
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model was applied to the diagnosis of common pediatric diseases. The results showed 
that the diagnostic accuracy of 55 common pediatric diseases was close to 90 %. 
Some diseases such as acute upper respiratory tract infection had a diagnostic 
accuracy of 95 %, and the average score of the artificial intelligence model was higher 
than that of the doctor group. Therefore, artificial intelligence can assist early disease 
diagnosis through a large amount of medical data, improve diagnosis efficiency and 
reduce misdiagnosis rate, solve the problem of insufficient medical service capacity to 
a certain extent, and improve the fairness and accessibility of health services. In the 
field of neonatal disease screening, with the promotion of genetic screening 
technology and the inclusion of more genetic metabolic diseases in screening, 
accurate and rapid judgment of multiple disease risks has become an urgent need for 
clinical front-line. Artificial intelligence has obvious advantages in the interpretation 
and risk prediction of genetic metabolic disease gene results, which not only 
significantly reduces the false positive results and saves medical resources ; for areas 
with weak clinical foundation and urgent need to carry out neonatal genetic metabolic 
disease gene screening projects, artificial intelligence tools will become a strong 
support for clinicians, promote the standardization and standardization of genetic 
metabolic disease diagnosis, and improve the efficiency and accuracy of neonatal 
genetic metabolic disease gene screening system. 
According to the preliminary development of neonatal genetic screening in 
China, we will retrospectively collect and analyze the genetic screening data and 
tandem mass spectrometry data of 40,000 newborns in the participating units of the 
South China Neonatal Genetic Screening Alliance, so as to understand the incidence 
of neonatal genetic diseases in China and the characteristics of the carrying rate and 
mutation sites of related gene pathogenic variations, and provide scientific research 
basis for the feasibility, clinical value and social benefits of large-scale genetic 
screening. In addition, our previous studies have found that the mutation sites with 
high carrying rate ( more than 1 ‰ ) in many populations are mutations of unknown 
clinical significance ( VUS ). The pathogenic interpretation of such mutations has 
brought serious confusion to clinicians and brought pending trouble to the subjects. 

                                       
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How to accurately analyze the pathogenicity of a large number of high-frequency 
VUS mutations has become a key problem to be solved in neonatal genetic screening. 
Therefore, we intend to use 40,000 cases of gene screening data and tandem mass 
spectrometry data, and establish a risk assessment model for neonatal genetic 
metabolic disease gene screening results through protein function artificial 
intelligence method, in order to solve the pathogenicity problem of high-frequency 
VUS and provide reliable research basis for clinical practice, so as to quickly and 
accurately identify high-risk children, reduce the false positive rate of gene screening, 
and improve the accuracy and system efficiency of neonatal gene screening.  
In addition, we will also explore the use of neonatal gene screening data and 
tandem mass spectrometry data, as well as machine learning algorithms such as 
random forest, support vector machine, elastic network, and multi-layer perceptron to 
construct prediction models for common genetic diseases, and strive to achieve 
accurate diagnosis and prediction of common genetic diseases using simple tandem 
mass spectrometry metabolomics data, and expand the application range of tandem 
mass spectrometry technology for disease detection. 
1.2 Creativity  
This study is a multi-center cooperative study of the South China Neonatal 
Genetic Screening Alliance. Through the analysis of 40,000 cases of neonatal genetic 
screening data and MS / MS data, the incidence of common genetic diseases in 
newborns in China and the carrying rate of related gene pathogenic mutations are 
deeply understood, and the types of diseases, gene selection, gene screening process, 
interpretation of gene screening results, follow-up and treatment of screening positive 
patients and other related contents of neonatal genetic screening are preliminarily 
clarified, so as to provide scientific research basis for the feasibility, clinical value and 
social benefits of large-scale genetic screening.  
In addition, this study will clarify the correlation between genotypes and 
metabolic profiles of common genetic diseases in newborns, and explore the 
feasibility of using protein function artificial intelligence analysis platform and 
tandem mass spectrometry metabolite data to accurately predict the pathogenicity of 

                                       
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ectopic sites of genetic metabolic lesions through protein function prediction and 
metabolic level analysis of high-frequency mutation sites of genes, that is, to solve the 
pathogenicity of mutations with unknown clinical significance. It will also expand the 
types of disease diagnosis of neonatal MS / MS technology.  
This study will also establish a risk assessment model for neonatal genetic 
metabolic disease gene screening results through artificial intelligence methods. By 
identifying the pathogenicity of mutation sites, especially those with unclear clinical 
significance, it can quickly identify high-risk children and reduce false positive rates, 
thereby improving the accuracy and system efficiency of neonatal genetic screening. 
2. Research objective 
( 1 ) Gene sequencing data ( 138 genes related to 133 common genetic diseases ) and 
tandem mass spectrometry metabolomics data ( 11 amino acids and 28 acylcarnitines ) 
of about 40,000 newborns from the South China Neonatal Genetic Screening Alliance 
participating units were collected and collated to complete the database construction 
of genes and mass spectrometry.  
( 2 ) A retrospective analysis of the incidence, pathogenic mutations, suspected 
pathogenic mutations, and the carrying rate of unexplained mutations of 133 common 
neonatal genetic diseases in 40,000 cases was conducted, and the distribution of 
high-frequency variation sites in the population was counted, providing a scientific 
basis for the selection of mutation sites for large-scale genetic screening of neonatal 
genetic diseases.  
( 3 ) Protein function prediction and enzyme catalytic activity analysis were 
performed on high-frequency mutation sites of genetic metabolic diseases ( IEM ). 
Characteristic metabolites were analyzed and verified for mutation sites with low 
enzyme catalytic activity. The feasibility of using protein function artificial 
intelligence analysis platform and tandem mass spectrometry metabolite data to 
accurately predict the pathogenicity of genetic metabolic lesions was explored.  
( 4 ) Explore the use of genome and metabolome big data and machine learning 
algorithms such as Random forest, Support Vector Machine, Elastic net, Multilayer 
Perceptron to construct prediction models for common genetic diseases, and strive to 
achieve accurate diagnosis and prediction of common genetic diseases using simple 

                                       
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tandem mass spectrometry metabolome data, and expand the application range of 
tandem mass spectrometry technology for disease detection. 
3. Research scheme and technical route 
3.1 Research Design 
This study is a multi-center cooperative study of the South China Neonatal 
Genetic Screening Alliance. The principal investigator ( PI ) and project leader of this 
study are Hao Hu, chief physician of pediatrics of the Sixth Affiliated Hospital of Sun 
Yat-sen University, who plans to include 123 cooperative units of the South China 
Neonatal Genetic Screening Alliance. In this study, 40,000 neonatal genetic screening 
data and MS / MS data were retrospectively analyzed through multi-center 
cooperation. The collection date was from January 2019 to August 2022.  
Through the statistical analysis of neonatal genetic screening data ( 138 genes 
related to 133 common genetic diseases ), the incidence of common genetic diseases 
in newborns in China, the carrying rate of pathogenic variation and the 
high-frequency variation sites of the population were clarified, and the 
epidemiological characteristics of newborns in China were studied.  
Through the statistical analysis of neonatal genetic screening data and MS / MS 
metabolomics data ( 11 amino acids and 28 acylcarnitines ), the correlation between 
gene and metabolism will be explored, and the pathogenicity of high-frequency VUS 
mutation sites will be identified by using protein function artificial intelligence 
analysis platform and tandem mass spectrometry metabolite data.  
The prediction model of common genetic diseases is constructed by using 
machine learning algorithms such as random forest, support vector machine, elastic 
network and multi-layer perceptron, so as to realize the accurate diagnosis of common 
genetic diseases through tandem mass spectrometry metabolomics data, and expand 
2-3 kinds of diseases that can be detected by MS / MS technology. 
3.1 Sample size 
This study plans to collect genetic screening data ( 138 genes related to 133 
common genetic diseases ) and tandem mass spectrometry metabolomics data ( 11 

                                       
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amino acids and 28 acylcarnitines ) of about 40,000 newborns from January 2019 to 
August 2022 in 123 cooperative units of the South China Neonatal Genetic Screening 
Alliance.  
3.3 data collection 
In the course of the study, no random or any treatment driven by the research program 
will be performed or provided to the subjects. The researchers will view the 
patient 's medical history and laboratory reports, and determine the patient 's 
eligibility according to the inclusion and exclusion criteria.  
( 1 ) The types of diseases detected by genetic screening : including 133 kinds of 
hereditary diseases with high incidence in neonates. The types of diseases 
include genetic metabolic diseases such as amino acid metabolic diseases, 
organic acid metabolic diseases and fatty acid metabolic diseases within the 
scope of all neonatal tandem mass spectrometry screening, as well as other 
genetic diseases that cannot be screened by tandem mass spectrometry but have 
high incidence and early detection of great clinical significance and social 
benefits for children and families, such as lysosomal diseases, glycogen 
accumulation diseases, hepatolenticular degeneration, hereditary deafness, 
β-thalassemia, etc.  
( 2 ) In this study, the data analysis of genes and the interpretation of test reports are 
based on the implementation of industry consensus or guidelines for genetic 
testing of genetic diseases such as ' Discussion on the standardized consensus of 
the whole process of clinical detection of genetic diseases second-generation 
sequencing '. The analysis and interpretation of test data refer to the ' ACMG 
genetic variation classification criteria and guidelines ' issued by the American 
Society of Medical Genetics and Genomics ( ACMG ) in conjunction with the 
Molecular Pathology Society ( AMP ) and the domestic ' genetic variation 
classification criteria and guidelines ( 2017 ) '. The variation was divided into 
five grades : pathogenic ( P, pathogenic possibility > 0.99 ), suspected pathogenic 
( LP, pathogenic possibility 0.95-0.99 ), unknown clinical significance ( VUS, 
pathogenic possibility 0.01-0.90 ), suspected benign ( LB, pathogenic possibility 

                                       
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0.001-0.01 ), benign ( B, pathogenic possibility < 0.001 ). The results of gene 
detection in this study only classified pathogenic and possible pathogenic 
mutations as positive mutations, and homozygotes and compound heterozygotes 
of autosomal recessive inheritance, single heterozygotes and homozygotes of 
autosomal dominant inheritance, and hemizygotes of X-linked inheritance were 
classified as disease positive. The unclear clinical significance is not directly 
used as the basis for clinical diagnosis. It is necessary to combine the clinical, if 
necessary, to carry out the corresponding family member verification, auxiliary 
examination, clinical follow-up. 
( 3 ) MS / MS data collection and standardization : The collected data include ( 1 ) 
laboratory background information including instrument type, reagent type, 
quality control material type, laboratory quality control rules, filter paper type, 
normal population range ; ( 2 ) Quality control data included quality control 
number, quality control type, quality control batch number, amino acid internal 
standard batch number, acylcarnitine internal standard batch number, factory 
time, experimental time, and test value of each quality control analyte ; ( 3 ) 
Screening test data include screening number, gestational age, breastfeeding 
method, native place ( province, city ), newborn gender, birth date, birth weight, 
sample number, blood collection time, etc. ; ( 4 ) The data of confirmed cases 
included screening number, confirmed disease, blood ammonia test, blood gas 
analysis, blood routine, liver function test, urine GC-MS test, imaging 
examination, gene detection information and other tests. The fluctuation range of 
the detection concentration of the corresponding batch of quality control samples 
was within the target value ± 3 s. Finally, the data were collected and sorted into 
the negative database and the positive database. In order to eliminate the 
influence of regional differences, the series detection index data were 
standardized.  
( 4 ) By analyzing the correlation between large sample gene data and tandem mass 
spectrometry data and the correction of clinical prognosis, a neonatal genetic 
disease gene screening database and an artificial intelligence risk assessment 

                                       
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model were established. Construction and selection of modeling indicators, 
select core indicators through information gain and correlation coefficient, and 
construct combined features ; in the model training stage, the gene screening data 
is divided into training set and test set at a ratio of 8 ∶ 2, and the ensemble 
learning models such as random forest, gradient boosting tree algorithm and 
artificial neural network are trained to select the best model. Model evaluation, 
the model selection process first meets the detection rate of 1, and then compares 
the false positive rate to select the optimal training model. Model verification, 
using a new batch of big data samples, through model calculation, output 
high-risk and low-risk sample lists, and compare and analyze with the original 
screening results. 
3.4 Inclusion criteria 
( 1 ) Newborns who underwent genetic screening and tandem mass spectrometry at 
the same time.  
( 2 ) Age : 0-28 days, gestational age 37-42 weeks. 
3.5 Excluded criteria 
Data that meets any of the following conditions need to be eliminated :  
( 1 ) Neonatal data with unclear clinical basic information ;  
( 2 ) Lack of traceability core information data ;  
( 3 ) The data that the test results cannot be analyzed and interpreted. 
3.6 Observation items 
( 1 ) Positive detection rate of common genetic diseases in neonates : the results of 
genetic testing of enrolled neonates showed that the proportion of homozygous 
and compound heterozygotes of autosomal recessive inheritance, single 
heterozygote and homozygous of autosomal dominant inheritance, and 
hemizygote of X-linked inheritance accounted for the total number of people 
tested.  
( 2 ) The corresponding pathogenic genes and pathogenic variation carrying rate of 
common neonatal genetic diseases : that is, the proportion of the total number of 
occurrences of pathogenic variation, suspected pathogenic variation and 

                                       
16 
 
unknown clinical significance variation in the genetic test results of the enrolled 
neonates according to the ' ACMG genetic variation classification criteria and 
guidelines ' and ' genetic variation classification criteria and guidelines ( 2017 ) '.  
( 3 ) The carrying rate of high-frequency mutation sites in the population : that is, the 
mutation sites with a population carrying rate greater than 1 ‰.  
( 4 ) Establish a complete protein structure and catalytic activity analysis process, and 
develop artificial intelligence protein analysis tools for pathogenicity prediction 
of mutation sites.  
( 5 ) Preliminary identification of the pathogenicity of high-frequency VUS mutation 
sites : The protein structure and enzyme catalytic activity characteristics of 
high-frequency VUS mutation sites were predicted by artificial intelligence tools, 
and the metabolic spectrum differences between positive, carrier and negative 
samples of high-frequency VUS mutation sites were clarified by combining with 
tandem mass spectrometry metabolite data, which provided a basis for the 
pathogenicity judgment of VUS mutation.  
( 6 ) Gene-metabolism correlation : machine learning algorithms such as random 
forest, support vector machine, elastic network and multi-layer perceptron are 
used to construct prediction models of common genetic diseases, so as to realize 
accurate diagnosis of common genetic diseases through tandem mass 
spectrometry metabolomics data, and expand 2-3 diseases that can be diagnosed 
by tandem mass spectrometry. 
3.7 Statistical processing 
IBM-SPSS 26.0 statistical software and R language ( Version 4.1.0 ) were used 
for statistical analysis of the test results, and the positive rate of neonatal genetic 
disease screening ( common positive disease cases, common positive sites ) was 
counted. The carrying rate of common mutation sites in newborns was counted. The 
measurement data were expressed as mean ± standard deviation ( x  ± s ), and t test 
was used for comparison between groups. The count variables were expressed as 
values or rates ( % ), and the comparison between groups was tested. Unconditional 
logistic regression analysis was used to evaluate the correlation between genotype and 

                                       
17 
 
clinical phenotype. Generalized multifactor dimensionality reduction ( GMDR ) was 
used to analyze gene-gene interaction and genotype-phenotype correlation. Based on 
tandem mass spectrometry data, machine learning algorithms such as random forest, 
support vector machine, elastic network, and multi-layer perceptron were used to 
construct genotype prediction models. Accuracy, Balanced accuracy, sensitivity, and 
specificity were used to evaluate the prediction effect of the model. 
4. Ethics in clinical research 
4.1 Data source 
The gene sequencing data and MS / MS metabolic data of 40,000 newborns were 
from 123 cooperative units of the South China Neonatal Gene Screening Alliance.In 
this study, the data table established by Microsoft Excel was used. The neonatal gene 
data and tandem mass spectrometry of the multi-center cooperative units were 
transmitted through the Excel data table. Effective measures will be taken to strictly 
record, clean and check the data. Multi-centers ensure the authenticity, accuracy and 
completeness of the neonatal gene sequencing data and MS / MS metabolic data 
provided, and all data and test reports can be traced. In addition, the data management, 
pay attention to the confidentiality of the data, to ensure the privacy of patients and 
their families. 
4.2 Informed consent 
This study is a retrospective study. Subjects have signed informed consent from 
parents or guardians when doing neonatal MS / MS metabolic disease screening or 
genetic screening. The informed consent form clearly states that the test data can be 
used for scientific research after removing personal privacy information. Therefore, 
the application for exemption from informed consent. 
4.3 Benefits of participating in research 
There is no direct economic benefit for all the test subjects included in this study, 
but for the positive children included in this study, free first-generation sequencing 
verification is provided, and professional genetic counseling and clinical treatment 
advice are provided to parents by pediatric clinicians with the consent of the parents 
of the children. 

                                       
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4.4 Privacy protection measures 
All the data of the subjects during the study period will be entered into the computer 
for confidential storage and analysis. If necessary, the relevant institutions may review 
the records to confirm the authenticity, accuracy and integrity of the data. The data 
obtained from the study may also be published in academic journals, but the names of 
the subjects will not be published, and the privacy of the subjects will be kept 
confidential.  
All selected populations do not involve special populations, and patient privacy 
information is strictly protected. 
4.5 Data Recording 
The results of this study will be recorded in human genetic resources in China. 
5. Research progress 
September 01, 2022 - December 31, 2022: Prepare programs and projects, sign 
cooperation agreements with partners. 
January 01,2023 to December 31,2024: (1) Data processing: Collect gene sequencing 
data and MS / MS data, and organize the data to establish a database of genes and 
mass spectrometry. (2) Data analysis : Gene data and mass spectrometry were 
analyzed by statistical software. Data, get the target statistical results. (3) 
Establishment of protein structure and catalytic activity analysis process, development 
of artificial Intelligent protein function analysis tool. (4) Completed the preliminary 
identification of the pathogenicity of high-frequency VUS mutation sites. 
January 01, 2025 - December 31, 2025: (1) To study the correlation between gene and 
metabolism, and to construct a string of common genetic diseases. Mass spectrometry 
prediction model, and increase the use of MS / MS technology for diagnosis The 
disease, and to verify. (2) Write the conclusion report, complete the paper, submission 
and publication work.  
 
 
 

                                       
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6. Reference 
[1] 
Report 
on 
prevention 
and 
treatment 
of birth 
defects 
in 
China 
( 
2012 
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