Premature infants, sga catch-up growth management and its cmds risk early warning system

Abstract

The present application relates to the technical field of medical systems, and specifically designs a preterm infant and SGA catch-up growth management and its CMDs risk early warning system, through establishing a preterm infant and SGA optimal catch-up growth fine management mode and a long-term CMDs risk monitoring and early warning platform intelligent management system, and a related risk detection and early warning platform, early detection of unsuitable catch-up growth and high-risk CMDs of preterm infants / SGA is promoted, early intervention and risk management are implemented, and finally the long-term CMDs chronic disease burden is reduced.

Description

Technical Field

[0001] This invention relates to the field of medical system technology, specifically to catch-up growth management for premature infants and SGA, and an early warning system for CMDs risks. Background Technology

[0002] Given the large number of premature infants and small for gestational age (SGA) infants, exploring early prevention of preterm birth and the long-term risk of cardiovascular metabolic diseases (CMDs) based on the DOHaD (Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor of the Doctor) theory can bring significant cost-effectiveness and social benefits. Research evidence shows that accelerated telomere shortening under high oxidative stress levels can lead to premature aging of tissues and organs, which is a key factor in the development of CMDs. Previous studies have found that high oxidative stress in SGA infants can promote accelerated telomere shortening, suggesting a close relationship with its long-term CMD development.

[0003] Studies have also found that early catch-up growth after birth may be more likely to cause early metabolic abnormalities than premature infants or SGA infants themselves. However, there is currently a lack of a refined management system for the most suitable catch-up growth for premature infants and SGA infants in China. It is impossible to use learning models to perform regression calculations on indicators of a child over a normal cycle, predict the difference between risk indicators and reference indicators, and issue a risk monitoring warning on the platform if the difference exceeds the threshold. Summary of the Invention

[0004] The purpose of this invention is to address the current lack of a refined management system for the most suitable catch-up growth of premature infants and SGA groups, and the inability to use learning models to perform regression calculations on indicators of a child over a normal cycle for risk monitoring and early warning.

[0005] This invention will establish a refined management model for the optimal catch-up growth of premature infants and SGA, as well as an intelligent management system for long-term CMDs risk monitoring and early warning, and a related risk detection and early warning platform. This will promote the early detection of premature infants / SGA who are not suitable for catch-up growth and are at high risk of CMDs, so as to implement early intervention and risk management, and ultimately reduce the long-term chronic disease burden of CMDs.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] A catch-up growth management system for preterm infants and SGA, and an early warning system for CMDs (Critical Diseases), characterized in that: the system is based on the DOHaD (Doctor of Development and Habitat) theory and includes an information collection and grouping module, a catch-up difference analysis module, an early warning factor analysis module, a risk assessment module, and a statistical analysis module.

[0008] The information collection grouping module divides the data collected through the HIS system and questionnaire surveys into four groups: preterm SGA, full-term SGA, preterm AGA (appropriate for gestational age), and full-term AGA.

[0009] The catch-up difference analysis module uses the Z-score method to analyze the incidence of excessive rapid catch-up growth (ERCG), rapid catch-up growth (RCG), appropriate catch-up growth (ACG), slow catch-up growth (SCG), and almost no catch-up growth (NCG) in high-risk infants of different groups, as well as the differences in catch-up growth patterns, to obtain early postnatal physical indicators.

[0010] The aforementioned early warning factor analysis model detects changes in peripheral blood leukocyte telomere length (LTL) and gut microbiota in school-aged children of different groups, compares the differences in LTL and gut microbiota profiles among school-aged children in different catch-up growth groups of each cohort, and obtains the association between LTL, gut microbiota and the risk of CMDs in school-aged children of each group. At the same time, it detects the level of oxidative stress (OS) and obtains the association between high oxidative stress and rapid catch-up growth leading to accelerated shortening of LTL and gut microbiota imbalance.

[0011] The risk assessment module includes the following tests for school-aged children in each group: glucose and lipid metabolism indicators, adipokines and growth factors, imaging tests, body composition analysis, and blood pressure.

[0012] The statistical analysis module compares the differences in the incidence of high-risk CMDs among school-aged children in different catch-up growth groups of preterm SGA, full-term SGA, preterm AGA, and full-term AGA groups. After correcting for confounding factors using multiple regression analysis, the catch-up growth patterns of early postnatal physical indicators of high-risk infants in each group are obtained, and inappropriate catch-up growth patterns and preterm and SGA infants with high CMDs risk are identified in the early stages.

[0013] Information on premature infants and SGAs is collected and grouped by the information collection module, then enters the catch-up difference analysis module, the early warning factor analysis module, and the risk assessment module. After analysis by these modules, the data is input into the statistical analysis module, which outputs CMDs risk warning information.

[0014] Furthermore, the early postnatal physical indicators include weight, length, head circumference, weight at length, BMI, skinfold thickness, waist-to-hip ratio, waist-to-height ratio, catch-up growth initiation time (Tb) for each early postnatal physical indicator, time to complete catch-up growth (Tc), and catch-up growth rate at different stages. The peripheral blood leukocyte telomere length was detected by RT-PCR; the changes in gut microbiota were detected by 16S-rRNA high-throughput sequencing; the glucose and lipid metabolism indicators were obtained by collecting peripheral venous blood, including HbA1c, C-peptide, TC, TG, HDL, VLDL, LDL, Apo-A1, and 1,25-(OH)2-VitD3, OGTT test, and HOMA-IR calculation; the adipocyte cytokines included leptin and adiponectin; the growth factors included IGF-1 and IGF-BP3; the imaging tests included ultrasound examination of fatty liver and carotid intima-media thickness (IMT); and the body composition analysis was performed by bioelectrical impedance analysis of body composition, including body fat percentage.

[0015] Furthermore, the detection of oxidative stress levels is achieved by separately detecting the oxidative stress levels of preterm SGA, full-term SGA, and preterm AGA cohorts, and comparing the differences in oxidative stress levels between different catch-up growth groups and the control group of full-term AGA.

[0016] Furthermore, after correcting for confounding factors using multiple regression analysis, the differences in oxidative stress levels were used to generate the association between catch-up growth patterns of various physical indicators and oxidative stress levels at different stages in the early postnatal period.

[0017] Furthermore, after correcting for confounding factors using multiple regression analysis, the differences in oxidative stress levels were obtained, showing the association between oxidative stress levels and changes in TL and gut microbiota in fast-catch-up preterm and SGA infants. This further revealed the association between high oxidative stress and the accelerated shortening of TL and gut microbiota imbalance caused by fast-catch-up growth.

[0018] Furthermore, the association between the high oxidative stress state and rapid catch-up growth leading to accelerated shortening of telomere length (TL) and gut microbiota imbalance was investigated using a low-protein diet to establish an intrauterine growth retardation (IUGR) rat model. Rats were divided into a late rapid catch-up growth group (IUGR-RR), an early catch-up growth group (IUGR-RC), and a control group (AGA-CC). Differences in oxidative stress levels in peripheral blood, pancreas, liver, skeletal muscle, and adipose tissue were compared among the groups at 3, 9, and 12 months of age. Additionally, telomere length was compared among the groups. Differences in levels and changes in gut microbiota were investigated to determine their association with oxidative stress levels in rapidly catching-up growth (IUGR) pups and with the later development of obesity, diabetes (including hyperglycemia and insulin resistance), and hyperlipidemia. By supplementing pups in the later rapid catch-up growth group and the early catch-up growth group with coenzyme Q10 (CoQ10) to downregulate their oxidative stress, the study obtained the differences in the long-term risk of obesity, diabetes, and hyperlipidemia in rapidly catching-up growth IUGR pups after improving their high oxidative stress state, as well as changes in their total tract growth (TL) and gut microbiota.

[0019] Furthermore, the TL detection uses the ΔCt method of the reference gene, i.e. The relative telomere length is assessed by calculating the telomere length ratio; furthermore, this invention calculates the telomere shortening rate by detecting changes in telomere length in various tissues and organs of a prospective study cohort, LTL and IUGR, at multiple time points. .

[0020] Furthermore, the high risk of CMDs includes the presence of symptoms such as obesity, hyperglycemia, insulin resistance, lipid metabolism disorders, hypertension, and fatty liver. The confounding factors include maternal pregnancy, neonatal birth status, infant feeding, childhood lifestyle and behavioral factors, as well as nutritional and disease factors.

[0021] This invention also provides a platform for catch-up growth management of preterm infants and SGA, as well as an early warning platform for CMDs risks. This platform, based on collected data and statistical results, establishes a statistical model using machine learning methods to view the catch-up growth patterns of preterm infants and SGA, and the correlation between various factors and CMD risk outcomes. It completes a learning model for catch-up growth management of preterm infants and SGA, and uses this learning model to perform regression calculations on indicators of the child's normal life cycle to predict the difference between risk indicators and reference indicators. If the difference exceeds a threshold, a risk monitoring warning will be issued on the platform.

[0022] Furthermore, after checking the various data entered into the system, the learning model uses 20% of the data for testing and verification, achieving an effective identification rate of 95% or higher for CMDs risks.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0024] In this application, a catch-up growth management and long-term cardiovascular and metabolic disease risk monitoring and early warning platform for preterm infants and SGAs is established to identify early signs of excessively rapid or failed catch-up growth, asymmetrical catch-up growth, and high risk of CMDs (obesity, hyperglycemia, insulin resistance, lipid metabolism disorders, hypertension, fatty liver), including systematic assessment and risk identification of nutrient intake. By closely integrating clinical and basic research, a follow-up cohort for preterm infants and SGAs is established. Through long-term standardized management and follow-up, the clinical diagnosis and treatment level of preterm infants and SGAs can be significantly improved.

[0025] Intelligent and standardized management systems can help clinicians identify premature and SGA infants who are not suitable for catch-up growth and are at high risk of CMDs in the early stages, so as to implement early intervention and risk stratification management, increase the health index of this population after birth, improve their quality of life, reduce the burden on society and families, promote the long-term standardized management of premature and SGA infants, and promote the development of maternal and child health. Attached Figure Description

[0026] Figure 1 A schematic diagram of the technical route for cohort studies of preterm birth and SGA;

[0027] Figure 2 To analyze the growth trajectory of different body weights in full-term SGA using a latent category growth model;

[0028] Figure 3 A technical roadmap for validating the association between high oxidative stress and rapid catch-up growth leading to accelerated shortening of total trace (TL) and gut microbiota imbalance using an IUGR rat model. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention.

[0030] Therefore, the following detailed description of embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0031] It should be noted that, unless otherwise specified, the embodiments and features and technical solutions in the present invention can be combined with each other.

[0032] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0033] In the description of this invention, it should be noted that the terms "upper," "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. These terms are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. In addition, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] Example 1: See Figure 1 As shown,

[0035] This embodiment provides a catch-up growth management system for preterm infants and SGA, and an early warning system for CMDs risks. The system is based on the DOHaD theory and includes an information collection and grouping module, a catch-up difference analysis module, an early warning factor analysis module, a risk assessment module, and a statistical analysis module.

[0036] The information collection grouping module divides the data collected through the HIS system and questionnaire surveys into four groups: preterm SGA, full-term SGA, preterm AGA (appropriate for gestational age), and full-term AGA. The data includes maternal pregnancy, newborn birth status, infant feeding, parental health status, basic family information, and childhood lifestyle, nutrition, and disease factors. The follow-up period decreases with the child's age, specifically: 0-6 months: once a month; 6-12 months: once every 2 months; 1-2 years: once every 3 months; 2-8 years: once every 6 months.

[0037] See Figure 2As shown, the catch-up difference analysis module uses the Z-score method to analyze high-risk infants in different groups. Referring to the WHO's 2006 reference standards for growth and development in children aged 0-5 years, it calculates the age-specific weight z-score (WAZ), age-specific length z-score (LAZ), and age-specific body mass index z-score (BMIAZ) at each follow-up time point. Using MPlus8.0 latent variable modeling software, the latent class growth model (LCGA) method is employed to analyze the weight gain trajectory of full-term SGA infants. The results show that, according to the Bayesian information criterion in LCGA, when the subjects are divided into 2-6 classes, the BIC values ​​are 4103.42, 3652.44, 3427.69, 3239.00, and 3293.68, respectively, with 5 classes representing the optimal number of groups with the lowest BIC. Therefore, this study suggests that there are five different weight gain trajectories in the full-term SGA in this study cohort, including excessive rapid catch-up growth (ERCG) (class 1, 10.9%), rapid catch-up growth (RCG) (class 2, 17.9%), appropriate catch-up growth (ACG) (class 3, 53.0%), slow catch-up growth (SCG) (class 4, 13.4%), and almost no catch-up growth (NCG) (class 5, 4.8%).

[0038] The aforementioned early warning factor analysis model detects changes in peripheral blood leukocyte telomere length (LTL) and gut microbiota in school-aged children of different groups, compares the differences in LTL and gut microbiota profiles among school-aged children in different catch-up growth groups of each cohort, and obtains the association between LTL, gut microbiota and the risk of CMDs in school-aged children of each group. At the same time, it detects the level of oxidative stress (OS) and obtains the association between high oxidative stress and rapid catch-up growth leading to accelerated shortening of LTL and gut microbiota imbalance.

[0039] The risk assessment module includes the following tests for school-aged children in each group: glucose and lipid metabolism indicators, adipokines and growth factors, imaging tests, body composition analysis, and blood pressure.

[0040] The statistical analysis module compares the differences in the incidence of high-risk CMDs among school-aged children in different catch-up growth groups of preterm SGA, full-term SGA, preterm AGA, and full-term AGA groups. After correcting for confounding factors using multiple regression analysis, the catch-up growth patterns of early postnatal physical indicators of high-risk infants in each group are obtained, and inappropriate catch-up growth patterns and preterm and SGA infants with high CMDs risk are identified in the early stages.

[0041] Information on premature infants and SGAs is collected and grouped by the information collection module, then enters the catch-up difference analysis module, the early warning factor analysis module, and the risk assessment module. After analysis by these modules, the data is input into the statistical analysis module, which outputs CMDs risk warning information.

[0042] Furthermore, the early postnatal physical indicators include weight, length, head circumference, weight at length, BMI, skinfold thickness, waist-to-hip ratio, waist-to-height ratio, catch-up growth initiation time (Tb) for each early postnatal physical indicator, time to complete catch-up growth (Tc), and catch-up growth rate at different stages. The peripheral blood leukocyte telomere length was detected by RT-PCR; the changes in gut microbiota were detected by 16S-rRNA high-throughput sequencing; the glucose and lipid metabolism indicators were obtained by collecting peripheral venous blood, including HbA1c, C-peptide, TC, TG, HDL, VLDL, LDL, Apo-A1, and 1,25-(OH)2-VitD3, OGTT test, and HOMA-IR calculation; the adipocyte cytokines included leptin and adiponectin; the growth factors included IGF-1 and IGF-BP3; the imaging tests included ultrasound examination of fatty liver and carotid intima-media thickness (IMT); and the body composition analysis was performed by bioelectrical impedance analysis of body composition, including body fat percentage.

[0043] Furthermore, the detection of oxidative stress levels is achieved by separately detecting the oxidative stress levels of preterm SGA, full-term SGA, and preterm AGA cohorts, and comparing the differences in oxidative stress levels between different catch-up growth groups and the control group of full-term AGA.

[0044] Furthermore, after correcting for confounding factors using multiple regression analysis, the differences in oxidative stress levels were used to generate the association between catch-up growth patterns of various physical indicators and oxidative stress levels at different stages in the early postnatal period.

[0045] Furthermore, after correcting for confounding factors using multiple regression analysis, the differences in oxidative stress levels were obtained, showing the association between oxidative stress levels and changes in TL and gut microbiota in fast-catch-up preterm and SGA infants. This further revealed the association between high oxidative stress and the accelerated shortening of TL and gut microbiota imbalance caused by fast-catch-up growth.

[0046] Furthermore, the association between the high oxidative stress state and rapid catch-up growth leading to accelerated shortening of TL and gut microbiota imbalance was established by using a low-protein diet to create an intrauterine growth retardation (IUGR) rat model, which was divided into a late rapid catch-up growth group (IUGR-RR), an early catch-up growth group (IUGR-RC), and a control group (AGA-CC).

[0047] Rats in the IUGR-RC group exhibited catch-up growth immediately after birth, and this catch-up growth was prolonged, especially in the 0-3 week period, with significantly higher weight and BMI increases compared to the IUGR-RR and control groups. Rats in the IUGR-RC group showed catch-up growth within one week of birth, which continued until 20 weeks, while rats in the IUGR-RR group showed catch-up growth at 3 weeks of birth, but this was shorter in duration and the rate of weight gain was slower. By comparing the catch-up growth of rats in each group from 0 to 3 weeks after birth, it was found that the IUGR-RC group rats... , All were significantly higher than those in the IUGR-RR group (0.64±0.12 per week vs. -0.42±0.06 per week, P<0.001; 0.50±0.15 per week vs. -0.65±0.10 per week, P<0.001) and the control group (0.64±0.12 vs. -0.001±0.05, P<0.001; 0.50±0.15 vs. 0.01±0.02, P<0.001). The weight gain rate of rats in the IUGR-RC group from 0 to 3 weeks was approximately 2.3 times that of the IUGR-RC group (40.83±10.81 g / wk vs. 17.55±5.79 g / wk, P<0.001), and the weight gain rate from 3 to 20 weeks after birth was increased compared with both the IUGR-RR group and the control group (P<0.05).

[0048] IUGR rats have an increased risk of lipid metabolism disorders in the later stages of rapid catch-up growth in the early postnatal period: Comparison of lipid metabolism indicators among different groups of pups revealed that serum TG, TC, and LDL levels in the IUGR-RC and IUGR-RC groups were higher than those in the Control group (P < 0.05), while there was no significant difference in serum HDL levels among the groups (P > 0.05), suggesting that lipid metabolism disorders occur in the later stages of rapid catch-up growth in IUGR pups.

[0049] IUGR rats experience increased oxidative stress levels in the early postnatal catch-up growth phase, leading to accelerated telomere shortening in tissues and organs. Measurements of 8-isoprostaglandins in various tissues were used to investigate this effect. The results showed that the oxidative stress levels in the adipose tissue, pancreas, and liver tissue of rats in the IUGR-RC and IUGR-RR groups were significantly higher than those in the Control group (P < 0.05), and the levels in the IUGR-RC group were higher than those in the IUGR-RR group (P < 0.05), indicating that the adipose tissue, pancreas, and liver tissue of IUGR fast-catch-growth rats were under high oxidative stress. The relative telomere length (TL) of tissues was measured by qRT-PCR, and the telomere length ratio (T / S ratio) was calculated using the ΔCt method of the reference gene. The results showed that there was no significant difference in the relative telomere length of liver and pancreas tissues among the three-month-old rat groups (P > 0.05). At nine months of age, the telomere length of pups in the IUGR-RC and IUGR-RR groups was shorter than that in the Control group, and the shortening was more significant in the RC group than in the RR group (P < 0.05), indicating that the telomere length shortens rapidly under continuous high oxidative stress in IUGR fast-catch-growth rats after birth.

[0050] Early rapid catch-up growth in IUGR rats led to premature aging of tissues and organs. By detecting the aging marker sirtuin3 protein level in various tissues, it was found that the expression level of sirtuin3 protein in the pancreas and liver tissues of IUGR-RC and IUGR-RR rats was significantly higher than that in the control group, and the level in the IUGR-RC group was higher than that in the IUGR-RR group (P<0.05).

[0051] Rapid catch-up growth in IUGR rats during early postnatal period can lead to TERT nuclear-mitochondrial translocation, protecting mitochondrial function from damage caused by high oxidative stress and potentially contributing to accelerated telomere shortening. Immunofluorescence co-localization and Western blotting showed that TERT translocation from the nucleus to the mitochondria occurred in pancreatic tissue of IUGR rats. qRT-PCR was used to determine the translocation of TERT from the nucleus to the mitochondria. The relative copy number of mitochondrial DNA (mtDNA) in liver and pancreatic tissues was assessed. The results showed no statistically significant difference in the relative copy number of mtDNA in liver and pancreatic tissues between the IUGR-RR and IUGR-RC catch-up growth rat groups and the Control group (P > 0.05). A positive correlation was observed (r=1.372, P<0.05), suggesting that TERT translocation from the nucleus to the mitochondria in IUGR-catch-growth rats may be involved in protecting mitochondria from oxidative stress damage; pancreatic tissue of IUGR-RC rats Significantly positively correlated with OS level (r=1.861, P<0.01) and negatively correlated with TL level (r=-1.273, P<0.01), suggesting that the rapid catch-up growth of IUGR rats leads to a state of high oxidative stress, which promotes the increased translocation of TERT from the nucleus to the mitochondria and cytoplasm, and is an important reason for the accelerated shortening of TL.

[0052] Furthermore, the TL detection uses the ΔCt method of the reference gene, i.e. The relative telomere length is assessed by calculating the telomere length ratio; furthermore, this invention calculates the telomere shortening rate by detecting changes in telomere length in various tissues and organs of a prospective study cohort, LTL and IUGR, at multiple time points. .

[0053] Furthermore, the high risk of CMDs includes the presence of symptoms such as obesity, hyperglycemia, insulin resistance, lipid metabolism disorders, hypertension, and fatty liver. The confounding factors include maternal pregnancy, neonatal birth status, infant feeding, childhood lifestyle and behavioral factors, as well as nutritional and disease factors.

[0054] This invention also provides a platform for catch-up growth management of preterm infants and SGA (Special Generalized Toxic-Grade A) infants, as well as an early warning platform for CMD (Congenital Myocardial Defects) risks. This platform is supported by comprehensive data collected using the aforementioned methods and statistical analysis results. Through the initial establishment of a statistical model, a software analysis model is built using machine learning to analyze the catch-up growth patterns of preterm infants and SGA infants, and to analyze the correlation between various factors and CMD risk outcomes, thus completing a learning model for catch-up growth management of preterm infants and SGA infants. The resulting learning model can be used for long-term CMD risk monitoring and early warning. After the various data obtained are entered into the system, the learning model can perform regression calculations on indicators for a typical child cycle to predict the difference between the risk indicator and the reference indicator. If the difference exceeds a threshold, a risk monitoring warning will be issued on the platform, facilitating effective early risk identification and timely intervention. Simultaneously, this model can be used to develop a user-friendly app system for doctors and parents, allowing them to view and query relevant information promptly, and receive push notifications of potential risks through the risk prediction platform.

[0055] Furthermore, after checking the various data entered into the system, the learning model uses 20% of the data for testing and verification, achieving an effective identification rate of 95% or higher for CMDs risks.

[0056] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.

Claims

1. A catch-up growth management system for preterm infants and small-for-gestational-age (SGA) infants, and an early warning system for the risk of long-term cardiovascular and metabolic diseases (CMDs), characterized by: It includes modules for information collection and grouping, catch-up difference analysis, early warning factor analysis, risk assessment, and statistical analysis. The information collection and grouping module divides the data collected through the HIS system and questionnaires into four groups: preterm SGA, full-term SGA, preterm AGA, and full-term AGA. The catch-up difference analysis module: referring to the growth and development reference standards for children aged 0-5 years, calculates the age-specific weight Z-score, age-specific height Z-score, and age-specific body mass index Z-score at each follow-up time point. It uses the latent category growth model method to analyze the weight growth trajectory and uses the Z-score method to analyze the incidence rates of excessively rapid catch-up growth, rapid catch-up growth, moderate catch-up growth, slow catch-up growth, and almost no catch-up growth in high-risk infants of different groups, as well as the differences in catch-up growth patterns, to obtain early postnatal physical indicators. The early warning factor analysis module detects changes in peripheral blood leukocyte telomere length (LTL) and gut microbiota in school-aged children of different groups, compares the differences in LTL and gut microbiota profiles among school-aged children in different catch-up growth groups of each cohort, obtains the association between LTL, gut microbiota and the risk of CMDs in school-aged children of each group, and detects oxidative stress levels at the same time. The oxidative stress level was detected by measuring the oxidative stress levels of preterm SGA, full-term SGA, and preterm AGA cohorts, and comparing the differences in oxidative stress levels between different catch-up growth groups and the control group of full-term AGA. After correcting for confounding factors using multiple regression analysis, the differences in oxidative stress levels were found to be associated with oxidative stress levels in rapidly catching-up preterm and small-for-gestational-age infants, as well as changes in tissue telomere relative length (TL) and gut microbiota. Furthermore, the association between high oxidative stress and rapid catch-up growth leading to accelerated TL shortening and gut microbiota imbalance was obtained. The association between high oxidative stress and rapid catch-up growth leading to accelerated shortening of telomere length (TL) and gut microbiota imbalance was established using a low-protein diet in an intrauterine growth retardation (IUGR) rat model. Rats were divided into a late rapid catch-up growth group, an early catch-up growth group, and a control group. The differences in oxidative stress levels in peripheral blood, pancreas, liver, skeletal muscle, and adipose tissue were compared among the groups at 3, 9, and 12 months of age. Furthermore, the differences in telomere length and gut microbiota changes among the groups were compared to obtain their association with oxidative stress levels in IUGR rats undergoing rapid catch-up growth and with the later development of obesity, diabetes, and hyperlipidemia. By administering coenzyme Q10 to the late rapid catch-up growth group and the early catch-up growth group to downregulate their oxidative stress, the risk differences in the long-term development of obesity, diabetes, and hyperlipidemia, as well as changes in TL and gut microbiota, were obtained after improving the high oxidative stress state in IUGR rats undergoing rapid catch-up growth. The risk assessment module includes the following tests for school-aged children in each group: glucose and lipid metabolism indicators, adipokines and growth factors, imaging tests, body composition analysis, and blood pressure. The statistical analysis module compares the differences in the incidence of high-risk CMDs among school-aged children in different catch-up growth groups of preterm SGA, full-term SGA, preterm AGA, and full-term AGA groups. After correcting for confounding factors using multiple regression analysis, the early postnatal physical indicators of high-risk infants in each group are obtained to identify unsuitable catch-up growth patterns and preterm and small-for-gestational-age infants with high CMDs in the early stages. Information on premature infants and SGAs is collected and grouped by the information collection module, then enters the catch-up difference analysis module, the early warning factor analysis module, and the risk assessment module. After analysis by these modules, the data is input into the statistical analysis module, which outputs CMDs risk warning information.

2. The system as described in claim 1, characterized in that: The early postnatal physical indicators include weight, length, head circumference, weight at length, BMI, skinfold thickness, waist-to-hip ratio, waist-to-height ratio, catch-up growth initiation time (Tb) for each postnatal physical indicator, time to complete catch-up growth (Tc), and catch-up growth rate at different stages. The peripheral blood leukocyte telomere length was detected by RT-PCR, the changes in gut microbiota were detected by 16S-rRNA high-throughput sequencing, the adipocyte kinases included leptin and adiponectin, and the growth factors included IGF-1 and IGF-BP3.

3. The system as described in claim 2, characterized in that: The TL detection uses a reference gene. Law, that is The relative telomere length was assessed by calculating the telomere length ratio; changes in telomere length in various tissues and organs of catch-up growing rats were studied by detecting LTL and IUGR at multiple time points, and the rate of telomere shortening was calculated. .

4. The system as described in claim 2, characterized in that: The high-risk CMDs include the presence of symptoms such as obesity, hyperglycemia, insulin resistance, lipid metabolism disorders, hypertension, and fatty liver. The confounding factors include infant feeding, childhood lifestyle and behavior, and nutritional and disease factors.

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