Method for predicting metabolic risk in offspring based on parental constitution and assisted reproductive technology

Abstract

This invention discloses a method for predicting metabolic risk in offspring based on parental physique and assisted reproductive technology, belonging to the field of medical intelligent technology. This method, for the first time, achieves accurate prediction of abnormalities in specific metabolic indicators in offspring by quantifying the interaction between specific ART technology and parental BMI, filling a blind spot in the assessment dimensions. It expands the focus of ICSI offspring safety monitoring from traditional neurodevelopmental disorders to the more basic cardiovascular metabolic profile, improving the quality and comprehensiveness of overall birth defects and long-term health monitoring. The technology is simpler to control, operate, and use, allowing clinicians to easily and effectively block the transmission of metabolic risks from paternal obesity by simply monitoring offspring BMI and controlling their early energy intake through a single clinical procedure—closely monitoring their BMI and controlling their early energy intake.

Description

Technical Field

[0001] This invention relates to medical intelligent technology, specifically to a method for predicting metabolic risks in offspring based on parental constitution and assisted reproductive technology. Background Technology

[0002] In assessing the safety of offspring assisted reproductive technologies (ART) and the impact of parental body mass index (BMI) on the long-term metabolic health of offspring, existing "technical approaches" (i.e., clinical research methods and known principles) mainly include: ART techniques (such as in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) procedures (such as in vitro gamete manipulation and embryo cryopreservation and thawing) occur during critical developmental windows affecting genome methylation, which may be associated with adverse perinatal outcomes (such as macrosomia) or long-term health risks in offspring. It is also known that parental obesity (high-fat environment) is intergenerationally transmitted in naturally conceived populations, increasing the risk of obesity, hypertension, and type 2 diabetes in offspring.

[0003] Despite some exploration of ART security in existing technologies, the following obvious "blind spots" and shortcomings still exist:

[0004] 1. The safety assessment of ICSI technology suffers from limitations in endpoint indicators: Most studies on the safety of ICSI offspring focus excessively on neurodevelopmental disorders, while paying very little attention to its long-term effects on metabolic profiles. Reasoning: Clinical practice has long neglected the deep biochemical link between nervous system development and basal metabolic characteristics (such as blood glucose and cholesterol levels). ICSI bypasses natural sperm selection by directly injecting sperm heads, potentially increasing the likelihood of using DNA-fragmented sperm, thereby causing early epigenetic changes. However, the specific long-term metabolic manifestations of these changes have not been individually quantified previously.

[0005] 2. The independent effects of embryo freezing (FET) on long-term cardiovascular metabolism remain unclear. Previous meta-analyses and randomized controlled trials (RCTs) have primarily reported short-term / perinatal risks associated with frozen embryo transfer, such as gestational hypertension, large-for-gestational-age infants, and increased birth weight. However, no studies have specifically compared the long-term cardiovascular metabolic changes in offspring. Reason for this: Previous studies failed to effectively isolate the "freezing / thawing intervention" from the "obesity-inducing intrauterine environment caused by maternal obesity" and perform interaction analyses, leaving the long-term metabolic safety of FET technology itself uncertain.

[0006] 3. The lack of assessment of the intergenerational effects of paternal obesity in specific ART populations presents a problem: Although animal studies and natural conception studies have suggested the adverse effects of paternal obesity, the specific impact of paternal obesity on the cardiometabolic characteristics of offspring in this special and metabolically vulnerable group (conceived through ART) is completely unexplored. Reasons for this: Traditional perspectives often focus on the role of the maternal intrauterine environment in the metabolic programming of the fetus, frequently neglecting the paternal factor; furthermore, there is a lack of high-quality ART cohorts that include detailed paternal BMI data and long-term serological follow-up of offspring.

[0007] Current technologies cannot determine the impact of poor parental health on offspring metabolic health in assisted reproductive technologies, and cannot guide infertile patients in choosing clinical strategies before pregnancy or embryo transfer, thus affecting doctors' ability to predict metabolic disorders in offspring and formulate medical interventions. Summary of the Invention

[0008] The purpose of this invention is to provide a method for predicting metabolic risks in offspring based on parental physical condition and assisted reproductive technology, in order to solve the problem that existing technologies cannot determine the impact of poor parental physical condition on the metabolic health of offspring in assisted reproductive technology.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting metabolic risk in offspring based on parental physique and assisted reproductive technology, comprising the following steps:

[0010] Data collection: Before infertile couples undergo ART treatment, collect and record the age, height, and weight of both parents, calculate their pre-pregnancy BMI, and record the ART fertilization method and embryo transfer strategy used.

[0011] Offspring follow-up and testing: After the offspring are born, at specific age ranges, their height, weight, waist circumference, and blood pressure are measured and recorded. Fasting blood samples are also drawn to test biochemical indicators including fasting blood glucose, fasting insulin, triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol.

[0012] Data standardization and modeling: The absolute values ​​of the biochemical indicators measured above are input into the calculation module. Based on the total follow-up population data or WHO child growth standards, the Z-scores and metabolic comprehensive scores of each biochemical indicator are calculated. A linear mixed model is used to adjust and control confounding factors.

[0013] Risk assessment and intervention output: Based on the model comparison results, risk warnings are output.

[0014] Preferably, the BMI parameter range standards for both parents include: the standard for determining normal weight for the father is 18.5 kg / m². 2 ≤BMI≤23.9kg / m 2The criteria for determining whether a mother or father is overweight is a BMI ≥ 24 kg / m². 2 The criteria for determining obesity in fathers is a BMI ≥ 28 kg / m². 2 The specific age range for the offspring detection records is singleton children aged 4-10 / 11 years.

[0015] Preferably, the Z-score formula is Z-score = (observed value - mean of specific age and gender) / standard deviation of specific age and gender.

[0016] Preferably, the metabolic comprehensive score includes a scoring model MSS-1 and a scoring model MSS-2. The calculation formula for the scoring model MSS-1 is: MSS-1 = BMI + systolic blood pressure + insulin + triglycerides - high-density lipoprotein cholesterol. The calculation formula for the scoring model MSS-2 is: MSS-2 = waist circumference to height ratio + systolic blood pressure + blood glucose + triglycerides - high-density lipoprotein cholesterol.

[0017] Preferably, the confounding factors include parental age, parental smoking history, offspring sex, and postpartum energy intake.

[0018] Preferably, the risk warning assessment rules include:

[0019] For ICSI technology, when conception is achieved through ICSI and the father is overweight / obese (BMI ≥ 24.0 kg / m²), the prediction system should indicate that the offspring have significantly reduced fasting blood glucose and total cholesterol levels, suggesting potential neurodevelopmental-related biochemical risks. It is recommended that the father lose weight before pregnancy.

[0020] For frozen embryo transfer (FET), the system assesses the mother's pre-pregnancy BMI. If the mother's BMI is ≥24.0 kg / m², the system will determine the appropriate level of pregnancy. 2 Furthermore, if frozen embryo transfer is used, the system should output a high cardiovascular and metabolic risk warning, because its offspring will have significantly higher BMI Z-scores, systolic blood pressure Z-scores, and higher metabolic comprehensive scores.

[0021] Regarding the intergenerational effects of paternal obesity, the system confirmed through mediation effect analysis that when BMI ≥ 28.0 kg / m², paternal obesity will directly lead to a significant increase in offspring BMI, systolic blood pressure, and insulin resistance (HOMA-IR). The system should provide guidance that controlling early energy intake and strictly controlling offspring BMI can effectively block the transmission of this metabolic risk.

[0022] Compared with existing technologies, the method for predicting metabolic risk in offspring based on parental physique and assisted reproductive technology provided by this invention, for the first time, achieves accurate prediction of abnormalities in specific metabolic indicators in offspring by quantifying the interaction between specific ART technologies and parental BMI. This fills a blind spot in the assessment dimensions and expands the focus of ICSI offspring safety monitoring from traditional neurodevelopmental disorders to a more fundamental cardiovascular metabolic profile, improving the quality and comprehensiveness of overall birth defect and long-term health monitoring. The technology is simpler to control, operate, and use, greatly simplifying the process of blocking and controlling metabolic diseases. Clinicians no longer need to take complex genetic or drug interventions; they can simply and effectively block the transmission of metabolic risks from paternal obesity through a single clinical procedure—closely monitoring offspring BMI and controlling their early energy intake. This approach improves the efficiency of reproductive clinical decision-making by enhancing efficiency and saving medical data, providing highly specific fertility guidance for infertile patients, saving unnecessary medical intervention costs, and fundamentally improving the metabolic health of children conceived through ICSI by providing early risk warnings. In the long run, it can significantly save the medical resources and treatment costs used by the whole society to treat childhood / adolescent obesity, hypertension, and early type 2 diabetes. It also pioneers a brand-new ART eugenic assessment model, breaking the inherent perception that frozen embryos are absolutely safe or that only the mother's physique affects the fetus. It provides a new data processing and risk assessment tool that combines specific ART interventions with obesity-inducing environments for cross-risk rating, giving existing reproductive center electronic medical record systems a new and useful function of early warning of the long-term cardiovascular and metabolic health of offspring. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0024] Figure 1 A flowchart of the prediction method provided in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0026] As attached Figure 1 As shown:

[0027] Example:

[0028] This invention provides a method for predicting metabolic risk in offspring based on parental constitution and assisted reproductive technology, comprising the following steps:

[0029] Data collection: Before infertile couples undergo ART treatment, the age, height, and weight of both parents are collected and recorded, and their pre-pregnancy BMI is calculated. The standard range for the BMI parameters of both parents includes: the standard for normal weight for the father is 18.5 kg / m². 2 ≤BMI≤23.9kg / m 2 The criteria for determining whether a mother or father is overweight is a BMI ≥ 24 kg / m². 2 The criteria for determining obesity in fathers is a BMI ≥ 28 kg / m². 2 Record the ART fertilization method used (conventional IVF or ICSI) and the embryo transfer strategy (fresh embryo transfer or frozen embryo transfer).

[0030] Offspring follow-up and testing: After the birth of offspring, especially in a specific age range (such as 4-11 years old), height, weight, waist circumference, and blood pressure of offspring are measured and recorded, and fasting blood samples are drawn to test biochemical indicators including fasting blood glucose, fasting insulin, triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c).

[0031] Data standardization and modeling: The absolute values ​​of the biochemical indicators measured above are input into the calculation module. Based on the total follow-up population data or WHO child growth standards, the Z-scores and metabolic comprehensive scores (MSS-1 and MSS-2) of each biochemical indicator are calculated. A linear mixed model is used to control for confounding factors such as parental age, parental smoking history, offspring sex and postpartum energy intake.

[0032] Risk assessment and intervention output: Based on the model comparison results, risk warnings are output.

[0033] The Z-score formula is Z-score = (observed value - mean for specific age and gender) / standard deviation for specific age and gender. The metabolic comprehensive score includes scoring model MSS-1 and scoring model MSS-2. The calculation formula for scoring model MSS-1 is: MSS-1 = BMI + systolic blood pressure + insulin + triglycerides - high-density lipoprotein cholesterol (HDL-c). The calculation formula for scoring model MSS-2 is: MSS-2 = waist-to-height ratio + systolic blood pressure + blood glucose + triglycerides - high-density lipoprotein cholesterol (HDL-c).

[0034] The risk warning assessment rules include:

[0035] Regarding ICSI technology, when conception is achieved through ICSI and the father is overweight / obese (BMI ≥ 24.0 kg / m²), the prediction system should indicate that the offspring (4-11 years old) have significantly reduced fasting blood glucose and total cholesterol levels, suggesting potential neurodevelopmental-related biochemical risks. It is recommended that the father lose weight before pregnancy.

[0036] For frozen embryo transfer (FET), the system assesses the mother's pre-pregnancy BMI. If the mother's BMI is ≥24.0 kg / m², the system will determine the appropriate level of pregnancy. 2 Furthermore, if frozen embryo transfer is used, the system should output a high cardiovascular and metabolic risk warning, because its offspring will have significantly higher BMI Z-scores, systolic blood pressure Z-scores, and higher metabolic comprehensive scores (MSS-1, MSS-2).

[0037] Regarding the intergenerational effects of paternal obesity, the system confirmed through mediation effect analysis that when BMI ≥ 28.0 kg / m², paternal obesity directly leads to a significant increase in offspring BMI, systolic blood pressure, and insulin resistance (HOMA-IR). The system should provide guidance that controlling early energy intake in offspring and strictly controlling offspring BMI (blocking 57.48% to 94.75% of the mediation effect) can effectively block the transmission of this metabolic risk.

[0038] By employing the above-mentioned method for predicting metabolic risk in offspring based on parental constitution and assisted reproductive technology, the following can be achieved:

[0039] Improved precision and quality (enhanced precision medicine and risk prediction capabilities) have significantly improved the accuracy of risk prediction: Traditional medicine often speaks generally of "parental obesity affecting offspring." This method, for the first time, quantifies the interaction between specific ART techniques (such as ICSI versus conventional IVF, frozen embryos versus fresh embryos) and parental BMI, achieving precise prediction of abnormalities in specific metabolic indicators in offspring (such as fasting blood glucose, total cholesterol, and systolic blood pressure Z-score). For example, it clearly indicates that frozen embryo transfer only significantly increases offspring BMI and systolic blood pressure Z-score when the mother is overweight / obese, while no such adverse association is found in mothers of normal weight. It fills a gap in assessment dimensions: expanding the focus of ICSI offspring safety monitoring from traditional neurodevelopmental disorders to more fundamental cardiovascular metabolic profiles (such as lower fasting blood glucose and LDL cholesterol), improving the quality and comprehensiveness of overall birth defect and long-term health monitoring.

[0040] The ease of control, operation, and use (a significant simplification of clinical intervention targets) greatly simplifies the process of blocking and controlling metabolic diseases: studies have confirmed a strong association between paternal obesity and adverse cardiometabolic outcomes in ART offspring. More importantly, this method establishes that "offspring BMI" is the primary mediator of this intergenerational transmission, mediating 57.48% to 94.75% of the metabolic deterioration effect. This means that clinicians can easily and effectively block the transmission of metabolic risks from paternal obesity through a single clinical procedure—closely monitoring offspring BMI and controlling their early energy intake—without resorting to complex genetic or pharmacological interventions.

[0041] Increased efficiency and reduced medical resources (procedures) improve the efficiency of reproductive clinical decision-making: This method provides highly specific "pre-conception guidance" for infertile patients. For example, for overweight / obese female patients, the system can quickly assist doctors in making decisions, prioritizing fresh embryo transfer over frozen embryo transfer, thereby avoiding potential cardiovascular and metabolic diseases in offspring at the source. It also saves unnecessary medical intervention costs: Through proactive risk warnings (such as encouraging fathers undergoing ICSI treatment to lose weight before pregnancy), it fundamentally improves the metabolic health of children conceived through ICSI, and in the long run, can significantly save society's medical resources and treatment costs for treating childhood / adolescent obesity, hypertension, and early-stage type 2 diabetes.

[0042] The emergence of useful capabilities (the birth of novel clinical screening and eugenic tools) has pioneered a completely new ART eugenic assessment model: This invention breaks the conventional wisdom that "frozen embryos are absolutely safe" or "only maternal health affects the fetus." It provides a novel data processing and risk assessment tool that combines "specific ART interventions (such as embryo freezing, ICSI sperm injection)" with "obesity-inducing environments (overweight parents)" for cross-risk rating, giving existing reproductive center electronic medical record systems a new useful capability of "early warning of long-term cardiovascular and metabolic health of offspring."

[0043] Experimental Example 1:

[0044] Families who received ART treatment between January 2006 and December 2017 were recruited, including those who conceived through in vitro fertilization / intracytoplasmic sperm injection (IVF / ICSI). Exclusion criteria included: use of donor eggs / sperm (N=1140), incomplete anthropometric data (N=212), underweight fathers (N=209), and non-participation in the birth cohort (N=2168). From 2014 to 2021, BMI, blood pressure, blood glucose, and blood lipid data of offspring were prospectively collected at birth, 6 months of age, 1–2 years, 3–4 years, 5–6 years, 7–9 years, and 10 years and older. A total of 15,415 offspring were included, with 27,698 visits. After excluding offspring aged 11 years and older and female offspring, 7,816 male offspring were ultimately included, with 14,196 visits. The study followed epidemiological observational study reporting guidelines.

[0045] Offspring were divided into two groups to investigate the risk of metabolic changes caused by ICSI: (1) ICSI conception; (2) IVF conception. Subsequent groupings were analyzed based on ART method and paternal BMI status: (1) ICSI + overweight / obese paternal; (2) IVF + overweight / obese paternal; (3) ICSI + normal paternal weight; (4) IVF + normal paternal weight. The following factors were also analyzed for their impact on offspring metabolic outcomes: (1) singleton pregnancy; (2) percutaneous epididymal sperm aspiration (PESA) / testicular sperm extraction (TESA)-ICSI versus conventional ICSI.

[0046] According to the standards of the China Obesity Working Group, parents' pre-conception BMI is divided into: overweight / obese (BMI≥24.0kg / m²) and normal weight (18.5kg / m²≤BMI<24.0kg / m²).

[0047] At each follow-up visit, a physical examination of the offspring was performed by a pediatrician and nurse. Height (±0.1cm), weight (±0.1kg), and chest / waist circumference were measured twice and the average was taken. Blood pressure was measured in the right arm while the offspring were seated, three times consecutively, and the average of the last two measurements was taken. Fasting blood samples were collected overnight, centrifuged immediately after collection, temporarily stored at 4℃, and transferred to the biobank within 24 hours for storage at −80℃ for analysis. The methods for measuring fasting blood glucose, fasting insulin, triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in the offspring were the same as previously reported.

[0048] The outcome was the age- and sex-adjusted Z-scores of cardiometabolic variables in offspring conceived through IVF / ICSI. The mean differences in BMI Z-scores, systolic / diastolic blood pressure Z-scores, blood glucose Z-scores, insulin Z-scores, Homeostasis Model for Insulin Resistance Assessment (HOMA-IR) Z-scores, triglyceride Z-scores, total cholesterol Z-scores, LDL-C Z-scores, and HDL-C Z-scores were analyzed between groups.

[0049] The definitions and calculations of offspring BMI and HOMA-IR are the same as previously reported. The formula for calculating the age-sex adjusted Z-score (every 6 months) is: (measured value − age − sex mean) / age − sex standard deviation. The data are from all offspring conceived through ART at our center.

[0050] Statistical analysis was performed using R 3.6.3 software. For baseline characteristics, normally distributed continuous variables were expressed as mean ± standard deviation, and comparisons between groups were performed using one-way ANOVA and t-tests. Non-normally distributed continuous variables were expressed as median [interquartile range (IQR)], and comparisons between groups were performed using the Wilcoxon rank-sum test. Categorical variables were expressed as frequency (percentage), and comparisons between groups were performed using the χ² test.

[0051] This study employed a repeated measures design using a linear mixed model. The random effect consisted of a unique offspring number, while fixed effects included ICSI treatment, paternal BMI, paternal age, maternal age, maternal BMI, parity, multiple pregnancy, parental smoking, frozen embryo transfer, and offspring age. Interaction p-values ​​were calculated, with p < 0.05 considered statistically significant. The residual normality, homogeneity of variance, and observational independence of the linear mixed model were verified using residual histograms, residual fit plots, and residual autocorrelation plots. The power analysis of the linear mixed model was performed using the R package "simr". Based on Monte Carlo simulation, this study could detect the absolute effect size of a fasting blood glucose Z-score of 0.17 between the IVF and ICSI groups, with a power of 88% at a significance level of 0.05. Post-hoc analysis between groups calculated 95% confidence intervals (CIs) using Bonferroni correction (p = 0.05 / n), with p < 0.025 considered statistically significant.

[0052] A total of 14,196 visits were conducted to offspring conceived through IVF / ICSI. Of these, 9,642 (67.92%) had overweight / obese fathers, and 4,554 (32.08%) had fathers of normal weight. Compared to IVF offspring, ICSI offspring had significantly lower paternal age, paternal BMI, maternal age, maternal BMI, and parental smoking rates, and a significantly higher proportion of first pregnancies. Furthermore, the ICSI group had significantly higher oocyte retrieval numbers, higher number of high-quality D3 embryos, and a higher frozen embryo transfer rate, with significantly lower total gonadotropin doses. The mean age of the offspring was 2.35 ± 1.94 years.

[0053] First, we analyzed the association between ICSI treatment and metabolic changes in offspring. After adjusting for paternal age, maternal age, maternal BMI, parity, multiple pregnancy, parental smoking, frozen embryo transfer, and offspring age, there were no significant differences in BMI Z-score, waist-to-height ratio Z-score, and systolic / diastolic blood pressure Z-score between ICSI and IVF offspring aged 0–1 year, 2–3 years, and 4–11 years. Interestingly, in offspring aged 4–11 years, the ICSI group had significantly lower fasting blood glucose Z-score, total cholesterol Z-score, and LDL-C Z-score than the IVF group (fasting blood glucose Z-score: adjusted mean difference −0.13, 95% CI: −0.23 to −0.03; total cholesterol Z-score: adjusted mean difference −0.13, 95% CI: −0.23 to −0.02; LDL-C Z-score: adjusted mean difference −0.12, 95% CI: −0.22 to −0.01). There were no significant differences in triglyceride Z-scores and HDL-CZ scores between the two groups.

[0054] To determine whether the average blood lipid levels were within the normal range for children of this age, age-specific total cholesterol, LDL-C, and HDL-C levels were assessed in children undergoing ICSI and IVF, with mmol / L and mg / dL units provided for comparison with previous studies. Results showed that children undergoing ICSI had lower total cholesterol and LDL-C levels than children undergoing IVF, and the difference gradually increased with age. Singleton analysis showed that offspring of ICSI had significantly lower fasting blood glucose Z-scores and triglyceride Z-scores than offspring undergoing IVF (fasting blood glucose Z-score: adjusted mean difference −0.19, 95% CI: −0.30 to −0.07; triglyceride Z-score: adjusted mean difference −0.12, 95% CI: −0.23 to −0.01).

[0055] This study further explored the potential mediating role of paternal BMI on metabolic changes in offspring conceived through ICSI. Among offspring whose fathers were overweight / obese, 3145 pregnancies (32.75%) were conceived via ICSI. In offspring aged 4–11 years with overweight / obese fathers, the fasting blood glucose Z-score and total cholesterol Z-score were significantly lower in the ICSI group than in the IVF group (fasting blood glucose Z-score: adjusted mean difference −0.20, 95% CI: −0.32 to −0.08; total cholesterol Z-score: adjusted mean difference −0.15, 95% CI: −0.27 to −0.02). The fasting blood glucose Z-score results were consistent in singleton offspring (adjusted mean difference −0.24, 95% CI: −0.38 to −0.10).

[0056] Of the offspring whose fathers had normal weight, 1603 pregnancies (35.19%) were conceived via ICSI. In offspring aged 4–11 years, the systolic blood pressure Z-score was significantly lower in the ICSI group than in the IVF group (adjusted mean difference −0.21, 95% CI: −0.37 to −0.05). Results were similar in singleton offspring (adjusted mean difference −0.25, 95% CI: −0.45 to −0.05).

[0057] Given the potential epigenetic differences between traditional ICSI and PESA / TESA-ICSI, this study analyzed the association between paternal overweight / obesity, ICSI method, and metabolic changes in offspring. A total of 837 offspring visits following PESA / TESA-ICSI were included. In offspring with paternal overweight / obesity, the HDL-C Z-score was significantly lower in the PESA / TESA-ICSI group than in the traditional ICSI group (adjusted mean difference −0.30, 95% CI: −0.56 to −0.03). In both the paternal overweight / obesity and normal weight groups, there were no significant differences in other metabolic parameters between offspring of PESA / TESA-ICSI and traditional ICSI.

[0058] This study found that ICSI treatment is associated with metabolic changes in male offspring, manifested as lower fasting blood glucose, total cholesterol, and LDL-C levels. Maternal overweight / obesity significantly mediated this association. Offspring of overweight / obese fathers who underwent ICSI had lower fasting blood glucose, total cholesterol, and LDL-C levels than offspring who underwent IVF; while offspring of fathers with normal weight who underwent ICSI had lower systolic blood pressure than offspring who underwent IVF. This study fills a current research gap by exploring the metabolic effects of ICSI.

[0059] Experimental Example 2:

[0060] This study included 2,741 singleton offspring aged 4 to 11 years, all of whom conceived through in vitro fertilization / intracytoplasmic sperm injection (ICSI). Exclusion criteria included: mothers with low pre-pregnancy weight, those who did not undergo embryo transfer (artificial insemination with husband's sperm / donor sperm, intrauterine transfer, ovulation induction), those who received donor eggs, and those whose parents' anthropometric data were incomplete. The offspring were further divided into a fresh embryo transfer group and a frozen embryo transfer group.

[0061] Based on Chinese adult standards, mothers' pre-pregnancy BMI is divided into: overweight / obese group: BMI≥24kg / m²; normal weight group: 18.5~23.9kg / m².

[0062] We collected information on the parents' preconception characteristics and ART procedures from their ART medical records. Embryos in the frozen group underwent vitrification.

[0063] Offspring measurements: Height (accurate to 0.1cm), weight (accurate to 0.1kg): measured twice with an automatic electronic scale, wearing light clothing; waist circumference: measured twice manually with a soft measuring tape; blood pressure: measured twice with an electronic blood pressure monitor on the right arm while seated, and the average value was taken.

[0064] Offspring were sampled overnight on an empty stomach and tested within 24 hours: fasting insulin: electrochemiluminescence immunoassay; fasting blood glucose: hexokinase method; triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c): homogeneous detection method.

[0065] Offspring dietary patterns were assessed using a food frequency questionnaire, referencing the "Chinese Dietary Guidelines (2016)". Daily dietary energy intake was estimated using Nutrition Star software 1.0.

[0066] The age- and sex-standardized Z-scores of cardiometabolic indicators were used as the outcome, including growth indicators, systolic / diastolic blood pressure, blood glucose, insulin, lipid profile, and metabolic comprehensive score.

[0067] Z-score calculation formula: (measured value - mean of the same age and gender) / standard deviation of the same age and gender.

[0068] BMI = weight (kg) / height (m)²; waist-to-height ratio = waist circumference (cm) / height (cm); Homeostasis Model for Insulin Resistance (HOMA-IR) = fasting blood glucose (mmol / L) × fasting insulin (mIU / L) / 22.5.

[0069] Metabolic Comprehensive Score Definition:

[0070] Metabolic Score MSS-1 = BMI + Systolic Blood Pressure + Insulin + Triglycerides - High-Density Lipoprotein Cholesterol (HDL-c).

[0071] Metabolic Score MSS-2 = waist-to-height ratio + systolic blood pressure + blood glucose + triglycerides - high-density lipoprotein cholesterol (HDL-c).

[0072] R3.6.3 software was used for analysis. The χ² test was used for categorical variables, the t-test for normally distributed continuous variables, and the Mann-Whitney U test for non-normally distributed continuous variables.

[0073] We used uncorrected and corrected mixed linear models to compare the effects of embryo freezing on maternal overweight / obesity versus normal weight. Interaction p-values ​​were calculated, with p < 0.05 considered statistically significant.

[0074] The adjusted model included: father's age, mother's age, father's BMI, mother's BMI, offspring's age, sex, and postnatal energy intake. Differences between groups were assessed post-hoc, with estimated mean differences and 95% confidence intervals (CIs) calculated. A Bonferroni-adjusted P < 0.025 was considered statistically significant.

[0075] Of the 2741 offspring, 965 (35.21%) were overweight / obese mothers, of whom 396 (41.04%) conceived via frozen embryo transfer. The mean BMI for overweight / obese mothers was 26.8 ± 2.55 kg / m², while for normal-weight mothers it was 21.5 ± 1.47 kg / m². In both the overweight / obese and normal-weight groups, the parents of offspring conceived via frozen embryo transfer were younger than those in the fresh embryo transfer group. 1046 (38.16%) offspring conceived via blastocyst transfer (P<0.01). In the normal-weight mother group, the risk of gestational hypertension was significantly higher in offspring conceived via frozen embryo transfer than in the fresh embryo transfer group; no significant difference was observed in the overweight / obese mother group. The median age of the offspring was 5 years, with males comprising 52.5%, and most were in Tanner stage I. In the overweight / obese mother group, the Tanner breast stage of offspring conceived via frozen embryo transfer was significantly lower than that in the fresh embryo transfer group (P<0.01).

[0076] After adjusting for parental age, father's BMI, offspring age and sex, maternal BMI and embryo transfer strategy showed a significant multiplicative interaction on offspring BMIz score, systolic blood pressure z score, LDL-cz score, metabolic comprehensive score 1, and metabolic comprehensive score 2.

[0077] In the overweight / obese mother group, offspring of frozen embryo transfer had significantly higher BMI Z-scores, systolic blood pressure Z-scores, metabolic comprehensive score MSS-1, and metabolic comprehensive score MSS-2 than those of fresh embryo transfer. In the normal-weight mother group, offspring of frozen embryo transfer had significantly lower diastolic blood pressure Z-scores, fasting blood glucose Z-scores, and LDL-c Z-scores than those of fresh embryo transfer.

[0078] Even after additional correction for postnatal energy intake in offspring, a significant interaction between maternal BMI and embryo transfer strategy remained in the metabolic comprehensive score (MSS-1 and MSS-2). In the overweight / obese mother group, offspring from frozen embryo transfer showed significantly higher scores on these indicators; in the normal-weight mother group, offspring from frozen embryo transfer had lower LDL-cZ scores.

[0079] The offspring were divided into two subgroups based on their follow-up age: 4–5 years and 6–11 years.

[0080] 4–5 years old: After adjusting for multiple confounding factors, there was no statistically significant difference in cardiac metabolic parameters between the two groups;

[0081] 6–11 years old: In the overweight / obese mother group, offspring of frozen embryo transfer had significantly higher BMI z-scores, systolic blood pressure z-scores, and metabolic comprehensive scores 1 and 2; there were no significant differences in the normal weight mother group.

[0082] Sensitivity analysis (women with 4-15 oocytes retrieved and optimal ovarian response): In the overweight / obese mother group, differences in cardiometabolic parameters between offspring of frozen and fresh embryo transfer still existed; in the normal weight mother group, offspring of frozen embryo transfer had lower diastolic blood pressure Z-scores and LDL-c Z-scores.

[0083] Sensitivity analysis of the cleavage stage / blastocyst transfer subgroup:

[0084] Cleavage-stage transfer: In the overweight / obese mother group, offspring with frozen embryos had significantly higher levels of multiple indicators; in the normal-weight mother group, only the diastolic blood pressure z-score was higher.

[0085] Blastocyst transfer: In the overweight / obese mother group, offspring with frozen embryos had higher triglyceride Z-scores and metabolic comprehensive scores (MSS-1 and 2), and lower HDL-c Z-scores.

[0086] The association between maternal pre-pregnancy BMI-modified frozen embryo transfer and cardiometabolic changes in offspring was investigated. Adverse cardiometabolic changes in offspring only occurred when there was simultaneous exposure to frozen embryo transfer and maternal pre-pregnancy overweight / obesity.

[0087] Experiment Example 3:

[0088] A total of 23,008 offspring conceived through assisted reproductive technology were selected. Inclusion criteria included singleton births and children aged 4–10 years. Ultimately, 2,047 singleton offspring were included, with a total of 2,890 medical visits. Of these, 1,304 had only one medical visit, 650 had two, 86 had three, and 7 had four. Participants with incomplete body mass index (BMI) information were excluded. All data were analyzed based on anthropometric parameters of the children's biological parents.

[0089] Body mass index (BMI) was classified according to Chinese adult standards as follows: obese (BMI ≥ 28 kg / m²), overweight (24–27.9 kg / m²), normal weight (18.5–23.9 kg / m²), and underweight (< 18.5 kg / m²). The normal-weight fathers were used as a reference, while the obese, overweight, and underweight fathers were considered the exposure group.

[0090] Maternal and perinatal factors that may influence the association between paternal obesity and offspring cardiometabolic health were included as potential confounding variables, including paternal age, maternal age, maternal body mass index, parity, sex of offspring, infertility factors, type of assisted reproductive technology, parental education level, and parental smoking status.

[0091] Children were divided into high-energy intake groups based on the median age (per year) and sex, with the upper 50 percentile being the high-energy intake group and the lower 50 percentile being the low-energy intake group. Within each energy intake group, children were further divided into obese, overweight, normal-weight, and underweight subgroups based on their father's body mass index (BMI).

[0092] Nurses collect parental characteristics and health data from assisted reproductive technology medical records. Before infertility treatment, nurses record the father's and mother's age, body mass index, parents' education level, parity, infertility factors, type of assisted reproductive technology, and parents' smoking status.

[0093] Pediatricians and nurses performed physical examinations on children at each visit, measuring height (accurate to 0.1 cm), weight (accurate to 0.1 kg), and chest / waist circumference twice and recording the average values. Blood pressure was measured using the same method as previous studies: blood pressure was measured in the right arm while the child was seated, for a total of 3 measurements, and the average of the last two measurements was recorded.

[0094] Venous blood was collected overnight after fasting. All blood samples were processed according to standard procedures: centrifugation immediately after collection, temporary storage at 4°C, and delivery to the biobank within 24 hours for storage at -80°C until testing. The methods for measuring fasting blood glucose, fasting insulin, triglycerides, total cholesterol, LDL cholesterol, and HDL cholesterol levels in children were the same as in previous studies.

[0095] Parents are required to complete a 7-day dietary review questionnaire for their child at each visit. The questionnaire is based on the "Chinese Dietary Guidelines (2016)" and records the child's intake and frequency of grains, fish, meat, dairy products, eggs, vegetables, and fruits within a week. The average daily dietary energy intake is calculated using Nutrition Star software 1.0 (Shanghai Zhending Company).

[0096] Given the difficulty in applying a single "normal range" to children's growth and development, this study calculated age- and sex-adjusted Z-scores to compare cardiometabolic changes in offspring from different fathers' body mass index (BMI) groups. The Z-score calculation formula was: (measured value - mean for age and sex) / standard deviation for age and sex, based on the total population of offspring conceived through assisted reproductive technology aged 4–10 years at our center (N=3722), while also referencing the World Health Organization's Child Growth Standards (2006 edition). The definitions of BMI, waist-to-height ratio, and insulin resistance homeostasis model assessment were the same as in previous studies.

[0097] To reduce non-response bias, multiple imputation was performed using predicted mean matching. Missing variables included offspring height (n=6), weight (n=6), waist circumference (n=8), systolic blood pressure (n=14), diastolic blood pressure (n=14), blood glucose (n=67), insulin (n=70), triglycerides (n=61), total cholesterol (n=63), LDL cholesterol (n=61), and HDL cholesterol (n=61). Data were analyzed using R3.6.3 software with the "Mice" package.

[0098] Data analysis was performed using R3.6.3 software. Normally distributed continuous data were expressed as mean ± standard deviation, and one-way ANOVA was used for comparisons between groups. Non-normally distributed continuous data were expressed as median (interquartile range). Count data were expressed as frequency (percentage), and chi-square test or Fisher's exact test was used for comparisons between groups.

[0099] This study employed a repeated measures design and a linear mixed model analysis. The random effects were unique offspring numbers, while the fixed effects included father's body mass index status (categorical variable), offspring age, father's age, mother's age, mother's body mass index, parity, offspring sex, parents' education level, and parents' smoking status.

[0100] This study included 2047 offspring, of whom 1574 had only one visit and 743 had repeated visits. Reasons for loss to follow-up included: not reaching the age for follow-up after the initial visit, loss of contact with parents, refusal to follow up, or dropping out of the cohort. Based on the random missing data assumption, the linear mixture model automatically handled missing data for the outcome variable.

[0101] Simultaneously, the interaction between paternal obesity and offspring sex and energy intake was analyzed to assess the effects on cardiometabolic changes. Post-hoc comparisons were made between obese and non-obese offspring, and the mean difference and 95% confidence interval were calculated. Bonferroni correction was used to control for Type I error (α=0.05 / n).

[0102] The number of fathers observed in the obese, overweight, normal weight, and underweight groups were 506 (17.51%), 1056 (36.54%), 1272 (44.0%), and 56 (1.9%), respectively. The fathers' age gradually decreased from the obese group to the underweight group. The mothers in the obese and overweight groups had higher age and body mass index than the normal weight group. There were no significant differences in female infertility factors (polycystic ovary syndrome, fallopian tube peritoneal factors, and uterine factors) among the four groups. The proportion of male infertility factors gradually increased from the obese group to the underweight group, possibly related to the fact that most sperm donors were normal / underweight. There were no significant differences in the types of assisted reproductive technologies used between the husband's sperm group and the donor sperm group.

[0103] There were no significant differences in the prevalence of gestational diabetes, gestational hypertension, and weight gain during pregnancy among the four maternal groups. The number of offspring living with their biological fathers was 497 (98.2%) for obese fathers, 991 (93.8%) for overweight fathers, 840 (66.0%) for normal-weight fathers, and 29 (51.8%) for underweight fathers. The mean age of the offspring was 6.15 ± 1.47 years, and 1471 (49.39%) were male.

[0104] The father's body mass index (BMI) was significantly correlated with offspring's BMI, waist-to-height ratio, systolic blood pressure, diastolic blood pressure, fasting insulin, and insulin resistance homeostasis model assessment, among other cardiometabolic indicators (P<0.01). For every 1 unit increase in the father's BMI, the offspring's BMI increased by 0.16 units.

[0105] After multivariate adjustment, compared with the normal-weight group of fathers, the mean difference in BMIZ score among offspring in the obese group was 0.53 (95% confidence interval: 0.37–0.68), 0.17 (95% confidence interval: 0.05–0.30) for the overweight group, and -0.55 (95% confidence interval: -0.95–-0.15) for the underweight group; the corresponding systolic blood pressure Z-score was 0.21 (95% confidence interval: 0.07–0.68). 35), 0.10 (95% confidence interval: -0.01 to 0.21), -0.24 (95% confidence interval: -0.59 to 0.11); the corresponding Z-scores for the insulin resistance homeostasis model assessment were 0.31 (95% confidence interval: 0.16 to 0.46), 0.09 (95% confidence interval: -0.02 to 0.21), and -0.11 (95% confidence interval: -0.48 to 0.28). Using the World Health Organization's child growth standards as a reference, the association between paternal obesity and offspring BMI Z-scores was consistent. There were no significant differences in blood lipid indicators among the four groups.

[0106] Mediation analysis showed that offspring BMI mediated a 57.48%–94.75% association between paternal body mass index and offspring cardiometabolic outcomes such as systolic blood pressure Z-score, insulin Z-score, and insulin resistance homeostasis model assessment Z-score.

[0107] Subgroup analysis of offspring who did not live with their biological fathers (N=533) showed that among offspring whose biological fathers were overweight / obese, those living with overweight / obese fathers had significantly higher triglyceride Z-scores than those living with normal / low-weight fathers (mean difference: 0.71, 95% confidence interval: 0.23–1.19); there were no statistically significant differences in other metabolic indicators.

[0108] Fatherly obesity was significantly associated with BMI and glucose metabolism indicators in both male and female offspring. Compared with daughters of fathers with normal weight, daughters of obese fathers had significantly higher BMI Z-scores (mean difference: 0.59, 95% confidence interval: 0.38–0.81), waist-to-height ratio Z-scores (mean difference: 0.51, 95% confidence interval: 0.29–0.72), systolic blood pressure Z-scores (mean difference: 0.34, 95% confidence interval: 0.15–0.54), diastolic blood pressure Z-scores (mean difference: 0.27, 95% confidence interval: 0.07–0.46), fasting insulin Z-scores (mean difference: 0.29, 95% confidence interval: 0.09–0.50), and insulin resistance homeostasis model assessment Z-scores (mean difference: 0.28, 95% confidence interval: 0.08–0.49). The sons of obese fathers had significantly higher BMI Z-scores (mean difference: 0.49, 95% confidence interval: 0.28–0.70), waist-to-height ratio Z-scores (mean difference: 0.37, 95% confidence interval: 0.15–0.58), fasting insulin Z-scores (mean difference: 0.33, 95% confidence interval: 0.13–0.53), and insulin resistance homeostasis model assessment Z-scores (mean difference: 0.33, 95% confidence interval: 0.13–0.54) than the sons of fathers with normal weight.

[0109] In both the high and low energy intake subgroups, the BMI Z-score and waist-to-height ratio Z-score of offspring from obese fathers were significantly higher than those of offspring from fathers of normal weight (P<0.004, Bonferroni corrected). In the high energy intake subgroup, the systolic and diastolic blood pressure Z-scores of daughters from obese fathers were significantly higher than those of daughters from fathers of normal weight (P<0.004, Bonferroni corrected). The association between glucose and lipid parameters between the offspring of obese and normal-weight fathers was unclear in either the high or low energy intake subgroups.

[0110] The above experiments suggest that paternal obesity is associated with poor cardiometabolic phenotypes in offspring conceived through assisted reproductive technology. Offspring BMI is a potential mediating factor in the association between paternal obesity and metabolic abnormalities in offspring.

[0111] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the metabolic risk of offspring based on parental phenotypes and assisted reproductive technologies, characterized in that, Includes the following steps: Data collection: Before infertile couples undergo ART treatment, collect and record the age, height, and weight of both parents, calculate their pre-pregnancy BMI, and record the ART fertilization method and embryo transfer strategy used. Offspring follow-up and testing: After the offspring are born, at specific age ranges, their height, weight, waist circumference, and blood pressure are measured and recorded. Fasting blood samples are also drawn to test biochemical indicators including fasting blood glucose, fasting insulin, triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Data standardization and modeling: The absolute values ​​of the biochemical indicators measured above are input into the calculation module. Based on the total follow-up population data or WHO child growth standards, the Z-scores and metabolic comprehensive scores of each biochemical indicator are calculated. A linear mixed model is used to adjust and control confounding factors. Risk assessment and intervention output: Based on the model comparison results, risk warnings are output.

2. The method for predicting the metabolic risk of offspring based on the parent's constitution and assisted reproductive technology according to claim 1, characterized in that, The BMI range standards for both parents include: the standard for normal weight for the father is 18.5 kg / m². 2 ≤BMI≤23.9kg / m 2 The criteria for determining whether a mother or father is overweight is a BMI ≥ 24 kg / m². 2 The criteria for determining obesity in fathers is a BMI ≥ 28 kg / m². 2 The specific age range for the offspring detection records is singleton children aged 4-10 / 11 years.

3. The method for predicting the metabolic risk of offspring based on the parent's constitution and assisted reproductive technology according to claim 1, characterized in that, The Z-score formula is: Z-score = (observed value - mean for specific age and gender) / standard deviation for specific age and gender.

4. The method for predicting the metabolic risk of offspring based on the parent's constitution and assisted reproductive technology according to claim 1, characterized in that, The metabolic comprehensive score includes scoring model MSS-1 and scoring model MSS-2. The calculation formula for scoring model MSS-1 is: MSS-1 = BMI + systolic blood pressure + insulin + triglycerides - high-density lipoprotein cholesterol. The calculation formula for scoring model MSS-2 is: MSS-2 = waist circumference to height ratio + systolic blood pressure + blood glucose + triglycerides - high-density lipoprotein cholesterol.

5. The method for predicting the metabolic risk of offspring based on the parent's constitution and assisted reproductive technology according to claim 1, characterized in that, The confounding factors include parental age, parental smoking history, offspring sex, and postpartum energy intake.

6. The method for predicting the metabolic risk of offspring based on the parent's constitution and assisted reproductive technology according to claim 1, characterized in that, The risk warning assessment rules include: For ICSI technology, when conception is achieved via ICSI and the father is overweight / obese (BMI ≥ 24.0 kg / m²), 2 At that time, the prediction system indicated that the offspring had significantly lower fasting blood glucose and total cholesterol levels, suggesting potential neurodevelopmental-related biochemical risks, and recommended that the father lose weight before pregnancy; For frozen embryo transfer FET, the system determines the mother's pre-pregnancy BMI, if the mother's BMI ≥ 24.0 kg / m 2 , and adopts frozen embryo transfer, the system outputs high cardiovascular metabolic risk warning, because the offspring will have higher BMI Z score, systolic blood pressure Z score and higher metabolic syndrome score; For the intergenerational effect of father's obesity, the system confirms through the mediation effect analysis that when BMI ≥ 28.0 kg / m 2 2, that is, father's obesity will directly lead to significant increase of child's BMI, systolic blood pressure and insulin resistance, and the system should output the guidance: control the early energy intake of the child and strictly control the child's BMI.

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