Current neonatal congenital heart disease (CHD) screening strategies face significant challenges in low-income or underdeveloped regions due to a shortage of experienced physicians. Therefore, we aim to develop a cost-effective method to identify high-risk populations, supplementing neonatal screening. A multicenter case-control study was conducted in three hospitals in Zhejiang, China, from September 2022 to July 2024. Pregnant women were surveyed using a self-designed questionnaire, and newborns were diagnosed using echocardiography. We utilized three steps to establish a prediction model for neonatal CHD. Initially, LASSO regression was used to screen variables. Subsequently, five representative machine learning (ML) algorithms, including SVM, XGBoost, Random Forest (RF), Logistic Regression (LR) and LightGBM, were applied to establish the CHD risk prediction models. Finally, the Shapley Additive exPlanation (SHAP) method and logistic regression were adopted to identify key risk factors for CHD. A total of 1,633 mother-infant pairs were included, with 437 infants diagnosed with CHD. Among the five machine learning models, XGBoost showed the best performance, with an AUC of 0.724 on the internal test set and 0.706 on the external validation cohort. The internal AUCs for RF, SVM, LightGBM, and LR were 0.673, 0.647, 0.672, and 0.649, respectively. The key risk factors for CHD were identified as COVID-19 infection within the three months prior to the last menstrual period, gestational medication use and viral infections during pregnancy. The odds ratios (ORs) with 95

Background Gestational age at birth is shaped by complex maternal and paternal factors prior to conception, yet few studies have integrated multidimensional clinical indicators from both parents to estimate their quantitative effects.Methods Using a large retrospective cohort with routinely collected preconception health data from both mothers and fathers, we applied a double machine learning (DML) framework combining Lasso, Random Forest and XGBoost regressors to estimate associations between parental biomarkers and gestational age at birth. Model interpretability was enhanced through Shapley Additive Explanations (SHAP) analysis, stratified interaction testing and estimation of individualized treatment effects (ITE).Results Elevated maternal fasting glucose, alanine aminotransferase, platelet count and anti-hepatitis B core seropositivity were consistently associated with shortened gestational age at birth across all DML models. For instance, a 10 mmol/L increase in fasting glucose corresponded to 2.8-4.9 days shorter gestation. Paternal Treponema pallidum seropositivity and increased monocyte proportion, defined as the fraction of monocytes within total circulating white blood cells, also demonstrated significant associations, with the former linked to gestational shortening and the latter to modest extension. Stratified and interaction analyses revealed that paternal immune markers modified the associations of maternal metabolic indicators with gestational age at birth. SHAP-based interpretation confirmed model consistency and ITE analysis indicated marked heterogeneity, particularly for fasting glucose.Conclusions This study demonstrates the utility of interpretable DML methods for quantifying the effects of multidimensional preconception health indicators from both parents on gestational age at birth. Our findings support integrating maternal and paternal screening into preconception risk assessments to enable early, targeted prevention of preterm birth. Given the established links between preterm birth and neonatal surgical conditions, early risk identification via parental screening may help inform perinatal care strategies and optimize resource allocation in pediatric surgery.

Congenital heart disease (CHD) is the most prevalent birth defect. Ozone and heatwave exposure during pregnancy could increase the risk of adverse birth outcomes. Green space might be associated with beneficial birth outcomes. The research on the combined effects of those exposures on CHD is limited. Therefore, we conducted a multicenter case–control study based on a surveillance system in Zhejiang Province, China, to explore the effect of ozone, heatwave, and green space exposure during early pregnancy on CHD and their interaction. The inverse distance weighting method and normalized difference vegetation index were applied to assess maternal ozone and green space exposure, respectively. The heatwave definition is from the National Oceanic and Atmospheric Administration. Our study reveals positive associations of heatwave and ozone exposure with CHD (ozone: OR = 1.07, 95% CI: 1.02, 1.13; heatwave: OR = 1.29, 95% CI: 1.18, 1.40), and green space in different buffers around residence exerted protective effects on CHD, with ORs ranging from 0.93 to 0.94. Associations between ozone and CHD were weakened among participants with higher NDVI. Ozone’s effects on CHD were stronger with the increased duration of heatwave exposure. Our study indicates that ozone and heatwave exposure could increase the risk of CHD, and high green space is a protective factor for CHD. Meanwhile, high green space exposure could attenuate the effect of ozone on CHD, but heatwave exposure strengthened it.

Background A good prognosis of congenital heart disease (CHD) depends on early diagnosis and intervention. Under the current screening conditions, a significant proportion still go undetected. Metabolomics, as a phenotype‐correlated research methodology, remains underused in the study of CHD, which could provide the possibility to screen neonatal CHD efficiently. Methods Data for the analysis are from >22 000 neonates captured in the Network Platform for CHD from April 2020 to November 2021 in 11 cities in China. After data matching and quality control, a total of 22 674 neonates were finally included and divided into the CHD group (n=1823), nonsignificant CHD group (n=17 968), and normal group (n=2748). Demographic and clinical characteristics and tandem mass spectrometry‐based metabolic data for genetic and metabolic disease screening were gathered and compared for all groups. Machine learning models based on metabolic biomarkers were constructed to screen CHD in neonates. Results After quality control, 22 539 neonates were ultimately included. Among them, 1823 were diagnosed with CHD, 17 968 were nonsignificant CHD, and 2748 were normal. A total of 46 distinguishing metabolic biomarkers were identified, and we found that the CHD group had significantly lower levels of 17‐hydroxyprogesterone (CHD versus nonsignificant CHD, P<0.001, log2 fold change=−0.16; CHD versus normal, P<0.001, log2 fold change=−0.15). We constructed CHD and ventricular septal disease screening models based on metabolic biomarkers. The best fitting model achieved an area under the receiver operating characteristic curve of 0.745 (95% CI, 0.696–0.791). Conclusions This study reveals the unique metabolic profile of neonates with CHD. The screening model demonstrates considerable potential in early neonatal CHD screening and reflects significant value from a health economics perspective.

AIM:Ozone is a photochemical pollutant, with exposure levels typically peaking during warm seasons in urban areas with intense sunlight and high traffic emissions. The rising annual average tropospheric ozone concentration risks children's health. This paper provided an in-depth overview of research on ambient ozone exposure and child health. METHODS:A meticulous search was conducted within the PubMed database from 1 January 1964 to 4 October 2024 to identify epidemiological studies on the association between ambient ozone exposure and children's health. In addition, this study innovatively compiled the average ozone exposure levels from each epidemiological investigation. RESULTS:In total, 85 papers were included. Ambient ozone exposure's effects remain controversial in most diseases. Consistent evidence exists that ambient ozone exposure is a risk factor for low birth weight, asthma, respiratory disease and obesity in children, even if ozone concentrations are below the World Health Organization (WHO) standard. Both long-term ozone exposure and short-term spikes in ozone concentrations can harm children's health. CONCLUSION:Current ambient ozone air quality standards inadequately protect children's health and urgently require revision. Further research is needed to furnish evidence concerning the contentious aspects of research and to offer guidance to child health practitioners and public health policymakers.

Background: Congenital heart disease (CHD) is the most common congenital anomaly, but whether the COVID-19 pandemic affects its prevalence is unknown. We aimed to compare the incidence of CHD during the COVID-19 pandemic with that before the pandemic in China. Methods: This multicenter retrospective observational study involved all newborns in seven representative cities of China between 01 September 2019, and 31 December 2021. All the newborns underwent pulse oximetry monitoring combined with cardiac murmur auscultation in the first 6 h to 72 h after birth for CHD screening. We defined fetuses born in and beyond September 2020 as the exposed group, and before as the non-exposed group. The incidence of CHD and specific heart abnormalities, including atrial septal defect (ASD) and ventricular septal defect (VSD), before and during the COVID-19 pandemic were compared. Results: The study included 492,662 newborns; 217,003 newborns born before September 2020 and 275,659 newborns born in and beyond September 2020. There were 3115 patients with CHD in total during the whole study period. Of those, 1055 (September 2019 to August 2020) and 2060 (September 2020 to December 2021) were less and more affected by the pandemic, respectively. There was a significant increase in the incidence of CHD in the early stage of the COVID-19 pandemic (7.78 per 1000 births) compared to that before the pandemic (4.86 per 1000 births) (p < 0.001). The birth prevalence of ASD and VSD significantly increased during the pandemic from 3.991 per 1000 births to 4.717 per 1000 births (p = 0.008) and from 1.650 per 1000 births to 3.508 per 1000 births (p < 0.001), respectively. Conclusions: The incidence of CHD increased during the COVID-19 pandemic, which was possibly related to the reallocation of medical resources, increased psychological pressure, and increased socioeconomic deprivation, though underlying mechanisms remain unclear.

While prenatal PM2.5 exposure constitutes an established risk factor for congenital heart defects (CHDs), the modifying role of greenness exposure in this association remains underexplored. We conducted a retrospective cohort study analyzed 1,356,420 birth records (11,803 CHD cases) from Zhejiang Province, China (2018-2023). Prenatal exposure to PM2.5 and its major constituents was estimated using satellite-derived speciation models, and greenness was assessed via the normalized difference vegetation index (NDVI). Generalized additive models (GAMs) with a quasibinomial logit link and restricted maximum likelihood were used to model non-linear associations and interactions. Mixture effects and joint exposure-response surfaces were estimated using fast Bayesian kernel machine regression (fbKMR). Causal mediation analysis under a counterfactual framework was used to assess indirect effects of PM2.5 in the greenness-CHD relationship. We identified predominantly J-shaped exposure-response relationships between key PM2.5 constituents and CHD risk, with organic matter (OM) and black carbon (BC) exhibiting the steepest risk increases. Mixture modeling via fbKMR revealed a monotonic increase in CHD risk with joint pollutant exposure. NDVI showed a robust U-shaped association with CHDs, with lowest risk at moderate greenness. Seasonal analysis highlighted strong BC and OM effects in autumn and winter, sulfate in spring and winter, and a threshold pattern for ammonium in summer. Spatial heterogeneity was evident, with eastern coastal cities (e.g., Ningbo, Jiaxing) showing pronounced risk increases above 35 μg/m3. Septal-type CHDs exhibited consistent positive associations with PM2.5, while complex subtypes showed weaker patterns. Mediation analysis indicated that PM2.5 accounted for 5.6-15.7 % of the greenness-CHD association, with BC showing the strongest mediation effect. Our findings underscore the cumulative toxicity of PM2.5 mixtures and the protective yet nonlinear role of greenness. Region- and season-specific strategies that integrate pollution control and green infrastructure may help mitigate CHD risk.

Congenital heart disease (CHD) is a common birth defect in children. Intelligent auscultation algorithms have been proven to reduce the subjectivity of diagnoses and alleviate the workload of doctors. However, the development of this algorithm has been limited by the lack of reliable, standardized, and publicly available pediatric heart sound databases. Therefore, the objective of this research is to develop a large-scale, high-standard, high-quality, and accurately labeled pediatric CHD heart sound database. Method: From 2020 to 2022, we collaborated with experienced cardiac surgeons from three general children's hospitals to collect heart sound signals from 1259 participants using electronic stethoscopes. To ensure the accuracy of the labels, the labels for all data were confirmed by two cardiac experts. To establish the baseline of ZCHsound, we extracted 84 features and used machine learning models to evaluate the performance of the classification task. Results: The ZCHSound database was divided into two datasets: one is a high-quality, filtered clean heart sound dataset, and the other is a low-quality, noisy heart sound dataset. In the evaluation of the high-quality dataset, our random forest ensemble model achieved an F1 score of 90.3% in the classification task of normal and pathological heart sounds. Conclusion: This study has successfully established a large-scale, high-quality, rigorously standardized pediatric CHD sound database with precise disease diagnosis. This database not only provides important learning resources for clinical doctors in auscultation knowledge but also offers valuable data support for algorithm engineers in developing intelligent auscultation algorithms.

BACKGROUND AND AIM: The few studies that examined the association between PM2.5 constituents and congenital heart diseases, and the role of residential greenness played between them. We examined the mediation and interaction effects of green space on the association between fine particulate matter (PM2.5) and its constituents during early pregnancy (gestational week 3rd to week 8th) and postnatal CHDs. METHOD: We executed a register system-based case-control study on 7904 singleton live births between 2019 and 2022 in China. In present study, individual PM2.5 exposure is estimated by ordinary kriging interpolation method, and residential green space index is evaluated using normalized difference vegetation index (NDVI). We applied multiple regression model to estimate the impact of PM2.5 associated with CHDs, and further conducted causal mediation analysis to estimate the mediating effects of green space on PM2.5 related CHDs. Additionally, we speculated the potential multiplicative interaction between PM2.5 and greenness exposure and its influence on CHD. RESULTS: Maternal PM2.5 exposure during early pregnancy was related with increased risks for CHDs while the impact for residential NDVI was the opposite which had OR of 1.046 (95% CI:1.040-1.051), and OR of 0.966 (95% CI:0.963-0.967), respectively, per inter quantile range increment in multivariate models. The causal mediation analysis indicated that greenness mediated generally 5.44% of the correlationship between prenatal PM2.5 exposure and CHD. We identified multiplicative interactions between maternal exposure to NDVI and PM2.5 for CHD with OR-interaction = 1.043 (95%CI: 1.037-1.048). CONCLUSIONS: This study revealed harmful associations between maternal PM2.5 exposure and CHDs in gestational week 3 to 8. And such associations were partially reduced when exposed to residential greenness.

A birth cohort study is an epidemiological study that enrolls a group of people who were born during a specific period of time and follows up with them from the prenatal period to adulthood and even old age.The establishment of a birth cohort study enables the exploration of the etiology of diseases and enriches the understanding and awareness of health status among the reproductive population.

We aim to explore the link between maternal weekly temperature exposure and CHD in offspring and identify the relative contributions from heat and cold and from moderate and extreme atmospheric temperature. From January 2019 to December 2020, newborns who were diagnosed with CHD by echocardiography in the Network Platform for Congenital Heart Disease (NPCHD) from 11 cities in eastern China were enrolled in the present study. We appraised the exposure lag response relationship between temperature and CHDs in the distributed lag nonlinear model and further probed the pooled estimates by multivariate meta-analysis. We further performed the exposure–response curves in extreme temperature (5 th percentile for cold and 95 th for hot events). We also delve into the cumulative risk ratios (CRRs) of temperature on CHDs in general and subgroups. In this study, 5904 of 983, 523 infants were diagnosed with CHDs. The temperature-CHD combination performed positive significance in two exposure windows, gestational weeks 10–16 and 26–31, and reached the maximum effect in the 28th week. Compared with extreme cold (5 th , 6.14℃), these effects were higher in extreme heat (95 th , 29.26℃). The cumulative exposure–response curve showed a steep nonlinear rise in the hot tail but showed non-significance at low temperatures. In this range, the CRRs of temperature showed an increment to a ceiling of 3.781 (95% CI: 1.460–10.723). The temperature- CHD curves for both sex groups showed a general growth trend. No statistical significance was observed between these two groups ( P = 0.106). The cumulative effect of the temperature related CHD was significant in regions with lower education levels (maximum CRR was 9.282 (3.019–28.535)). A degree centigrade increase in temperature exposure was associated with the increment of CHD risk in the first and second trimesters, especially in extreme heat. Neonates born in lower education regions were more vulnerable to temperature-related CHDs.

Objective To describe the temporal trend of the number of new congenital heart disease (CHD) cases among newborns in Jinhua from 2019 to 2020 and explored an appropriate model to fit and forecast the tendency of CHD. Methods Data on CHD from 2019 to 2020 was collected from a health information system. We counted the number of newborns with CHD weekly and separately used the additive Holt-Winters ES method and ARIMA model to fit and predict the number of CHD for newborns in Jinhua. By comparing the mean square error, rooted mean square error and mean absolute percentage error of each approach, we evaluated the effects of different approaches for predicting the number of CHD in newborns. Results A total of 1135 newborns, including 601 baby girls and 534 baby boys, were admitted for CHD from HIS in Jinhua during the 2-year study period. The prevalence of CHD among newborns in Jinhua in 2019 was 0.96%. Atrial septal defect was diagnosed the most frequently among all newborns with CHD. The number of CHD cases among newborns remained stable in 2019 and 2020. There were fewer cases in spring and summer, while cases peaked in November and December. The ARIMA(2,1,1) model relatively offered advantages over the additive Holt-winters ES method in predicting the number of newborns with CHD, while the accuracy of ARIMA(2,1,1) was not very ideal. Conclusions The diagnosis of CHD is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors’ influence and try to incorporate them into the models.

骶尾部畸胎瘤是胎儿期常见肿瘤之一,巨大畸胎瘤可能增加胎儿心脏负荷,导致胎儿水肿、心功能衰竭等严重问题.宫外产时处理最初应用于胎儿严重膈疝(fetoscopic endoluminal tracheal oc-clusion,FETO)手术后气管内堵塞物的取出,之后应用范围逐步扩大,对胎儿气道梗阻或心肺功能不全发挥了重要作用.本文报告1例应用宫外产时处理技术治疗的胎儿巨大骶尾部畸胎瘤合并心功能衰竭,探讨骶尾部畸胎瘤宫外产时处理的应用指征,总结宫外产时处理的围术期管理要点.

Congenital heart disease(CHD)is the most common type of birth defect in China.According to a national study,the overall prevalences of CHD and critical CHD were 8.98 per 1000 live births and 1.46 per 1000 live births,respectively,in China[1].CHD,particularly critical CHD,not only seri-ously affects the quality of life of patients and their family,but creates huge economic and mental burdens on the fam-ily and on society as a whole[2].Early diagnosis of CHD is imperative for early intervention and treatment.For early diagnosis of CHD,CHD screening in all newborns was first initiated in Shanghai,China on June 1,2016[3].Since July 30,2018,the China National Health Commission initiated newborn screening program for CHD;"double index meth-ods"(cardiac auscultation plus pulse oximetry)were used to screen CHD for newborns 6 hours and 72 hours after birth.

Congenital heart diseases (CHD) are the most common birth defects, and the early diagnosis of CHD is crucial for CHD therapy. However, there are relatively few studies on intelligent auscultation for pediatric CHD, due to the fact that effective cooperation of the patient is required for the acquisition of useable heart sounds by electronic stethoscopes, yet the quality of heart sounds in pediatric is poor compared to adults due to the factors such as crying and breath sounds. This paper presents a novel pediatric CHD intelligent auscultation method based on electronic stethoscope. Firstly, a pediatric CHD heart sound database with a total of 941 PCG signal is established. Then a segment-based heart sound segmentation algorithm is proposed, which is based on PCG segment to achieve the segmentation of cardiac cycles, and therefore can reduce the influence of local noise to the global. Finally, the accurate classification of CHD is achieved using a majority voting classifier with Random Forest and Adaboost classifier based on 84 features containing time domain and frequency domain. Experimental results show that the performance of the proposed method is competitive, and the accuracy, sensitivity, specificity and f1-score of classification for CHD are 0.953, 0.946, 0.961 and 0.953 respectively.

Although low-density lipoprotein cholesterol (LDL-C) has been considered as a risk factor of atherosclerotic cardiovascular disease, limited studies can be available to evaluate the association of LDL-C with risk of mortality in the general population. This study aimed to examine the association of LDL-C level with risk of mortality using a propensity-score weighting method in a Chinese population, based on the health examination data. We performed a retrospective cohort study with 65,517 participants aged 40 years or older in Ningbo city, Zhejiang. LDL-C levels were categorized as five groups according to the Chinese dyslipidemia guidelines in adults. To minimize potential biases resulting from a complex array of covariates, we implemented a generalized boosted model to generate propensity-score weights on covariates. Then, we used Cox proportional hazard regression models with all-cause and cause-specific mortality as the dependent variables to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs). During the 439,186.5 person years of follow-up, 2403 deaths occurred. Compared with the median LDL-C group (100–130 mg/dL), subjects with extremely low LDL-C levels (group 1) had a higher risk of deaths from all-cause (HR = 2.53, 95% CI:1.80–3.53), CVD (HR = 1.84, 95% CI: 1.28–2.61), ischemic stroke (HR = 2.29, 95% CI:1.32–3.94), hemorrhagic stroke (HR = 3.49, 95% CI: 1.57–7.85), and cancer (HR = 2.12, 95% CI: 1.04–4.31) while the corresponding HRs in LDL-C group 2 were relatively lower than that in group 1. Low LDL-C levels were associated with an increased risk of all-cause, CVD, ischemic stroke, hemorrhagic stroke, and cancer mortality in the Chinese population.

Objective: To examine the association between siesta and hypertension by sex and nighttime sleep duration among Chinese adults aged >= 35 years in Yinzhou, Ningbo City. Methods: All data were obtained from physical examinations and structured questionnaires. A total of 44, 652 participants were included. Logistic regression models were applied to calculate odds ratios and 95% confidence intervals for the association between siesta and hypertension. Results: When compared with no siesta, siesta durations of 60 similar to 89 min (OR = 1.10, 95% CI:1.04-1.17) and >= 90 min (OR = 1.21, 95% CI:1.08-1.36) were associated with higher risk of hypertension in women. But no significant association was observed in men. Siesta durations of 30 similar to 59 min (OR = 1.09, 95% CI:1.00-1.19) and 60-89 min (OR = 1.10, 95% CI:1.05-1.16) were associated with hypertension in people with 6 similar to 8 h sleep, and this association appeared seemingly stronger with >= 90 min siesta either in short (<6 h) sleepers (OR = 1.20, 95% CI: 0.99-1.47) or in long (>8 h) sleepers (OR = 1.29, 95% CI: 1.00-1.68). However, in short sleepers, 60 similar to 89 min siesta seemed to be associated with decreased risk of hypertension (OR = 0.95, 95% CI: 0.85-1.06); while in long sleepers, the same range of siesta seemed to be associated with increased risk of hypertension (OR = 1.11, 95% CI: 0.93-1.34). Conclusion: Long siesta was associated with increased risk of hypertension in women but not in men. Not too long siesta may be related to decreased risk of hypertension in short sleepers but not in people with adequate or even long sleep. These findings warrant further examination with prospective studies and laboratory investigations. (C) 2021 Elsevier B.V. All rights reserved.

Background and aims: High-density lipoprotein cholesterol (HDL-C) concentration and variability are both important factors of cardiovascular disease (CVD) and mortality. We aimed to explore the associations of HDL-C and longitudinal change in HDL-C with risk of mortality. Methods and results: We recruited a total of 69,163 participants aged 1 mmol/L and 2 mmol/L were associated with a higher risk of CVD mortality (HRs: 1.23 (95% CI: 1.01-1.50) and 1.37 (95% CI: 1.03-1.82), respectively). Compared with the stable group ([-0.1, +0.1 mmol/L]), a large decrease ([-0.5, 0.3 mmol/L]) and very large decrease (<-0.5 mmol/L) in HDL-C were associated with a higher risk of non-accidental mortality (HRs: 1.40 (95% CI: 1.21-1.63) and 1.78 (95% CI: 1.44-2.20), respectively). Similar results were observed for CVD mortality and cancer mortality. Conclusion: Extremely low or high HDL-C and a large decrease or very large decrease in HDL-C were associated with a higher risk of cause-specific mortality. Monitoring of HDL-C may have utility in identifying individuals at higher risk of mortality. (c) 2021 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

OBJECTIVE:. To compare the metabolic profile of women with spontaneous premature ovarian insufficiency (POI) with that of age-matched healthy controls.STUDY DESIGN:. A cross-sectional case-control study was conducted using 1:1 matching by age. Women below the age of 40 with spontaneous POI who did not receive any medication (n = 303) and age-matched healthy women (n = 303) were included in this study.MAIN OUTCOME MEASURES:. Metabolic profiles, including serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), glucose, uric acid, urea and creatinine, were compared between women with POI and controls. For women with POI, factors associated with the metabolic profile were analyzed.RESULTS:. Women with POI were more likely to exhibit increased serum levels of TG (β, 0.155; 95% CI, 0.086, 0.223) and glucose (0.067; 0.052, 0.083), decreased levels of HDL-C (-0.087; -0.123, -0.051), LDL-C (-0.047; -0.091, -0.003) and uric acid (-0.053; -0.090, -0.015), and impaired kidney function (urea [0.070; 0.033, 0.107]; creatinine [0.277; 0.256, 0.299]; eGFR [-0.234; -0.252, -0.216]) compared with controls after adjusting for age and BMI. BMI, parity, gravidity, FSH and E2 levels were independent factors associated with the metabolic profile of women with POI.CONCLUSION:. Women with POI exhibited abnormalities in lipid metabolism, glucose metabolism, and a decrease in kidney function. In women with POI, early detection and lifelong management of metabolic abnormalities are needed.